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Apache Paimon 使用 Kafka CDC 获取数据
   

Apache Paimon 使用 Kafka CDC 获取数据

a.依赖准备

flink-sql-connector-kafka-*.jar

b.支持的文件格式

Flink提供了几种Kafka CDC格式:Canal、Debezium、Ogg和Maxwell JSON。

如果Kafka的Topic中的消息是使用Change Data Capture(CDC)工具从另一个数据库捕获的change event,那么可以使用Paimon Kafka CDC,将INSERT、UPDATE、DELETE消息写入到paimon表中。

注意:

JSON源可能缺少一些信息。例如,Ogg和Maxwell格式标准不包含字段类型;当将JSON源写入Flink Kafka Sink时,它只会保留数据和行类型并删除其他信息。

通常,debezium-json包含“schema”字段,Paimon将从中检索数据类型。确保debezium json具有此字段,否则Paimon将使用“STRING”类型。

  • 如果缺少字段类型,Paimon将默认使用“STRING”类型。
  • 如果缺少数据库名称或表名,则无法进行数据库同步,但仍然可以进行表同步。
  • 如果缺少主键,该作业可能会创建非主键表,可以在表synchronization中提交作业时设置主键。

    c.同步表

    在Flink DataStream作业中使用 KafkaSyncTableAction 或直接通过flink run,可以将Kafka的一个Topic中的一个或多个表同步到一个Paimon表中。

    /bin/flink run \
        /path/to/paimon-flink-action-0.7.0-incubating.jar \
        kafka_sync_table
        --warehouse  \
        --database  \
        --table  \
        [--partition_keys ] \
        [--primary_keys ] \
        [--type_mapping to-string] \
        [--computed_column <'column-name=expr-name(args[, ...])'> [--computed_column ...]] \
        [--kafka_conf  [--kafka_conf  ...]] \
        [--catalog_conf  [--catalog_conf  ...]] \
        [--table_conf  [--table_conf  ...]]
    
    ConfigurationDescription
    –warehouseThe path to Paimon warehouse.
    –databaseThe database name in Paimon catalog.
    –tableThe Paimon table name.
    –partition_keysThe partition keys for Paimon table. If there are multiple partition keys, connect them with comma, for example “dt,hh,mm”.
    –primary_keysThe primary keys for Paimon table. If there are multiple primary keys, connect them with comma, for example “buyer_id,seller_id”.
    –type_mappingIt is used to specify how to map MySQL data type to Paimon type. Supported options:“tinyint1-not-bool”: maps MySQL TINYINT(1) to TINYINT instead of BOOLEAN.“to-nullable”: ignores all NOT NULL constraints (except for primary keys). This is used to solve the problem that Flink cannot accept the MySQL ‘ALTER TABLE ADD COLUMN column type NOT NULL DEFAULT x’ operation.“to-string”: maps all MySQL types to STRING.“char-to-string”: maps MySQL CHAR(length)/VARCHAR(length) types to STRING.“longtext-to-bytes”: maps MySQL LONGTEXT types to BYTES.“bigint-unsigned-to-bigint”: maps MySQL BIGINT UNSIGNED, BIGINT UNSIGNED ZEROFILL, SERIAL to BIGINT. You should ensure overflow won’t occur when using this option.
    –computed_columnThe definitions of computed columns. The argument field is from Kafka topic’s table field name. See here for a complete list of configurations.
    –kafka_confThe configuration for Flink Kafka sources. Each configuration should be specified in the format key=value. properties.bootstrap.servers, topic/topic-pattern, properties.group.id, and value.format are required configurations, others are optional.See its document for a complete list of configurations.
    –catalog_confThe configuration for Paimon catalog. Each configuration should be specified in the format “key=value”. See here for a complete list of catalog configurations.
    –table_confThe configuration for Paimon table sink. Each configuration should be specified in the format “key=value”. See here for a complete list of table configurations.

    如果指定的Paimon表不存在,此操作将自动创建该表,它的结构将从所有指定的Kafka的Topic的表中派生出来,它从Topic中获取最早的非DDL数据解析模式。

    如果Paimon表已经存在,其模式将与所有指定的Kafka的Topic表的模式进行比较。

    示例1:

    /bin/flink run \
        /path/to/paimon-flink-action-0.7.0-incubating.jar \
        kafka_sync_table \
        --warehouse hdfs:///path/to/warehouse \
        --database test_db \
        --table test_table \
        --partition_keys pt \
        --primary_keys pt,uid \
        --computed_column '_year=year(age)' \
        --kafka_conf properties.bootstrap.servers=127.0.0.1:9020 \
        --kafka_conf topic=order \
        --kafka_conf properties.group.id=123456 \
        --kafka_conf value.format=canal-json \
        --catalog_conf metastore=hive \
        --catalog_conf uri=thrift://hive-metastore:9083 \
        --table_conf bucket=4 \
        --table_conf changelog-producer=input \
        --table_conf sink.parallelism=4
    

    如果启动同步作业时kafka的Topic中不包含消息,则必须在提交作业之前手动创建表,只能定义分区键和主键,剩下的列将由同步作业添加。

    注意:在这种情况下,不应该使用-partition_keys或-primary_keys,因为这些键是在创建表时定义的,不能修改。此外,如果指定了计算列,还应该定义用于计算列的所有参数列。

    示例2:如果想同步具有主键“id INT”的表,并且要计算分区键“part=date_format(create_time,yyyy-MM-dd)”,可以先创建这样的表(其他列可以省略)

