Spark configuration

Session configuration

When you create a PathlingContext within your Spark application, it will detect the presence of an existing SparkSession and use it. If there is no existing session, it will create one for you with some sensible default configuration. You can override this default configuration by passing a SparkSession object to the PathlingContext constructor.

This can be useful if you want to set other Spark configuration, for example to increase the available memory.

The session that you provide must have the Pathling library API on the classpath. You can also optionally enable Delta Lake support. Here is an example of how to programmatically configure a session that has Delta enabled:

Python

from pathling import PathlingContext
from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.config(
            "spark.jars.packages",
            "au.csiro.pathling:library-runtime:7.0.1,"
            "io.delta:delta-spark_2.12:3.1.0,"
    )
    .config(
            "spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension"
    )
    .config(
            "spark.sql.catalog.spark_catalog",
            "org.apache.spark.sql.delta.catalog.DeltaCatalog",
    ).getOrCreate()
)

pc = PathlingContext.create(spark)

R

library(sparklyr)
library(pathling)

sc <- spark_connect(master = "local",
                    packages = c(paste("au.csiro.pathling:library-runtime:", pathling_version()), 
                                 "io.delta:delta-spark_2.12:3.1.0"),
                    config = list("sparklyr.shell.conf" = c(
                      "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension",
                      "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"
                    )), version = "3.4.0")

pc <- pathling_connect(sc)

Scala

import au.csiro.pathling.library.PathlingContext

val spark = SparkSession.builder
  .config("spark.jars.packages", "au.csiro.pathling:library-runtime:7.0.1," +
      "io.delta:delta-spark_2.12:3.1.0")
  .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")
  .config("spark.sql.catalog.spark_catalog",
    "org.apache.spark.sql.delta.catalog.DeltaCatalog")
  .getOrCreate()

val pc = PathlingContext.create(spark)

Java

import au.csiro.pathling.library.PathlingContext;
import org.apache.spark.sql.SparkSession;

class MyApp {

    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder()
            .config("spark.jars.packages", 
                    "au.csiro.pathling:library-runtime:7.0.1," +
                    "io.delta:delta-spark_2.12:3.1.0")
            .config("spark.sql.extensions", 
                    "io.delta.sql.DeltaSparkSessionExtension")
            .config("spark.sql.catalog.spark_catalog",
                    "org.apache.spark.sql.delta.catalog.DeltaCatalog")
            .getOrCreate();
        PathlingContext pc = PathlingContext.create(spark);
    }
}

Cluster configuration

If you are running your own Spark cluster, or using a Docker image (such as jupyter/all-spark-notebook), you will need to configure Pathling as a Spark package.

You can do this by adding the following to your spark-defaults.conf file:

spark.jars.packages au.csiro.pathling:library-runtime:[some version]

See the Configuration page of the Spark documentation for more information about spark.jars.packages and other related configuration options.

To create a Pathling notebook Docker image, your Dockerfile might look like this:

FROM jupyter/all-spark-notebook

USER root
RUN echo "spark.jars.packages au.csiro.pathling:library-runtime:[some version]" >> /usr/local/spark/conf/spark-defaults.conf

USER ${NB_UID}

RUN pip install --quiet --no-cache-dir pathling && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"