Retro Eye care Haitian Deep Dark Default

Sharding Algorithm

Background

ShardingSphere built-in algorithms provide a variety of sharding algorithms, which can be divided into automatic sharding algorithms, standard sharding algorithms, composite sharding algorithms, and hint sharding algorithms, and can meet the needs of most business scenarios of users.

Additionally, considering the complexity of business scenarios, the built-in algorithm also provides a way to customize the sharding algorithm. Users can complete complex sharding logic by writing java code.

It should be noted that the sharding logic of the automatic sharding algorithm is automatically managed by ShardingSphere and needs to be used by configuring the autoTables sharding rules.

Parameters

Auto Sharding Algorithm

Modulo Sharding Algorithm

Type: MOD

Attributes:

Name DataType Description
sharding-count int Sharding count

Hash Modulo Sharding Algorithm

Type: HASH_MOD

Attributes:

Name DataType Description
sharding-count int Sharding count

Volume Based Range Sharding Algorithm

Type: VOLUME_RANGE

Attributes:

Name DataType Description
range-lower long Range lower bound, throw exception if lower than bound
range-upper long Range upper bound, throw exception if upper than bound
sharding-volume long Sharding volume

Boundary Based Range Sharding Algorithm

Type: BOUNDARY_RANGE

Attributes:

Name DataType Description
sharding-ranges String Range of sharding border, multiple boundaries separated by commas

Auto Interval Sharding Algorithm

Type: AUTO_INTERVAL

Attributes:

Name DataType Description
datetime-lower String Shard datetime begin boundary, pattern: yyyy-MM-dd HH:mm:ss
datetime-upper String Shard datetime end boundary, pattern: yyyy-MM-dd HH:mm:ss
sharding-seconds long Max seconds for the data in one shard, allows sharding key timestamp format seconds with time precision, but time precision after seconds is automatically erased

Standard Sharding Algorithm

Apache ShardingSphere built-in standard sharding algorithm are:

Inline Sharding Algorithm

With Groovy expressions that uses the default implementation of the InlineExpressionParser SPI, InlineShardingStrategy provides single-key support for the sharding operation of = and IN in SQL. Simple sharding algorithms can be used through a simple configuration to avoid laborious Java code developments. For example, t_user_$->{u_id % 8} means table t_user is divided into 8 tables according to u_id, with table names from t_user_0 to t_user_7. Please refer to Inline Expression for more details.

Type: INLINE

Attributes:

Name DataType Description Default Value
algorithm-expression String Inline expression sharding algorithm -
allow-range-query-with-inline-sharding (?) boolean Whether range query is allowed. Note: range query will ignore sharding strategy and conduct full routing false

Interval Sharding Algorithm

This algorithm actively ignores the time zone information of datetime-pattern. This means that when datetime-lower, datetime-upper and the incoming shard key contain time zone information, time zone conversion will not occur due to time zone inconsistencies. When the incoming sharding key is java.time.Instant, there is a special case, which will carry the time zone information of the system and convert it into the string format of datetime-pattern, and then proceed to the next sharding.

Type: INTERVAL

Attributes:

Name DataType Description Default Value
datetime-pattern String Timestamp pattern of sharding value, must can be transformed to Java LocalDateTime. For example: yyyy-MM-dd HH:mm:ss, yyyy-MM-dd or HH:mm:ss etc. But GGGGy-MM etc. related to java.time.chrono.JapaneseDate are not supported -
datetime-lower String Datetime sharding lower boundary, pattern is defined datetime-pattern -
datetime-upper (?) String Datetime sharding upper boundary, pattern is defined datetime-pattern Now
sharding-suffix-pattern String Suffix pattern of sharding data sources or tables, must can be transformed to Java LocalDateTime, must be consistent with datetime-interval-unit. For example: yyyyMM -
datetime-interval-amount (?) int Interval of sharding value, after which the next shard will be entered 1
datetime-interval-unit (?) String Unit of sharding value interval, must can be transformed to Java ChronoUnit’s Enum value. For example: MONTHS DAYS

Complex Sharding Algorithm

Complex Inline Sharding Algorithm

Please refer to Inline Expression for more details.

Type: COMPLEX_INLINE

Name DataType Description Default Value
sharding-columns (?) String sharding column names -
algorithm-expression String Inline expression sharding algorithm -
allow-range-query-with-inline-sharding (?) boolean Whether range query is allowed. Note: range query will ignore sharding strategy and conduct full routing false

Hint Sharding Algorithm

Hint Inline Sharding Algorithm

Please refer to Inline Expression for more details.

Type: COMPLEX_INLINE

Name DataType Description Default Value
algorithm-expression String Inline expression sharding algorithm ${value}

Class Based Sharding Algorithm

Realize custom extension by configuring the sharding strategy type and algorithm class name. CLASS_BASED allows additional custom properties to be passed into the algorithm class. The passed properties can be retrieved through the java.util.Properties class instance with the property name props. Refer to Git’s org.apache.shardingsphere.example.extension.sharding.algortihm.classbased.fixture.ClassBasedStandardShardingAlgorithmFixture.

Type：CLASS_BASED

Attributes：

Name DataType Description
strategy String Sharding strategy type, support STANDARD, COMPLEX or HINT (case insensitive)
algorithmClassName String Fully qualified name of sharding algorithm

Procedure

  1. When using data sharding, configure the corresponding data sharding algorithm under the shardingAlgorithms attribute.

Sample

rules:
- !SHARDING
  tables:
    t_order: 
      actualDataNodes: ds_${0..1}.t_order_${0..1}
      tableStrategy: 
        standard:
          shardingColumn: order_id
          shardingAlgorithmName: t_order_inline
      keyGenerateStrategy:
        column: order_id
        keyGeneratorName: snowflake
    t_order_item:
      actualDataNodes: ds_${0..1}.t_order_item_${0..1}
      tableStrategy:
        standard:
          shardingColumn: order_id
          shardingAlgorithmName: t_order_item_inline
      keyGenerateStrategy:
        column: order_item_id
        keyGeneratorName: snowflake
    t_account:
      actualDataNodes: ds_${0..1}.t_account_${0..1}
      tableStrategy:
        standard:
          shardingAlgorithmName: t_account_inline
      keyGenerateStrategy:
        column: account_id
        keyGeneratorName: snowflake
  defaultShardingColumn: account_id
  bindingTables:
    - t_order,t_order_item
  defaultDatabaseStrategy:
    standard:
      shardingColumn: user_id
      shardingAlgorithmName: database_inline
  defaultTableStrategy:
    none:
  
  shardingAlgorithms:
    database_inline:
      type: INLINE
      props:
        algorithm-expression: ds_${user_id % 2}
    t_order_inline:
      type: INLINE
      props:
        algorithm-expression: t_order_${order_id % 2}
    t_order_item_inline:
      type: INLINE
      props:
        algorithm-expression: t_order_item_${order_id % 2}
    t_account_inline:
      type: INLINE
      props:
        algorithm-expression: t_account_${account_id % 2}
  keyGenerators:
    snowflake:
      type: SNOWFLAKE

- !BROADCAST
  tables:
    - t_address