Spark1.1.0 Spark SQL Programming Guide

Spark SQL Programming Guide

Overview
Getting Started
Data Sources

RDDs

Inferring the Schema Using Reflection
Programmatically Specifying the Schema

Parquet Files

Loading Data Programmatically
Configuration

JSON Datasets
Hive Tables

Performance Tuning

Caching Data In Memory
Other Configuration Options

Other SQL Interfaces

Running the Thrift JDBC server
Running the Spark SQL CLI

Compatibility with Other Systems

Migration Guide for Shark User

Scheduling
Reducer number
Caching

Compatibility with Apache Hive

Deploying in Existing Hive Warehouses
Supported Hive Features
Unsupported Hive Functionality

Writing Language-Integrated Relational Queries
Spark SQL DataType Reference

Overview

Scala
Java
Python

Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of RDD, SchemaRDD.
SchemaRDDs are composed of Row objects, along with a schema that describes
the data types of each column in the row. A SchemaRDD is similar to a table in a traditional relational database. A SchemaRDD can be created from an existing RDD, a Parquet file,
a JSON dataset, or by running HiveQL against data stored in Apache Hive.
All of the examples on this page use sample data included in the Spark distribution and can be run in the

spark-shell

.

Spark SQL is currently an alpha component. While we will minimize API changes, some APIs may change in future releases.

Getting Started

Scala
Java
Python

The entry point into all relational functionality in Spark is the SQLContext class,
or one of its descendants. To create a basic SQLContext, all you need is a SparkContext.

[code]val sc: SparkContext // An existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
import sqlContext.createSchemaRDD

In addition to the basic SQLContext, you can also create a HiveContext, which provides a superset of the functionality provided by the basic SQLContext. Additional features include the ability to write queries using
the more complete HiveQL parser, access to HiveUDFs, and the ability to read data from Hive tables. To use a HiveContext, you do not need to have an existing Hive setup, and all of the data sources available to a SQLContext are still available. HiveContext
is only packaged separately to avoid including all of Hive’s dependencies in the default Spark build. If these dependencies are not a problem for your application then using HiveContext is recommended for the 1.2 release of Spark. Future releases will focus
on bringing SQLContext up to feature parity with a HiveContext.

The specific variant of SQL that is used to parse queries can also be selected using the

spark.sql.dialect

option. This parameter
can be changed using either the

setConf

method on a SQLContext or by using a

SET
 key=value

command in SQL. For a SQLContext, the only dialect available is “sql” which uses a simple SQL parser provided by Spark SQL. In a HiveContext, the default is “hiveql”, though “sql” is also available. Since the HiveQL parser is much more complete,
this is recommended for most use cases.

Data Sources

Spark SQL supports operating on a variety of data sources through the

SchemaRDD

interface. A SchemaRDD can be operated on as normal
RDDs and can also be registered as a temporary table. Registering a SchemaRDD as a table allows you to run SQL queries over its data. This section describes the various methods for loading data into a SchemaRDD.

RDDs

Spark SQL supports two different methods for converting existing RDDs into SchemaRDDs. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection based approach leads to more concise code and works
well when you already know the schema while writing your Spark application.

The second method for creating SchemaRDDs is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows you to construct SchemaRDDs when the columns and their types
are not known until runtime.

Inferring the Schema Using Reflection

Scala
Java
Python

The Scala interaface for Spark SQL supports automatically converting an RDD containing case classes to a SchemaRDD. The case class defines the schema of the table. The names of the arguments to the case class are
read using reflection and become the names of the columns. Case classes can also be nested or contain complex types such as Sequences or Arrays. This RDD can be implicitly converted to a SchemaRDD and then be registered as a table. Tables can be used in subsequent
SQL statements.

[code]// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
import sqlContext.createSchemaRDD

// Define the schema using a case class.
// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
// you can use custom classes that implement the Product interface.
case class Person(name: String, age: Int)

// Create an RDD of Person objects and register it as a table.
val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt))
people.registerTempTable("people")

// SQL statements can be run by using the sql methods provided by sqlContext.
val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
teenagers.map(t => "Name: " + t(0)).collect().foreach(println)

Programmatically Specifying the Schema

Scala
Java
Python

When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a

SchemaRDD

can
be created programmatically with three steps.

