JavaScript Unit Test Best Practices — Test Categories

Unit tests are very useful for checking how our app is working.

Otherwise, we run into all kinds of issues later on.

In this article, we’ll look at some best practices we should follow when writing JavaScript unit tests.

Tag Our Tests

We should tag our tests so that they can run when commits are being made or builds are being run.

This way, we can, we can do sanity checks before we commit anything by running the most critical tests.

For instance, we can write:

describe("user service", function() {
  describe("log in #sanity", function() {
    test("can log in with the right username and password #sanity", function() {
      //...
    });
  });
});

#javascript #testing

What is GEEK

Buddha Community

ElectroDB: A DynamoDB Library to Make Single Table Designs Easier

ElectroDB

 ElectroDB is a DynamoDB library to ease the use of having multiple entities and complex hierarchical relationships in a single DynamoDB table.

---

 

Introducing: The NEW ElectroDB Playground

 

Try out and share ElectroDB Models, Services, and Single Table Design at electrodb.fun

---

Features

• Use with your existing tables - ElectroDB simplifies building DocumentClient parameters, so you can use it with existing tables/data.
• Attribute Schema Enforcement - Define a schema for your entities with enforced attribute validation, defaults, types, aliases, and more.
• Easily Compose Hierarchical Access Patterns - Plan and design hierarchical keys for your indexes to multiply your possible access patterns.
• Single Table Entity Segregation - Entities created with ElectroDB will not conflict with other entities when using a single table.
• Simplified Sort Key Condition Querying - Write efficient sort key queries by easily building compose keys.
• Simplified Filter Composition - Easily create complex readable filters for DynamoDB queries without worrying about the implementation of ExpressionAttributeNames, ExpressionAttributeValues, and FilterExpressions.
• Simplified Update Expression Composition - Easily compose type safe update operations without having to format tedious ExpressionAttributeNames, ExpressionAttributeValues, and UpdateExpressions.
• Easily Query Across Entities - Define "collections" to create powerful/idiomatic queries that return multiple entities in a single request.
• Automatic Index Selection - Use .find() or .match() methods to dynamically and efficiently query based on defined sort key structures.
• Simplified Pagination API - Use .page() to easily paginate through result sets.
• Use With Your Existing Solution - If you are already using DynamoDB, and want to use ElectroDB, use custom Composite Attribute Templates to leverage your existing key structures.
• TypeScript Support - Strong TypeScript support for both Entities and Services now in Beta.
• Query Directly via the Terminal - Execute queries against your Entities, Services, Models directly from the command line.
• Stand Up Rest Server for Entities - Stand up a REST Server to interact with your Entities, Services, Models for easier prototyping.

---

Turn this

tasks
  .patch({ 
    team: "core",
    task: "45-662", 
    project: "backend"
  })
  .set({ status: "open" })
  .add({ points: 5 })
  .append({ 
    comments: [{
      user: "janet",
      body: "This seems half-baked."
    }] 
  })
  .where(( {status}, {eq} ) => eq(status, "in-progress"))
  .go();

Into This

{
    "UpdateExpression": "SET #status = :status_u0, #points = #points + :points_u0, #comments = list_append(#comments, :comments_u0), #updatedAt = :updatedAt_u0, #gsi1sk = :gsi1sk_u0",
    "ExpressionAttributeNames": {
        "#status": "status",
        "#points": "points",
        "#comments": "comments",
        "#updatedAt": "updatedAt",
        "#gsi1sk": "gsi1sk"
    },
    "ExpressionAttributeValues": {
        ":status0": "in-progress",
        ":status_u0": "open",
        ":points_u0": 5,
        ":comments_u0": [
            {
                "user": "janet",
                "body": "This seems half-baked."
            }
        ],
        ":updatedAt_u0": 1630977029015,
        ":gsi1sk_u0": "$assignments#tasks_1#status_open"
    },
    "TableName": "your_table_name",
    "Key": {
        "pk": "$taskapp#team_core",
        "sk": "$tasks_1#project_backend#task_45-662"
    },
    "ConditionExpression": "attribute_exists(pk) AND attribute_exists(sk) AND #status = :status0"
}

---

Table of Contents

• ElectroDB
  • Features
  • Table of Contents
• Project Goals
• Installation
• Usage
• Entities and Services
• Entities
• Services
  • TypeScript Support
    • TypeScript Services
  • Join
    • Independent Models
    • Joining Entity instances to a Service
    • Joining models to a Service
    • Joining Entities or Models with an alias
    • Joining Entities at Service construction for TypeScript
  • Model
    • Model Properties
    • Service Options
  • Attributes
    • Simple Syntax
    • Expanded Syntax
      • Attribute Definition
      • Enum Attributes
      • Map Attributes
      • List Attributes
      • Set Attributes
      • Attribute Getters and Setters
      • Attribute Watching
        • Attribute Watching: Watch All
        • Attribute Watching Examples
      • Calculated Attributes
      • Virtual Attributes
      • CreatedAt and UpdatedAt Attributes
      • Attribute Validation
  • Indexes
    • Indexes Without Sort Keys
    • Indexes With Sort Keys
    • Numeric Keys
    • Index Casing
  • Facets
  • Composite Attributes
    • Composite Attribute Arrays
    • Composite Attribute Templates
      • Templates and Composite Attribute Arrays
  • Composite Attribute and Index Considerations
    • Attributes as Indexes
  • Collections
    • Collection Queries vs Entity Queries
    • Collection Response Structure
  • Sub-Collections
    • Sub-Collection Entities
  • Index and Collection Naming Conventions
    • Index Naming Conventions
  • Collection Naming Conventions
  • Filters
    • Defined on the model
    • Defined via Filter method after query operators
    • Multiple Filters
  • Where
    • FilterExpressions
    • ConditionExpressions
    • Where with Complex Attributes
    • Attributes and Operations
    • Multiple Where Clauses
  • Parse
• Building Queries
  • Using composite attributes to make hierarchical keys
    • Shopping Mall Stores
  • Query App Records
    • Partition Key Composite Attributes
  • Sort Key Operations
  • Query Chains
    • Query Method
    • Get Method
    • Batch Get
    • Delete Method
    • Batch Write Delete Records
    • Put Record
    • Batch Write Put Records
    • Update Record
      • Updates to Composite Attributes
      • Update Method: Set
      • Update Method: Remove
      • Update Method: Add
      • Update Method: Subtract
      • Update Method: Append
      • Update Method: Delete
      • Update Method: Data
    • Update Method: Complex Data Types
    • Scan Records
    • Remove Method
    • Patch Record
    • Create Record
    • Find Records
    • Match Records
    • Access Pattern Queries
      • Begins With Queries
  • Collection Chains
  • Execute Queries
    • Params
    • Go
    • Page
      • Entity Pagination
      • Service Pagination
      • Pager Query Options
        • Pagination Example
  • Query Examples
  • Query Options
• Errors:
  • No Client Defined On Model
  • Invalid Identifier
  • Invalid Key Composite Attribute Template
  • Duplicate Indexes
  • Collection Without An SK
  • Duplicate Collections
  • Missing Primary Index
  • Invalid Attribute Definition
  • Invalid Model
  • Invalid Options
  • Duplicate Index Fields
  • Duplicate Index Composite Attributes
  • Incompatible Key Composite Attribute Template
  • Invalid Index With Attribute Name
  • Invalid Collection on Index With Attribute Field Names
  • Missing Composite Attributes
  • Missing Table
  • Invalid Concurrency Option
  • Invalid Pages Option
  • Invalid Limit Option
  • Invalid Attribute
  • AWS Error
  • Unknown Errors
  • Invalid Last Evaluated Key
  • No Owner For Pager
  • Pager Not Unique
• Examples
  • Employee App
    • Employee App Requirements
    • App Entities
    • Query Records
      • All tasks and employee information for a given employee
      • Find all employees and office details for a given office
      • Tasks for a given employee
      • Tasks for a given project
      • Find office locations
      • Find employee salaries and titles
      • Find employee birthdays or anniversaries
      • Find direct reports
  • Shopping Mall Property Management App
    • Shopping Mall Requirements
    • Access Patterns are accessible on the StoreLocation
    • PUT Record
      • Add a new Store to the Mall
    • UPDATE Record
      • Change the Stores Lease Date
    • GET Record
      • Retrieve a specific Store in a Mall
    • DELETE Record
      • Remove a Store location from the Mall
    • Query Mall Records
      • All Stores in a particular mall
      • All Stores in a particular mall building
      • Find the store located in unit B47
      • Stores by Category at Mall
      • Stores by upcoming lease
      • Stores will renewals for Q4
      • Spite-stores with release renewals this year
      • All Latte Larrys in a particular mall building
• Exported TypeScript Types
  • EntityRecord Type
  • EntityItem Type
  • CollectionItem Type
  • CreateEntityItem Type
  • UpdateEntityItem Type
  • UpdateAddEntityItem Type
  • UpdateSubtractEntityItem Type
  • UpdateAppendEntityItem Type
  • UpdateRemoveEntityItem Type
  • UpdateDeleteEntityItem Type
• Using ElectroDB With Existing Data
• Electro CLI
• Version 1 Migration
  • New schema format/breaking key format change
  • The renaming of index property Facets to Composite and Template
  • Get Method to Return null
• Coming Soon

---

Project Goals

ElectroDB focuses on simplifying the process of modeling, enforcing data constraints, querying across entities, and formatting complex DocumentClient parameters. Three important design considerations we're made with the development of ElectroDB:

1. ElectroDB should be able to be useful without having to query the database itself [read more].
2. ElectroDB should be able to be added to a project that already has been established tables, data, and access patterns [read more].
3. ElectroDB should not require additional design considerations on top of those made for DynamoDB, and therefore should be able to be removed from a project at any time without sacrifice.

Installation

Install from NPM

npm install electrodb --save

Usage

Require/import Entity and/or Service from electrodb:

const {Entity, Service} = require("electrodb");
// or 
import {Entity, Service} from "electrodb";

Entities and Services

To see full examples of ElectroDB in action, go to the Examples section.

Entity allows you to create separate and individual business objects in a DynamoDB table. When queried, your results will not include other Entities that also exist the same table. This allows you to easily achieve single table design as recommended by AWS. For more detail, read Entities.

Service allows you to build relationships across Entities. A service imports Entity Models, builds individual Entities, and creates Collections to allow cross Entity querying. For more detail, read Services.

You can use Entities independent of Services, you do not need to import models into a Service to use them individually. However, If you intend to make queries that join or span multiple Entities you will need to use a Service.

Entities

In ElectroDB an Entity is represents a single business object. For example, in a simple task tracking application, one Entity could represent an Employee and or a Task that is assigned to an employee.

Require or import Entity from electrodb:

const {Entity} = require("electrodb");
// or
import {Entity} from "electrodb";

When using TypeScript, for strong type checking, be sure to either add your model as an object literal to the Entity constructor or create your model using const assertions with the as const syntax.

Services

In ElectroDB a Service represents a collection of related Entities. Services allow you to build queries span across Entities. Similar to Entities, Services can coexist on a single table without collision. You can use Entities independent of Services, you do not need to import models into a Service to use them individually. However, you do you need to use a Service if you intend make queries that join multiple Entities.

Require:

const {Service} = require("electrodb");
// or
import {Service} from "electrodb";

TypeScript Support

Previously it was possible to generate type definition files (.d.ts) for you Models, Entities, and Services with the Electro CLI. New with version 0.10.0 is TypeScript support for Entities and Services.

As of writing this, this functionality is still a work in progress, and enforcement of some of ElectroDB's query constraints have still not been written into the type checks. Most notably are the following constraints not yet enforced by the type checker, but are enforced at query runtime:

• Sort Key Composite Attribute order is not strongly typed. Sort Key Composite Attributes must be provided in the order they are defined on the model to build the key appropriately. This will not cause an error at query runtime, be sure your partial Sort Keys are provided in accordance with your model to fully leverage Sort Key queries. For more information about composite attribute ordering see the section on Composite Attributes.
• Put/Create/Update/Patch/Delete/Create operations that partially impact index composite attributes are not statically typed. When performing a put or update type operation that impacts a composite attribute of a secondary index, ElectroDB performs a check at runtime to ensure all composite attributes of that key are included. This is detailed more in the section Composite Attribute and Index Considerations.
• Use of the params method does not yet return strict types.
• Use of the raw or includeKeys query options do not yet impact the returned types.

If you experience any issues using TypeScript with ElectroDB, your feedback is very important, please create a GitHub issue, and it can be addressed.

See the section Exported TypeScript Types to read more about the useful types exported from ElectroDB.

TypeScript Services

New with version 0.10.0 is TypeScript support. To ensure accurate types with, TypeScript users should create their services by passing an Object literal or const object that maps Entity alias names to Entity instances.

const table = "my_table_name";
const employees = new Entity(EmployeesModel, { client, table });
const tasks = new Entity(TasksModel, { client, table });
const TaskApp = new Service({employees, tasks});

The property name you assign the entity will then be "alias", or name, you can reference that entity by through the Service. Aliases can be useful if you are building a service with multiple versions of the same entity or wish to change the reference name of an entity without impacting the schema/key names of that entity.

Services take an optional second parameter, similar to Entities, with a client and table. Using this constructor interface, the Service will utilize the values from those entities, if they were provided, or be passed values to override the client or table name on the individual entities.

Not yet available for TypeScript, this pattern will also accept Models, or a mix of Entities and Models, in the same object literal format.

Join

When using JavaScript, use join to add Entities or Models onto a Service.

NOTE: If using TypeScript, see Joining Entities at Service construction for TypeScript to learn how to "join" entities for use in a TypeScript project.

Independent Models

let table = "my_table_name";
let employees = new Entity(EmployeesModel, { client, table });
let tasks = new Entity(TasksModel, { client, table });

Joining Entity instances to a Service

// Joining Entity instances to a Service
let TaskApp = new Service("TaskApp", { client, table });
TaskApp
    .join(employees) // available at TaskApp.entities.employees
    .join(tasks);    // available at TaskApp.entities.tasks

Joining models to a Service

let TaskApp = new Service("TaskApp", { client, table });
TaskApp
    .join(EmployeesModel) // available at TaskApp.entities.employees (based on entity name in model)
    .join(TasksModel);    // available at TaskApp.entities.tasks (based on entity name in model)

Joining Entities or Models with an alias

let TaskApp = new Service("TaskApp", { client, table });
TaskApp
    .join("personnel", EmployeesModel) // available at TaskApp.entities.personnel
    .join("directives", TasksModel); // available at TaskApp.entities.directives

Joining Entities at Service construction for TypeScript

let TaskApp = new Service({
    personnel: EmployeesModel, // available at TaskApp.entities.personnel
    directives: TasksModel, // available at TaskApp.entities.directives
});

When joining a Model/Entity to a Service, ElectroDB will perform a number of validations to ensure that Entity conforms to expectations collectively established by all joined Entities.

• Entity names must be unique across a Service.
• Collection names must be unique across a Service.
• All Collections map to on the same DynamoDB indexes with the same index field names. See Indexes.
• Partition Key [Composite Attributes](#composite attribute-arrays) on a Collection must have the same attribute names and labels (if applicable). See Attribute Definitions.
• The name of the Service in the Model must match the Name defined on the Service instance.
• Joined instances must be type Model or Entity.
• If the attributes of an Entity have overlapping names with other attributes in that service, they must all have compatible or matching attribute definitions.
• All models conform to the same model format. If you created your model prior to ElectroDB version 0.9.19 see section Version 1 Migration.

Model

Create an Entity's schema. In the below example.

const DynamoDB = require("aws-sdk/clients/dynamodb");
const {Entity, Service} = require("electrodb");
const client = new DynamoDB.DocumentClient();
const EmployeesModel = {
    model: {
        entity: "employees",
        version: "1",
        service: "taskapp",
    },
    attributes: {
        employee: {
            type: "string",
            default: () => uuidv4(),
        },
        firstName: {
            type: "string",
            required: true,
        },
        lastName: {
            type: "string",
            required: true,
        },
        office: {
            type: "string",
            required: true,
        },
        title: {
            type: "string",
            required: true,
        },
        team: {
            type: ["development", "marketing", "finance", "product", "cool cats and kittens"],
            required: true,
        },
        salary: {
            type: "string",
            required: true,
        },
        manager: {
            type: "string",
        },
        dateHired: {
            type: "string",
            validate: /^\d{4}-\d{2}-\d{2}$/gi
        },
        birthday: {
            type: "string",
            validate: /^\d{4}-\d{2}-\d{2}$/gi
        },
    },
    indexes: {
        employee: {
            pk: {
                field: "pk",
                composite: ["employee"],
            },
            sk: {
                field: "sk",
                composite: [],
            },
        },
        coworkers: {
            index: "gsi1pk-gsi1sk-index",
            collection: "workplaces",
            pk: {
                field: "gsi1pk",
                composite: ["office"],
            },
            sk: {
                field: "gsi1sk",
                composite: ["team", "title", "employee"],
            },
        },
        teams: {
            index: "gsi2pk-gsi2sk-index",
            pk: {
                field: "gsi2pk",
                composite: ["team"],
            },
            sk: {
                field: "gsi2sk",
                composite: ["title", "salary", "employee"],
            },
        },
        employeeLookup: {
            collection: "assignments",
            index: "gsi3pk-gsi3sk-index",
            pk: {
                field: "gsi3pk",
                composite: ["employee"],
            },
            sk: {
                field: "gsi3sk",
                composite: [],
            },
        },
        roles: {
            index: "gsi4pk-gsi4sk-index",
            pk: {
                field: "gsi4pk",
                composite: ["title"],
            },
            sk: {
                field: "gsi4sk",
                composite: ["salary", "employee"],
            },
        },
        directReports: {
            index: "gsi5pk-gsi5sk-index",
            pk: {
                field: "gsi5pk",
                composite: ["manager"],
            },
            sk: {
                field: "gsi5sk",
                composite: ["team", "office", "employee"],
            },
        },
    },
};

const TasksModel = {
    model: {
        entity: "tasks",
        version: "1",
        service: "taskapp",
    },
    attributes: {
        task: {
            type: "string",
            default: () => uuid(),
        },
        project: {
            type: "string",
        },
        employee: {
            type: "string",
        },
        description: {
            type: "string",
        },
    },
    indexes: {
        task: {
            pk: {
                field: "pk",
                composite: ["task"],
            },
            sk: {
                field: "sk",
                composite: ["project", "employee"],
            },
        },
        project: {
            index: "gsi1pk-gsi1sk-index",
            pk: {
                field: "gsi1pk",
                composite: ["project"],
            },
            sk: {
                field: "gsi1sk",
                composite: ["employee", "task"],
            },
        },
        assigned: {
            collection: "assignments",
            index: "gsi3pk-gsi3sk-index",
            pk: {
                field: "gsi3pk",
                composite: ["employee"],
            },
            sk: {
                field: "gsi3sk",
                composite: ["project", "task"],
            },
        },
    },
};

Model Properties

Property	Description
model.service	Name of the application using the entity, used to namespace all entities
model.entity	Name of the entity that the schema represents
model.version	(optional) The version number of the schema, used to namespace keys
attributes	An object containing each attribute that makes up the schema
indexes	An object containing table indexes, including the values for the table's default Partition Key and Sort Key

Service Options

Optional second parameter

Property	Description
table	The name of the dynamodb table in aws.
client	(optional) An instance of the docClient from the aws-sdk for use when querying a DynamoDB table. This is optional if you wish to only use the params functionality, but required if you actually need to query against a database.

Attributes

Attributes define an Entity record. The AttributeName represents the value your code will use to represent an attribute.

Pro-Tip: Using the field property, you can map an AttributeName to a different field name in your table. This can be useful to utilize existing tables, existing models, or even to reduce record sizes via shorter field names. For example, you may refer to an attribute as organization but want to save the attribute with a field name of org in DynamoDB.

Simple Syntax

Assign just the type of the attribute directly to the attribute name. Types currently supported options are "string", "number", "boolean", an array of strings representing a fixed set of possible values, or "any" which disables value type checking on that attribute.

attributes: {
    <AttributeName>: "string" | "number" | "boolean" | "list" | "map" | "set" | "any" | string[] | ReadonlyArray<string> 
}

Expanded Syntax

Use the expanded syntax build out more robust attribute options.

attributes: {
    <AttributeName>: {
        type: "string" | "number" | "boolean" | "list" | "map" | "set" | "any" | ReadonlyArray<string>;
        required?: boolean;
        default?: <type> | (() => <type>);
        validate?: RegExp | ((value: <type>) => void | string);
        field?: string;
        readOnly?: boolean;
        label?: string;
        cast?: "number"|"string"|"boolean";
        get?: (attribute: <type>, schema: any) => <type> | void | undefined;
        set?: (attribute?: <type>, schema?: any) => <type> | void | undefined; 
        watch: "*" | string[]
    }
}

NOTE: When using get/set in TypeScript, be sure to use the ?: syntax to denote an optional attribute on set

Attribute Definition

Property	Type	Required	Types	Description
type	string, ReadonlyArray<string>, string[]	yes	all	Accepts the values: "string", "number" "boolean", "map", "list", "set", an array of strings representing a finite list of acceptable values: ["option1", "option2", "option3"], or "any" which disables value type checking on that attribute.
required	boolean	no	all	Flag an attribute as required to be present when creating a record. This attribute also acts as a type of NOT NULL flag, preventing it from being removed directly.
hidden	boolean	no	all	Flag an attribute as hidden to remove the property from results before they are returned.
default	value, () => value	no	all	Either the default value itself or a synchronous function that returns the desired value. Applied before set and before required check.
validate	RegExp, (value: any) => void, (value: any) => string	no	all	Either regex or a synchronous callback to return an error string (will result in exception using the string as the error's message), or thrown exception in the event of an error.
field	string	no	all	The name of the attribute as it exists in DynamoDB, if named differently in the schema attributes. Defaults to the AttributeName as defined in the schema.
readOnly	boolean	no	all	Prevents an attribute from being updated after the record has been created. Attributes used in the composition of the table's primary Partition Key and Sort Key are read-only by default. The one exception to readOnly is for properties that also use the watch property, read attribute watching for more detail.
label	string	no	all	Used in index composition to prefix key composite attributes. By default, the AttributeName is used as the label.
cast	"number", "string", "boolean"	no	all	Optionally cast attribute values when interacting with DynamoDB. Current options include: "number", "string", and "boolean".
set	(attribute, schema) => value	no	all	A synchronous callback allowing you to apply changes to a value before it is set in params or applied to the database. First value represents the value passed to ElectroDB, second value are the attributes passed on that update/put
get	(attribute, schema) => value	no	all	A synchronous callback allowing you to apply changes to a value after it is retrieved from the database. First value represents the value passed to ElectroDB, second value are the attributes retrieved from the database.
watch	Attribute[], "*"	no	root-only	Define other attributes that will always trigger your attribute's getter and setter callback after their getter/setter callbacks are executed. Only available on root level attributes.
properties	{[key: string]: Attribute}	yes*	map	Define the properties available on a "map" attribute, required if your attribute is a map. Syntax for map properties is the same as root level attributes.
items	Attribute	yes*	list	Define the attribute type your list attribute will contain, required if your attribute is a list. Syntax for list items is the same as a single attribute.
items	"string"	"number"	yes*	set

Enum Attributes

When using TypeScript, if you wish to also enforce this type make sure to us the as const syntax. If TypeScript is not told this array is Readonly, even when your model is passed directly to the Entity constructor, it will not resolve the unique values within that array.

