Spring Boot + React Redux example: Build a CRUD App

In this tutorial, we will learn how to build a full stack React Redux + Spring Boot example with a CRUD App. The back-end server uses Spring Boot with Spring Web MVC for REST APIs and Spring Data JPA for interacting with embedded database (H2 database). Front-end side is made with React, Redux, React Router, Axios & Bootstrap.

Full Article: https://bezkoder.com/spring-boot-react-redux-example/

Spring Boot React Redux example Overview

We will build a full-stack Spring Boot & React Redux Tutorial Application in that:

Each Tutorial has id, title, description, published status.
We can create, retrieve, update, delete Tutorials.
We can also find Tutorials by title.

The images below shows screenshots of our System.

Create a Tutorial:

spring-boot-react-redux-example-crud-create-tutorial

Retrieve all Tutorials:

spring-boot-react-redux-example-crud-retrieve-tutorial

Click on Edit button to retrieve an item:

spring-boot-react-redux-example-crud-retrieve-one-tutorial

On this Page, you can:

change status to Published using Publish button
remove the Tutorial from Database using Delete button
update the Tutorial details on Database with Update button

spring-boot-react-redux-example-crud-update-tutorial

Search items by title:

spring-boot-react-redux-example-crud-search-tutorial

Architecture of Spring Boot React Redux example

This is the application architecture we're gonna build:

spring-boot-react-redux-example-crud-architecture

Spring Boot exports REST Apis using Spring Web MVC & interacts with embedded H2 Database using Spring JPA
React Client sends HTTP Requests and retrieves HTTP Responses using Axios, consume data on Redux which provides state to the Components. React Router is used for navigating to pages.

You can also find the Spring Restful Apis that works with other databases here:
- Spring JPA + PostgreSQL
- Spring JPA + MySQL
- Spring Data + MongoDB

Spring Boot Rest Apis Back-end

Overview

These are APIs that Spring Boot App will export:

Methods	Urls	Actions
POST	/api/tutorials	create new Tutorial
GET	/api/tutorials	retrieve all Tutorials
GET	/api/tutorials/:id	retrieve a Tutorial by `:id`
PUT	/api/tutorials/:id	update a Tutorial by `:id`
DELETE	/api/tutorials/:id	delete a Tutorial by `:id`
DELETE	/api/tutorials	delete all Tutorials
GET	/api/tutorials?title=[keyword]	find all Tutorials which title contains `keyword`

- We make CRUD operations & finder methods with Spring Data JPA's JpaRepository. - The database will be H2 Database (in memory or on disk) by configuring project dependency & datasource.

Technology

Java 8
Spring Boot 2.4 (with Spring Web MVC, Spring Data JPA)
H2 Database
Maven 3.6.1

Project Structure

spring-boot-react-redux-example-crud-server-project-structure

– Tutorial data model class corresponds to entity and table tutorials.
– TutorialRepository is an interface that extends JpaRepository for CRUD methods and custom finder methods. It will be autowired in TutorialController.
– TutorialController is a RestController which has request mapping methods for RESTful requests such as: getAllTutorials, createTutorial, updateTutorial, deleteTutorial, findByPublished…
– Configuration for Spring Datasource, JPA & Hibernate in application.properties.
– pom.xml contains dependencies for Spring Boot and H2 Database.

React Redux Front-end

Overview

This is React components that we're gonna implement:

spring-boot-react-redux-example-crud-client-components

– The App component is a container with React Router. It has navbar that links to routes paths.

– Three components that dispatch actions to Redux Thunk Middleware which uses TutorialDataService to call Rest API.

TutorialsList component gets and displays Tutorials.
Tutorial component has form for editing Tutorial's details based on :id.
AddTutorial component has form for submission new Tutorial.

– TutorialDataService uses axios to make HTTP requests and receive responses.

This diagram shows how Redux elements work in our React Application:

spring-boot-react-redux-example-crud-store-architecture

We’re gonna create Redux store for storing tutorials data. Other React Components will work with the Store via dispatching an action.

The reducer will take the action and return new state.