Create an RDD of

Row

s from the original RDD;
Create the schema represented by a

StructType

matching the structure of

Row

s
in the RDD created in Step 1.
Apply the schema to the RDD of

Row

s via

applySchema

method
provided by

SQLContext

.

For example:

[code]// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

// Create an RDD
val people = sc.textFile("examples/src/main/resources/people.txt")

// The schema is encoded in a string
val schemaString = "name age"

// Import Spark SQL data types and Row.
import org.apache.spark.sql._

// Generate the schema based on the string of schema
val schema =
  StructType(
    schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))

// Convert records of the RDD (people) to Rows.
val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim))

// Apply the schema to the RDD.
val peopleSchemaRDD = sqlContext.applySchema(rowRDD, schema)

// Register the SchemaRDD as a table.
peopleSchemaRDD.registerTempTable("people")

// SQL statements can be run by using the sql methods provided by sqlContext.
val results = sqlContext.sql("SELECT name FROM people")

// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
results.map(t => "Name: " + t(0)).collect().foreach(println)

Parquet Files

Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that
automatically preserves the schema of the original data.

Loading Data Programmatically

Using the data from the above example:

Scala
Java
Python

[code]// sqlContext from the previous example is used in this example.
// createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD.
import sqlContext.createSchemaRDD

val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.

// The RDD is implicitly converted to a SchemaRDD by createSchemaRDD, allowing it to be stored using Parquet.
people.saveAsParquetFile("people.parquet")

// Read in the parquet file created above.  Parquet files are self-describing so the schema is preserved.
// The result of loading a Parquet file is also a SchemaRDD.
val parquetFile = sqlContext.parquetFile("people.parquet")

//Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerTempTable("parquetFile")
val teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
teenagers.map(t => "Name: " + t(0)).collect().foreach(println)

Configuration

Configuration of Parquet can be done using the

setConf

method on SQLContext or by running

SET
 key=value

commands using SQL.

Property Name	Default	Meaning
spark.sql.parquet.binaryAsString	false	Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do not differentiate between binary data and strings when writing out the Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
spark.sql.parquet.cacheMetadata	false	Turns on caching of Parquet schema metadata. Can speed up querying of static data.
spark.sql.parquet.compression.codec	snappy	Sets the compression codec use when writing Parquet files. Acceptable values include: uncompressed, snappy, gzip, lzo.

JSON Datasets

Scala
Java
Python

Spark SQL can automatically infer the schema of a JSON dataset and load it as a SchemaRDD. This conversion can be done using one of two methods in a SQLContext:

jsonFile

- loads data from a directory of JSON files where each line of the files is a JSON object.

jsonRdd

- loads data from an existing RDD where each element of the RDD is a string containing a JSON
object.

[code]// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files.
val path = "examples/src/main/resources/people.json"
// Create a SchemaRDD from the file(s) pointed to by path
val people = sqlContext.jsonFile(path)

// The inferred schema can be visualized using the printSchema() method.
people.printSchema()
// root
//  |-- age: IntegerType
//  |-- name: StringType

// Register this SchemaRDD as a table.
people.registerTempTable("people")

// SQL statements can be run by using the sql methods provided by sqlContext.
val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

// Alternatively, a SchemaRDD can be created for a JSON dataset represented by
// an RDD[String] storing one JSON object per string.
val anotherPeopleRDD = sc.parallelize(
  """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)

Hive Tables

Spark SQL also supports reading and writing data stored in Apache Hive. However, since Hive has a large number of dependencies, it is not included in the
default Spark assembly. In order to use Hive you must first run “

sbt/sbt -Phive assembly/assembly

” (or use

-Phive

for
maven). This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar must also be present on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to access
data stored in Hive.

Configuration of Hive is done by placing your

hive-site.xml

file in

conf/

.

Scala
Java
Python

When working with Hive one must construct a

HiveContext

, which inherits from

SQLContext

,
and adds support for finding tables in in the MetaStore and writing queries using HiveQL. Users who do not have an existing Hive deployment can still create a HiveContext. When not configured by the hive-site.xml, the context automatically creates

metastore_db

and

warehouse

in
the current directory.

