Nat  Grady

Nat Grady

1661252125

ShinyTree: Shiny integration with The jsTree Library

shinyTree

The shinyTree package enables Shiny application developers to use the jsTree library in their applications.

shiny tree screenshot

Installation

You can install the latest development version of the code using the devtools R package.

# Install devtools, if you haven't already.
install.packages("devtools")

library(devtools)
install_github("shinyTree/shinyTree")

Getting Started

01-simple (Live Demo)

library(shiny)
runApp(system.file("examples/01-simple", package = "shinyTree"))

A simple example to demonstrate the usage of the shinyTree package.

02-attributes (Live Demo)

library(shiny)
runApp(system.file("examples/02-attributes", package = "shinyTree"))

Manage properties of your tree by adding attributes to your list when rendering.

03-checkbox (Live Demo)

library(shiny)
runApp(system.file("examples/03-checkbox", package = "shinyTree"))

Use checkboxes to allow users to more easily manage which nodes are selected.

04-selected (Live Demo)

library(shiny)
runApp(system.file("examples/04-selected", package = "shinyTree"))

An example demonstrating how to set an input to the value of the currently selected node in the tree.

05-structure (Live Demo)

library(shiny)
runApp(system.file("examples/05-structure", package = "shinyTree"))

Demonstrates the low-level usage of a shinyTree as an input in which all attributes describing the state of the tree can be read.

06-search (Live Demo)

library(shiny)
runApp(system.file("examples/06-search", package = "shinyTree"))

An example showing the use of the search plugin to allow users to more easily navigate the nodes in your tree.

07-drag-and-drop (Live Demo)

library(shiny)
runApp(system.file("examples/07-drag-and-drop", package = "shinyTree"))

An example demonstrating the use of the drag-and-drop feature which allows the user to reorder the nodes in the tree.

08-class (Live Demo)

library(shiny)
runApp(system.file("examples/08-class", package = "shinyTree"))

An example demonstrating the use of the ability to style nodes using custom classes.

09-themes

library(shiny)
runApp(system.file("examples/09-themes", package = "shinyTree"))

An example demonstrating the use of built-in tree themes.

10-node-ids

library(shiny)
runApp(system.file("examples/10-node-ids", package = "shinyTree"))

An example demonstrating the ability to label and return node identifiers and classes.

11-tree-update

library(shiny)
runApp(system.file("examples/11-tree-update", package = "shinyTree"))

An example demonstrating the ability to update a tree with a new tree model. This was broken in the original version as the tree was destroyed upon initialization.

12-types

library(shiny)
runApp(system.file("examples/12-types", package = "shinyTree"))

An example demonstrating node types with custom icons.

13-icons

library(shiny)
runApp(system.file("examples/13-icons", package = "shinyTree"))

An example demonstrating various ways to use icons on nodes.

14-files

library(shiny)
runApp(system.file("examples/14-files", package = "shinyTree"))

Demonstrates how to create a file browser tree.

15-files

library(shiny)
runApp(system.file("examples/15-data", package = "shinyTree"))

Demonstrates how to attach and retreive metadata from a node.

16-async

library(shiny)
runApp(system.file("examples/16-async", package = "shinyTree"))

Demonstrates how to render a tree asynchronously.

17-contextmenu

library(shiny)
runApp(system.file("examples/17-contextmenu", package = "shinyTree"))

Demonstrates how to enable the contextmenu.

18-modules

library(shiny)
runApp(system.file("examples/18-modules/app.R", package="shinyTree"))
runApp(system.file("examples/18-modules/app_types.R", package="shinyTree"))

Demonstrates how to use shinyTree with shiny modules.

19-data.tree

library(shiny)
runApp(system.file("examples/19-data.tree", package = "shinyTree"))

Demonstrates how to pass a data.tree to shinyTree.

20-api

library(shiny)
runApp(system.file("examples/20-api", package = "shinyTree"))

An example demonstrating how to extend the operations on the tree to the rest of the jsTree's core functionality.

21-options

library(shiny)
runApp(system.file("examples/21-options/app_setState_refresh.R", package="shinyTree"))

Demonstrates how to fine-tune shinyTree's behaviour with options. Specifically: When internal jstree code calls set_state or refresh, a callback is made so that the shiny server is notified and observe and observeEvents for the tree are fired. This can be useful if the developer would like observe and observeEvents to run after using updateTree. (By default, updateTree does not run observe or observeEvent because it is assumed that the shiny application knows that the tree is being changed already.)