Technology

React 17/16
react-redux 7.2.3
redux 4.0.5
redux-thunk 2.3.0
react-router-dom 5.2.0
axios 0.21.1
bootstrap 4

Project Structure

![spring-boot-react-redux-example-crud-client-project-structure](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/k0pmxledyj2pe0iru91w.png)

package.json contains main modules: react, react-router-dom, react-redux, redux, redux-thunk, axios & bootstrap.
App is the container that has Router & navbar.
There are 3 components: TutorialsList, Tutorial, AddTutorial.
http-common.js initializes axios with HTTP base Url and headers.
TutorialDataService has methods for sending HTTP requests to the Apis.
.env configures port for this React CRUD App.

About Redux elements that we’re gonna use:

actions folder contains the action creator (tutorials.js for CRUD operations and searching).
reducers folder contains the reducer (tutorials.js) which updates the application state corresponding to dispatched action.

For more steps and Source code, please visit:
https://bezkoder.com/spring-boot-react-redux-example/

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Spring Boot + React Redux example: Build a CRUD App

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Enhance Amazon Aurora Read/Write Capability with ShardingSphere-JDBC

1. Introduction

Amazon Aurora is a relational database management system (RDBMS) developed by AWS(Amazon Web Services). Aurora gives you the performance and availability of commercial-grade databases with full MySQL and PostgreSQL compatibility. In terms of high performance, Aurora MySQL and Aurora PostgreSQL have shown an increase in throughput of up to 5X over stock MySQL and 3X over stock PostgreSQL respectively on similar hardware. In terms of scalability, Aurora achieves enhancements and innovations in storage and computing, horizontal and vertical functions.

Aurora supports up to 128TB of storage capacity and supports dynamic scaling of storage layer in units of 10GB. In terms of computing, Aurora supports scalable configurations for multiple read replicas. Each region can have an additional 15 Aurora replicas. In addition, Aurora provides multi-primary architecture to support four read/write nodes. Its Serverless architecture allows vertical scaling and reduces typical latency to under a second, while the Global Database enables a single database cluster to span multiple AWS Regions in low latency.

Aurora already provides great scalability with the growth of user data volume. Can it handle more data and support more concurrent access? You may consider using sharding to support the configuration of multiple underlying Aurora clusters. To this end, a series of blogs, including this one, provides you with a reference in choosing between Proxy and JDBC for sharding.

1.1 Why sharding is needed

AWS Aurora offers a single relational database. Primary-secondary, multi-primary, and global database, and other forms of hosting architecture can satisfy various architectural scenarios above. However, Aurora doesn’t provide direct support for sharding scenarios, and sharding has a variety of forms, such as vertical and horizontal forms. If we want to further increase data capacity, some problems have to be solved, such as cross-node database Join, associated query, distributed transactions, SQL sorting, page turning, function calculation, database global primary key, capacity planning, and secondary capacity expansion after sharding.

1.2 Sharding methods

It is generally accepted that when the capacity of a MySQL table is less than 10 million, the time spent on queries is optimal because at this time the height of its BTREE index is between 3 and 5. Data sharding can reduce the amount of data in a single table and distribute the read and write loads to different data nodes at the same time. Data sharding can be divided into vertical sharding and horizontal sharding.

1. Advantages of vertical sharding

Address the coupling of business system and make clearer.
Implement hierarchical management, maintenance, monitoring, and expansion to data of different businesses, like micro-service governance.
In high concurrency scenarios, vertical sharding removes the bottleneck of IO, database connections, and hardware resources on a single machine to some extent.

2. Disadvantages of vertical sharding

After splitting the library, Join can only be implemented by interface aggregation, which will increase the complexity of development.
After splitting the library, it is complex to process distributed transactions.
There is a large amount of data on a single table and horizontal sharding is required.

3. Advantages of horizontal sharding

There is no such performance bottleneck as a large amount of data on a single database and high concurrency, and it increases system stability and load capacity.
The business modules do not need to be split due to minor modification on the application client.

4. Disadvantages of horizontal sharding

Transaction consistency across shards is hard to be guaranteed;
The performance of associated query in cross-library Join is poor.
It’s difficult to scale the data many times and maintenance is a big workload.

