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

This example uses two CSV tables. We will merge them into a single table, convert that table into Parquet format, and transfer it to Managed Service for ClickHouse®.

Required paid resourcesRequired paid resources

The support cost for this solution includes:

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

Getting startedGetting started

Set up your infrastructure:

Prepare your test dataPrepare your test data

In this example, we will use two CSV tables:

• coords.csv contains the vehicle’s geographic coordinates.
• sensors.csv contains vehicle speed and 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. In the input bucket, create a folder named csv and upload your CSV files to it.

Run data processing in Yandex Data ProcessingRun data processing in Yandex Data Processing

Merge two tables into one and upload the resulting table as a Parquet file to the processing results bucket you created earlier :

1. Prepare a script file:

   1. Create a local file named join-tables.py and paste the following script into 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 on 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. In your script, specify the following:

      • Name of the input bucket containing original CSV tables.
      • Name of the output bucket for the Parquet file containing the joined tables.

   3. Create a folder named scripts in the input bucket and upload the join-tables.py file to it.

2. Create a PySpark job with the file path to your script specified in the Main python file field: s3a://<input_bucket_name>/scripts/join-tables.py.

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

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 paste the following script into it:

      parquet-to-ch.py

      from pyspark.sql import SparkSession
      
      # Creating a Spark session
      spark = SparkSession.builder.appName("ParquetClickhouse").getOrCreate()
      
      # Reading data from the Parquet file
      parquetFile = spark.read.parquet("s3a://<output_bucket_name>/parquet/*.parquet")
      
      # Specifying the port and other ClickHouse® cluster settings
      jdbcPort = 8443
      jdbcHostname = "c-<cluster_ID>.rw.mdb.yandexcloud.net"
      jdbcDatabase = "db1"
      jdbcUrl = f"jdbc:clickhouse://{jdbcHostname}:{jdbcPort}/{jdbcDatabase}?ssl=true"
      
      # Loading the table from your Parquet file into the ClickHouse® `measurements` table
      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. In your script, specify the following:

      • Name of the bucket containing your Parquet file.
      • Managed Service for ClickHouse® cluster ID.
      • ClickHouse® password.

   3. Upload the parquet-to-ch.py file to the input data bucket’s scripts folder.

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

3. Wait for the job to complete, then verify that the joined table has been transferred to the cluster:

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

   2. Run this query:

      SELECT * FROM measurements;
      

   If the data export is successful, you will receive the joined table in response.

Delete the resources you createdDelete the resources you created

Some resources are not free of charge. Delete the resources you no longer need to avoid paying for them:

1. Delete all objects from the buckets.

2. Delete the other resources depending on how you created them:

   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.

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