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    表格存储：同步数据到数据表

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    表格存储：同步数据到数据表

    更新时间：Feb 19, 2024

    Tablestore Sink Connector会根据订阅的主题轮询地从Kafka中拉取消息，并对消息记录进行解析，然后将数据批量导入到Tablestore的数据表。

    前提条件

    • 已安装Kafka，并且已启动ZooKeeper和Kafka。更多信息，请参见Kafka官方文档。

    • 已开通表格存储服务，创建实例以及创建数据表。具体操作，请参见使用流程。

      说明

      您也可以通过Tablestore Sink Connector自动创建目标数据表，此时需要配置auto.create为true。

    • 已获取AccessKey。具体操作，请参见获取AccessKey。

    步骤一：部署Tablestore Sink Connector

    1. 通过以下任意一种方式获取Tablestore Sink Connector。

      • 通过GitHub下载源码并编译。源码的GitHub路径为Tablestore Sink Connector源码。

        1. 通过Git工具执行以下命令下载Tablestore Sink Connector源码。

          git clone https://github.com/aliyun/kafka-connect-tablestore.git

        2. 进入到下载的源码目录后，执行以下命令进行Maven打包。

          mvn clean package -DskipTests

          编译完成后，生成的压缩包（例如kafka-connect-tablestore-1.0.jar）会存放在target目录。

      • 直接下载编译完成的kafka-connect-tablestore压缩包。

    2. 将压缩包复制到各个节点的$KAFKA_HOME/libs目录下。

    步骤二：启动Tablestore Sink Connector

    Tablestore Sink Connector具有standalone模式和distributed模式两种工作模式。请根据实际选择。

    standalone模式的配置步骤如下：

    1. 根据实际修改worker配置文件connect-standalone.properties和connector配置文件connect-tablestore-sink-quickstart.properties。

      • worker配置文件connect-standalone.properties的配置示例

        worker配置中包括Kafka连接参数、序列化格式、提交偏移量的频率等配置项。此处以Kafka官方示例为例介绍。更多信息，请参见Kafka Connect。

        # Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# These are defaults. This file just demonstrates how to override some settings.
bootstrap.servers=localhost:9092

# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will
# need to configure these based on the format they want their data in when loaded from or stored into Kafka
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
# Converter-specific settings can be passed in by prefixing the Converter's setting with the converter we want to apply
# it to
key.converter.schemas.enable=true
value.converter.schemas.enable=true

offset.storage.file.filename=/tmp/connect.offsets
# Flush much faster than normal, which is useful for testing/debugging
offset.flush.interval.ms=10000

# Set to a list of filesystem paths separated by commas (,) to enable class loading isolation for plugins
# (connectors, converters, transformations). The list should consist of top level directories that include 
# any combination of: 
# a) directories immediately containing jars with plugins and their dependencies
# b) uber-jars with plugins and their dependencies
# c) directories immediately containing the package directory structure of classes of plugins and their dependencies
# Note: symlinks will be followed to discover dependencies or plugins.
# Examples: 
# plugin.path=/usr/local/share/java,/usr/local/share/kafka/plugins,/opt/connectors,
#plugin.path=

      • connector配置文件connect-tablestore-sink-quickstart.properties的配置示例

        connector配置中包括连接器类、表格存储连接参数、数据映射等配置项。更多信息，请参见配置说明。

        # 设置连接器名称。
name=tablestore-sink
# 指定连接器类。
connector.class=TableStoreSinkConnector
# 设置最大任务数。
tasks.max=1
# 指定导出数据的Kafka的Topic列表。
topics=test

# 以下为Tablestore连接参数配置。
# Tablestore实例的Endpoint。
tablestore.endpoint=https://xxx.xxx.ots.aliyuncs.com
# 填写AccessKey ID和AccessKey Secret。
tablestore.access.key.id =xxx
tablestore.access.key.secret=xxx
# Tablestore实例名称。
tablestore.instance.name=xxx

# 用于指定表格存储目标表名称的格式字符串，其中<topic>作为原始Topic的占位符。默认值为<topic>。
# Examples:
# table.name.format=kafka_<topic>，主题为test的消息记录将写入表名为kafka_test的数据表。
# table.name.format=

# 主键模式，默认值为kafka。
# 将以<topic>_<partition>（Kafka主题和分区，用"_"分隔）和<offset>（消息记录在分区中的偏移量）作为Tablestore数据表的主键。
# primarykey.mode=

# 自动创建目标表，默认值为false。
auto.create=true

    2. 进入到$KAFKA_HOME目录后，执行以下命令启动standalone模式。

      bin/connect-standalone.sh config/connect-standalone.properties config/connect-tablestore-sink-quickstart.properties

    distributed模式的配置步骤如下：

    1. 根据实际修改worker配置文件connect-distributed.properties。

      worker配置中包括Kafka连接参数、序列化格式、提交偏移量的频率等配置项，还包括存储各connectors相关信息的Topic，建议您提前手动创建相应Topic。此处以Kafka官方示例为例介绍。更多信息，请参见Kafka Connect。

      • offset.storage.topic：用于存储各connectors相关offset的Compact Topic。

      • config.storage.topic：用于存储connector和task相关配置的Compact Topic，此Topic的Parition数必须设置为1。

      • status.storage.topic：用于存储Kafka Connect状态信息的Compact Topic。

      ##
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##

# This file contains some of the configurations for the Kafka Connect distributed worker. This file is intended
# to be used with the examples, and some settings may differ from those used in a production system, especially
# the `bootstrap.servers` and those specifying replication factors.

