Viewing Metrics and Dimensions in Managed Service for Apache Flink - Managed Service for Apache Flink
Application MetricsKinesis Data Streams Connector MetricsAmazon MSK Connector MetricsApache Zeppelin MetricsViewing CloudWatch MetricsMetrics
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Amazon Managed Service for Apache Flink was previously known as Amazon Kinesis Data Analytics for Apache Flink.

Viewing Metrics and Dimensions in Managed Service for Apache Flink

This topic contains the following sections:

When your Managed Service for Apache Flink processes a data source, Managed Service for Apache Flink reports the following metrics and dimensions to Amazon CloudWatch.

Application Metrics

Metric Unit Description Level Usage Notes
backPressuredTimeMsPerSecond* Milliseconds The time (in milliseconds) this task or operator is back pressured per second. Task, Operator, Parallelism

*Available for Managed Service for Apache Flink applications running Flink version 1.13 only.

These metrics can be useful in identifying bottlenecks in an application.
busyTimeMsPerSecond* Milliseconds The time (in milliseconds) this task or operator is busy (neither idle nor back pressured) per second. Can be NaN, if the value could not be calculated. Task, Operator, Parallelism

*Available for Managed Service for Apache Flink applications running Flink version 1.13 only.

These metrics can be useful in identifying bottlenecks in an application.
cpuUtilization Percentage Overall percentage of CPU utilization across task managers. For example, if there are five task managers, Managed Service for Apache Flink publishes five samples of this metric per reporting interval. Application You can use this metric to monitor minimum, average, and maximum CPU utilization in your application. The CPUUtilization metric only accounts for CPU usage of the TaskManager JVM process running inside the container.
containerCPUUtilization Percentage Overall percentage of CPU utilization across task manager containers in Flink application cluster. For example, if there are five task managers, correspondingly there are five TaskManager containers and Managed Service for Apache Flink publishes 2 * five samples of this metric per 1 minute reporting interval. Application

It is calculated per container as:

Total CPU time (in seconds) consumed by container * 100 / Container CPU limit (in CPUs/seconds)

The CPUUtilization metric only accounts for CPU usage of the TaskManager JVM process running inside the container. There are other components running outside the JVM within the same container. The containerCPUUtilization metric gives you a more complete picture, including all processes in terms of CPU exhaustion at the container and failures resulting from that.
containerMemoryUtilization Percentage Overall percentage of memory utilization across task manager containers in Flink application cluster. For example, if there are five task managers, correspondingly there are five TaskManager containers and Managed Service for Apache Flink publishes 2 * five samples of this metric per 1 minute reporting interval. Application

It is calculated per container as:

Container memory usage (bytes) * 100 / Container memory limit as per pod deployment spec (in bytes)

The HeapMemoryUtilization and ManagedMemoryUtilzations metrics only account for specific memory metrics like Heap Memory Usage of TaskManager JVM or Managed Memory (memory usage outside JVM for native processes like RocksDB State Backend). The containerMemoryUtilization metric gives you a more complete picture by including the working set memory, which is a better tracker of total memory exhaustion. Upon its exhaustion, it will result in Out of Memory Error for the TaskManager pod.
containerDiskUtilization Percentage Overall percentage of disk utilization across task manager containers in Flink application cluster. For example, if there are five task managers, correspondingly there are five TaskManager containers and Managed Service for Apache Flink publishes 2 * five samples of this metric per 1 minute reporting interval. Application

It is calculated per container as:

Disk usage in bytes * 100 / Disk Limit for container in bytes

For containers, it represents utilization of the filesystem on which root volume of the container is set up.
currentInputWatermark Milliseconds The last watermark this application/operator/task/thread has received Application, Operator, Task, Parallelism This record is only emitted for dimensions with two inputs. This is the minimum value of the last received watermarks.
currentOutputWatermark Milliseconds The last watermark this application/operator/task/thread has emitted Application, Operator, Task, Parallelism
downtime Milliseconds For jobs currently in a failing/recovering situation, the time elapsed during this outage. Application This metric measures the time elapsed while a job is failing or recovering. This metric returns 0 for running jobs and -1 for completed jobs. If this metric is not 0 or -1, this indicates that the Apache Flink job for the application failed to run.
fullRestarts Count The total number of times this job has fully restarted since it was submitted. This metric does not measure fine-grained restarts. Application You can use this metric to evaluate general application health. Restarts can occur during internal maintenance by Managed Service for Apache Flink. Restarts higher than normal can indicate a problem with the application.
heapMemoryUtilization Percentage Overall heap memory utilization across task managers. For example, if there are five task managers, Managed Service for Apache Flink publishes five samples of this metric per reporting interval. Application You can use this metric to monitor minimum, average, and maximum heap memory utilization in your application. The HeapMemoryUtilization only accounts for specific memory metrics like Heap Memory Usage of TaskManager JVM.
idleTimeMsPerSecond* Milliseconds The time (in milliseconds) this task or operator is idle (has no data to process) per second. Idle time excludes back pressured time, so if the task is back pressured it is not idle. Task, Operator, Parallelism

*Available for Managed Service for Apache Flink applications running Flink version 1.13 only.

