Autoscaler 参数自动调整

Amazon EMR 7.2.0 及更高版本测量会应用自动扩缩决策所需的实际重启时间。在 7.1.0 及更低版本中，必须使用配置 job.autoscaler.restart.time 来手动配置估计的最长重启时间。通过使用配置 job.autoscaler.restart.time-tracking.enabled，您只需输入第一次缩放的重启时间。之后，Operator 会记录实际的重启时间，并将其用于后续缩放。

要启用此跟踪，请使用以下命令：

job.autoscaler.restart.time-tracking.enabled: true

以下是重新缩放时间估计的相关配置。

下面是一个示例部署规范，可用来尝试重新缩放时间估计：

apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
  name: autoscaling-example
spec:
  flinkVersion: v1_18
  flinkConfiguration:

    # Autoscaler parameters
    job.autoscaler.enabled: "true"
    job.autoscaler.scaling.enabled: "true"
    job.autoscaler.stabilization.interval: "5s"
    job.autoscaler.metrics.window: "1m"
    
    job.autoscaler.restart.time-tracking.enabled: "true"
    job.autoscaler.restart.time: "2m"
    job.autoscaler.restart.time-tracking.limit: "10m"
    
    jobmanager.scheduler: adaptive
    taskmanager.numberOfTaskSlots: "1"
    pipeline.max-parallelism: "12"

  executionRoleArn: <JOB ARN>
  emrReleaseLabel: emr-7.8.0-flink-latest
  jobManager:
    highAvailabilityEnabled: false
    storageDir: s3://<s3_bucket>/flink/autoscaling/ha/
    replicas: 1
    resource:
      memory: "1024m"
      cpu: 0.5
  taskManager:
    resource:
      memory: "1024m"
      cpu: 0.5
  job:
    jarURI: s3://<s3_bucket>/some-job-with-back-pressure
    parallelism: 1
    upgradeMode: stateless

要模拟背压，请使用以下部署规范。

job:
    jarURI: s3://<s3_bucket>/pyflink-script.py
    entryClass: "org.apache.flink.client.python.PythonDriver"
    args: ["-py", "/opt/flink/usrlib/pyflink-script.py"]
    parallelism: 1
    upgradeMode: stateless

将以下 Python 脚本上传到 S3 存储桶。

import logging
import sys
import time
import random

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment

TABLE_NAME="orders"
QUERY=f"""
CREATE TABLE {TABLE_NAME} (
  id INT,
  order_time AS CURRENT_TIMESTAMP,
  WATERMARK FOR order_time AS order_time - INTERVAL '5' SECONDS
)
WITH (
  'connector' = 'datagen',
  'rows-per-second'='10',
  'fields.id.kind'='random',
  'fields.id.min'='1',
  'fields.id.max'='100'
);
"""

def create_backpressure(i):
    time.sleep(2)
    return i

def autoscaling_demo():
    env = StreamExecutionEnvironment.get_execution_environment()
    t_env = StreamTableEnvironment.create(env)
    t_env.execute_sql(QUERY)
    res_table = t_env.from_path(TABLE_NAME)

    stream =  t_env.to_data_stream(res_table) \
      .shuffle().map(lambda x: create_backpressure(x))\
      .print()
    env.execute("Autoscaling demo")

if __name__ == '__main__':
    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(message)s")
    autoscaling_demo()

要验证重缩时间估计是否有效，请确保已启用 Flink Operator 的 DEBUG 级别日志记录。下面的示例演示了如何更新 Helm 图表文件 values.yaml。然后重新安装更新后的 Helm 图表并再次运行 Flink 作业。

log4j-operator.properties: |+
  # Flink Operator Logging Overrides
  rootLogger.level = DEBUG

获取主容器组（pod）的名称。

ip=$(kubectl get configmap -n $NAMESPACE <job-name>-cluster-config-map -o json | jq -r ".data[\"org.apache.flink.k8s.leader.restserver\"]" | awk -F: '{print $2}' | awk -F '/' '{print $3}')

kubectl get pods -n $NAMESPACE -o json | jq -r ".items[] | select(.status.podIP == \"$ip\") | .metadata.name"

运行以下命令，获取指标评估中使用的实际重启时间。

kubectl logs <FLINK-OPERATOR-POD-NAME> -c flink-kubernetes-operator -n <OPERATOR-NAMESPACE> -f | grep "Restart time used in scaling summary computation"

您应该会看到类似下面的日志。请注意，只有第一次缩放才会使用 job.autoscaler.restart.time。后续缩放使用观察到的重启时间。

2024-05-16 17:17:32,590 o.a.f.a.ScalingExecutor        [DEBUG][default/autoscaler-example] Restart time used in scaling summary computation: PT2M
2024-05-16 17:19:03,787 o.a.f.a.ScalingExecutor        [DEBUG][default/autoscaler-example] Restart time used in scaling summary computation: PT14S
2024-05-16 17:19:18,976 o.a.f.a.ScalingExecutor        [DEBUG][default/autoscaler-example] Restart time used in scaling summary computation: PT14S
2024-05-16 17:20:50,283 o.a.f.a.ScalingExecutor        [DEBUG][default/autoscaler-example] Restart time used in scaling summary computation: PT14S
2024-05-16 17:22:21,691 o.a.f.a.ScalingExecutor        [DEBUG][default/autoscaler-example] Restart time used in scaling summary computation: PT14S

