Samuel Tucker

Samuel Tucker

1549237772

How to join results of multiple tables in Spring JPA repository

I'm new to Spring and I'm unable to figure out how to join multiple tables to return some result. I tried to implement a small Library application as shown below.

My Entity Classes - Book, Customer, Bookings

Book.java - books available in the library

@Entity
@Table(name = "books")
public class Book {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = "id", columnDefinition = "int")
private int id;

@NotNull(message = "Book name cannot be null")
@Column(name = "book_name", columnDefinition = "VARCHAR(255)")
private String bookName;

@Column(name = "author", columnDefinition = "VARCHAR(255)")
private String author;

// getters and setters

public Book() {}

public Book(String bookName, String author) {
    this.bookName = bookName;
    this.author = author;
}

}

Customer.java - Customers registered in the library

@Entity
@Table(name = “customer”, uniqueConstraints = {@UniqueConstraint(columnNames = {“phone”})})
public class Customer {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = “id”, columnDefinition = “int”)
private int id;

@NotNull(message = "Customer name cannot be null")
@Column(name = "name", columnDefinition = "VARCHAR(255)")
private String name;

@Column(name = "phone", columnDefinition = "VARCHAR(15)")
private String phone;

@Column(name = "registered", columnDefinition = "DATETIME")
private String registered;

// getters and setters

public Customer() {}

public Customer(String name, String phone, String registered) {
    this.name = name;
    this.phone = phone;
    this.registered = registered;
}

}

Booking.java - All the bookings made by the customers

@Entity
@Table(name = “bookings”)
public class Booking {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = “id”, columnDefinition = “int”)
private int id;

@NotNull(message = "Book id cannot be null")
@Column(name = "book_id", columnDefinition = "int")
private int bookId;

@NotNull(message = "Customer id cannot be null")
@Column(name = "customer_id", columnDefinition = "int")
private int customerId;

@Column(name = "issue_date", columnDefinition = "DATETIME")
private String issueDate;

@Column(name = "return_date", columnDefinition = "DATETIME")
private String returnDate;

// getters and setters

public Booking() {}

public Booking(int bookId, int customerId, String issueDate) {
    this.bookId = bookId;
    this.customerId = customerId;
    this.issueDate = issueDate;
}

}

Now the table schemas for the respective entities are as follows:

books:
±----------±-------------±-----±----±--------±---------------+
| Field | Type | Null | Key | Default | Extra |
±----------±-------------±-----±----±--------±---------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| book_name | varchar(255) | NO | | NULL | |
| author | varchar(255) | YES | | NULL | |
±----------±-------------±-----±----±--------±---------------+
id - primary key

customer:
±-----------±-------------±-----±----±------------------±------------------+
| Field | Type | Null | Key | Default | Extra |
±-----------±-------------±-----±----±------------------±------------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| name | varchar(255) | NO | | NULL | |
| registered | datetime | YES | | CURRENT_TIMESTAMP | DEFAULT_GENERATED |
| phone | varchar(15) | YES | UNI | NULL | |
±-----------±-------------±-----±----±------------------±------------------+
id - primary key

bookings:
±------------±---------±-----±----±------------------±------------------+
| Field | Type | Null | Key | Default | Extra |
±------------±---------±-----±----±------------------±------------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| book_id | int(11) | NO | MUL | NULL | |
| customer_id | int(11) | NO | MUL | NULL | |
| issue_date | datetime | YES | | CURRENT_TIMESTAMP | DEFAULT_GENERATED |
| return_date | datetime | YES | | NULL | |
±------------±---------±-----±----±------------------±------------------+
id - primary key
book_id - foreign key references books.id
customer_id - foreign key references customer.id

Now What I want to do is given some booking critieria like customer phone or author name etc., I want to return all the bookings related to that order. I’ll show a sample Booking api to explain.