This may be desirable, however, as enforcing the type value can require consumers of your model to do more work to resolve the type beyond just the type string.

NOTE: Regardless of using TypeScript or JavaScript, ElectroDB will enforce values supplied match the supplied array of values at runtime.

The following example shows the differences in how TypeScript may enforce your enum value:

attributes: {
  myEnumAttribute1: {
      type: ["option1", "option2", "option3"]        // TypeScript enforces as `string[]`
  },
  myEnumAttribute2: {
    type: ["option1", "option2", "option3"] as const // TypeScript enforces as `"option1" | "option2" | "option3" | undefined`
  },
  myEnumAttribute3: {
    required: true,
    type: ["option1", "option2", "option3"] as const // TypeScript enforces as `"option1" | "option2" | "option3"`
  }
}

Map Attributes

Map attributes leverage DynamoDB's native support for object-like structures. The attributes within a Map are defined under the properties property; a syntax that mirrors the syntax used to define root level attributes. You are not limited in the types of attributes you can nest inside a map attribute.

attributes: {
  myMapAttribute: {
    type: "map",
    properties: {
      myStringAttribute: {
        type: "string"
      },
      myNumberAttribute: {
        type: "number"
      }
    }
  }
}

List Attributes

List attributes model array-like structures with DynamoDB's List type. The elements of a List attribute are defined using the items property. Similar to Map properties, ElectroDB does not restrict the types of items that can be used with a list.

attributes: {
  myStringList: { 
    type: "list",
    items: {
      type: "string"
    },
  },
  myMapList: {
    myMapAttribute: {
      type: "map",
      properties: {
        myStringAttribute: {
          type: "string"
        },
        myNumberAttribute: {
          type: "number"
        }
      }
    }
  }
}

Set Attributes

The Set attribute is arguably DynamoDB's most powerful type. ElectroDB supports String and Number Sets using the items property set as either "string" or "number".

In addition to having the same modeling benefits you get with other attributes, ElectroDB also simplifies the use of Sets by removing the need to use DynamoDB's special createSet class to work with Sets. ElectroDB Set Attributes accept Arrays, JavaScript native Sets, and objects from createSet as values. ElectroDB will manage the casting of values to a DynamoDB Set value prior to saving and ElectroDB will also convert Sets back to JavaScript arrays on retrieval.

NOTE: If you are using TypeScript, Sets are currently typed as Arrays to simplify the type system. Again, ElectroDB will handle the conversion of these Arrays without the need to use client.createSet().

attributes: {
  myStringSet: {
    type: "set",
    items: "string"
  },
  myNumberSet: {
    type: "set",
    items: "number"
  }
}

Attribute Getters and Setters

Using get and set on an attribute can allow you to apply logic before and just after modifying or retrieving a field from DynamoDB. Both callbacks should be pure synchronous functions and may be invoked multiple times during one query.

The first argument in an attribute's get or set callback is the value received in the query. The second argument, called "item", in an attribute's is an object containing the values of other attributes on the item as it was given or retrieved. If your attribute uses watch, the getter or setter of attribute being watched will be invoked before your getter or setter and the updated value will be on the "item" argument instead of the original.

NOTE: Using getters/setters on Composite Attributes is not recommended without considering the consequences of how that will impact your keys. When a Composite Attribute is supplied for a new record via a put or create operation, or is changed via a patch or updated operation, the Attribute's set callback will be invoked prior to formatting/building your record's keys on when creating or updating a record.

ElectroDB invokes an Attribute's get method in the following circumstances:

1. If a field exists on an item after retrieval from DynamoDB, the attribute associated with that field will have its getter method invoked.
2. After a put or create operation is performed, attribute getters are applied against the object originally received and returned.
3. When using ElectroDB's attribute watching functionality, an attribute will have its getter callback invoked whenever the getter callback of any "watched" attributes are invoked. Note: The getter of an Attribute Watcher will always be applied after the getters for the attributes it watches.

ElectroDB invokes an Attribute's set callback in the following circumstances:

1. Setters for all Attributes will always be invoked when performing a create or put operation.
2. Setters will only be invoked when an Attribute is modified when performing a patch or update operation.
3. When using ElectroDB's attribute watching functionality, an attribute will have its setter callback invoked whenever the setter callback of any "watched" attributes are invoked. Note: The setter of an Attribute Watcher will always be applied after the setters for the attributes it watches.

NOTE: As of ElectroDB 1.3.0, the watch property is only possible for root level attributes. Watch is currently not supported for nested attributes like properties on a "map" or items of a "list".

Attribute Watching

Attribute watching is a powerful feature in ElectroDB that can be used to solve many unique challenges with DynamoDB. In short, you can define a column to have its getter/setter callbacks called whenever another attribute's getter or setter callbacks are called. If you haven't read the section on Attribute Getters and Setters, it will provide you with more context about when an attribute's mutation callbacks are called.

Because DynamoDB allows for a flexible schema, and ElectroDB allows for optional attributes, it is possible for items belonging to an entity to not have all attributes when setting or getting records. Sometimes values or changes to other attributes will require corresponding changes to another attribute. Sometimes, to fully leverage some advanced model denormalization or query access patterns, it is necessary to duplicate some attribute values with similar or identical values. This functionality has many uses; below are just a few examples of how you can use watch:

NOTE: Using the watch property impacts the order of which getters and setters are called. You cannot watch another attribute that also uses watch, so ElectroDB first invokes the getters or setters of attributes without the watch property, then subsequently invokes the getters or setters of attributes who use watch.

myAttr: { 
  type: "string",
  watch: ["otherAttr"],
  set: (myAttr, {otherAttr}) => {
    // Whenever "myAttr" or "otherAttr" are updated from an `update` or `patch` operation, this callback will be fired. 
    // Note: myAttr or otherAttr could be independently undefined because either attribute could have triggered this callback
  },
  get: (myAttr, {otherAttr}) => {
    // Whenever "myAttr" or "otherAttr" are retrieved from a `query` or `get` operation, this callback will be fired. 
    // Note: myAttr or otherAttr could be independently undefined because either attribute could have triggered this callback.
  } 
}

Attribute Watching: Watch All

If your attributes needs to watch for any changes to an item, you can model this by supplying the watch property a string value of "*"

myAttr: { 
  type: "string",
  watch: "*", // "watch all"
  set: (myAttr, allAttributes) => {
    // Whenever an `update` or `patch` operation is performed, this callback will be fired. 
    // Note: myAttr or the attributes under `allAttributes` could be independently undefined because either attribute could have triggered this callback
  },
  get: (myAttr, allAttributes) => {
    // Whenever a `query` or `get` operation is performed, this callback will be fired. 
    // Note: myAttr or the attributes under `allAttributes` could be independently undefined because either attribute could have triggered this callback
  } 
}

Attribute Watching Examples

Example 1 - A calculated attribute that depends on the value of another attribute:

In this example, we have an attribute "fee" that needs to be updated any time an item's "price" attribute is updated. The attribute "fee" uses watch to have its setter callback called any time "price" is updated via a put, create, update, or patch operation.

{
  model: {
    entity: "products",
    service: "estimator",
    version: "1"
  },
  attributes: {
    product: {
      type: "string"
    },
    price: {
      type: "number",
              required: true
    },
    fee: {
      type: "number",
              watch: ["price"],
              set: (_, {price}) => {
        return price * .2;
      }
    }
  },
  indexes: {
    pricing: {
      pk: {
        field: "pk",
                composite: ["product"]
      },
      sk: {
        field: "sk",
                composite: []
      }
    }
  }
}

Example 2 - Making a virtual attribute that never persists to the database:

In this example we have an attribute "displayPrice" that needs its getter called anytime an item's "price" attribute is retrieved. The attribute "displayPrice" uses watch to return a formatted price string based whenever an item with a "price" attribute is queried. Additionally, "displayPrice" always returns undefined from its setter callback to ensure that it will never write data back to the table.

{
  model: {
    entity: "services",
    service: "costEstimator",
    version: "1"
  },
  attributes: {
    service: {
      type: "string"
    },
    price: {
      type: "number",
      required: true
    },
    displayPrice: {
      type: "string",
      watch: ["price"],
      get: (_, {price}) => {
        return "$" + price;  
      },
      set: () => undefined
    }
  },
  indexes: {
    pricing: {
      pk: {
        field: "pk",
        composite: ["service"]
      },
      sk: {
        field: "sk",
        composite: []
      }
    }
  }
}

Example 3 - Creating a more filter-friendly version of an attribute without impacting the original attribute:

In this example we have an attribute "descriptionSearch" which will help our users easily filter for transactions by "description". To ensure our filters will not take into account a description's character casing, descriptionSearch duplicates the value of "description" so it can be used in filters without impacting the original "description" value. Without ElectroDB's watch functionality, to accomplish this you would either have to duplicate this logic or cause permanent modification to the property itself. Additionally, the "descriptionSearch" attribute has used hidden:true to ensure this value will not be presented to the user.

{
  model: {
    entity: "transaction",
    service: "bank",
    version: "1"
  },
  attributes: {
    accountNumber: {
      type: "string"
    },
    transactionId: {
      type: "string"
    },
    amount: {
      type: "number",
    },
    description: {
      type: "string",
    },
    descriptionSearch: {
      type: "string",
      hidden: true,
      watch: ["description"],
      set: (_, {description}) => {
        if (typeof description === "string") {
            return description.toLowerCase();
        }
      }
    }
  },
  indexes: {
    transactions: {
      pk: {
        field: "pk",
        composite: ["accountNumber"]
      },
      sk: {
        field: "sk",
        composite: ["transactionId"]
      }
    }
  }
}

Example 4 - Creating an updatedAt property:

In this example we can easily create both updatedAt and createdAt attributes on our model. createdAt will use ElectroDB's set and readOnly attribute properties, while updatedAt will make use of readOnly, and watch with the "watchAll" syntax: {watch: "*"}. By supplying an asterisk, instead of an array of attribute names, attributes can be defined to watch all changes to all attributes.

Using watch in conjunction with readOnly is another powerful modeling technique. This combination allows you to model attributes that can only be modified via the model and not via the user. This is useful for attributes that need to be locked down and/or strictly calculated.

Notable about this example is that both updatedAt and createdAt use the set property without using its arguments. The readOnly only prevents modification of an attributes on update, and patch. By disregarding the arguments passed to set, the updatedAt and createdAt attributes are then effectively locked down from user influence/manipulation.

{
  model: {
    entity: "transaction",
    service: "bank",
    version: "1"
  },
  attributes: {
    accountNumber: {
      type: "string"
    },
    transactionId: {
      type: "string"
    },
    description: {
      type: "string",
    },
    createdAt: {
      type: "number",
      readOnly: true,
      set: () => Date.now()
    },
    updatedAt: {
      type: "number",
      readOnly: true,
      watch: "*",
      set: () => Date.now()
    },
    
  },
  indexes: {
    transactions: {
      pk: {
        field: "pk",
        facets: ["accountNumber"]
      },
      sk: {
        field: "sk",
        facets: ["transactionId"]
      }
    }
  }
}

Calculated Attributes

See: Attribute Watching (Example 1).

Virtual Attributes

See: Attribute Watching (Example 2).

CreatedAt and UpdatedAt Attributes

See: Attribute Watching (Example 4).

Attribute Validation

The validation property allows for multiple function/type signatures. Here the different combinations ElectroDB supports: signature | behavior ----------------------- | -------- Regexp | ElectroDB will call .test(val) on the provided regex with the value passed to this attribute (value: T) => string | If a string value with length is returned, the text will be considered the reason the value is invalid. It will generate a new exception this text as the message. (value: T) => boolean | If a boolean value is returned, true or truthy values will signify than a value is invalid while false or falsey will be considered valid. (value: T) => void | A void or undefined value is returned, will be treated as successful, in this scenario you can throw an Error yourself to interrupt the query

Indexes

When using ElectroDB, indexes are referenced by their AccessPatternName. This allows you to maintain generic index names on your DynamoDB table, but reference domain specific names while using your ElectroDB Entity. These will often be referenced as "Access Patterns".

All DynamoDB table start with at least a PartitionKey with an optional SortKey, this can be referred to as the "Table Index". The indexes object requires at least the definition of this Table Index Partition Key and (if applicable) Sort Key.

In your model, the Table Index this is expressed as an Access Pattern without an index property. For Secondary Indexes, use the index property to define the name of the index as defined on your DynamoDB table.

Within these AccessPatterns, you define the PartitionKey and (optionally) SortKeys that are present on your DynamoDB table and map the key's name on the table with the field property.

indexes: {
    [AccessPatternName]: {
        pk: {
            field: string; 
            composite: AttributeName[];
            template?: string;
        },
        sk?: {
            field: string;
            composite: AttributesName[];
            template?: string;
        },
        index?: string
        collection?: string | string[]
    }
}

Property	Type	Required	Description
pk	object	yes	Configuration for the pk of that index or table
pk.composite	`string	string[]`	yes
pk.template	string	no	A string that represents the template in which attributes composed to form a key (see Composite Attribute Templates below for more on this functionality).
pk.field	string	yes	The name of the attribute as it exists in DynamoDB, if named differently in the schema attributes.
pk.casing	default	upper	lower
sk	object	no	Configuration for the sk of that index or table
sk.composite	`string	string[]`	no
sk.template	string	no	A string that represents the template in which attributes composed to form a key (see Composite Attribute Templates below for more on this functionality).
sk.field	string	yes	The name of the attribute as it exists in DynamoDB, if named differently in the schema attributes.
pk.casing	default	upper	lower
index	string	no	Required when the Index defined is a Secondary Index; but is left blank for the table's primary index.
collection	`string	string[]`	no

Indexes Without Sort Keys

When using indexes without Sort Keys, that should be expressed as an index without an sk property at all. Indexes without an sk cannot have a collection, see Collections for more detail.

NOTE: It is generally recommended to always use Sort Keys when using ElectroDB as they allow for more advanced query opportunities. Even if your model doesn't need an additional property to define a unique record, having an sk with no defined composite attributes (e.g. an empty array) still opens the door to many more query opportunities like collections.

// ElectroDB interprets as index *not having* an SK.
{
  indexes: {
    myIndex: {
      pk: {
        field: "pk",
        composite: ["id"]
      }
    }
  }
}

Indexes With Sort Keys

When using indexes with Sort Keys, that should be expressed as an index with an sk property. If you don't wish to use the Sort Key in your model, but it does exist on the table, simply use an empty for the composite property. An empty array is still very useful, and opens the door to more query opportunities and access patterns like collections.

// ElectroDB interprets as index *having* SK, but this model doesnt assign any composite attributes to it.
{
  indexes: {
    myIndex: {
      pk: {
        field: "pk",
        composite: ["id"]
      },
      sk: {
        field: "sk",
        composite: []
      }
    }
  }
}

Numeric Keys

If you have an index where the Partition or Sort Keys are expected to be numeric values, you can accomplish this with the template property on the index that requires numeric keys. Define the attribute used in the composite template as type "number", and then create a template string with only the attribute's name.

For example, this model defines both the Partition and Sort Key as numeric:

const schema = {
  model: {
    entity: "numeric",
    service: "example",
    version: "1"
  },
  attributes: {
    number1: {
      type: "number" // defined as number
    },
    number2: {
      type: "number"  // defined as number
    }
  },
  indexes: {
    record: {
      pk: {
        field: "pk",
        template: "${number1}" // will build PK as numeric value 
      },
      sk: {
        field: "sk",
        template: "${number2}" // will build SK as numeric value
      }
    }
  }
}

Index Casing

DynamoDB is a case-sensitive data store, and therefore it is common to convert the casing of keys to uppercase or lowercase prior to saving, updating, or querying data to your table. ElectroDB, by default, will lowercase all keys when preparing query parameters. For those who are using ElectroDB with an existing dataset, have preferences on upper or lowercase, or wish to not convert case at all, this can be configured on an index key field basis.

In the example below, we are configuring the casing ElectroDB will use individually for the Partition Key and Sort Key on the GSI "gis1". For the index's PK, mapped to gsi1pk, we ElectroDB will convert this key to uppercase prior to its use in queries. For the index's SK, mapped to gsi1pk, we ElectroDB will not convert the case of this key prior to its use in queries.

{
  indexes: {
    myIndex: {
      index: "gsi1",
      pk: {
        field: "gsi1pk",
        casing: "upper", // Acct_0120 -> ACCT_0120
        composite: ["organizationId"]
      },
      sk: {
        field: "gsi1sk",
        casing: "none", // Acct_0120 -> Acct_0120 
        composite: ["accountId"]
      }
    }
  }
}

NOTE: Casing is a very important decision when modeling your data in DynamoDB. While choosing upper/lower is largely a personal preference, once you have begun loading records in your table it can be difficult to change your casing after the fact. Unless you have good reason, allowing for mixed case keys can make querying data difficult because it will require database consumers to always have a knowledge of their data's case.

Casing Option	Effect
default	The default for keys is lowercase, or lower
lower	Will convert the key to lowercase prior it its use
upper	Will convert the key to uppercase prior it its use
none	Will not perform any casing changes when building keys

Facets

As of version 0.11.1, "Facets" have been renamed to "Composite Attributes", and all documentation has been updated to reflect that change.

• To learn about the latest syntax, checkout Composite Attributes.
• To learn about why this change was made in preparation for 1.0 checkout Renaming Facets.

Composite Attributes

A Composite Attribute is a segment of a key based on one of the attributes. Composite Attributes are concatenated together from either a Partition Key, or a Sort Key key, which define an index.

NOTE: Only attributes with a type of "string", "number", "boolean", or string[] (enum) can be used as composite attributes.

There are two ways to provide composite:

1. As a Composite Attribute Array
2. As a Composite Attribute Template

For example, in the following Access Pattern, "locations" is made up of the composite attributes storeId, mallId, buildingId and unitId which map to defined attributes in the model:

// Input
{
    storeId: "STOREVALUE",
    mallId: "MALLVALUE",
    buildingId: "BUILDINGVALUE",
    unitId: "UNITVALUE"
};

// Output:
{
    pk: '$mallstoredirectory_1#storeId_storevalue',
    sk: '$mallstores#mallid_mallvalue#buildingid_buildingvalue#unitid_unitvalue'
}

For PK values, the service and version values from the model are prefixed onto the key.

For SK values, the entity value from the model is prefixed onto the key.

Composite Attribute Arrays

Within a Composite Attribute Array, each element is the name of the corresponding Attribute defined in the Model. The attributes chosen, and the order in which they are specified, will translate to how your composite keys will be built by ElectroDB.

NOTE: If the Attribute has a label property, that will be used to prefix the composite attributes, otherwise the full Attribute name will be used.

attributes: {
    storeId: {
        type: "string",
        label: "sid",
    },
    mallId: {
        type: "string",
        label: "mid",
    },
    buildingId: {
        type: "string",
        label: "bid",
    },
    unitId: {
        type: "string",
        label: "uid",
    }
},
indexes: {
    locations: {
        pk: {
            field: "pk",
            composite: ["storeId"]
        },
        sk: {
            field: "sk",
            composite: ["mallId", "buildingId", "unitId"]
        }
    }
}
    
// Input
{
    storeId: "STOREVALUE",
    mallId: "MALLVALUE",
    buildingId: "BUILDINGVALUE",
    unitId: "UNITVALUE"
};

// Output:
{
    pk: '$mallstoredirectory_1#sid_storevalue',
    sk: '$mallstores#mid_mallvalue#bid_buildingvalue#uid_unitvalue'
}

Composite Attribute Templates

In a Composite Template, you provide a formatted template for ElectroDB to use when making keys. Composite Attribute Templates allow for potential ElectroDB adoption on already established tables and records.

Attributes are identified by surrounding the attribute with ${...} braces. For example, the syntax ${storeId} will match storeId attribute in the model.

Convention for a composing a key use the # symbol to separate attributes, and for labels to attach with underscore. For example, when composing both the mallId and buildingId would be expressed as mid_${mallId}#bid_${buildingId}.

NOTE: ElectroDB will not prefix templated keys with the Entity, Project, Version, or Collection. This will give you greater control of your keys but will limit ElectroDB's ability to prevent leaking entities with some queries.

ElectroDB will continue to always add a trailing delimiter to composite attributes with keys are partially supplied. The section on BeginsWith Queries goes into more detail about how ElectroDB builds indexes from composite attributes.

{
    model: {
        entity: "MallStoreCustom",
        version: "1",
        service: "mallstoredirectory"
    },
  attributes: {
      storeId: {
          type: "string"
      },
      mallId: {
          type: "string"
      },
      buildingId: {
          type: "string"
      },
      unitId: {
          type: "string"
      }
  },
  indexes: {
      locations: {
          pk: {
              field: "pk",
              template: "sid_${storeId}"
          },
          sk: {
              field: "sk",
              template: "mid_${mallId}#bid_${buildingId}#uid_${unitId}"
          }
      }
  }
}


// Input
{
    storeId: "STOREVALUE",
    mallId: "MALLVALUE",
    buildingId: "BUILDINGVALUE",
    unitId: "UNITVALUE"
};

// Output:
{
    pk: 'sid_storevalue',
    sk: 'mid_mallvalue#bid_buildingvalue#uid_unitvalue'
}

Templates and Composite Attribute Arrays

The example above shows indexes defined only with the template property. This property alone is enough to work with ElectroDB, however it can be useful to also include a composite array with the names of the Composite Attributes included in the template string. Doing so achieves the following benefits:

ElectroDB will enforce that the template you have supplied actually resolves to the composite attributes specified in the array.

If you use ElectroDB with TypeScript, supplying the composite array will ensure the indexes' Composite Attributes are typed just the same as if you had not used a composite template.

An example of using template while also using composite:

{
  indexes: {
    locations: {
      pk: {
        field: "pk",
        template: "sid_${storeId}"
        composite: ["storeId"]
      },
      sk: {
        field: "sk",
        template: "mid_${mallId}#bid_${buildingId}#uid_${unitId}",
        composite: ["mallId", "buildingId", "unitId"]
      }
    }
  }
}

Composite Attribute and Index Considerations

As described in the above two sections (Composite Attributes, Indexes), ElectroDB builds your keys using the attribute values defined in your model and provided on your query. Here are a few considerations to take into account when thinking about how to model your indexes:

Your table's primary Partition and Sort Keys cannot be changed after a record has been created. Be mindful of not to use Attributes that have values that can change as composite attributes for your primary table index.