Based on the analysis above, and the available studis on popular sharding middleware, we selected ShardingSphere, an open source product, combined with Amazon Aurora to introduce how the combination of these two products meets various forms of sharding and how to solve the problems brought by sharding.

ShardingSphere is an open source ecosystem including a set of distributed database middleware solutions, including 3 independent products, Sharding-JDBC, Sharding-Proxy & Sharding-Sidecar.

2. ShardingSphere introduction:

The characteristics of Sharding-JDBC are:

With the client end connecting directly to the database, it provides service in the form of jar and requires no extra deployment and dependence.
It can be considered as an enhanced JDBC driver, which is fully compatible with JDBC and all kinds of ORM frameworks.
Applicable in any ORM framework based on JDBC, such as JPA, Hibernate, Mybatis, Spring JDBC Template or direct use of JDBC.
Support any third-party database connection pool, such as DBCP, C3P0, BoneCP, Druid, HikariCP;
Support any kind of JDBC standard database: MySQL, Oracle, SQLServer, PostgreSQL and any databases accessible to JDBC.
Sharding-JDBC adopts decentralized architecture, applicable to high-performance light-weight OLTP application developed with Java

Hybrid Structure Integrating Sharding-JDBC and Applications

Sharding-JDBC’s core concepts

Data node: The smallest unit of a data slice, consisting of a data source name and a data table, such as ds_0.product_order_0.

Actual table: The physical table that really exists in the horizontal sharding database, such as product order tables: product_order_0, product_order_1, and product_order_2.

Logic table: The logical name of the horizontal sharding databases (tables) with the same schema. For instance, the logic table of the order product_order_0, product_order_1, and product_order_2 is product_order.

Binding table: It refers to the primary table and the joiner table with the same sharding rules. For example, product_order table and product_order_item are sharded by order_id, so they are binding tables with each other. Cartesian product correlation will not appear in the multi-tables correlating query, so the query efficiency will increase greatly.

Broadcast table: It refers to tables that exist in all sharding database sources. The schema and data must consist in each database. It can be applied to the small data volume that needs to correlate with big data tables to query, dictionary table and configuration table for example.

3. Testing ShardingSphere-JDBC

3.1 Example project

Download the example project code locally. In order to ensure the stability of the test code, we choose shardingsphere-example-4.0.0 version.

git clone https://github.com/apache/shardingsphere-example.git

Project description：

shardingsphere-example
  ├── example-core
  │   ├── config-utility
  │   ├── example-api
  │   ├── example-raw-jdbc
  │   ├── example-spring-jpa #spring+jpa integration-based entity，repository
  │   └── example-spring-mybatis
  ├── sharding-jdbc-example
  │   ├── sharding-example
  │   │   ├── sharding-raw-jdbc-example
  │   │   ├── sharding-spring-boot-jpa-example #integration-based sharding-jdbc functions
  │   │   ├── sharding-spring-boot-mybatis-example
  │   │   ├── sharding-spring-namespace-jpa-example
  │   │   └── sharding-spring-namespace-mybatis-example
  │   ├── orchestration-example
  │   │   ├── orchestration-raw-jdbc-example
  │   │   ├── orchestration-spring-boot-example #integration-based sharding-jdbc governance function
  │   │   └── orchestration-spring-namespace-example
  │   ├── transaction-example
  │   │   ├── transaction-2pc-xa-example #sharding-jdbc sample of two-phase commit for a distributed transaction
  │   │   └──transaction-base-seata-example #sharding-jdbc distributed transaction seata sample
  │   ├── other-feature-example
  │   │   ├── hint-example
  │   │   └── encrypt-example
  ├── sharding-proxy-example
  │   └── sharding-proxy-boot-mybatis-example
  └── src/resources
        └── manual_schema.sql

Configuration file description:

application-master-slave.properties #read/write splitting profile
application-sharding-databases-tables.properties #sharding profile
application-sharding-databases.properties       #library split profile only
application-sharding-master-slave.properties    #sharding and read/write splitting profile
application-sharding-tables.properties          #table split profile
application.properties                         #spring boot profile

Code logic description:

The following is the entry class of the Spring Boot application below. Execute it to run the project.

