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Spring Boot: Profile Config, YAML Property File

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.

Spring Boot: Profile Config, YAML Property File

Having issues selecting your profile configuration within your Spring Boot application? We’ll walk you through selecting a YAML property file and get you going!

In the video below, we take a closer look at the Spring boot — Selecting profile configuration — YAML property file | Spring Boot tutorial. Let’s get started!

#java #tutorial #spring boot #video #spring boot tutorial #spring boot tutorial for beginners

Sigrid  Farrell

Sigrid Farrell

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How To Read the Properties File Outside the Jar in Spring Boot - DZone Java

In the video presentation attached, we take a closer look at how to read the properties file, which is outside the Jar in Spring Boot! Let’s get started!

In the video below, we take a closer look at how to read the properties file, which is outside the Jar in Spring Boot! First, we’ll externalize the properties file. Let’s get started!

#spring boot #video #jar #properties file #properties files

How to Configure the Interceptor With Spring Boot Application

In the video in this article, we take a closer look at how to configure the interceptor with the Spring Boot application! Let’s take a look!

#spring boot #spring boot tutorial #interceptor #interceptors #spring boot interceptor #spring boot tutorial for beginners

Swift Tips: A Collection Useful Tips for The Swift Language

SwiftTips

The following is a collection of tips I find to be useful when working with the Swift language. More content is available on my Twitter account!

Property Wrappers as Debugging Tools

Property Wrappers allow developers to wrap properties with specific behaviors, that will be seamlessly triggered whenever the properties are accessed.

While their primary use case is to implement business logic within our apps, it's also possible to use Property Wrappers as debugging tools!

For example, we could build a wrapper called @History, that would be added to a property while debugging and would keep track of all the values set to this property.

import Foundation

@propertyWrapper
struct History<Value> {
    private var value: Value
    private(set) var history: [Value] = []

    init(wrappedValue: Value) {
        self.value = wrappedValue
    }
    
    var wrappedValue: Value {
        get { value }

        set {
            history.append(value)
            value = newValue
        }
    }
    
    var projectedValue: Self {
        return self
    }
}

// We can then decorate our business code
// with the `@History` wrapper
struct User {
    @History var name: String = ""
}

var user = User()

// All the existing call sites will still
// compile, without the need for any change
user.name = "John"
user.name = "Jane"

// But now we can also access an history of
// all the previous values!
user.$name.history // ["", "John"]

Localization through String interpolation

Swift 5 gave us the possibility to define our own custom String interpolation methods.

This feature can be used to power many use cases, but there is one that is guaranteed to make sense in most projects: localizing user-facing strings.

import Foundation

extension String.StringInterpolation {
    mutating func appendInterpolation(localized key: String, _ args: CVarArg...) {
        let localized = String(format: NSLocalizedString(key, comment: ""), arguments: args)
        appendLiteral(localized)
    }
}


/*
 Let's assume that this is the content of our Localizable.strings:
 
 "welcome.screen.greetings" = "Hello %@!";
 */

let userName = "John"
print("\(localized: "welcome.screen.greetings", userName)") // Hello John!

Implementing pseudo-inheritance between structs

If you’ve always wanted to use some kind of inheritance mechanism for your structs, Swift 5.1 is going to make you very happy!

Using the new KeyPath-based dynamic member lookup, you can implement some pseudo-inheritance, where a type inherits the API of another one 🎉

(However, be careful, I’m definitely not advocating inheritance as a go-to solution 🙃)

import Foundation

protocol Inherits {
    associatedtype SuperType
    
    var `super`: SuperType { get }
}

extension Inherits {
    subscript<T>(dynamicMember keyPath: KeyPath<SuperType, T>) -> T {
        return self.`super`[keyPath: keyPath]
    }
}

struct Person {
    let name: String
}

@dynamicMemberLookup
struct User: Inherits {
    let `super`: Person
    
    let login: String
    let password: String
}

let user = User(super: Person(name: "John Appleseed"), login: "Johnny", password: "1234")

user.name // "John Appleseed"
user.login // "Johnny"

Composing NSAttributedString through a Function Builder

Swift 5.1 introduced Function Builders: a great tool for building custom DSL syntaxes, like SwiftUI. However, one doesn't need to be building a full-fledged DSL in order to leverage them.

For example, it's possible to write a simple Function Builder, whose job will be to compose together individual instances of NSAttributedString through a nicer syntax than the standard API.

import UIKit

@_functionBuilder
class NSAttributedStringBuilder {
    static func buildBlock(_ components: NSAttributedString...) -> NSAttributedString {
        let result = NSMutableAttributedString(string: "")
        
        return components.reduce(into: result) { (result, current) in result.append(current) }
    }
}

extension NSAttributedString {
    class func composing(@NSAttributedStringBuilder _ parts: () -> NSAttributedString) -> NSAttributedString {
        return parts()
    }
}

let result = NSAttributedString.composing {
    NSAttributedString(string: "Hello",
                       attributes: [.font: UIFont.systemFont(ofSize: 24),
                                    .foregroundColor: UIColor.red])
    NSAttributedString(string: " world!",
                       attributes: [.font: UIFont.systemFont(ofSize: 20),
                                    .foregroundColor: UIColor.orange])
}

Using switch and if as expressions

Contrary to other languages, like Kotlin, Swift does not allow switch and if to be used as expressions. Meaning that the following code is not valid Swift:

let constant = if condition {
                  someValue
               } else {
                  someOtherValue
               }

A common solution to this problem is to wrap the if or switch statement within a closure, that will then be immediately called. While this approach does manage to achieve the desired goal, it makes for a rather poor syntax.

To avoid the ugly trailing () and improve on the readability, you can define a resultOf function, that will serve the exact same purpose, in a more elegant way.

import Foundation

func resultOf<T>(_ code: () -> T) -> T {
    return code()
}

let randomInt = Int.random(in: 0...3)

let spelledOut: String = resultOf {
    switch randomInt {
    case 0:
        return "Zero"
    case 1:
        return "One"
    case 2:
        return "Two"
    case 3:
        return "Three"
    default:
        return "Out of range"
    }
}

print(spelledOut)

Avoiding double negatives within guard statements

A guard statement is a very convenient way for the developer to assert that a condition is met, in order for the execution of the program to keep going.

However, since the body of a guard statement is meant to be executed when the condition evaluates to false, the use of the negation (!) operator within the condition of a guard statement can make the code hard to read, as it becomes a double negative.