When updating/patching an Attribute that is also a composite attribute for secondary index, ElectroDB will perform a runtime check that the operation will leave a key in a partially built state. For example: if a Sort Key is defined as having the Composite Attributes ["prop1", "prop2", "prop3"], than an update to the prop1 Attribute will require supplying the prop2 and prop3 Attributes as well. This prevents a loss of key fidelity because ElectroDB is not able to update a key partially in place with its existing values.

As described and detailed in [Composite Attribute Arrays](#composite attribute-arrays), you can use the label property on an Attribute shorten a composite attribute's prefix on a key. This can allow trim down the length of your keys.

Attributes as Indexes

It may be the case that an index field is also an attribute. For example, if a table was created with a Primary Index partition key of accountId, and that same field is used to store the accountId value used by the application. The following are a few examples of how to model that schema with ElectroDB:

NOTE: If you have the unique opportunity to use ElectroDB with a new project, it is strongly recommended to use generically named index fields that are separate from your business attributes.

Using composite

When your attribute's name, or field property on an attribute, matches the field property on an indexes' pk or sk ElectroDB will forego its usual index key prefixing.

{
  model: {
    entity: "your_entity_name",
    service: "your_service_name",
    version: "1"
  },
  attributes: {
    accountId: {
      type: "string"
    },
    productNumber: {
      type: "number"
    }
  },
  indexes: {
    products: {
      pk: {
        field: "accountId",
        composite: ["accountId"]
      },
      sk: {
        field: "productNumber",
        composite: ["productNumber"]
      }
    }
  }
}

Using template

Another approach allows you to use the template property, which allows you to format exactly how your key should be built when interacting with DynamoDB. In this case composite is optional when using template, but including it helps with TypeScript typing.

{
  model: {
    entity: "your_entity_name",
    service: "your_service_name",
    version: "1"
  },
  attributes: {
    accountId: {
      type: "string" // string and number types are both supported
    }      
  },
  indexes: {
    "your_access_pattern_name": {
      pk: {
        field: "accountId",
        composite: ["accountId"], // `composite` is optional when using `template` but is required when using TypeScript
        template: "${accountId}"
      },
      sk: {...}
    }
  }
}

Advanced use of template

When your string attribute is also an index key, and using key templates, you can also add static prefixes and postfixes to your attribute. Under the covers, ElectroDB will leverage this template while interacting with DynamoDB but will allow you to maintain a relationship with the attribute value itself.

For example, given the following model:

{
  model: {
    entity: "your_entity_name",
    service: "your_service_name",
    version: "1"
  },
  attributes: {
    accountId: {
      type: "string" // only string types are both supported for this example
    },
    organizationId: {
      type: "string"
    },
    name: {
      type: "string"
    }
  },
  indexes: {
    "your_access_pattern_name": {
      pk: {
        field: "accountId",
        composite: ["accountId"],
        template: "prefix_${accountId}_postfix"
      },
      sk: {
        field: "organizationId",
        composite: ["organizationId"]
      }
    }
  }
}

ElectroDB will accept a get request like this:

await myEntity.get({
  accountId: "1111-2222-3333-4444",
  organizationId: "AAAA-BBBB-CCCC-DDDD"
}).go()

Query DynamoDB with the following params (note the pre/postfix on accountId):

NOTE: ElectroDB defaults keys to lowercase, though this can be configured using Index Casing.

{
  Key: {
    accountId: "prefix_1111-2222-3333-4444_postfix",
    organizationId: `aaaa-bbbb-cccc-dddd`, 
  },
  TableName: 'your_table_name'
}

When returned from a query, however, ElectroDB will return the following and trim the key of it's prefix and postfix:

{
  accountId: "prefix_1111-2222-3333-4444_postfix",
  organizationId: `aaaa-bbbb-cccc-dddd`,
}
name: "your_item_name"

Collections

A Collection is a grouping of Entities with the same Partition Key and allows you to make efficient query across multiple entities. If your background is SQL, imagine Partition Keys as Foreign Keys, a Collection represents a View with multiple joined Entities.

NOTE: ElectroDB Collections use DynamoDB queries to retrieve results. One query is made to retrieve results for all Entities (the benefits of single table design), however like the query method, ElectroDB will paginate through all results for a given query.

Collections are defined on an Index, and the name of the collection should represent what the query would return as a pseudo Entity. Additionally, Collection names must be unique across a Service.

NOTE: A collection name should be unique to a single common index across entities.

const DynamoDB = require("aws-sdk/clients/dynamodb");
const table = "projectmanagement";
const client = new DynamoDB.DocumentClient();

const employees = new Entity({
  model: {
    entity: "employees",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    employeeId: {
      type: "string"
    },
    organizationId: {
      type: "string"
    },
    name: {
      type: "string"
    },
    team: {
      type: ["jupiter", "mercury", "saturn"]
    }
  },
  indexes: {
    staff: {
      pk: {
        field: "pk",
        composite: ["organizationId"]
      },
      sk: {
        field: "sk",
        composite: ["employeeId"]
      }
    },
    employee: {
      collection: "assignments",
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: [],
      },
    }
  }
}, { client, table })

const tasks = new Entity({
  model: {
    entity: "tasks",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    taskId: {
      type: "string"
    },
    employeeId: {
      type: "string"
    },
    projectId: {
      type: "string"
    },
    title: {
      type: "string"
    },
    body: {
      type: "string"
    }
  },
  indexes: {
    project: {
      pk: {
        field: "pk",
        composite: ["projectId"]
      },
      sk: {
        field: "sk",
        composite: ["taskId"]
      }
    },
    assigned: {
      collection: "assignments",
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: ["projectId"],
      },
    }
  }
}, { client, table });

const TaskApp = new Service({employees, tasks});

await TaskApp.collections
    .assignments({employeeId: "JExotic"})
    .go();

// Equivalent Parameters
{
  "TableName": 'projectmanagement',
  "ExpressionAttributeNames": { '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  "ExpressionAttributeValues": { ':pk': '$taskapp_1#employeeid_joeexotic', ':sk1': '$assignments' },
  "KeyConditionExpression": '#pk = :pk and begins_with(#sk1, :sk1)',
  "IndexName": 'gsi2'
}

Collection Queries vs Entity Queries

To query across entities, collection queries make use of ElectroDB's Sort Key structure, which prefixes Sort Key fields with the collection name. Unlike an Entity Query, Collection Queries only leverage Composite Attributes from an access pattern's Partition Key.

To better explain how Collection Queries are formed, here is a juxtaposition of an Entity Query's parameters vs a Collection Query's parameters:

Entity Query

await TaskApp.entities
    .tasks.query
    .assigned({employeeId: "JExotic"})
    .go();

// Equivalent Parameters
{
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  ExpressionAttributeValues: {
    ':pk': '$taskapp#employeeid_jexotic',
    ':sk1': '$assignments#tasks_1'
  },
  IndexName: 'gsi2'
}

Collection Query

await TaskApp.collections
    .assignments({employeeId: "JExotic"})
    .go();

// Equivalent Parameters
{
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  ExpressionAttributeValues: { ':pk': '$taskapp#employeeid_jexotic', ':sk1': '$assignments' },
  IndexName: 'gsi2'
}

The notable difference between the two is how much of the Sort Key is specified at query time.

Entity Query:

ExpressionAttributeValues: { ':sk1': '$assignments#tasks_1' },

Collection Query:

ExpressionAttributeValues: { ':sk1': '$assignments' },

Collection Response Structure

Unlike Entity Queries which return an array, Collection Queries return an object. This object will have a key for every Entity name (or Entity Alias) associated with that Collection, and an array for all results queried that belong to that Entity.

For example, using the "TaskApp" models defined above, we would expect the following response from a query to the "assignments" collection:

let results = await TaskApp.collections
        .assignments({employeeId: "JExotic"})
        .go();

{
    tasks: [...],    // tasks for employeeId "JExotic" 
    employees: [...] // employee record(s) with employeeId "JExotic"
}

Because the Tasks and Employee Entities both associated their index (gsi2) with the same collection name (assignments), ElectroDB is able to associate the two entities via a shared Partition Key. As stated in the collections section, querying across Entities by PK can be comparable to querying across a foreign key in a traditional relational database.

Sub-Collections

Sub-Collections are an extension of Collection functionality that allow you to model more advanced access patterns. Collections and Sub-Collections are defined on Indexes via a property called collection, as either a string or string array respectively.

The following is an example of functionally identical collections, implemented as a string (referred to as a "collection") and then as a string array (referred to as sub-collections):

As a string (collection):

{
  collection: "assignments"
  pk: {
    field: "pk",
    composite: ["employeeId"]
  },
  sk: {
    field: "sk",
    composite: ["projectId"]
  }
}

As a string array (sub-collections):

{
  collection: ["assignments"]
  pk: {
    field: "pk",
            composite: ["employeeId"]
  },
  sk: {
    field: "sk",
            composite: ["projectId"]
  }
}

Both implementations above will create a "collections" method called assignments when added to a Service.

const results = await TaskApp.collections
    .assignments({employeeId: "JExotic"})
    .go();

The advantage to using a string array to define collections is the ability to express sub-collections. Below is an example of three entities using sub-collections, followed by an explanation of their sub-collection definitions:

Sub-Collection Entities

import {Entity, Service} from "electrodb"
import DynamoDB from "aws-sdk/clients/dynamodb";
const table = "projectmanagement";
const client = new DynamoDB.DocumentClient();

const employees = new Entity({
  model: {
    entity: "employees",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    employeeId: {
      type: "string"
    },
    organizationId: {
      type: "string"
    },
    name: {
      type: "string"
    },
    team: {
      type: ["jupiter", "mercury", "saturn"] as const
    }
  },
  indexes: {
    staff: {
      pk: {
        field: "pk",
        composite: ["organizationId"]
      },
      sk: {
        field: "sk",
        composite: ["employeeId"]
      }
    },
    employee: {
      collection: "contributions",
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: [],
      },
    }
  }
}, { client, table })

const tasks = new Entity({
  model: {
    entity: "tasks",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    taskId: {
      type: "string"
    },
    employeeId: {
      type: "string"
    },
    projectId: {
      type: "string"
    },
    title: {
      type: "string"
    },
    body: {
      type: "string"
    }
  },
  indexes: {
    project: {
      collection: "overview",
      pk: {
        field: "pk",
        composite: ["projectId"]
      },
      sk: {
        field: "sk",
        composite: ["taskId"]
      }
    },
    assigned: {
      collection: ["contributions", "assignments"] as const,
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: ["projectId"],
      },
    }
  }
}, { client, table });

const projectMembers = new Entity({
  model: {
    entity: "projectMembers",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    employeeId: {
      type: "string"
    },
    projectId: {
      type: "string"
    },
    name: {
      type: "string"
    },
  },
  indexes: {
    members: {
      collection: "overview",
      pk: {
        field: "pk",
        composite: ["projectId"]
      },
      sk: {
        field: "sk",
        composite: ["employeeId"]
      }
    },
    projects: {
      collection: ["contributions", "assignments"] as const,
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: [],
      },
    }
  }
}, { client, table }); 

const TaskApp = new Service({employees, tasks, projectMembers});

TypeScript Note: Use as const syntax when defining collection as a string array for improved type support

The last line of the code block above creates a Service called TaskApp using the Entity instances created above its declaration. By creating a Service, ElectroDB will identify and validate the sub-collections defined across all three models. The result in this case are four unique collections: "overview", "contributions", and "assignments".

The simplest collection to understand is overview. This collection is defined on the table's Primary Index, composed of a projectId in the Partition Key, and is currently implemented by two Entities: tasks and projectMembers. If another entity were to be added to our service, it could "join" this collection by implementing an identical Partition Key composite (projectId) and labeling itself as part of the overview collection. The following is an example of using the overview collection:

// overview
const results = await TaskApp.collections
    .overview({projectId: "SD-204"})
    .go();

// results 
{ 
  tasks: [...],         // tasks associated with projectId "SD-204
  projectMembers: [...] // employees of project "SD-204"
}

// parameters
{
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#pk': 'pk', '#sk1': 'sk' },
  ExpressionAttributeValues: { ':pk': '$taskapp#projectid_sd-204', ':sk1': '$overview' }
}

Unlike overview, the collections contributions, and assignments are more complex.

In the case of contributions, all three entities implement this collection on the gsi2 index, and compose their Partition Key with the employeeId attribute. The assignments collection, however, is only implemented by the tasks and projectMembers Entities. Below is an example of using these collections:

NOTE: Collection values of collection: "contributions" and collection: ["contributions"] are interpreted by ElectroDB as being the same implementation.

// contributions
const results = await TaskApp.collections
        .contributions({employeeId: "JExotic"})
        .go();

// results 
{
  tasks: [...], // tasks assigned to employeeId "JExotic" 
  projectMembers: [...], // projects with employeeId "JExotic"
  employees: [...] // employee record(s) with employeeId "JExotic"
}

{
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  ExpressionAttributeValues: { ':pk': '$taskapp#employeeid_jexotic', ':sk1': '$contributions' },
  IndexName: 'gsi2'
}

// assignments
const results = await TaskApp.collections
        .assignments({employeeId: "JExotic"})
        .go();

// results 
{
  tasks: [...],          // tasks assigned to employeeId "JExotic" 
  projectMembers: [...], // projects with employeeId "JExotic"
}

{
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  ExpressionAttributeValues: {
    ':pk': '$taskapp#employeeid_jexotic',
    ':sk1': '$contributions#assignments'
  },
  IndexName: 'gsi2'
}

Looking above we can see that the assignments collection is actually a subset of the results that could be queried with the contributions collection. The power behind having the assignments sub-collection is the flexibility to further slice and dice your cross-entity queries into more specific and performant queries.

If you're interested in the naming used in the collection and access pattern definitions above, checkout the section on Naming Conventions

Index and Collection Naming Conventions

ElectroDB puts an emphasis on allowing users to define more domain specific naming. Instead of referring to indexes by their name on the table, ElectroDB allows users to define their indexes as Access Patterns.

Please refer to the Entities defined in the section Sub-Collection Entities as the source of examples within this section.

Index Naming Conventions

The following is an access pattern on the "employees" entity defined here:

staff: {
  pk: {
    field: "pk",
    composite: ["organizationId"]
  },
  sk: {
    field: "sk",
    composite: ["employeeId"]
  }
}

This Access Pattern is defined on the table's Primary Index (note the lack of an index property), is given the name staff, and is composed of an organiztionId and an employeeId.

When deciding on an Access Pattern name, ask yourself, "What would the array of items returned represent if I only supplied the Partition Key". In this example case, the entity defines an "Employee" by its organizationId and employeeId. If you performed a query against this index, and only provided organizationId you would then expect to receive all Employees for that Organization. From there, the name staff was chosen because the focus becomes "What are these Employees to that Organization?".

This convention also becomes evident when you consider Access Pattern name becomes the name of the method you use query that index.

await employee.query.staff({organizationId: "nike"}).go();

Collection Naming Conventions

The following are access patterns on entities defined here:

// employees entity
employee: {
  collection: "contributions",
  index: "gsi2",
  pk: {
    field: "gsi2pk",
    composite: ["employeeId"],
  },
  sk: {
    field: "gsi2sk",
    composite: [],
  },
}

// tasks entity
assigned: {
  collection: ["contributions", "assignments"],
  index: "gsi2",
  pk: {
    field: "gsi2pk",
    composite: ["employeeId"],
  },
  sk: {
    field: "gsi2sk",
    composite: ["projectId"],
  },
}

// projectMembers entity
projects: {
  collection: ["contributions", "assignments"] as const,
  index: "gsi2",
  pk: {
    field: "gsi2pk",
    composite: ["employeeId"],
  },
  sk: {
    field: "gsi2sk",
    composite: [],
  },
}

In the case of the entities above, we see an example of a sub-collection. ElectroDB will use the above definitions to generate two collections: contributions, assignments.

The considerations for naming a collection are nearly identical to the considerations for naming an index: What do the query results from supplying just the Partition Key represent? In the case of collections you must also consider what the results represent across all of the involved entities, and the entities that may be added in the future.

For example, the contributions collection is named such because when given an employeeId we receive the employee's details, the tasks the that employee, and the projects where they are currently a member.

In the case of assignments, we receive a subset of contributions when supplying an employeeId: Only the tasks and projects they are "assigned" are returned.

Filters

Filters are no longer the preferred way to add FilterExpressions. Checkout the Where section to find out about how to apply FilterExpressions and ConditionExpressions.

Building thoughtful indexes can make queries simple and performant. Sometimes you need to filter results down further. By adding Filters to your model, you can extend your queries with custom filters. Below is the traditional way you would add a filter to Dynamo's DocumentClient directly alongside how you would accomplish the same using a Filter function.

{
  "IndexName": "idx2",
  "TableName": "StoreDirectory",
  "ExpressionAttributeNames": {
    "#rent": "rent",
    "#discount": "discount",
    "#pk": "idx2pk",
    "#sk1": "idx2sk"
  },
  "ExpressionAttributeValues": {
    ":rent1": "2000.00",
    ":rent2": "5000.00",
    ":discount1": "1000.00",
    ":pk": "$mallstoredirectory_1#mallid_eastpointe",
    ":sk1": "$mallstore#leaseenddate_2020-04-01#rent_",
    ":sk2": "$mallstore#leaseenddate_2020-07-01#rent_"
  },
  "KeyConditionExpression": ",#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2",
  "FilterExpression": "(#rent between :rent1 and :rent2) AND #discount <= :discount1"
}

Defined on the model

Deprecated but functional with 1.x

Filters can be defined on the model and used in your query chain.

/**
    * Filter by low rent a specific mall or a leaseEnd withing a specific range  
    * @param {Object} attributes - All attributes from the model with methods for each filter operation  
    * @param {...*} values - Values passed when calling the filter in a query chain.
**/
filters: {
    rentPromotions: function(attributes, minRent, maxRent, promotion)  {
        let {rent, discount} = attributes;
        return `
            ${rent.between(minRent, maxRent)} AND ${discount.lte(promotion)}
        `
    }
}


let StoreLocations  =  new Entity(model, {table: "StoreDirectory"});
let maxRent = "5000.00";
let minRent = "2000.00";
let promotion = "1000.00";
let stores = await MallStores.query
    .stores({ mallId: "EastPointe" })
    .between({ leaseEndDate:  "2020-04-01" }, { leaseEndDate:  "2020-07-01" })
    .rentPromotions(minRent, maxRent, promotion)
    .go();

// Equivalent Parameters
{
  IndexName: 'idx2',
  TableName: 'StoreDirectory',
  ExpressionAttributeNames: {
    '#rent': 'rent',
    '#discount': 'discount',
    '#pk': 'idx2pk',
    '#sk1': 'idx2sk'
  },
  ExpressionAttributeValues: {
    ':rent1': '2000.00',
    ':rent2': '5000.00',
    ':discount1': '1000.00',
    ':pk': '$mallstoredirectory_1#mallid_eastpointe',
    ':sk1': '$mallstore#leaseenddate_2020-04-01#rent_',
    ':sk2': '$mallstore#leaseenddate_2020-07-01#rent_'
  },
  KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
  FilterExpression: '(#rent between :rent1 and :rent2) AND #discount <= :discount1'
}

Defined via Filter method after query operators

Filters are no longer the preferred way to add FilterExpressions. Checkout the Where section to find out about how to apply FilterExpressions and ConditionExpressions.

The easiest way to use filters is to use them inline in your query chain.

let StoreLocations  =  new Entity(model, {table: "StoreDirectory"});
let maxRent = "5000.00";
let minRent = "2000.00";
let promotion = "1000.00";
let stores  =  await StoreLocations.query
    .leases({ mallId: "EastPointe" })
    .between({ leaseEndDate:  "2020-04-01" }, { leaseEndDate:  "2020-07-01" })
    .filter(({rent, discount}) => `
        ${rent.between(minRent, maxRent)} AND ${discount.lte(promotion)}
    `)
    .go();

// Equivalent Parameters
{
  IndexName: 'idx2',
  TableName: 'StoreDirectory',
  ExpressionAttributeNames: {
    '#rent': 'rent',
    '#discount': 'discount',
    '#pk': 'idx2pk',
    '#sk1': 'idx2sk'
  },
  ExpressionAttributeValues: {
    ':rent1': '2000.00',
    ':rent2': '5000.00',
    ':discount1': '1000.00',
    ':pk': '$mallstoredirectory_1#mallid_eastpointe',
    ':sk1': '$mallstore#leaseenddate_2020-04-01#rent_',
    ':sk2': '$mallstore#leaseenddate_2020-07-01#rent_'
  },
  KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
  FilterExpression: '(#rent between :rent1 and :rent2) AND #discount <= :discount1'
}

Filter functions allow you to write a FilterExpression without having to worry about the complexities of expression attributes. To accomplish this, ElectroDB injects an object attributes as the first parameter to all Filter Functions. This object contains every Attribute defined in the Entity's Model with the following operators as methods:

operator | example | result | ----------- | -------------------------------- |
gte | rent.gte(maxRent) | #rent >= :rent1 gt | rent.gt(maxRent) | #rent > :rent1 lte | rent.lte(maxRent) | #rent <= :rent1 lt | rent.lt(maxRent) | #rent < :rent1 eq | rent.eq(maxRent) | #rent = :rent1 ne | rent.ne(maxRent) | #rent <> :rent1 begins | rent.begins(maxRent) | begins_with(#rent, :rent1) exists | rent.exists() | attribute_exists(#rent) notExists | rent.notExists() | attribute_not_exists(#rent) contains | rent.contains(maxRent) | contains(#rent = :rent1) notContains | rent.notContains(maxRent) | not contains(#rent = :rent1) between | rent.between(minRent, maxRent) | (#rent between :rent1 and :rent2) name | rent.name() | #rent value | rent.value(maxRent) | :rent1

This functionality allows you to write the remaining logic of your FilterExpression with ease. Add complex nested and/or conditions or other FilterExpression logic while ElectroDB handles the ExpressionAttributeNames and ExpressionAttributeValues.

Multiple Filters

Filters are no longer the preferred way to add FilterExpressions. Checkout the Where section to find out about how to apply FilterExpressions and ConditionExpressions.

It is possible to chain together multiple filters. The resulting FilterExpressions are concatenated with an implicit AND operator.

let MallStores = new Entity(model, {table: "StoreDirectory"});
let stores = await MallStores.query
    .leases({ mallId: "EastPointe" })
    .between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
    .filter(({ rent, discount }) => `
        ${rent.between("2000.00", "5000.00")} AND ${discount.eq("1000.00")}
    `)
    .filter(({ category }) => `
        ${category.eq("food/coffee")}
    `)
    .go();

// Equivalent Parameters
{
  TableName: 'StoreDirectory',
  ExpressionAttributeNames: {
    '#rent': 'rent',
    '#discount': 'discount',
    '#category': 'category',
    '#pk': 'idx2pk',
    '#sk1': 'idx2sk'
  },
  ExpressionAttributeValues: {
    ':rent1': '2000.00',
    ':rent2': '5000.00',
    ':discount1': '1000.00',
    ':category1': 'food/coffee',
    ':pk': '$mallstoredirectory_1#mallid_eastpointe',
    ':sk1': '$mallstore#leaseenddate_2020-04-01#storeid_',
    ':sk2': '$mallstore#leaseenddate_2020-07-01#storeid_'
  },
  KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
  IndexName: 'idx2',
  FilterExpression: '(#rent between :rent1 and :rent2) AND (#discount = :discount1 AND #category = :category1)'
}

Where

The where() method is an improvement on the filter() method. Unlike filter, where will be compatible with upcoming features related to complex types.

