Help Center > > User Guide> Managing an Existing Cluster> Job Management> Running a SparkSql Job

Running a SparkSql Job

Updated at: Aug 17, 2021 GMT+08:00

You can submit programs developed by yourself to MRS to execute them, and obtain the results. This section describes how to submit a SparkSQL job on the MRS console. SparkSQL jobs are used for data query and analysis. Both SQL statements and scripts are supported. If SQL statements contain sensitive information, use Spark Script to submit them.

Prerequisites

You have uploaded the program packages and data files required for running jobs to OBS or HDFS.

Submitting a Job on the GUI

  1. Log in to the MRS management console.
  2. Choose Clusters > Active Clusters, select a running cluster, and click its name to switch to the cluster details page.
  3. If Kerberos authentication is enabled for the cluster, perform the following steps. If Kerberos authentication is not enabled for the cluster, skip this step.

    In the Basic Information area on the Dashboard tab page, click Click to synchronize on the right side of IAM User Sync to synchronize IAM users. For details, see Synchronizing IAM Users to MRS.

    • When the policy of the user group to which the IAM user belongs changes from MRS ReadOnlyAccess to MRS CommonOperations, MRS FullAccess, or MRS Administrator, wait for 5 minutes until the new policy takes effect after the synchronization is complete because the SSSD (System Security Services Daemon) cache of cluster nodes needs time to be updated. Then, submit a job. Otherwise, the job may fail to be submitted.
    • When the policy of the user group to which the IAM user belongs changes from MRS CommonOperations, MRS FullAccess, or MRS Administrator to MRS ReadOnlyAccess, wait for 5 minutes until the new policy takes effect after the synchronization is complete because the SSSD cache of cluster nodes needs time to be updated.

  4. Click the Jobs tab.
  5. Click Create. On the displayed Create Job page, set Type to SparkSql and configure SparkSql job information by referring to Table 1.

    Table 1 Job configuration information

    Parameter

    Description

    Name

    Job name. It contains 1 to 64 characters. Only letters, digits, hyphens (-), and underscores (_) are allowed.

    NOTE:

    You are advised to set different names for different jobs.

    SQL Type

    Submission type of the SQL statement

    • SQL
    • Script

    SQL Statement

    This parameter is valid only when SQL Type is set to SQL. Enter the SQL statement to be executed, and then click Check to check whether the SQL statement is correct. If you want to submit and execute multiple statements at the same time, use semicolons (;) to separate them.

    SQL File

    This parameter is valid only when SQL Type is set to Script. The path of the SQL file to be executed must meet the following requirements:

    • Contains a maximum of 1,023 characters, excluding special characters such as ;|&><'$. The parameter value cannot be empty or full of spaces.
    • The path of the program to be executed can be stored in HDFS or OBS. The path varies depending on the file system.
      • OBS: The path must start with obs://. Example: obs://wordcount/program/xxx.jar
      • HDFS: The path must start with /user. For details about how to import data to HDFS, see Importing Data.
    • For SparkScript and HiveScript, the path must end with .sql. For MapReduce, the path must end with .jar. For Flink and SparkSubmit, the path must end with .jar or .py. The .sql, .jar, and .py are case-insensitive.
    NOTE:

    A file path on OBS can start with obs://. To submit jobs in this format, you need to configure permissions for accessing OBS.

    • If the OBS permission control function is enabled during cluster creation, you can use the obs:// directory without extra configuration.
    • If the OBS permission control function is not enabled or is not supported when you create a cluster, configure the function by following instructions in Accessing OBS.

    Program Parameter

    (Optional) Used to configure optimization parameters such as threads, memory, and vCPUs for the job to optimize resource usage and improve job execution performance.

    Table 2 describes the common parameters of a running program.

    Service Parameter

    (Optional) It is used to modify service parameters for the job. The parameter modification applies only to the current job. To make the modification take effect permanently for the cluster, follow instructions in Configuring Service Parameters.

    To add multiple parameters, click on the right. To delete a parameter, click Delete on the right.

    Table 3 lists the common service configuration parameters.

    Command Reference

    Command submitted to the background for execution when a job is submitted.

    Table 2 Program parameters

    Parameter

    Description

    Example Value

    --conf

    Task configuration items to be added.

    spark.executor.memory=2G

    --driver-memory

    Running memory of a driver.

    2G

    --num-executors

    Number of executors to be started.

    5

    --executor-cores

    Number of executor cores.

    2

    --jars

    Additional dependency packages of a task, which is used to add the external dependency packages to the task.

    -

    --executor-memory

    Executor memory.

    2G

    Table 3 Service parameters

    Parameter

    Description

    Example Value

    fs.obs.access.key

    Key ID for accessing OBS.

    -

    fs.obs.secret.key

    Key corresponding to the key ID for accessing OBS.

    -

  6. Confirm job configuration information and click OK.

    After the job is created, you can manage it.

Submitting a Job in the Background

In MRS 3.x and later versions, the default installation path of the client is /opt/Bigdata/client. In MRS 3.x and earlier versions, the default installation path is /opt/client. For details, see the actual situation.

  1. Create a user for submitting jobs. For details, see Creating a User.

    In this example, a machine-machine user used in the user development scenario has been created, and user groups (hadoop and supergroup), the primary group (supergroup), and role permissions (System_administrator and default) have been correctly assigned to the user.

  2. On MRS Manager, choose System > User. In the Operation column of the new user, choose More > Download Authentication Credential.
  3. Log in to the node where the Spark client is located, upload the user authentication credential created in 2 to the /opt/ directory of the cluster, and run the following command to decompress the package:

    tar –xvf MRSTest _xxxxxx_keytab.tar

    After the decompression, you obtain the user.keytab and krb5.conf files.

  4. Before performing operations on the cluster, run the following commands:

    source /opt/Bigdata/client/bigdata_env

    cd $SPARK_HOME

  5. Open the spark-sql CLI and run the following SQL statement:

    ./bin/spark-sql --conf spark.yarn.principal=MRSTest --conf spark.yarn.keytab=/opt/user.keytab

    To execute the SQL file, you need to upload the SQL file (for example, to the /opt/ directory). After the file is uploaded, run the following command:

    ./bin/spark-sql --conf spark.yarn.principal=MRSTest --conf spark.yarn.keytab=/opt/user.keytab -f /opt/script.sql

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