Skip to main content
版本：Next

监控指标

概览​

我们为节点增加了指标计算。 用户添加 with 选项 inlong.metric.labels 后 Sort 会计算指标，inlong.metric.labels 选项的值由三部分构成：groupId={groupId}&streamId={streamId}&nodeId={nodeId}。 用户可以使用 metric reporter 去上报数据。

指标​

支持的 extract 节点​

指标名Extract 节点描述
groupId_streamId_nodeId_numRecordsInkafka,mongodb-cdc,mysql-cdc,oracle-cdc,postgresql-cdc,pulsar,sqlserver-cdc输入记录数
groupId_streamId_nodeId_numBytesInkafka,mongodb-cdc,mysql-cdc,oracle-cdc,postgresql-cdc,pulsar,sqlserver-cdc输入字节数
groupId_streamId_nodeId_numRecordsInPerSecondkafka,mongodb-cdc,mysql-cdc,oracle-cdc,postgresql-cdc,pulsar,sqlserver-cdc每秒输入记录数
groupId_streamId_nodeId_numBytesInPerSecondkafka,mongodb-cdc,mysql-cdc,oracle-cdc,postgresql-cdc,pulsar,sqlserver-cdc每秒输入字节数

支持表级别指标​

它是用于整库同步场景

指标名Extract 节点描述
groupId_streamId_nodeId_database_table_numRecordsInmysql-cdc输入记录数
groupId_streamId_nodeId_database_schema_table_numRecordsInoracle-cdc,postgresql-cdc输入记录数
groupId_streamId_nodeId_database_collection_numRecordsInmongodb-cdc输入记录数
groupId_streamId_nodeId_database_table_numBytesInmysql-cdc输入字节数
groupId_streamId_nodeId_database_schema_table_numBytesInoracle-cdc,postgresql-cdc输入字节数
groupId_streamId_nodeId_database_collection_numBytesInmongodb-cdc输入字节数
groupId_streamId_nodeId_database_table_numRecordsInPerSecondmysql-cdc每秒输入记录数
groupId_streamId_nodeId_database_schema_table_numRecordsInPerSecondoracle-cdc,postgresql-cdc每秒输入记录数
groupId_streamId_nodeId_database_collection_numRecordsInPerSecondmongodb-cdc每秒输入记录数
groupId_streamId_nodeId_database_table_numBytesInPerSecondmysql-cdc每秒输入字节数
groupId_streamId_nodeId_database_schema_table_numBytesInPerSecondoracle-cdc,postgresql-cdc每秒输入字节数
groupId_streamId_nodeId_database_collection_numBytesInPerSecondmongodb-cdc每秒输入字节数
groupId_streamId_nodeId_database_collection_numSnapshotCreatepostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc尝试创建Checkpoint数
groupId_streamId_nodeId_database_collection_numSnapshotErrorpostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc创建Checkpoint异常数
groupId_streamId_nodeId_database_collection_numSnapshotCompletepostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc创建Checkpoint成功数
groupId_streamId_nodeId_database_collection_snapshotToCheckpointTimeLagpostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc从开始创建Checkpoint到完成创建延迟（毫秒）
groupId_streamId_nodeId_database_collection_numDeserializeSuccesspostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc反序列化成功数
groupId_streamId_nodeId_database_collection_numDeserializeSuccesspostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc反序列化异常数
groupId_streamId_nodeId_database_collection_deserializeTimeLagpostgresql-cdc,pulsar,mongodb-cdc,sqlserver-cdc反序列化延迟（毫秒）

支持的 load 节点​

指标名Load 节点描述
groupId_streamId_nodeId_numRecordsOutclickhouse,elasticsearch,greenplum,hbase,
hdfs,hive,iceberg,kafka,
mysql,oracle,postgresql,sqlserver,tdsql-postgresql		输出记录数
groupId_streamId_nodeId_numBytesOutclickhouse,elasticsearch,greenplum,hbase,
hdfs,hive,iceberg,kafka,
mysql,oracle,postgresql,sqlserver,tdsql-postgresql		输出字节数
groupId_streamId_nodeId_numRecordsOutPerSecondclickhouse,elasticsearch,greenplum,
hbase,hdfs,hive,iceberg,
kafka,mysql,oracle,postgresql,sqlserver,tdsql-postgresql		每秒输出记录数
groupId_streamId_nodeId_numBytesOutPerSecondclickhouse,elasticsearch,greenplum,
hbase,hdfs,hive,iceberg,kafka,
mysql,oracle,postgresql,sqlserver,tdsql-postgresql		每秒输出字节数
groupId_streamId_nodeId_dirtyRecordsOutclickhouse,elasticsearch,greenplum,hbase,
hdfs,hive,iceberg,kafka,mysql,
oracle,postgresql,sqlserver,tdsql-postgresql		输出脏数据记录数
groupId_streamId_nodeId_dirtyBytesOutclickhouse,elasticsearch,greenplum,hbase,
hdfs,hive,iceberg,kafka,mysql,
oracle,postgresql,sqlserver,tdsql-postgresql		输出脏数据字节数