A nice trick to avoid such double negatives is to encapsulate the use of the ! operator within a new property or function, whose name does not include a negative.

import Foundation

extension Collection {
    var hasElements: Bool {
        return !isEmpty
    }
}

let array = Bool.random() ? [1, 2, 3] : []

guard array.hasElements else { fatalError("array was empty") }

print(array)

Defining a custom init without loosing the compiler-generated one

It's common knowledge for Swift developers that, when you define a struct, the compiler is going to automatically generate a memberwise init for you. That is, unless you also define an init of your own. Because then, the compiler won't generate any memberwise init.

Yet, there are many instances where we might enjoy the opportunity to get both. As it turns out, this goal is quite easy to achieve: you just need to define your own init in an extension rather than inside the type definition itself.

import Foundation

struct Point {
    let x: Int
    let y: Int
}

extension Point {
    init() {
        x = 0
        y = 0
    }
}

let usingDefaultInit = Point(x: 4, y: 3)
let usingCustomInit = Point()

Implementing a namespace through an empty enum

Swift does not really have an out-of-the-box support of namespaces. One could argue that a Swift module can be seen as a namespace, but creating a dedicated Framework for this sole purpose can legitimately be regarded as overkill.

Some developers have taken the habit to use a struct which only contains static fields to implement a namespace. While this does the job, it requires us to remember to implement an empty private init(), because it wouldn't make sense for such a struct to be instantiated.

It's actually possible to take this approach one step further, by replacing the struct with an enum. While it might seem weird to have an enum with no case, it's actually a very idiomatic way to declare a type that cannot be instantiated.

import Foundation

enum NumberFormatterProvider {
    static var currencyFormatter: NumberFormatter {
        let formatter = NumberFormatter()
        formatter.numberStyle = .currency
        formatter.roundingIncrement = 0.01
        return formatter
    }
    
    static var decimalFormatter: NumberFormatter {
        let formatter = NumberFormatter()
        formatter.numberStyle = .decimal
        formatter.decimalSeparator = ","
        return formatter
    }
}

NumberFormatterProvider() // ❌ impossible to instantiate by mistake

NumberFormatterProvider.currencyFormatter.string(from: 2.456) // $2.46
NumberFormatterProvider.decimalFormatter.string(from: 2.456) // 2,456

Using Never to represent impossible code paths

Never is quite a peculiar type in the Swift Standard Library: it is defined as an empty enum enum Never { }.

While this might seem odd at first glance, it actually yields a very interesting property: it makes it a type that cannot be constructed (i.e. it possesses no instances).

This way, Never can be used as a generic parameter to let the compiler know that a particular feature will not be used.

import Foundation

enum Result<Value, Error> {
    case success(value: Value)
    case failure(error: Error)
}

func willAlwaysSucceed(_ completion: @escaping ((Result<String, Never>) -> Void)) {
    completion(.success(value: "Call was successful"))
}

willAlwaysSucceed( { result in
    switch result {
    case .success(let value):
        print(value)
    // the compiler knows that the `failure` case cannot happen
    // so it doesn't require us to handle it.
    }
})

Providing a default value to a Decodable enum

Swift's Codable framework does a great job at seamlessly decoding entities from a JSON stream. However, when we integrate web-services, we are sometimes left to deal with JSONs that require behaviors that Codable does not provide out-of-the-box.

For instance, we might have a string-based or integer-based enum, and be required to set it to a default value when the data found in the JSON does not match any of its cases.

We might be tempted to implement this via an extensive switch statement over all the possible cases, but there is a much shorter alternative through the initializer init?(rawValue:):

import Foundation

enum State: String, Decodable {
    case active
    case inactive
    case undefined
    
    init(from decoder: Decoder) throws {
        let container = try decoder.singleValueContainer()
        let decodedString = try container.decode(String.self)
        
        self = State(rawValue: decodedString) ?? .undefined
    }
}

let data = """
["active", "inactive", "foo"]
""".data(using: .utf8)!

let decoded = try! JSONDecoder().decode([State].self, from: data)

print(decoded) // [State.active, State.inactive, State.undefined]

Another lightweight dependency injection through default values for function parameters

Dependency injection boils down to a simple idea: when an object requires a dependency, it shouldn't create it by itself, but instead it should be given a function that does it for him.

Now the great thing with Swift is that, not only can a function take another function as a parameter, but that parameter can also be given a default value.

When you combine both those features, you can end up with a dependency injection pattern that is both lightweight on boilerplate, but also type safe.

import Foundation

protocol Service {
    func call() -> String
}

class ProductionService: Service {
    func call() -> String {
        return "This is the production"
    }
}

class MockService: Service {
    func call() -> String {
        return "This is a mock"
    }
}

typealias Provider<T> = () -> T

class Controller {
    
    let service: Service
    
    init(serviceProvider: Provider<Service> = { return ProductionService() }) {
        self.service = serviceProvider()
    }
    
    func work() {
        print(service.call())
    }
}

let productionController = Controller()
productionController.work() // prints "This is the production"

let mockedController = Controller(serviceProvider: { return MockService() })
mockedController.work() // prints "This is a mock"

Lightweight dependency injection through protocol-oriented programming

Singletons are pretty bad. They make your architecture rigid and tightly coupled, which then results in your code being hard to test and refactor. Instead of using singletons, your code should rely on dependency injection, which is a much more architecturally sound approach.

But singletons are so easy to use, and dependency injection requires us to do extra-work. So maybe, for simple situations, we could find an in-between solution?

One possible solution is to rely on one of Swift's most know features: protocol-oriented programming. Using a protocol, we declare and access our dependency. We then store it in a private singleton, and perform the injection through an extension of said protocol.

This way, our code will indeed be decoupled from its dependency, while at the same time keeping the boilerplate to a minimum.

import Foundation

protocol Formatting {
    var formatter: NumberFormatter { get }
}

private let sharedFormatter: NumberFormatter = {
    let sharedFormatter = NumberFormatter()
    sharedFormatter.numberStyle = .currency
    return sharedFormatter
}()

extension Formatting {
    var formatter: NumberFormatter { return sharedFormatter }
}

class ViewModel: Formatting {
    var displayableAmount: String?
    
    func updateDisplay(to amount: Double) {
        displayableAmount = formatter.string(for: amount)
    }
}

let viewModel = ViewModel()

viewModel.updateDisplay(to: 42000.45)
viewModel.displayableAmount // "$42,000.45"

Getting rid of overabundant [weak self] and guard

Callbacks are a part of almost all iOS apps, and as frameworks such as RxSwift keep gaining in popularity, they become ever more present in our codebase.

Seasoned Swift developers are aware of the potential memory leaks that @escaping callbacks can produce, so they make real sure to always use [weak self], whenever they need to use self inside such a context. And when they need to have self be non-optional, they then add a guard statement along.