Booking Controller:

@RestController
@RequestMapping(“/bookings”)
public class BookingController {
@Autowired
BookingService bookingService;

// some booking apis which return Booking objects

@GetMapping
public List<Booking> getAllBookingsBy(@RequestParam("phone") String phone,
                                     @RequestParam("authors") List<String> authors) {
    return bookingService.getAllBy(phone, authors);
}

}

Booking Service class:

@Service
public class BookService {
@Autowired
private BookRepository bookRepository;

// some booking service methods

// get all bookings booked by a customer with matching phone number and books written by a given list of authors
public List<Booking> getAllBy(String phone, List<String> authors) {
return bookingRepository.queryBy(phone, authors);

}
}

Booking Repository Class:

@Repository
public interface BookingRepository extends JpaRepository<Booking, Integer> {
// some booking repository methods

@Query(value = "SELECT * FROM bookings bs WHERE " +
        "EXISTS (SELECT 1 FROM customer c WHERE bs.customer_id = c.id AND c.phone = :phone) " +
        "AND EXISTS (SELECT 1 FROM books b WHERE b.id = bs.book_id AND b.author IN :authors)",
        nativeQuery = true)
List&lt;Booking&gt; queryBy(@Param("phone") String phone,
                        @Param("authors") List&lt;String&gt; authors);

}

Now hitting the shown booking controller 'll return a booking object which looks like this :

[
{
“id”: 3,
“book_id”: 5,
“customer_id”: 2,
“issue_date”: “2019-02-04 01:45:21”,
“return_date”: null
}
]

But I don’t want it like that, I want to return along with them the name of the customer for that booking and also the name of the book. So I want the booking objects returned by the controller to look like this:

[
{
“id”: 3,
“book_id”: 5,
“customer_id”: 2,
“issue_date”: “2019-02-04 01:45:21”,
“return_date”: null,
“customer_name”: “Cust 2”,
“book_name”: “Book_2_2”,
}
]

Can someone please help in doing this? I’m stuck as I’m unable to proceed from here.

#java #spring

What is GEEK

Buddha Community

Tubo Man

1550025192

What you do is wrong. You are returning Booking and you expect that it magicaly deserialize into an entity that contains join information like Book Name. But in your select query on the repository you have selected the Booking. The way things are at your implementation the Booking does not hold information about the Book.

First you need to separate what you will deserialize as JSON and what you will use as persistence layer towards your spring data.

1.Make a @OneToOne/@OneToMany relationship from Booking to Book as a start. 
2.Change your query to do eager fetching on the entity/collection you have mapped as Book.
3. Make a POJO and annotate it with JSON annotations the way you want it to be returned by the controller.
4. Map between your persistence object / Booking with hidrated collection on Book and your newly created POJO

Actualy if you map as OneToOne the default initialization becomes EAGER so your query becomes a bit unnessesary.

If we presume you have your mappings right in the persistent layer your query will look like this:

@Query(value = "SELECT * FROM bookings bs WHERE " +
            "bs.customer.phone = :phone) " +
            "AND  bs.book.author IN :authors)")

Here is your mapping documentation from Hibernate>http://docs.jboss.org/hibernate/orm/5.4/userguide/html_single/Hibernate_User_Guide.html#associations

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.

Connor Mills

Connor Mills

1659063683

HTML, CSS & JavaScript Project: Build Cocktail App

In this tutorial, we will learn how to create a cocktail app with HTML, CSS and Javascript.

Create a cocktail app where the user can search a cocktail of choice and the app displays the ingredients and instructions to make the cocktail. We use 'The Cocktail DB' API to fetch information required for our app.

Project Folder Structure:

Before we start coding let us take look at the project folder structure. We create a project folder called – ‘Cocktail App’. Inside this folder, we have three files. These files are index.html, style.css and script.js.

HTML:

We start with the HTML code. First, copy the code below and paste it into your HTML document.

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Cocktail App</title>
    <!-- Google Font -->
    <link
      href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&display=swap"
      rel="stylesheet"
    />
    <!-- Stylesheet -->
    <link rel="stylesheet" href="style.css" />
  </head>
  <body>
    <div class="container">
      <div class="search-container">
        <input
          type="text"
          placeholder="Type a cocktail name..."
          id="user-inp"
          value="margarita"
        />
        <button id="search-btn">Search</button>
      </div>
      <div id="result"></div>
    </div>
    <!-- Script -->
    <script src="script.js"></script>
  </body>
</html>

CSS:

Next, we style this app using CSS. For this copy, the code provided to you below and paste it into your stylesheet.

