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How to build a CRUD REST API using Spring Boot, JPA/Hibernate and MySQL

In this article we will learn how to develop a CRUD RESTFul API with Spring Boot 2 + JPA/Hibernate and MySQL as database.

The end goal of these articles is to deploy this application on Oracle Cloud. But first, we will develop and test the application using a local database.

Before we get started, here is a list of what we need to complete this tutorial:

The API we will develop

We will create a Contact Resource exposing three services using RESTFul URIs and HTTP methods:

  • Retrieve all contacts - @GetMapping(“/contacts)
  • Get details of specific contact - @GetMapping(“/contacts/{id}”)
  • Delete a contact - @DeleteMapping(“/contacts/{id}”)
  • Create a new contact - @PostMapping(“/contacts)
  • Update existing contact details - @PutMapping(“/contacts/{id}”)

Creating the REST Spring Boot application

To create the Spring Boot application, we’ll use start.spring.io, which will provide us some bootstrap code (main class and pom.xml files). We need the WebJPALombok and MySQL packages for this example:

Look at the From the above diagram, we have specified the following details:

Generate: Maven Project
Java Version: 1.8 (Default)
Spring Boot:2.1.1
Group: com.loiane
Artifact: spring-cloud-mysql
Dependencies: Web, JPA, Lombok, MySQL

After entering all details, click on Generate Project button, download the zip file, extract its contents to your workspace and open it in your favorite IDE.

Project Structure

The following screenshot shows the structure of the project we will create.

Creating the model (JPA Entity)

The first class we will create is the JPA Entity. We will create a class Contact with a primary key id:

The model is the same as part 1 of this article series, but we will specify the how the ID will be auto generated because we are no longer using H2 database.

@AllArgsConstructor
@NoArgsConstructor
@Data
@Entity
public class Contact {

@Id

@GeneratedValue(strategy = GenerationType.IDENTITY)

private Long id;


private String name;

private String email;

private String phone;

}

The following annotations are from project Lombok and help us keep our classes (specially model/POJO) cleaner without the getters and setters:

  • AllArgsConstructor: automatically creates a class construtor with all arguments (properties).
  • NoArgsConstructor: automatically creates an empty class construtor with all arguments (properties).
  • Data: creates toStringequalshashCodegetters and setters.

Creating the JPA Repository

To easily access the methods to manipulate the Contact table, we just need to create an interface that extends JpaRepository passing the class that represents our model and the type of the primary key as generic arguments:

@Repository

public interface ContactRepository

extends JpaRepository<Contact, Long> { }

The JpaRepository interface provides a simple and easy way to access all the CRUD operations.

Creating the Controller

To access our data, we will also need a Controller.

@RestController

@RequestMapping({“/contacts”})

public class ContactController {



private ContactRepository repository;


ContactController(ContactRepository contactRepository) {

this.repository = contactRepository;

}


// CRUD methods here

}

The @RestController annotation contains the @Controller and @ResponseBody annotations. The @Controller annotation represents a class with endpoints and the @ResponseBody indicates indicates a method return value should be bound to the web response body (according to the documentation).

The @RequestMapping(“/contacts”) indicates that the url of the API in this controller will start with /contacts, so we will be able to access http://localhost:8080/contacts as our endpoint.

Please note we are not using the @Autowired annotation to automatically inject the repository. We are using dependency injection through the constructor as it is a recommended best practice.

As this is a simple example, we are not creating a Service class to iterate with the repository. Creating a service layer is a good practice as we can keep our controller class clean and add any required business logic to the service instead.

Let’s take a look at each method next.

Retrieve All Contacts (GET /contacts)

@GetMapping

public List findAll(){

return repository.findAll();

}

The findAll method is going to retrieve all the records from the database (select * from contact).

As this is a RESTful API, we can omit the @RequestMapping(value=“/contacts”, method=RequestMethod.GET) and simply use @GetMapping.