Building thoughtful indexes can make queries simple and performant. Sometimes you need to filter results down further or add conditions to an update/patch/put/create/delete/remove action.

FilterExpressions

Below is the traditional way you would add a FilterExpression to Dynamo's DocumentClient directly alongside how you would accomplish the same using the where method.

animals.query
  .exhibit({habitat: "Africa"})
  .where(({isPregnant, offspring}, {exists, eq}) => `
    ${eq(isPregnant, true)} OR ${exists(offspring)}
  `)
  .go()

{
  "KeyConditionExpression": "#pk = :pk and begins_with(#sk1, :sk1)",
  "TableName": "zoo_manifest",
  "ExpressionAttributeNames": {
    "#isPregnant": "isPregnant",
    "#offspring": "offspring",
    "#pk": "gsi1pk",
    "#sk1": "gsi1sk"
  },
  "ExpressionAttributeValues": {
    ":isPregnant0": true,
    ":pk": "$zoo#habitat_africa",
    ":sk1": "$animals_1#enclosure_"
  },
  "IndexName": "gsi1pk-gsi1sk-index",
  "FilterExpression": "#isPregnant = :isPregnant0 OR attribute_exists(#offspring)"
}

ConditionExpressions

Below is the traditional way you would add a ConditionExpression to Dynamo's DocumentClient directly alongside how you would accomplish the same using the where method.

animals.update({
    animal: "blackbear",
    name: "Isabelle"
  })
  // no longer pregnant because Ernesto was born!
  .set({
    isPregnant: false,
    lastEvaluation: "2021-09-12",
    lastEvaluationBy: "stephanie.adler"
  })
  // welcome to the world Ernesto!
  .append({
    offspring: [{
      name: "Ernesto",
      birthday: "2021-09-12",
      note: "healthy birth, mild pollen allergy"
    }]
  })
  // using the where clause can guard against making
  // updates against stale data
  .where(({isPregnant, lastEvaluation}, {lt, eq}) => `
    ${eq(isPregnant, true)} AND ${lt(lastEvaluation, "2021-09-12")}
  `)
  .go()

{
  "UpdateExpression": "SET #isPregnant = :isPregnant_u0, #lastEvaluation = :lastEvaluation_u0, #lastEvaluationBy = :lastEvaluationBy_u0, #offspring = list_append(#offspring, :offspring_u0)",
  "ExpressionAttributeNames": {
    "#isPregnant": "isPregnant",
    "#lastEvaluation": "lastEvaluation",
    "#lastEvaluationBy": "lastEvaluationBy",
    "#offspring": "offspring"
  },
  "ExpressionAttributeValues": {
    ":isPregnant0": true,
    ":lastEvaluation0": "2021-09-12",
    ":isPregnant_u0": false,
    ":lastEvaluation_u0": "2021-09-12",
    ":lastEvaluationBy_u0": "stephanie.adler",
    ":offspring_u0": [
      {
        "name": "Ernesto",
        "birthday": "2021-09-12",
        "note": "healthy birth, mild pollen allergy"
      }
    ]
  },
  "TableName": "zoo_manifest",
  "Key": {
    "pk": "$zoo#animal_blackbear",
    "sk": "$animals_1#name_isabelle"
  },
  "ConditionExpression": "#isPregnant = :isPregnant0 AND #lastEvaluation < :lastEvaluation0"
}

Where with Complex Attributes

ElectroDB supports using the where() method with DynamoDB's complex attribute types: map, list, and set. When using the injected attributes object, simply drill into the attribute itself to apply your update directly to the required object.

The following are examples on how to filter on complex attributes:

Example 1: Filtering on a map attribute

animals.query
    .farm({habitat: "Africa"})
    .where(({veterinarian}, {eq}) => eq(veterinarian.name, "Herb Peterson"))
    .go()

Example 1: Filtering on an element in a list attribute

animals.query
  .exhibit({habitat: "Tundra"})
  .where(({offspring}, {eq}) => eq(offspring[0].name, "Blitzen"))
  .go()

Attributes and Operations

Where functions allow you to write a FilterExpression or ConditionExpression without having to worry about the complexities of expression attributes. To accomplish this, ElectroDB injects an object attributes as the first parameter to all Filter Functions, and an object operations, as the second parameter. Pass the properties from the attributes object to the methods found on the operations object, along with inline values to set filters and conditions.

NOTE: where callbacks must return a string. All method on the operation object all return strings, so you can return the results of the operation method or use template strings compose an expression.

// A single filter operation 
animals.update({habitat: "Africa", enclosure: "5b"})
  .set({keeper: "Joe Exotic"})
  .where((attr, op) => op.eq(attr.dangerous, true))
  .go();

// A single filter operation w/ destructuring
animals.update({animal: "tiger", name: "janet"})
  .set({keeper: "Joe Exotic"})
  .where(({dangerous}, {eq}) => eq(dangerous, true))
  .go();

// Multiple conditions - `op`
animals.update({animal: "tiger", name: "janet"})
  .set({keeper: "Joe Exotic"})
  .where((attr, op) => `
    ${op.eq(attr.dangerous, true)} AND ${op.notExists(attr.lastFed)}
  `)
  .go();

// Multiple usages of `where` (implicit AND)
animals.update({animal: "tiger", name: "janet"})
  .set({keeper: "Joe Exotic"})
  .where((attr, op) => `
    ${op.eq(attr.dangerous, true)} OR ${op.notExists(attr.lastFed)}
  `)
  .where(({birthday}, {between}) => {
    const today = Date.now();
    const lastMonth = today - 1000 * 60 * 60 * 24 * 30;
    return between(birthday, lastMonth, today);
  })
  .go();

// "dynamic" filtering
function getAnimals(habitat, keepers) {
  const query = animals.query.exhibit({habitat});
  for (const name of keepers) {
    query.where(({keeper}, {eq}) => eq(keeper, name));
  }
  return query.go();
}

const keepers = [
  "Joe Exotic",
  "Carol Baskin"
];

getAnimals("RainForest", keepers);

The attributes object contains every Attribute defined in the Entity's Model. The operations object contains the following methods:

operator	example	result
eq	eq(rent, maxRent)	#rent = :rent1
ne	eq(rent, maxRent)	#rent <> :rent1
gte	gte(rent, value)	#rent >= :rent1
gt	gt(rent, maxRent)	#rent > :rent1
lte	lte(rent, maxRent)	#rent <= :rent1
lt	lt(rent, maxRent)	#rent < :rent1
begins	begins(rent, maxRent)	begins_with(#rent, :rent1)
exists	exists(rent)	attribute_exists(#rent)
notExists	notExists(rent)	attribute_not_exists(#rent)
contains	contains(rent, maxRent)	contains(#rent = :rent1)
notContains	notContains(rent, maxRent)	not contains(#rent = :rent1)
between	between(rent, minRent, maxRent)	(#rent between :rent1 and :rent2)
name	name(rent)	#rent
value	value(rent, maxRent)	:rent1

Multiple Where Clauses

It is possible to include chain multiple where clauses. The resulting FilterExpressions (or ConditionExpressions) are concatenated with an implicit AND operator.

let MallStores = new Entity(model, {table: "StoreDirectory"});
let stores = await MallStores.query
    .leases({ mallId: "EastPointe" })
    .between({ leaseEndDate: "2020-04-01" }, { leaseEndDate: "2020-07-01" })
    .where(({ rent, discount }, {between, eq}) => `
        ${between(rent, "2000.00", "5000.00")} AND ${eq(discount, "1000.00")}
    `)
    .where(({ category }, {eq}) => `
        ${eq(category, "food/coffee")}
    `)
    .go();

// Equivalent Parameters
{
  TableName: 'StoreDirectory',
  ExpressionAttributeNames: {
    '#rent': 'rent',
    '#discount': 'discount',
    '#category': 'category',
    '#pk': 'idx2pk',
    '#sk1': 'idx2sk'
  },
  ExpressionAttributeValues: {
    ':rent1': '2000.00',
    ':rent2': '5000.00',
    ':discount1': '1000.00',
    ':category1': 'food/coffee',
    ':pk': '$mallstoredirectory_1#mallid_eastpointe',
    ':sk1': '$mallstore#leaseenddate_2020-04-01#storeid_',
    ':sk2': '$mallstore#leaseenddate_2020-07-01#storeid_'
  },
  KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
  IndexName: 'idx2',
  FilterExpression: '(#rent between :rent1 and :rent2) AND (#discount = :discount1 AND #category = :category1)'
}

Parse

The parse method can be given a DocClient response and return a typed and formatted ElectroDB item.

ElectroDB's parse() method accepts results from get, delete, put, update, query, and scan operations, applies all the same operations as though the item was retrieved by ElectroDB itself, and will return null (or empty array for query results) if the item could not be parsed.

const myEntity = new Entity({...});
const getResults = docClient.get({...}).promise();
const queryResults = docClient.query({...}).promise();
const updateResults = docClient.update({...}).promise(); 
const formattedGetResults = myEntity.parse(getResults);
const formattedQueryResults = myEntity.parse(formattedQueryResults);

Parse also accepts an optional options object as a second argument (see the section Query Options to learn more). Currently, the following query options are relevant to the parse() method:

Option | Default | Notes ----------------- : ------- | ----- ignoreOwnership | true | This property defaults to true here, unlike elsewhere in the application when it defaults to false. You can overwrite the default here with your own preference.

Building Queries

For hands-on learners: the following example can be followed along with and executed on runkit: https://runkit.com/tywalch/electrodb-building-queries

ElectroDB queries use DynamoDB's query method to find records based on your table's indexes.

NOTE: By default, ElectroDB will paginate through all items that match your query. To limit the number of items ElectroDB will retrieve, read more about the Query Options pages and limit, or use the ElectroDB Pagination API for fine-grain pagination support.

Forming a composite Partition Key and Sort Key is a critical step in planning Access Patterns in DynamoDB. When planning composite keys, it is crucial to consider the order in which they are composed. As of the time of writing this documentation, DynamoDB has the following constraints that should be taken into account when planning your Access Patterns:

1. You must always supply the Partition Key in full for all queries to DynamoDB.
2. You currently only have the following operators available on a Sort Key: begins_with, between, >, >=, <, <=, and Equals.
3. To act on single record, you will need to know the full Partition Key and Sort Key for that record.

Using composite attributes to make hierarchical keys

Carefully considering your Composite Attribute order will allow ElectroDB to express hierarchical relationships and unlock more available Access Patterns for your application.

For example, let's say you have a StoreLocations Entity that represents Store Locations inside Malls:

Shopping Mall Stores

let schema = {  
    model: {
      service: "MallStoreDirectory",  
      entity: "MallStore",
      version: "1",    
    },  
    attributes: {
        cityId: {
            type: "string",
            required: true,
        }, 
        mallId: {  
            type: "string",  
            required: true,  
        },  
        storeId: {  
            type: "string",  
            required: true,  
        },  
        buildingId: {  
            type: "string",  
            required: true,  
        },  
        unitId: {  
            type: "string",  
            required: true,
        },  
        category: {  
            type: [
                "spite store",
                "food/coffee", 
                "food/meal", 
                "clothing", 
                "electronics", 
                "department", 
                "misc"
            ],  
            required: true  
        },  
        leaseEndDate: {  
            type: "string",  
            required: true  
        },
        rent: {
            type: "string",
            required: true,
            validate: /^(\d+\.\d{2})$/
        },
        discount: {
            type: "string",
            required: false,
            default: "0.00",
            validate: /^(\d+\.\d{2})$/
        }  
    },  
    indexes: {  
        stores: {  
            pk: {
                field: "pk",
                composite: ["cityId", "mallId"]
            }, 
            sk: {
                field: "sk",
                composite: ["buildingId", "storeId"]
            }  
        },  
        units: {  
            index: "gis1pk-gsi1sk-index",  
            pk: {
                field: "gis1pk",
                composite: ["mallId"]
            },  
            sk: {
                field: "gsi1sk",
                composite: ["buildingId", "unitId"]
            }  
        },
        leases: {
            index: "gis2pk-gsi2sk-index",
            pk: {
                field: "gis2pk",
                composite: ["storeId"]
            },  
            sk: {
                field: "gsi2sk",
                composite: ["leaseEndDate"]
            }  
        }
    }
};
const StoreLocations = new Entity(schema, {table: "StoreDirectory"});

Query App Records

Examples in this section using the MallStore schema defined above, and available for interacting with here: https://runkit.com/tywalch/electrodb-building-queries

All queries start from the Access Pattern defined in the schema.

const MallStore = new Entity(schema, {table: "StoreDirectory"}); 
// Each Access Pattern is available on the Entity instance
// MallStore.query.stores()
// MallStore.query.malls()

Partition Key Composite Attributes

All queries require (at minimum) the Composite Attributes included in its defined Partition Key. Composite Attributes you define on the Sort Key can be partially supplied, but must be supplied in the order they are defined.

Important: Composite Attributes must be supplied in the order they are composed when invoking the Access Pattern. This is because composite attributes are used to form a concatenated key string, and if attributes supplied out of order, it is not possible to fill the gaps in that concatenation.

const MallStore = new Entity({
  model: {
    service: "mallmgmt",
    entity: "store", 
    version: "1"
  },
  attributes: {
    cityId: "string",
    mallId: "string",
    storeId: "string",
    buildingId: "string",
    unitId: "string",
    name: "string",
    description: "string",
    category: "string"
  },
  indexes: {
    stores: {
      pk: {
        field: "pk",
        composite: ["cityId", "mallId"]
      },
      sk: {
        field: "sk",
        composite: ["storeId", "unitId"]
      }
    }
  }
}, {table: "StoreDirectory"});

const cityId = "Atlanta1";
const mallId = "EastPointe";
const storeId = "LatteLarrys";
const unitId = "B24";
const buildingId = "F34";

// Good: Includes at least the PK
StoreLocations.query.stores({cityId, mallId});

// Good: Includes at least the PK, and the first SK attribute
StoreLocations.query.stores({cityId, mallId, storeId});

// Good: Includes at least the PK, and the all SK attributes   
StoreLocations.query.stores({cityId, mallId, storeId, unitId});

// Bad: No PK composite attributes specified, will throw
StoreLocations.query.stores();

// Bad: Not All PK Composite Attributes included (cityId), will throw
StoreLocations.query.stores({mallId});

// Bad: Composite Attributes not included in order, will NOT throw, but will ignore `unitId` because `storeId` was not supplied as well
StoreLocations.query.stores({cityId, mallId, unitId});

Sort Key Operations

operator	use case
begins	Keys starting with a particular set of characters.
between	Keys between a specified range.
gt	Keys less than some value
gte	Keys less than or equal to some value
lt	Keys greater than some value
lte	Keys greater than or equal to some value

Each record represents one Store location. All Stores are located in Malls we manage.

To satisfy requirements for searching based on location, you could use the following keys: Each StoreLocations record would have a Partition Key with the store's storeId. This key alone is not enough to identify a particular store. To solve this, compose a Sort Key for the store's location attribute ordered hierarchically (mall/building/unit): ["mallId", "buildingId", "unitId"].

The StoreLocations entity above, using just the stores Index alone enables four Access Patterns:

1. All LatteLarrys locations in all Malls
2. All LatteLarrys locations in one Mall
3. All LatteLarrys locations inside a specific Mall
4. A specific LatteLarrys inside of a Mall and Building

Query Chains

Queries in ElectroDB are built around the Access Patterns defined in the Schema and are capable of using partial key Composite Attributes to create performant lookups. To accomplish this, ElectroDB offers a predictable chainable API.

Examples in this section using the StoreLocations schema defined above and can be directly experiment with on runkit: https://runkit.com/tywalch/electrodb-building-queries

The methods: Get (get), Create (put), Update (update), and Delete (delete) *require all composite attributes described in the Entities' primary PK and SK.

Query Method

ElectroDB queries use DynamoDB's query method to find records based on your table's indexes. To read more about queries checkout the section Building Queries

NOTE: By default, ElectroDB will paginate through all items that match your query. To limit the number of items ElectroDB will retrieve, read more about the Query Options pages and limit, or use the ElectroDB Pagination API for fine-grain pagination support.

Get Method

Provide all Table Index composite attributes in an object to the get method. In the event no record is found, a value of null will be returned.

NOTE: As part of ElectroDB's roll out of 1.0.0, a breaking change was made to the get method. Prior to 1.0.0, the get method would return an empty object if a record was not found. This has been changed to now return a value of null in this case.

let results = await StoreLocations.get({
    storeId: "LatteLarrys", 
    mallId: "EastPointe", 
    buildingId: "F34", 
    cityId: "Atlanta1"
}).go();

// Equivalent Params:
// {
//   Key: {
//     pk: "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
//     sk: "$mallstore_1#buildingid_f34#storeid_lattelarrys"
//   },
//   TableName: 'StoreDirectory'
// }

Batch Get

Provide all Table Index composite attributes in an array of objects to the get method to perform a BatchGet query.

NOTE: Performing a BatchGet will return a response structure unique to BatchGet: a two-dimensional array with the results of the query and any unprocessed records. See the example below. Additionally, when performing a BatchGet the .params() method will return an array of parameters, rather than just the parameters for one docClient query. This is because ElectroDB BatchGet queries larger than the docClient's limit of 100 records.

If the number of records you are requesting is above the BatchGet threshold of 100 records, ElectroDB will make multiple requests to DynamoDB and return the results in a single array. By default, ElectroDB will make these requests in series, one after another. If you are confident your table can handle the throughput, you can use the Query Option concurrent. This value can be set to any number greater than zero, and will execute that number of requests simultaneously.

For example, 150 records (50 records over the DynamoDB maximum):

The default value of concurrent will be 1. ElectroDB will execute a BatchGet request of 100, then after that request has responded, make another BatchGet request for 50 records.

If you set the Query Option concurrent to 2, ElectroDB will execute a BatchGet request of 100 records, and another BatchGet request for 50 records without waiting for the first request to finish.

It is important to consider your Table's throughput considerations when setting this value.

let [results, unprocessed] = await StoreLocations.get([
    {
        storeId: "LatteLarrys", 
        mallId: "EastPointe", 
        buildingId: "F34", 
        cityId: "Atlanta1"
    },
    {
        storeId: "MochaJoes", 
        mallId: "WestEnd", 
        buildingId: "A21", 
        cityId: "Madison2"
    }   
]).go({concurrent: 1}); // `concurrent` value is optional and default's to `1`

// Equivalent Params:
// {
//   "RequestItems": {
//     "electro": {
//       "Keys": [
//         {
//           "pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
//           "sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
//         },
//         {
//           "pk": "$mallstoredirectory#cityid_madison2#mallid_westend",
//           "sk": "$mallstore_1#buildingid_a21#storeid_mochajoes"
//         }
//       ]
//     }
//   }
// }

The two-dimensional array returned by batch get most easily used when deconstructed into two variables, in the above case: results and unprocessed.

The results array are records that were returned DynamoDB as Responses on the BatchGet query. They will appear in the same format as other ElectroDB queries.

Elements of the unprocessed array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the composite attributes defined in the Table Index. This is in keeping with DynamoDB's practice of returning only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as composite attributes, but you can pass the query option {unprocessed:"raw"} override this behavior and return the Keys as they came from DynamoDB.

Delete Method

Provide all Table Index composite attributes in an object to the delete method to delete a record.

await StoreLocations.delete({
    storeId: "LatteLarrys", 
    mallId: "EastPointe", 
    buildingId: "F34", 
    cityId: "Atlanta1"
}).go();

// Equivalent Params:
// {
//   Key: {
//     pk: "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
//     sk: "$mallstore_1#buildingid_f34#storeid_lattelarrys"
//   },
//   TableName: 'StoreDirectory'
// }

Batch Write Delete Records

Provide all table index composite attributes in an array of objects to the delete method to batch delete records.

NOTE: Performing a Batch Delete will return an array of "unprocessed" records. An empty array signifies all records were processed. If you want the raw DynamoDB response you can always use the option {raw: true}, more detail found here: Query Options. Additionally, when performing a BatchWrite the .params() method will return an array of parameters, rather than just the parameters for one docClient query. This is because ElectroDB BatchWrite queries larger than the docClient's limit of 25 records.

If the number of records you are requesting is above the BatchWrite threshold of 25 records, ElectroDB will make multiple requests to DynamoDB and return the results in a single array. By default, ElectroDB will make these requests in series, one after another. If you are confident your table can handle the throughput, you can use the Query Option concurrent. This value can be set to any number greater than zero, and will execute that number of requests simultaneously.

For example, 75 records (50 records over the DynamoDB maximum):

The default value of concurrent will be 1. ElectroDB will execute a BatchWrite request of 25, then after that request has responded, make another BatchWrite request for 25 records, and then another.

If you set the Query Option concurrent to 2, ElectroDB will execute a BatchWrite request of 25 records, and another BatchGet request for 25 records without waiting for the first request to finish. After those two have finished it will execute another BatchWrite request for 25 records.

It is important to consider your Table's throughput considerations when setting this value.

let unprocessed = await StoreLocations.delete([
    {
        storeId: "LatteLarrys", 
        mallId: "EastPointe", 
        buildingId: "F34", 
        cityId: "LosAngeles1"
    },
    {
        storeId: "MochaJoes", 
        mallId: "EastPointe", 
        buildingId: "F35", 
        cityId: "LosAngeles1"
    }
]).go({concurrent: 1}); // `concurrent` value is optional and default's to `1` 

// Equivalent Params:
{
  "RequestItems": {
    "StoreDirectory": [
      {
        "DeleteRequest": {
          "Key": {
            "pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
            "sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
          }
        }
      },
      {
        "DeleteRequest": {
          "Key": {
            "pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
            "sk": "$mallstore_1#buildingid_f35#storeid_mochajoes"
          }
        }
      }
    ]
  }
}

Elements of the unprocessed array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the composite attributes defined in the Table Index. This is in keeping with DynamoDB's practice of returning only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as composite attributes, but you can pass the query option {unprocessed:"raw"} override this behavior and return the Keys as they came from DynamoDB.

Put Record

Provide all required Attributes as defined in the model to create a new record. ElectroDB will enforce any defined validations, defaults, casting, and field aliasing. A Put operation will trigger the default, and set attribute callbacks when writing to DynamoDB. By default, after performing a put() or create() operation, ElectroDB will format and return the record through the same process as a Get/Query. This process will invoke the get callback on all included attributes. If this behaviour is not desired, use the Query Option response:"none" to return a null value.

