flink获取kafka数据(flink消费kafka的offset与checkpoint)

生产环境有个作业,逻辑很简单,读取kafka的数据,然后使用hive catalog,实时写入hbase,hive,redis。使用的flink版本为1.11.1。

为了防止写入hive的文件数量过多,我设置了checkpoint为30分钟。

env.enableCheckpointing(1000 * 60 * 30); // 1000 * 60 * 30 => 30 minutes

达到的效果就是每30分钟生成一个文件,如下:

hive> dfs -ls /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/ ; Found 10 items -rw-r--r-- 3 hdfs hive 0 2020-10-18 01:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/_SUCCESS -rw-r--r-- 3 hdfs hive 248895 2020-10-18 00:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-0-10911 -rw-r--r-- 3 hdfs hive 306900 2020-10-18 00:50 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-0-10912 -rw-r--r-- 3 hdfs hive 208227 2020-10-18 01:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-0-10913 -rw-r--r-- 3 hdfs hive 263586 2020-10-18 00:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-1-10911 -rw-r--r-- 3 hdfs hive 307723 2020-10-18 00:50 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-1-10912 -rw-r--r-- 3 hdfs hive 196777 2020-10-18 01:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-1-10913 -rw-r--r-- 3 hdfs hive 266984 2020-10-18 00:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-2-10911 -rw-r--r-- 3 hdfs hive 338992 2020-10-18 00:50 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-2-10912 -rw-r--r-- 3 hdfs hive 216655 2020-10-18 01:20 /opt/user/hive/warehouse/dw.db/ods_analog_zgyp/dt_day=2020-10-18/dt_hour=00/part-5b7b6d44-993a-4af7-b7ee-1a8ab64d3453-2-10913 hive>

但是,同时也观察到归属于这个作业的kafka消费组积压数量,每分钟消费数量,明显具有周期性消费峰值。

比如,对于每30分钟时间间隔度的一个观察,前面25分钟的“每分钟消费数量”都是为0,然后,后面5分钟的“每分钟消费数量”为300k。同理,“消费组积压数量”也出现同样情况,积压数量一直递增,但是到了30分钟的间隔,就下降到数值0。如图。

flink获取kafka数据(flink消费kafka的offset与checkpoint)(1)

消费组每分钟消费数量

flink获取kafka数据(flink消费kafka的offset与checkpoint)(2)

消费组积压数量

但其实,通过对hbase,hive,redis的观察,数据是实时写入的,并不存在前面25分钟没有消费数据的情况。

查阅资料得知,flink会自己维护一份kafka的offset,然后checkpoint时间点到了,再把offset更新回kafka。

为了验证这个观点,“flink在checkpoint的时候,才把消费kafka的offset更新回kafka”,同时,观察,savepoint机制是否会重复消费kafka,我尝试写一个程序,逻辑很简单,就是从topic "test"读取数据,然后写入topic "test2"。特别说明,这个作业的checkpoint是1分钟。

package com.econ.powercloud.jobsTest; import org.apache.flink.api.common.serialization.SimpleStringSchema; import org.apache.flink.api.java.utils.ParameterTool; import org.apache.flink.kafka.shaded.org.apache.kafka.clients.producer.ProducerRecord; import org.apache.flink.streaming.api.CheckpointingMode; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer; import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema; import javax.annotation.Nullable; import java.util.Properties; public class TestKafkaOffsetCheckpointJob { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.enableCheckpointing(1000 * 60); ParameterTool parameterTool = ParameterTool.fromArgs(args); String bootstrapServers = parameterTool.get("bootstrap.servers") == null ? "localhost:9092" : parameterTool.get("bootstrap.servers"); Properties properties = new Properties(); properties.setProperty("bootstrap.servers", bootstrapServers); properties.setProperty("group.id", "prod-econ-flink-TestKafkaOffsetCheckpointJob-local"); properties.setProperty("transaction.timeout.ms", String.valueOf(1000 * 60 * 5)); String topic = "test"; FlinkKafkaConsumer<String> stringFlinkKafkaConsumer = new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties); DataStreamSource<String> stringDataStreamSource = env.addSource(stringFlinkKafkaConsumer); String producerTopic = "test2"; FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(producerTopic, new KafkaSerializationSchema<String>() { @Override public ProducerRecord<byte[], byte[]> serialize(String element, @Nullable Long timestamp) { return new ProducerRecord<>(producerTopic, element.getBytes()); } }, properties, FlinkKafkaProducer.Semantic.EXACTLY_ONCE); stringDataStreamSource.addSink(kafkaProducer); env.execute("TestKafkaOffsetCheckpointJob"); } }

