Managing Optimizers

The optimizer is the execution unit for performing self-optimizing tasks on a table. To isolate optimizing tasks on different tables and support the deployment of optimizers in different environments, Amoro has proposed the concepts of optimizer containers and optimizer groups:

  • Optimizer container: Encapsulate the deployment method of optimizers, there are three implementations for now: flink container based on Flink streaming job, local container based on Java Application, and external container based on manually started by users.
  • Optimizer group: A collection of optimizers, where each table must select an optimizer group to perform optimizing tasks on it. Tables under the same optimizer group contribute resources to each other, and tables under different optimizer groups can be isolated in terms of optimizer resources.
  • Optimizer: The specific unit that performs optimizing tasks, usually with multiple concurrent units.

Optimizer container

Before using self-optimizing, you need to configure the container information in the configuration file. Opimizer container represents a specific set of runtime environment configuration, and the scheduling scheme of optimizer in that runtime environment. container includes three types: flink, local, and external.

Local container

Local conatiner is a way to start Optimizer by local process and supports multi-threaded execution of Optimizer tasks. It is recommended to be used only in demo or local deployment scenarios. If the environment variable for jdk is not configured, the user can configure java_home to point to the jdk root directory. If already configured, this configuration item can be ignored.

  - name: localContainer
    container-impl: com.netease.arctic.optimizer.LocalOptimizerContainer
      export.JAVA_HOME: "/opt/java"   # JDK environment

Flink container is a way to start Optimizer through Flink jobs. With Flink, you can easily deploy Optimizer on yarn clusters to support large-scale data scenarios. To use flink container, you need to add a new container configuration. The required properties include “flink-home”, and all environment variables that need to be exported can be configured in the “export.{env_arg}” property of the container’s properties. The commonly used configurations are as follows:

  • flink-home, download the Flink installation package and unzip it. Take Flink-1.12.7 as an example, download , assuming that it is extracted to /opt/ directory, then configure the value /opt/ flink-1.12.7/. Since the Flink distribution does not come with the hadoop compatible package flink-shaded-hadoop-2-uber-x.y.z.jar, you need to download it and copy it to the FLINK_HOME/lib directory. The flink-shaded-hadoop-2-uber-2.7.5-10.0.jar is generally sufficient and can be downloaded at:
  • HADOOP_CONF_DIR, which holds the configuration files for the hadoop cluster (including hdfs-site.xml, core-site.xml, yarn-site.xml ). If the hadoop cluster has kerberos authentication enabled, you need to prepare an additional krb5.conf and a keytab file for the user to submit tasks
  • JVM_ARGS, you can configure flink to run additional configuration parameters, here is an example of configuring krb5.conf, specify the address of krb5.conf to be used by Flink when committing via
  • HADOOP_USER_NAME, the username used to submit tasks to yarn
  • FLINK_CONF_DIR, the directory where flink_conf.yaml is located
  - name: flinkContainer
    container-impl: com.netease.arctic.optimizer.FlinkOptimizerContainer
      flink-home: /opt/flink/        #flink install home
      export.HADOOP_CONF_DIR: /etc/hadoop/conf/       #hadoop config dir
      export.HADOOP_USER_NAME: hadoop       #hadoop user submit on yarn
      export.JVM_ARGS:       #flink launch jvm args, like kerberos config when ues kerberos
      export.FLINK_CONF_DIR: /etc/hadoop/conf/        #flink config dir

External container

External container refers to the way in which the user manually starts the optimizer. The system has a built-in external container called external, so you don’t need to configure it manually.

Optimizer group

Optimizer group (optimizer resource group) is a concept introduced to divide Optimizer resources. An Optimizer Group can contain several optimizers with the same container implementation to facilitate the expansion and contraction of the resource group.

Add optimizer group

You can add an optimizer group on the Amoro dashboard by following these steps:

1.Click the “Add Group” button in the top left corner of the Optimizer Groups page. add-optimizer-group

2.Configure the newly added Optimizer group. config-optimizer-group

The following configuration needs to be filled in:

  • name: the name of the optimizer group, which can be seen in the list of optimizer groups on the front-end page.
  • container: the name of a container configured in containers.
  • properties: the default configuration under this group, is used as a configuration parameter for tasks when the optimize page is scaled out.

The optimizer group supports the following properties:

Property Container type Required Default Description
scheduling-policy All No quota The scheduler group scheduling policy, the default value is quota, it will be scheduled according to the quota resources configured for each table, the larger the table quota is, the more optimizer resources it can take. There is also a configuration balanced that will balance the scheduling of each table, the longer the table has not been optimized, the higher the scheduling priority will be.
taskmanager.memory flink Yes N/A The memory size of Flink TaskManager.
jobmanager.memory flink Yes N/A The memory size of Flink JobManager.
memory local Yes N/A The memory size of the local optimizer Java process.

Edit optimizer group

You can click the edit button on the Optimizer Groups page to modify the configuration of the Optimizer group.

Remove optimizer group

You can click the remove button on the Optimizer Groups page to delete the optimizer group, but only if the group is not referenced by any catalog or table and no optimizer belonging to this group is running.

Optimizer Scale-out and Release

Scale out optimizer

You can click the Scale-Out button on the Optimizer Groups page to scale out the optimizer for the corresponding optimizer group, and then click OK to start the optimizer for this optimizer group according to the parallelism configuration. If the optimizer runs normally, you will see an optimizer with the status RUNNING on the Optimizers page.


Release optimizer

You can click the Release button on the Optimizer page to release the optimizer.

release optimizer

Currently, only pptimizer scaled through the dashboard can be released on dashboard.

Deploy external optimizer

You can submit optimizer in your own Flink task development platform or local Flink environment with the following configuration. The main parameters include:

flink run -m yarn-cluster  -ytm {EXECUTOR_TASKMANAGER_MEMORY} -yjm {EXECUTOR_JOBMANAGER_MEMORY}  -c com.netease.arctic.optimizer.flink.FlinkOptimizer  {AMORO_HOME}/plugin/optimize/OptimizeJob.jar -a {AMS_THRIFT_SERVER_URL} -g {OPTIMIZE_GROUP_NAME} -p {EXECUTOR_PARALLELISM} -m {EXECUTOR_MEMORY}  --hb 10000

The description of the relevant parameters is shown in the following table:

Property Description
-ytm EXECUTOR_TASKMANAGER_MEMORY Flink task task manager memory size.
-yjm EXECUTOR_JOBMANAGER_MEMORY Flink task job mamanger memory size.
AMORO_HOME Amoro home directory
-a AMS_THRIFT_SERVER_URL The address of the AMS thrift service, for example: thrift://, can be obtained from the config.yaml configuration.
-g OPTIMIZE_GROUP_NAME Group name created in advance under external container.
-p EXECUTOR_PARALLELISM Optimizer parallelism usage.
-hb 10000 Heart beat interval with ams, should be smaller than configuration ams.optimizer.heart-beat-timeout in AMS configuration conf/config.yaml which is 60000 milliseconds by default