manticorecao/CosId

Universal, flexible, high-performance distributed ID generator. | 通用、灵活、高性能的分布式 ID 生成器

CosId Universal, flexible, high-performance distributed ID generator

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Introduction

CosId aims to provide a universal, flexible and high-performance distributed ID generator. Two types of ID generators are currently provided:

• SnowflakeId : Stand-alone TPS performance：4,096,000 JMH Benchmark , It mainly solves two major problems of SnowflakeId: machine number allocation problem and clock backwards problem and provide a more friendly and flexible experience.
• SegmentId: Get a segment (Step) ID every time to reduce the network IO request frequency of the IdSegment distributor and improve performance.
  • IdSegmentDistributor:
    • RedisIdSegmentDistributor: IdSegment distributor based on Redis.
    • JdbcIdSegmentDistributor: The Jdbc-based IdSegment distributor supports various relational databases.
  • SegmentChainId(recommend):SegmentChainId (lock-free) is an enhancement of SegmentId, the design diagram is as follows. PrefetchWorker maintains a safe distance, so that SegmentChainId achieves approximately AtomicLong TPS performance (Step 1000): 127,439,148+/s JMH Benchmark .
    • PrefetchWorker maintains a safe distance (safeDistance), and supports dynamic safeDistance expansion and contraction based on hunger status.

SnowflakeId

SnowflakeId is a distributed ID algorithm that uses Long (64-bit) bit partition to generate ID. The general bit allocation scheme is : timestamp (41-bit) + machineId (10-bit) + sequence (12-bit) = 63-bit。

• 41-bit timestamp = (1L<<41)/(1000/3600/365) approximately 69 years of timestamp can be stored, that is, the usable absolute time is EPOCH + 69 years. Generally, we need to customize EPOCH as the product development time. In addition, we can increase the number of allocated bits by compressing other areas， The number of timestamp bits to extend the available time.
• 10-bit machineId = (1L<<10) = 1024 That is, 1024 copies of the same business can be deployed (there is no master-slave copy in the Kubernetes concept, and the definition of Kubernetes is directly used here) instances. Generally, there is no need to use so many, so it will be redefined according to the scale of deployment.
• 12-bit sequence = (1L<<12) * 1000 = 4096000 That is, a single machine can generate about 409W ID per second, and a global same-service cluster can generate 4096000*1024=4194304000=4.19 billion (TPS).

It can be seen from the design of SnowflakeId:

• 👍 The first 41-bit are a timestamp,So SnowflakeId is local monotonically increasing, and affected by global clock synchronization SnowflakeId is global trend increasing.
• 👍 SnowflakeId does not have a strong dependency on any third-party middleware, and its performance is also very high.
• 👍 The bit allocation scheme can be flexibly configured according to the needs of the business system to achieve the optimal use effect.
• 👎 Strong reliance on the local clock, potential clock moved backwards problems will cause ID duplication.
• 👎 The machineId needs to be set manually. If the machineId is manually assigned during actual deployment, it will be very inefficient.

---

CosId-SnowflakeId

It mainly solves two major problems of SnowflakeId: machine number allocation problem and clock backwards problem and provide a more friendly and flexible experience.

MachineIdDistributor

Currently CosId provides the following three MachineId distributors.

ManualMachineIdDistributor

cosid:
  snowflake:
    machine:
      distributor:
        type: manual
        manual:
          machine-id: 0

Manually distribute MachineId

StatefulSetMachineIdDistributor

cosid:
  snowflake:
    machine:
      distributor:
        type: stateful_set

Use the stable identification ID provided by the StatefulSet of Kubernetes as the machine number.

RedisMachineIdDistributor

cosid:
  snowflake:
    machine:
      distributor:
        type: redis

Use Redis as the distribution store for the machine number.