Retrieve a Contact by ID (GET /contacts/{id})

@GetMapping(path = {“/{id}”})

public ResponseEntity<Contact> findById(@PathVariable long id){

return repository.findById(id)

.map(record -> ResponseEntity.ok().body(record))

.orElse(ResponseEntity.notFound().build());

}

The @PathVariable annotation binds the method parameter id with the path variable {id}.

we will go to the database and will try to retrieve the contact (select * from contact where id = ?). If a contact is found, we return it (HTTP 200 - OK), else, we return HTTP 404 -Not Found.

Create a new Contact (POST /contacts)

@PostMapping

public Contact create(@RequestBody Contact contact){

return repository.save(contact);

}

The @RequestBody annotation indicates a method parameter should be bound to the body of the web request. This means the method expects the following content from que request (in JSON format):

{

“name”: “Java”,

“email”: “java@email.com”,

“phone”: “(111) 111-1111”

}

Spring will automatically parse the request and create a variable of type Contact with its contents. Then, it will save it. The id of the contact will be null, therefore the save method will perform an insert into the Contact table.

Update a Contact (PUT /contacts)

@PutMapping(value=“/{id}”)

public ResponseEntity<Contact> update(@PathVariable(“id”) long id,

@RequestBody Contact contact){

return repository.findById(id)

.map(record -> {

record.setName(contact.getName());

record.setEmail(contact.getEmail());

record.setPhone(contact.getPhone());

Contact updated = repository.save(record);

return ResponseEntity.ok().body(updated);

}).orElse(ResponseEntity.notFound().build());

}

In order to update a contact, we need to inform its ID in the path variable. We also need to pass the updated contact.

Next, we will try to find the contact to be updated. If the contact is found, we update the values from the record from the database with the values passed as parameter to the method and save it. In this case, as the record exists, an update statement will performed in the contact table. We also return the updated contact. In case the contact is not found, it returns HTTP 404.

Remove a Contact (DELETE /contacts/{id})

@DeleteMapping(path ={“/{id}”})

public ResponseEntity<?> delete(@PathVariable(“id”) long id) {

return repository.findById(id)

.map(record -> {

repository.deleteById(id);

return ResponseEntity.ok().build();

}).orElse(ResponseEntity.notFound().build());

}

To remove a contact, we first need to retrieve it from the database. In case it is found, we delete it passing its ID and return HTTP 200 to indicate the deletion was performed successfully. In case the contact is not found, we return HTTP 404.

Initializing the MySQL database

As a last step, we are going to insert a few records in the MySQL contact table by declaring a Bean that returns a CommandLineRunner - this step is optional in case you already using an existing database/table.

@SpringBootApplication

public class SpringCloudMysqlApplication {



public static void main(String[] args) {
	SpringApplication.run(SpringCloudMysqlApplication.class, args);
}

@Bean
CommandLineRunner init(ContactRepository repository) {
	return args -&gt; {
		repository.deleteAll();
		LongStream.range(1, 11)
				.mapToObj(i -&gt; {
					Contact c = new Contact();
					c.setName("Contact " + i);
					c.setEmail("contact" + i + "@email.com");
					c.setPhone("(111) 111-1111");
					return c;
				})
				.map(v -&gt; repository.save(v))
				.forEach(System.out::println);
	};
}



}

Configuring MySQL Database

In order for our code to be able to connect to a MySQL database, we also need to inform the connection details. We are going to add these details inside src/maind/resources/application.properties:

## Spring DATASOURCE (DataSourceAutoConfiguration & DataSourceProperties)

spring.datasource.url=jdbc:mysql://localhost:3306/mydatabase?useSSL=false

spring.datasource.username=root

spring.datasource.password=root



The SQL dialect makes Hibernate generate better SQL for the chosen database


spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5InnoDBDialect

In case you want Hibernate to automatically generate the table in the database, we can use the following configuration as well:

# Hibernate ddl auto (create, create-drop, validate, update)

spring.jpa.hibernate.ddl-auto=update

Although this makes easier during the development, this is not recommended in production. For production, you might want to create a functional ID (user/password) that can perform all CRUD operations in the tables (DML (Data Manipulation Language) commands), but cannot perform any DDL (Data Definition Language) commands (Create, Drop, Alter tables, and so on).

