Metrics and dimensions in Managed Service for Apache Flink - Managed Service for Apache Flink
Application metricsKinesis Data Streams connector metricsAmazon MSK connector metricsApache Zeppelin metrics

Amazon Managed Service for Apache Flink was previously known as Amazon Kinesis Data Analytics for Apache Flink.

Metrics and dimensions in Managed Service for Apache Flink

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

*To apply the SUM statistic over a period of time (second/minute):

  • Select the metric at the correct Level. If you’re tracking the metric for an Operator, you need to select the corresponding operator metrics.

  • As Managed Service for Apache Flink takes 4 metric snapshots per minute, the following metric math should be used: m1/4 where m1 is the SUM statistic over a period (second/minute)

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

*To apply the SUM statistic over a period of time (second/minute):

  • Select the metric at the correct Level. If you’re tracking the metric for an Operator, you need to select the corresponding operator metrics.

  • As Managed Service for Apache Flink takes 4 metric snapshots per minute, the following metric math should be used: m1/4 where m1 is the SUM statistic over a period (second/minute)

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

*To apply the SUM statistic over a period of time (second/minute):

  • Select the metric at the correct Level. If you’re tracking the metric for an Operator, you need to select the corresponding operator metrics.

  • As Managed Service for Apache Flink takes 4 metric snapshots per minute, the following metric math should be used: m1/4 where m1 is the SUM statistic over a period (second/minute)

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 Application, Operator, Task, Parallelism

*To apply the SUM statistic over a period of time (second/minute):

  • Select the metric at the correct Level. If you’re tracking the metric for an Operator, you need to select the corresponding operator metrics.

  • As Managed Service for Apache Flink takes 4 metric snapshots per minute, the following metric math should be used: m1/4 where m1 is the SUM statistic over a period (second/minute)

The number of records this operator or task has dropped due to arriving late.
numRecordsOutPerSecond* Count/Second The total number of records this application, operator or task has emitted per second. Application, Operator, Task, Parallelism

*To apply the SUM statistic over a period of time (second/minute):

  • Select the metric at the correct Level. If you’re tracking the metric for an Operator, you need to select the corresponding operator metrics.

  • As Managed Service for Apache Flink takes 4 metric snapshots per minute, the following metric math should be used: m1/4 where m1 is the SUM statistic over a period (second/minute)

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.
KPUs* Count The total number of KPUs used by the application. Application

*This metric receives one sample per billing period (one hour). To visualize the number of KPUs over time, use MAX or AVG over a period of at least one (1) hour.

The KPU count includes the orchestration KPU. For more information, see Managed Service for Apache Flink Pricing.

Kinesis Data Streams connector metrics

AWS 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

AWS 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, AWS 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