The execution logic of demo is as follows:

3.2 Verifying read/write splitting

As business grows, the write and read requests can be split to different database nodes to effectively promote the processing capability of the entire database cluster. Aurora uses a reader/writer endpoint to meet users' requirements to write and read with strong consistency, and a read-only endpoint to meet the requirements to read without strong consistency. Aurora's read and write latency is within single-digit milliseconds, much lower than MySQL's binlog-based logical replication, so there's a lot of loads that can be directed to a read-only endpoint.

Through the one primary and multiple secondary configuration, query requests can be evenly distributed to multiple data replicas, which further improves the processing capability of the system. Read/write splitting can improve the throughput and availability of system, but it can also lead to data inconsistency. Aurora provides a primary/secondary architecture in a fully managed form, but applications on the upper-layer still need to manage multiple data sources when interacting with Aurora, routing SQL requests to different nodes based on the read/write type of SQL statements and certain routing policies.

ShardingSphere-JDBC provides read/write splitting features and it is integrated with application programs so that the complex configuration between application programs and database clusters can be separated from application programs. Developers can manage the Shard through configuration files and combine it with ORM frameworks such as Spring JPA and Mybatis to completely separate the duplicated logic from the code, which greatly improves the ability to maintain code and reduces the coupling between code and database.

3.2.1 Setting up the database environment

Create a set of Aurora MySQL read/write splitting clusters. The model is db.r5.2xlarge. Each set of clusters has one write node and two read nodes.

3.2.2 Configuring Sharding-JDBC

application.properties spring boot Master profile description:

You need to replace the green ones with your own environment configuration.

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#Activate master-slave configuration item so that sharding-jdbc can use master-slave profile
spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-master-slave.properties sharding-jdbc profile description:

spring.shardingsphere.datasource.names=ds_master,ds_slave_0,ds_slave_1
# data souce-master
spring.shardingsphere.datasource.ds_master.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master.password=Your master DB password
spring.shardingsphere.datasource.ds_master.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master.jdbc-url=Your primary DB data sourceurl spring.shardingsphere.datasource.ds_master.username=Your primary DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_0.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_0.jdbc-url=Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_0.username= Your slave DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_1.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_1.jdbc-url= Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_1.username= Your slave DB username
# Routing Policy Configuration
spring.shardingsphere.masterslave.load-balance-algorithm-type=round_robin
spring.shardingsphere.masterslave.name=ds_ms
spring.shardingsphere.masterslave.master-data-source-name=ds_master
spring.shardingsphere.masterslave.slave-data-source-names=ds_slave_0,ds_slave_1
# sharding-jdbc configures the information storage mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log，and you can see the conversion from logical SQL to actual SQL from the print
spring.shardingsphere.props.sql.show=true

3.2.3 Test and verification process description

Test environment data initialization: Spring JPA initialization automatically creates tables for testing.

Write data to the master instance

As shown in the ShardingSphere-SQL log figure below, the write SQL is executed on the ds_master data source.

Data query operations are performed on the slave library.

As shown in the ShardingSphere-SQL log figure below, the read SQL is executed on the ds_slave data source in the form of polling.

[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] Rule Type: master-slave 
[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] SQL: select orderentit0_.order_id as order_id1_1_, orderentit0_.address_id as address_2_1_, 
orderentit0_.status as status3_1_, orderentit0_.user_id as user_id4_1_ from t_order orderentit0_ ::: DataSources: ds_slave_0 
---------------------------- Print OrderItem Data -------------------
Hibernate: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, orderiteme1_.user_id 
as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] Rule Type: master-slave 
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] SQL: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, 
orderiteme1_.user_id as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id ::: DataSources: ds_slave_1

Note: As shown in the figure below, if there are both reads and writes in a transaction, Sharding-JDBC routes both read and write operations to the master library. If the read/write requests are not in the same transaction, the corresponding read requests are distributed to different read nodes according to the routing policy.