To create the contact table, we can use the following SQL script:

CREATE TABLE mydatabase.contact (

id INT NOT NULL,

name VARCHAR(255) NULL,

email VARCHAR(255) NULL,

phone VARCHAR(45) NULL,

PRIMARY KEY (id));

ALTER TABLE mydatabase.contact

CHANGE COLUMN id id INT(11) NOT NULL AUTO_INCREMENT ,

ADD UNIQUE INDEX id_UNIQUE (id ASC);

Testing the APIs

We can test the API using Postman. If you use Visual Studio Code, you can also use the REST Client extension.

File for testing using the REST Client extension is also included in the source code

Conclusion

In this article, we developed and tested a CRUD API connecting to a real database locally. 

I hope this tutorial will surely help and you if you liked this tutorial, please consider sharing it with others

 

#spring-boot #java #hibernate #mysql #web-development

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How to build a CRUD REST API using Spring Boot, JPA/Hibernate and MySQL

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.

Wilford  Pagac

Wilford Pagac

1594289280

What is REST API? An Overview | Liquid Web

What is REST?

The REST acronym is defined as a “REpresentational State Transfer” and is designed to take advantage of existing HTTP protocols when used for Web APIs. It is very flexible in that it is not tied to resources or methods and has the ability to handle different calls and data formats. Because REST API is not constrained to an XML format like SOAP, it can return multiple other formats depending on what is needed. If a service adheres to this style, it is considered a “RESTful” application. REST allows components to access and manage functions within another application.

REST was initially defined in a dissertation by Roy Fielding’s twenty years ago. He proposed these standards as an alternative to SOAP (The Simple Object Access Protocol is a simple standard for accessing objects and exchanging structured messages within a distributed computing environment). REST (or RESTful) defines the general rules used to regulate the interactions between web apps utilizing the HTTP protocol for CRUD (create, retrieve, update, delete) operations.

What is an API?

An API (or Application Programming Interface) provides a method of interaction between two systems.

What is a RESTful API?

A RESTful API (or application program interface) uses HTTP requests to GET, PUT, POST, and DELETE data following the REST standards. This allows two pieces of software to communicate with each other. In essence, REST API is a set of remote calls using standard methods to return data in a specific format.

The systems that interact in this manner can be very different. Each app may use a unique programming language, operating system, database, etc. So, how do we create a system that can easily communicate and understand other apps?? This is where the Rest API is used as an interaction system.

When using a RESTful API, we should determine in advance what resources we want to expose to the outside world. Typically, the RESTful API service is implemented, keeping the following ideas in mind:

  • Format: There should be no restrictions on the data exchange format
  • Implementation: REST is based entirely on HTTP
  • Service Definition: Because REST is very flexible, API can be modified to ensure the application understands the request/response format.
  • The RESTful API focuses on resources and how efficiently you perform operations with it using HTTP.

The features of the REST API design style state:

  • Each entity must have a unique identifier.
  • Standard methods should be used to read and modify data.
  • It should provide support for different types of resources.
  • The interactions should be stateless.

For REST to fit this model, we must adhere to the following rules:

  • Client-Server Architecture: The interface is separate from the server-side data repository. This affords flexibility and the development of components independently of each other.
  • Detachment: The client connections are not stored on the server between requests.
  • Cacheability: It must be explicitly stated whether the client can store responses.
  • Multi-level: The API should work whether it interacts directly with a server or through an additional layer, like a load balancer.

#tutorials #api #application #application programming interface #crud #http #json #programming #protocols #representational state transfer #rest #rest api #rest api graphql #rest api json #rest api xml #restful #soap #xml #yaml

Debbie Clay

Debbie Clay

1555665034

Build a Rest API with Spring Boot using MySQL and JPA

In that case, I found a very clean and elegant framework called Spring Boot to build a back end.

