我试图在本地Docker容器中使用Kafka Connect(使用官方的Confluent映像),以便将DB2数据推送到Openshift(在AWS上)上的Kafka集群。我将Confluent JDBC连接器与DB2 JDBC-Jar一起使用。 由于我将SMT与“ transforms.createKey”(用于创建密钥)一起使用,并且表中的密钥列具有不同的名称,因此我具有不同的连接器配置。
这是我的步骤:
到目前为止,一切正常,我可以看到我的数据已推送到集群。但是,一旦我通过post调用添加了第二个JDBC连接器,第一个连接器就会停止将数据推送到集群,而第二个连接器会启动,并继续加载和推送数据。在很短的时间内,似乎两个连接器都将数据推送到群集,但是我假设这可能是来自连接器1的数据仍被刷新。 问题是,a)甚至跟踪日志也没有显示出有意义的错误(至少对我而言),b)两次尝试之间显示的错误有所不同(我一直删除所有主题和容器)。
我假设这不是错误,而是需要适当设置的配置的组合,并且/或者我对某些基本的Kafka Connect核心功能缺乏了解。我已经尝试添加和更改各种配置,但不幸的是到目前为止,还没有任何解决方法。我已经尝试了很多,但没有运气。我已经附加了我最近两次尝试的日志以及配置。
有人知道我可以采用哪种配置,或者为了解决此问题需要研究什么? 感谢您的帮助-谢谢!
Kafka: 2.0.0
Docker image: confluentinc/cp-kafka-connect:5.0.0
DB2: 10.5
JDBC Jar: db2jcc4.jar with version 4.19.76
记录第一次尝试:
[2018-12-17 13:09:15,683] ERROR Invalid call to OffsetStorageWriter flush() while already flushing, the framework should not allow this (org.apache.kafka.connect.storage.OffsetStorageWriter)
[2018-12-17 13:09:15,684] ERROR WorkerSourceTask{id=db2-jdbc-source-0} Task threw an uncaught and unrecoverable exception (org.apache.kafka.connect.runtime.WorkerTask)
org.apache.kafka.connect.errors.ConnectException: OffsetStorageWriter is already flushing
at org.apache.kafka.connect.storage.OffsetStorageWriter.beginFlush(OffsetStorageWriter.java:110)
at org.apache.kafka.connect.runtime.WorkerSourceTask.commitOffsets(WorkerSourceTask.java:409)
at org.apache.kafka.connect.runtime.WorkerSourceTask.execute(WorkerSourceTask.java:238)
at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:175)
at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:219)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
[2018-12-17 13:09:15,686] ERROR WorkerSourceTask{id=db2-jdbc-source-0} Task is being killed and will not recover until manually restarted (org.apache.kafka.connect.runtime.WorkerTask)
[2018-12-17 13:09:15,686] INFO [Producer clientId=producer-4] Closing the Kafka producer with timeoutMillis = 30000 ms. (org.apache.kafka.clients.producer.KafkaProducer)
[2018-12-17 13:09:20,682] ERROR Graceful stop of task db2-jdbc-source-0 failed. (org.apache.kafka.connect.runtime.Worker)
[2018-12-17 13:09:20,682] INFO Finished stopping tasks in preparation for rebalance (org.apache.kafka.connect.runtime.distributed.DistributedHerder)
记录第二次尝试:
[2018-12-17 14:01:31,658] INFO Stopping task db2-jdbc-source-0 (org.apache.kafka.connect.runtime.Worker)
[2018-12-17 14:01:31,689] INFO Stopped connector db2-jdbc-source (org.apache.kafka.connect.runtime.Worker)
[2018-12-17 14:01:31,784] INFO WorkerSourceTask{id=db2-jdbc-source-0} Committing offsets (org.apache.kafka.connect.runtime.WorkerSourceTask)
[2018-12-17 14:01:31,784] INFO WorkerSourceTask{id=db2-jdbc-source-0} flushing 20450 outstanding messages for offset commit (org.apache.kafka.connect.runtime.WorkerSourceTask)
[2018-12-17 14:01:36,733] ERROR Graceful stop of task db2-jdbc-source-0 failed. (org.apache.kafka.connect.runtime.Worker)
[2018-12-17 14:01:36,733] INFO Finished stopping tasks in preparation for rebalance (org.apache.kafka.connect.runtime.distributed.DistributedHerder)
screenshot of incoming messages per second in the Kafka cluster
Kafka Connect Docker环境变量:
-e CONNECT_BOOTSTRAP_SERVERS=my_kafka_cluster:443 \