* {
  padding: 0;
  margin: 0;
  box-sizing: border-box;
  font-family: "Poppins", sans-serif;
}
body {
  height: 100vh;
  background: linear-gradient(#5372f0 50%, #000000 50%);
}
.container {
  position: absolute;
  transform: translate(-50%, -50%);
  top: 50%;
  left: 50%;
  width: 90vw;
  max-width: 37.5em;
  background-color: #ffffff;
  padding: 1.8em;
  border-radius: 0.6em;
  box-shadow: 0 1em 3em rgba(2, 9, 38, 0.25);
}
.search-container {
  display: grid;
  grid-template-columns: 9fr 3fr;
  gap: 1em;
  margin-bottom: 1.2em;
}
.search-container input {
  font-size: 1em;
  padding: 0.6em 0.3em;
  border: none;
  outline: none;
  color: #1f194c;
  border-bottom: 1.5px solid #1f194c;
}
.search-container input:focus {
  border-color: #5372f0;
}
.search-container button {
  font-size: 1em;
  border-radius: 2em;
  background-color: #5372f0;
  border: none;
  outline: none;
  color: #ffffff;
  cursor: pointer;
}
#result {
  color: #575a7b;
  line-height: 2em;
}
#result img {
  display: block;
  max-width: 12.5em;
  margin: auto;
}
#result h2 {
  font-size: 1.25em;
  margin: 0.8em 0 1.6em 0;
  text-align: center;
  text-transform: uppercase;
  font-weight: 600;
  letter-spacing: 0.05em;
  color: #1f194c;
  position: relative;
}
#result h2:before {
  content: "";
  position: absolute;
  width: 15%;
  height: 3px;
  background-color: #5372f0;
  left: 42.5%;
  bottom: -0.3em;
}
#result h3 {
  font-size: 1.1em;
  font-weight: 600;
  margin-bottom: 0.2em;
  color: #1f194c;
}
#result ul {
  margin-bottom: 1em;
  margin-left: 1.8em;
  display: grid;
  grid-template-columns: auto auto;
}
#result li {
  margin-bottom: 0.3em;
}
#result p {
  text-align: justify;
  font-weight: 400;
  font-size: 0.95em;
}
.msg {
  text-align: center;
}
@media screen and (max-width: 600px) {
  .container {
    font-size: 14px;
  }
}

Javascript:

Lastly, we implement the functionality using Javascript. Now copy the code below and paste it into your script file.

let result = document.getElementById("result");
let searchBtn = document.getElementById("search-btn");
let url = "https://thecocktaildb.com/api/json/v1/1/search.php?s=";
let getInfo = () => {
  let userInp = document.getElementById("user-inp").value;
  if (userInp.length == 0) {
    result.innerHTML = `<h3 class="msg">The input field cannot be empty</h3>`;
  } else {
    fetch(url + userInp)
      .then((response) => response.json())
      .then((data) => {
        document.getElementById("user-inp").value = "";
        console.log(data);
        console.log(data.drinks[0]);
        let myDrink = data.drinks[0];
        console.log(myDrink.strDrink);
        console.log(myDrink.strDrinkThumb);
        console.log(myDrink.strInstructions);
        let count = 1;
        let ingredients = [];
        for (let i in myDrink) {
          let ingredient = "";
          let measure = "";
          if (i.startsWith("strIngredient") && myDrink[i]) {
            ingredient = myDrink[i];
            if (myDrink[`strMeasure` + count]) {
              measure = myDrink[`strMeasure` + count];
            } else {
              measure = "";
            }
            count += 1;
            ingredients.push(`${measure} ${ingredient}`);
          }
        }
        console.log(ingredients);
        result.innerHTML = `
      <img src=${myDrink.strDrinkThumb}>
      <h2>${myDrink.strDrink}</h2>
      <h3>Ingredients:</h3>
      <ul class="ingredients"></ul>
      <h3>Instructions:</h3>
      <p>${myDrink.strInstructions}</p>
      `;
        let ingredientsCon = document.querySelector(".ingredients");
        ingredients.forEach((item) => {
          let listItem = document.createElement("li");
          listItem.innerText = item;
          ingredientsCon.appendChild(listItem);
        });
      })
      .catch(() => {
        result.innerHTML = `<h3 class="msg">Please enter a valid input</h3>`;
      });
  }
};
window.addEventListener("load", getInfo);
searchBtn.addEventListener("click", getInfo);

📁 Download Source Code:  https://www.codingartistweb.com

#html #css #javascript 

Spring Data JPA: Easy to Implement JPA Based Repositories

Spring Data JPA

Spring Data JPA, part of the larger Spring Data family, makes it easy to easily implement JPA based repositories. This module deals with enhanced support for JPA based data access layers. It makes it easier to build Spring-powered applications that use data access technologies.