Previously, in JavaScript development, I used:

  1. Mongoose — an ORM (Object Relational Mapping) for Mongo DB
  2. Sequelize — an ORM for MySQL

For Java-related development, there are lot of ORM’s like Hibernate, JPA (Java Persistence API) & Java Object Oriented Querying.

I choose to build with JPA which is traditionally used in Java applications.

It was very interesting, and took about one week to finish as I had to learn Spring Boot (There are a lot of annotations “@” and other cool kinds of stuff to learn), JPA, and Hibernate along the way.

All this magic is mostly done by the annotations (“@” symbol) used in Spring Boot.

Creating a Spring Boot Maven Project

Let’s create a Spring Boot Maven Project Application using this link.

Maven” is a project management tool used to manage dependency management. It’s just like Node Package Manager (NPM) in the JS development environment.

We have package.json in NodeJS for dependency management and pom.xml in Spring Boot for dependency management.

In Group, write whatever the name you want. Usually, the domain name of the organization is written right to left.

For example our domain name is www.javaAPI.com, so the group name could be com.javaAPI.www

Then in the Artifact type the name of the folder you want.

On the right side, add the following dependencies:

  1. Mongoose — an ORM (Object Relational Mapping) for Mongo DB
  2. Sequelize — an ORM for MySQL

Then click “Generate Project”. You will find a rar file — extract it. Then open that folder in your favorite IDE.

Click on the com.rest.API and you will find an ApiApplication.java file as follows:

package com.rest.API;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class ApiApplication {

public static void main(String[] args) {
      SpringApplication.run(ApiApplication.class, args);
   }
}

This code is enough to start your server. Normally spring boot runs on localhost:8080.

Type in your terminal as follows:

mvn spring-boot:run
See your localhost running in the web browser at port 8080. It looks blank as we haven’t done anything yet.

Let’s explore the files and their tags

If you have a look at the pom.xml file you may notice that the dependencies you put in when creating the application in Spring Initialize like MySQL, JPA, and Web will be inside a tag.

The starter and tester dependencies are the core for creating the Spring Boot Application to serve on the server.

Now, let’s move to APIApplication.java which is the main file.

package com.rest.API;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class ApiApplication {

public static void main(String[] args) {
      SpringApplication.run(ApiApplication.class, args);
   }
}

Here the name of the package is in the first line of the code. Using that package name, you can import any class, method, or instances in another package file.

After that, two modules are imported from “org.springframework.boot” package.

  1. Mongoose — an ORM (Object Relational Mapping) for Mongo DB
  2. Sequelize — an ORM for MySQL

Since Spring boot is the latest application developing framework of Spring, it needs the packages of Spring Application as well as its specific packages.

After that @SpringBootApplication Annotation is used. This Annotation consists of annotation which is used in Spring:

  1. Mongoose — an ORM (Object Relational Mapping) for Mongo DB
  2. Sequelize — an ORM for MySQL

These are the annotations used to start the Spring Boot Application to run on a server.

Here is an article I have written about Annotation & their uses in Java.

Let’s create Model for our data

Let’s create a Model class to save, retrieve, update and delete the details of a book.

For that, I have to create a new package named model and inside that creating a Book.java class to put my code.

package com.rest.API.model;

import javax.persistence.*;
import javax.validation.constraints.NotBlank;

@Entity
@Table(name = "books")

public class Book {
    @Id
    @GeneratedValue
    private Long id;

@NotBlank
    private String book_name;

@NotBlank
    private String author_name;

@NotBlank
    private String isbn;

public Book(){
        super();
    }

public Book(Long id, String book_name, String author_name, String isbn) {
        super();
        this.id = id;
        this.book_name = book_name;
        this.author_name = author_name;
        this.isbn=isbn;
    }

public Long getId() {
        return id;
    }

public void setId(Long id) {
        this.id = id;
    }

public String getBook_name() {
        return book_name;
    }

public void setBook_name(String book_name) {
        this.book_name = book_name;
    }

public String getAuthor_name() {
        return author_name;
    }

public void setAuthor_name(String author_name) {
        this.author_name = author_name;
    }

public String getIsbn() {
        return isbn;
    }

public void setIsbn(String isbn) {
        this.isbn = isbn;
    }

}

Here I’m using JPA (Java Persistence API) which is a collection of classes and methods to continuously store data into a database.