Consequently, this syntax of a [weak self] followed by a guard rapidly tends to appear everywhere in the codebase. The good thing is that, through a little protocol-oriented trick, it's actually possible to get rid of this tedious syntax, without loosing any of its benefits!

import Foundation
import PlaygroundSupport

PlaygroundPage.current.needsIndefiniteExecution = true

protocol Weakifiable: class { }

extension Weakifiable {
    func weakify(_ code: @escaping (Self) -> Void) -> () -> Void {
        return { [weak self] in
            guard let self = self else { return }
            
            code(self)
        }
    }
    
    func weakify<T>(_ code: @escaping (T, Self) -> Void) -> (T) -> Void {
        return { [weak self] arg in
            guard let self = self else { return }
            
            code(arg, self)
        }
    }
}

extension NSObject: Weakifiable { }

class Producer: NSObject {
    
    deinit {
        print("deinit Producer")
    }
    
    private var handler: (Int) -> Void = { _ in }
    
    func register(handler: @escaping (Int) -> Void) {
        self.handler = handler
        
        DispatchQueue.main.asyncAfter(deadline: .now() + 1.0, execute: { self.handler(42) })
    }
}

class Consumer: NSObject {
    
    deinit {
        print("deinit Consumer")
    }
    
    let producer = Producer()
    
    func consume() {
        producer.register(handler: weakify { result, strongSelf in
            strongSelf.handle(result)
        })
    }
    
    private func handle(_ result: Int) {
        print("🎉 \(result)")
    }
}

var consumer: Consumer? = Consumer()

consumer?.consume()

DispatchQueue.main.asyncAfter(deadline: .now() + 2.0, execute: { consumer = nil })

// This code prints:
// 🎉 42
// deinit Consumer
// deinit Producer

Solving callback hell with function composition

Asynchronous functions are a big part of iOS APIs, and most developers are familiar with the challenge they pose when one needs to sequentially call several asynchronous APIs.

This often results in callbacks being nested into one another, a predicament often referred to as callback hell.

Many third-party frameworks are able to tackle this issue, for instance RxSwift or PromiseKit. Yet, for simple instances of the problem, there is no need to use such big guns, as it can actually be solved with simple function composition.

import Foundation

typealias CompletionHandler<Result> = (Result?, Error?) -> Void

infix operator ~>: MultiplicationPrecedence

func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ second: @escaping (T, CompletionHandler<U>) -> Void) -> (CompletionHandler<U>) -> Void {
    return { completion in
        first({ firstResult, error in
            guard let firstResult = firstResult else { completion(nil, error); return }
            
            second(firstResult, { (secondResult, error) in
                completion(secondResult, error)
            })
        })
    }
}

func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ transform: @escaping (T) -> U) -> (CompletionHandler<U>) -> Void {
    return { completion in
        first({ result, error in
            guard let result = result else { completion(nil, error); return }
            
            completion(transform(result), nil)
        })
    }
}

func service1(_ completionHandler: CompletionHandler<Int>) {
    completionHandler(42, nil)
}

func service2(arg: String, _ completionHandler: CompletionHandler<String>) {
    completionHandler("🎉 \(arg)", nil)
}

let chainedServices = service1
    ~> { int in return String(int / 2) }
    ~> service2

chainedServices({ result, _ in
    guard let result = result else { return }
    
    print(result) // Prints: 🎉 21
})

Transform an asynchronous function into a synchronous one

Asynchronous functions are a great way to deal with future events without blocking a thread. Yet, there are times where we would like them to behave in exactly such a blocking way.

Think about writing unit tests and using mocked network calls. You will need to add complexity to your test in order to deal with asynchronous functions, whereas synchronous ones would be much easier to manage.

Thanks to Swift proficiency in the functional paradigm, it is possible to write a function whose job is to take an asynchronous function and transform it into a synchronous one.

import Foundation

func makeSynchrone<A, B>(_ asyncFunction: @escaping (A, (B) -> Void) -> Void) -> (A) -> B {
    return { arg in
        let lock = NSRecursiveLock()
        
        var result: B? = nil
        
        asyncFunction(arg) {
            result = $0
            lock.unlock()
        }
        
        lock.lock()
        
        return result!
    }
}

func myAsyncFunction(arg: Int, completionHandler: (String) -> Void) {
    completionHandler("🎉 \(arg)")
}

let syncFunction = makeSynchrone(myAsyncFunction)

print(syncFunction(42)) // prints 🎉 42

Using KeyPaths instead of closures

Closures are a great way to interact with generic APIs, for instance APIs that allow to manipulate data structures through the use of generic functions, such as filter() or sorted().

The annoying part is that closures tend to clutter your code with many instances of {, } and $0, which can quickly undermine its readably.

A nice alternative for a cleaner syntax is to use a KeyPath instead of a closure, along with an operator that will deal with transforming the provided KeyPath in a closure.

import Foundation

prefix operator ^

prefix func ^ <Element, Attribute>(_ keyPath: KeyPath<Element, Attribute>) -> (Element) -> Attribute {
    return { element in element[keyPath: keyPath] }
}

struct MyData {
    let int: Int
    let string: String
}

let data = [MyData(int: 2, string: "Foo"), MyData(int: 4, string: "Bar")]

data.map(^\.int) // [2, 4]
data.map(^\.string) // ["Foo", "Bar"]

Bringing some type-safety to a userInfo Dictionary

Many iOS APIs still rely on a userInfo Dictionary to handle use-case specific data. This Dictionary usually stores untyped values, and is declared as follows: [String: Any] (or sometimes [AnyHashable: Any].

Retrieving data from such a structure will involve some conditional casting (via the as? operator), which is prone to both errors and repetitions. Yet, by introducing a custom subscript, it's possible to encapsulate all the tedious logic, and end-up with an easier and more robust API.

import Foundation

typealias TypedUserInfoKey<T> = (key: String, type: T.Type)

extension Dictionary where Key == String, Value == Any {
    subscript<T>(_ typedKey: TypedUserInfoKey<T>) -> T? {
        return self[typedKey.key] as? T
    }
}

let userInfo: [String : Any] = ["Foo": 4, "Bar": "forty-two"]

let integerTypedKey = TypedUserInfoKey(key: "Foo", type: Int.self)
let intValue = userInfo[integerTypedKey] // returns 4
type(of: intValue) // returns Int?

let stringTypedKey = TypedUserInfoKey(key: "Bar", type: String.self)
let stringValue = userInfo[stringTypedKey] // returns "forty-two"
type(of: stringValue) // returns String?

Lightweight data-binding for an MVVM implementation

MVVM is a great pattern to separate business logic from presentation logic. The main challenge to make it work, is to define a mechanism for the presentation layer to be notified of model updates.