提交作业:

[econ@dev-hadoop-node-c ~]$ /opt/flink-1.11.1/bin/flink run -m dev-hadoop-node-c:8081 -c "com.econ.powercloud.jobsTest.TestKafkaOffsetCheckpointJob" -d ~/powercloud-flink-1.0.20201016.jar --bootstrap.servers localhost:9092 Job has been submitted with JobID 5fdd14f7fd3c93287635c9d61180d8a6 [econ@dev-hadoop-node-c ~]$

使用"kafka-console-producer.sh"往topic "test"生成消息"a1":

RdeMacBook-Pro:kafka r$ ./bin/kafka-console-producer.sh --topic test --broker-list localhost: >a1 >

使用"kafka-console-consumer.sh"消费topic "test2"的消息:

RdeMacBook-Pro:kafka r$ ./bin/kafka-console-consumer.sh --topic test2 --bootstrap-server localhost:9092 a1

证明作业逻辑本身没有问题,实现' 从topic "test"读取数据,然后写入topic "test2" '。

使用"kafka-consumer-groups.sh"观察消费组"prod-econ-flink-TestKafkaOffsetCheckpointJob-local"的积压数量,重点观察指标"LAG",可以看到LAG为1 :

RdeMacBook-Pro:kafka r$ ./bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --group prod-econ-flink-TestKafkaOffsetCheckpointJob-local --describe; date; Consumer group 'prod-econ-flink-TestKafkaOffsetCheckpointJob-local' has no active members. TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID test 1 3 3 0 - - - test 0 3 3 0 - - - test 2 5 6 1 - - - 2020年10月18日 星期日 20时09分45秒 CST RdeMacBook-Pro:kafka r$

证明flink消费了kafka数据后,不会更新offset到kafka。

停止作业:

[econ@dev-hadoop-node-c ~]$ /opt/flink-1.11.1/bin/flink stop -m dev-hadoop-node-c:8081 5fdd14f7fd3c93287635c9d61180d8a6 Suspending job "5fdd14f7fd3c93287635c9d61180d8a6" with a savepoint. Savepoint completed. Path: hdfs://nameservice1/flink1.11/flink-savepoints/savepoint-5fdd14-53dfd9f8eccd [econ@dev-hadoop-node-c ~]$

再次启动作业,但是,不使用上面生成的savepoint:

[econ@dev-hadoop-node-c ~]$ /opt/flink-1.11.1/bin/flink run -m dev-hadoop-node-c:8081 -c "com.econ.powercloud.jobsTest.TestKafkaOffsetCheckpointJob" -d ~/powercloud-flink-1.0.20201016.jar --bootstrap.servers localhost:9092 Job has been submitted with JobID 130568a2eeec96296237ed3e1f280f83 [econ@dev-hadoop-node-c ~]$

观察topic "test2",发现,同样的数据"a1"被生产进入:

RdeMacBook-Pro:kafka r$ ./bin/kafka-console-consumer.sh --topic test2 --bootstrap-server localhost:9092 a1 a1

证明:flink在没有使用savepoint的时候,消费kafka的offset还是从kafka自身获取。

再仔细观察topic "test"的“消费组积压数量”,注意在"20时10分05秒"还观察到积压数值1,但是在"20时10分08秒"就发现积压数值都是0.