@Entity — used to denote that this class is going to be an Entity in the database.

@Table — which takes some values like the name you are going to name your table

**@Id **— denotes that the id is the primary key / identifying key for this table

@NotBlank — is used to say that these attributes should not be blank.

Other than that there is an empty constructor which has a super method to satisfy the JPA customs. Getter and setter methods are usually in a POJO class (Plain old Java object).

Creating the Repository

Next, we are going to create a repository package to deal with database management in Java.

Create an Interface called BookRepository.java inside the repository package.

package com.rest.API.repository;

import com.rest.API.model.Book;
import org.springframework.data.jpa.repository.JpaRepository;
import org.springframework.stereotype.Repository;

@Repository
public interface BookRepository extends JpaRepository<Book, Long> {

}

I have imported the JpaRepository package to use that repository in the BookRepository interface by connecting my most recently coded Book model to do CRUD operations.

There are already built-in methods in those repositories to do CRUD operations.

Eg:

.findAll() - to get All datas
.save()    - to save the got Data
.delete()  - to delete the data

Inside the <> tag we are taking the Model name we are going to use and the Primary key’s datatype.

@Repository: Annotation used to Indicate the DAO (Data Access Object) component in the persistence layer.

It tells the compiler that the interface is going to use the Repository to do database activities.

Creating Controller and Exception Handling

Create a new package called controller, andinside that create a BookController.java file which contains the endpoints.

package com.rest.API.controller;

import com.rest.API.exception.BookNotFoundException;
import com.rest.API.model.Book;
import com.rest.API.repository.BookRepository;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;
import org.springframework.http.ResponseEntity;
import javax.validation.Valid;
import java.util.List;

@RestController
public class BookController {

@Autowired
    BookRepository bookRepository;

// Get All Notes
    @GetMapping("/books")
    public List<Book> getAllNotes() {
        return bookRepository.findAll();
    }

// Create a new Note
    @PostMapping("/books")
    public Book createNote(@Valid @RequestBody Book book) {
        return bookRepository.save(book);
    }

// Get a Single Note
    @GetMapping("/books/{id}")
    public Book getNoteById(@PathVariable(value = "id") Long bookId) throws BookNotFoundException {
        return bookRepository.findById(bookId)
                .orElseThrow(() -> new BookNotFoundException(bookId));
    }

// Update a Note
    @PutMapping("/books/{id}")
    public Book updateNote(@PathVariable(value = "id") Long bookId,
                           @Valid @RequestBody Book bookDetails) throws BookNotFoundException {

Book book = bookRepository.findById(bookId)
                .orElseThrow(() -> new BookNotFoundException(bookId));

book.setBook_name(bookDetails.getBook_name());
        book.setAuthor_name(bookDetails.getAuthor_name());
        book.setIsbn(bookDetails.getIsbn());

Book updatedBook = bookRepository.save(book);

return updatedBook;
    }

// Delete a Note
    @DeleteMapping("/books/{id}")
    public ResponseEntity<?> deleteBook(@PathVariable(value = "id") Long bookId) throws BookNotFoundException {
        Book book = bookRepository.findById(bookId)
                .orElseThrow(() -> new BookNotFoundException(bookId));

bookRepository.delete(book);

return ResponseEntity.ok().build();
    }
}

The first imported package is for the Book Not Found exception (for which we are going to create a file in a bit).

Explanation of Annotations we used here:

  1. Mongoose — an ORM (Object Relational Mapping) for Mongo DB
  2. Sequelize — an ORM for MySQL

So what is Domain Object…?