RxSwift is a perfect choice to solve such a problem. Yet, some developers don't feel confortable with leveraging a third-party library for such a central part of their architecture.

For those situation, it's possible to define a lightweight Variable type, that will make the MVVM pattern very easy to use!

import Foundation

class Variable<Value> {
    var value: Value {
        didSet {
            onUpdate?(value)
        }
    }
    
    var onUpdate: ((Value) -> Void)? {
        didSet {
            onUpdate?(value)
        }
    }
    
    init(_ value: Value, _ onUpdate: ((Value) -> Void)? = nil) {
        self.value = value
        self.onUpdate = onUpdate
        self.onUpdate?(value)
    }
}

let variable: Variable<String?> = Variable(nil)

variable.onUpdate = { data in
    if let data = data {
        print(data)
    }
}

variable.value = "Foo"
variable.value = "Bar"

// prints:
// Foo
// Bar

Using typealias to its fullest

The keyword typealias allows developers to give a new name to an already existing type. For instance, Swift defines Void as a typealias of (), the empty tuple.

But a less known feature of this mechanism is that it allows to assign concrete types for generic parameters, or to rename them. This can help make the semantics of generic types much clearer, when used in specific use cases.

import Foundation

enum Either<Left, Right> {
    case left(Left)
    case right(Right)
}

typealias Result<Value> = Either<Value, Error>

typealias IntOrString = Either<Int, String>

Writing an interruptible overload of forEach

Iterating through objects via the forEach(_:) method is a great alternative to the classic for loop, as it allows our code to be completely oblivious of the iteration logic. One limitation, however, is that forEach(_:) does not allow to stop the iteration midway.

Taking inspiration from the Objective-C implementation, we can write an overload that will allow the developer to stop the iteration, if needed.

import Foundation

extension Sequence {
    func forEach(_ body: (Element, _ stop: inout Bool) throws -> Void) rethrows {
        var stop = false
        for element in self {
            try body(element, &stop)
            
            if stop {
                return
            }
        }
    }
}

["Foo", "Bar", "FooBar"].forEach { element, stop in
    print(element)
    stop = (element == "Bar")
}

// Prints:
// Foo
// Bar

Optimizing the use of reduce()

Functional programing is a great way to simplify a codebase. For instance, reduce is an alternative to the classic for loop, without most the boilerplate. Unfortunately, simplicity often comes at the price of performance.

Consider that you want to remove duplicate values from a Sequence. While reduce() is a perfectly fine way to express this computation, the performance will be sub optimal, because of all the unnecessary Array copying that will happen every time its closure gets called.

That's when reduce(into:_:) comes into play. This version of reduce leverages the capacities of copy-on-write type (such as Array or Dictionnary) in order to avoid unnecessary copying, which results in a great performance boost.

import Foundation

func time(averagedExecutions: Int = 1, _ code: () -> Void) {
    let start = Date()
    for _ in 0..<averagedExecutions { code() }
    let end = Date()
    
    let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
    
    print("time: \(duration)")
}

let data = (1...1_000).map { _ in Int(arc4random_uniform(256)) }


// runs in 0.63s
time {
    let noDuplicates: [Int] = data.reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}

// runs in 0.15s
time {
    let noDuplicates: [Int] = data.reduce(into: [], { if !$0.contains($1) { $0.append($1) } } )
}

Avoiding hardcoded reuse identifiers

UI components such as UITableView and UICollectionView rely on reuse identifiers in order to efficiently recycle the views they display. Often, those reuse identifiers take the form of a static hardcoded String, that will be used for every instance of their class.

Through protocol-oriented programing, it's possible to avoid those hardcoded values, and instead use the name of the type as a reuse identifier.

import Foundation
import UIKit

protocol Reusable {
    static var reuseIdentifier: String { get }
}

extension Reusable {
    static var reuseIdentifier: String {
        return String(describing: self)
    }
}

extension UITableViewCell: Reusable { }

extension UITableView {
    func register<T: UITableViewCell>(_ class: T.Type) {
        register(`class`, forCellReuseIdentifier: T.reuseIdentifier)
    }
    func dequeueReusableCell<T: UITableViewCell>(for indexPath: IndexPath) -> T {
        return dequeueReusableCell(withIdentifier: T.reuseIdentifier, for: indexPath) as! T
    }
}

class MyCell: UITableViewCell { }

let tableView = UITableView()

tableView.register(MyCell.self)
let myCell: MyCell = tableView.dequeueReusableCell(for: [0, 0])

Defining a union type

The C language has a construct called union, that allows a single variable to hold values from different types. While Swift does not provide such a construct, it provides enums with associated values, which allows us to define a type called Either that implements a union of two types.

import Foundation

enum Either<A, B> {
    case left(A)
    case right(B)
    
    func either(ifLeft: ((A) -> Void)? = nil, ifRight: ((B) -> Void)? = nil) {
        switch self {
        case let .left(a):
            ifLeft?(a)
        case let .right(b):
            ifRight?(b)
        }
    }
}

extension Bool { static func random() -> Bool { return arc4random_uniform(2) == 0 } }

var intOrString: Either<Int, String> = Bool.random() ? .left(2) : .right("Foo")

intOrString.either(ifLeft: { print($0 + 1) }, ifRight: { print($0 + "Bar") })

If you're interested by this kind of data structure, I strongly recommend that you learn more about Algebraic Data Types.

Asserting that classes have associated NIBs and vice-versa

Most of the time, when we create a .xib file, we give it the same name as its associated class. From that, if we later refactor our code and rename such a class, we run the risk of forgetting to rename the associated .xib.

While the error will often be easy to catch, if the .xib is used in a remote section of its app, it might go unnoticed for sometime. Fortunately it's possible to build custom test predicates that will assert that 1) for a given class, there exists a .nib with the same name in a given Bundle, 2) for all the .nib in a given Bundle, there exists a class with the same name.

import XCTest

public func XCTAssertClassHasNib(_ class: AnyClass, bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
    let associatedNibURL = bundle.url(forResource: String(describing: `class`), withExtension: "nib")
    
    XCTAssertNotNil(associatedNibURL, "Class \"\(`class`)\" has no associated nib file", file: file, line: line)
}

public func XCTAssertNibHaveClasses(_ bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
    guard let bundleName = bundle.infoDictionary?["CFBundleName"] as? String,
        let basePath = bundle.resourcePath,
        let enumerator = FileManager.default.enumerator(at: URL(fileURLWithPath: basePath),
                                                    includingPropertiesForKeys: nil,
                                                    options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants]) else { return }
    
    var nibFilesURLs = [URL]()
    
    for case let fileURL as URL in enumerator {
        if fileURL.pathExtension.uppercased() == "NIB" {
            nibFilesURLs.append(fileURL)
        }
    }
    
    nibFilesURLs.map { $0.lastPathComponent }
        .compactMap { $0.split(separator: ".").first }
        .map { String($0) }
        .forEach {
            let associatedClass: AnyClass? = bundle.classNamed("\(bundleName).\($0)")
            
            XCTAssertNotNil(associatedClass, "File \"\($0).nib\" has no associated class", file: file, line: line)
        }
}

XCTAssertClassHasNib(MyFirstTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertClassHasNib(MySecondTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
        
XCTAssertNibHaveClasses(Bundle(for: AppDelegate.self))

Many thanks Benjamin Lavialle for coming up with the idea behind the second test predicate.