This example includes an optional conditional expression

await StoreLocations
  .put({
      cityId: "Atlanta1",
      storeId: "LatteLarrys",
      mallId: "EastPointe",
      buildingId: "BuildingA1",
      unitId: "B47",
      category: "food/coffee",
      leaseEndDate: "2020-03-22",
      rent: "4500.00"
  })
  .where((attr, op) => op.eq(attr.rent, "4500.00"))
  .go()

// Equivalent Params:
{
  "Item": {
    "cityId": "Atlanta1",
    "mallId": "EastPointe",
    "storeId": "LatteLarrys",
    "buildingId": "BuildingA1",
    "unitId": "B47",
    "category": "food/coffee",
    "leaseEndDate": "2020-03-22",
    "rent": "4500.00",
    "discount": "0.00",
    "pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_buildinga1#storeid_lattelarrys",
    "gis1pk": "$mallstoredirectory#mallid_eastpointe",
    "gsi1sk": "$mallstore_1#buildingid_buildinga1#unitid_b47",
    "gis2pk": "$mallstoredirectory#storeid_lattelarrys",
    "gsi2sk": "$mallstore_1#leaseenddate_2020-03-22",
    "__edb_e__": "MallStore",
    "__edb_v__": "1"
  },
  "TableName": "StoreDirectory",
  "ConditionExpression": "#rent = :rent_w1",
  "ExpressionAttributeNames": {
    "#rent": "rent"
  },
  "ExpressionAttributeValues": {
    ":rent_w1": "4500.00"
  }
}

Batch Write Put Records

Provide all required Attributes as defined in the model to create records as an array to .put(). ElectroDB will enforce any defined validations, defaults, casting, and field aliasing. Another convenience ElectroDB provides, is accepting BatchWrite arrays larger than the 25 record limit. This is achieved making multiple, "parallel", requests to DynamoDB for batches of 25 records at a time. A failure with any of these requests will cause the query to throw, so be mindful of your table's configured throughput.

NOTE: Performing a Batch Put will return an array of "unprocessed" records. An empty array signifies all records returned were processed. If you want the raw DynamoDB response you can always use the option {raw: true}, more detail found here: Query Options. Additionally, when performing a BatchWrite the .params() method will return an array of parameters, rather than just the parameters for one docClient query. This is because ElectroDB BatchWrite queries larger than the docClient's limit of 25 records.

If the number of records you are requesting is above the BatchWrite threshold of 25 records, ElectroDB will make multiple requests to DynamoDB and return the results in a single array. By default, ElectroDB will make these requests in series, one after another. If you are confident your table can handle the throughput, you can use the Query Option concurrent. This value can be set to any number greater than zero, and will execute that number of requests simultaneously.

For example, 75 records (50 records over the DynamoDB maximum):

The default value of concurrent will be 1. ElectroDB will execute a BatchWrite request of 25, then after that request has responded, make another BatchWrite request for 25 records, and then another.

If you set the Query Option concurrent to 2, ElectroDB will execute a BatchWrite request of 25 records, and another BatchGet request for 25 records without waiting for the first request to finish. After those two have finished it will execute another BatchWrite request for 25 records.

It is important to consider your Table's throughput considerations when setting this value.

let unprocessed = await StoreLocations.put([
    {
        cityId: "LosAngeles1",
        storeId: "LatteLarrys",
        mallId: "EastPointe",
        buildingId: "F34",
        unitId: "a1",
        category: "food/coffee",
        leaseEndDate: "2022-03-22",
        rent: "4500.00"
    },
    {
        cityId: "LosAngeles1",
        storeId: "MochaJoes",
        mallId: "EastPointe",
        buildingId: "F35",
        unitId: "a2",
        category: "food/coffee",
        leaseEndDate: "2021-01-22",
        rent: "1500.00"
    }
]).go({concurrent: 1}); // `concurrent` value is optional and default's to `1`

// Equivalent Params:
{
  "RequestItems": {
    "StoreDirectory": [
      {
        "PutRequest": {
          "Item": {
            "cityId": "LosAngeles1",
            "mallId": "EastPointe",
            "storeId": "LatteLarrys",
            "buildingId": "F34",
            "unitId": "a1",
            "category": "food/coffee",
            "leaseEndDate": "2022-03-22",
            "rent": "4500.00",
            "discount": "0.00",
            "pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
            "sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys",
            "gis1pk": "$mallstoredirectory#mallid_eastpointe",
            "gsi1sk": "$mallstore_1#buildingid_f34#unitid_a1",
            "gis2pk": "$mallstoredirectory#storeid_lattelarrys",
            "gsi2sk": "$mallstore_1#leaseenddate_2022-03-22",
            "__edb_e__": "MallStore",
            "__edb_v__": "1"
          }
        }
      },
      {
        "PutRequest": {
          "Item": {
            "cityId": "LosAngeles1",
            "mallId": "EastPointe",
            "storeId": "MochaJoes",
            "buildingId": "F35",
            "unitId": "a2",
            "category": "food/coffee",
            "leaseEndDate": "2021-01-22",
            "rent": "1500.00",
            "discount": "0.00",
            "pk": "$mallstoredirectory#cityid_losangeles1#mallid_eastpointe",
            "sk": "$mallstore_1#buildingid_f35#storeid_mochajoes",
            "gis1pk": "$mallstoredirectory#mallid_eastpointe",
            "gsi1sk": "$mallstore_1#buildingid_f35#unitid_a2",
            "gis2pk": "$mallstoredirectory#storeid_mochajoes",
            "gsi2sk": "$mallstore_1#leaseenddate_2021-01-22",
            "__edb_e__": "MallStore",
            "__edb_v__": "1"
          }
        }
      }
    ]
  }
}

Elements of the unprocessed array are unlike results received from a query. Instead of containing all the attributes of a record, an unprocessed record only includes the composite attributes defined in the Table Index. This is in keeping with DynamoDB's practice of returning only Keys in the case of unprocessed records. For convenience, ElectroDB will return these keys as composite attributes, but you can pass the query option {unprocessed:"raw"} override this behavior and return the Keys as they came from DynamoDB.

Update Record

Update Methods are available after the method update() is called, and allow you to perform alter an item stored dynamodb. The methods can be used (and reused) in a chain to form update parameters, when finished with .params(), or an update operation, when finished with .go(). If your application requires the update method to return values related to the update (e.g. via the ReturnValues DocumentClient parameters), you can use the Query Option {response: "none" | "all_old" | "updated_old" | "all_new" | "updated_new"} with the value that matches your need. By default, the Update operation returns an empty object when using .go().

ElectroDB will validate an attribute's type when performing an operation (e.g. that the subtract() method can only be performed on numbers), but will defer checking the logical validity your update operation to the DocumentClient. If your query performs multiple mutations on a single attribute, or perform other illogical operations given nature of an item/attribute, ElectroDB will not validate these edge cases and instead will simply pass back any error(s) thrown by the Document Client.

Update Method	Attribute Types	Parameter
set	number string boolean enum map list set any	object
remove	number string boolean enum map list set any	array
add	number any set	object
subtract	number	object
append	any list	object
delete	any set	object
data	*	callback

Updates to Composite Attributes

ElectroDB adds some constraints to update calls to prevent the accidental loss of data. If an access pattern is defined with multiple composite attributes, then ElectroDB ensure the attributes cannot be updated individually. If an attribute involved in an index composite is updated, then the index key also must be updated, and if the whole key cannot be formed by the attributes supplied to the update, then it cannot create a composite key without overwriting the old data.

This example shows why a partial update to a composite key is prevented by ElectroDB:

{
  "index": "my-gsi",
  "pk": {
    "field": "gsi1pk",
    "composite": ["attr1"]
  },
  "sk": {
    "field": "gsi1sk",
    "composite": ["attr2", "attr3"]
  }
}

The above secondary index definition would generate the following index keys:

{
  "gsi1pk": "$service#attr1_value1",
  "gsi1sk": "$entity_version#attr2_value2#attr3_value6"
}

If a user attempts to update the attribute attr2, then ElectroDB has no way of knowing value of the attribute attr3 or if forming the composite key without it would overwrite its value. The same problem exists if a user were to update attr3, ElectroDB cannot update the key without knowing each composite attribute's value.

In the event that a secondary index includes composite values from the table's primary index, ElectroDB will draw from the values supplied for the update key to address index gaps in the secondary index. For example:

For the defined indexes:

{
  "accessPattern1": {
    "pk": {
      "field": "pk",
      "composite": ["attr1"]
    },
    "sk": {
      "field": "sk",
      "composite": ["attr2"]
    }
  },
  "accessPattern2": {
    "index": "my-gsi",
    "pk": {
      "field": "gsi1pk",
      "composite": ["attr3"]
    },
    "sk": {
      "field": "gsi1sk",
      "composite": ["attr2", "attr4"]
    }
  }
}

A user could update attr4 alone because ElectroDB is able to leverage the value for attr2 from values supplied to the update() method:

entity.update({ attr1: "value1", attr2: "value2" })
  .set({ attr4: "value4" })
  .go();

{
  "UpdateExpression": "SET #attr4 = :attr4_u0, #gsi1sk = :gsi1sk_u0, #attr1 = :attr1_u0, #attr2 = :attr2_u0",
  "ExpressionAttributeNames": {
    "#attr4": "attr4",
    "#gsi1sk": "gsi1sk",
    "#attr1": "attr1",
    "#attr2": "attr2"
  },
  "ExpressionAttributeValues": {
    ":attr4_u0": "value6",
    // This index was successfully built
    ":gsi1sk_u0": "$update-edgecases_1#attr2_value2#attr4_value6",
    ":attr1_u0": "value1",
    ":attr2_u0": "value2"
  },
  "TableName": "test_table",
  "Key": { 
    "pk": "$service#attr1_value1", 
    "sk": "$entity_version#attr2_value2" 
  }
}

Note: Included in the update are all attributes from the table's primary index. These values are automatically included on all updates in the event an update results in an insert.

Update Method: Set

The set() method will accept all attributes defined on the model. Provide a value to apply or replace onto the item.

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .set({category: "food/meal"})
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "SET #category = :category",
  "ExpressionAttributeNames": {
    "#category": "category"
  },
  "ExpressionAttributeValues": {
    ":category_w1": "food/coffee",
    ":category": "food/meal"
  },
  "TableName": "StoreDirectory",
  "Key": {
    "pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_f34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category_w1"
}

Update Method: Remove

The remove() method will accept all attributes defined on the model. Unlike most other update methods, the remove() method accepts an array with the names of the attributes that should be removed.

NOTE that the attribute property required functions as a sort of NOT NULL flag. Because of this, if a property exists as required:true it will not be possible to remove that property in particular. If the attribute is a property is on "map", and the "map" is not required, then the "map" can be removed.

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .remove(["category"])
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "REMOVE #category",
  "ExpressionAttributeNames": {
    "#category": "category"
  },
  "ExpressionAttributeValues": {
    ":category0": "food/coffee"
  },
  "TableName": "StoreDirectory",
  "Key": {
    "pk": "$mallstoredirectory#cityid_atlanta#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_a34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category0"
}

Update Method: Add

The add() method will accept attributes with type number, set, and any defined on the model. In the case of a number attribute, provide a number to add to the existing attribute's value on the item.

If the attribute is defined as any, the syntax compatible with the attribute type set will be used. For this reason, do not use the attribute type any to represent a number.

const newTenant = client.createSet("larry");

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .add({
      rent: 100,         // "number" attribute
      tenant: ["larry"]  // "set" attribute
    })
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "SET #rent = #rent + :rent0 ADD #tenant :tenant0",
  "ExpressionAttributeNames": {
    "#category": "category",
    "#rent": "rent",
    "#tenant": "tenant"
  },
  "ExpressionAttributeValues": {
    ":category0": "food/coffee",
    ":rent0": 100,
    ":tenant0": ["larry"]
  },
  "TableName": "StoreDirectory",
  "Key": {
    "pk": "$mallstoredirectory#cityid_atlanta#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_a34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category0"
}

Update Method: Subtract

The subtract() method will accept attributes with type number. In the case of a number attribute, provide a number to subtract from the existing attribute's value on the item.

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .subtract({deposit: 500})
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "SET #deposit = #deposit - :deposit0",
  "ExpressionAttributeNames": {
    "#category": "category",
    "#deposit": "deposit"
  },
  "ExpressionAttributeValues": {
    ":category0": "food/coffee",
    ":deposit0": 500
  },
  "TableName": "StoreDirectory",
  "Key": {
    "pk": "$mallstoredirectory#cityid_atlanta#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_a34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category0"
}

Update Method: Append

The append() method will accept attributes with type any. This is a convenience method for working with DynamoDB lists, and is notably different that set because it will add an element to an existing array, rather than overwrite the existing value.

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .append({
      rentalAgreement: [{
        type: "ammendment", 
        detail: "no soup for you"
      }]
    })
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "SET #rentalAgreement = list_append(#rentalAgreement, :rentalAgreement0)",
  "ExpressionAttributeNames": {
    "#category": "category",
    "#rentalAgreement": "rentalAgreement"
  },
  "ExpressionAttributeValues": {
    ":category0": "food/coffee",
    ":rentalAgreement0": [
      {
        "type": "ammendment",
        "detail": "no soup for you"
      }
    ]
  },
  "TableName": "StoreDirectory",
  "Key": {
    "pk": "$mallstoredirectory#cityid_atlanta#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_a34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category0"
}

Update Method: Delete

The delete() method will accept attributes with type any or set . This operation removes items from a the contract attribute, defined as a set attribute.

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .delete({contact: ['555-345-2222']})
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "DELETE #contact :contact0",
  "ExpressionAttributeNames": {
    "#category": "category",
    "#contact": "contact"
  },
  "ExpressionAttributeValues": {
    ":category0": "food/coffee",
    ":contact0": "555-345-2222"
  },
  "TableName": "StoreDirectory",
  "Key": {
    "pk": "$mallstoredirectory#cityid_atlanta#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_a34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category0"
}

Update Method: Data

The data() allows for different approach to updating your item, by accepting a callback with a similar argument signature to the where clause.

The callback provided to the data method is injected with an attributes object as the first parameter, and an operations object as the second parameter. All operations accept an attribute from the attributes object as a first parameter, and optionally accept a second value parameter.

As mentioned above, this method is functionally similar to the where clause with one exception: The callback provided to data() is not expected to return a value. When you invoke an injected operation method, the side effects are applied directly to update expression you are building.

operation	example	result	description
set	set(category, value)	#category = :category0	Add or overwrite existing value
add	add(tenant, name)	#tenant :tenant1	Add value to existing set attribute (used when provided attribute is of type any or set)
add	add(rent, amount)	#rent = #rent + :rent0	Mathematically add given number to existing number on record
subtract	subtract(deposit, amount)	#deposit = #deposit - :deposit0	Mathematically subtract given number from existing number on record
remove	remove(petFee)	#petFee	Remove attribute/property from item
append	append(rentalAgreement, amendment)	#rentalAgreement = list_append(#rentalAgreement, :rentalAgreement0)	Add element to existing list attribute
delete	delete(tenant, name)	#tenant :tenant1	Remove item from existing set attribute
del	del(tenant, name)	#tenant :tenant1	Alias for delete operation
name	name(rent)	#rent	Reference another attribute's name, can be passed to other operation that allows leveraging existing attribute values in calculating new values
value	value(rent, value)	:rent1	Create a reference to a particular value, can be passed to other operation that allows leveraging existing attribute values in calculating new values

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .data((a, o) => {
        const newTenant = a.value(attr.tenant, "larry");
        o.set(a.category, "food/meal");   // electrodb "enum"   -> dynamodb "string"
        o.add(a.tenant, newTenant);       // electrodb "set"    -> dynamodb "set"
        o.add(a.rent, 100);               // electrodb "number" -> dynamodb "number"
        o.subtract(a.deposit, 200);       // electrodb "number" -> dynamodb "number"
        o.remove(a.leaseEndDate);         // electrodb "string" -> dynamodb "string"
        o.append(a.rentalAgreement, [{    // electrodb "list"   -> dynamodb "list"
            type: "ammendment",           // electrodb "map"    -> dynamodb "map"
            detail: "no soup for you"
        }]);
        o.delete(a.tags, ['coffee']);     // electrodb "set"    -> dynamodb "set"
        o.del(a.contact, '555-345-2222'); // electrodb "string" -> dynamodb "string"
        o.add(a.fees, op.name(a.petFee)); // electrodb "number" -> dynamodb "number"
        o.add(a.leaseHolders, newTenant); // electrodb "set"    -> dynamodb "set"
    })
    .where((attr, op) => op.eq(attr.category, "food/coffee"))
    .go()

// Equivalent Params:
{
  "UpdateExpression": "SET #category = :category_u0, #rent = #rent + :rent_u0, #deposit = #deposit - :deposit_u0, #rentalAgreement = list_append(#rentalAgreement, :rentalAgreement_u0), #totalFees = #totalFees + #petFee REMOVE #leaseEndDate, #gsi2sk ADD #tenant :tenant_u0, #leaseHolders :tenant_u0 DELETE #tags :tags_u0, #contact :contact_u0",
  "ExpressionAttributeNames": {
  "#category": "category",
    "#tenant": "tenant",
    "#rent": "rent",
    "#deposit": "deposit",
    "#leaseEndDate": "leaseEndDate",
    "#rentalAgreement": "rentalAgreement",
    "#tags": "tags",
    "#contact": "contact",
    "#totalFees": "totalFees",
    "#petFee": "petFee",
    "#leaseHolders": "leaseHolders",
    "#gsi2sk": "gsi2sk"
  },
  "ExpressionAttributeValues": {
    ":category0": "food/coffee",
    ":category_u0": "food/meal",
    ":tenant_u0": ["larry"],
    ":rent_u0": 100,
    ":deposit_u0": 200,
    ":rentalAgreement_u0": [{
      "type": "amendment",
      "detail": "no soup for you"
    }],
    ":tags_u0": ["coffee"], // <- DynamoDB Set
    ":contact_u0": ["555-345-2222"], // <- DynamoDB Set 
    },
  "TableName": "electro",
  "Key": {
    "pk": `$mallstoredirectory#cityid_12345#mallid_eastpointe`,
    "sk": "$mallstore_1#buildingid_a34#storeid_lattelarrys"
  },
  "ConditionExpression": "#category = :category0"
}

Update Method: Complex Data Types

ElectroDB supports updating DynamoDB's complex types (list, map, set) with all of its Update Methods.

When using the chain methods set, add, subtract, remove, append, and delete, you can access map properties, list elements, and set items by supplying the json path of the property as the name of the attribute.

The data() method also allows for working with complex types. Unlike using the update chain methods, the data() method ensures type safety when using TypeScript. When using the injected attributes object, simply drill into the attribute itself to apply your update directly to the required object.

The following are examples on how update complex attributes, using both with chain methods and the data() method.

Example 1: Set property on a map attribute

Specifying a property on a map attribute is expressed with dot notation.

// via Chain Method
await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .set({'mapAttribute.mapProperty':  "value"})
    .go();

// via Data Method 
await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .data(({mapAttribute}, {set}) => set(mapAttribute.mapProperty, "value"))
    .go()

Example 2: Removing an element from a list attribute

Specifying an index on a list attribute is expressed with square brackets containing the element's index number.

// via Chain Method
await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .remove(['listAttribute[0]'])
    .go();

// via Data Method 
await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .data(({listAttribute}, {remove}) => remove(listAttribute[0]))
    .go();

Example 3: Adding an item to a set attribute, on a map attribute, that is an element of a list attribute

All other complex structures are simply variations on the above two examples.

// Set values must use the DocumentClient to create a `set`
const newSetValue = StoreLocations.client.createSet("setItemValue"); 

// via Data Method 
await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .add({'listAttribute[1].setAttribute': newSetValue})
    .go();

await StoreLocations
    .update({cityId, mallId, storeId, buildingId})
    .data(({listAttribute}, {add}) => {
        add(listAttribute[1].setAttribute, newSetValue)
    })
    .go();

Scan Records

When scanning for rows, you can use filters the same as you would any query. For more information on filters, see the Where section.

Note: Scan functionality will be scoped to your Entity. This means your results will only include records that match the Entity defined in the model.

await StoreLocations.scan
    .where(({category}, {eq}) => `
        ${eq(category, "food/coffee")} OR ${eq(category, "spite store")}  
    `)
    .where(({leaseEndDate}, {between}) => `
        ${between(leaseEndDate, "2020-03", "2020-04")}
    `)
    .go()

// Equivalent Params:
{
  "TableName": "StoreDirectory",
  "ExpressionAttributeNames": {
    "#category": "category",
    "#leaseEndDate": "leaseEndDate",
    "#pk": "pk",
    "#sk": "sk",
    "#__edb_e__": "__edb_e__",
    "#__edb_v__": "__edb_v__"
  },
  "ExpressionAttributeValues": {
    ":category_w1": "food/coffee",
    ":category_w2": "spite store",
    ":leaseEndDate_w1": "2020-03",
    ":leaseEndDate_w2": "2020-04",
    ":pk": "$mallstoredirectory#cityid_",
    ":sk": "$mallstore_1#buildingid_",
    ":__edb_e__": "MallStore",
    ":__edb_v__": "1"
  },
  "FilterExpression": "begins_with(#pk, :pk) AND #__edb_e__ = :__edb_e__ AND #__edb_v__ = :__edb_v__ AND begins_with(#sk, :sk) AND (#category = :category_w1 OR #category = :category_w2) AND (#leaseEndDate between :leaseEndDate_w1 and :leaseEndDate_w2)"
}

Remove Method

A convenience method for delete with ConditionExpression that the item being deleted exists. Provide all Table Index composite attributes in an object to the remove method to remove the record.

await StoreLocations.remove({
    storeId: "LatteLarrys", 
    mallId: "EastPointe", 
    buildingId: "F34", 
    cityId: "Atlanta1"
}).go();

// Equivalent Params:
// {
//   Key: {
//     pk: "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
//     sk: "$mallstore_1#buildingid_f34#storeid_lattelarrys"
//   },
//   TableName: 'StoreDirectory'
//   ConditionExpression: 'attribute_exists(pk) AND attribute_exists(sk)'
// }

Patch Record

In DynamoDB, update operations by default will insert a record if record being updated does not exist. In ElectroDB, the patch method will utilize the attribute_exists() parameter dynamically to ensure records are only "patched" and not inserted when updating.

For more detail on how to use the patch() method, see the section Update Record to see all the transferable requirements and capabilities available to patch().

Create Record

In DynamoDB, put operations by default will overwrite a record if record being updated does not exist. In ElectroDB, the patch method will utilize the attribute_not_exists() parameter dynamically to ensure records are only "created" and not overwritten when inserting new records into the table.