23-file-icons

library(shiny)
library(shinyTree)
runApp(system.file("examples/23-file-icons", package = "shinyTree"))

An example demonstrating how to create a file tree with individual icons.

Known Bugs

See the Issues page for information on outstanding issues.

Download Details:

Author: shinyTree
Source Code: https://github.com/shinyTree/shinyTree 
License: View license

#r #tree #integration 

What is GEEK

Buddha Community

ShinyTree: Shiny integration with The jsTree Library
Nat  Grady

Nat Grady

1661252125

ShinyTree: Shiny integration with The jsTree Library

shinyTree

The shinyTree package enables Shiny application developers to use the jsTree library in their applications.

shiny tree screenshot

Installation

You can install the latest development version of the code using the devtools R package.

# Install devtools, if you haven't already.
install.packages("devtools")

library(devtools)
install_github("shinyTree/shinyTree")

Getting Started

01-simple (Live Demo)

library(shiny)
runApp(system.file("examples/01-simple", package = "shinyTree"))

A simple example to demonstrate the usage of the shinyTree package.

02-attributes (Live Demo)

library(shiny)
runApp(system.file("examples/02-attributes", package = "shinyTree"))

Manage properties of your tree by adding attributes to your list when rendering.

03-checkbox (Live Demo)

library(shiny)
runApp(system.file("examples/03-checkbox", package = "shinyTree"))

Use checkboxes to allow users to more easily manage which nodes are selected.

04-selected (Live Demo)

library(shiny)
runApp(system.file("examples/04-selected", package = "shinyTree"))

An example demonstrating how to set an input to the value of the currently selected node in the tree.

05-structure (Live Demo)

library(shiny)
runApp(system.file("examples/05-structure", package = "shinyTree"))

Demonstrates the low-level usage of a shinyTree as an input in which all attributes describing the state of the tree can be read.

06-search (Live Demo)

library(shiny)
runApp(system.file("examples/06-search", package = "shinyTree"))

An example showing the use of the search plugin to allow users to more easily navigate the nodes in your tree.

07-drag-and-drop (Live Demo)

library(shiny)
runApp(system.file("examples/07-drag-and-drop", package = "shinyTree"))

An example demonstrating the use of the drag-and-drop feature which allows the user to reorder the nodes in the tree.

08-class (Live Demo)

library(shiny)
runApp(system.file("examples/08-class", package = "shinyTree"))

An example demonstrating the use of the ability to style nodes using custom classes.

09-themes

library(shiny)
runApp(system.file("examples/09-themes", package = "shinyTree"))

An example demonstrating the use of built-in tree themes.

10-node-ids

library(shiny)
runApp(system.file("examples/10-node-ids", package = "shinyTree"))

An example demonstrating the ability to label and return node identifiers and classes.

11-tree-update

library(shiny)
runApp(system.file("examples/11-tree-update", package = "shinyTree"))

An example demonstrating the ability to update a tree with a new tree model. This was broken in the original version as the tree was destroyed upon initialization.

12-types

library(shiny)
runApp(system.file("examples/12-types", package = "shinyTree"))

An example demonstrating node types with custom icons.

13-icons

library(shiny)
runApp(system.file("examples/13-icons", package = "shinyTree"))

An example demonstrating various ways to use icons on nodes.

14-files

library(shiny)
runApp(system.file("examples/14-files", package = "shinyTree"))

Demonstrates how to create a file browser tree.

15-files

library(shiny)
runApp(system.file("examples/15-data", package = "shinyTree"))

Demonstrates how to attach and retreive metadata from a node.

16-async

library(shiny)
runApp(system.file("examples/16-async", package = "shinyTree"))

Demonstrates how to render a tree asynchronously.

17-contextmenu

library(shiny)
runApp(system.file("examples/17-contextmenu", package = "shinyTree"))

Demonstrates how to enable the contextmenu.

18-modules

library(shiny)
runApp(system.file("examples/18-modules/app.R", package="shinyTree"))
runApp(system.file("examples/18-modules/app_types.R", package="shinyTree"))

Demonstrates how to use shinyTree with shiny modules.