Small footprint type-erasing with functions

Seasoned Swift developers know it: a protocol with associated type (PAT) "can only be used as a generic constraint because it has Self or associated type requirements". When we really need to use a PAT to type a variable, the goto workaround is to use a type-erased wrapper.

While this solution works perfectly, it requires a fair amount of boilerplate code. In instances where we are only interested in exposing one particular function of the PAT, a shorter approach using function types is possible.

import Foundation
import UIKit

protocol Configurable {
    associatedtype Model
    
    func configure(with model: Model)
}

typealias Configurator<Model> = (Model) -> ()

extension UILabel: Configurable {
    func configure(with model: String) {
        self.text = model
    }
}

let label = UILabel()
let configurator: Configurator<String> = label.configure

configurator("Foo")

label.text // "Foo"

Performing animations sequentially

UIKit exposes a very powerful and simple API to perform view animations. However, this API can become a little bit quirky to use when we want to perform animations sequentially, because it involves nesting closure within one another, which produces notoriously hard to maintain code.

Nonetheless, it's possible to define a rather simple class, that will expose a really nicer API for this particular use case 👌

import Foundation
import UIKit

class AnimationSequence {
    typealias Animations = () -> Void
    
    private let current: Animations
    private let duration: TimeInterval
    private var next: AnimationSequence? = nil
    
    init(animations: @escaping Animations, duration: TimeInterval) {
        self.current = animations
        self.duration = duration
    }
    
    @discardableResult func append(animations: @escaping Animations, duration: TimeInterval) -> AnimationSequence {
        var lastAnimation = self
        while let nextAnimation = lastAnimation.next {
            lastAnimation = nextAnimation
        }
        lastAnimation.next = AnimationSequence(animations: animations, duration: duration)
        return self
    }
    
    func run() {
        UIView.animate(withDuration: duration, animations: current, completion: { finished in
            if finished, let next = self.next {
                next.run()
            }
        })
    }
}

var firstView = UIView()
var secondView = UIView()

firstView.alpha = 0
secondView.alpha = 0

AnimationSequence(animations: { firstView.alpha = 1.0 }, duration: 1)
            .append(animations: { secondView.alpha = 1.0 }, duration: 0.5)
            .append(animations: { firstView.alpha = 0.0 }, duration: 2.0)
            .run()

Debouncing a function call

Debouncing is a very useful tool when dealing with UI inputs. Consider a search bar, whose content is used to query an API. It wouldn't make sense to perform a request for every character the user is typing, because as soon as a new character is entered, the result of the previous request has become irrelevant.

Instead, our code will perform much better if we "debounce" the API call, meaning that we will wait until some delay has passed, without the input being modified, before actually performing the call.

import Foundation

func debounced(delay: TimeInterval, queue: DispatchQueue = .main, action: @escaping (() -> Void)) -> () -> Void {
    var workItem: DispatchWorkItem?
    
    return {
        workItem?.cancel()
        workItem = DispatchWorkItem(block: action)
        queue.asyncAfter(deadline: .now() + delay, execute: workItem!)
    }
}

let debouncedPrint = debounced(delay: 1.0) { print("Action performed!") }

debouncedPrint()
debouncedPrint()
debouncedPrint()

// After a 1 second delay, this gets
// printed only once to the console:

// Action performed!

Providing useful operators for Optional booleans

When we need to apply the standard boolean operators to Optional booleans, we often end up with a syntax unnecessarily crowded with unwrapping operations. By taking a cue from the world of three-valued logics, we can define a couple operators that make working with Bool? values much nicer.

import Foundation

func && (lhs: Bool?, rhs: Bool?) -> Bool? {
    switch (lhs, rhs) {
    case (false, _), (_, false):
        return false
    case let (unwrapLhs?, unwrapRhs?):
        return unwrapLhs && unwrapRhs
    default:
        return nil
    }
}

func || (lhs: Bool?, rhs: Bool?) -> Bool? {
    switch (lhs, rhs) {
    case (true, _), (_, true):
        return true
    case let (unwrapLhs?, unwrapRhs?):
        return unwrapLhs || unwrapRhs
    default:
        return nil
    }
}

false && nil // false
true && nil // nil
[true, nil, false].reduce(true, &&) // false

nil || true // true
nil || false // nil
[true, nil, false].reduce(false, ||) // true

Removing duplicate values from a Sequence

Transforming a Sequence in order to remove all the duplicate values it contains is a classic use case. To implement it, one could be tempted to transform the Sequence into a Set, then back to an Array. The downside with this approach is that it will not preserve the order of the sequence, which can definitely be a dealbreaker. Using reduce() it is possible to provide a concise implementation that preserves ordering:

import Foundation

extension Sequence where Element: Equatable {
    func duplicatesRemoved() -> [Element] {
        return reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
    }
}

let data = [2, 5, 2, 3, 6, 5, 2]

data.duplicatesRemoved() // [2, 5, 3, 6]

Shorter syntax to deal with optional strings

Optional strings are very common in Swift code, for instance many objects from UIKit expose the text they display as a String?. Many times you will need to manipulate this data as an unwrapped String, with a default value set to the empty string for nil cases.

While the nil-coalescing operator (e.g. ??) is a perfectly fine way to a achieve this goal, defining a computed variable like orEmpty can help a lot in cleaning the syntax.

import Foundation
import UIKit

extension Optional where Wrapped == String {
    var orEmpty: String {
        switch self {
        case .some(let value):
            return value
        case .none:
            return ""
        }
    }
}

func doesNotWorkWithOptionalString(_ param: String) {
    // do something with `param`
}

let label = UILabel()
label.text = "This is some text."

doesNotWorkWithOptionalString(label.text.orEmpty)

Encapsulating background computation and UI update

Every seasoned iOS developers knows it: objects from UIKit can only be accessed from the main thread. Any attempt to access them from a background thread is a guaranteed crash.