These metrics can be useful in identifying bottlenecks in an application.
lastCheckpointSize Bytes The total size of the last checkpoint Application You can use this metric to determine running application storage utilization.

If this metric is increasing in value, this may indicate that there is an issue with your application, such as a memory leak or bottleneck.
lastCheckpointDuration Milliseconds The time it took to complete the last checkpoint Application This metric measures the time it took to complete the most recent checkpoint. If this metric is increasing in value, this may indicate that there is an issue with your application, such as a memory leak or bottleneck. In some cases, you can troubleshoot this issue by disabling checkpointing.
managedMemoryUsed* Bytes The amount of managed memory currently used. Application, Operator, Task, Parallelism

*Available for Managed Service for Apache Flink applications running Flink version 1.13 only.

This relates to memory managed by Flink outside the Java heap. It is used for the RocksDB state backend, and is also available to applications.
managedMemoryTotal* Bytes The total amount of managed memory. Application, Operator, Task, Parallelism

*Available for Managed Service for Apache Flink applications running Flink version 1.13 only.

This relates to memory managed by Flink outside the Java heap. It is used for the RocksDB state backend, and is also available to applications. The ManagedMemoryUtilzations metric only accounts for specific memory metrics like Managed Memory (memory usage outside JVM for native processes like RocksDB State Backend)
managedMemoryUtilization* Percentage Derived by managedMemoryUsed/managedMemoryTotal Application, Operator, Task, Parallelism

*Available for Managed Service for Apache Flink applications running Flink version 1.13 only.

This relates to memory managed by Flink outside the Java heap. It is used for the RocksDB state backend, and is also available to applications.
numberOfFailedCheckpoints Count The number of times checkpointing has failed. Application You can use this metric to monitor application health and progress. Checkpoints may fail due to application problems, such as throughput or permissions issues.
numRecordsIn Count The total number of records this application, operator, or task has received. Application, Operator, Task, Parallelism The metric's Level specifies whether this metric measures the total number of records the entire application, a specific operator, or a specific task has received.
numRecordsInPerSecond Count/Second The total number of records this application, operator or task has received per second. Application, Operator, Task, Parallelism The metric's Level specifies whether this metric measures the total number of records the entire application, a specific operator, or a specific task has received per second.
numRecordsOut Count The total number of records this application, operator or task has emitted. Application, Operator, Task, Parallelism The metric's Level specifies whether this metric measures the total number of records the entire application, a specific operator, or a specific task has emitted.
numLateRecordsDropped Count The number of records this operator or task has dropped due to arriving late. Application, Operator, Task, Parallelism
numRecordsOutPerSecond Count/Second The total number of records this application, operator or task has emitted per second. Application, Operator, Task, Parallelism The metric's Level specifies whether this metric measures the total number of records the entire application, a specific operator, or a specific task has emitted per second.
oldGenerationGCCount Count The total number of old garbage collection operations that have occurred across all task managers. Application
oldGenerationGCTime Milliseconds The total time spent performing old garbage collection operations. Application You can use this metric to monitor sum, average, and maximum garbage collection time.
threadCount Count The total number of live threads used by the application. Application This metric measures the number of threads used by the application code. This is not the same as application parallelism.
uptime Milliseconds The time that the job has been running without interruption. Application You can use this metric to determine if a job is running successfully. This metric returns -1 for completed jobs.

Kinesis Data Streams Connector Metrics

Amazon emits all records for Kinesis Data Streams in addition to the following:

Metric Unit Description Level Usage Notes
millisbehindLatest Milliseconds The number of milliseconds the consumer is behind the head of the stream, indicating how far behind current time the consumer is. Application (for Stream), Parallelism (for ShardId)

  • A value of 0 indicates that record processing is caught up, and there are no new records to process at this moment. A particular shard's metric can be specified by stream name and shard id.