ClockBackwardsSynchronizer

cosid:
  snowflake:
    clock-backwards:
      spin-threshold: 10
      broken-threshold: 2000

The default DefaultClockBackwardsSynchronizer clock moved backwards synchronizer uses active wait synchronization strategy, spinThreshold (default value 10 milliseconds) is used to set the spin wait threshold, when it is greater than spinThreshold, use thread sleep to wait for clock synchronization, if it exceeds BrokenThreshold (default value 2 seconds) will directly throw a ClockTooManyBackwardsException exception.

MachineStateStorage

public class MachineState {
  public static final MachineState NOT_FOUND = of(-1, -1);
  private final int machineId;
  private final long lastTimeStamp;

  public MachineState(int machineId, long lastTimeStamp) {
    this.machineId = machineId;
    this.lastTimeStamp = lastTimeStamp;
  }

  public int getMachineId() {
    return machineId;
  }

  public long getLastTimeStamp() {
    return lastTimeStamp;
  }

  public static MachineState of(int machineId, long lastStamp) {
    return new MachineState(machineId, lastStamp);
  }
}

cosid:
  snowflake:
    machine:
      state-storage:
        local:
          state-location: ./cosid-machine-state/

The default LocalMachineStateStorage local machine state storage uses a local file to store the machine number and the most recent timestamp, which is used as a MachineState cache.

ClockSyncSnowflakeId

cosid:
  snowflake:
    share:
      clock-sync: true

The default SnowflakeId will directly throw a ClockBackwardsException when a clock moved backwards occurs, while using the ClockSyncSnowflakeId will use the ClockBackwardsSynchronizer to actively wait for clock synchronization to regenerate the ID, providing a more user-friendly experience.

SafeJavaScriptSnowflakeId

SnowflakeId snowflakeId = SafeJavaScriptSnowflakeId.ofMillisecond(1);

The Number.MAX_SAFE_INTEGER of JavaScript has only 53-bit. If the 63-bit SnowflakeId is directly returned to the front end, the value will overflow. Usually we can convert SnowflakeId to String type or customize SnowflakeId Bit allocation is used to shorten the number of bits of SnowflakeId so that ID does not overflow when it is provided to the front end.

SnowflakeFriendlyId (Can parse SnowflakeId into a more readable SnowflakeIdState)

cosid:
  snowflake:
    share:
      friendly: true

public class SnowflakeIdState {

    private final long id;

    private final int machineId;

    private final long sequence;

    private final LocalDateTime timestamp;
    /**
     * {@link #timestamp}-{@link #machineId}-{@link #sequence}
     */
    private final String friendlyId;
}

public interface SnowflakeFriendlyId extends SnowflakeId {

    SnowflakeIdState friendlyId(long id);

    SnowflakeIdState ofFriendlyId(String friendlyId);

    default SnowflakeIdState friendlyId() {
        long id = generate();
        return friendlyId(id);
    }
}

        SnowflakeFriendlyId snowflakeFriendlyId=new DefaultSnowflakeFriendlyId(snowflakeId);
        SnowflakeIdState idState = snowflakeFriendlyId.friendlyId();
        idState.getFriendlyId(); //20210623131730192-1-0

SegmentId

RedisIdSegmentDistributor

cosid:
  segment:
    enabled: true
    distributor:
      type: redis

JdbcIdSegmentDistributor

Initialize the cosid table

create table if not exists cosid
(
    name            varchar(100) not null comment '{namespace}.{name}',
    last_max_id     bigint       not null default 0,
    last_fetch_time bigint       not null,
    constraint cosid_pk
        primary key (name)
) engine = InnoDB;

spring:
  datasource:
    url: jdbc:mysql://localhost:3306/test_db
    username: root
    password: root
cosid:
  segment:
    enabled: true
    distributor:
      type: jdbc
      jdbc:
        enable-auto-init-cosid-table: false
        enable-auto-init-id-segment: true

After enabling enable-auto-init-id-segment:true, the application will try to create the idSegment record when it starts to avoid manual creation. Similar to the execution of the following initialization sql script, there is no need to worry about misoperation, because name is the primary key.