# A list of host/port pairs to use for establishing the initial connection to the Kafka cluster.
bootstrap.servers=localhost:9092

# unique name for the cluster, used in forming the Connect cluster group. Note that this must not conflict with consumer group IDs
group.id=connect-cluster

# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will
# need to configure these based on the format they want their data in when loaded from or stored into Kafka
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
# Converter-specific settings can be passed in by prefixing the Converter's setting with the converter we want to apply
# it to
key.converter.schemas.enable=true
value.converter.schemas.enable=true

# Topic to use for storing offsets. This topic should have many partitions and be replicated and compacted.
# Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
offset.storage.topic=connect-offsets
offset.storage.replication.factor=1
#offset.storage.partitions=25

# Topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated,
# and compacted topic. Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
config.storage.topic=connect-configs
config.storage.replication.factor=1

# Topic to use for storing statuses. This topic can have multiple partitions and should be replicated and compacted.
# Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
status.storage.topic=connect-status
status.storage.replication.factor=1
#status.storage.partitions=5

# Flush much faster than normal, which is useful for testing/debugging
offset.flush.interval.ms=10000

# These are provided to inform the user about the presence of the REST host and port configs 
# Hostname & Port for the REST API to listen on. If this is set, it will bind to the interface used to listen to requests.
#rest.host.name=
#rest.port=8083

# The Hostname & Port that will be given out to other workers to connect to i.e. URLs that are routable from other servers.
#rest.advertised.host.name=
#rest.advertised.port=

# Set to a list of filesystem paths separated by commas (,) to enable class loading isolation for plugins
# (connectors, converters, transformations). The list should consist of top level directories that include 
# any combination of: 
# a) directories immediately containing jars with plugins and their dependencies
# b) uber-jars with plugins and their dependencies
# c) directories immediately containing the package directory structure of classes of plugins and their dependencies
# Examples: 
# plugin.path=/usr/local/share/java,/usr/local/share/kafka/plugins,/opt/connectors,
#plugin.path=

    2. 进入到$KAFKA_HOME目录后，执行以下命令启动distributed模式。

      重要

      您需要在每个节点上均启动worker进程。

      bin/connect-distributed.sh config/connect-distributed.properties

    3. 通过REST API管理connectors。更多信息，请参见REST API。

      1. 在config路径下创建connect-tablestore-sink-quickstart.json文件并填写以下示例内容。

        connector配置文件以JSON格式指定参数键值对，包括连接器类、表格存储连接参数、数据映射等配置项。更多信息，请参见配置说明。

        {
  "name": "tablestore-sink",
  "config": {
    "connector.class":"TableStoreSinkConnector",
    "tasks.max":"1",
    "topics":"test",
    "tablestore.endpoint":"https://xxx.xxx.ots.aliyuncs.com",
    "tablestore.access.key.id":"xxx",
    "tablestore.access.key.secret":"xxx",
    "tablestore.instance.name":"xxx",
    "table.name.format":"<topic>",
    "primarykey.mode":"kafka",
    "auto.create":"true"
  }
}

      2. 执行以下命令启动一个Tablestore Sink Connector。

        curl -i -k  -H "Content-type: application/json" -X POST -d @config/connect-tablestore-sink-quickstart.json http://localhost:8083/connectors

        其中http://localhost:8083/connectors为Kafka REST服务的地址，请根据实际修改。

    步骤三：生产新的记录

    1. 进入到$KAFKA_HOME目录后，执行以下命令启动一个控制台生产者。

      bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test

      配置项说明请参见下表。

      配置项

      示例值

      描述

      --broker-list

      localhost:9092

      Kafka集群broker地址和端口。

      --topic

      test

      主题名称。启动Tablestore Sink Connector时默认会自动创建Topic，您也可以选择手动创建。

    2. 向主题test中写入一些新的消息。

      • Struct类型消息

        {
    "schema":{
        "type":"struct",
        "fields":[
            {
                "type":"int32",
                "optional":false,
                "field":"id"
            },
            {
                "type":"string",
                "optional":false,
                "field":"product"
            },
            {
                "type":"int64",
                "optional":false,
                "field":"quantity"
            },
            {
                "type":"double",
                "optional":false,
                "field":"price"
            }
        ],
        "optional":false,
        "name":"record"
    },
    "payload":{
        "id":1,
        "product":"foo",
        "quantity":100,
        "price":50
    }
}

      • Map类型消息

        {
    "schema":{
        "type":"map",
        "keys":{
            "type":"string",
            "optional":false
        },
        "values":{
            "type":"int32",
            "optional":false
        },
        "optional":false
    },
    "payload":{
        "id":1
    }
}

    3. 登录表格存储控制台查看数据。

      表格存储实例中将自动创建一张数据表，表名为test，表中数据如下图所示。其中第一行数据为Map类型消息导入结果，第二行数据为Struct类型消息导入结果。fig_datanew