Reputation: 457

What are SparkSession Config Options

I am trying to use SparkSession to convert JSON data of a file to RDD with Spark Notebook. I already have the JSON file.

 val spark = SparkSession
   .builder()
   .appName("jsonReaderApp")
   .config("config.key.here", configValueHere)
   .enableHiveSupport()
   .getOrCreate()
val jread = spark.read.json("search-results1.json")

I am very new to spark and do not know what to use for config.key.here and configValueHere.

Upvotes: 27

Answers (5)

Clay

Reputation: 2736

SparkSession

To get all the "various Spark parameters as key-value pairs" for a SparkSession, “The entry point to programming Spark with the Dataset and DataFrame API," run the following (this is using Spark Python API, Scala would be very similar).

import pyspark
from pyspark import SparkConf
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
SparkConf().getAll()

or without importing SparkConf:

spark.sparkContext.getConf().getAll()

Depending on which API you are using, see one of the following:

You can get a deeper level list of SparkSession configuration options by running the code below. Most are the same, but there are a few extra ones. I am not sure if you can change these.

spark.sparkContext._conf.getAll()

SparkContext

To get all the "various Spark parameters as key-value pairs" for a SparkContext, the "Main entry point for Spark functionality," ... "connection to a Spark cluster," ... and "to create RDDs, accumulators and broadcast variables on that cluster,” run the following.

import pyspark
from pyspark import SparkConf, SparkContext 
spark_conf = SparkConf().setAppName("test")
spark = SparkContext(conf = spark_conf)
SparkConf().getAll()

Depending on which API you are using, see one of the following:

Spark parameters

You should get a list of tuples that contain the "various Spark parameters as key-value pairs" similar to the following:

[(u'spark.eventLog.enabled', u'true'),
 (u'spark.yarn.appMasterEnv.PYSPARK_PYTHON', u'/<yourpath>/parcels/Anaconda-4.2.0/bin/python'),
 ...
 ...
 (u'spark.yarn.jars', u'local:/<yourpath>/lib/spark2/jars/*')]

Depending on which API you are using, see one of the following:

https://spark.apache.org/docs/latest/api/scala/org/apache/spark/SparkConf.html
https://spark.apache.org/docs/latest//api/python/reference/api/pyspark.SparkConf.html
https://spark.apache.org/docs/latest/api/java/org/apache/spark/SparkConf.html
https://spark.apache.org/docs/3.3.2/api/R/reference/sparkR.conf.html (for SparkR, sparkConfig can only be set from sparkR.session(sparkConfig=list()))

For a complete list of Spark properties, see:
http://spark.apache.org/docs/latest/configuration.html#viewing-spark-properties

Setting Spark parameters

Each tuple is ("spark.some.config.option", "some-value") which you can set in your application with:

SparkSession

spark = (
    SparkSession
    .builder
    .appName("Your App Name")
    .config("spark.some.config.option1", "some-value")
    .config("spark.some.config.option2", "some-value")
    .getOrCreate())

sc = spark.sparkContext

SparkContext

spark_conf = (
    SparkConf()
    .setAppName("Your App Name")
    .set("spark.some.config.option1", "some-value")
    .set("spark.some.config.option2", "some-value"))

sc = SparkContext(conf = spark_conf)

spark-defaults

You can also set the Spark parameters in a spark-defaults.conf file:

spark.some.config.option1 some-value
spark.some.config.option2 "some-value"

then run your Spark application with spark-submit (pyspark):

spark-submit \
--properties-file path/to/your/spark-defaults.conf \
--name "Your App Name" \
--py-files path/to/your/supporting/pyspark_files.zip \
--class Main path/to/your/pyspark_main.py

Upvotes: 54

kar09

Reputation: 431

The easiest way to set some config:

spark.conf.set("spark.sql.shuffle.partitions", 500).

Where spark refers to a SparkSession, that way you can set configs at runtime. It's really useful when you want to change configs again and again to tune some spark parameters for specific queries.

Upvotes: 5

Jeff A.

Reputation: 81

This is how it worked for me to add spark or hive settings in my scala:

{
    val spark = SparkSession
        .builder()
        .appName("StructStreaming")
        .master("yarn")
        .config("hive.merge.mapfiles", "false")
        .config("hive.merge.tezfiles", "false")
        .config("parquet.enable.summary-metadata", "false")
        .config("spark.sql.parquet.mergeSchema","false")
        .config("hive.merge.smallfiles.avgsize", "160000000")
        .enableHiveSupport()
        .config("hive.exec.dynamic.partition", "true")
        .config("hive.exec.dynamic.partition.mode", "nonstrict")
        .config("spark.sql.orc.impl", "native")
        .config("spark.sql.parquet.binaryAsString","true")
        .config("spark.sql.parquet.writeLegacyFormat","true")
        //.config(“spark.sql.streaming.checkpointLocation”, “hdfs://pp/apps/hive/warehouse/dev01_landing_initial_area.db”)
        .getOrCreate()
}

Upvotes: 7