A Put operation will trigger the default, and set attribute callbacks when writing to DynamoDB. By default, after writing to DynamoDB, ElectroDB will format and return the record through the same process as a Get/Query, which will invoke the get callback on all included attributes. If this behaviour is not desired, use the Query Option response:"none" to return a null value.

await StoreLocations
  .create({
      cityId: "Atlanta1",
      storeId: "LatteLarrys",
      mallId: "EastPointe",
      buildingId: "BuildingA1",
      unitId: "B47",
      category: "food/coffee",
      leaseEndDate: "2020-03-22",
      rent: "4500.00"
  })
  .where((attr, op) => op.eq(attr.rent, "4500.00"))
  .go()

// Equivalent Params:
{
  "Item": {
    "cityId": "Atlanta1",
    "mallId": "EastPointe",
    "storeId": "LatteLarrys",
    "buildingId": "BuildingA1",
    "unitId": "B47",
    "category": "food/coffee",
    "leaseEndDate": "2020-03-22",
    "rent": "4500.00",
    "discount": "0.00",
    "pk": "$mallstoredirectory#cityid_atlanta1#mallid_eastpointe",
    "sk": "$mallstore_1#buildingid_buildinga1#storeid_lattelarrys",
    "gis1pk": "$mallstoredirectory#mallid_eastpointe",
    "gsi1sk": "$mallstore_1#buildingid_buildinga1#unitid_b47",
    "gis2pk": "$mallstoredirectory#storeid_lattelarrys",
    "gsi2sk": "$mallstore_1#leaseenddate_2020-03-22",
    "__edb_e__": "MallStore",
    "__edb_v__": "1"
  },
  "TableName": "StoreDirectory",
  "ConditionExpression": "attribute_not_exists(pk) AND attribute_not_exists(sk) AND #rent = :rent_w1",
  "ExpressionAttributeNames": {
    "#rent": "rent"
  },
  "ExpressionAttributeValues": {
    ":rent_w1": "4500.00"
  }
}

Find Records

DynamoDB offers three methods to query records: get, query, and scan. In ElectroDB, there is a fourth type: find. Unlike get and query, the find method does not require you to provide keys, but under the covers it will leverage the attributes provided to choose the best index to query on. Provide the find method will all properties known to match a record and ElectroDB will generate the most performant query it can to locate the results. This can be helpful with highly dynamic querying needs. If an index cannot be satisfied with the attributes provided, scan will be used as a last resort.

NOTE: The Find method is similar to the Match method with one exception: The attributes you supply directly to the .find() method will only be used to identify and fulfill your index access patterns. Any values supplied that do not contribute to a composite key will not be applied as query filters. Furthermore, if the values you provide do not resolve to an index access pattern, then a table scan will be performed. Use the where() chain method to further filter beyond keys, or use Match for the convenience of automatic filtering based on the values given directly to that method.

await StoreLocations.find({
    mallId: "EastPointe",
    buildingId: "BuildingA1",
}).go()

// Equivalent Params:
{
  "KeyConditionExpression": "#pk = :pk and begins_with(#sk1, :sk1)",
  "TableName": "StoreDirectory",
  "ExpressionAttributeNames": {
    "#mallId": "mallId",
    "#buildingId": "buildingId",
    "#pk": "gis1pk",
    "#sk1": "gsi1sk"
  },
  "ExpressionAttributeValues": {
    ":mallId1": "EastPointe",
    ":buildingId1": "BuildingA1",
    ":pk": "$mallstoredirectory#mallid_eastpointe",
    ":sk1": "$mallstore_1#buildingid_buildinga1#unitid_"
  },
  "IndexName": "gis1pk-gsi1sk-index",
}

Match Records

Match is a convenience method based off of ElectroDB's find method. Similar to Find, Match does not require you to provide keys, but under the covers it will leverage the attributes provided to choose the best index to query on.

Match differs from Find in that it will also include all supplied values into a query filter.

await StoreLocations.find({
    mallId: "EastPointe",
    buildingId: "BuildingA1",
    leaseEndDate: "2020-03-22",
    rent: "1500.00"
}).go()

// Equivalent Params:
{
  "KeyConditionExpression": "#pk = :pk and begins_with(#sk1, :sk1)",
  "TableName": "StoreDirectory",
  "ExpressionAttributeNames": {
    "#mallId": "mallId",
    "#buildingId": "buildingId",
    "#leaseEndDate": "leaseEndDate",
    "#rent": "rent",
    "#pk": "gis1pk",
    "#sk1": "gsi1sk"
  },
  "ExpressionAttributeValues": {
    ":mallId1": "EastPointe",
    ":buildingId1": "BuildingA1",
    ":leaseEndDate1": "2020-03-22",
    ":rent1": "1500.00",
    ":pk": "$mallstoredirectory#mallid_eastpointe",
    ":sk1": "$mallstore_1#buildingid_buildinga1#unitid_"
  },
  "IndexName": "gis1pk-gsi1sk-index",
  "FilterExpression": "#mallId = :mallId1 AND#buildingId = :buildingId1 AND#leaseEndDate = :leaseEndDate1 AND#rent = :rent1"
}

After invoking the Access Pattern with the required Partition Key Composite Attributes, you can now choose what Sort Key Composite Attributes are applicable to your query. Examine the table in Sort Key Operations for more information on the available operations on a Sort Key.

Access Pattern Queries

When you define your indexes in your model, you are defining the Access Patterns of your entity. The composite attributes you choose, and their order, ultimately define the finite set of index queries that can be made. The more you can leverage these index queries the better from both a cost and performance perspective.

Unlike Partition Keys, Sort Keys can be partially provided. We can leverage this to multiply our available access patterns and use the Sort Key Operations: begins, between, lt, lte, gt, and gte. These queries are more performant and cost-effective than filters. The costs associated with DynamoDB directly correlate to how effectively you leverage Sort Key Operations.

For a comprehensive and interactive guide to build queries please visit this runkit: https://runkit.com/tywalch/electrodb-building-queries.

Begins With Queries

One important consideration when using Sort Key Operations to make is when to use and not to use "begins".

It is possible to supply partially supply Sort Key composite attributes. Sort Key attributes must be provided in the order they are defined, but it's possible to provide only a subset of the Sort Key Composite Attributes to ElectroDB. By default, when you supply a partial Sort Key in the Access Pattern method, ElectroDB will create a beginsWith query. The difference between that and using .begins() is that, with a .begins() query, ElectroDB will not post-pend the next composite attribute's label onto the query.

The difference is nuanced and makes better sense with an example, but the rule of thumb is that data passed to the Access Pattern method should represent values you know strictly equal the value you want.

The following examples will use the following Access Pattern definition for units:

{
  "units": {
    "index": "gis1pk-gsi1sk-index",
    "pk": {
      "field": "gis1pk",
      "composite attributes": [
        "mallId"
      ]
    },
    "sk": {
      "field": "gsi1sk",
      "composite attributes": [
        "buildingId",
        "unitId"
      ]
    }
  }
}

The names you have given to your indexes on your entity model/schema express themselves as "Access Pattern" methods on your Entity's query object:

// Example #1, access pattern `units`
StoreLocations.query.units({mallId, buildingId}).go();
// -----------------------^^^^^^^^^^^^^^^^^^^^^^

Data passed to the Access Pattern method is considered to be full, known, data. In the above example, we are saying we know the mallId, buildingId and unitId.

Alternatively, if you only know the start of a piece of data, use .begins():

// Example #2
StoreLocations.query.units({mallId}).begins({buildingId}).go();
// ---------------------------------^^^^^^^^^^^^^^^^^^^^^

Data passed to the .begins() method is considered to be partial data. In the second example, we are saying we know the mallId and buildingId, but only know the beginning of unitId.

For the above queries we see two different sort keys:

1. "$mallstore_1#buildingid_f34#unitid_"
2. "$mallstore_1#buildingid_f34"

The first example shows how ElectroDB post-pends the label of the next composite attribute (unitId) on the Sort Key to ensure that buildings such as "f340" are not included in the query. This is useful to prevent common issues with overloaded sort keys like accidental over-querying.

The second example allows you to make queries that do include buildings such as "f340" or "f3409" or "f340356346".

For these reasons it is important to consider that attributes passed to the Access Pattern method are considered to be full, known, data.

Collection Chains

Collections allow you to query across Entities. They can be used on Service instance.

const DynamoDB = require("aws-sdk/clients/dynamodb");
const table = "projectmanagement";
const client = new DynamoDB.DocumentClient();

const employees = new Entity({
  model: {
    entity: "employees",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    employeeId: {
      type: "string"
    },
    organizationId: {
      type: "string"
    },
    name: {
      type: "string"
    },
    team: {
      type: ["jupiter", "mercury", "saturn"]
    }
  },
  indexes: {
    staff: {
      pk: {
        field: "pk",
        composite: ["organizationId"]
      },
      sk: {
        field: "sk",
        composite: ["employeeId"]
      }
    },
    employee: {
      collection: "assignments",
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: [],
      },
    }
  }
}, { client, table })

const tasks = new Entity({
  model: {
    entity: "tasks",
    version: "1",
    service: "taskapp",
  },
  attributes: {
    taskId: {
      type: "string"
    },
    employeeId: {
      type: "string"
    },
    projectId: {
      type: "string"
    },
    title: {
      type: "string"
    },
    body: {
      type: "string"
    }
  },
  indexes: {
    project: {
      pk: {
        field: "pk",
        composite: ["projectId"]
      },
      sk: {
        field: "sk",
        composite: ["taskId"]
      }
    },
    assigned: {
      collection: "assignments",
      index: "gsi2",
      pk: {
        field: "gsi2pk",
        composite: ["employeeId"],
      },
      sk: {
        field: "gsi2sk",
        composite: [],
      },
    }
  }
}, { client, table });

const TaskApp = new Service({employees, tasks});

Available on your Service are two objects: entites and collections. Entities available on entities have the same capabilities as they would if created individually. When a Model added to a Service with join however, its Collections are automatically added and validated with the other Models joined to that Service. These Collections are available on collections.

TaskApp.collections.assignments({employeeId: "JExotic"}).params();  

// Results
{
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  ExpressionAttributeValues: { ':pk': '$taskapp_1#employeeid_joeexotic', ':sk1': '$assignments' },
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  IndexName: 'gsi3'
}

Collections do not have the same query functionality and as an Entity, though it does allow for inline filters like an Entity. The attributes available on the filter object include all attributes across entities.

TaskApp.collections
    .assignments({employee: "CBaskin"})
    .filter((attributes) => `
        ${attributes.project.notExists()} OR ${attributes.project.contains("murder")}
    `)

// Results
{
  TableName: 'projectmanagement',
  ExpressionAttributeNames: { '#project': 'project', '#pk': 'gsi2pk', '#sk1': 'gsi2sk' },
  ExpressionAttributeValues: {
    ':project1': 'murder',
    ':pk': '$taskapp_1#employeeid_carolbaskin',
    ':sk1': '$assignments'
  },
  KeyConditionExpression: '#pk = :pk and begins_with(#sk1, :sk1)',
  IndexName: 'gsi2',
  FilterExpression: '\n\t\tattribute_not_exists(#project) OR contains(#project, :project1)\n\t'
}

Execute Queries

Lastly, all query chains end with either a .go() or a .params() method invocation. These will either execute the query to DynamoDB (.go()) or return formatted parameters for use with the DynamoDB docClient (.params()).

Both .params() and .go() take a query configuration object which is detailed more in the section Query Options.

Params

The params method ends a query chain, and synchronously formats your query into an object ready for the DynamoDB docClient.

For more information on the options available in the config object, checkout the section Query Options.

let config = {};
let stores = MallStores.query
    .leases({ mallId })
    .between(
      { leaseEndDate:  "2020-06-01" }, 
      { leaseEndDate:  "2020-07-31" })
    .filter(attr) => attr.rent.lte("5000.00"))
    .params(config);

// Results:
{
  IndexName: 'idx2',
  TableName: 'electro',
  ExpressionAttributeNames: { '#rent': 'rent', '#pk': 'idx2pk', '#sk1': 'idx2sk' },
  ExpressionAttributeValues: {
    ':rent1': '5000.00',
    ':pk': '$mallstoredirectory_1#mallid_eastpointe',
    ':sk1': '$mallstore#leaseenddate_2020-06-01#rent_',
    ':sk2': '$mallstore#leaseenddate_2020-07-31#rent_'
  },
  KeyConditionExpression: '#pk = :pk and #sk1 BETWEEN :sk1 AND :sk2',
  FilterExpression: '#rent <= :rent1'
}

Go

The go method ends a query chain, and asynchronously queries DynamoDB with the client provided in the model.

For more information on the options available in the config object, check out the section Query Options.

let config = {};
let stores = MallStores.query
    .leases({ mallId })
    .between(
        { leaseEndDate:  "2020-06-01" }, 
        { leaseEndDate:  "2020-07-31" })
    .filter(({rent}) => rent.lte("5000.00"))
    .go(config);

Page

NOTE: By Default, ElectroDB queries will paginate through all results with the go() method. ElectroDB's page() method can be used to manually iterate through DynamoDB query results.

The page method ends a query chain, and asynchronously queries DynamoDB with the client provided in the model. Unlike the .go(), the .page() method returns a tuple.

The first element for a page query is the "pager": an object contains the composite attributes that make up the ExclusiveStartKey that is returned by the DynamoDB client. This is very useful in multi-tenant applications where only some composite attributes are exposed to the client, or there is a need to prevent leaking keys between entities. If there is no ExclusiveStartKey this value will be null. On subsequent calls to .page(), pass the results returned from the previous call to .page() or construct the composite attributes yourself.

The "pager" includes the associated entity's Identifiers.

NOTE: It is highly recommended to use the query option pager: "raw"" flag when using .page() with scan operations. This is because when using scan on large tables the docClient may return an ExclusiveStartKey for a record that does not belong to entity making the query (regardless of the filters set). In these cases ElectroDB will return null (to avoid leaking the keys of other entities) when further pagination may be needed to find your records.

The second element is the results of the query, exactly as it would be returned through a query operation.

NOTE: When calling .page() the first argument is reserved for the "page" returned from a previous query, the second parameter is for Query Options. For more information on the options available in the config object, check out the section Query Options.

Entity Pagination

let [next, stores] = await MallStores.query
    .leases({ mallId })
    .page(); // no "pager" passed to `.page()`

let [pageTwo, moreStores] = await MallStores.query
    .leases({ mallId })
    .page(next, {}); // the "pager" from the first query (`next`) passed to the second query

// page:
// { 
//   storeId: "LatteLarrys", 
//   mallId: "EastPointe", 
//   buildingId: "BuildingA1", 
//   unitId: "B47"
//   __edb_e__: "MallStore",
//   __edb_v__: "version" 
// }

// stores
// [{
//   mall: '3010aa0d-5591-4664-8385-3503ece58b1c',
//   leaseEnd: '2020-01-20',
//   sector: '7d0f5c19-ec1d-4c1e-b613-a4cc07eb4db5',
//   store: 'MNO',
//   unit: 'B5',
//   id: 'e0705325-d735-4fe4-906e-74091a551a04',
//   building: 'BuildingE',
//   category: 'food/coffee',
//   rent: '0.00'
// },
// {
//   mall: '3010aa0d-5591-4664-8385-3503ece58b1c',
//   leaseEnd: '2020-01-20',
//   sector: '7d0f5c19-ec1d-4c1e-b613-a4cc07eb4db5',
//   store: 'ZYX',
//   unit: 'B9',
//   id: 'f201a1d3-2126-46a2-aec9-758ade8ab2ab',
//   building: 'BuildingI',
//   category: 'food/coffee',
//   rent: '0.00'
// }]

Service Pagination

NOTE: By Default, ElectroDB will paginate through all results with the query() method. ElectroDB's page() method can be used to manually iterate through DynamoDB query results.

Pagination with services is also possible. Similar to Entity Pagination, calling the .page() method returns a [pager, results] tuple. Also, similar to pagination on Entities, the pager object returned by default is a deconstruction of the returned LastEvaluatedKey.

Pager Query Options

The .page() method also accepts Query Options just like the .go() and .params() methods. Unlike those methods, however, the .page() method accepts Query Options as the second parameter (the first parameter is reserved for the "pager").

A notable Query Option, that is available only to the .page() method, is an option called pager. This property defines the post-processing ElectroDB should perform on a returned LastEvaluatedKey, as well as how ElectroDB should interpret an incoming pager, to use as an ExclusiveStartKey.

NOTE: Because the "pager" object is destructured from the keys DynamoDB returns as the LastEvaluatedKey, these composite attributes differ from the record's actual attribute values in one important way: Their string values will all be lowercase. If you intend to use these attributes in ways where their casing will matter (e.g. in a where filter), keep in mind this may result in unexpected outcomes.

The three options for the query option pager are as follows:

// LastEvaluatedKey
{
  pk: '$taskapp#country_united states of america#state_oregon',
  sk: '$offices_1#city_power#zip_34706#office_mobile branch',
  gsi1pk: '$taskapp#office_mobile branch',
  gsi1sk: '$workplaces#offices_1'
}

"named" (default): By default, ElectroDB will deconstruct the LastEvaluatedKey returned by the DocClient into it's individual composite attribute parts. The "named" option, chosen by default, also includes the Entity's column "identifiers" -- this is useful with Services where destructured pagers may be identical between more than one Entity in that Service.

// {pager: "named"} | {pager: undefined} 
{  
  "city": "power",
  "country": "united states of america",
  "state": "oregon",
  "zip": "34706",
  "office": "mobile branch",
  "__edb_e__": "offices",
  "__edb_v__": "1"
}

"item": Similar to "named", however without the Entity's "identifiers". If two Entities with a service have otherwise identical index definitions, using the "item" pager option can result in errors while paginating a Collection. If this is not a concern with your Service, or you are paginating with only an Entity, this option could be preferable because it has fewer properties.

// {pager: "item"} 
{  
  "city": "power",
  "country": "united states of america",
  "state": "oregon",
  "zip": "34706",
  "office": "mobile branch",
}

"raw": The "raw" option returns the LastEvaluatedKey as it was returned by the DynamoDB DocClient.

// {pager: "raw"} 
{
  pk: '$taskapp#country_united states of america#state_oregon',
  sk: '$offices_1#city_power#zip_34706#office_mobile branch',
  gsi1pk: '$taskapp#office_mobile branch',
  gsi1sk: '$workplaces#offices_1'
}

Pagination Example

Simple pagination example:

async function getAllStores(mallId) {
  let stores = [];
  let pager = null;

  do {
    let [next, results] = await MallStores.query
      .leases({ mallId })
      .page(pager);
    stores = [...stores, ...results]; 
    pager = next;
  } while(pager !== null);
  
  return stores;
} 

Query Examples

For a comprehensive and interactive guide to build queries please visit this runkit: https://runkit.com/tywalch/electrodb-building-queries.

const cityId = "Atlanta1";
const mallId = "EastPointe";
const storeId = "LatteLarrys";
const unitId = "B24";
const buildingId = "F34";
const june = "2020-06";
const july = "2020-07"; 
const discount = "500.00";
const maxRent = "2000.00";
const minRent = "5000.00";

// Lease Agreements by StoreId
await StoreLocations.query.leases({storeId}).go()

// Lease Agreement by StoreId for March 22nd 2020
await StoreLocations.query.leases({storeId, leaseEndDate: "2020-03-22"}).go()

// Lease agreements by StoreId for 2020
await StoreLocations.query.leases({storeId}).begins({leaseEndDate: "2020"}).go()

// Lease Agreements by StoreId after March 2020
await StoreLocations.query.leases({storeId}).gt({leaseEndDate: "2020-03"}).go()

// Lease Agreements by StoreId after, and including, March 2020
await StoreLocations.query.leases({storeId}).gte({leaseEndDate: "2020-03"}).go()

// Lease Agreements by StoreId before 2021
await StoreLocations.query.leases({storeId}).lt({leaseEndDate: "2021-01"}).go()

// Lease Agreements by StoreId before February 2021
await StoreLocations.query.leases({storeId}).lte({leaseEndDate: "2021-02"}).go()

// Lease Agreements by StoreId between 2010 and 2020
await StoreLocations.query
    .leases({storeId})
    .between(
        {leaseEndDate: "2010"}, 
        {leaseEndDate: "2020"})
    .go()

// Lease Agreements by StoreId after, and including, 2010 in the city of Atlanta and category containing food
await StoreLocations.query
    .leases({storeId})
    .gte({leaseEndDate: "2010"})
    .where((attr, op) => `
        ${op.eq(attr.cityId, "Atlanta1")} AND ${op.contains(attr.category, "food")}
    `)
    .go()
    
// Rents by City and Store who's rent discounts match a certain rent/discount criteria
await StoreLocations.query
    .units({mallId})
    .begins({leaseEndDate: june})
    .rentDiscount(discount, maxRent, minRent)
    .go()

// Stores by Mall matching a specific category
await StoreLocations.query
    .units({mallId})
    .byCategory("food/coffee")
    .go()

Query Options

Query options can be added the .params(), .go() and .page() to change query behavior or add customer parameters to a query.

By default, ElectroDB enables you to work with records as the names and properties defined in the model. Additionally, it removes the need to deal directly with the docClient parameters which can be complex for a team without as much experience with DynamoDB. The Query Options object can be passed to both the .params() and .go() methods when building you query. Below are the options available:

{
  params?: object;
  table?: string;
  raw?: boolean;
  includeKeys?: boolean;
  pager?: "raw" | "named" | "item";
  originalErr?: boolean;
  concurrent?: number;
  unprocessed?: "raw" | "item";
  response?: "default" | "none" | "all_old" | "updated_old" | "all_new" | "updated_new";
  ignoreOwnership?: boolean;
  limit?: number;
  pages?: number;
};

Option	Default	Description
params	{}	Properties added to this object will be merged onto the params sent to the document client. Any conflicts with ElectroDB will favor the params specified here.
table	(from constructor)	Use a different table than the one defined in the Service Options
raw	false	Returns query results as they were returned by the docClient.
includeKeys	false	By default, ElectroDB does not return partition, sort, or global keys in its response.
pager	"named"	Used in with pagination (.pages()) calls to override ElectroDBs default behaviour to break apart LastEvaluatedKeys records into composite attributes. See more detail about this in the sections for Pager Query Options.
originalErr	false	By default, ElectroDB alters the stacktrace of any exceptions thrown by the DynamoDB client to give better visibility to the developer. Set this value equal to true to turn off this functionality and return the error unchanged.
concurrent	1	When performing batch operations, how many requests (1 batch operation == 1 request) to DynamoDB should ElectroDB make at one time. Be mindful of your DynamoDB throughput configurations
unprocessed	"item"	Used in batch processing to override ElectroDBs default behaviour to break apart DynamoDBs Unprocessed records into composite attributes. See more detail about this in the sections for BatchGet, BatchDelete, and BatchPut.
response	"default"	Used as a convenience for applying the DynamoDB parameter ReturnValues. The options here are the same as the parameter values for the DocumentClient except lowercase. The "none" option will cause the method to return null and will bypass ElectroDB's response formatting -- useful if formatting performance is a concern.
ignoreOwnership	false	By default, ElectroDB interrogates items returned from a query for the presence of matching entity "identifiers". This helps to ensure other entities, or other versions of an entity, are filtered from your results. If you are using ElectroDB with an existing table/dataset you can turn off this feature by setting this property to true.
limit	none	A target for the number of items to return from DynamoDB. If this option is passed, Queries on entities and through collections will paginate DynamoDB until this limit is reached or all items for that query have been returned.
pages	∞	How many DynamoDB pages should a query iterate through before stopping. By default ElectroDB paginate through all results for your query.

