[Yandex Cloud documentation](../../index.md) > [Tutorials](../index.md) > [Building a data platform](index.md) > Working with jobs Yandex Data Processing > Running jobs from a remote host # Running jobs from remote hosts that are not part of the Yandex Data Processing cluster This tutorial describes how to use the [spark-submit utility](https://spark.apache.org/docs/latest/submitting-applications.html#submitting-applications) to run Spark jobs in the Yandex Data Processing cluster from hosts that are not part of the cluster. {% note info %} You can also run jobs in the Yandex Data Processing cluster from Yandex DataSphere. For more information, see [this concept](../../datasphere/concepts/data-processing.md#existing-clusters). {% endnote %} To run jobs from remote hosts that are not part of the cluster: 1. [Set up your infrastructure](#infra). 1. [Run the jobs](#spark-submit). If you no longer need the resources you created, [delete them](#clear-out). ## Getting started {#before-you-begin} Sign up for Yandex Cloud and create a [billing account](../../billing/concepts/billing-account.md): 1. Navigate to the [management console](https://console.yandex.cloud) and log in to Yandex Cloud or create a new account. 1. On the **[Yandex Cloud Billing](https://center.yandex.cloud/billing/accounts)** page, make sure you have a billing account linked and it has the `ACTIVE` or `TRIAL_ACTIVE` [status](../../billing/concepts/billing-account-statuses.md). If you do not have a billing account, [create one](../../billing/quickstart/index.md) and [link](../../billing/operations/pin-cloud.md) a cloud to it. If you have an active billing account, you can create or select a [folder](../../resource-manager/concepts/resources-hierarchy.md#folder) for your infrastructure on the [cloud page](https://console.yandex.cloud/cloud). [Learn more about clouds and folders here](../../resource-manager/concepts/resources-hierarchy.md). ### Required paid resources {#paid-resources} * Yandex Data Processing cluster: use of computing resources with a Yandex Data Processing markup, use of network drives, retrieval and storage of logs, amount of outgoing traffic (see [Yandex Data Processing pricing](../../data-proc/pricing.md)). * Public IP addresses if public access is enabled for cluster hosts (see [Yandex Virtual Private Cloud pricing](../../vpc/pricing.md)). * VM instance: use of computing resources, storage, public IP address, and OS (see [Yandex Compute Cloud pricing](../../compute/pricing.md)). ## Set up your infrastructure {#infra} Create and configure a host to run jobs remotely on the Yandex Data Processing cluster: {% list tabs %} - Image version 1.4 1. [Create a Yandex Data Processing cluster](../../data-proc/operations/cluster-create.md). 1. [Create a VM](../../compute/operations/vm-create/create-linux-vm.md) running Ubuntu 16.04 LTS. 1. To provide network access to the Yandex Data Processing cluster hosting this VM, [set up security groups](../../data-proc/operations/security-groups.md) for the cluster. 1. [Connect](../../compute/operations/vm-connect/ssh.md#vm-connect) to the VM over SSH: ```bash ssh -A @ ``` 1. Copy the repository settings from any of the Yandex Data Processing cluster hosts. To do this, run a sequence of commands on the VM you created. 1. Copy the repository address: ```bash ssh root@ \ "cat /etc/apt/sources.list.d/yandex-dataproc.list" | \ sudo tee /etc/apt/sources.list.d/yandex-dataproc.list ``` 1. Copy the GPG key to verify Debian package signatures: ```bash ssh root@ \ "cat /srv/dataproc.gpg" | sudo apt-key add - ``` 1. Update the repository cache: ```bash sudo apt update ``` 1. Install the required packages: ```bash sudo apt install openjdk-8-jre-headless hadoop-client hadoop-hdfs spark-core spark-python ``` {% note info %} You need the `spark-python` package only to run PySpark jobs. {% endnote %} 1. Copy the Hadoop and Spark configuration files: ```bash sudo -E scp -r \ root@:/etc/hadoop/conf/* \ /etc/hadoop/conf/ && \ sudo -E scp -r \ root@:/etc/spark/conf/* \ /etc/spark/conf/ ``` 1. Create a user named `sparkuser` to run jobs: ```bash sudo useradd sparkuser && \ ssh root@ " hadoop fs -mkdir /user/sparkuser sudo -u hdfs hdfs dfs -chown sparkuser:sparkuser /user/sparkuser sudo -u hdfs hdfs dfs -ls /user/sparkuser " ``` - Image version 2.0 1. [Create a Yandex Data Processing cluster](../../data-proc/operations/cluster-create.md). 