Still, running a costly computation on the background, and then using it to update the UI can be a common pattern.

In such cases you can rely on asyncUI to encapsulate all the boilerplate code.

import Foundation
import UIKit

func asyncUI<T>(_ computation: @autoclosure @escaping () -> T, qos: DispatchQoS.QoSClass = .userInitiated, _ completion: @escaping (T) -> Void) {
    DispatchQueue.global(qos: qos).async {
        let value = computation()
        DispatchQueue.main.async {
            completion(value)
        }
    }
}

let label = UILabel()

func costlyComputation() -> Int { return (0..<10_000).reduce(0, +) }

asyncUI(costlyComputation()) { value in
    label.text = "\(value)"
}

Retrieving all the necessary data to build a debug view

A debug view, from which any controller of an app can be instantiated and pushed on the navigation stack, has the potential to bring some real value to a development process. A requirement to build such a view is to have a list of all the classes from a given Bundle that inherit from UIViewController. With the following extension, retrieving this list becomes a piece of cake 🍰

import Foundation
import UIKit
import ObjectiveC

extension Bundle {
    func viewControllerTypes() -> [UIViewController.Type] {
        guard let bundlePath = self.executablePath else { return [] }
        
        var size: UInt32 = 0
        var rawClassNames: UnsafeMutablePointer<UnsafePointer<Int8>>!
        var parsedClassNames = [String]()
        
        rawClassNames = objc_copyClassNamesForImage(bundlePath, &size)
        
        for index in 0..<size {
            let className = rawClassNames[Int(index)]
            
            if let name = NSString.init(utf8String:className) as String?,
                NSClassFromString(name) is UIViewController.Type {
                parsedClassNames.append(name)
            }
        }
        
        return parsedClassNames
            .sorted()
            .compactMap { NSClassFromString($0) as? UIViewController.Type }
    }
}

// Fetch all view controller types in UIKit
Bundle(for: UIViewController.self).viewControllerTypes()

I share the credit for this tip with Benoît Caron.

Defining a function to map over dictionaries

Update As it turns out, map is actually a really bad name for this function, because it does not preserve composition of transformations, a property that is required to fit the definition of a real map function.

Surprisingly enough, the standard library doesn't define a map() function for dictionaries that allows to map both keys and values into a new Dictionary. Nevertheless, such a function can be helpful, for instance when converting data across different frameworks.

import Foundation

extension Dictionary {
    func map<T: Hashable, U>(_ transform: (Key, Value) throws -> (T, U)) rethrows -> [T: U] {
        var result: [T: U] = [:]
        
        for (key, value) in self {
            let (transformedKey, transformedValue) = try transform(key, value)
            result[transformedKey] = transformedValue
        }
        
        return result
    }
}

let data = [0: 5, 1: 6, 2: 7]
data.map { ("\($0)", $1 * $1) } // ["2": 49, "0": 25, "1": 36]

A shorter syntax to remove nil values

Swift provides the function compactMap(), that can be used to remove nil values from a Sequence of optionals when calling it with an argument that just returns its parameter (i.e. compactMap { $0 }). Still, for such use cases it would be nice to get rid of the trailing closure.

The implementation isn't as straightforward as your usual extension, but once it has been written, the call site definitely gets cleaner 👌

import Foundation

protocol OptionalConvertible {
    associatedtype Wrapped
    func asOptional() -> Wrapped?
}

extension Optional: OptionalConvertible {
    func asOptional() -> Wrapped? {
        return self
    }
}

extension Sequence where Element: OptionalConvertible {
    func compacted() -> [Element.Wrapped] {
        return compactMap { $0.asOptional() }
    }
}

let data = [nil, 1, 2, nil, 3, 5, nil, 8, nil]
data.compacted() // [1, 2, 3, 5, 8]

Dealing with expirable values

It might happen that your code has to deal with values that come with an expiration date. In a game, it could be a score multiplier that will only last for 30 seconds. Or it could be an authentication token for an API, with a 15 minutes lifespan. In both instances you can rely on the type Expirable to encapsulate the expiration logic.

import Foundation

struct Expirable<T> {
    private var innerValue: T
    private(set) var expirationDate: Date
    
    var value: T? {
        return hasExpired() ? nil : innerValue
    }
    
    init(value: T, expirationDate: Date) {
        self.innerValue = value
        self.expirationDate = expirationDate
    }
    
    init(value: T, duration: Double) {
        self.innerValue = value
        self.expirationDate = Date().addingTimeInterval(duration)
    }
    
    func hasExpired() -> Bool {
        return expirationDate < Date()
    }
}

let expirable = Expirable(value: 42, duration: 3)

sleep(2)
expirable.value // 42
sleep(2)
expirable.value // nil

I share the credit for this tip with Benoît Caron.

Using parallelism to speed-up map()

Almost all Apple devices able to run Swift code are powered by a multi-core CPU, consequently making a good use of parallelism is a great way to improve code performance. map() is a perfect candidate for such an optimization, because it is almost trivial to define a parallel implementation.

import Foundation

extension Array {
    func parallelMap<T>(_ transform: (Element) -> T) -> [T] {
        let res = UnsafeMutablePointer<T>.allocate(capacity: count)
        
        DispatchQueue.concurrentPerform(iterations: count) { i in
            res[i] = transform(self[i])
        }
        
        let finalResult = Array<T>(UnsafeBufferPointer(start: res, count: count))
        res.deallocate(capacity: count)
        
        return finalResult
    }
}

let array = (0..<1_000).map { $0 }

func work(_ n: Int) -> Int {
    return (0..<n).reduce(0, +)
}

array.parallelMap { work($0) }

🚨 Make sure to only use parallelMap() when the transform function actually performs some costly computations. Otherwise performances will be systematically slower than using map(), because of the multithreading overhead.

Measuring execution time with minimum boilerplate

During development of a feature that performs some heavy computations, it can be helpful to measure just how much time a chunk of code takes to run. The time() function is a nice tool for this purpose, because of how simple it is to add and then to remove when it is no longer needed.

import Foundation

func time(averagedExecutions: Int = 1, _ code: () -> Void) {
    let start = Date()
    for _ in 0..<averagedExecutions { code() }
    let end = Date()
    
    let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
    
    print("time: \(duration)")
}

time {
    (0...10_000).map { $0 * $0 }
}
// time: 0.183973908424377

Running two pieces of code in parallel

Concurrency is definitely one of those topics were the right encapsulation bears the potential to make your life so much easier. For instance, with this piece of code you can easily launch two computations in parallel, and have the results returned in a tuple.

import Foundation

func parallel<T, U>(_ left: @autoclosure () -> T, _ right: @autoclosure () -> U) -> (T, U) {
    var leftRes: T?
    var rightRes: U?
    