insert into cosid
    (name, last_max_id, last_fetch_time)
    value
    ('namespace.name', 0, unix_timestamp());

SegmentChainId

cosid:
  segment:
    enabled: true
    mode: chain
    chain:
      safe-distance: 5
      prefetch-worker:
        core-pool-size: 2
        prefetch-period: 1s
    distributor:
      type: redis
    share:
      offset: 0
      step: 100
    provider:
      bizC:
        offset: 10000
        step: 100
      bizD:
        offset: 10000
        step: 100

IdGeneratorProvider

cosid:
  snowflake:
    provider:
      bizA:
        #      timestamp-bit:
        sequence-bit: 12
      bizB:
        #      timestamp-bit:
        sequence-bit: 12

IdGenerator idGenerator = idGeneratorProvider.get("bizA");

In actual use, we generally do not use the same IdGenerator for all business services, but different businesses use different IdGenerator, then IdGeneratorProvider exists to solve this problem, and it is the container of IdGenerator , You can get the corresponding IdGenerator by the business name.

CosIdPlugin (MyBatis Plugin)

Kotlin DSL

    implementation("me.ahoo.cosid:cosid-mybatis:${cosidVersion}")

@Target({ElementType.FIELD})
@Documented
@Retention(RetentionPolicy.RUNTIME)
public @interface CosId {
    String value() default IdGeneratorProvider.SHARE;
    
    boolean friendlyId() default false;
}

public class LongIdEntity {

    @CosId(value = "safeJs")
    private Long id;

    public Long getId() {
        return id;
    }

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

public class FriendlyIdEntity {

    @CosId(friendlyId = true)
    private String id;

    public String getId() {
        return id;
    }

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

@Mapper
public interface OrderRepository {
    @Insert("insert into t_table (id) value (#{id});")
    void insert(LongIdEntity order);

    @Insert({
            "<script>",
            "insert into t_friendly_table (id)",
            "VALUES" +
                    "<foreach item='item' collection='list' open='' separator=',' close=''>" +
                    "(#{item.id})" +
                    "</foreach>",
            "</script>"})
    void insertList(List<FriendlyIdEntity> list);
}

        LongIdEntity entity = new LongIdEntity();
        entityRepository.insert(entity);
        /**
         * {
         *   "id": 208796080181248
         * }
         */
        return entity;

ShardingSphere Plugin

Kotlin DSL

    implementation("me.ahoo.cosid:cosid-shardingsphere:${cosidVersion}")

CosIdKeyGenerateAlgorithm (Distributed-Id)

spring:
  shardingsphere:
    rules:
      sharding:
        key-generators:
          cosid:
            type: COSID
            props:
              id-name: __share__

Interval-based time range sharding algorithm

• Ease of use: supports multiple data types (Long/LocalDateTime/DATE/ String / SnowflakeId),The official implementation is to first convert to a string and then convert to LocalDateTime, the conversion success rate is affected by the time formatting characters.
• Performance: Compared to org.apache.shardingsphere.sharding.algorithm.sharding.datetime.IntervalShardingAlgorithm,The performance is 1200~4000 times higher.

PreciseShardingValue	RangeShardingValue

• CosIdIntervalShardingAlgorithm
  • type: COSID_INTERVAL
• SnowflakeIntervalShardingAlgorithm
  • type: COSID_INTERVAL_SNOWFLAKE

spring:
  shardingsphere:
    rules:
      sharding:
        sharding-algorithms:
          alg-name:
            type: COSID_INTERVAL_{type_suffix}
            props:
              logic-name-prefix: logic-name-prefix
              id-name: cosid-name
              datetime-lower: 2021-12-08 22:00:00
              datetime-upper: 2022-12-01 00:00:00
              sharding-suffix-pattern: yyyyMM
              datetime-interval-unit: MONTHS
              datetime-interval-amount: 1

CosIdModShardingAlgorithm

• Performance: Compared to org.apache.shardingsphere.sharding.algorithm.sharding.datetime.IntervalShardingAlgorithm,The performance is 1200~4000 times higher.And it has higher stability and no serious performance degradation.