@Override
@Transactional // When a transaction is started, both read and write in the transaction go through the master library. When closed, read goes through the slave library and write goes through the master library
public void processSuccess() throws SQLException {
    System.out.println("-------------- Process Success Begin ---------------");
    List<Long> orderIds = insertData();
    printData();
    deleteData(orderIds);
    printData();
    System.out.println("-------------- Process Success Finish --------------");
}

3.2.4 Verifying Aurora failover scenario

The Aurora database environment adopts the configuration described in Section 2.2.1.

3.2.4.1 Verification process description

Start the Spring-Boot project

2. Perform a failover on Aurora’s console

3. Execute the Rest API request

4. Repeatedly execute POST (http://localhost:8088/save-user) until the call to the API failed to write to Aurora and eventually recovered successfully.

5. The following figure shows the process of executing code failover. It takes about 37 seconds from the time when the latest SQL write is successfully performed to the time when the next SQL write is successfully performed. That is, the application can be automatically recovered from Aurora failover, and the recovery time is about 37 seconds.

3.3 Testing table sharding-only function

3.3.1 Configuring Sharding-JDBC

application.properties spring boot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
#spring.profiles.active=sharding-databases
#Activate sharding-tables configuration items
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
# spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-tables.properties sharding-jdbc profile description

## configure primary-key policy
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# configure the binding relation of t_order and t_order_item
spring.shardingsphere.sharding.binding-tables[0]=t_order,t_order_item
# configure broadcast tables
spring.shardingsphere.sharding.broadcast-tables=t_address
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

3.3.2 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC routing rules are configured, the client executes DDL, and Sharding-JDBC automatically creates corresponding tables according to the table splitting rules. If t_address is a broadcast table, create a t_address because there is only one master instance. Two physical tables t_order_0 and t_order_1 will be created when creating t_order.

2. Write operation

As shown in the figure below, Logic SQL inserts a record into t_order. When Sharding-JDBC is executed, data will be distributed to t_order_0 and t_order_1 according to the table splitting rules.

When t_order and t_order_item are bound, the records associated with order_item and order are placed on the same physical table.

3. Read operation

As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.

The join query operations on order and order_item under the unbound table will traverse all shards.

3.4 Testing database sharding-only function

3.4.1 Setting up the database environment

Create two instances on Aurora: ds_0 and ds_1

When the sharding-spring-boot-jpa-example project is started, tables t_order, t_order_item，t_address will be created on two Aurora instances.

3.4.2 Configuring Sharding-JDBC

application.properties springboot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

# Activate sharding-databases configuration items
spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-databases.properties sharding-jdbc profile description

spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= spring.shardingsphere.datasource.ds_0.username= 
spring.shardingsphere.datasource.ds_0.password=
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url= 
spring.shardingsphere.datasource.ds_1.username= 
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_0

spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

3.4.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, physical tables will be created on ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the slave library are written on the table in the corresponding instance according to the slave library field and routing policy.

3. Read operation

Query order is routed to the corresponding Aurora instance according to the routing rules of the slave library .

Query Address. Since address is a broadcast table, an instance of address will be randomly selected and queried from the nodes used.

As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.

3.5 Verifying sharding function

3.5.1 Setting up the database environment

As shown in the figure below, create two instances on Aurora: ds_0 and ds_1

When the sharding-spring-boot-jpa-example project is started, physical tables t_order_01, t_order_02, t_order_item_01，and t_order_item_02 and global table t_address will be created on two Aurora instances.

3.5.2 Configuring Sharding-JDBC

application.properties springboot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
# Activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-databases.properties sharding-jdbc profile description

spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= 306/dev?useSSL=false&characterEncoding=utf-8
spring.shardingsphere.datasource.ds_0.username= 
spring.shardingsphere.datasource.ds_0.password=
spring.shardingsphere.datasource.ds_0.max-active=16
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url= 
spring.shardingsphere.datasource.ds_1.username= 
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.datasource.ds_1.max-active=16
# default library splitting policy
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
# Tables that do not meet the library splitting policy are placed on ds_0
spring.shardingsphere.sharding.default-data-source-name=ds_0
# t_order table splitting policy
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
# t_order_item table splitting policy
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mdoe
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

3.5.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s sharding and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the sub-library are written to the table on the corresponding instance according to the slave library field and routing policy.