    DispatchQueue.concurrentPerform(iterations: 2, execute: { id in
        if id == 0 {
            leftRes = left()
        } else {
            rightRes = right()
        }
    })
    
    return (leftRes!, rightRes!)
}

let values = (1...100_000).map { $0 }

let results = parallel(values.map { $0 * $0 }, values.reduce(0, +))

Making good use of #file, #line and #function

Swift exposes three special variables #file, #line and #function, that are respectively set to the name of the current file, line and function. Those variables become very useful when writing custom logging functions or test predicates.

import Foundation

func log(_ message: String, _ file: String = #file, _ line: Int = #line, _ function: String = #function) {
    print("[\(file):\(line)] \(function) - \(message)")
}

func foo() {
    log("Hello world!")
}

foo() // [MyPlayground.playground:8] foo() - Hello world!

Comparing Optionals through Conditional Conformance

Swift 4.1 has introduced a new feature called Conditional Conformance, which allows a type to implement a protocol only when its generic type also does.

With this addition it becomes easy to let Optional implement Comparable only when Wrapped also implements Comparable:

import Foundation

extension Optional: Comparable where Wrapped: Comparable {
    public static func < (lhs: Optional, rhs: Optional) -> Bool {
        switch (lhs, rhs) {
        case let (lhs?, rhs?):
            return lhs < rhs
        case (nil, _?):
            return true // anything is greater than nil
        case (_?, nil):
            return false // nil in smaller than anything
        case (nil, nil):
            return true // nil is not smaller than itself
        }
    }
}

let data: [Int?] = [8, 4, 3, nil, 12, 4, 2, nil, -5]
data.sorted() // [nil, nil, Optional(-5), Optional(2), Optional(3), Optional(4), Optional(4), Optional(8), Optional(12)]

Safely subscripting a Collection

Any attempt to access an Array beyond its bounds will result in a crash. While it's possible to write conditions such as if index < array.count { array[index] } in order to prevent such crashes, this approach will rapidly become cumbersome.

A great thing is that this condition can be encapsulated in a custom subscript that will work on any Collection:

import Foundation

extension Collection {
    subscript (safe index: Index) -> Element? {
        return indices.contains(index) ? self[index] : nil
    }
}

let data = [1, 3, 4]

data[safe: 1] // Optional(3)
data[safe: 10] // nil

Easier String slicing using ranges

Subscripting a string with a range can be very cumbersome in Swift 4. Let's face it, no one wants to write lines like someString[index(startIndex, offsetBy: 0)..<index(startIndex, offsetBy: 10)] on a regular basis.

Luckily, with the addition of one clever extension, strings can be sliced as easily as arrays 🎉

import Foundation

extension String {
    public subscript(value: CountableClosedRange<Int>) -> Substring {
        get {
            return self[index(startIndex, offsetBy: value.lowerBound)...index(startIndex, offsetBy: value.upperBound)]
        }
    }
    
    public subscript(value: CountableRange<Int>) -> Substring {
        get {
            return self[index(startIndex, offsetBy: value.lowerBound)..<index(startIndex, offsetBy: value.upperBound)]
        }
    }
    
    public subscript(value: PartialRangeUpTo<Int>) -> Substring {
        get {
            return self[..<index(startIndex, offsetBy: value.upperBound)]
        }
    }
    
    public subscript(value: PartialRangeThrough<Int>) -> Substring {
        get {
            return self[...index(startIndex, offsetBy: value.upperBound)]
        }
    }
    
    public subscript(value: PartialRangeFrom<Int>) -> Substring {
        get {
            return self[index(startIndex, offsetBy: value.lowerBound)...]
        }
    }
}

let data = "This is a string!"

data[..<4]  // "This"
data[5..<9] // "is a"
data[10...] // "string!"

Concise syntax for sorting using a KeyPath

By using a KeyPath along with a generic type, a very clean and concise syntax for sorting data can be implemented:

import Foundation

extension Sequence {
    func sorted<T: Comparable>(by attribute: KeyPath<Element, T>) -> [Element] {
        return sorted(by: { $0[keyPath: attribute] < $1[keyPath: attribute] })
    }
}

let data = ["Some", "words", "of", "different", "lengths"]

data.sorted(by: \.count) // ["of", "Some", "words", "lengths", "different"]

If you like this syntax, make sure to checkout KeyPathKit!

Manufacturing cache-efficient versions of pure functions

By capturing a local variable in a returned closure, it is possible to manufacture cache-efficient versions of pure functions. Be careful though, this trick only works with non-recursive function!

import Foundation

func cached<In: Hashable, Out>(_ f: @escaping (In) -> Out) -> (In) -> Out {
    var cache = [In: Out]()
    
    return { (input: In) -> Out in
        if let cachedValue = cache[input] {
            return cachedValue
        } else {
            let result = f(input)
            cache[input] = result
            return result
        }
    }
}

let cachedCos = cached { (x: Double) in cos(x) }

cachedCos(.pi * 2) // value of cos for 2π is now cached

Simplifying complex conditions with pattern matching

When distinguishing between complex boolean conditions, using a switch statement along with pattern matching can be more readable than the classic series of if {} else if {}.

import Foundation

let expr1: Bool
let expr2: Bool
let expr3: Bool

if expr1 && !expr3 {
    functionA()
} else if !expr2 && expr3 {
    functionB()
} else if expr1 && !expr2 && expr3 {
    functionC()
}

switch (expr1, expr2, expr3) {
    
case (true, _, false):
    functionA()
case (_, false, true):
    functionB()
case (true, false, true):
    functionC()
default:
    break
}

Easily generating arrays of data

Using map() on a range makes it easy to generate an array of data.

import Foundation

func randomInt() -> Int { return Int(arc4random()) }

let randomArray = (1...10).map { _ in randomInt() }

Using @autoclosure for cleaner call sites

Using @autoclosure enables the compiler to automatically wrap an argument within a closure, thus allowing for a very clean syntax at call sites.

import UIKit

extension UIView {
    class func animate(withDuration duration: TimeInterval, _ animations: @escaping @autoclosure () -> Void) {
        UIView.animate(withDuration: duration, animations: animations)
    }
}

let view = UIView()

UIView.animate(withDuration: 0.3, view.backgroundColor = .orange)

Observing new and old value with RxSwift

When working with RxSwift, it's very easy to observe both the current and previous value of an observable sequence by simply introducing a shift using skip().

