Uso de alarmas de CloudWatch con Amazon Managed Service para Apache Flink - Managed Service para Apache Flink
Revisión de alarmas recomendadas

Amazon Managed Service para Apache Flink Amazon (Amazon MSF) se denominaba anteriormente Amazon Kinesis Data Analytics para Apache Flink.

Uso de alarmas de CloudWatch con Amazon Managed Service para Apache Flink

Con las alarmas de métricas de Amazon CloudWatch, puede ver una métrica determinada durante el periodo especificado. La alarma realiza una o varias acciones según el valor de la métrica o expresión con respecto a un umbral durante varios períodos de tiempo. Un ejemplo de acción es el envío de una enviar una notificación a un tema de Amazon Simple Notification Service (Amazon SNS).

Para obtener más información sobre las alarmas de CloudWatch, consulte Uso de las alarmas de Amazon CloudWatch.

Esta sección contiene las alarmas recomendadas para supervisar el servicio gestionado para las aplicaciones de Apache Flink.

La tabla describe las alarmas recomendadas y tiene las siguientes columnas:

  • Expresión métrica: la métrica o expresión métrica que se va a comprobar si se compara con el umbral.

  • Estadística: la estadística que se utiliza para comprobar la métrica, por ejemplo, el promedio.

  • Umbral: el uso de esta alarma requiere que determine un umbral que defina el límite del rendimiento esperado de la aplicación. Debe determinar este umbral supervisando la aplicación en condiciones normales.

  • Descripción: causas que podrían activar esta alarma y posibles soluciones para esta afección.

Expresiones de métricas Estadística Umbral Descripción
tiempo de inactividad > 0 Average 0 A downtime greater than zero indicates that the application has failed. If the value is larger than 0, the application is not processing any data. Recommended for all applications. The Tiempo de inactividad metric measures the duration of an outage. A downtime greater than zero indicates that the application has failed. For troubleshooting, see La aplicación se está reiniciando.
RATE (numberOfFailedCheckpoints) > 0 Average 0 This metric counts the number of failed checkpoints since the application started. Depending on the application, it can be tolerable if checkpoints fail occasionally. But if checkpoints are regularly failing, the application is likely unhealthy and needs further attention. We recommend monitoring RATE(numberOfFailedCheckpoints) to alarm on the gradient and not on absolute values. Recommended for all applications. Use this metric to monitor application health and checkpointing progress. The application saves state data to checkpoints when it's healthy. Checkpointing can fail due to timeouts if the application isn't making progress in processing the input data. For troubleshooting, see Agotamiento del tiempo para llegar al punto de control.
Operator.numRecordsOutPerSecond < threshold Average The minimum number of records emitted from the application during normal conditions. Recommended for all applications. Falling below this threshold can indicate that the application isn't making expected progress on the input data. For troubleshooting, see El rendimiento es demasiado lento.
records_lag_max|millisbehindLatest > threshold Maximum The maximum expected latency during normal conditions. If the application is consuming from Kinesis or Kafka, these metrics indicate if the application is falling behind and needs to be scaled in order to keep up with the current load. This is a good generic metric that is easy to track for all kinds of applications. But it can only be used for reactive scaling, i.e., when the application has already fallen behind. Recommended for all applications. Use the records_lag_max metric for a Kafka source, or the millisbehindLatest for a Kinesis stream source. Rising above this threshold can indicate that the application isn't making expected progress on the input data. For troubleshooting, see El rendimiento es demasiado lento.
lastCheckpointDuration > threshold Maximum The maximum expected checkpoint duration during normal conditions. Monitors how much data is stored in state and how long it takes to take a checkpoint. If checkpoints grow or take long, the application is continuously spending time on checkpointing and has less cycles for actual processing. At some points, checkpoints may grow too large or take so long that they fail. In addition to monitoring absolute values, customers should also considering monitoring the change rate with RATE(lastCheckpointSize) and RATE(lastCheckpointDuration). If the lastCheckpointDuration continuously increases, rising above this threshold can indicate that the application isn't making expected progress on the input data, or that there are problems with application health such as backpressure. For troubleshooting, see Crecimiento de estado ilimitado.
lastCheckpointSize > threshold Maximum The maximum expected checkpoint size during normal conditions. Monitors how much data is stored in state and how long it takes to take a checkpoint. If checkpoints grow or take long, the application is continuously spending time on checkpointing and has less cycles for actual processing. At some points, checkpoints may grow too large or take so long that they fail. In addition to monitoring absolute values, customers should also considering monitoring the change rate with RATE(lastCheckpointSize) and RATE(lastCheckpointDuration). If the lastCheckpointSize continuously increases, rising above this threshold can indicate that the application is accumulating state data. If the state data becomes too large, the application can run out of memory when recovering from a checkpoint, or recovering from a checkpoint might take too long. For troubleshooting, see Crecimiento de estado ilimitado.
heapMemoryUtilization > threshold Maximum This gives a good indication of the overall resource utilization of the application and can be used for proactive scaling unless the application is I/O bound. The maximum expected heapMemoryUtilization size during normal conditions, with a recommended value of 90 percent. You can use this metric to monitor the maximum memory utilization of task managers across the application. If the application reaches this threshold, you need to provision more resources. You do this by enabling automatic scaling or increasing the application parallelism. For more information about increasing resources, see Implementación del escalado de aplicaciones.
cpuUtilization > threshold Maximum This gives a good indication of the overall resource utilization of the application and can be used for proactive scaling unless the application is I/O bound. The maximum expected cpuUtilization size during normal conditions, with a recommended value of 80 percent. You can use this metric to monitor the maximum CPU utilization of task managers across the application. If the application reaches this threshold, you need to provision more resources You do this by enabling automatic scaling or increasing the application parallelism. For more information about increasing resources, see Implementación del escalado de aplicaciones.
threadsCount > threshold Maximum The maximum expected threadsCount size during normal conditions. You can use this metric to watch for thread leaks in task managers across the application. If this metric reaches this threshold, check your application code for threads being created without being closed.
(OldGarbageCollectionTime * 100) /60_000 durante un período de 1 minuto”) > threshold Maximum The maximum expected oldGarbageCollectionTime duration. We recommend setting a threshold such that typical garbage collection time is 60 percent of the specified threshold, but the correct threshold for your application will vary. If this metric is continually increasing, this can indicate that there is a memory leak in task managers across the application.
RATE(oldGarbageCollectionCount) > threshold Maximum The maximum expected oldGarbageCollectionCount under normal conditions. The correct threshold for your application will vary. If this metric is continually increasing, this can indicate that there is a memory leak in task managers across the application.
Operator.currentOutputWatermark - Operator.currentInputWatermark > threshold Minimum The minimum expected watermark increment under normal conditions. The correct threshold for your application will vary. If this metric is continually increasing, this can indicate that either the application is processing increasingly older events, or that an upstream subtask has not sent a watermark in an increasingly long time.