RdeMacBook-Pro:kafka r$ ./bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --group prod-econ-flink-TestKafkaOffsetCheckpointJob-local --describe; date; Consumer group 'prod-econ-flink-TestKafkaOffsetCheckpointJob-local' has no active members. TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID test 1 3 3 0 - - - test 0 3 3 0 - - - test 2 5 6 1 - - - 2020年10月18日 星期日 20时10分05秒 CST RdeMacBook-Pro:kafka r$ ./bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --group prod-econ-flink-TestKafkaOffsetCheckpointJob-local --describe; date; Consumer group 'prod-econ-flink-TestKafkaOffsetCheckpointJob-local' has no active members. TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID test 1 3 3 0 - - - test 0 3 3 0 - - - test 2 6 6 0 - - - 2020年10月18日 星期日 20时10分08秒 CST RdeMacBook-Pro:kafka r$

这是因为,在"20:10:06"完成了一次checkpoint,把offset更新回kafka。

flink获取kafka数据(flink消费kafka的offset与checkpoint)(3)

Flink Checkpoint History

下面接着测试flink使用savepoint的情况下,是否会重复消费kafka数据。

使用"kafka-console-producer.sh"往topic "test"生成消息"a2":

RdeMacBook-Pro:kafka r$ ./bin/kafka-console-producer.sh --topic test --broker-list localhost:9092 >a1 >a2 >

使用"kafka-console-consumer.sh"消费topic "test2"的消息:

RdeMacBook-Pro:kafka r$ ./bin/kafka-console-consumer.sh --topic test2 --bootstrap-server localhost:9092 a1 a1 a2

停止作业:

[econ@dev-hadoop-node-c ~]$ /opt/flink-1.11.1/bin/flink stop -m dev-hadoop-node-c:8081 bb8b4ba7ddaad869c6469fab5e81d179 Suspending job "bb8b4ba7ddaad869c6469fab5e81d179" with a savepoint. Savepoint completed. Path: hdfs://nameservice1/flink1.11/flink-savepoints/savepoint-bb8b4b-99016a1c3e60 [econ@dev-hadoop-node-c ~]$

观察topic "test"的“消费组积压数量”,发现LAG还是1:

RdeMacBook-Pro:kafka r$ ./bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --group prod-econ-flink-TestKafkaOffsetCheckpointJob-local --describe; date; Consumer group 'prod-econ-flink-TestKafkaOffsetCheckpointJob-local' has no active members. TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID test 1 3 4 1 - - - test 0 3 3 0 - - - test 2 6 6 0 - - - 2020年10月18日 星期日 20时28分39秒 CST RdeMacBook-Pro:kafka r$

flink使用savepoint启动作业,注意参数"-s":

[econ@dev-hadoop-node-c ~]$ /opt/flink-1.11.1/bin/flink run -m dev-hadoop-node-c:8081 -c "com.econ.powercloud.jobsTest.TestKafkaOffsetCheckpointJob" -d -s 'hdfs://nameservice1/flink1.11/flink-savepoints/savepoint-bb8b4b-99016a1c3e60' ~/powercloud-flink-1.0.20201016.jar --bootstrap.servers localhost:9092 Job has been submitted with JobID d6cb6e1a6f9c0816ac4b61a1df38ddeb [econ@dev-hadoop-node-c ~]$

观察"kafka-console-consumer.sh"消费topic "test2"的情况,没有新的消息被打印:

RdeMacBook-Pro:kafka r$ ./bin/kafka-console-consumer.sh --topic test2 --bootstrap-server localhost:9092 a1 a1 a2

再观察“消费组积压数量”,发现LAG值已经全部是0。

RdeMacBook-Pro:kafka r$ ./bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --group prod-econ-flink-TestKafkaOffsetCheckpointJob-local --describe; date; Consumer group 'prod-econ-flink-TestKafkaOffsetCheckpointJob-local' has no active members. TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID test 1 4 4 0 - - - test 0 3 3 0 - - - test 2 6 6 0 - - - 2020年10月18日 星期日 20时31分43秒 CST RdeMacBook-Pro:kafka r$

证明:flink使用savepoint启动作业,不会重复消费kafka数据,也会正确更新kafka的offset。

重申,以上试验证明:

  1. flink消费了kafka数据后,不会更新offset到kafka,直到checkpoint完成。
  2. flink在没有使用savepoint重启作业的时候,消费kafka的offset还是从kafka自身获取,存在重复消费数据的情况。
  3. flink使用savepoint重启作业,不会重复消费kafka数据,也会正确更新kafka的offset。
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