Errors:

Error Code	Description
1000s	Configuration Errors
2000s	Invalid Queries
3000s	User Defined Errors
4000s	DynamoDB Errors
5000s	Unexpected Errors

No Client Defined On Model

Code: 1001

Why this occurred: If a DynamoDB DocClient is not passed to the constructor of an Entity or Service (client), ElectroDB will be unable to query DynamoDB. This error will only appear when a query(using go()) is made because ElectroDB is still useful without a DocClient through the use of it's params() method.

What to do about it: For an Entity be sure to pass the DocClient as the second param to the constructor:

new Entity(schema, {client})

For a Service, the client is passed the same way, as the second param to the constructor:

new Service("", {client});

Invalid Identifier

Code: 1002

Why this occurred: You tried to modify the entity identifier on an Entity.

What to do about it: Make sure you have spelled the identifier correctly or that you actually passed a replacement.

Invalid Key Composite Attribute Template

Code: 1003

Why this occurred: You are trying to use the custom Key Composite Attribute Template, and the format you passed is invalid.

What to do about it: Checkout the section on [Composite Attribute Templates](#composite attribute-templates) and verify your template conforms to the rules detailed there.

Duplicate Indexes

Code: 1004

Why this occurred: Your model contains duplicate indexes. This could be because you accidentally included an index twice or even forgot to add an index name on a secondary index, which would be interpreted as "duplicate" to the Table's Primary index.

What to do about it: Double-check the index names on your model for duplicate indexes. The error should specify which index has been duplicated. It is also possible that you have forgotten to include an index name. Each table must have at least one Table Index (which does not include an index property in ElectroDB), but all Secondary and Local indexes must include an index property with the name of that index as defined on the table.

{
  indexes: {
    index1: {
      index: "idx1", // <-- duplicate "idx1"
      pk: {},
      sk: {}
    },
    index2: {
      index: "idx1", // <-- duplicate "idx1"
      pk: {},
      sk: {}
    }
  }
}

Collection Without An SK

Code: 1005

Why this occurred: You have added a collection to an index that does not have an SK. Because Collections are used to help query across entities via the Sort Key, not having a Sort Key on an index defeats the purpose of a Collection.

What to do about it: If your index does have a Sort Key, but you are unsure of how to inform electro without setting composite attributes to the SK, add the SK object to the index and use an empty array for Composite Attributes:

// ElectroDB interprets as index *not having* an SK.
{
  indexes: {
    myIndex: {
      pk: {
        field: "pk",
        composite: ["id"]
      }
    }
  }
}

// ElectroDB interprets as index *having* SK, but this model doesnt attach any composite attributes to it.
{
  indexes: {
    myIndex: {
      pk: {
        field: "pk",
        composite: ["id"]
      },
      sk: {
        field: "sk",
        composite: []
      }
    }
  }
}

Duplicate Collections

Code: 1006

Why this occurred: You have assigned the same collection name to multiple indexes. This is not allowed because collection names must be unique.

What to do about it: Determine a new naming scheme

Missing Primary Index

Code: 1007

Why this occurred: DynamoDB requires the definition of at least one Primary Index on the table. In Electro this is defined as an Index without an index property. Each model needs at least one, and the composite attributes used for this index must ensure each composite represents a unique record.

What to do about it: Identify the index you're using as the Primary Index and ensure it does not have an index property on its definition.

// ElectroDB interprets as the Primary Index because it lacks an `index` property.
{
  indexes: {
    myIndex: {
      pk: {
        field: "pk",
        composite: ["org"]
      },
      sk: {
        field: "sk",
        composite: ["id"]
      }
    }
  }
}

// ElectroDB interprets as a Global Secondary Index because it has an `index` property.
{
  indexes: {
    myIndex: {
      index: "gsi1"
      pk: {
        field: "gsipk1",
        composite: ["org"]
      },
      sk: {
        field: "gsisk1",
        composite: ["id"]
      }
    }
  }
}

Invalid Attribute Definition

Code: 1008

Why this occurred: Some attribute on your model has an invalid configuration.

What to do about it: Use the error to identify which column needs to examined, double-check the properties on that attribute. Checkout the section on Attributes for more information on how they are structured.

Invalid Model

Code: 1009

Why this occurred: Some properties on your model are missing or invalid.

What to do about it: Checkout the section on Models to verify your model against what is expected.

Invalid Options

Code: 1010

Why this occurred: Some properties on your options object are missing or invalid.

What to do about it: Checkout the section on Model/Service Options to verify your model against what is expected.

Duplicate Index Fields

Code: 1014

Why this occurred: An Index in your model references the same field twice across indexes. The field property in the definition of an index is a mapping to the name of the field assigned to the PK or SK of an index.

What to do about it: This is likely a typo, if not double-check the names of the fields you assigned to be the PK and SK of your index, these field names must be unique.

Duplicate Index Composite Attributes

Code: 1015

Why this occurred: Within one index you tried to use the same composite attribute in both the PK and SK. A composite attribute may only be used once within an index. With ElectroDB it is not uncommon to use the same value as both the PK and SK when a Sort Key exists on a table -- this usually is done because some value is required in that column but for that entity it is not necessary. If this is your situation remember that ElectroDB does put a value in the SortKey even if does not include a composite attribute, checkout this section for more information.

What to do about it: Determine how you can change your access pattern to not duplicate the composite attribute. Remember that an empty array for an SK is valid.

Incompatible Key Composite Attribute Template

Code: 1017

Why this occurred: You are trying to use the custom Key Composite Attribute Template, and a Composite Attribute Array on your model, and they do not contain identical composite attributes.

What to do about it: Checkout the section on [Composite Attribute Templates](#composite attribute-templates) and verify your template conforms to the rules detailed there. Both properties must contain the same attributes and be provided in the same order.

Invalid Index With Attribute Name

Code: 1018

Why this occurred: ElectroDB's design revolves around best practices related to modeling in single table design. This includes giving indexed fields generic names. If the PK and SK fields on your table indexes also match the names of attributes on your Entity you will need to make special considerations to make sure ElectroDB can accurately map your data.

What to do about it: Checkout the section Using ElectroDB with existing data to learn more about considerations to make when using attributes as index fields.

Invalid Collection on Index With Attribute Field Names

Code: 1019

Why this occurred: Collections allow for unique access patterns to be modeled between entities. It does this by appending prefixes to your key composites. If an Entity leverages an attribute field as an index key, ElectroDB will be unable to prefix your value because that would result in modifying the value itself.

What to do about it: Checkout the section Collections to learn more about collections, as well as the section Using ElectroDB with existing data to learn more about considerations to make when using attributes as index fields.

Missing Composite Attributes

Code: 2002

Why this occurred: The current request is missing some composite attributes to complete the query based on the model definition. Composite Attributes are used to create the Partition and Sort keys. In DynamoDB Partition keys cannot be partially included, and Sort Keys can be partially include they must be at least passed in the order they are defined on the model.

What to do about it: The error should describe the missing composite attributes, ensure those composite attributes are included in the query or update the model to reflect the needs of the access pattern.

Missing Table

Code: 2003f

Why this occurred: You never specified a Table for DynamoDB to use.

What to do about it: Tables can be defined on the Service Options object when you create an Entity or Service, or if that is not known at the time of creation, it can be supplied as a Query Option and supplied on each query individually. If can be supplied on both, in that case the Query Option will override the Service Option.

Invalid Concurrency Option

Code: 2004

Why this occurred: When performing a bulk operation (Batch Get, Batch Delete Records, Batch Put Records) you can pass a Query Options called concurrent, which impacts how many batch requests can occur at the same time. Your value should pass the test of both, !isNaN(parseInt(value)) and parseInt(value) > 0.

What to do about it:
Expect this error only if you're providing a concurrency option. Double-check the value you are providing is the value you expect to be passing, and that the value passes the tests listed above.

Invalid Pages Option

Code: 2005

Why this occurred: When performing a query Query you can pass a Query Options called pages, which impacts how many DynamoDB pages a query should iterate through. Your value should pass the test of both, !isNaN(parseInt(value)) and parseInt(value) > 0.

What to do about it: Expect this error only if you're providing a pages option. Double-check the value you are providing is the value you expect to be passing, and that the value passes the tests listed above.

Invalid Limit Option

Code: 2006

Why this occurred: When performing a query Query you can pass a Query Options called limit, which impacts how many DynamoDB items a query should return. Your value should pass the test of both, !isNaN(parseInt(value)) and parseInt(value) > 0.

What to do about it: Expect this error only if you're providing a limit option. Double-check the value you are providing is the value you expect to be passing, and that the value passes the tests listed above.

Invalid Attribute

Code: 3001

Why this occurred: The value received for a validation either failed type expectations (e.g. a "number" instead of a "string"), or the user provided "validate" callback on an attribute rejected a value.

What to do about it: Examine the error itself for more precise detail on why the failure occurred. The error object itself should have a property called "fields" which contains an array of every attribute that failed validation, and a reason for each. If the failure originated from a "validate" callback, the originally thrown error will be accessible via the cause property the corresponding element within the fields array.1

Below is the type definition for an ElectroValidationError:

ElectroValidationError<T extends Error = Error> extends ElectroError {
    readonly name: "ElectroValidationError"
    readonly code: number;
    readonly date: number;
    readonly isElectroError: boolean;
    ref: {
        readonly code: number;
        readonly section: string;
        readonly name: string;
        readonly sym: unique symbol;
    }
    readonly fields: ReadonlyArray<{
        /**
         * The json path to the attribute that had a validation error
         */
        readonly field: string;

        /**
         * A description of the validation error for that attribute
         */
        readonly reason: string;

        /**
         * Index of the value passed (present only in List attribute validation errors)
         */
        readonly index: number | undefined;

        /**
         * The error thrown from the attribute's validate callback (if applicable)
         */
        readonly cause: T | undefined;
    }>
}

AWS Error

Code: 4001

Why this occurred: DynamoDB did not like something about your query.

What to do about it: By default ElectroDB tries to keep the stack trace close to your code, ideally this can help you identify what might be going on. A tip to help with troubleshooting: use .params() to get more insight into how your query is converted to DocClient params.

Unknown Errors

Invalid Last Evaluated Key

Code: 5003

Why this occurred: Likely you were calling .page() on a scan. If you weren't please make an issue and include as much detail about your query as possible.

What to do about it: When paginating with scan queries, it is highly recommended that the query option, {pager: "raw"}. This is because when using scan on large tables the docClient may return an ExclusiveStartKey for a record that does not belong to entity making the query (regardless of the filters set). In these cases ElectroDB will return null (to avoid leaking the keys of other entities) when further pagination may be needed to find your records.

// example
myModel.scan.page(null, {pager: "raw"});

No Owner For Pager

Code: 5004

Why this occurred: When using pagination with a Service, ElectroDB will try to identify which Entity is associated with the supplied pager. This error can occur when you supply an invalid pager, or when you are using a different pager option to a pager than what was used when retrieving it. Consult the section on Pagination to learn more.

What to do about it: If you are sure the pager you are passing to .page() is the same you received from .page() this could be an unexpected error. To mitigate the issue use the Query Option {pager: "raw"} and please open a support issue.

Pager Not Unique

Code: 5005

Why this occurred: When using pagination with a Service, ElectroDB will try to identify which Entity is associated with the supplied pager option. This error can occur when you supply a pager that resolves to more than one Entity. This can happen if your entities share the same composite attributes for the index you are querying on, and you are using the Query Option {pager: "item""}.

What to do about it: Because this scenario is possible with otherwise well considered/thoughtful entity models, the default pager type used by ElectroDB is "named". To avoid this error, you will need to use either the "raw" or "named" pager options for any index that could result in an ambiguous Entity owner.

Examples

Want to just play with ElectroDB instead of read about it? Try it out for yourself! https://runkit.com/tywalch/electrodb-building-queries

Employee App

For an example, lets look at the needs of application used to manage Employees. The application Looks at employees, offices, tasks, and projects.

Employee App Requirements

1. As a Project Manager, I need to find all tasks and details on a specific employee.
2. As a Regional Manager, I need to see all details about an office and its employees
3. As an Employee, I need to see all my Tasks.
4. As a Product Manager, I need to see all the tasks for a project.
5. As a Client, I need to find a physical office close to me.
6. As a Hiring manager, I need to find employees with comparable salaries.
7. As HR, I need to find upcoming employee birthdays/anniversaries
8. As HR, I need to find all the employees that report to a specific manager

App Entities

const EmployeesModel = {
    model: {
      entity: "employees",
      version: "1",
      service: "taskapp",  
    },
    attributes: {
        employee: "string",
        firstName: "string",
        lastName: "string",
        office: "string",
        title: "string",
        team: ["development", "marketing", "finance", "product"],
        salary: "string",
        manager: "string",
        dateHired: "string",
        birthday: "string",
    },
    indexes: {
        employee: {
            pk: {
                field: "pk",
                composite: ["employee"],
            },
            sk: {
                field: "sk",
                composite: [],
            },
        },
        coworkers: {
            index: "gsi1pk-gsi1sk-index",
            collection: "workplaces",
            pk: {
                field: "gsi1pk",
                composite: ["office"],
            },
            sk: {
                field: "gsi1sk",
                composite: ["team", "title", "employee"],
            },
        },
        teams: {
            index: "gsi2pk-gsi2sk-index",
            pk: {
                field: "gsi2pk",
                composite: ["team"],
            },
            sk: {
                field: "gsi2sk",
                composite: ["title", "salary", "employee"],
            },
        },
        employeeLookup: {
            collection: "assignements",
            index: "gsi3pk-gsi3sk-index",
            pk: {
                field: "gsi3pk",
                composite: ["employee"],
            },
            sk: {
                field: "gsi3sk",
                composite: [],
            },
        },
        roles: {
            index: "gsi4pk-gsi4sk-index",
            pk: {
                field: "gsi4pk",
                composite: ["title"],
            },
            sk: {
                field: "gsi4sk",
                composite: ["salary", "employee"],
            },
        },
        directReports: {
            index: "gsi5pk-gsi5sk-index",
            pk: {
                field: "gsi5pk",
                composite: ["manager"],
            },
            sk: {
                field: "gsi5sk",
                composite: ["team", "office", "employee"],
            },
        },
    },
    filters: {
        upcomingCelebrations: (attributes, startDate, endDate) => {
            let { dateHired, birthday } = attributes;
            return `${dateHired.between(startDate, endDate)} OR ${birthday.between(
                startDate,
                endDate,
            )}`;
        },
    },
};

const TasksModel = {
    model: {
        entity: "tasks",
        version: "1",
        service: "taskapp",  
    }, 
    attributes: {
        task: "string",
        project: "string",
        employee: "string",
        description: "string",
    },
    indexes: {
        task: {
            pk: {
                field: "pk",
                composite: ["task"],
            },
            sk: {
                field: "sk",
                composite: ["project", "employee"],
            },
        },
        project: {
            index: "gsi1pk-gsi1sk-index",
            pk: {
                field: "gsi1pk",
                composite: ["project"],
            },
            sk: {
                field: "gsi1sk",
                composite: ["employee", "task"],
            },
        },
        assigned: {
            collection: "assignements",
            index: "gsi3pk-gsi3sk-index",
            pk: {
                field: "gsi3pk",
                composite: ["employee"],
            },
            sk: {
                field: "gsi3sk",
                composite: ["project", "task"],
            },
        },
    },
};

const OfficesModel = {
    model: {
          entity: "offices",
          version: "1",
          service: "taskapp",  
      }, 
    attributes: {
        office: "string",
        country: "string",
        state: "string",
        city: "string",
        zip: "string",
        address: "string",
    },
    indexes: {
        locations: {
            pk: {
                field: "pk",
                composite: ["country", "state"],
            },
            sk: {
                field: "sk",
                composite: ["city", "zip", "office"],
            },
        },
        office: {
            index: "gsi1pk-gsi1sk-index",
            collection: "workplaces",
            pk: {
                field: "gsi1pk",
                composite: ["office"],
            },
            sk: {
                field: "gsi1sk",
                composite: [],
            },
        },
    },
};

Join models on a new Service called EmployeeApp

const DynamoDB = require("aws-sdk/clients/dynamodb");
const client = new DynamoDB.DocumentClient({region: "us-east-1"});
const { Service } = require("electrodb");
const table = "projectmanagement";
const EmployeeApp = new Service("EmployeeApp", { client, table });

EmployeeApp
    .join(EmployeesModel) // EmployeeApp.entities.employees
    .join(TasksModel)     // EmployeeApp.entities.tasks
    .join(OfficesModel);  // EmployeeApp.entities.tasks

Query Records

All tasks and employee information for a given employee

Fulfilling Requirement #1.

EmployeeApp.collections.assignements({employee: "CBaskin"}).go();

Returns the following:

{
    employees: [{
        employee: "cbaskin",
        firstName: "carol",
        lastName: "baskin",
        office: "big cat rescue",
        title: "owner",
        team: "cool cats and kittens",
        salary: "1,000,000",
        manager: "",
        dateHired: "1992-11-04",
        birthday: "1961-06-06",
    }],
    tasks: [{
        task: "Feed tigers",
        description: "Prepare food for tigers to eat",
        project: "Keep tigers alive",
        employee: "cbaskin"
    }, {
        task: "Fill water bowls",
        description: "Ensure the tigers have enough water",
        project: "Keep tigers alive",
        employee: "cbaskin"
    }]
}

Find all employees and office details for a given office

Fulfilling Requirement #2.

EmployeeApp.collections.workplaces({office: "big cat rescue"}).go()

Returns the following:

{
    employees: [{
        employee: "cbaskin",
        firstName: "carol",
        lastName: "baskin",
        office: "big cat rescue",
        title: "owner",
        team: "cool cats and kittens",
        salary: "1,000,000",
        manager: "",
        dateHired: "1992-11-04",
        birthday: "1961-06-06",
    }],
    offices: [{
        office: "big cat rescue",
        country: "usa",
        state: "florida",
        city: "tampa",
        zip: "12345",
        address: "123 Kitty Cat Lane"
    }]
}

Tasks for a given employee

Fulfilling Requirement #3.

EmployeeApp.entities.tasks.query.assigned({employee: "cbaskin"}).go();

Returns the following:

[
    {
        task: "Feed tigers",
        description: "Prepare food for tigers to eat",
        project: "Keep tigers alive",
        employee: "cbaskin"
    }, {
        task: "Fill water bowls",
        description: "Ensure the tigers have enough water",
        project: "Keep tigers alive",
        employee: "cbaskin"
    }
]

Tasks for a given project

Fulfilling Requirement #4.

EmployeeApp.entities.tasks.query.project({project: "Murder Carol"}).go();

Returns the following:

[
    {
        task: "Hire hitman",
        description: "Find someone to murder Carol",
        project: "Murder Carol",
        employee: "jexotic"
    }
];

Find office locations

Fulfilling Requirement #5.

EmployeeApp.entities.office.locations({country: "usa", state: "florida"}).go()

Returns the following:

[
    {
        office: "big cat rescue",
        country: "usa",
        state: "florida",
        city: "tampa",
        zip: "12345",
        address: "123 Kitty Cat Lane"
    }
]

Find employee salaries and titles

Fulfilling Requirement #6.

EmployeeApp.entities.employees
    .roles({title: "animal wrangler"})
    .lte({salary: "150.00"})
    .go()

Returns the following:

[
    {
        employee: "ssaffery",
        firstName: "saff",
        lastName: "saffery",
        office: "gw zoo",
        title: "animal wrangler",
        team: "keepers",
        salary: "105.00",
        manager: "jexotic",
        dateHired: "1999-02-23",
        birthday: "1960-07-11",
    }
]

Find employee birthdays or anniversaries

Fulfilling Requirement #7.

EmployeeApp.entities.employees
    .workplaces({office: "gw zoo"})
    .upcomingCelebrations("2020-05-01", "2020-06-01")
    .go()

Returns the following:

[
    {
        employee: "jexotic",
        firstName: "joe",
        lastName: "maldonado-passage",
        office: "gw zoo",
        title: "tiger king",
        team: "founders",
        salary: "10000.00",
        manager: "jlowe",
        dateHired: "1999-02-23",
        birthday: "1963-03-05",
    }
]

Find direct reports

Fulfilling Requirement #8.

EmployeeApp.entities.employees
    .reports({manager: "jlowe"})
    .go()

Returns the following:

[
    {
        employee: "jexotic",
        firstName: "joe",
        lastName: "maldonado-passage",
        office: "gw zoo",
        title: "tiger king",
        team: "founders",
        salary: "10000.00",
        manager: "jlowe",
        dateHired: "1999-02-23",
        birthday: "1963-03-05",
    }
]

Shopping Mall Property Management App

For an example, lets look at the needs of application used to manage Shopping Mall properties. The application assists employees in the day-to-day operations of multiple Shopping Malls.

Shopping Mall Requirements

1. As a Maintenance Worker, I need to know which stores are currently in each Mall down to the Building they are located.
2. As a Helpdesk Employee, I need to locate related stores in Mall locations by Store Category.
3. As a Property Manager, I need to identify upcoming leases in need of renewal.