3. Read operation

The read operation is similar to the library split function verification described in section2.4.3.

3.6 Testing database sharding, table sharding and read/write splitting function

3.6.1 Setting up the database environment

The following figure shows the physical table of the created database instance.

3.6.2 Configuring Sharding-JDBC

application.properties spring boot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

# activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
spring.profiles.active=sharding-master-slave

application-sharding-master-slave.properties sharding-jdbc profile description

The url, name and password of the database need to be changed to your own database parameters.

spring.shardingsphere.datasource.names=ds_master_0,ds_master_1,ds_master_0_slave_0,ds_master_0_slave_1,ds_master_1_slave_0,ds_master_1_slave_1
spring.shardingsphere.datasource.ds_master_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0.username= 
spring.shardingsphere.datasource.ds_master_0.password=
spring.shardingsphere.datasource.ds_master_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_0.username= 
spring.shardingsphere.datasource.ds_master_0_slave_0.password=
spring.shardingsphere.datasource.ds_master_0_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_1.username= 
spring.shardingsphere.datasource.ds_master_0_slave_1.password=
spring.shardingsphere.datasource.ds_master_0_slave_1.max-active=16
spring.shardingsphere.datasource.ds_master_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1.jdbc-url= 
spring.shardingsphere.datasource.ds_master_1.username= 
spring.shardingsphere.datasource.ds_master_1.password=
spring.shardingsphere.datasource.ds_master_1.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_0.jdbc-url=
spring.shardingsphere.datasource.ds_master_1_slave_0.username=
spring.shardingsphere.datasource.ds_master_1_slave_0.password=
spring.shardingsphere.datasource.ds_master_1_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_1_slave_1.username=admin
spring.shardingsphere.datasource.ds_master_1_slave_1.password=
spring.shardingsphere.datasource.ds_master_1_slave_1.max-active=16
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_master_0
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# master/slave data source and slave data source configuration
spring.shardingsphere.sharding.master-slave-rules.ds_0.master-data-source-name=ds_master_0
spring.shardingsphere.sharding.master-slave-rules.ds_0.slave-data-source-names=ds_master_0_slave_0, ds_master_0_slave_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.master-data-source-name=ds_master_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.slave-data-source-names=ds_master_1_slave_0, ds_master_1_slave_1
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

3.6.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the slave library are written to the table on the corresponding instance according to the slave library field and routing policy.

3. Read operation

The join query operations on order and order_item under the binding table are shown below.

3. Conclusion

As an open source product focusing on database enhancement, ShardingSphere is pretty good in terms of its community activitiy, product maturity and documentation richness.

Among its products, ShardingSphere-JDBC is a sharding solution based on the client-side, which supports all sharding scenarios. And there’s no need to introduce an intermediate layer like Proxy, so the complexity of operation and maintenance is reduced. Its latency is theoretically lower than Proxy due to the lack of intermediate layer. In addition, ShardingSphere-JDBC can support a variety of relational databases based on SQL standards such as MySQL/PostgreSQL/Oracle/SQL Server, etc.

However, due to the integration of Sharding-JDBC with the application program, it only supports Java language for now, and is strongly dependent on the application programs. Nevertheless, Sharding-JDBC separates all sharding configuration from the application program, which brings relatively small changes when switching to other middleware.

In conclusion, Sharding-JDBC is a good choice if you use a Java-based system and have to to interconnect with different relational databases — and don’t want to bother with introducing an intermediate layer.

Author

Sun Jinhua

A senior solution architect at AWS, Sun is responsible for the design and consult on cloud architecture. for providing customers with cloud-related design and consulting services. Before joining AWS, he ran his own business, specializing in building e-commerce platforms and designing the overall architecture for e-commerce platforms of automotive companies. He worked in a global leading communication equipment company as a senior engineer, responsible for the development and architecture design of multiple subsystems of LTE equipment system. He has rich experience in architecture design with high concurrency and high availability system, microservice architecture design, database, middleware, IOT etc.

