Viewing Kinesis Data Analytics Metrics and Dimensions - Amazon Kinesis Data Analytics

Viewing Kinesis Data Analytics Metrics and Dimensions

When your Kinesis Data Analytics for Apache Flink application processes a data source, Kinesis Data Analytics reports the following metrics and dimensions to Amazon CloudWatch.

Application Metrics

Metric Unit Description Level Usage Notes
KPUs Count The number of Kinesis Processing Units (KPUs) currently in use. Application The application can dynamically scale the number of KPUs if autoscaling is enabled. For more information, see Automatic Scaling.
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.
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.
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.
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.
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.
lastCheckpointSize Bytes The total size of the last checkpoint Application You can use this metric to determine running application storage utilization. Determine the application's storage utilization as follows:
(<lastCheckpointSize> + <application's disk usage>) / (<Number of KPUs> * 50)

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.

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.
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. This value is calculated for all task managers using the following formula:
(Heap.Used / Heap.Committed)
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.
oldGenerationGCCount Count The total number of old garbage collection operations that have occurred across all task managers. Application
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.
numRecordsIn Count The total number of records this operator or task has received. Application, Operator, Task, Parallelism
numRecordsInPerSecond Count/Second The total number of records this operator or task has received per second. Application, Operator, Task, Parallelism
numRecordsOut Count The total number of records this operator or task has emitted. Application, Operator, Task, Parallelism You can use this metric to determine the total data sent by the application, operator, task, or parallelism over time
numRecordsOutPerSecond Count/Second The total number of records this operator or task has emitted per second. Application, Operator, Task, Parallelism
numLateRecordsDropped Count The number of records this operator or task has dropped due to arriving late. Application, Operator, Task, Parallelism
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

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

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

  1. Open the CloudWatch console at https://console.aws.amazon.com/cloudwatch/.

  2. In the navigation pane, choose Metrics.

  3. In the CloudWatch Metrics by Category pane for Amazon Kinesis Data Analytics, choose a metrics category.

  4. 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 Level in the Apache Log4j documentation.