Create a new Entity using the StoreLocations schema defined above

const DynamoDB = require("aws-sdk/clients/dynamodb");
const client = new DynamoDB.DocumentClient();
const StoreLocations = new Entity(model, {client, table: "StoreLocations"});

Access Patterns are accessible on the StoreLocation

PUT Record

Add a new Store to the Mall

await StoreLocations.create({
    mallId: "EastPointe",
    storeId: "LatteLarrys",
    buildingId: "BuildingA1",
    unitId: "B47",
    category: "spite store",
    leaseEndDate: "2020-02-29",
    rent: "5000.00",
}).go();

Returns the following:

{
    "mallId": "EastPointe",
    "storeId": "LatteLarrys",
    "buildingId": "BuildingA1",
    "unitId": "B47",
    "category": "spite store",
    "leaseEndDate": "2020-02-29",
    "rent": "5000.00",
    "discount": "0.00"
}

---

UPDATE Record

Change the Stores Lease Date

When updating a record, you must include all Composite Attributes associated with the table's primary PK and SK.

let storeId = "LatteLarrys";
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
await StoreLocations.update({storeId, mallId, buildingId, unitId}).set({
    leaseEndDate: "2021-02-28"
}).go();

Returns the following:

{
    "leaseEndDate": "2021-02-28"
}

GET Record

Retrieve a specific Store in a Mall

When retrieving a specific record, you must include all Composite Attributes associated with the table's primary PK and SK.

let storeId = "LatteLarrys";
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
await StoreLocations.get({storeId, mallId, buildingId, unitId}).go();

Returns the following:

{
    "mallId": "EastPointe",
    "storeId": "LatteLarrys",
    "buildingId": "BuildingA1",
    "unitId": "B47",
    "category": "spite store",
    "leaseEndDate": "2021-02-28",
    "rent": "5000.00",
    "discount": "0.00"
}

DELETE Record

Remove a Store location from the Mall

When removing a specific record, you must include all Composite Attributes associated with the table's primary PK and SK.

let storeId = "LatteLarrys";
let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let storeId = "LatteLarrys";
await StoreLocations.delete({storeId, mallId, buildingId, unitId}).go();

Returns the following:

{}

Query Mall Records

All Stores in a particular mall

Fulfilling Requirement #1.

let mallId = "EastPointe";
let stores = await StoreLocations.malls({mallId}).query().go();

All Stores in a particular mall building

Fulfilling Requirement #1.

let mallId = "EastPointe";
let buildingId = "BuildingA1";
let stores = await StoreLocations.malls({mallId}).query({buildingId}).go();

Find the store located in unit B47

Fulfilling Requirement #1.

let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let stores = await StoreLocations.malls({mallId}).query({buildingId, unitId}).go();

Stores by Category at Mall

Fulfilling Requirement #2.

let mallId = "EastPointe";
let category = "food/coffee";
let stores = await StoreLocations.malls({mallId}).byCategory(category).go();

Stores by upcoming lease

Fulfilling Requirement #3.

let mallId = "EastPointe";
let q2StartDate = "2020-04-01";
let stores = await StoreLocations.leases({mallId}).lt({leaseEndDate: q2StateDate}).go();

Stores will renewals for Q4

Fulfilling Requirement #3.

let mallId = "EastPointe";
let q4StartDate = "2020-10-01";
let q4EndDate = "2020-12-31";
let stores = await StoreLocations.leases(mallId)
    .between (
      {leaseEndDate: q4StartDate}, 
      {leaseEndDate: q4EndDate})
    .go();

Spite-stores with release renewals this year

Fulfilling Requirement #3.

let mallId = "EastPointe";
let yearStarDate = "2020-01-01";
let yearEndDate = "2020-12-31";
let storeId = "LatteLarrys";
let stores = await StoreLocations.leases(mallId)
    .between (
      {leaseEndDate: yearStarDate}, 
      {leaseEndDate: yearEndDate})
    .filter(attr => attr.category.eq("Spite Store"))
    .go();

All Latte Larrys in a particular mall building

let mallId = "EastPointe";
let buildingId = "BuildingA1";
let unitId = "B47";
let storeId = "LatteLarrys";
let stores = await StoreLocations.malls({mallId}).query({buildingId, storeId}).go();

Exported TypeScript Types

The following types are exported for easier use while using ElectroDB with TypeScript:

EntityRecord Type

The EntityRecord type is an object containing every attribute an Entity's model.

Definition:

type EntityRecord<E extends Entity<any, any, any, any>> =
    E extends Entity<infer A, infer F, infer C, infer S>
        ? Item<A,F,C,S,S["attributes"]>
        : never;

Use:

type EntiySchema = EntityRecord<typeof MyEntity>

EntityItem Type

This type represents an item as it is returned from a query. This is different from the EntityRecord in that this type reflects the required, hidden, default, etc properties defined on the attribute.

Definition:

export type EntityItem<E extends Entity<any, any, any, any>> =
  E extends Entity<infer A, infer F, infer C, infer S>
  ? ResponseItem<A, F, C, S>
  : never;

Use:

type Thing = EntityItem<typeof MyEntityInstance>;

CollectionItem Type

This type represents the value returned from a collection query, and is similar to EntityItem.

Use:

type CollectionResults = CollectionItem<typeof MyServiceInstance, "collectionName">

CreateEntityItem Type

This type represents an item that you would pass your entity's put or create method

Definition:

export type CreateEntityItem<E extends Entity<any, any, any, any>> =
  E extends Entity<infer A, infer F, infer C, infer S>
  ? PutItem<A, F, C, S>
  : never;

Use:

type NewThing = CreateEntityItem<typeof MyEntityInstance>;

UpdateEntityItem Type

This type represents an item that you would pass your entity's set method when using create or update.

Definition:

export type UpdateEntityItem<E extends Entity<any, any, any, any>> =
  E extends Entity<infer A, infer F, infer C, infer S>
  ? SetItem<A, F, C, S>
  : never;

Use:

type UpdateProperties = UpdateEntityItem<typeof MyEntityInstance>;

UpdateAddEntityItem Type

This type represents an item that you would pass your entity's add method when using create or update.

Definition:

export type UpdateAddEntityItem<E extends Entity<any, any, any, any>> =
    E extends Entity<infer A, infer F, infer C, infer S>
        ? AddItem<A, F, C, S>
        : never;

UpdateSubtractEntityItem Type

This type represents an item that you would pass your entity's subtract method when using create or update.

Definition:

export type UpdateSubtractEntityItem<E extends Entity<any, any, any, any>> =
    E extends Entity<infer A, infer F, infer C, infer S>
        ? SubtractItem<A, F, C, S>
        : never;

UpdateAppendEntityItem Type

This type represents an item that you would pass your entity's append method when using create or update.

Definition:

export type UpdateAppendEntityItem<E extends Entity<any, any, any, any>> =
    E extends Entity<infer A, infer F, infer C, infer S>
        ? AppendItem<A, F, C, S>
        : never;

UpdateRemoveEntityItem Type

This type represents an item that you would pass your entity's remove method when using create or update.

Definition:

export type UpdateRemoveEntityItem<E extends Entity<any, any, any, any>> =
    E extends Entity<infer A, infer F, infer C, infer S>
        ? RemoveItem<A, F, C, S>
        : never;

UpdateDeleteEntityItem Type

This type represents an item that you would pass your entity's delete method when using create or update.

Definition:

export type UpdateDeleteEntityItem<E extends Entity<any, any, any, any>> =
    E extends Entity<infer A, infer F, infer C, infer S>
        ? DeleteItem<A, F, C, S>
        : never;

Using ElectroDB With Existing Data

When using ElectroDB with an existing table and/or data model, there are a few configurations you may need to make to your ElectroDB model. Read the sections below to see if any of the following cases fits your particular needs.

Whenever using ElectroDB with existing tables/data, it is best to use the Query Option ignoreOwnership. ElectroDB leaves some meta-data on items to help ensure data queried and returned from DynamoDB does not leak between entities. Because your data was not made by ElectroDB, these checks could impede your ability to return data.

// when building params
.params({ignoreOwnership: true})
// when querying the table
.go({ignoreOwnership: true})
// when using pagination
.page(null, {ignoreOwnership: true})

Your existing index fields have values with mixed case:

DynamoDB is case-sensitive, and ElectroDB will lowercase key values by default. In the case where you modeled your data with uppercase, or did not apply case modifications, ElectroDB can be configured to match this behavior. Checkout the second on Index Casing to read more.

You have index field names that match attribute names:

With Single Table Design, it is encouraged to give index fields a generic name, like pk, sk, gsi1pk, etc. In reality, it is also common for tables to have index fields that are named after the domain itself, like accountId, organizationId, etc.

ElectroDB tries to abstract away your when working with DynamoDB, so instead of defining pk or sk in your model's attributes, you define them as indexes and map other attributes onto those fields as a composite. Using separate item fields for keys, then for the actual attributes you use in your application, you can leverage more advanced modeling techniques in DynamoDB.

If your existing table uses non-generic fields that also function as attributes, checkout the section Attributes as Indexes to learn more about how ElectroDB handles these types of indexes.

Electro CLI

NOTE: The ElectroCLI is currently in a beta phase and subject to change.

Electro is a CLI utility toolbox for extending the functionality of ElectroDB. Current functionality of the CLI allows you to:

1. Execute queries against your Entities, Services, Models directly from the command line.
2. Dynamically stand up an HTTP Service to interact with your Entities, Services, Models.

For usage and installation details you can learn more here.

Version 1 Migration

This section is to detail any breaking changes made on the journey to a stable 1.0 product.

New schema format/breaking key format change

It became clear when I added the concept of a Service that the "version" paradigm of having the version in the PK wasn't going to work. This is because collection queries use the same PK for all entities and this would prevent some entities in a Service to change versions without impacting the service as a whole. The better more is the place the version in the SK after the entity name so that all version of an entity can be queried. This will work nicely into the migration feature I have planned that will help migrate between model versions.

To address this change, I decide it would be best to change the structure for defining a model, which is then used as heuristic to determine where to place the version in the key (PK or SK). This has the benefit of not breaking existing models, but does increase some complexity in the underlying code.

Additionally, a change was made to the Service class. New Services would take a string of the service name instead of an object as before.

In the old scheme, version came after the service name (see ^).

pk: $mallstoredirectory_1#mall_eastpointe
                        ^
sk: $mallstores#building_buildinga#store_lattelarrys

In the new scheme, version comes after the entity name (see ^).

pk: $mallstoredirectory#mall_eastpointe

sk: $mallstores_1#building_buildinga#store_lattelarrys
                ^

In practice the change looks like this for use of Entity:

const  DynamoDB  =  require("aws-sdk/clients/dynamodb");
const {Entity} = require("electrodb");
const client = new DynamoDB.DocumentClient();
const table = "dynamodb_table_name";

// old way
let old_schema = {
  entity: "model_name",
  service: "service_name",
  version: "1",
  table: table,
  attributes: {...},
  indexes: {...}
};
new Entity(old_schema, {client});

// new way
let new_schema = {
  model: {
    entity: "model_name",
    service: "service_name",
    version: "1",
  },
  attributes: {...},
  indexes: {...}
};
new Entity(new_schema, {client, table});

Changes to usage of Service would look like this:

const  DynamoDB  =  require("aws-sdk/clients/dynamodb");
const {Service} = require("electrodb");
const client = new DynamoDB.DocumentClient();
const table = "dynamodb_table_name";

// old way
new Service({
  service: "service_name",
  version: "1",
  table: table,
}, {client});

// new way
new Service("service_name", {client, table});

// new way (for better TypeScript support)
new Service({entity1, entity2, ...})

The renaming of index property Facets to Composite and Template

In preparation of moving the codebase to version 1.0, ElectroDB will now accept the facets property as either the composite and/or template properties. Using the facets property is still accepted by ElectroDB but will be deprecated sometime in the future (tbd).

This change stems from the fact the facets is already a defined term in the DynamoDB space and that definition does not fit the use-case of how ElectroDB uses the term. To avoid confusion from new developers, the facets property shall now be called composite (as in Composite Attributes) when supplying an Array of attributes, and template while supplying a string. These are two independent fields for two reasons:

ElectroDB will validate the Composite Attributes provided map to those in the template (more validation is always nice).

Allowing for the composite array to be supplied independently will allow for Composite Attributes to remained typed even when using a Composite Attribute Template.

Get Method to Return null

1.0.0 brings back a null response from the get() method when a record could not be found. Prior to 1.0.0 ElectroDB returned an empty object.

Coming Soon

• Default query options defined on the model to give more general control of interactions with the Entity.

Download Details:
Author: tywalch
Official Website: https://github.com/tywalch/electrodb 
License: MIT
 

#electrodb #dynamodb

Software Testing 101: Regression Tests, Unit Tests, Integration Tests

Automation and segregation can help you build better software
If you write automated tests and deliver them to the customer, he can make sure the software is working properly. And, at the end of the day, he paid for it.

Ok. We can segregate or separate the tests according to some criteria. For example, “white box” tests are used to measure the internal quality of the software, in addition to the expected results. They are very useful to know the percentage of lines of code executed, the cyclomatic complexity and several other software metrics. Unit tests are white box tests.

#testing #software testing #regression tests #unit tests #integration tests

Where To Learn Test Programming — July 2020 Edition

What do you do when you have lots of free time on your hands? Why not learn test programming strategies and approaches?

When you’re looking for places to learn test programming, Test Automation University has you covered. From API testing through visual validation, you can hone your skills and learn new approaches on TAU.

We introduced five new TAU courses from April through June, and each of them can help you expand your knowledge, learn a new approach, and improve your craft as a test automation engineer. They are:

These courses add to the other three courses we introduced in January through March 2020:

• IntelliJ for Test Automation Engineers (3 hrs 41 min)
• Cucumber with JavaScript (1 hr 22 min)
• Python Programming (2 hrs)

Each of these courses can give you a new set of skills.

Let’s look at each in a little detail.

Mobile Automation With Appium in JavaScript

Orane Findley teaches Mobile Automation with Appium in JavaScript. Orane walks through all the basics of Appium, starting with what it is and where it runs.

“Appium is an open-source tool for automating native, web, and hybrid applications on different platforms.”

In the introduction, Orane describes the course parts:

• Setup and Dependencies — installing Appium and setting up your first project
• Working with elements by finding them, sending values, clicking, and submitting
• Creating sessions, changing screen orientations, and taking screenshots
• Timing, including TimeOuts and Implicit Waits
• Collecting attributes and data from an element
• Selecting and using element states
• Reviewing everything to make it all make sense

The first chapter, broken into five parts, gets your system ready for the rest of the course. You’ll download and install a Java Developer Kit, a stable version of Node.js, Android Studio and Emulator (for a mobile device emulator), Visual Studio Code for an IDE, Appium Server, and a sample Appium Android Package Kit. If you get into trouble, you can use the Test Automation University Slack channel to get help from Orane. Each subchapter contains the links to get to the proper software. Finally, Orane has you customize your configuration for the course project.

Chapter 2 deals with elements and screen interactions for your app. You can find elements on the page, interact with those elements, and scroll the page to make other elements visible. Orane breaks the chapter into three distinct subchapters so you can become competent with each part of finding, scrolling, and interacting with the app. The quiz comes at the end of the third subchapter.

The remaining chapters each deal with specific bullets listed above: sessions and screen capture, timing, element attributes, and using element states. The final summary chapter ensures you have internalized the key takeaways from the course. Each of these chapters includes its quiz.

When you complete this course successfully, you will have both a certificate of completion and the code infrastructure available on your system to start testing mobile apps using Appium.

Selenium WebDriver With Python

Andrew Knight, who blogs as The Automation Panda, teaches the course on Selenium WebDriver with Python. As Andrew points out, Python has become a popular language for test automation. If you don’t know Python at all, he points you to Jess Ingrassellino’s great course, Python for Test Programming, also on Test Automation University.

In the first chapter, Andrew has you write your first test. Not in Python, but Gherkin. If you have never used Gherkin syntax, it helps you structure your tests in pseudocode that you can translate into any language of your choice. Andrew points out that it’s important to write your test steps before you write test code — and Gherkin makes this process straightforward.

The second chapter goes through setting up a pytest, the test framework Andrew uses. He assumes you already have Python 3.8 installed. Depending on your machine, you may need to do some work (Macs come with Python 2.7.16 installed, which is old and won’t work. Andrew also goes through the pip package manager to install pipenv. He gives you a GitHub link to his test code for the project. And, finally, he creates a test using the Gherkin codes as comments to show you how a test runs in pytest.

In the third chapter, you set up Selenium Webdriver to work with specific browsers, then create your test fixture in the pytest. Andrew reminds you to download the appropriate browser driver for the browser you want to test — for example, chromedriver to drive Chrome and geckodriver to drive Firefox. Once you use pipenv to install Selenium, you begin your test fixture. One thing to remember is to call an explicit quit for your webdriver after a test.

Chapter 4 goes through page objects, and how you abstract page object details to simplify your test structure. Chapter 5 goes through element locator structures and how to use these in Python. And, in Chapter 6, Andrew goes through some common webdriver calls and how to use them in your tests. These first six chapters cover the basics of testing with Python and Selenium.

Now that you have the basics down, the final three chapters review some advanced ideas: testing with multiple browsers, handling race conditions, and running your tests in parallel. This course gives you specific skills around Python and Selenium on top of what you can get from the Python for Test Programming course.

#tutorial #performance #testing #automation #test automation #automated testing #visual testing #visual testing best practices #testing tutorial

Quality Assurance Services | Best QA team for Hire

Software testing is often underrated when it comes to tight deadlines and deliverables. Developers try to compromise in software testing phases, and this leads to more confusion and client dissatisfaction. A professional software developer is the one who remains calm an ensures that the developed solutions go through proper checks and trouble shooting well before the software is due to be delivered or deployed. There are situations when the software development companies do not have an in-house testing team or lack certain tools and resources. In this case, they use third party software testing companies to complete the process.

Software Testing-All you need to know about

Software testing, we all know is a mandatory process that needs to be carried out during, after and beyond the development of any software solution. It helps identify major and minor glitches. Early and regular identification, can help programmers ensure that the finished product is error free. Imagine, developing a software and deploying it without any testing or quality checks? What would be the result? The answers could be endless and rolling out a software this way could jeopardize an entire software development organization.

The lack of proper and robust software testing is a major problem facing development companies. This can be due to many reasons. So when integrating a successful testing processes into software development, a development company should keep a few things in mind.

What software testing actually means

Verifying whether software meets the expected parameters. It is not a superficial but an in-depth process
A healthy process involves investigating the product to find any information that is of value to the stakeholders.
Trying to identify problems before software is released to stakeholders
An integral step in software development that is often underrated is constant communication with stakeholders and collaboration to improve the product
It is important to keep these points in mind when searching for a good software testing partner. Getting software testing done by a third-party vendor is not an easy task because it can be hard to determine if a company will be a good partner or a bad one. Your knowledge of proper testing and quality assurance processes comes into play here.

How to excel in manual quality testing

Proper Organization
Organization is an important step not only in testing but other processes too. When we talk about manual testing, it becomes necessary to create an organized structure to store all the relevant details of the project so that no integral information is lost or missed. The stored information and communication details are helpful when creating a successful testing strategy and revisiting information exchanged earlier.

Writing detailed bug reports
Bug reports submitted by the tester are of the utmost importance because these reports contain reports of genuine issue in the application. A detailed report is a great benefit to developers because they can just go through it and see what needs to be done next. If the bug report is not clear or it is incomplete, it creates confusion and may lead to misunderstandings or delayed delivery dates.

Clarity in test cases
The optimal size for a test case is 3 to 8 steps. Clarity in test cases can determine the efficiency in your testing tasks. Just like a detailed bug report is important similarly clear and precise test cases are required to carry out and effective testing process. Test cases should not be very long, the longer the test case the trickier it will be for the developer to work through it.

Besides clear case studies and complete bug reports, the tester must have ability to be focused and stay positive while testing the software application. These features enhance the testing process and make it more reliable.

Tools used for robust software testing

Where manual testing is considered a priority in a software development process, automated testing aids the process and makes it more effective.

Good testing tools:

1. Selenium
Selenium is an automated testing tool that runs on various OS systems like Linux, Windows, and iOS. It is a testing framework to perform web application testing. Selenium helps the testers to write tests in various programming languages like Java, PHP, C#, Python, Groovy, Ruby, and Perl. It provides a record and review of the features. These records are easy to learn and utilize. Selenium proudly supports some of the largest browser vendors. Major browsers often include Selenium as a native part of their browser. Selenium is undoubtedly the base for most of the other software testing tools in general.

2. Tosca Testsuite
Tosca Testsuite by Tricentis uses model-based test automation to automate software testing.

Tosca Testsuite comes with the following capabilities:

• Plan and design test case
• Test data provisioning
• Service virtualization network
• Tests mobile apps
• Integration management
• Risk coverage

3. TestingWhiz
The enterprise package of TestingWhiz offers automated testing solutions like web testing, software testing, database testing, API testing, mobile app testing, regression testing and more. It is the testing tool that offers code-less scripting and involves processes such as optimization, automation and cross-browser testing.

Other features include:

• Keyword-driven, data-driven testing, and distributed testing
• Browser Extension Testing
• Object Eye Internal Recorder
• SMTP Integration
• Bug tracking tools- Jira, Mantis, TFS and FogBugz
• Test management tools – HP Quality Center, Zephyr, TestRail, and Microsoft VSTS
• Customized Recording Rule

4. TestComplete
TestComplete is a functional testing platform that offers various solutions to automate testing for desktop, web, and mobile applications.

TestComplete offers the following features:

• GUI testing
• Scripting Language Support – JavaScript, Python, VBScript, JScript, DelphiScript, C++Script & C# Script
• Test visualizer
• Scripted testing
• Test recording and playback

5. Ranorex
Ranorex Studio covers testing of all desktop, web, and mobile applications.

Ranorex offers the following features:

• GUI recognition
• Reusable test codes
• Bug detection
• Integration with various tools
• Record and playback

6. HPE Unified Functional Testing
Formerly known as HP QuickTest Professional, HPE Unified Functional Testing offers testing automation for functional and regression testing for software applications.

Visual Basic Scripting Edition is used by this tool to register the test processes and operates the various objects and controls in testing the applications.

Other features are:

• Mercury Business Process Testing
• Unique Smart Object Recognition
• Error handling mechanism
• Creation of parameters for objects, checkpoints, and data-driven tables
• Automated documentation

7. Sahi
This open-source testing tool is written in Java and JavaScript programming languages.

Sahi provides the following features:

• Performs multi-browser testing
• Supports ExtJS, ZK, Dojo, YUI, etc. frameworks
• Record and playback on the browser testing

8. Watir
Watir, pronounced as ‘Water’, is an open-source testing tool made up of Ruby libraries to automate web application testing.

Watir offers the following features:

• Tests any language-based web application
• Cross-browser testing
• Compatible with business-driven development tools like RSpec, Cucumber, and Test/Unit
• Tests web page’s buttons, forms, links, and their responses

9. Telerik TestStudio

Telerik TestStudio offers one solution to automate desktop, web, and mobile application testing including UI, load, and performance testing.

Telerik TestStudio offers various compatibilities like:

• Support for programming languages like HTML, AJAX, ASP.NET, JavaScript, Silverlight, WPF, and MVC
• Integration with Visual Basic Studio 2010 and 2012
• Record and playback
• Cross-browser testing
• Manual testing
• Integration with bug tracking tools

10. Katalon Studio

Katalon Studio by Katalon LLC is built on top of Appium with a specialized IDE interface for API, web and mobile testing. This tool includes a full package of powerful features that help overcome common challenges in web UI test automation.

Katalon Studio consists of the following features:

• XPath, object re-identification
• Built-in object repository
• Supports Java/Groovy scripting languages
• Built-in support for Image-based testing
• Support Continuous Integration tools like Jenkins & TeamCity
• Supports Duel-editor Interface
• Customizable execution workflow

Conclusion
Software testing is the whole and sole process in which the application’s actual working state can be diagnosed. Some software development organizations get it done in house whereas, some require outsourcing. Software testing is a vast topic and its process vary from organization to organization. But it is a critical step and should be carried out by any reliable team.

#software testing best practices #software test strategies #software testing strategies #software testing approach #qa strategies #testing best practices

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

• Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
• Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
• While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

• Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
• Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
• End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr