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

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®.

Getting startedGetting started

Set up your infrastructure:

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.

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.

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