1. [Create a VM](../../compute/operations/vm-create/create-linux-vm.md) running Ubuntu 20.04 LTS. 1. To provide network access to the Yandex Data Processing cluster hosting this VM, [set up security groups](../../data-proc/operations/security-groups.md) for the cluster. 1. [Connect](../../compute/operations/vm-connect/ssh.md#vm-connect) to the VM over SSH: ```bash ssh -A @ ``` 1. Copy the repository settings from any of the Yandex Data Processing cluster hosts. To do this, run a sequence of commands on the VM you created. 1. Copy the repository address: ```bash ssh ubuntu@ \ "cat /etc/apt/sources.list.d/yandex-dataproc.list" | \ sudo tee /etc/apt/sources.list.d/yandex-dataproc.list ``` 1. Copy the GPG key to verify Debian package signatures: ```bash ssh ubuntu@ \ "cat /srv/dataproc.gpg" | sudo apt-key add - ``` 1. Update the repository cache: ```bash sudo apt update ``` 1. Install the required packages: ```bash sudo apt install openjdk-8-jre-headless hadoop-client hadoop-hdfs spark-core spark-python ``` {% note info %} You need the `spark-python` package only to run PySpark jobs. {% endnote %} 1. Copy the Hadoop and Spark configuration files: ```bash sudo -E scp -r \ ubuntu@:/etc/hadoop/conf/* \ /etc/hadoop/conf/ && \ sudo -E scp -r \ ubuntu@:/etc/spark/conf/* \ /etc/spark/conf/ ``` 1. Create a user named `sparkuser` to run jobs: ```bash sudo useradd sparkuser && \ ssh ubuntu@ " hadoop fs -mkdir /user/sparkuser sudo -u hdfs hdfs dfs -chown sparkuser:sparkuser /user/sparkuser sudo -u hdfs hdfs dfs -ls /user/sparkuser " ``` {% endlist %} ## Running a job {#spark-submit} {% list tabs %} - Spark job 1. Run a job using this command: ```bash sudo -u sparkuser spark-submit \ --master yarn \ --deploy-mode cluster \ --class org.apache.spark.examples.SparkPi \ /usr/lib/spark/examples/jars/spark-examples.jar 1000 ``` Result: ```text 20/04/19 16:43:58 INFO client.RMProxy: Connecting to ResourceManager at rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net/ 10.13.13.18:8032 20/04/19 16:43:58 INFO client.AHSProxy: Connecting to Application History server at rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net/10.13.13.18:10200 20/04/19 16:43:58 INFO yarn.Client: Requesting a new application from cluster with 4 NodeManagers ... 20/04/19 16:43:58 INFO yarn.Client: Preparing resources for our AM container 20/04/19 16:43:58 INFO yarn.Client: Uploading resource file:/usr/lib/spark/examples/jars/spark-examples.jar -> hdfs:// rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net/user/sparkuser/.sparkStaging/application_1586176069782_0003/ spark-examples.jar 20/04/19 16:43:58 INFO yarn.Client: Uploading resource file:/etc/spark/conf/hive-site.xml -> hdfs:// rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net/user/sparkuser/.sparkStaging/application_1586176069782_0003/hive-site. xml 20/04/19 16:43:58 INFO yarn.Client: Uploading resource file:/tmp/spark-6dff3163-089b-4634-8f74-c8301d424567/ __spark_conf__8717606866210190000.zip -> hdfs://rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net/user/sparkuser/. sparkStaging/application_1586176069782_0003/__spark_conf__.zip 20/04/19 16:44:00 INFO yarn.Client: Submitting application application_1586176069782_0003 to ResourceManager 20/04/19 16:44:00 INFO impl.YarnClientImpl: Submitted application application_1586176069782_0003 20/04/19 16:44:01 INFO yarn.Client: Application report for application_1586176069782_0003 (state: ACCEPTED) 20/04/19 16:44:01 INFO yarn.Client: client token: N/A diagnostics: AM container is launched, waiting for AM container to Register with RM ApplicationMaster host: N/A ApplicationMaster RPC port: -1 queue: default start time: 1587314639386 final status: UNDEFINED tracking URL: http://rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net:8088/proxy/application_1586176069782_0003/ user: sparkuser 20/04/19 16:44:05 INFO yarn.Client: Application report for application_1586176069782_0003 (state: RUNNING) 20/04/19 16:44:05 INFO yarn.Client: client token: N/A diagnostics: N/A ApplicationMaster host: rc1b-dataproc-d-9cd9yoenm4npsznt.mdb.yandexcloud.net ApplicationMaster RPC port: 41648 queue: default start time: 1587314639386 final status: UNDEFINED tracking URL: http://rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net:8088/proxy/application_1586176069782_0003/ user: sparkuser 20/04/19 16:44:06 INFO yarn.Client: Application report for application_1586176069782_0003 (state: RUNNING) 20/04/19 16:44:07 INFO yarn.Client: Application report for application_1586176069782_0003 (state: RUNNING) 20/04/19 16:44:08 INFO yarn.Client: Application report for application_1586176069782_0003 (state: RUNNING) 20/04/19 16:44:09 INFO yarn.Client: Application report for application_1586176069782_0003 (state: FINISHED) 20/04/19 16:44:09 INFO yarn.Client: client token: N/A diagnostics: N/A ApplicationMaster host: rc1b-dataproc-d-9cd9yoenm4npsznt.mdb.yandexcloud.net ApplicationMaster RPC port: 41648 queue: default start time: 1587314639386 final status: SUCCEEDED tracking URL: http://rc1b-dataproc-m-ds7lj5gnnnqggbqd.mdb.yandexcloud.net:8088/proxy/application_1586176069782_0003/ user: sparkuser 20/04/19 16:44:09 INFO util.ShutdownHookManager: Shutdown hook called 20/04/19 16:44:09 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-6dff3163-089b-4634-8f74-c8301d424567 20/04/19 16:44:09 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-826498b1-8dec-4229-905e-921203b7b1d0 ``` 1. Check the job execution status using the [yarn application](https://hadoop.apache.org/docs/r2.10.0/hadoop-yarn/hadoop-yarn-site/YarnCommands.html#application) utility: ```bash yarn application -status application_1586176069782_0003 ``` Result: ```text 20/04/19 16:47:03 INFO client.RMProxy: Connecting to ResourceManager at rc1b-dataproc-m-ds7lj5gn********.mdb.yandexcloud.net/10.13.13.18:8032 20/04/19 16:47:03 INFO client.AHSProxy: Connecting to Application History server at rc1b-dataproc-m-ds7lj5gn********.mdb.yandexcloud.net/10.13.13.18:10200 Application Report : Application-Id : application_1586176069782_0003 Application-Name : org.apache.spark.examples.SparkPi Application-Type : SPARK User : sparkuser Queue : default Application Priority : 0 Start-Time : 1587314639386 Finish-Time : 1587314647621 Progress : 100% State : FINISHED Final-State : SUCCEEDED Tracking-URL : rc1b-dataproc-m-ds7lj5gn********.mdb.yandexcloud.net:18080/history/application_1586176069782_0003/1 RPC Port : 41648 AM Host : rc1b-dataproc-d-9cd9yoen********.mdb.yandexcloud.net Aggregate Resource Allocation : 141510 MB-seconds, 11 vcore-seconds Aggregate Resource Preempted : 0 MB-seconds, 0 vcore-seconds Log Aggregation Status : SUCCEEDED Diagnostics : Unmanaged Application : false Application Node Label Expression : AM container Node Label Expression : TimeoutType : LIFETIME ExpiryTime : UNLIMITED RemainingTime : -1seconds ``` 1. View logs from all running containers using the [yarn logs](https://hadoop.apache.org/docs/r2.10.0/hadoop-yarn/hadoop-yarn-site/YarnCommands.html#logs) utility: ```bash sudo -u sparkuser yarn logs \ -applicationId application_1586176069782_0003 | grep "Pi is" ``` Result: ```text Pi is roughly 3.14164599141646 ``` - PySpark job 1. On the VM, create a file named `month_stat.py` with the following code: ```python import sys from pyspark import SparkContext, SparkConf from pyspark.sql import SQLContext def main(): conf = SparkConf().setAppName("Month Stat - Python") conf.set("fs.s3a.aws.credentials.provider", "org.apache.hadoop.fs.s3a.AnonymousAWSCredentialsProvider") sc = SparkContext(conf=conf) sql = SQLContext(sc) df = sql.read.parquet("s3a://yc-mdb-examples/dataproc/example01/set01") defaultFS = sc._jsc.hadoopConfiguration().get("fs.defaultFS") month_stat = df.groupBy("Month").count() month_stat.repartition(1).write.format("csv").save(defaultFS+"/tmp/month_stat") if __name__ == "__main__": main() ``` 1. Copy the `month_stat.py` file on the cluster's master host: ```bash sudo -E scp month_stat.py @:~/month_stat.py ``` For image version 2.0, specify the `ubuntu` user; for image version 1.4, specify `root`. 1. Run the application: ```bash sudo -u sparkuser spark-submit \ --master yarn \ --deploy-mode cluster \ month_stat.py ``` 1. The result will be exported to HDFS on the cluster. You can list the files you got using this command: ```bash ssh @ "hdfs dfs -ls /tmp/month_stat" ``` For image version 2.0, specify the `ubuntu` user; for image version 1.4, specify `root`. {% endlist %} {% note info %} You can view the job logs and search data in them using [Yandex Cloud Logging](../../logging/index.md). For more information, see [Working with logs](../../data-proc/operations/logging.md). {% endnote %} ## Delete the resources you created {#clear-out} Some resources are not free of charge. Delete the resources you no longer need to avoid paying for them: 1. [Delete the Yandex Data Processing cluster](../../data-proc/operations/cluster-delete.md). 1. [Delete the VM](../../compute/operations/vm-control/vm-delete.md).