Implementing a data access layer of an application has been cumbersome for quite a while. Too much boilerplate code has to be written to execute simple queries as well as perform pagination, and auditing. Spring Data JPA aims to significantly improve the implementation of data access layers by reducing the effort to the amount that’s actually needed. As a developer you write your repository interfaces, including custom finder methods, and Spring will provide the implementation automatically.

Features

  • Implementation of CRUD methods for JPA Entities
  • Dynamic query generation from query method names
  • Transparent triggering of JPA NamedQueries by query methods
  • Implementation domain base classes providing basic properties
  • Support for transparent auditing (created, last changed)
  • Possibility to integrate custom repository code
  • Easy Spring integration with custom namespace

Code of Conduct

This project is governed by the Spring Code of Conduct. By participating, you are expected to uphold this code of conduct. Please report unacceptable behavior to spring-code-of-conduct@pivotal.io.

Getting Started

Here is a quick teaser of an application using Spring Data Repositories in Java:

public interface PersonRepository extends CrudRepository<Person, Long> {

  List<Person> findByLastname(String lastname);

  List<Person> findByFirstnameLike(String firstname);
}

@Service
public class MyService {

  private final PersonRepository repository;

  public MyService(PersonRepository repository) {
    this.repository = repository;
  }

  public void doWork() {

    repository.deleteAll();

    Person person = new Person();
    person.setFirstname("Oliver");
    person.setLastname("Gierke");
    repository.save(person);

    List<Person> lastNameResults = repository.findByLastname("Gierke");
    List<Person> firstNameResults = repository.findByFirstnameLike("Oli*");
 }
}

@Configuration
@EnableJpaRepositories("com.acme.repositories")
class AppConfig {

  @Bean
  public DataSource dataSource() {
    return new EmbeddedDatabaseBuilder().setType(EmbeddedDatabaseType.H2).build();
  }

  @Bean
  public JpaTransactionManager transactionManager(EntityManagerFactory emf) {
    return new JpaTransactionManager(emf);
  }

  @Bean
  public JpaVendorAdapter jpaVendorAdapter() {
    HibernateJpaVendorAdapter jpaVendorAdapter = new HibernateJpaVendorAdapter();
    jpaVendorAdapter.setDatabase(Database.H2);
    jpaVendorAdapter.setGenerateDdl(true);
    return jpaVendorAdapter;
  }

  @Bean
  public LocalContainerEntityManagerFactoryBean entityManagerFactory() {
    LocalContainerEntityManagerFactoryBean lemfb = new LocalContainerEntityManagerFactoryBean();
    lemfb.setDataSource(dataSource());
    lemfb.setJpaVendorAdapter(jpaVendorAdapter());
    lemfb.setPackagesToScan("com.acme");
    return lemfb;
  }
}

Maven configuration

Add the Maven dependency:

<dependency>
  <groupId>org.springframework.data</groupId>
  <artifactId>spring-data-jpa</artifactId>
  <version>${version}.RELEASE</version>
</dependency>

If you’d rather like the latest snapshots of the upcoming major version, use our Maven snapshot repository and declare the appropriate dependency version.

<dependency>
  <groupId>org.springframework.data</groupId>
  <artifactId>spring-data-jpa</artifactId>
  <version>${version}.BUILD-SNAPSHOT</version>
</dependency>

<repository>
  <id>spring-libs-snapshot</id>
  <name>Spring Snapshot Repository</name>
  <url>https://repo.spring.io/libs-snapshot</url>
</repository>

Getting Help

Having trouble with Spring Data? We’d love to help!

