Dependency Injection with Spring Code Review

Dependency Injection with Spring Code Review

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A code review of a Dependency Injection example using the Spring Framework. See more at my website spring framework.guru

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Enhance Amazon Aurora Read/Write Capability with ShardingSphere-JDBC

1. Introduction

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

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

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

1.1 Why sharding is needed

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

1.2 Sharding methods

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

1. Advantages of vertical sharding

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

2. Disadvantages of vertical sharding

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

3. Advantages of horizontal sharding

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

4. Disadvantages of horizontal sharding

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

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

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

2. ShardingSphere introduction:

The characteristics of Sharding-JDBC are:

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_item，t_address will be created on two Aurora instances.

3.4.2 Configuring Sharding-JDBC

application.properties springboot master profile description

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

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

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

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

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

 

3.4.3 Test and verification process description

1. DDL operation

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

2. Write operation

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

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

3. Read operation

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

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

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

3.5 Verifying sharding function

3.5.1 Setting up the database environment

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

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

3.5.2 Configuring Sharding-JDBC

application.properties springboot master profile description

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

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

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

 

3.5.3 Test and verification process description

1. DDL operation

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

2. Write operation

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

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

3. Read operation

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

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

3.6.1 Setting up the database environment

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

3.6.2 Configuring Sharding-JDBC

application.properties spring boot master profile description

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

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

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

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

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

 

3.6.3 Test and verification process description

1. DDL operation

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

2. Write operation

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

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

3. Read operation

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

3. Conclusion

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

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

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

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

Author

Sun Jinhua

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

How to Use Hotwire Rails

Introduction

We are back with another exciting and much-talked-about Rails tutorial on how to use Hotwire with the Rails application. This Hotwire Rails tutorial is an alternate method for building modern web applications that consume a pinch of JavaScript.

Rails 7 Hotwire is the default front-end framework shipped with Rails 7 after it was launched. It is used to represent HTML over the wire in the Rails application. Previously, we used to add a hotwire-rails gem in our gem file and then run rails hotwire: install. However, with the introduction of Rails 7, the gem got deprecated. Now, we use turbo-rails and stimulus rails directly, which work as Hotwire’s SPA-like page accelerator and Hotwire’s modest JavaScript framework.

What is Hotwire?

Hotwire is a package of different frameworks that help to build applications. It simplifies the developer’s work for writing web pages without the need to write JavaScript, and instead sending HTML code over the wire.

Introduction to The Hotwire Framework:

1. Turbo:

It uses simplified techniques to build web applications while decreasing the usage of JavaScript in the application. Turbo offers numerous handling methods for the HTML data sent over the wire and displaying the application’s data without actually loading the entire page. It helps to maintain the simplicity of web applications without destroying the single-page application experience by using the below techniques:

Turbo Frames: Turbo Frames help to load the different sections of our markup without any dependency as it divides the page into different contexts separately called frames and updates these frames individually.
Turbo Drive: Every link doesn’t have to make the entire page reload when clicked. Only the HTML contained within the tag will be displayed.
Turbo Streams: To add real-time features to the application, this technique is used. It helps to bring real-time data to the application using CRUD actions.

2. Stimulus

It represents the JavaScript framework, which is required when JS is a requirement in the application. The interaction with the HTML is possible with the help of a stimulus, as the controllers that help those interactions are written by a stimulus.

3. Strada

Not much information is available about Strada as it has not been officially released yet. However, it works with native applications, and by using HTML bridge attributes, interaction is made possible between web applications and native apps.

Simple diagrammatic representation of Hotwire Stack:

Prerequisites For Hotwire Rails Tutorial

As we are implementing the Ruby on Rails Hotwire tutorial, make sure about the following installations before you can get started.

• Ruby on Rails
• Hotwire gem
• PostgreSQL/SQLite (choose any one database)
• Turbo Rails
• Stimulus.js

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Create a new Rails Project

Find the following commands to create a rails application.

mkdir ~/projects/railshotwire
cd ~/projects/railshotwire
echo "source 'https://rubygems.org'" > Gemfile
echo "gem 'rails', '~> 7.0.0'" >> Gemfile
bundle install  
bundle exec rails new . --force -d=postgresql

Now create some files for the project, up till now no usage of Rails Hotwire can be seen.
Fire the following command in your terminal.

