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.
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()
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:
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:
sparkR.session(sparkConfig=list()))For a complete list of Spark properties, see:
http://spark.apache.org/docs/latest/configuration.html#viewing-spark-properties
Each tuple is ("spark.some.config.option", "some-value") which you can set in your application with:
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
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)
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
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()
}
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.
In simple terms, values set in "config" method are automatically propagated to both SparkConf and SparkSession's own configuration.
for eg : you can refer to https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-sql-settings.html to understand how hive warehouse locations are set for SparkSession using config option
To know about the this api you can refer to : https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/sql/SparkSession.Builder.html
Every Spark config option is expolained at: http://spark.apache.org/docs/latest/configuration.html
You can set these at run-time as in your example above or through the config file given to spark-submit
