Flink autoscaler - Amazon EMR
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Flink autoscaler

Amazon EMR releases 6.15.0 and higher support Flink autoscaler. The job autoscaler functionality collects metrics from running Flink streaming jobs, and automatically scales the individual job vertexes. This reduces the backpressure and satisfies the utilization target that you set.

For more information, see the Autoscaler section of the Apache Flink Kubernetes Operator documentation.

  • Flink autoscaler is supported with Amazon EMR 6.15.0 and higher.

  • Flink autoscaler is supported only for streaming jobs.

  • Only adaptive scheduler is supported. The default scheduler is not supported.

  • We recommend that you enable cluster scaling to allow dynamic resource provision. Amazon EMR managed scaling is preferred `because the metric evaluation occurs every 5–10 seconds. At this interval, your cluster can more readily adjust to the change in the required cluster resources.

Use the following steps to enable the Flink autoscaler when you create an Amazon EMR on EC2 cluster.

  1. In the Amazon EMR console, create a new EMR cluster:

    1. Choose Amazon EMR release emr-6.15.0 or higher. Select the Flink application bundle, and select any other applications that you might want to include on your cluster.

    2. For the Cluster scaling and provisioning option, select Use EMR-managed scaling.

  2. In the Software settings section, enter the following configuration to enable Flink autoscaler. For testing scenarios, set the decision interval, metrics window interval, and stabilization interval to a lower value so that the job immediately makes a scaling decision for easier verification.

    [ { "Classification": "flink-conf", "Properties": { "job.autoscaler.enabled": "true", "jobmanager.scheduler": "adaptive", "job.autoscaler.stabilization.interval": "60s", "job.autoscaler.metrics.window": "60s", "job.autoscaler.decision.interval": "10s", "job.autoscaler.debug.logs.interval": "60s" } } ]
  3. Select or configure any other settings as you prefer them, and create the Flink autoscaler-enabled cluster.

This section covers most of the configurations that you can change based on your specific needs.


With time-based configurations like time, interval and window settings, the default unit when no unit is specified is milliseconds. So a value of 30 with no suffix equals 30 milliseconds. For other units of time, include the appropriate suffix of s for seconds, m for minutes, or h for hours.

Autoscaler fetches the job vertex level metrics for every few configurable time interval, converts them into scale actionables, estimates new job vertex parallelism, and recommends it to job scheduler. Metrics are collected only after the job restart time and cluster stabilization interval.

Config key Default value Description Example values
job.autoscaler.enabled false Enable autoscaling on your Flink cluster. true, false
job.autoscaler.decision.interval 60s Autoscaler decision interval. 30 (default unit is milliseconds), 5m, 1h
job.autoscaler.restart.time 3m Expected restart time to be used until the operator can determine it reliably from history. 30 (default unit is milliseconds), 5m, 1h
job.autoscaler.stabilization.interval 300s Stabilization period in which no new scaling will be executed. 30 (default unit is milliseconds), 5m, 1h
job.autoscaler.debug.logs.interval 300s Autoscaler debug logs interval. 30 (default unit is milliseconds), 5m, 1h

Autoscaler fetches the metrics, aggregates them over time based sliding window and these are evaluated into scaling decisions. The scaling decision history for each job vertex are utilised to estimate new parallelism. These have both age based expiry as well as history size (at-least 1).

Config key Default value Description Example values
job.autoscaler.metrics.window 600s Scaling metrics aggregation window size. 30 (default unit is milliseconds), 5m, 1h
job.autoscaler.history.max.count 3 Maximum number of past scaling decisions to retain per vertex. 1 to Integer.MAX_VALUE
job.autoscaler.history.max.age 24h Minimum number of past scaling decisions to retain per vertex. 30 (default unit is milliseconds), 5m, 1h

The parallelism of each job vertex is modified on the basis of target utilisation and bounded by the min-max parallelism limits. It’s not recommended to set target utilisation close to 100% (i.e value of 1) and the utilisation boundary works as a buffer to handle the intermediate load fluctuations.

Config key Default value Description Example values
job.autoscaler.target.utilization 0.7 Target vertex utilization. 0 - 1
job.autoscaler.target.utilization.boundary 0.4 Target vertex utilization boundary. Scaling won't be performed if the current processing rate is within [target_rate / (target_utilization - boundary), and (target_rate / (target_utilization + boundary)] 0 - 1
job.autoscaler.vertex.min-parallelism 1 The minimum parallelism that the autoscaler can use. 0 - 200
job.autoscaler.vertex.max-parallelism 200 The maximum parallelism the autoscaler can use. Note that this limit will be ignored if it is higher than the max parallelism configured in the Flink config or directly on each operator. 0 - 200

The job vertex needs extra resources to handle the pending events, or backlogs, that accumulate during the scale operation time period. This is also referred as the catch-up duration. If the time to process backlog exceeds the configured lag -threshold value, the job vertex target utilization increases to max level. This helps prevent unnecessary scaling operations while the backlog processes.

Config key Default value Description Example values
job.autoscaler.backlog-processing.lag-threshold 5m Lag threshold which will prevent unnecessary scalings while removing the pending messages responsible for the lag. 30 (default unit is milliseconds), 5m, 1h
job.autoscaler.catch-up.duration 15m The target duration for fully processing any backlog after a scaling operation. Set to 0 to disable backlog based scaling. 30 (default unit is milliseconds), 5m, 1h

Autoscaler doesn’t perform scale down operation immediately after a scale up operation within grace time period. This prevents un-necessary cycle of scale up-down-up-down operations caused by temporary load fluctuations.

We can use the scale down operation ratio to gradually decrease the parallelism and release resources to cater for temporary load spike. It also helps to prevent un-necessary minor scale up operation post major scale down operation.

We can detect an in-effective scale up operation based past job vertex scaling decision history to prevent further parallelism change.

Config key Default value Description Example values
job.autoscaler.scale-up.grace-period 1h Duration in which no scale down of a vertex is allowed after it has been scaled up. 30 (default unit is milliseconds), 5m, 1h
job.autoscaler.scale-down.max-factor 0.6 Max scale down factor. A value of 1 means no limit on scale down; 0.6 means job can only be scaled down with 60% of the original parallelism. 0 - 1
job.autoscaler.scale-up.max-factor 100000. Maximum scale up ratio. A value of 2.0 means job can only be scaled up with 200% of the current parallelism. 0 - Integer.MAX_VALUE
job.autoscaler.scaling.effectiveness.detection.enabled false Whether to enable detection of ineffective scaling operations and allowing the autoscaler to block further scale ups. true, false