[code]// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)

sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")

// Queries are expressed in HiveQL
sqlContext.sql("FROM src SELECT key, value").collect().foreach(println)

Performance Tuning

For some workloads it is possible to improve performance by either caching data in memory, or by turning on some experimental options.

Caching Data In Memory

Spark SQL can cache tables using an in-memory columnar format by calling

cacheTable("tableName")

. Then Spark SQL will scan only
required columns and will automatically tune compression to minimize memory usage and GC pressure. You can call

uncacheTable("tableName")

to
remove the table from memory.

Note that if you call

cache

rather than

cacheTable

,
tables will not be cached using the in-memory columnar format, and therefore

cacheTable

is strongly recommended for this
use case.

Configuration of in-memory caching can be done using the

setConf

method on SQLContext or by running

SET
 key=value

commands using SQL.

Property Name	Default	Meaning
spark.sql.inMemoryColumnarStorage.compressed	false	When set to true Spark SQL will automatically select a compression codec for each column based on statistics of the data.
spark.sql.inMemoryColumnarStorage.batchSize	1000	Controls the size of batches for columnar caching. Larger batch sizes can improve memory utilization and compression, but risk OOMs when caching data.

Other Configuration Options

The following options can also be used to tune the performance of query execution. It is possible that these options will be deprecated in future release as more optimizations are performed automatically.

Property Name	Default	Meaning
spark.sql.autoBroadcastJoinThreshold	10000	Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join. By setting this value to -1 broadcasting can be disabled. Note that currently statistics are only supported for Hive Metastore tables where the command `ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan` has been run.
spark.sql.codegen	false	When true, code will be dynamically generated at runtime for expression evaluation in a specific query. For some queries with complicated expression this option can lead to significant speed-ups. However, for simple queries this can actually slow down query execution.
spark.sql.shuffle.partitions	200	Configures the number of partitions to use when shuffling data for joins or aggregations.

Other SQL Interfaces

Spark SQL also supports interfaces for running SQL queries directly without the need to write any code.

Running the Thrift JDBC server

The Thrift JDBC server implemented here corresponds to the

HiveServer2

in
Hive 0.12. You can test the JDBC server with the beeline script that comes with either Spark or Hive 0.12.

To start the JDBC server, run the following in the Spark directory:

[code]./sbin/start-thriftserver.sh

The default port the server listens on is 10000. To listen on customized host and port, please set the

HIVE_SERVER2_THRIFT_PORT

and

HIVE_SERVER2_THRIFT_BIND_HOST

environment
variables. You may run

./sbin/start-thriftserver.sh --help

for a complete list of all available options. Now you can use beeline
to test the Thrift JDBC server:

[code]./bin/beeline

Connect to the JDBC server in beeline with:

[code]beeline> !connect jdbc:hive2://localhost:10000

Beeline will ask you for a username and password. In non-secure mode, simply enter the username on your machine and a blank password. For secure mode, please follow the instructions given in the beeline
documentation.

Configuration of Hive is done by placing your

hive-site.xml

file in

conf/

.

You may also use the beeline script that comes with Hive.

Running the Spark SQL CLI

The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute queries input from the command line. Note that the Spark SQL CLI cannot talk to the Thrift JDBC server.

To start the Spark SQL CLI, run the following in the Spark directory:

[code]./bin/spark-sql

Configuration of Hive is done by placing your

hive-site.xml

file in

conf/

.
You may run

./bin/spark-sql --help

for a complete list of all available options.

Compatibility with Other Systems

Migration Guide for Shark User

Scheduling

s To set a Fair Scheduler pool for a JDBC client session, users can set the

spark.sql.thriftserver.scheduler.pool

variable:

[code]SET spark.sql.thriftserver.scheduler.pool=accounting;

Reducer number

In Shark, default reducer number is 1 and is controlled by the property

mapred.reduce.tasks

. Spark SQL deprecates this property
in favor of

spark.sql.shuffle.partitions

, whose default value is 200. Users may customize this property via

SET

:

[code]SET spark.sql.shuffle.partitions=10;
SELECT page, count(*) c 
FROM logs_last_month_cached
GROUP BY page ORDER BY c DESC LIMIT 10;

You may also put this property in

hive-site.xml

to override the default value.

For now, the

mapred.reduce.tasks

property is still recognized, and is converted to

spark.sql.shuffle.partitions

automatically.