• For creating a default controller for the application

echo "class HomeController < ApplicationController" > app/controllers/home_controller.rb
echo "end" >> app/controllers/home_controller.rb

• For creating another controller for the application

echo "class OtherController < ApplicationController" > app/controllers/other_controller.rb
echo "end" >> app/controllers/home_controller.rb

• For creating routes for the application

echo "Rails.application.routes.draw do" > config/routes.rb
echo '  get "home/index"' >> config/routes.rb
echo '  get "other/index"' >> config/routes.rb
echo '  root to: "home#index"' >> config/routes.rb
echo 'end' >> config/routes.rb

• For creating a default view for the application

mkdir app/views/home
echo '<h1>This is Rails Hotwire homepage</h1>' > app/views/home/index.html.erb
echo '<div><%= link_to "Enter to other page", other_index_path %></div>' >> app/views/home/index.html.erb

• For creating another view for the application

mkdir app/views/other
echo '<h1>This is Another page</h1>' > app/views/other/index.html.erb
echo '<div><%= link_to "Enter to home page", root_path %></div>' >> app/views/other/index.html.erb

• For creating a database and schema.rb file for the application

bin/rails db:create
bin/rails db:migrate

• For checking the application run bin/rails s and open your browser, your running application will have the below view.

Additionally, you can clone the code and browse through the project. Here’s the source code of the repository: Rails 7 Hotwire application

Now, let’s see how Hotwire Rails can work its magic with various Turbo techniques.

Hotwire Rails: Turbo Drive

Go to your localhost:3000 on your web browser and right-click on the Inspect and open a Network tab of the DevTools of the browser.

Now click on go to another page link that appears on the home page to redirect from the home page to another page. In our Network tab, we can see that this action of navigation is achieved via XHR. It appears only the part inside HTML is reloaded, here neither the CSS is reloaded nor the JS is reloaded when the navigation action is performed.

By performing this action we can see that Turbo Drive helps to represent the HTML response without loading the full page and only follows redirect and reindeer HTML responses which helps to make the application faster to access.

Hotwire Rails: Turbo Frame

This technique helps to divide the current page into different sections called frames that can be updated separately independently when new data is added from the server.
Below we discuss the different use cases of Turbo frame like inline edition, sorting, searching, and filtering of data.

Let’s perform some practical actions to see the example of these use cases.

Make changes in the app/controllers/home_controller.rb file

#CODE

class HomeController < ApplicationController
   def turbo_frame_form
   end
   
   def turbo_frame submit
      extracted_anynumber = params[:any][:anynumber]
      render :turbo_frame_form, status: :ok, locals: {anynumber: extracted_anynumber,      comment: 'turbo_frame_submit ok' }
   end
end

Add app/views/home/turbo_frame_form.html.erb file to the application and add this content inside the file.

#CODE

<section>

    <%= turbo_frame_tag 'anyframe' do %>
            
      <div>
          <h2>Frame view</h2>
          <%= form_with scope: :any, url: turbo_frame_submit_path, local: true do |form| %>
              <%= form.label :anynumber, 'Type an integer (odd or even)', 'class' => 'my-0  d-inline'  %>
              <%= form.text_field :anynumber, type: 'number', 'required' => 'true', 'value' => "#{local_assigns[:anynumber] || 0}",  'aria-describedby' => 'anynumber' %>
              <%= form.submit 'Submit this number', 'id' => 'submit-number' %>
          <% end %>
      </div>
      <div>
        <h2>Data of the view</h2>
        <pre style="font-size: .7rem;"><%= JSON.pretty_generate(local_assigns) %></pre> 
      </div>
      
    <% end %>

</section>

Make some adjustments in routes.rb

#CODE

Rails.application.routes.draw do
  get 'home/index'
  get 'other/index'

  get '/home/turbo_frame_form' => 'home#turbo_frame_form', as: 'turbo_frame_form'
  post '/home/turbo_frame_submit' => 'home#turbo_frame_submit', as: 'turbo_frame_submit'


  root to: "home#index"
end

• Next step is to change homepage view in app/views/home/index.html.erb

#CODE

<h1>This is Rails Hotwire home page</h1>
<div><%= link_to "Enter to other page", other_index_path %></div>

<%= turbo_frame_tag 'anyframe' do %>        
  <div>
      <h2>Home view</h2>
      <%= form_with scope: :any, url: turbo_frame_submit_path, local: true do |form| %>
          <%= form.label :anynumber, 'Type an integer (odd or even)', 'class' => 'my-0  d-inline'  %>
          <%= form.text_field :anynumber, type: 'number', 'required' => 'true', 'value' => "#{local_assigns[:anynumber] || 0}",  'aria-describedby' => 'anynumber' %>
          <%= form.submit 'Submit this number', 'id' => 'submit-number' %>
      <% end %>
  <div>
<% end %>

After making all the changes, restart the rails server and refresh the browser, the default view will appear on the browser.