Reporting Issues

Spring Data uses GitHub as issue tracking system to record bugs and feature requests. If you want to raise an issue, please follow the recommendations below:

  • Before you log a bug, please search the issue tracker to see if someone has already reported the problem.
  • If the issue doesn’t exist already, create a new issue.
  • Please provide as much information as possible with the issue report, we like to know the version of Spring Data that you are using and JVM version, complete stack traces and any relevant configuration information.
  • If you need to paste code, or include a stack trace format it as code using triple backtick.
  • If possible try to create a test-case or project that replicates the issue. Attach a link to your code or a compressed file containing your code. Use an in-memory datatabase if possible or set the database up using Testcontainers.

Building from Source

You don’t need to build from source to use Spring Data (binaries in repo.spring.io), but if you want to try out the latest and greatest, Spring Data can be easily built with the maven wrapper. You also need JDK 1.8.

 $ ./mvnw clean install

If you want to build with the regular mvn command, you will need Maven v3.5.0 or above.

Also see CONTRIBUTING.adoc if you wish to submit pull requests, and in particular please sign the Contributor’s Agreement before your first non-trivial change.

Building reference documentation

Building the documentation builds also the project without running tests.

 $ ./mvnw clean install -Pdistribute

The generated documentation is available from target/site/reference/html/index.html.

Guides

The spring.io site contains several guides that show how to use Spring Data step-by-step:

Accessing Data with JPA: Learn how to work with JPA data persistence using Spring Data JPA.

Accessing JPA Data with REST is a guide to creating a REST web service exposing data stored with JPA through repositories.

Examples

Spring Data Examples contains example projects that explain specific features in more detail.

Download Details:
Author: spring-projects
Source Code: https://github.com/spring-projects/spring-data-jpa
License: Apache-2.0 License

#spring #spring-framework #spring-boot #java #jpa 

Samuel Tucker

Samuel Tucker

1549237772

How to join results of multiple tables in Spring JPA repository

I'm new to Spring and I'm unable to figure out how to join multiple tables to return some result. I tried to implement a small Library application as shown below.

My Entity Classes - Book, Customer, Bookings

Book.java - books available in the library

@Entity
@Table(name = "books")
public class Book {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = "id", columnDefinition = "int")
private int id;

@NotNull(message = "Book name cannot be null")
@Column(name = "book_name", columnDefinition = "VARCHAR(255)")
private String bookName;

@Column(name = "author", columnDefinition = "VARCHAR(255)")
private String author;

// getters and setters

public Book() {}

public Book(String bookName, String author) {
    this.bookName = bookName;
    this.author = author;
}

}

Customer.java - Customers registered in the library

@Entity
@Table(name = “customer”, uniqueConstraints = {@UniqueConstraint(columnNames = {“phone”})})
public class Customer {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = “id”, columnDefinition = “int”)
private int id;

@NotNull(message = "Customer name cannot be null")
@Column(name = "name", columnDefinition = "VARCHAR(255)")
private String name;

@Column(name = "phone", columnDefinition = "VARCHAR(15)")
private String phone;

@Column(name = "registered", columnDefinition = "DATETIME")
private String registered;

// getters and setters

public Customer() {}

public Customer(String name, String phone, String registered) {
    this.name = name;
    this.phone = phone;
    this.registered = registered;
}

}

Booking.java - All the bookings made by the customers

@Entity
@Table(name = “bookings”)
public class Booking {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = “id”, columnDefinition = “int”)
private int id;

@NotNull(message = "Book id cannot be null")
@Column(name = "book_id", columnDefinition = "int")
private int bookId;

@NotNull(message = "Customer id cannot be null")
@Column(name = "customer_id", columnDefinition = "int")
private int customerId;

@Column(name = "issue_date", columnDefinition = "DATETIME")
private String issueDate;

@Column(name = "return_date", columnDefinition = "DATETIME")
private String returnDate;

// getters and setters

public Booking() {}

public Booking(int bookId, int customerId, String issueDate) {
    this.bookId = bookId;
    this.customerId = customerId;
    this.issueDate = issueDate;
}

