This topic describes how to use the Spark-SQL command-line tool and provides sample code.

Prerequisites

The Spark-SQL package is obtained.

You can click here to download the dla-spark-toolkit.tar.gz package or use the following wget command to download this package.
wget https://dla003.oss-cn-hangzhou.aliyuncs.com/dla_spark_toolkit_1/dla-spark-toolkit.tar.gz
After you download the package, decompress it.
 tar zxvf dla-spark-toolkit.tar.gz
Note To use the Spark-SQL command-line tool, make sure that JDK 8 or later is installed.

Procedure

  1. View help information.
    • Run the following command to view the help information:
      cd /path/to/dla-spark-toolkit
      ./bin/spark-sql --help
    • After the preceding command is executed, the following result is returned:
      ./spark-sql [options] [cli option]
      
      Options:
        --keyId                     Your ALIYUN_ACCESS_KEY_ID, required
        --secretId                  Your ALIYUN_ACCESS_KEY_SECRET, required
        --regionId                  Your Cluster Region Id, required
        --vcName                    Your Virtual Cluster Name, required
        --oss-keyId                 Your ALIYUN_ACCESS_KEY_ID to upload local resource to oss
                                    default is same as --keyId
        --oss-secretId              Your ALIYUN_ACCESS_KEY_SECRET, default is same as --secretId
        --oss-endpoint              Oss endpoint where the resource will upload. default is http://oss-$regionId.aliyuncs.com
        --oss-upload-path           the user oss path where the resource will upload
                                    If you want to upload a local jar package to the OSS directory,
                                    you need to specify this parameter
      
      
      
        --class CLASS_NAME          Your application's main class (for Java / Scala apps).
        --name NAME                 A name of your application.
        --jars JARS                 Comma-separated list of jars to include on the driver
                                    and executor classpaths.
      
        --conf PROP=VALUE           Arbitrary Spark configuration property
        --help, -h                  Show this help message and exit.
        --driver-resource-spec      Indicates the resource specifications used by the driver:
                                    small | medium | large
        --executor-resource-spec    Indicates the resource specifications used by the executor:
                                    small | medium | large
        --num-executors             Number of executors to launch
        --properties-file           Default properties file location, only local files are supported
        --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place
                                    on the PYTHONPATH for Python apps.
        --files FILES               Comma-separated list of files to be placed in the working
                                    directory of each executor. File paths of these files
                                    in executors can be accessed via SparkFiles.get(fileName).
                                    Specially, you can pass in a custom log output format file named `log4j.properties`
                                    Note: The file name must be `log4j.properties` to take effect
      
        --status job_id             If given, requests the status and details of the job specified
        --verbose                   print more messages, enable spark-submit print job status and more job details.
      
        List Spark Job Only:
        --list                      List Spark Job, should use specify --vcName and --regionId
        --pagenumber, -pn           Set page number which want to list (default: 1)
        --pagesize, -ps             Set page size which want to list (default: 1)
      
        Get Job Log Only:
        --get-log job_id            Get job log
      
        Kill Spark Job Only:
        --kill job_id,job_id        Comma-separated list of job to kill spark job with specific ids
      
        Spark Offline SQL options:
        -e <quoted-query-string>   SQL from command line
        -f <filename>              SQL from files
  2. Use the spark-defaults.conf file to configure common parameters.
    The spark-defaults.conf file allows you to configure the following parameters. Only the common parameters under spark conf are listed.
    #  cluster information
    # AccessKeyId
    #keyId =
    #  AccessKeySecret
    #secretId =
    #  RegionId
    #regionId =
    #  set vcName
    #vcName =
    #  set OssUploadPath, if you need upload local resource
    #ossUploadPath =
    
    ##spark conf
    #  driver specifications : small 1c4g | medium 2c8g | large 4c16g
    #spark.driver.resourceSpec =
    #  executor instance number
    #spark.executor.instances =
    #  executor specifications : small 1c4g | medium 2c8g | large 4c16g
    #spark.executor.resourceSpec =
    #  when use ram,  role arn
    #spark.dla.roleArn =
    #  when use option -f or -e, set catalog implementation
    #spark.sql.catalogImplementation =
    #  config dla oss connectors
    #spark.dla.connectors = oss
    #  config eni, if you want to use eni
    #spark.dla.eni.enable =
    #spark.dla.eni.vswitch.id =
    #spark.dla.eni.security.group.id =
    #  config log location, need an oss path to store logs
    #spark.dla.job.log.oss.uri =
    #  config spark access dla metadata
    #spark.sql.hive.metastore.version = dla
    Note
    • The spark-submit scripts automatically read the spark-defaults.conf file in the conf folder.
    • Command line parameters take precedence over the spark-defaults.conf file.
    • For mappings between regions and regionIds, see Regions and zones.
  3. Submit an offline SQL job.
    The Spark-SQL command-line tool provides -e to execute multiple SQL statements that are separated by semicolons (;). This tool also provides -f to execute statements in an SQL file. Each SQL statement in the file ends with a semicolon (;). You can place the configuration specified by the conf field into the spark-defaults.conf file in the conf folder and submit the file in the following format:
    $ ./bin/spark-sql \
    --verbose \
    --name offlinesql \
    -e "select * from t1;insert into table t1 values(4,'test');select * from t1" 
    
    
    ## You can also place SQL statements in the file. Separate statements in the file with semicolons (;) and use the -f option to specify the directory where the SQL file is saved.
    
    $ ./bin/spark-sql \
    --verbose \
    --name offlinesql \
    -f /path/to/your/sql/file
    
    
    ## The following result is returned:
    ++++++++++++++++++executing sql: select * from t1
    | id|name|
    |  1|  zz|
    |  2|  xx|
    |  3|  yy|
    |  4|test|
    ++++++++++++++++++ end ++++++++++++++++++
    ++++++++++++++++++executing sql: insert into table t1 values(4,'test')
    ||
    ++++++++++++++++++ end ++++++++++++++++++
    ++++++++++++++++++executing sql: select * from t1
    | id|name|
    |  1|  zz|
    |  2|  xx|
    |  3|  yy|
    |  4|test|