PreciseShardingValue	RangeShardingValue

spring:
  shardingsphere:
    rules:
      sharding:
        sharding-algorithms:
          alg-name:
            type: COSID_MOD
            props:
              mod: 4
              logic-name-prefix: t_table_

Examples

CosId-Examples

http://localhost:8008/swagger-ui/index.html#/

Installation

Gradle

Kotlin DSL

    val cosidVersion = "1.7.5";
    implementation("me.ahoo.cosid:cosid-spring-boot-starter:${cosidVersion}")

Maven

<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

    <modelVersion>4.0.0</modelVersion>
    <artifactId>demo</artifactId>
    <properties>
        <cosid.version>1.7.5</cosid.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>me.ahoo.cosid</groupId>
            <artifactId>cosid-spring-boot-starter</artifactId>
            <version>${cosid.version}</version>
        </dependency>
    </dependencies>

</project>

application.yaml

spring:
  shardingsphere:
    datasource:
      names: ds0,ds1
      ds0:
        type: com.zaxxer.hikari.HikariDataSource
        driver-class-name: com.mysql.cj.jdbc.Driver
        jdbcUrl: jdbc:mysql://localhost:3306/cosid_db_0
        username: root
        password: root
      ds1:
        type: com.zaxxer.hikari.HikariDataSource
        driver-class-name: com.mysql.cj.jdbc.Driver
        jdbcUrl: jdbc:mysql://localhost:3306/cosid_db_1
        username: root
        password: root
    props:
      sql-show: true
    rules:
      sharding:
        binding-tables:
          - t_order,t_order_item
        tables:
          cosid:
            actual-data-nodes: ds0.cosid
          t_table:
            actual-data-nodes: ds0.t_table_$->{0..1}
            table-strategy:
              standard:
                sharding-column: id
                sharding-algorithm-name: table-inline
          t_friendly_table:
            actual-data-nodes: ds0.t_friendly_table
          t_order:
            actual-data-nodes: ds$->{0..1}.t_order
            database-strategy:
              standard:
                sharding-column: order_id
                sharding-algorithm-name: order-db-inline
            key-generate-strategy:
              column: order_id
              key-generator-name: order
          t_order_item:
            actual-data-nodes: ds$->{0..1}.t_order_item
            database-strategy:
              standard:
                sharding-column: order_id
                sharding-algorithm-name: order-db-inline
          t_date_log:
            actual-data-nodes: ds0.t_date_log_202112
            key-generate-strategy:
              column: id
              key-generator-name: snowflake
            table-strategy:
              standard:
                sharding-column: create_time
                sharding-algorithm-name: data-log-interval
          t_date_time_log:
            actual-data-nodes: ds0.t_date_time_log_202112
            key-generate-strategy:
              column: id
              key-generator-name: snowflake
            table-strategy:
              standard:
                sharding-column: create_time
                sharding-algorithm-name: data-time-log-interval
          t_timestamp_log:
            actual-data-nodes: ds0.t_timestamp_log_202112
            key-generate-strategy:
              column: id
              key-generator-name: snowflake
            table-strategy:
              standard:
                sharding-column: create_time
                sharding-algorithm-name: timestamp-log-interval
          t_snowflake_log:
            actual-data-nodes: ds0.t_snowflake_log_202112
            table-strategy:
              standard:
                sharding-column: id
                sharding-algorithm-name: snowflake-log-interval
        sharding-algorithms:
          table-inline:
            type: COSID_MOD
            props:
              mod: 2
              logic-name-prefix: t_table_
          order-db-inline:
            type: COSID_MOD
            props:
              mod: 2
              logic-name-prefix: ds
          data-log-interval:
            type: COSID_INTERVAL_DATE
            props:
              logic-name-prefix: t_date_log_
              datetime-lower: 2021-12-08 22:00:00
              datetime-upper: 2022-12-01 00:00:00
              sharding-suffix-pattern: yyyyMM
              datetime-interval-unit: MONTHS
              datetime-interval-amount: 1
          data-time-log-interval:
            type: COSID_INTERVAL_LDT
            props:
              logic-name-prefix: t_date_time_log_
              datetime-lower: 2021-12-08 22:00:00
              datetime-upper: 2022-12-01 00:00:00
              sharding-suffix-pattern: yyyyMM
              datetime-interval-unit: MONTHS
              datetime-interval-amount: 1
          timestamp-log-interval:
            type: COSID_INTERVAL_TS
            props:
              logic-name-prefix: t_timestamp_log_
              datetime-lower: 2021-12-08 22:00:00
              datetime-upper: 2022-12-01 00:00:00
              sharding-suffix-pattern: yyyyMM
              datetime-interval-unit: MONTHS
              datetime-interval-amount: 1
          snowflake-log-interval:
            type: COSID_INTERVAL_SNOWFLAKE
            props:
              logic-name-prefix: t_snowflake_log_
              id-name: snowflake
              datetime-lower: 2021-12-08 22:00:00
              datetime-upper: 2022-12-01 00:00:00
              sharding-suffix-pattern: yyyyMM
              datetime-interval-unit: MONTHS
              datetime-interval-amount: 1
        key-generators:
          snowflake:
            type: COSID
            props:
              id-name: snowflake
          order:
            type: COSID
            props:
              id-name: order