import RxSwift

let values = Observable.of(4, 8, 15, 16, 23, 42)

let newAndOld = Observable.zip(values, values.skip(1)) { (previous: $0, current: $1) }
    .subscribe(onNext: { pair in
        print("current: \(pair.current) - previous: \(pair.previous)")
    })

//current: 8 - previous: 4
//current: 15 - previous: 8
//current: 16 - previous: 15
//current: 23 - previous: 16
//current: 42 - previous: 23

Implicit initialization from literal values

Using protocols such as ExpressibleByStringLiteral it is possible to provide an init that will be automatically when a literal value is provided, allowing for nice and short syntax. This can be very helpful when writing mock or test data.

import Foundation

extension URL: ExpressibleByStringLiteral {
    public init(stringLiteral value: String) {
        self.init(string: value)!
    }
}

let url: URL = "http://www.google.fr"

NSURLConnection.canHandle(URLRequest(url: "http://www.google.fr"))

Achieving systematic validation of data

Through some clever use of Swift private visibility it is possible to define a container that holds any untrusted value (such as a user input) from which the only way to retrieve the value is by making it successfully pass a validation test.

import Foundation

struct Untrusted<T> {
    private(set) var value: T
}

protocol Validator {
    associatedtype T
    static func validation(value: T) -> Bool
}

extension Validator {
    static func validate(untrusted: Untrusted<T>) -> T? {
        if self.validation(value: untrusted.value) {
            return untrusted.value
        } else {
            return nil
        }
    }
}

struct FrenchPhoneNumberValidator: Validator {
    static func validation(value: String) -> Bool {
       return (value.count) == 10 && CharacterSet(charactersIn: value).isSubset(of: CharacterSet.decimalDigits)
    }
}

let validInput = Untrusted(value: "0122334455")
let invalidInput = Untrusted(value: "0123")

FrenchPhoneNumberValidator.validate(untrusted: validInput) // returns "0122334455"
FrenchPhoneNumberValidator.validate(untrusted: invalidInput) // returns nil

Implementing the builder pattern with keypaths

With the addition of keypaths in Swift 4, it is now possible to easily implement the builder pattern, that allows the developer to clearly separate the code that initializes a value from the code that uses it, without the burden of defining a factory method.

import UIKit

protocol With {}

extension With where Self: AnyObject {
    @discardableResult
    func with<T>(_ property: ReferenceWritableKeyPath<Self, T>, setTo value: T) -> Self {
        self[keyPath: property] = value
        return self
    }
}

extension UIView: With {}

let view = UIView()

let label = UILabel()
    .with(\.textColor, setTo: .red)
    .with(\.text, setTo: "Foo")
    .with(\.textAlignment, setTo: .right)
    .with(\.layer.cornerRadius, setTo: 5)

view.addSubview(label)

🚨 The Swift compiler does not perform OS availability checks on properties referenced by keypaths. Any attempt to use a KeyPath for an unavailable property will result in a runtime crash.

I share the credit for this tip with Marion Curtil.

Storing functions rather than values

When a type stores values for the sole purpose of parametrizing its functions, it’s then possible to not store the values but directly the function, with no discernable difference at the call site.

import Foundation

struct MaxValidator {
    let max: Int
    let strictComparison: Bool
    
    func isValid(_ value: Int) -> Bool {
        return self.strictComparison ? value < self.max : value <= self.max
    }
}

struct MaxValidator2 {
    var isValid: (_ value: Int) -> Bool
    
    init(max: Int, strictComparison: Bool) {
        self.isValid = strictComparison ? { $0 < max } : { $0 <= max }
    }
}

MaxValidator(max: 5, strictComparison: true).isValid(5) // false
MaxValidator2(max: 5, strictComparison: false).isValid(5) // true

Defining operators on function types

Functions are first-class citizen types in Swift, so it is perfectly legal to define operators for them.

import Foundation

let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }

func ||(_ lhs: @escaping (Int) -> Bool, _ rhs: @escaping (Int) -> Bool) -> (Int) -> Bool {
    return { value in
        return lhs(value) || rhs(value)
    }
}

(firstRange || secondRange)(2) // true
(firstRange || secondRange)(4) // false
(firstRange || secondRange)(6) // true

Typealiases for functions

Typealiases are great to express function signatures in a more comprehensive manner, which then enables us to easily define functions that operate on them, resulting in a nice way to write and use some powerful API.

import Foundation

typealias RangeSet = (Int) -> Bool

func union(_ left: @escaping RangeSet, _ right: @escaping RangeSet) -> RangeSet {
    return { left($0) || right($0) }
}

let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }

let unionRange = union(firstRange, secondRange)

unionRange(2) // true
unionRange(4) // false

Encapsulating state within a function

By returning a closure that captures a local variable, it's possible to encapsulate a mutable state within a function.

import Foundation

func counterFactory() -> () -> Int {
    var counter = 0
    
    return {
        counter += 1
        return counter
    }
}

let counter = counterFactory()

counter() // returns 1
counter() // returns 2

Generating all cases for an Enum

⚠️ Since Swift 4.2, allCases can now be synthesized at compile-time by simply conforming to the protocol CaseIterable. The implementation below should no longer be used in production code.

Through some clever leveraging of how enums are stored in memory, it is possible to generate an array that contains all the possible cases of an enum. This can prove particularly useful when writing unit tests that consume random data.

import Foundation

enum MyEnum { case first; case second; case third; case fourth }

protocol EnumCollection: Hashable {
    static var allCases: [Self] { get }
}

extension EnumCollection {
    public static var allCases: [Self] {
        var i = 0
        return Array(AnyIterator {
            let next = withUnsafePointer(to: &i) {
                $0.withMemoryRebound(to: Self.self, capacity: 1) { $0.pointee }
            }
            if next.hashValue != i { return nil }
            i += 1
            return next
        })
    }
}

extension MyEnum: EnumCollection { }

MyEnum.allCases // [.first, .second, .third, .fourth]

Using map on optional values

The if-let syntax is a great way to deal with optional values in a safe manner, but at times it can prove to be just a little bit to cumbersome. In such cases, using the Optional.map() function is a nice way to achieve a shorter code while retaining safeness and readability.

import UIKit

let date: Date? = Date() // or could be nil, doesn't matter
let formatter = DateFormatter()
let label = UILabel()

if let safeDate = date {
    label.text = formatter.string(from: safeDate)
}

label.text = date.map { return formatter.string(from: $0) }

label.text = date.map(formatter.string(from:)) // even shorter, tough less readable

📣 NEW 📣 Swift Tips are now available on YouTube 👇

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Author: vincent-pradeilles
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License: MIT license
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