Application Metrics Kinesis Data Streams Connector Metrics Amazon MSK Connector Metrics Apache Zeppelin Metrics Viewing CloudWatch Metrics Metrics

Viewing Kinesis Data Analytics Metrics and Dimensions

This topic contains the following sections:

When your Amazon Kinesis Data Analytics for Apache Flink processes a data source, Kinesis Data Analytics 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 KDA 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 KDA 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, Kinesis Data Analytics 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 Kinesis Data Analytics 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 Kinesis Data Analytics 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 Kinesis Data Analytics 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 Kinesis Data Analytics. 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, Kinesis Data Analytics 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 KDA 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 KDA 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 KDA 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 KDA 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

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`

Viewing CloudWatch Metrics

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

To view metrics using the CloudWatch console

Open the CloudWatch console at https://console.aws.amazon.com/cloudwatch/.
In the navigation pane, choose Metrics.
In the CloudWatch Metrics by Category pane for Amazon Kinesis Data Analytics, choose a metrics category.
In the upper pane, scroll to view the full list of metrics.

To view metrics using the AWS 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. Kinesis Data Analytics for Apache Flink supports the following metrics levels:

Application: The application only reports the highest level of metrics for each application. Kinesis Data Analytics 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.

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