19-data.tree

library(shiny)
runApp(system.file("examples/19-data.tree", package = "shinyTree"))

Demonstrates how to pass a data.tree to shinyTree.

20-api

library(shiny)
runApp(system.file("examples/20-api", package = "shinyTree"))

An example demonstrating how to extend the operations on the tree to the rest of the jsTree's core functionality.

21-options

library(shiny)
runApp(system.file("examples/21-options/app_setState_refresh.R", package="shinyTree"))

Demonstrates how to fine-tune shinyTree's behaviour with options. Specifically: When internal jstree code calls set_state or refresh, a callback is made so that the shiny server is notified and observe and observeEvents for the tree are fired. This can be useful if the developer would like observe and observeEvents to run after using updateTree. (By default, updateTree does not run observe or observeEvent because it is assumed that the shiny application knows that the tree is being changed already.)

23-file-icons

library(shiny)
library(shinyTree)
runApp(system.file("examples/23-file-icons", package = "shinyTree"))

An example demonstrating how to create a file tree with individual icons.

Known Bugs

See the Issues page for information on outstanding issues.

Download Details:

Author: shinyTree
Source Code: https://github.com/shinyTree/shinyTree 
License: View license

#r #tree #integration 

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:

  1. With the client end connecting directly to the database, it provides service in the form of jar and requires no extra deployment and dependence.
  2. It can be considered as an enhanced JDBC driver, which is fully compatible with JDBC and all kinds of ORM frameworks.
  3. Applicable in any ORM framework based on JDBC, such as JPA, Hibernate, Mybatis, Spring JDBC Template or direct use of JDBC.
  4. Support any third-party database connection pool, such as DBCP, C3P0, BoneCP, Druid, HikariCP;
  5. Support any kind of JDBC standard database: MySQL, Oracle, SQLServer, PostgreSQL and any databases accessible to JDBC.
  6. 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

  1. 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_itemt_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.

Brandon  Adams

Brandon Adams

1625629740

What is a Library? Using Libraries in Code Tutorial | C Library Examples

In this tutorial, we’ll be talking about what a library is and how they are useful. We will be looking at some examples in C, including the C Standard I/O Library and the C Standard Math Library, but these concepts can be applied to many different languages. Thank you for watching and happy coding!

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1600756660

QuickBooks Amazon Integration | Steps to Integrate | +1(844)313-4856

QuickBooks accounting software always surprises with its amazing features. The addition of QuickBooks in Amazon bring more profits. The aim of this integration is to monitor and track the entire process more accurately to increase the business economy. This article will provide a brief description of “QuickBooks Amazon Integration” along with its advanced features.