支持表级别指标​

它是用于整库同步场景

指标名Load node描述
groupId_streamId_nodeId_database_table_numRecordsOutdoris,iceberg,starRocks输出记录数据
groupId_streamId_nodeId_database_schema_table_numRecordsOutpostgresql输出记录数据
groupId_streamId_nodeId_topic_numRecordsOutkafka输出记录数据
groupId_streamId_nodeId_database_table_numBytesOutdoris,iceberg,starRocks输出字节数据
groupId_streamId_nodeId_database_schema_table_numBytesOutpostgresql输出字节数据
groupId_streamId_nodeId_topic_numBytesOutkafka输出字节数据
groupId_streamId_nodeId_database_table_numRecordsOutPerSeconddoris,iceberg,starRocks每秒记录数据
groupId_streamId_nodeId_database_schema_table_numRecordsOutPerSecondpostgresql每秒记录数据
groupId_streamId_nodeId_topic_numRecordsOutPerSecondkafka每秒记录数据
groupId_streamId_nodeId_database_table_numBytesOutPerSeconddoris,iceberg,starRocks每秒输出字节数量
groupId_streamId_nodeId_database_schema_table_numBytesOutPerSecondpostgresql每秒输出字节数量
groupId_streamId_nodeId_topic_numBytesOutPerSecondkafka每秒输出字节数量
groupId_streamId_nodeId_database_table_dirtyRecordsOutdoris,iceberg,starRocks输出脏数据记录数
groupId_streamId_nodeId_database_schema_table_dirtyRecordsOutpostgresql输出脏数据记录数
groupId_streamId_nodeId_topic_dirtyRecordsOutkafka输出脏数据记录数
groupId_streamId_nodeId_database_table_dirtyBytesOutdoris,iceberg,starRocks输出脏数据字节数据
groupId_streamId_nodeId_database_schema_table_dirtyBytesOutpostgresql输出脏数据字节数据
groupId_streamId_nodeId_topic_dirtyBytesOutkafka输出脏数据字节数据
groupId_streamId_nodeId_numSerializeSuccessstarRocks序列化成功数
groupId_streamId_nodeId_numSerializeErrorstarRocks序列化异常数
groupId_streamId_nodeId_serializeTimeLagstarRocks序列化延迟（毫秒）
groupId_streamId_nodeId_numSnapshotCreatestarRocks尝试创建Checkpoint数
groupId_streamId_nodeId_numSnapshotErrorstarRocks创建Checkpoint异常数
groupId_streamId_nodeId_numSnapshotCompletestarRocks创建Checkpoint成功数
groupId_streamId_nodeId_snapshotToCheckpointTimeLagstarRocks从开始创建Checkpoint到完成创建延迟（毫秒）

用法​

这里将介绍一个同步 MYSQL 数据到 PostgreSQL 的例子，同时介绍指标的使用。

  • flink sql 的使用

 create table `table_groupId_streamId_nodeId1`(
     `id` INT,
    `name` INT,
    `age` STRING,
    PRIMARY KEY(`id`) NOT ENFORCED)
    WITH (
        'connector' = 'mysql-cdc-inlong',
        'hostname' = 'xxxx',
        'username' = 'xxx',
        'password' = 'xxx',
        'database-name' = 'test',
        'scan.incremental.snapshot.enabled' = 'true',
        'server-time-zone' = 'GMT+8',
        'table-name' = 'user',
        'inlong.metric' = 'mysqlGroup&mysqlStream&mysqlNode1'
);

 CREATE TABLE `table_groupId_streamId_nodeId2`(
     PRIMARY KEY (`id`) NOT ENFORCED,
     `id` INT,
     `name` STRING,
     `age` INT)
     WITH (
         'connector' = 'jdbc-inlong',
         'url' = 'jdbc:postgresql://ip:5432/postgres',
         'username' = 'postgres',
         'password' = 'inlong',
         'table-name' = 'public.user',
         'inlong.metric.labels' = 'groupId=xxgroup&streamId=xxstream&nodeId=xxnode'
         );

 INSERT INTO `table_groupId_streamId_nodeId2`
 SELECT
     `id`,
     `name`,
     `age`
 FROM `table_groupId_streamId_nodeId1`;
  • 要将指标上报到外部系统，我们可以在 flink-conf.yaml 中添加 metric report 配置（以Prometheus为例）
metric.reporters: promgateway
metrics.reporter.promgateway.class: org.apache.flink.metrics.prometheus.PrometheusPushGatewayReporter
metrics.reporter.promgateway.host: ip
metrics.reporter.promgateway.port: 9091
metrics.reporter.promgateway.interval: 60 SECONDS

ip 和 port 是你的 pushgateway 的配置。

  • 执行上面的sql后，我们可以访问 pushgateway 的 url: http://ip:port

当我们使用的 metric report 是 org.apache.flink.metrics.prometheus.PrometheusPushGatewayReporter 指标名将添加前缀 flink_taskmanager_job_task_operator。
我们可以看到完整的指标名如下:
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numRecordsIn,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numBytesIn,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numRecordsInPerSecond,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numBytesInPerSecond,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numRecordsOut,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numBytesOut,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numRecordsOutPerSecond,
flink_taskmanager_job_task_operator_groupId_streamId_nodeId_numBytesOutPerSecond.