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

Performance: It delivers optimal performance.
Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
UI design: React Native enables you to design simple and responsive UI for your mobile app.
3rd party plugins: This framework supports 3rd party plugins.
Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
Near-native user experience: React Native provides several native components, and these are platform agnostic.
The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Lawrence Lesch

1677668905

TS-mockito: Mocking Library for TypeScript

TS-mockito

Mocking library for TypeScript inspired by http://mockito.org/

1.x to 2.x migration guide

Main features

Strongly typed
IDE autocomplete
Mock creation (mock) (also abstract classes) #example
Spying on real objects (spy) #example
Changing mock behavior (when) via:
- thenReturn - return value #example
- thenThrow - throw an error #example
- thenCall - call custom method #example
- thenResolve - resolve promise #example
- thenReject - rejects promise #example
Checking if methods were called with given arguments (verify)
- anything, notNull, anyString, anyOfClass etc. - for more flexible comparision
- once, twice, times, atLeast etc. - allows call count verification #example
- calledBefore, calledAfter - allows call order verification #example
Resetting mock (reset, resetCalls) #example, #example
Capturing arguments passed to method (capture) #example
Recording multiple behaviors #example
Readable error messages (ex. 'Expected "convertNumberToString(strictEqual(3))" to be called 2 time(s). But has been called 1 time(s).')

Installation

npm install ts-mockito --save-dev

Usage

Basics

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance from mock
let foo:Foo = instance(mockedFoo);

// Using instance in source code
foo.getBar(3);
foo.getBar(5);

// Explicit, readable verification
verify(mockedFoo.getBar(3)).called();
verify(mockedFoo.getBar(anything())).called();

Stubbing method calls

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub method before execution
when(mockedFoo.getBar(3)).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.getBar(3));

// prints null, because "getBar(999)" was not stubbed
console.log(foo.getBar(999));

Stubbing getter value

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub getter before execution
when(mockedFoo.sampleGetter).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.sampleGetter);

Stubbing property values that have no getters

Syntax is the same as with getter values.

Please note, that stubbing properties that don't have getters only works if Proxy object is available (ES6).

Call count verification

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(2);
foo.getBar(2);
foo.getBar(3);

// Call count verification
verify(mockedFoo.getBar(1)).once();               // was called with arg === 1 only once
verify(mockedFoo.getBar(2)).twice();              // was called with arg === 2 exactly two times
verify(mockedFoo.getBar(between(2, 3))).thrice(); // was called with arg between 2-3 exactly three times
verify(mockedFoo.getBar(anyNumber()).times(4);    // was called with any number arg exactly four times
verify(mockedFoo.getBar(2)).atLeast(2);           // was called with arg === 2 min two times
verify(mockedFoo.getBar(anything())).atMost(4);   // was called with any argument max four times
verify(mockedFoo.getBar(4)).never();              // was never called with arg === 4

Call order verification

// Creating mock
let mockedFoo:Foo = mock(Foo);
let mockedBar:Bar = mock(Bar);

// Getting instance
let foo:Foo = instance(mockedFoo);
let bar:Bar = instance(mockedBar);

// Some calls
foo.getBar(1);
bar.getFoo(2);

// Call order verification
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(2));    // foo.getBar(1) has been called before bar.getFoo(2)
verify(mockedBar.getFoo(2)).calledAfter(mockedFoo.getBar(1));    // bar.getFoo(2) has been called before foo.getBar(1)
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(999999));    // throws error (mockedBar.getFoo(999999) has never been called)

Throwing errors

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(10)).thenThrow(new Error('fatal error'));

let foo:Foo = instance(mockedFoo);
try {
    foo.getBar(10);
} catch (error:Error) {
    console.log(error.message); // 'fatal error'
}

Custom function

You can also stub method with your own implementation

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

when(mockedFoo.sumTwoNumbers(anyNumber(), anyNumber())).thenCall((arg1:number, arg2:number) => {
    return arg1 * arg2; 
});

// prints '50' because we've changed sum method implementation to multiply!
console.log(foo.sumTwoNumbers(5, 10));

Resolving / rejecting promises

You can also stub method to resolve / reject promise

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.fetchData("a")).thenResolve({id: "a", value: "Hello world"});
when(mockedFoo.fetchData("b")).thenReject(new Error("b does not exist"));

Resetting mock calls

You can reset just mock call counter

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(1);
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
resetCalls(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset

You can also reset calls of multiple mocks at once resetCalls(firstMock, secondMock, thirdMock)

Resetting mock

Or reset mock call counter with all stubs

// Creating mock
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn("one").