Now in the field enter any digit, after entering the digit click on submit button, and as the submit button is clicked we can see the Turbo Frame in action in the below screen, we can observe that the frame part changed, the first title and first link didn’t move.

Hotwire Rails: Turbo Streams

Turbo Streams deliver page updates over WebSocket, SSE or in response to form submissions by only using HTML and a series of CRUD-like operations, you are free to say that either

• Update the piece of HTML while responding to all the other actions like the post, put, patch, and delete except the GET action.
• Transmit a change to all users, without reloading the browser page.

This transmit can be represented by a simple example.

• Make changes in app/controllers/other_controller.rb file of rails application

#CODE

class OtherController < ApplicationController

  def post_something
    respond_to do |format|
      format.turbo_stream {  }
    end
  end

   end

Add the below line in routes.rb file of the application

#CODE

post '/other/post_something' => 'other#post_something', as: 'post_something'

Superb! Rails will now attempt to locate the app/views/other/post_something.turbo_stream.erb template at any moment the ‘/other/post_something’ endpoint is reached.

For this, we need to add app/views/other/post_something.turbo_stream.erb template in the rails application.

#CODE

<turbo-stream action="append" target="messages">
  <template>
    <div id="message_1">This changes the existing message!</div>
  </template>
</turbo-stream>

This states that the response will try to append the template of the turbo frame with ID “messages”.

Now change the index.html.erb file in app/views/other paths with the below content.

#CODE

<h1>This is Another page</h1>
<div><%= link_to "Enter to home page", root_path %></div>

<div style="margin-top: 3rem;">
  <%= form_with scope: :any, url: post_something_path do |form| %>
      <%= form.submit 'Post any message %>
  <% end %>
  <turbo-frame id="messages">
    <div>An empty message</div>
  </turbo-frame>
</div>

• After making all the changes, restart the rails server and refresh the browser, and go to the other page.

• Once the above screen appears, click on the Post any message button

This action shows that after submitting the response, the Turbo Streams help the developer to append the message, without reloading the page.

Another use case we can test is that rather than appending the message, the developer replaces the message. For that, we need to change the content of app/views/other/post_something.turbo_stream.erb template file and change the value of the action attribute from append to replace and check the changes in the browser.

#CODE

<turbo-stream action="replace" target="messages">
  <template>
    <div id="message_1">This changes the existing message!</div>
  </template>
</turbo-stream>

When we click on Post any message button, the message that appear below that button will get replaced with the message that is mentioned in the app/views/other/post_something.turbo_stream.erb template

Stimulus

There are some cases in an application where JS is needed, therefore to cover those scenarios we require Hotwire JS tool. Hotwire has a JS tool because in some scenarios Turbo-* tools are not sufficient. But as we know that Hotwire is used to reduce the usage of JS in an application, Stimulus considers HTML as the single source of truth. Consider the case where we have to give elements on a page some JavaScript attributes, such as data controller, data-action, and data target. For that, a stimulus controller that can access elements and receive events based on those characteristics will be created.

Make a change in app/views/other/index.html.erb template file in rails application

#CODE

<h1>This is Another page</h1>
<div><%= link_to "Enter to home page", root_path %></div>

<div style="margin-top: 2rem;">
  <%= form_with scope: :any, url: post_something_path do |form| %>
      <%= form.submit 'Post something' %>
  <% end %>
  <turbo-frame id="messages">
    <div>An empty message</div>
  </turbo-frame>
</div>

<div style="margin-top: 2rem;">
  <h2>Stimulus</h2>  
  <div data-controller="hello">
    <input data-hello-target="name" type="text">
    <button data-action="click->hello#greet">
      Greet
    </button>
    <span data-hello-target="output">
    </span>
  </div>
</div>

Make changes in the hello_controller.js in path app/JavaScript/controllers and add a stimulus controller in the file, which helps to bring the HTML into life.