It simply says that Domain Object == Business Object.

They are usually represented by entities and value objects related to the endpoint we are giving to get the data from the database.

  1. Autowired: This annotation is used to wire the bean classes automatically.

For that, you need to know about “What is a bean Class…?

Basically, a Java Bean Class is a simple class which encapsulates many objects into it.

This is an article I wrote on Java Bean Classes.

The following are the Mapping Annotations for the endpoints to perform CRUD Operations.

  1. GetMapping: This is an interface which contains the path of the endpoint to perform a Get method. This GetMapping interface uses the RequestMapping interface which can have the “path, value, params, headers” method to perform the Get method in earlier Spring versions.

Now it’s simplified by using GetMapping.

  1. PostMapping: This is an interface which contains the path of the endpoint to perform the Post method.

  2. PutMapping: This is an interface which contains the path of the endpoint to perform the Put method to Update.

  3. DeleteMapping: This is an interface which contains the path of the endpoint to perform the Delete method.

In the final lines, you probably noticed the “ResponseEntity” keyword.

What is that…??

It’s a Java class which inherits HttpEntity class to manipulate the HTTP Responses. Whether the request of the connection is “OK” or if there are any problems, throw an exception from the HttpEntity class.

orElseThrow(): This is a method found in the Optional class in Java8 which was introduced to handle Exceptions. The optional class provides various utility methods to check the presence or absence of an object, which helps to deal with NullPointerException.

orElseThrow is a method that Returns value if present, otherwise invokes an exception.

Creating a NotFoundException if there is no such book_id

As orElseThrow method throws a NotFound Exception. The following is the Exception Handling part. Create a BookNotFoundException.java file inside exception package.

package com.rest.API.exception;

public class BookNotFoundException extends Exception {

private long book_id;

public BookNotFoundException(long book_id) {
        super(String.format("Book is not found with id : '%s'", book_id));
        }

}

The created class extends the Superclass of Exception. In the constructor, I’m passing the book_id & prints the exception.

So, that’s it…

We have finished the REST API part. Now you can build the app (which was explained in Part 1) and do some Testings with Postman.

Connecting with MySql Database

Inside the application.properties of your resources folder, add the following:

## Spring DATASOURCE (DataSourceAutoConfiguration & DataSourceProperties)
spring.datasource.url = jdbc:mysql://localhost:3306/library
spring.datasource.username = root //normally put your MySQL username 
spring.datasource.password = YOUR_MYSQL_PASSWORD

## Hibernate Properties
# The SQL dialect makes Hibernate generate better SQL for the chosen database
spring.jpa.properties.hibernate.dialect = org.hibernate.dialect.MySQL5InnoDBDialect

# Hibernate ddl auto (create, create-drop, validate, update)
spring.jpa.hibernate.ddl-auto = update

That’s it.

We have built a basic REST API in Spring Boot. Congrats!

If anything is wrong or need to be corrected, please let me know in the comments section.

Happy Coding!

#rest #api #spring-boot #mysql #jpa

Sigrid  Farrell

Sigrid Farrell

1624096385

Spring Boot CRUD Operations

In the video in this article, we take a closer look at the Spring Boot CRUD Operations Example alongside Exception Handling!

In the video below, we take a closer look at the Spring Boot CRUD Operations example with exception handling. Let’s get started!

#spring boot #spring boot tutorial for beginners #crud #crud #crud #spring boot crud operations

REST API In Laravel Example

Hello Friends,

Today I will give you information about REST API, REST API is an application program interface that uses HTTP requests to GET, PUT, POST and DELETE data.

In this tutorial I am going to perform CRUD operation using REST API and you can learn how to create REST API with authentication using passport in laravel 6/7 application. here we will get data from API.

REST API In Laravel Example

https://websolutionstuff.com/post/rest-api-in-laravel

#rest api in laravel example #php #rest api #crud operation using rest api #rest api with passport #laravel rest api crud