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
console.log(foo.getBar(1));               // "one" - as defined in stub
console.log(foo.getBar(1));               // "one" - as defined in stub
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
reset(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset
console.log(foo.getBar(1));               // null - previously added stub has been removed

You can also reset multiple mocks at once reset(firstMock, secondMock, thirdMock)

Capturing method arguments

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

// Call method
foo.sumTwoNumbers(1, 2);

// Check first arg captor values
const [firstArg, secondArg] = capture(mockedFoo.sumTwoNumbers).last();
console.log(firstArg);    // prints 1
console.log(secondArg);    // prints 2

You can also get other calls using first(), second(), byCallIndex(3) and more...

Recording multiple behaviors

You can set multiple returning values for same matching values

const mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(anyNumber())).thenReturn('one').thenReturn('two').thenReturn('three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinitely

Another example with specific values

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(1)).thenReturn('one').thenReturn('another one');
when(mockedFoo.getBar(2)).thenReturn('two');

let foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(2));    // two
console.log(foo.getBar(1));    // another one
console.log(foo.getBar(1));    // another one - this is last defined behavior for arg '1' so it will be repeated
console.log(foo.getBar(2));    // two
console.log(foo.getBar(2));    // two - this is last defined behavior for arg '2' so it will be repeated

Short notation:

const mockedFoo:Foo = mock(Foo);

// You can specify return values as multiple thenReturn args
when(mockedFoo.getBar(anyNumber())).thenReturn('one', 'two', 'three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinity

Possible errors:

const mockedFoo:Foo = mock(Foo);

// When multiple matchers, matches same result:
when(mockedFoo.getBar(anyNumber())).thenReturn('one');
when(mockedFoo.getBar(3)).thenReturn('one');

const foo:Foo = instance(mockedFoo);
foo.getBar(3); // MultipleMatchersMatchSameStubError will be thrown, two matchers match same method call

Mocking interfaces

You can mock interfaces too, just instead of passing type to mock function, set mock function generic type Mocking interfaces requires Proxy implementation

let mockedFoo:Foo = mock<FooInterface>(); // instead of mock(FooInterface)
const foo: SampleGeneric<FooInterface> = instance(mockedFoo);

Mocking types

You can mock abstract classes

const mockedFoo: SampleAbstractClass = mock(SampleAbstractClass);
const foo: SampleAbstractClass = instance(mockedFoo);

You can also mock generic classes, but note that generic type is just needed by mock type definition

const mockedFoo: SampleGeneric<SampleInterface> = mock(SampleGeneric);
const foo: SampleGeneric<SampleInterface> = instance(mockedFoo);

Spying on real objects

You can partially mock an existing instance:

const foo: Foo = new Foo();
const spiedFoo = spy(foo);

when(spiedFoo.getBar(3)).thenReturn('one');

console.log(foo.getBar(3)); // 'one'
console.log(foo.getBaz()); // call to a real method

You can spy on plain objects too:

const foo = { bar: () => 42 };
const spiedFoo = spy(foo);

foo.bar();

console.log(capture(spiedFoo.bar).last()); // [42]

Thanks

Szczepan Faber (https://www.linkedin.com/in/szczepiq)
Sebastian Konkol (https://www.linkedin.com/in/sebastiankonkol)
Clickmeeting (http://clickmeeting.com)
Michał Stocki (https://github.com/michalstocki)
Łukasz Bendykowski (https://github.com/viman)
Andrey Ermakov (https://github.com/dreef3)
Markus Ende (https://github.com/Markus-Ende)
Thomas Hilzendegen (https://github.com/thomashilzendegen)
Johan Blumenberg (https://github.com/johanblumenberg)

Download Details:

Author: NagRock
Source Code: https://github.com/NagRock/ts-mockito
License: MIT license

#typescript #testing #mock

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