Importing data from Yandex Object Storage, processing and exporting it 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

Prepare the 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 the 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 the csv folder in the input bucket and upload the created 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 the table from the coords.csv file
      coords_df = spark.read.csv("s3a://<input_bucket_name>/csv/coords.csv", header=True)
      
      # Reading the table from the sensors.csv file
      sensors_df = spark.read.csv("s3a://<input_bucket_name>/csv/sensors.csv", header=True)
      
      # Joining the tables by the vehicle_id column
      joined_df = coords_df.join(sensors_df, on="vehicle_id", how="inner")
      
      # Saving the joined table to the 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. Create the scripts folder in the input bucket and upload the join-tables.py file to it.

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

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 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 the data from the 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 the table from the 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 input bucket's scripts folder.

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

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 of the Managed Service for ClickHouse® cluster as user1.

   2. Run the following query:

      SELECT * FROM measurements;
      

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

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