}

Now the table schemas for the respective entities are as follows:

books:
±----------±-------------±-----±----±--------±---------------+
| Field | Type | Null | Key | Default | Extra |
±----------±-------------±-----±----±--------±---------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| book_name | varchar(255) | NO | | NULL | |
| author | varchar(255) | YES | | NULL | |
±----------±-------------±-----±----±--------±---------------+
id - primary key

customer:
±-----------±-------------±-----±----±------------------±------------------+
| Field | Type | Null | Key | Default | Extra |
±-----------±-------------±-----±----±------------------±------------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| name | varchar(255) | NO | | NULL | |
| registered | datetime | YES | | CURRENT_TIMESTAMP | DEFAULT_GENERATED |
| phone | varchar(15) | YES | UNI | NULL | |
±-----------±-------------±-----±----±------------------±------------------+
id - primary key

bookings:
±------------±---------±-----±----±------------------±------------------+
| Field | Type | Null | Key | Default | Extra |
±------------±---------±-----±----±------------------±------------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| book_id | int(11) | NO | MUL | NULL | |
| customer_id | int(11) | NO | MUL | NULL | |
| issue_date | datetime | YES | | CURRENT_TIMESTAMP | DEFAULT_GENERATED |
| return_date | datetime | YES | | NULL | |
±------------±---------±-----±----±------------------±------------------+
id - primary key
book_id - foreign key references books.id
customer_id - foreign key references customer.id

Now What I want to do is given some booking critieria like customer phone or author name etc., I want to return all the bookings related to that order. I’ll show a sample Booking api to explain.

Booking Controller:

@RestController
@RequestMapping(“/bookings”)
public class BookingController {
@Autowired
BookingService bookingService;

// some booking apis which return Booking objects

@GetMapping
public List&lt;Booking&gt; getAllBookingsBy(@RequestParam("phone") String phone,
                                     @RequestParam("authors") List&lt;String&gt; authors) {
    return bookingService.getAllBy(phone, authors);
}

}

Booking Service class:

@Service
public class BookService {
@Autowired
private BookRepository bookRepository;

// some booking service methods

// get all bookings booked by a customer with matching phone number and books written by a given list of authors
public List&lt;Booking&gt; getAllBy(String phone, List&lt;String&gt; authors) {
return bookingRepository.queryBy(phone, authors);

}
}

Booking Repository Class:

@Repository
public interface BookingRepository extends JpaRepository<Booking, Integer> {
// some booking repository methods

@Query(value = "SELECT * FROM bookings bs WHERE " +
        "EXISTS (SELECT 1 FROM customer c WHERE bs.customer_id = c.id AND c.phone = :phone) " +
        "AND EXISTS (SELECT 1 FROM books b WHERE b.id = bs.book_id AND b.author IN :authors)",
        nativeQuery = true)
List&lt;Booking&gt; queryBy(@Param("phone") String phone,
                        @Param("authors") List&lt;String&gt; authors);

}

Now hitting the shown booking controller 'll return a booking object which looks like this :

[
{
“id”: 3,
“book_id”: 5,
“customer_id”: 2,
“issue_date”: “2019-02-04 01:45:21”,
“return_date”: null
}
]

But I don’t want it like that, I want to return along with them the name of the customer for that booking and also the name of the book. So I want the booking objects returned by the controller to look like this:

[
{
“id”: 3,
“book_id”: 5,
“customer_id”: 2,
“issue_date”: “2019-02-04 01:45:21”,
“return_date”: null,
“customer_name”: “Cust 2”,
“book_name”: “Book_2_2”,
}
]

Can someone please help in doing this? I’m stuck as I’m unable to proceed from here.

#java #spring

Introduction to Spring Boot and JDBCTemplate: Refactoring to SpringData JPA

Introduction to Spring Boot and JDBCTemplate: Refactoring to SpringData JPA.

Refactoring is the process of modifying a software system without changing its desirable behavior. It was necessary to have an application integrated with the relational database using the Spring JDBC Template in the first parts. The Spring JDBC Template is a powerful tool that facilitates productivity. However, there is a way to simplify the code even further with Spring Data JPA. The purpose of this post is to refactor the project to use Spring Data JPA.

Spring Data JPA, part of the larger Spring Data family, makes it easy to implement JPA-based repositories easily. This module deals with enhanced support for JPA-based data access layers. It makes it easier to build Spring-powered applications that use data access technologies.

A safe code refactoring requires the use of tests to ensure that the compartment is not changed. The use of tests, fortunately, is adopted as a minimum standard, including several methodologies such as TDD that preach the creation of tests at the beginning of the development process.

#java #tutorial #spring #spring data #java tutorial #spring tutorial #spring data jpa