cosid:
  namespace: ${spring.application.name}
  snowflake:
    enabled: true
    #    epoch: 1577203200000
    clock-backwards:
      spin-threshold: 10
      broken-threshold: 2000
    machine:
      #      stable: true
      #      machine-bit: 10
      #      instance-id: ${HOSTNAME}
      distributor:
        type: redis
      #        manual:
      #          machine-id: 0
      state-storage:
        local:
          state-location: ./cosid-machine-state/
    share:
      clock-sync: true
      friendly: true
    provider:
      order_item:
        #        timestamp-bit:
        sequence-bit: 12
      snowflake:
        sequence-bit: 12
      safeJs:
        machine-bit: 3
        sequence-bit: 9
  segment:
    enabled: true
    mode: chain
    chain:
      safe-distance: 5
      prefetch-worker:
        core-pool-size: 2
        prefetch-period: 1s
    distributor:
      type: redis
    share:
      offset: 0
      step: 100
    provider:
      order:
        offset: 10000
        step: 100
      longId:
        offset: 10000
        step: 100

JMH-Benchmark

• The development notebook : MacBook Pro (M1)
• All benchmark tests are carried out on the development notebook.
• Deploying Redis on the development notebook.

SnowflakeId

gradle cosid-core:jmh
# or
java -jar cosid-core/build/libs/cosid-core-1.7.5-jmh.jar -bm thrpt -wi 1 -rf json -f 1

Benchmark                                                    Mode  Cnt        Score   Error  Units
SnowflakeIdBenchmark.millisecondSnowflakeId_friendlyId      thrpt       4020311.665          ops/s
SnowflakeIdBenchmark.millisecondSnowflakeId_generate        thrpt       4095403.859          ops/s
SnowflakeIdBenchmark.safeJsMillisecondSnowflakeId_generate  thrpt        511654.048          ops/s
SnowflakeIdBenchmark.safeJsSecondSnowflakeId_generate       thrpt        539818.563          ops/s
SnowflakeIdBenchmark.secondSnowflakeId_generate             thrpt       4206843.941          ops/s

Throughput (ops/s) of SegmentChainId

Percentile-Sample (P9999=0.208 us/op) of SegmentChainId

In statistics, a percentile (or a centile) is a score below which a given percentage of scores in its frequency distribution falls (exclusive definition) or a score at or below which a given percentage falls (inclusive definition). For example, the 50th percentile (the median) is the score below which (exclusive) or at or below which (inclusive) 50% of the scores in the distribution may be found.