开源内置 Flink Autoscaler 使用大量指标来做出最佳缩放决策。但在计算时使用的默认值适用于大多数工作负载，对于给定作业来说可能不是最佳值。Amazon EMR on EKS 版本的 Flink Operator 中添加的自动调整功能会查看针对特定捕获指标观察到的历史趋势，然后相应地尝试计算为给定作业定制的最佳值。

要启用自动调整，必须完成以下操作：

下面是一个示例部署规范，可用来尝试自动调整。

apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
  name: autoscaling-example
spec:
  flinkVersion: v1_18
  flinkConfiguration:

    # Autotuning parameters
    kubernetes.operator.job.autoscaler.autotune.enable: "true"
    kubernetes.operator.job.autoscaler.autotune.metrics.history.max.count: "2"
    kubernetes.operator.job.autoscaler.autotune.metrics.restart.count: "1"
    metrics.job.status.enable: TOTAL_TIME

    # Autoscaler parameters
    kubernetes.operator.job.autoscaler.enabled: "true"
    kubernetes.operator.job.autoscaler.scaling.enabled: "true"
    kubernetes.operator.job.autoscaler.stabilization.interval: "5s"
    kubernetes.operator.job.autoscaler.metrics.window: "1m"

    jobmanager.scheduler: adaptive

    taskmanager.numberOfTaskSlots: "1"
    state.savepoints.dir: s3://<S3_bucket>/autoscaling/savepoint/
    state.checkpoints.dir: s3://<S3_bucket>/flink/autoscaling/checkpoint/
    pipeline.max-parallelism: "4"

  executionRoleArn: <JOB ARN>
  emrReleaseLabel: emr-6.14.0-flink-latest
  jobManager:
    highAvailabilityEnabled: true
    storageDir: s3://<S3_bucket>/flink/autoscaling/ha/
    replicas: 1
    resource:
      memory: "1024m"
      cpu: 0.5
  taskManager:
    resource:
      memory: "1024m"
      cpu: 0.5
  job:
    jarURI: s3://<S3_bucket>/some-job-with-back-pressure
    parallelism: 1
    upgradeMode: last-state

要模拟背压，请使用以下部署规范。

  job:
    jarURI: s3://<S3_bucket>/pyflink-script.py
    entryClass: "org.apache.flink.client.python.PythonDriver"
    args: ["-py", "/opt/flink/usrlib/pyflink-script.py"]
    parallelism: 1
    upgradeMode: last-state

将以下 Python 脚本上传到 S3 存储桶。

import logging
import sys
import time
import random

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment

TABLE_NAME="orders"
QUERY=f"""
CREATE TABLE {TABLE_NAME} (
  id INT,
  order_time AS CURRENT_TIMESTAMP,
  WATERMARK FOR order_time AS order_time - INTERVAL '5' SECONDS
)
WITH (
  'connector' = 'datagen',
  'rows-per-second'='10',
  'fields.id.kind'='random',
  'fields.id.min'='1',
  'fields.id.max'='100'
);
"""

def create_backpressure(i):
    time.sleep(2)
    return i

def autoscaling_demo():
    env = StreamExecutionEnvironment.get_execution_environment()
    t_env = StreamTableEnvironment.create(env)
    t_env.execute_sql(QUERY)
    res_table = t_env.from_path(TABLE_NAME)

    stream =  t_env.to_data_stream(res_table) \
      .shuffle().map(lambda x: create_backpressure(x))\
      .print()
    env.execute("Autoscaling demo")

if __name__ == '__main__':
    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(message)s")
    autoscaling_demo()

要验证 Autosutiner 是否正在运行，请使用以下命令。请注意，必须对 Flink Operator 使用您自己的主容器组（pod）信息。

首先，获取主容器组（pod）的名称。

ip=$(kubectl get configmap -n $NAMESPACE <job-name>-cluster-config-map -o json | jq -r ".data[\"org.apache.flink.k8s.leader.restserver\"]" | awk -F: '{print $2}' | awk -F '/' '{print $3}')

kubectl get pods -n $NAMESPACE -o json | jq -r ".items[] | select(.status.podIP == \"$ip\") | .metadata.name"

获取主容器组（pod）的名称后，您可以运行以下命令。

kubectl logs -n $NAMESPACE  -c flink-kubernetes-operator --follow <YOUR-FLINK-OPERATOR-POD-NAME>  | grep -E 'EmrEks|autotun|calculating|restart|autoscaler'

您应该会看到类似下面的日志。

[m[33m2023-09-13 20:10:35,941[m [36mc.a.c.f.k.o.a.EmrEksMetricsAutotuner[m [36m[DEBUG][flink/autoscaling-example] Using the latest Emr Eks Metric for calculating restart.time for autotuning: EmrEksMetrics(restartMetric=RestartMetric(restartingTime=65, numRestarts=1))

[m[33m2023-09-13 20:10:35,941[m [36mc.a.c.f.k.o.a.EmrEksMetricsAutotuner[m [32m[INFO ][flink/autoscaling-example] Calculated average restart.time metric via autotuning to be: PT0.065S