-e CONNECT_PRODUCER_BOOTSTRAP_SERVERS="my_kafka_cluster:443" \
-e CONNECT_REST_ADVERTISED_HOST_NAME="kafka-connect" \
-e CONNECT_REST_PORT=8083 \
-e CONNECT_GROUP_ID="kafka-connect-group" \
-e CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR=3 \
-e CONNECT_CONFIG_STORAGE_TOPIC="kafka-connect-config" \
-e CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR=3 \
-e CONNECT_OFFSET_STORAGE_TOPIC="kafka-connect-offset" \
-e CONNECT_OFFSET_FLUSH_INTERVAL_MS=15000 \
-e CONNECT_OFFSET_FLUSH_TIMEOUT_MS=60000 \
-e CONNECT_STATUS_STORAGE_REPLICATION_FACTOR=3 \
-e CONNECT_STATUS_STORAGE_TOPIC="kafka-connect-status" \
-e CONNECT_KEY_CONVERTER="io.confluent.connect.avro.AvroConverter" \
-e CONNECT_KEY_CONVERTER_SCHEMA_REGISTRY_URL=http://url_to_schemaregistry \
-e CONNECT_VALUE_CONVERTER="io.confluent.connect.avro.AvroConverter" \
-e CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL=http://url_to_schemaregistry \
-e CONNECT_INTERNAL_KEY_CONVERTER="org.apache.kafka.connect.json.JsonConverter" \
-e CONNECT_INTERNAL_KEY_CONVERTER_SCHEMAS_ENABLE="false" \
-e CONNECT_INTERNAL_VALUE_CONVERTER="org.apache.kafka.connect.json.JsonConverter" \
-e CONNECT_INTERNAL_VALUE_CONVERTER_SCHEMAS_ENABLE="false" \
-e CONNECT_PLUGIN_PATH=/usr/share/java \
-e CONNECT_PRODUCER_BUFFER_MEMORY="8388608" \
-e CONNECT_SECURITY_PROTOCOL="SSL" \
-e CONNECT_PRODUCER_SECURITY_PROTOCOL="SSL" \
-e CONNECT_SSL_TRUSTSTORE_LOCATION="/usr/share/kafka.client.truststore.jks" \
-e CONNECT_PRODUCER_SSL_TRUSTSTORE_LOCATION="/usr/share/kafka.client.truststore.jks" \
-e CONNECT_SSL_TRUSTSTORE_PASSWORD="my_ts_pw" \
-e CONNECT_PRODUCER_SSL_TRUSTSTORE_PASSWORD="my_ts_pw" \
-e CONNECT_LOG4J_LOGGERS=org.apache.kafka.connect.runtime.rest=WARN,org.reflections=ERROR \
-e CONNECT_LOG4J_ROOT_LOGLEVEL=INFO \
-e HOSTNAME=kafka-connect \
JDBC连接器(仅表和键列有所不同):
{
"name": "db2-jdbc-source",
"config":
{
"mode":"timestamp",
"debug":"true",
"batch.max.rows":"50",
"poll.interval.ms":"10000",
"timestamp.delay.interval.ms":"60000",
"timestamp.column.name":"IBMSNAP_LOGMARKER",
"connector.class":"io.confluent.connect.jdbc.JdbcSourceConnector" ,
"connection.url":"jdbc:db2://myip:myport/mydb:currentSchema=myschema;",
"connection.password":"mypw",
"connection.user":"myuser",
"connection.backoff.ms":"60000",
"dialect.name": "Db2DatabaseDialect",
"table.types": "TABLE",
"table.poll.interval.ms":"60000",
"table.whitelist":"MYTABLE1",
"tasks.max":"1",
"topic.prefix":"db2_",
"key.converter":"io.confluent.connect.avro.AvroConverter",
"key.converter.schema.registry.url":"http://url_to_schemaregistry",
"value.converter":"io.confluent.connect.avro.AvroConverter",
"value.converter.schema.registry.url":"http://url_to_schemaregistry",
"transforms":"createKey",
"transforms.createKey.type":"org.apache.kafka.connect.transforms.ValueToKey",
"transforms.createKey.fields":"MYKEY1"
}
}
答案 0 :(得分:0)
我最终发现了问题所在: 我在时间戳记模式下使用JDBC连接器,而不是timestamp + incrementing,因为我不能(总是)指定一个递增列。我知道这可能会导致问题,当多个具有相同时间戳的条目时,Connect无法知道已经读取了哪些条目。
我的大部分数据行具有相同的时间戳。当我添加第二个连接器时,存储了第一个连接器的当前时间戳,Connect开始重新平衡,因此失去了已读取该时间戳的哪些行的信息。当连接器重新启动并再次运行时,第一个连接器将继续执行“下一个时间戳记”,因此仅加载最新的行(仅占很小的一部分)。
我的错误是假设在这种情况下,第一个连接器将重新开始使用上一个时间戳,而不是继续使用“下一个时间戳”。我宁愿冒着重复的风险,而不是可能丢失的数据的风险。