Version: 1.3.2

Spark Engine

This article mainly introduces the installation, use and configuration of the Spark engine plugin in Linkis.

1. Preliminary work

1.1 Engine installation

If you wish to use the spark engine on your server, you need to ensure that the following environment variables are set correctly and that the engine's starting user has these environment variables.

It is strongly recommended that you check these environment variables for the executing user before executing a spark job.

Environment variable name	Environment variable content	Remarks
JAVA_HOME	JDK installation path	Required
HADOOP_HOME	Hadoop installation path	Required
HADOOP_CONF_DIR	Hadoop configuration path	required
HIVE_CONF_DIR	Hive configuration path	required
SPARK_HOME	Spark installation path	Required
SPARK_CONF_DIR	Spark configuration path	Required
python	python	It is recommended to use anaconda's python as the default python

1.2 Environment verification

Verify that Spark is successfully installed by pyspark

pyspark

#After entering the pyspark virtual environment, the spark logo appears, indicating that the environment is successfully installed
Welcome to
      ______
     /__/__ ___ _____/ /__
    _\ \/ _ \/ _ `/ __/ '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.4.3
      /_/

Using Python version 2.7.13 (default, Sep 30 2017 18:12:43)
SparkSession available as 'spark'.

2. Engine plugin installation default engine

The Spark engine plugin is included in the binary installation package released by linkis by default, and users do not need to install it additionally.

In theory Linkis supports all versions of spark2.x and above. The default supported version is Spark2.4.3. If you want to use another version of spark, such as spark2.1.0, you just need to modify the version of the plugin spark and compile it. Specifically, you can find the linkis-engineplugin-spark module, change the value of the <spark.version> tag in the maven dependency to 2.1.0, and then compile this module separately.

EngineConnPlugin engine plugin installation

3. Using the `spark` engine

3.1 Submitting tasks via `Linkis-cli`

# codeType correspondence py-->pyspark sql-->sparkSQL scala-->Spark scala
sh ./bin/linkis-cli -engineType spark-2.4.3 -codeType sql -code "show databases"  -submitUser hadoop -proxyUser hadoop

# You can specify the yarn queue in the submission parameter by -confMap wds.linkis.yarnqueue=dws
sh ./bin/linkis-cli -engineType spark-2.4.3 -codeType sql  -confMap wds.linkis.yarnqueue=dws -code "show databases"  -submitUser hadoop -proxyUser hadoop

More Linkis-Cli command parameter reference: Linkis-Cli usage

3.2 Submitting tasks through `Linkis SDK`

Linkis provides SDK of Java and Scala to submit tasks to Linkis server. For details, please refer to JAVA SDK Manual. For Spark tasks you only need to modify the EngineConnType and CodeType parameters in Demo:

Map<String, Object> labels = new HashMap<String, Object>();
labels.put(LabelKeyConstant.ENGINE_TYPE_KEY, "spark-2.4.3"); // required engineType Label
labels.put(LabelKeyConstant.USER_CREATOR_TYPE_KEY, "hadoop-IDE");// required execute user and creator
labels.put(LabelKeyConstant.CODE_TYPE_KEY, "sql"); // required codeType py,sql,scala

3.3 Submitting tasks by submitting the jar package

Through OnceEngineConn submit tasks (through the spark-submit submit jar package mission), submission for reference org.apache.linkis.com putation.Client.SparkOnceJobTest.

public class SparkOnceJobTest {

    public static void main(String[] args)  {

        LinkisJobClient.config().setDefaultServerUrl("http://127.0.0.1:9001");

        String submitUser = "linkis";
        String engineType = "spark";

        SubmittableSimpleOnceJob onceJob =
                // region
                LinkisJobClient.once().simple().builder()
                        .setCreateService("Spark-Test")
                        .setMaxSubmitTime(300000)
                        .setDescription("SparkTestDescription")
                        .addExecuteUser(submitUser)
                        .addJobContent("runType", "jar")
                        .addJobContent("spark.app.main.class", "org.apache.spark.examples.JavaWordCount")
                        // Parameters obtained from the submitted jar package
                        .addJobContent("spark.app.args", "hdfs:///tmp/test_word_count.txt") // WordCount test file
                        .addLabel("engineType", engineType + "-2.4.7")
                        .addLabel("userCreator", submitUser + "-IDE")
                        .addLabel("engineConnMode", "once")
                        .addStartupParam("spark.app.name", "spark-submit-jar-test-linkis") // Application Name on yarn 
                        .addStartupParam("spark.executor.memory", "1g")
                        .addStartupParam("spark.driver.memory", "1g")
                        .addStartupParam("spark.executor.cores", "1")
                        .addStartupParam("spark.executor.instance", "1")
                        .addStartupParam("spark.app.resource", "hdfs:///tmp/spark/spark-examples_2.11-2.3.0.2.6.5.0-292.jar")
                        .addSource("jobName", "OnceJobTest")
                        .build();
        // endregion
        onceJob.submit();
        onceJob.waitForCompleted(); //A temporary network interruption may cause an exception. It is recommended to modify the SDK later. If the SDK is in use at this stage, exception handling is required.
        // Temporary network failure will cause exceptions. It is recommended to modify the SDK later. For use at this stage, exception handling is required
        onceJob.waitForCompleted();
    }
}

