Code examples for Databricks Connect for Python


This article covers Databricks Connect for Databricks Runtime 13.0 and above.

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


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

from databricks.connect import DatabricksSession

spark = DatabricksSession.builder.getOrCreate()

df ="samples.nyctaxi.trips")

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.

from databricks.connect import DatabricksSession
from pyspark.sql.types import *
from datetime import date

spark = DatabricksSession.builder.getOrCreate()

# Create a Spark DataFrame consisting of high and low temperatures
# by airport code and date.
schema = StructType([
  StructField('AirportCode', StringType(), False),
  StructField('Date', DateType(), False),
  StructField('TempHighF', IntegerType(), False),
  StructField('TempLowF', IntegerType(), False)

data = [
  [ 'BLI', date(2021, 4, 3), 52, 43],
  [ 'BLI', date(2021, 4, 2), 50, 38],
  [ 'BLI', date(2021, 4, 1), 52, 41],
  [ 'PDX', date(2021, 4, 3), 64, 45],
  [ 'PDX', date(2021, 4, 2), 61, 41],
  [ 'PDX', date(2021, 4, 1), 66, 39],
  [ 'SEA', date(2021, 4, 3), 57, 43],
  [ 'SEA', date(2021, 4, 2), 54, 39],
  [ 'SEA', date(2021, 4, 1), 56, 41]

temps = spark.createDataFrame(data, schema)

# 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')

# 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.
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")

# 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')


The following example describes how to to write code that is portable between Databricks Connect for Databricks Runtime 13.3 LTS and above in environments where the DatabricksSession class is unavailable.

The following example uses the DatabricksSession class, or uses the SparkSession class if the DatabricksSession class is unavailable, to query the specified table and return the first 5 rows. This example uses the SPARK_REMOTE environment variable for authentication.

from pyspark.sql import SparkSession, DataFrame

def get_spark() -> SparkSession:
    from databricks.connect import DatabricksSession
    return DatabricksSession.builder.getOrCreate()
  except ImportError:
    return SparkSession.builder.getOrCreate()

def get_taxis(spark: SparkSession) -> DataFrame: