Help Center/ MapReduce Service/ User Guide (Ankara Region)/ FusionInsight Manager Operation Guide/ SQL Inspector/ Configuring Spark SQL Inspection
Updated on 2024-11-29 GMT+08:00
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Configuring Spark SQL Inspection

Scenario

You can configure rules for Spark SQL inspection on FusionInsight Manager and configure rule parameters as you need.

Prerequisites

  • The cluster client that contains the Spark service has been installed in the /opt/hadoopclient directory.
  • Spark is running properly.
  • A tenant, for example, sparkstatic1 has been added to Tenant Resources.
  • For a cluster with Kerberos authentication enabled, a service user has been created, for example, user sparkuser. The user belongs to groups hive, hadoop, and supergroup, the primary group is hive, and the bound role is set to sparkstatic1.

Constraints

  • The default dynamic validity period of a rule is 6 minutes.
  • Only SQL jobs are supported.
  • Interception and blocking rules will interrupt SQL queries, so you need to set parameters of these rules properly based on the site requirements.
  • The static rule static_0007 is not required for Spark because it has a Cartesian product restriction (controlled by spark.sql.crossJoin.enabled, which is true by default). If the spark parameter is false, the static_0007 rule will not take effect.
  • Dynamic rules do work on carbon tables.
  • The dynamic rule dynamic_0002 supports SELECT, ALTER TABLE ADD PARTITION and ALTER TABLE DROP PARTITION. If you use a batch deletion statement that contains judgment conditions, for example, ALTER TABLE DROP PARTITION (pt < 10), the statement will be intercepted before the dynamic_0002 rule because the number of partitions is limited by spark.sql.dropPartitionsInBatch.limit, which is defaulted to 1000.
  • Blocking rules has execution latency. For example, if the running_0004 rule is used and the threshold of the scanned data volume is 10 GB, the statement may be blocked when the data volume is 15 GB or higher due to the determination period and task concurrency.
  • If a job does not exceed the threshold until the last several tasks are complete before the block action is triggered, the job cannot be canceled.
  • Blocking rule running_0004: The SQL execution duration includes the execution duration on the Driver side and the job running duration. When the SQL execution is blocked on the Driver side, the job cannot be canceled even if the execution duration exceeds the value of interruption threshold. This problem may occur when INSERT OVERWRITE is performed on a large number of partitions where storage and compute are decoupled.

Procedure

  1. Log in to FusionInsight Manager, click Cluster, and choose SQL Inspector. The SQL Inspector page is displayed.
  2. Add rules for Spark by referring to Adding an SQL Inspection.

    For details about the rules supported by the Spark SQL engine, see MRS SQL Inspection Rules.

    For example, add a rule whose ID is static_0001 to check whether count distinct appears more than two times in the SQL statement. If so, the system displays a hint.

    Figure 1 Adding a Spark SQL inspection rule

  3. Log in to the node where the Spark client is installed and run the following command to switch to the client installation directory.

    cd /opt/hadoopclient

    Run the following command to set environment variables:

    source bigdata_env

    source Spark/component_env

  4. Perform user authentication for clusters in security mode (Kerberos authentication enabled). Skip this step for clusters in normal mode (Kerberos authentication disabled).

    kinit Spark operation user

    Example:

    kinit sparkuser

    Enter the password as prompted and change the password upon your first login.

  5. Run the following command to log in to the spark-sql client:

    cd opt/client/Spark/spark/bin

    ./spark-sql

  6. Run the following SQL statement on the client to check whether the current rule takes effect:

    Run the following statement to create a table:

    create table table1(id int, name string) stored as parquet

    Run the following statement to query data:

    select count(distinct id),count(distinct id),count(distinct id),count(distinct id),count(distinct id),count(distinct id) from table1;

    If the number of times count distinct appears in the statement exceeds the threshold configured in 2, the following information is displayed:

    WARNING:  static_0001 Occurrence num of 'COUNT(DISTINCT)'(6) reaches the hint threshold(2)
    If the action set in the rule is Intercept, the following information is displayed: 
    Error in query: static_0001 Occurrence num of 'COUNT(DISTINCT)'(6) reaches the intercept threshold(2)

    In Spark Beeline, you can obtain SQL inspection details from logs.

    1. Log in to FusionInsight Manager and choose Cluster > Services > Yarn. On the Dashboard page, click the link next to ResourceManager WebUI to enter the Yarn web UI.
    2. Click the ID of the target application on the All Applications page. The application details page is displayed.

    3. Click Logs of the application. On the displayed page, click stdout logs to view SQL inspection details.

    1. For more Spark SQL inspection rules, see MRS SQL Inspection Rules.
    2. You can view query info in the /opt/hadoopclient/Spark/spark/audit/query.log path if you are using the Spark client. The log contains detailed running information and the corresponding SQL inspection information.

Parent topic: SQL Inspector