3.4 Submitting tasks with `Restful API`

Scripts type includes sql、scala、python、data_calc(content type is json).

Restful API Usage

POST /api/rest_j/v1/entrance/submit
Content-Type: application/json
Token-Code: dss-AUTH
Token-User: linkis

{
    "executionContent": {
        // script content, type: sql, python, scala, json
        "code": "show databases",
        // script type: sql, py（pyspark）, scala, data_calc(json)
        "runType": "sql"
    },
    "params": {
        "variable": {
        },
        "configuration": {
            // spark startup parameters, not required
            "startup": {
                "spark.executor.memory": "1g",
                "spark.driver.memory": "1g",
                "spark.executor.cores": "1",
                "spark.executor.instances": 1
            }
        }
    },
    "source":  {
        // not required, file:/// or hdfs:///
        "scriptPath": "file:///tmp/hadoop/test.sql"
    },
    "labels": {
        // pattern：engineType-version
        "engineType": "spark-2.4.3",
        // userCreator: linkis is username。IDE is system that be configed in Linkis。
        "userCreator": "linkis-IDE"
    }
}

4. Engine configuration instructions

4.1 Default Configuration Description

Configuration	Default	Required	Description
wds.linkis.rm.instance	10	No	Maximum number of concurrent engines
spark.executor.cores	1	No	Number of spark executor cores
spark.driver.memory	1g	no	maximum concurrent number of spark executor instances
spark.executor.memory	1g	No	spark executor memory size
wds.linkis.engineconn.max.free.time	1h	No	Engine idle exit time
spark.python.version	python2	no	python version

4.2 Queue resource configuration

Because the execution of spark requires queue resources, you need to set up a queue that you can execute.

yarn

4.3 Configuration modification

If the default parameters are not satisfied, there are the following ways to configure some basic parameters

4.3.1 Management Console Configuration

Users can customize settings, such as the number of spark sessions executor and executor memory. These parameters are for users to set their own spark parameters more freely, and other spark parameters can also be modified, such as the python version of pyspark, etc. spark

Note: After modifying the configuration under the IDE tag, you need to specify -creator IDE to take effect (other tags are similar), such as:

sh ./bin/linkis-cli -creator IDE \
-engineType spark-2.4.3 -codeType sql \
-code "show databases"  \
-submitUser hadoop -proxyUser hadoop

4.3.2 Task interface configuration

Submit the task interface, configure it through the parameter params.configuration.runtime

Example of http request parameters
{
    "executionContent": {"code": "show databases;", "runType":  "sql"},
    "params": {
                    "variable": {},
                    "configuration": {
                            "runtime": {
                                "wds.linkis.rm.instance":"10"
                                }
                            }
                    },
    "labels": {
        "engineType": "spark-2.4.3",
        "userCreator": "hadoop-IDE"
    }
}

Linkis is managed through the engine tag, and the data table information involved is shown below.

linkis_ps_configuration_config_key: Insert the key and default values ​​​​of the configuration parameters of the engine
linkis_cg_manager_label: insert engine label such as: spark-2.4.3
linkis_ps_configuration_category: The directory association relationship of the insertion engine
linkis_ps_configuration_config_value: The configuration that the insertion engine needs to display
linkis_ps_configuration_key_engine_relation: The relationship between the configuration item and the engine

The initial data in the table related to the spark engine is as follows

-- set variable
SET @SPARK_LABEL="spark-2.4.3";
SET @SPARK_ALL=CONCAT('*-*,',@SPARK_LABEL);
SET @SPARK_IDE=CONCAT('*-IDE,',@SPARK_LABEL);