    CREATE TABLE test_db.test_table (
        id INT,                 -- primary key
        create_time TIMESTAMP,  -- the argument of computed column part
        part STRING,            -- partition key
        PRIMARY KEY (id, part) NOT ENFORCED
    ) PARTITIONED BY (part);
    

    启动同步作业

    /bin/flink run \
        /path/to/paimon-flink-action-0.7.0-incubating.jar \
        kafka_sync_table \
        --warehouse hdfs:///path/to/warehouse \
        --database test_db \
        --table test_table \
        --computed_column 'part=date_format(create_time,yyyy-MM-dd)' \
        ... (other conf)
    

    d.同步数据库

    通过在Flink DataStream作业中使用KafkaSyncDatabaseAction或直接通过flink run,可以将多个Topic或一个Topic同步到一个Paimon数据库中。

    /bin/flink run \
        /path/to/paimon-flink-action-0.7.0-incubating.jar \
        kafka_sync_database
        --warehouse  \
        --database  \
        [--table_prefix ] \
        [--table_suffix ] \
        [--including_tables ] \
        [--excluding_tables ] \
        [--type_mapping to-string] \
        [--kafka_conf  [--kafka_conf  ...]] \
        [--catalog_conf  [--catalog_conf  ...]] \
        [--table_conf  [--table_conf  ...]]
    
    ConfigurationDescription
    –warehouseThe path to Paimon warehouse.
    –databaseThe database name in Paimon catalog.
    –ignore_incompatibleIt is default false, in this case, if MySQL table name exists in Paimon and their schema is incompatible,an exception will be thrown. You can specify it to true explicitly to ignore the incompatible tables and exception.
    –table_prefixThe prefix of all Paimon tables to be synchronized. For example, if you want all synchronized tables to have “ods_” as prefix, you can specify “–table_prefix ods_”.
    –table_suffixThe suffix of all Paimon tables to be synchronized. The usage is same as “–table_prefix”.
    –including_tablesIt is used to specify which source tables are to be synchronized. You must use ‘|’ to separate multiple tables.Because ‘|’ is a special character, a comma is required, for example: ‘a|b|c’.Regular expression is supported, for example, specifying “–including_tables test|paimon.*” means to synchronize table ‘test’ and all tables start with ‘paimon’.
    –excluding_tablesIt is used to specify which source tables are not to be synchronized. The usage is same as “–including_tables”. “–excluding_tables” has higher priority than “–including_tables” if you specified both.
    –type_mappingIt is used to specify how to map MySQL data type to Paimon type. Supported options:“tinyint1-not-bool”: maps MySQL TINYINT(1) to TINYINT instead of BOOLEAN.“to-nullable”: ignores all NOT NULL constraints (except for primary keys). This is used to solve the problem that Flink cannot accept the MySQL ‘ALTER TABLE ADD COLUMN column type NOT NULL DEFAULT x’ operation.“to-string”: maps all MySQL types to STRING.“char-to-string”: maps MySQL CHAR(length)/VARCHAR(length) types to STRING.“longtext-to-bytes”: maps MySQL LONGTEXT types to BYTES.“bigint-unsigned-to-bigint”: maps MySQL BIGINT UNSIGNED, BIGINT UNSIGNED ZEROFILL, SERIAL to BIGINT. You should ensure overflow won’t occur when using this option.
    –kafka_confThe configuration for Flink Kafka sources. Each configuration should be specified in the format key=value. properties.bootstrap.servers, topic/topic-pattern, properties.group.id, and value.format are required configurations, others are optional.See its document for a complete list of configurations.
    –catalog_confThe configuration for Paimon catalog. Each configuration should be specified in the format “key=value”. See here for a complete list of catalog configurations.
    –table_confThe configuration for Paimon table sink. Each configuration should be specified in the format “key=value”. See here for a complete list of table configurations.

    只有带有主键的表才会同步。

    此操作将为所有表构建一个combined sink,对于要同步的每个Kafka Topic的表,如果相应的Paimon表不存在,此操作将自动创建该表,其模式将从所有指定的Kafka Topic的表中派生。

    如果Paimon表已经存在,并且其模式与从Kafka记录中解析的模式不同,则此操作将尝试模式演变。

    示例:从一个Kafka Topic同步到Paimon数据库。

    /bin/flink run \
        /path/to/paimon-flink-action-0.7.0-incubating.jar \
        kafka_sync_database \
        --warehouse hdfs:///path/to/warehouse \
        --database test_db \
        --kafka_conf properties.bootstrap.servers=127.0.0.1:9020 \
        --kafka_conf topic=order \
        --kafka_conf properties.group.id=123456 \
        --kafka_conf value.format=canal-json \
        --catalog_conf metastore=hive \
        --catalog_conf uri=thrift://hive-metastore:9083 \
        --table_conf bucket=4 \
        --table_conf changelog-producer=input \
        --table_conf sink.parallelism=4
    

    从多个Kafka Topic同步到Paimon数据库。

    /bin/flink run \
        /path/to/paimon-flink-action-0.7.0-incubating.jar \
        kafka_sync_database \
        --warehouse hdfs:///path/to/warehouse \
        --database test_db \
        --kafka_conf properties.bootstrap.servers=127.0.0.1:9020 \
        --kafka_conf topic=order\;logistic_order\;user \
        --kafka_conf properties.group.id=123456 \
        --kafka_conf value.format=canal-json \
        --catalog_conf metastore=hive \
        --catalog_conf uri=thrift://hive-metastore:9083 \
        --table_conf bucket=4 \
        --table_conf changelog-producer=input \
        --table_conf sink.parallelism=4
    
     
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文章名称:《Apache Paimon 使用 Kafka CDC 获取数据》
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