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In this article:

  • Required paid resources
  • Getting started
  • Prepare the test data
  • Process your data in Yandex Data Processing
  • Export your data to ClickHouse®
  • Delete the resources you created
  1. Tutorials
  2. Importing data from Object Storage, processing and exporting to Managed Service for ClickHouse®

Importing data from Object Storage, processing and exporting to Yandex Managed Service for ClickHouse®

Written by
Yandex Cloud
Updated at May 5, 2025
  • Required paid resources
  • Getting started
  • Prepare the test data
  • Process your data in Yandex Data Processing
  • Export your data to ClickHouse®
  • Delete the resources you created

This tutorial is based on a Data Stories use case of building an analytical stack powered by Yandex Cloud services. It included uploading data to a storage, processing it, and transforming it into a single data mart for visualization.


datastories logo

In this example, we use two CSV tables which we will merge into a single one, import it to Parquet format, and transfer to Managed Service for ClickHouse®.

Required paid resourcesRequired paid resources

The support cost includes:

  • Managed Service for ClickHouse® cluster fee: Using computing resources allocated to hosts (including ZooKeeper hosts) and disk space (see Managed Service for ClickHouse® pricing).
  • 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).
  • Fee for using public IP addresses for cluster hosts (see Virtual Private Cloud pricing).
  • Object Storage bucket fee: storing data and performing operations with it (see Object Storage pricing).
  • NAT gateway fee (see Virtual Private Cloud pricing).

Getting startedGetting started

Set up your infrastructure:

Manually
Terraform
  1. Create a service account named dataproc-s3-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. In dataproc-network, create a security group named dataproc-sg 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:

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

    • With public access to cluster hosts.
    • DB name: db1
    • Username: user1
  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 s3-dataproc-ch.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 s3-dataproc-ch.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.

Prepare the test dataPrepare the test data

In this example, we use the following two CSV tables:

  • coords.csv with vehicle geographic coordinates.
  • sensors.csv with information about speed and other vehicle operating parameters.

To prepare the test data:

  1. Copy the contents of the sample files below and save them locally in CSV format:

    • coords.csv
      vehicle_id,latitude,longitude,altitude
      iv9a94th6rztooxh5ur2,55.70329032,37.65472196,427.5
      022wsiz48h2ljxuz04x8,56.96149325,38.46541766,423.6
      a7fbbqjws4zqw85f6jue,54.99296663,36.79063999,426.2
      l7731117m6r6on4m633n,55.34740545,37.13175678,422.5
      6f9q6ienc4qfpdwd9nef,56.69752218,38.38871530,428.3
      
    • sensors.csv
      vehicle_id,speed,battery_voltage,cabin_temperature,fuel_level
      iv9a94th6rztooxh5ur2,0.0,25.5,17,5
      022wsiz48h2ljxuz04x8,55.5,54.5,21,22
      a7fbbqjws4zqw85f6jue,80.6,22.1,19,73
      l7731117m6r6on4m633n,40.9,76.0,25,23
      6f9q6ienc4qfpdwd9nef,64.8,90.8,21,32
      
  2. Create a folder named csv in the input bucket and upload the new CSV files to it.

Process your data in Yandex Data ProcessingProcess your data in Yandex Data Processing

Merge the data from the two tables into one and upload it in Parquet format to the bucket you previously created for processing results:

  1. Prepare a script file:

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

      join-tables.py
      from pyspark.sql import SparkSession
      
      # Creating a Spark session
      spark = SparkSession.builder.appName("JoinExample").getOrCreate()
      
      # Reading a table from coords.csv
      coords_df = spark.read.csv("s3a://<input_bucket_name>/csv/coords.csv", header=True)
      
      # Reading a table from sensors.csv
      sensors_df = spark.read.csv("s3a://<input_bucket_name>/csv/sensors.csv", header=True)
      
      # Joining tables by the vehicle_id column
      joined_df = coords_df.join(sensors_df, on="vehicle_id", how="inner")
      
      # Saving the joined table to a bucket in Parquet format
      joined_df.write.parquet("s3a://<output_bucket_name>/parquet/")
      
    2. Specify the following in the script:

      • Name of the input bucket that stores the original CSV tables.
      • Name of the output bucket where the Parquet file with the merged data will be saved.
    3. In the input bucket, create a folder named scripts and upload the join-tables.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/join-tables.py.

  3. Wait for the job to complete and check that the output bucket's parquet folder contains the part-00000-*** Parquet file.

Note

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

Export your data to ClickHouse®Export your data to ClickHouse®

Transfer the joined table from Object Storage to ClickHouse®:

  1. Prepare a script file:

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

      parquet-to-ch.py
      from pyspark.sql import SparkSession
      
      # Creating a Spark session
      spark = SparkSession.builder.appName("ParquetClickhouse").getOrCreate()
      
      # Reading data from a Parquet file
      parquetFile = spark.read.parquet("s3a://<output_bucket_name>/parquet/*.parquet")
      
      # Specifying the port and ClickHouse® cluster parameters
      jdbcPort = 8443
      jdbcHostname = "c-<cluster_ID>.rw.mdb.yandexcloud.net"
      jdbcDatabase = "db1"
      jdbcUrl = f"jdbc:clickhouse://{jdbcHostname}:{jdbcPort}/{jdbcDatabase}?ssl=true"
      
      # Transferring a table from a Parquet file to a ClickHouse® table named `measurements`
      parquetFile.write.format("jdbc") \
      .mode("error") \
      .option("url", jdbcUrl) \
      .option("dbtable", "measurements") \
      .option("createTableOptions", "ENGINE = MergeTree() ORDER BY vehicle_id") \
      .option("user","user1") \
      .option("password","<ClickHouse®_user_password>") \
      .save()
      
    2. Specify the following in the script:

      • Name of the bucket with the Parquet file.
      • Managed Service for ClickHouse® cluster ID.
      • ClickHouse® user password.
    3. Upload the parquet-to-ch.py file to the source data bucket’s scripts folder.

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

  3. Wait for the job to complete and make sure the joined table has been moved to the cluster:

    1. Connect to the db1 database in the Managed Service for ClickHouse® cluster as user1.

    2. Run this request:

      SELECT * FROM measurements;
      

    If the data export is successful, the response will contain the joined table.

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.

  2. 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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