SparkSession - a new entry point

1023

收藏 2016-09-19

We have been getting a lot of questions about thre relationship between SparkContext, SQLContext, and HiveContext in Spark 1.x. It was really strange to have "HiveContext" as an entry point when people want to use the DataFrame API. In Spark 2.0, we are introducing SparkSession, a new entry point that subsumes SQLContext and HiveContext. For backward compatibiilty, the two are preserved. SparkSession has many features, and here we demonstrate some of the more important ones.

While this notebook is written in Scala, similar (actually almost identical) APIs exist in Python and Java.

To read the companion blog post, click here: https://databricks.com/blog/2016/05/11/spark-2-0-technical-preview-easier-faster-and-smarter.html

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Creating a SparkSession

A SparkSession can be created using a builder pattern. The builder will automatically reuse an existing SparkContext if one exists; and create a SparkContext if it does not exist. Configuration options set in the builder are automatically propagated over to Spark and Hadoop during I/O.

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// A SparkSession can be created using a builder patternimport org.apache.spark.sql.SparkSessionval sparkSession = SparkSession.builder .master("local") .appName("my-spark-app") .config("spark.some.config.option", "config-value") .getOrCreate()

import org.apache.spark.sql.SparkSessionsparkSession: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@46d6b87c

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In Databricks notebooks and Spark REPL, the SparkSession has been created automatically and assigned to variable "spark".

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spark

res9: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@46d6b87c

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Unified entry point for reading data

SparkSession is the entry point for reading data, similar to the old SQLContext.read.

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val jsonData = spark.read.json("/home/webinar/person.json")

jsonData: org.apache.spark.sql.DataFrame = [email: string, iq: bigint ... 1 more field]

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display(jsonData)

matei@databricks.com

180

Matei Zaharia

rxin@databricks.com

Reynold Xin

name

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Running SQL queries

SparkSession can be used to execute SQL queries over data, getting the results back as a DataFrame (i.e. Dataset[Row]).

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display(spark.sql("select * from person"))

matei@databricks.com

180

Matei Zaharia

rxin@databricks.com

Reynold Xin

name

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Working with config options

SparkSession can also be used to set runtime configuration options, which can toggle optimizer behavior or I/O (i.e. Hadoop) behavior.

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spark.conf.set("spark.some.config", "abcd")

res12: org.apache.spark.sql.RuntimeConfig = org.apache.spark.sql.RuntimeConfig@55d93752

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spark.conf.get("spark.some.config")

res13: String = abcd

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And config options set can also be used in SQL using variable substitution.

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%sql select "${spark.some.config}"

abcd

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Working with metadata directly

SparkSession also includes a "catalog" method that contains methods to work with the metastore (i.e. data catalog). Methods there return Datasets so you can use the same Dataset API to play with them.

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// To get a list of tables in the current databaseval tables = spark.catalog.listTables()

tables: org.apache.spark.sql.Dataset[org.apache.spark.sql.catalog.Table] = [name: string, database: string ... 3 more fields]

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display(tables)

person

default

null

MANAGED

false

smart

default

null

MANAGED

false

name

database

description

tableType

isTemporary

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