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In this article:

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  1. Tutorials
  2. Exchanging data between Yandex Managed Service for ClickHouse® and Yandex Data Processing

Exchanging data between Yandex Managed Service for ClickHouse® and Yandex Data Processing

Written by
Yandex Cloud
Updated at May 5, 2025
  • Required paid resources
  • Getting started
  • Upload data from Managed Service for ClickHouse®
    • Create a table in the Managed Service for ClickHouse® cluster
    • Transfer the table from Managed Service for ClickHouse®
  • Export data to Managed Service for ClickHouse®
  • Delete the resources you created

With Yandex Data Processing, you can:

  • Upload data from Managed Service for ClickHouse® to Spark DataFrame.
  • Export data from Spark DataFrame to Managed Service for ClickHouse®.

If you no longer need the resources you created, delete them.

Required paid resourcesRequired paid resources

The support cost includes:

  • Yandex Data Processing cluster fee: Using VM computing resources and Compute Cloud network disks, and Cloud Logging for log management (see Yandex Data Processing pricing).
  • Managed Service for ClickHouse® cluster fee: Using computing resources allocated to hosts (including ZooKeeper hosts) and disk space (see Managed Service for ClickHouse® pricing).
  • NAT gateway fee (see Virtual Private Cloud pricing).
  • Object Storage bucket fee: Storing data and performing operations with it (see Object Storage pricing).
  • Fee for public IP addresses for cluster hosts (see Virtual Private Cloud pricing).

Getting startedGetting started

Set up your infrastructure:

Manually
Terraform
  1. Create a service account named dataproc-sa and assign the dataproc.agent and dataproc.provisioner roles to it.

  2. In Object Storage, create buckets and configure access to them:

    1. Create a bucket for the input data and grant the READ permission for this bucket to the cluster service account.
    2. Create a bucket for the processing output and grant the cluster service account READ and WRITE permissions for this bucket.
  3. Create a cloud network named dataproc-network.

  4. In dataproc-network, create a subnet in any availability zone.

  5. Set up a NAT gateway for the subnet you created.

  6. If using security groups, create a security group named dataproc-sg in dataproc-network and add the following rules to it:

    • One rule for inbound and another one for outbound service traffic:

      • Port range: 0-65535.
      • Protocol: Any (Any).
      • Source/Destination name: Security group.
      • Security group: Current (Self).
    • Rule for outgoing HTTPS traffic:

      • Port range: 443
      • Protocol: TCP
      • Destination name: CIDR
      • CIDR blocks: 0.0.0.0/0
    • Rule for outgoing TCP traffic on port 8443 to access ClickHouse®:

      • Port range: 8443
      • Protocol: TCP
      • Destination name: CIDR
      • CIDR blocks: 0.0.0.0/0
  7. Create a Yandex Data Processing cluster in any suitable host configuration with the following settings:

    • Components:
      • SPARK
      • YARN
      • HDFS
    • Service account: dataproc-sa.
    • Bucket name: Bucket you created for output data.
    • Network: dataproc-network.
    • Security groups: dataproc-sg.
  8. Create a Managed Service for ClickHouse® cluster of any suitable configuration with the following settings:

    • With public access to cluster hosts.
    • Database: db1.
    • User: user1.
  9. If using security groups in your Managed Service for ClickHouse® cluster, make sure they are configured correctly and allow connecting to the cluster.

  1. If you do not have Terraform yet, install it.

  2. Get the authentication credentials. You can add them to environment variables or specify them later in the provider configuration file.

  3. Configure and initialize a provider. There is no need to create a provider configuration file manually, you can download it.

  4. Place the configuration file in a separate working directory and specify the parameter values. If you did not add the authentication credentials to environment variables, specify them in the configuration file.

  5. Download the data-proc-data-exchange-with-mch.tf configuration file to the same working directory.

    This file describes:

    • Network.
    • Subnet.
    • NAT gateway and route table required for Yandex Data Processing.
    • Security groups required for the Yandex Data Processing and Managed Service for ClickHouse® clusters.
    • Service account required for the Yandex Data Processing cluster.
    • Service account required to create buckets in Object Storage.
    • Buckets for input and output data.
    • Yandex Data Processing cluster.
    • Managed Service for ClickHouse® cluster.
  6. Specify the following in the data-proc-data-exchange-with-mch.tf file:

    • folder_id: Cloud folder ID, same as in the provider settings.
    • input_bucket: Name of the input data bucket.
    • output_bucket: Name of the output data bucket.
    • dp_ssh_key: Absolute path to the public key for the Yandex Data Processing cluster. To learn more, see Connecting to a Yandex Data Processing host via SSH.
    • ch_password: ClickHouse® user password.
  7. Make sure the Terraform configuration files are correct using this command:

    terraform validate
    

    If there are any errors in the configuration files, Terraform will point them out.

  8. Create the required infrastructure:

    1. Run this command to view the planned changes:

      terraform plan
      

      If you described the configuration correctly, the terminal will display a list of the resources to update and their parameters. This is a verification step that does not apply changes to your resources.

    2. If everything looks correct, apply the changes:

      1. Run this command:

        terraform apply
        
      2. Confirm updating the resources.

      3. Wait for the operation to complete.

    All the required resources will be created in the specified folder. You can check resource availability and their settings in the management console.