Brief Description :
Make Amazon Accounting Automated - To find accuracy and less-time consuming process, amazon needs to integrate with QuickBooks accounting software. It makes the entire amazon accountancy automated to find better results and to reduce human efforts. In that way, they have more time to focus on all other services also. With these things, they will able to focus on each process to get potential profit.
With help of this integration, Amazon seller knows and understands all financial issues like never before. It helps to identify and manage your entire Amazon transaction data. After syncing with this approach, you can work more confidently to track FBA fees, inventory cost, taxes, shipping expenses, and many more processes. It introduces many advanced features such as Advanced Automation, Dedicated support, Full Analytics, Easy Installation, and Affordable cost.
After integration, you can easily import Amazon order related information into the QuickBooks self-employed to meet with easy techniques to categorize the business transaction. This amazing feature is only available via QuickBooks self-employed labs to choose the users. To integrate with your Amazon account, perform the below steps:
o From the upper right corner, choose the “Gear” icon
o Then choose “Labs” and then click on the “Turn it On” option
o After that, fill your Amazon credentials
o Then, it permits the connection to complete the process
Once you have integrated with Amazon, you can easily view a few orders at very first. Whenever transactions are imported via connected amazing to view order details, then they will easily be identified how many products have been attached to it. You have the option to split all transactions by order product.
While parting in QuickBooks Self-Employed, the transactions default to a split by thing, with the delivery and sales taxes for the allocated order relatively over the items.
New things you get in QuickBooks Amazon Integration:
Record your Orders:
∙ you can easily record order individually or summed up by month, week, day, or a specific settlement period with journal entries
∙ You can easily create invoices and sales Receipts
∙ In QuickBooks, consequently, update your inventory with each Amazon deal
∙ In QuickBooks, missing products automatically create
∙ Easy to access all details about record transactions such as Sales tax, discount promo codes, payment method. The shipping method, shipping address, and billing address
∙ Work with QuickBooks class-tracking, assemblies, bundle items, and group item
∙ Along with QuickBooks, You can work with all type of currencies
Sync Inventory:
∙ You can easily hold on to inventory levels updated with every sale and return
∙ Forecasting Inventory
∙ While adding stock to your QuickBooks, you find Automatically update inventory levels
∙ Sync items escorted by variations
∙ You can easily work with QuickBooks Enterprises advanced inventory module
∙ East to track numerous inventory sites
Sales tax Compliance:
∙ You can work for both single as well as multiple tax jurisdictions
∙ Guide Sales tax to an explicit product in QuickBooks for exact sales tax filing
Reports you want them:
∙ To find the reports in your manner, how Amazon Channel performing with a summary of transactions
∙ You get a clear view of all profits and losses by order, region, product, customer, and many more.
Listings:
∙ you can publish your products from QuickBooks to your Amazon store including images and all other related details.
∙ Bulk Export listing
Fully Automated:
Utilize the scheduler to automate all posting to find accuracy in real-time
Manage Refunds & cancellations:
You can easily make credit memos against the initial sale for exact transaction level accounting
Handle high volumes:
● You can easily record thousands of transactions per day
● You can easily record or utilize summaries to keep the QuickBooks company file smaller
Generate purchase orders:
● You find advanced configurable settings for generating POs
● Automated process to generate the vendor emails & purchase orders
It integrates with your all Amazon stores and capabilities such as amazon prime, Amazon.com, Amazon UK, Amazon EU, Amazon Canada, Amazon Mexico, FBA, and Amazon Seller central
Few things to remember:
No new transactions are made. Existing transactions imported from your bank or credit card coordinated to orders on Amazon and the order related items are added to them.
For Amazon to order details to show up in QuickBooks Self-Employed, you more likely than not associated with the record you use to buy items on Amazon. In any case, if the record isn’t associated, the transaction details won’t show up in light of the fact that they won’t match the transaction. So in case if you utilize two installment techniques on Amazon, just the items bought with the installment strategy associated with QuickBooks Self-Employed will appear in QuickBooks Self-Employed. Items bought with a gift card or limited-time credit won’t show up.
After you’ve spared the part transaction, your work will stay, regardless of whether you select to kill the Amazon combination. Just the actual order items will vanish from the transaction.
Conclusion:
I hope the above information helps you know about the benefits and all the necessary information about the “QuickBooks Amazon Integration” It helps to grow your business with new ideas. QuickBooks’ in-built features reduce all complex issues to make things easy and simple. Thank you for spending your quality time to read my blog. If you have any doubt regarding the QuickBooks Amazon integration . then you can call our QuickBooks Support team at +1-844-313-4856 or do live chat with our QuickBooks online support team 24x7.

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Einar  Hintz

Einar Hintz

1599364620

API Integration Practices and Patterns

We all hear it so often that we almost stop hearing it: “Integration is critical to meeting users’ needs.”

Integration work consumes 50%-80% of the time and budget of digital transformation projects, or building a digital platform, while innovation gets only the leftovers, according to SAP and Salesforce. And as everyone from legacy enterprises to SaaS startups launches new digital products, they all hit a point at which the product cannot unlock more value for users or continue to grow without making integration a feature.

If I were to sum up the one question behind all of the other questions that I hear from customers, enterprises, partners, and developers, it would be something like: “Is integration a differentiator that we should own? Or an undifferentiated but necessary feature that supports what we’re trying to accomplish?”

This Refcard won’t try to answer that question for you. Rather, no matter what type of development work you do, API integration is a fact of life today, like gravity. Why? Today, experience is paramount. The average enterprise uses more than 1,500 cloud applications (with the number growing by 20% each year). Every app needs to integrate with other systems in a fluid and ever-changing application ecosystem. So instead, I’ll share some of the common practices you’re likely to contend with as well as some patterns to consider.


This is a preview of the API Integrations Practices and Patterns Refcard. To read the entire Refcard, please download the PDF from the link above.

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