  • A value of -1 indicates that the service has not yet reported a value for the metric.
bytesRequestedPerFetch Bytes The bytes requested in a single call to getRecords. Application (for Stream), Parallelism (for ShardId)

Amazon MSK Connector Metrics

Amazon emits all records for Amazon MSK in addition to the following:

Metric Unit Description Level Usage Notes
currentoffsets N/A The consumer's current read offset, for each partition. A particular partition's metric can be specified by topic name and partition id. Application (for Topic), Parallelism (for PartitionId)
commitsFailed N/A The total number of offset commit failures to Kafka, if offset committing and checkpointing are enabled. Application, Operator, Task, Parallelism Committing offsets back to Kafka is only a means to expose consumer progress, so a commit failure does not affect the integrity of Flink's checkpointed partition offsets.
commitsSucceeded N/A The total number of successful offset commits to Kafka, if offset committing and checkpointing are enabled. Application, Operator, Task, Parallelism
committedoffsets N/A The last successfully committed offsets to Kafka, for each partition. A particular partition's metric can be specified by topic name and partition id. Application (for Topic), Parallelism (for PartitionId)
records_lag_max Count The maximum lag in terms of number of records for any partition in this window Application, Operator, Task, Parallelism
bytes_consumed_rate Bytes The average number of bytes consumed per second for a topic Application, Operator, Task, Parallelism

Apache Zeppelin Metrics

For Studio notebooks, Amazon emits the following metrics at the application level: KPUs, cpuUtilization, heapMemoryUtilization, oldGenerationGCTime, oldGenerationGCCount, and threadCount. In addition, it emits the metrics shown in the following table, also at the application level.

Metric Unit Description Prometheus name
zeppelinCpuUtilization Percentage Overall percentage of CPU utilization in the Apache Zeppelin server. process_cpu_usage
zeppelinHeapMemoryUtilization Percentage Overall percentage of heap memory utilization for the Apache Zeppelin server. jvm_memory_used_bytes
zeppelinThreadCount Count The total number of live threads used by the Apache Zeppelin server. jvm_threads_live_threads
zeppelinWaitingJobs Count The number of queued Apache Zeppelin jobs waiting for a thread. jetty_threads_jobs
zeppelinServerUptime Seconds The total time that the server has been up and running. process_uptime_seconds

Viewing CloudWatch Metrics

You can view CloudWatch metrics for your application using the Amazon CloudWatch console or the Amazon CLI.

To view metrics using the CloudWatch console

  1. Open the CloudWatch console at https://console.amazonaws.cn/cloudwatch/.

  2. In the navigation pane, choose Metrics.

  3. In the CloudWatch Metrics by Category pane for Managed Service for Apache Flink, choose a metrics category.

  4. In the upper pane, scroll to view the full list of metrics.

To view metrics using the Amazon CLI

  • At a command prompt, use the following command.

    aws cloudwatch list-metrics --namespace "AWS/KinesisAnalytics" --region region

Setting CloudWatch Metrics Reporting Levels

You can control the level of application metrics that your application creates. Managed Service for Apache Flink supports the following metrics levels:

  • Application: The application only reports the highest level of metrics for each application. Managed Service for Apache Flink metrics are published at the Application level by default.

  • Task: The application reports task-specific metric dimensions for metrics defined with the Task metric reporting level, such as number of records in and out of the application per second.

  • Operator: The application reports operator-specific metric dimensions for metrics defined with the Operator metric reporting level, such as metrics for each filter or map operation.

  • Parallelism: The application reports Task and Operator level metrics for each execution thread. This reporting level is not recommended for applications with a Parallelism setting above 64 due to excessive costs.

    Note

    You should only use this metric level for troubleshooting because of the amount of metric data that the service generates. You can only set this metric level using the CLI. This metric level is not available in the console.

The default level is Application. The application reports metrics at the current level and all higher levels. For example, if the reporting level is set to Operator, the application reports Application, Task, and Operator metrics.

You set the CloudWatch metrics reporting level using the MonitoringConfiguration parameter of the CreateApplication action, or the MonitoringConfigurationUpdate parameter of the UpdateApplication action. The following example request for the UpdateApplication action sets the CloudWatch metrics reporting level to Task:

{
   "ApplicationName": "MyApplication",  
   "CurrentApplicationVersionId": 4,
   "ApplicationConfigurationUpdate": { 
      "FlinkApplicationConfigurationUpdate": { 
         "MonitoringConfigurationUpdate": { 
            "ConfigurationTypeUpdate": "CUSTOM",
            "MetricsLevelUpdate": "TASK"
         }
      }
   }
}

You can also configure the logging level using the LogLevel parameter of the CreateApplication action or the LogLevelUpdate parameter of the UpdateApplication action. You can use the following log levels:

  • ERROR: Logs potentially recoverable error events.

  • WARN: Logs warning events that might lead to an error.

  • INFO: Logs informational events.

  • DEBUG: Logs general debugging events.

For more information about Log4j logging levels, see Custom Log Levels in the Apache Log4j documentation.