Upload data from Managed Service for ClickHouse®Upload data from Managed Service for ClickHouse®

Create a table in the Managed Service for ClickHouse® clusterCreate a table in the Managed Service for ClickHouse® cluster

  1. Connect to the Managed Service for ClickHouse® cluster's database named db1 as user1.

  2. Add test data to the database. As an example, a simple table is used with people's names and ages.

    1. Create a table:

      CREATE TABLE persons (
          `name` String,
          `age` UInt8) ENGINE = MergeTree ()
      ORDER BY
          `name`;
      
    2. Populate the table with data:

      INSERT INTO persons VALUES
         ('Anna', 19),
         ('Michael', 65),
         ('Alvar', 28),
         ('Lilith', 50),
         ('Max', 27),
         ('Jaimey', 34),
         ('Dmitry', 42),
         ('Qiang', 19),
         ('Augustyna', 20),
         ('Maria', 28);
      
    3. Check the result:

      SELECT * FROM persons;
      

Transfer the table from Managed Service for ClickHouse®Transfer the table from Managed Service for ClickHouse®

  1. Prepare a script file:

    1. Create a local file named ch-to-dataproc.py and copy the following script to it:

      ch-to-dataproc.py
      from pyspark.sql import SparkSession
      
      # Creating a Spark session
      spark = SparkSession.builder.appName("ClickhouseDataproc").getOrCreate()
      
      # Specifying the port and ClickHouse® cluster parameters
      jdbcPort = 8443
      jdbcHostname = "c-<ClickHouse®_cluster_ID>.rw.mdb.yandexcloud.net"
      jdbcDatabase = "db1"
      jdbcUrl = f"jdbc:clickhouse://{jdbcHostname}:{jdbcPort}/{jdbcDatabase}?ssl=true"
      
      # Transferring the persons table from ClickHouse® to DataFrame
      df = spark.read.format("jdbc") \
      .option("url", jdbcUrl) \
      .option("user","user1") \
      .option("password","<user1_password>") \
      .option("dbtable","persons") \
      .load()
      
      # Transferring DataFrame to the bucket for checking
      df.repartition(1).write.mode("overwrite") \
      .csv(path='s3a://<output_bucket_name>/csv', header=True, sep=',')
      
    2. Specify the following in the script:

      • Managed Service for ClickHouse® cluster ID.
      • user1 user password.
      • Output bucket name.
    3. In the input bucket, create a directory named scripts and upload the ch-to-dataproc.py file to it.

  2. Create a PySpark job by specifying the path to the script file in the Main python file field: s3a://<input_bucket_name>/scripts/ch-to-dataproc.py.

  3. Wait for the job to complete and make sure the output bucket's csv directory contains the source table.

Note

You can view the job logs and search data in them using Yandex Cloud Logging. For more information, see Working with logs.

Export data to Managed Service for ClickHouse®Export data to Managed Service for ClickHouse®

  1. Prepare a script file:

    1. Create a local file named dataproc-to-ch.py and copy the following script to it:

      dataproc-to-ch.py
      from pyspark.sql import SparkSession
      from pyspark.sql.types import *
      
      # Creating a Spark session
      spark = SparkSession.builder.appName("DataprocClickhouse").getOrCreate()
      
      # Creating a data schema
      schema = StructType([StructField('name', StringType(), True),
      StructField('age', IntegerType(), True)])
      
      # Creating DataFrame
      df = spark.createDataFrame([('Alim', 19),
                                  ('Fred' ,65),
                                  ('Guanmin' , 28),
                                  ('Till', 60),
                                  ('Almagul', 27),
                                  ('Mary', 34),
                                  ('Dmitry', 42)], schema)
      
      # Specifying the port and ClickHouse® cluster parameters
      jdbcPort = 8443
      jdbcHostname = "c-<ClickHouse®_cluster_ID>.rw.mdb.yandexcloud.net"
      jdbcDatabase = "db1"
      jdbcUrl = f"jdbc:clickhouse://{jdbcHostname}:{jdbcPort}/{jdbcDatabase}?ssl=true"
      
      # Transferring DataFrame to ClickHouse®
      df.write.format("jdbc") \
      .mode("error") \
      .option("url", jdbcUrl) \
      .option("dbtable", "people") \
      .option("createTableOptions", "ENGINE = MergeTree() ORDER BY age") \
      .option("user","user1") \
      .option("password","<ClickHouse®_database_password>") \
      .save()
      
    2. Specify the following in the script:

      • Managed Service for ClickHouse® cluster ID.
      • user1 user password.
    3. In the input bucket, create a directory named scripts and upload the dataproc-to-ch.py file to it.

  2. Create a PySpark job by specifying the path to the script file in the Main python file field: s3a://<input_bucket_name>/scripts/dataproc-to-ch.py.

  3. Wait for the job to complete and make sure the data has been transferred to Managed Service for ClickHouse®:

    1. Connect to the Managed Service for ClickHouse® cluster's database named db1 as user1.

    2. Run this request:

      SELECT * FROM people;
      

    If the import is successful, the response will contain a table with the data.

Note

You can view the job logs and search data in them using Yandex Cloud Logging. For more information, see Working with logs.

Delete the resources you createdDelete the resources you created

Some resources are not free of charge. To avoid paying for them, delete the resources you no longer need:

  1. Delete the objects from the buckets. Delete the other resources depending on how they were created:

    Manually
    Terraform
    1. Managed Service for ClickHouse® cluster
    2. Yandex Data Processing cluster
    3. Object Storage buckets
    4. Subnet
    5. Route table
    6. NAT gateway
    7. Cloud network
    8. Service account
    1. In the terminal window, go to the directory containing the infrastructure plan.

      Warning

      Make sure the directory has no Terraform manifests with the resources you want to keep. Terraform deletes all resources that were created using the manifests in the current directory.

    2. Delete resources:

      1. Run this command:

        terraform destroy
        
      2. Confirm deleting the resources and wait for the operation to complete.

      All the resources described in the Terraform manifests will be deleted.

ClickHouse® is a registered trademark of ClickHouse, Inc.

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