-- engine label
insert into `linkis_cg_manager_label` (`label_key`, `label_value`, `label_feature`, `label_value_size`, `update_time`, `create_time`) VALUES ('combined_userCreator_engineType', @SPARK_ALL, 'OPTIONAL', 2, now(), now());
insert into `linkis_cg_manager_label` (`label_key`, `label_value`, `label_feature`, `label_value_size`, `update_time`, `create_time`) VALUES ('combined_userCreator_engineType', @SPARK_IDE, 'OPTIONAL', 2, now(), now());

select @label_id := id from linkis_cg_manager_label where `label_value` = @SPARK_IDE;
insert into linkis_ps_configuration_category (`label_id`, `level`) VALUES (@label_id, 2);

-- configuration key
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('wds.linkis.rm.instance', 'Range: 1-20, unit: each', 'Maximum concurrent number of spark engine', '10', 'NumInterval', '[1,20]', '0 ', '0', '1', 'queue resources', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.executor.instances', 'value range: 1-40, unit: individual', 'maximum concurrent number of spark executor instances', '1', 'NumInterval', '[1,40]', '0', '0', '2', 'spark resource settings', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.executor.cores', 'Value range: 1-8, unit: number', 'Number of spark executor cores', '1', 'NumInterval', '[1,8]', ' 0', '0', '1','spark resource settings', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.executor.memory', 'value range: 1-15, unit: G', 'spark executor memory size', '1g', 'Regex', '^([1-9]|1 [0-5])(G|g)$', '0', '0', '3', 'spark resource settings', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.driver.cores', 'Value range: only 1, unit: number', 'Number of spark driver cores', '1', 'NumInterval', '[1,1]', '0 ', '1', '1', 'spark resource settings', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.driver.memory', 'value range: 1-15, unit: G', 'spark driver memory size','1g', 'Regex', '^([1-9]|1[ 0-5])(G|g)$', '0', '0', '1', 'spark resource settings', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('wds.linkis.engineconn.max.free.time', 'Value range: 3m,15m,30m,1h,2h', 'Engine idle exit time','1h', 'OFT', '[\ "1h\",\"2h\",\"30m\",\"15m\",\"3m\"]', '0', '0', '1', 'spark engine settings', ' spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.tispark.pd.addresses', NULL, NULL, 'pd0:2379', 'None', NULL, '0', '0', '1', 'tidb设置', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.tispark.tidb.addr', NULL, NULL, 'tidb', 'None', NULL, '0', '0', '1', 'tidb设置', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.tispark.tidb.password', NULL, NULL, NULL, 'None', NULL, '0', '0', '1', 'tidb设置', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.tispark.tidb.port', NULL, NULL, '4000', 'None', NULL, '0', '0', '1', 'tidb设置', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.tispark.tidb.user', NULL, NULL, 'root', 'None', NULL, '0', '0', '1', 'tidb设置', 'spark');
INSERT INTO `linkis_ps_configuration_config_key` (`key`, `description`, `name`, `default_value`, `validate_type`, `validate_range`, `is_hidden`, `is_advanced`, `level`, `treeName`, `engine_conn_type`) VALUES ('spark.python.version', 'Value range: python2,python3', 'python version','python2', 'OFT', '[\"python3\",\"python2\"]', ' 0', '0', '1', 'spark engine settings', 'spark');

-- key engine relation
insert into `linkis_ps_configuration_key_engine_relation` (`config_key_id`, `engine_type_label_id`)
(select config.id as `config_key_id`, label.id AS `engine_type_label_id` FROM linkis_ps_configuration_config_key config
INNER JOIN linkis_cg_manager_label label ON config.engine_conn_type = 'spark' and label.label_value = @SPARK_ALL);

-- engine default configuration
insert into `linkis_ps_configuration_config_value` (`config_key_id`, `config_value`, `config_label_id`)
(select `relation`.`config_key_id` AS `config_key_id`, '' AS `config_value`, `relation`.`engine_type_label_id` AS `config_label_id` FROM linkis_ps_configuration_key_engine_relation relation
INNER JOIN linkis_cg_manager_label label ON relation.engine_type_label_id = label.id AND label.label_value = @SPARK_ALL);

1. Preliminary work​

1.1 Engine installation​

1.2 Environment verification​

2. Engine plugin installation default engine​

3. Using the spark engine​

3.1 Submitting tasks via Linkis-cli​

3.2 Submitting tasks through Linkis SDK​

3.3 Submitting tasks by submitting the jar package​

3.4 Submitting tasks with Restful API​

4. Engine configuration instructions​

4.1 Default Configuration Description​

4.2 Queue resource configuration​

4.3 Configuration modification​

4.3.1 Management Console Configuration​

4.3.2 Task interface configuration​

4.4 Engine related data sheet​