#CODE

import { Controller } from "@hotwired/stimulus"

export default class extends Controller {
  static targets = [ "name", "output" ]

  greet() {
    this.outputTarget.textContent =
      `Hello, ${this.nameTarget.value}!`
  }
}

Go to your browser after making the changes in the code and click on Enter to other page link which will navigate to the localhost:3000/other/index page there you can see the changes implemented by the stimulus controller that is designed to augment your HTML with just enough behavior to make it more responsive.

With just a little bit of work, Turbo and Stimulus together offer a complete answer for applications that are quick and compelling.

Using Rails 7 Hotwire helps to load the pages at a faster speed and allows you to render templates on the server, where you have access to your whole domain model. It is a productive development experience in ROR, without compromising any of the speed or responsiveness associated with SPA.

Conclusion

We hope you were satisfied with our Rails Hotwire tutorial. Write to us at service@bacancy.com for any query that you want to resolve, or if you want us to share a tutorial on your query.

For more such solutions on RoR, check out our Ruby on Rails Tutorials. We will always strive to amaze you and cater to your needs.

Original article source at: https://www.bacancytechnology.com/

#rails #ruby 

Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

Basic building blocks of Softagram

• Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

• Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

• Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company

Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

…

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

Effective Code Reviews: A Primer

Peer code reviews as a process have increasingly been adopted by engineering teams around the world. And for good reason — code reviews have been proven to improve software quality and save developers’ time in the long run. A lot has been written about how code reviews help engineering teams by leading software engineering practitioners. My favorite is this quote by Karl Wiegers, author of the seminal paper on this topic, Humanizing Peer Reviews:

Peer review – an activity in which people other than the author of a software deliverable examine it for defects and improvement opportunities – is one of the most powerful software quality tools available. Peer review methods include inspections, walkthroughs, peer deskchecks, and other similar activities. After experiencing the benefits of peer reviews for nearly fifteen years, I would never work in a team that did not perform them.

It is worth the time and effort to put together a code review strategy and consistently follow it in the team. In essence, this has a two-pronged benefit: more pair of eyes looking at the code decreases the chances of bugs and bad design patterns entering your codebase, and embracing the process fosters knowledge sharing and positive collaboration culture in the team.

Here are 6 tips to ensure effective peer reviews in your team.

1. Keep the Changes Small and Focused

Code reviews require developers to look at someone else’s code, most of which is completely new most of the times. Too many lines of code to review at once requires a huge amount of cognitive effort, and the quality of review diminishes as the size of changes increases. While there’s no golden number of LOCs, it is recommended to create small pull-requests which can be managed easily. If there are a lot of changes going in a release, it is better to chunk it down into a number of small pull-requests.

2. Ensure Logical Coherence of Changes

Code reviews are the most effective when the changes are focused and have logical coherence. When doing refactoring, refrain from making behavioral changes. Similarly, behavioral changes should not include refactoring and style violation fixes. Following this convention prevents unintended changes creeping in unnoticed in the code base.

3. Have Automated Tests, and Track Coverage

Automated tests of your preferred flavor — units, integration tests, end-to-end tests, etc. help automatically ensure correctness. Consistently ensuring that changes proposed are covered by some kind of automated frees up time for more qualitative review; allowing for a more insightful and in-depth conversation on deeper issues.

4. Self-Review Changes Before Submitting for Peer Review

A change can implement a new feature or fix an existing issue. It is recommended that the requester submits only those changes that are complete, and tested for correctness manually. Before creating the pull-request, a quick glance on what changes are being proposed helps ensure that no extraneous files are added in the changeset. This saves tons of time for the reviewers.

5. Automate What Can Be Automated

Human review time is expensive, and the best use of a developer’s time is reviewing qualitative aspects of code — logic, design patterns, software architecture, and so on. Linting tools can help automatically take care of style and formatting conventions. Continuous Quality tools can help catch potential bugs, anti-patterns and security issues which can be fixed by the developer before they make a change request. Most of these tools integrate well with code hosting platforms as well.

6. Be Positive, Polite, and Respectful

Finally, be cognizant of the fact that people on both sides of the review are but human. Offer positive feedback, and accept criticism humbly. Instead of beating oneself upon the literal meaning of words, it really pays off to look at reviews as people trying to achieve what’s best for the team, albeit in possibly different ways. Being cognizant of this aspect can save a lot of resentment and unmitigated negativity.

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