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Code examples for Databricks Connect for Scala

  • Artikkeli

Note

This article covers Databricks Connect for Databricks Runtime 13.3 LTS and above.

This article provides code examples that use Databricks Connect for Scala. Databricks Connect enables you to connect popular IDEs, notebook servers, and custom applications to Azure Databricks clusters. See What is Databricks Connect?. For the Python version of this article, see Code examples for Databricks Connect for Python.

Note

Before you begin to use Databricks Connect, you must set up the Databricks Connect client.

Databricks provides several additional example applications that show how to use Databricks Connect. See the example applications for Databricks Connect repository in GitHub, specifically:

You can also use the following simpler code examples to experiment with Databricks Connect. These examples assume that you are using default authentication for Databricks Connect client setup.

This simple code example queries the specified table and then shows the specified table’s first 5 rows. To use a different table, adjust the call to spark.read.table.

import com.databricks.connect.DatabricksSession
import org.apache.spark.sql.SparkSession

object Main {
  def main(args: Array[String]): Unit = {
    val spark = DatabricksSession.builder().getOrCreate()
    val df = spark.read.table("samples.nyctaxi.trips")
    df.limit(5).show()
  }
}

This longer code example does the following:

  1. Creates an in-memory DataFrame.
  2. Creates a table with the name zzz_demo_temps_table within the default schema. If the table with this name already exists, the table is deleted first. To use a different schema or table, adjust the calls to spark.sql, temps.write.saveAsTable, or both.
  3. Saves the DataFrame’s contents to the table.
  4. Runs a SELECT query on the table’s contents.
  5. Shows the query’s result.
  6. Deletes the table.
import com.databricks.connect.DatabricksSession
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types._
import java.time.LocalDate

object Main {
  def main(args: Array[String]): Unit = {
    val spark = DatabricksSession.builder().getOrCreate()

    // Create a Spark DataFrame consisting of high and low temperatures
    // by airport code and date.
    val schema = StructType(
      Seq(
        StructField("AirportCode", StringType, false),
        StructField("Date", DateType, false),
        StructField("TempHighF", IntegerType, false),
        StructField("TempLowF", IntegerType, false)
      )
    )

    val data = Seq(
      ( "BLI", LocalDate.of(2021, 4, 3), 52, 43 ),
      ( "BLI", LocalDate.of(2021, 4, 2), 50, 38),
      ( "BLI", LocalDate.of(2021, 4, 1), 52, 41),
      ( "PDX", LocalDate.of(2021, 4, 3), 64, 45),
      ( "PDX", LocalDate.of(2021, 4, 2), 61, 41),
      ( "PDX", LocalDate.of(2021, 4, 1), 66, 39),
      ( "SEA", LocalDate.of(2021, 4, 3), 57, 43),
      ( "SEA", LocalDate.of(2021, 4, 2), 54, 39),
      ( "SEA", LocalDate.of(2021, 4, 1), 56, 41)
    )

    val temps = spark.createDataFrame(data).toDF(schema.fieldNames: _*)

    // Create a table on the Databricks cluster and then fill
    // the table with the DataFrame 's contents.
    // If the table already exists from a previous run,
    // delete it first.
    spark.sql("USE default")
    spark.sql("DROP TABLE IF EXISTS zzz_demo_temps_table")
    temps.write.saveAsTable("zzz_demo_temps_table")

    // Query the table on the Databricks cluster, returning rows
    // where the airport code is not BLI and the date is later
    // than 2021-04-01.Group the results and order by high
    // temperature in descending order.
    val df_temps = spark.sql("SELECT * FROM zzz_demo_temps_table " +
      "WHERE AirportCode != 'BLI' AND Date > '2021-04-01' " +
      "GROUP BY AirportCode, Date, TempHighF, TempLowF " +
      "ORDER BY TempHighF DESC")
    df_temps.show()

    // Results:
    // +------------+-----------+---------+--------+
    // | AirportCode|       Date|TempHighF|TempLowF|
    // +------------+-----------+---------+--------+
    // |        PDX | 2021-04-03|      64 |     45 |
    // |        PDX | 2021-04-02|      61 |     41 |
    // |        SEA | 2021-04-03|      57 |     43 |
    // |        SEA | 2021-04-02|      54 |     39 |
    // +------------+-----------+---------+--------+

    // Clean up by deleting the table from the Databricks cluster.
    spark.sql("DROP TABLE zzz_demo_temps_table")
  }
}

Note

The following example describes how to use the SparkSession class in cases where the DatabricksSession class in Databricks Connect is unavailable.

This example queries the specified table and returns the first 5 rows. This example uses the SPARK_REMOTE environment variable for authentication.

import org.apache.spark.sql.{DataFrame, SparkSession}

object Main {
  def main(args: Array[String]): Unit = {
    getTaxis(getSpark()).show(5)
  }

  private def getSpark(): SparkSession = {
    SparkSession.builder().getOrCreate()
  }

  private def getTaxis(spark: SparkSession): DataFrame = {
    spark.read.table("samples.nyctaxi.trips")
  }
}