Caching

The

shark.cache

table property no longer exists, and tables whose name end with

_cached

are
no longer automatically cached. Instead, we provide

CACHE TABLE

and

UNCACHE
 TABLE

statements to let user control table caching explicitly:

[code]CACHE TABLE logs_last_month;
UNCACHE TABLE logs_last_month;

NOTE:

CACHE TABLE tbl

is lazy, similar to

.cache

on
an RDD. This command only marks

tbl

to ensure that partitions are cached when calculated but doesn’t actually cache it until a
query that touches

tbl

is executed. To force the table to be cached, you may simply count the table immediately after executing

CACHE
 TABLE

:

[code]CACHE TABLE logs_last_month;
SELECT COUNT(1) FROM logs_last_month;

Several caching related features are not supported yet:

User defined partition level cache eviction policy
RDD reloading
In-memory cache write through policy

Compatibility with Apache Hive

Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently Spark SQL is based on Hive 0.12.0.

Deploying in Existing Hive Warehouses

The Spark SQL Thrift JDBC server is designed to be “out of the box” compatible with existing Hive installations. You do not need to modify your existing Hive Metastore or change the data placement or partitioning of your tables.

Supported Hive Features

Spark SQL supports the vast majority of Hive features, such as:

Hive query statements, including:

SELECT

GROUP BY

ORDER BY

CLUSTER BY

SORT BY

All Hive operators, including:

Relational operators (

=

,

⇔

,

==

,

<>

,

<

,

>

,

>=

,

<=

,
etc)
Arithmetic operators (

+

,

-

,

*

,

/

,

%

,
etc)
Logical operators (

AND

,

&&

,

OR

,

||

,
etc)
Complex type constructors
Mathematical functions (

sign

,

ln

,

cos

,
etc)
String functions (

instr

,

length

,

printf

,
etc)

User defined functions (UDF)
User defined aggregation functions (UDAF)
User defined serialization formats (SerDes)
Joins

JOIN

{LEFT|RIGHT|FULL} OUTER JOIN

LEFT SEMI JOIN

CROSS JOIN

Unions
Sub-queries

SELECT col FROM ( SELECT a + b AS col from t1) t2

Sampling
Explain
Partitioned tables
All Hive DDL Functions, including:

CREATE TABLE

CREATE TABLE AS SELECT

ALTER TABLE

Most Hive Data types, including:

TINYINT

SMALLINT

INT

BIGINT

BOOLEAN

FLOAT

DOUBLE

STRING

BINARY

TIMESTAMP

ARRAY<>

MAP<>

STRUCT<>

Unsupported Hive Functionality

Below is a list of Hive features that we don’t support yet. Most of these features are rarely used in Hive deployments.

Major Hive Features

Spark SQL does not currently support inserting to tables using dynamic partitioning.
Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL doesn’t support buckets yet.

Esoteric Hive Features

Tables with partitions using different input formats: In Spark SQL, all table partitions need to have the same input format.
Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions (e.g. condition “

key
 < 10

”), Spark SQL will output wrong result for the

NULL

tuple.

UNION

type and

DATE

type
Unique join
Single query multi insert
Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at the moment and only supports populating the sizeInBytes field of the hive metastore.

Hive Input/Output Formats

File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
Hadoop archive

Hive Optimizations

A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are less important due to Spark SQL’s in-memory computational model. Others are slotted for future releases of Spark SQL.

Block level bitmap indexes and virtual columns (used to build indexes)
Automatically convert a join to map join: For joining a large table with multiple small tables, Hive automatically converts the join into a map join. We are adding this auto conversion in the next release.
Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you need to control the degree of parallelism post-shuffle using “

SET
 spark.sql.shuffle.partitions=[num_tasks];

”.
Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still launches tasks to compute the result.
Skew data flag: Spark SQL does not follow the skew data flags in Hive.

STREAMTABLE

hint in join: Spark SQL does not follow the

STREAMTABLE

hint.
Merge multiple small files for query results: if the result output contains multiple small files, Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS metadata. Spark SQL does not support
that.

Writing Language-Integrated Relational Queries

Language-Integrated queries are experimental and currently only supported in Scala.

Spark SQL also supports a domain specific language for writing queries. Once again, using the data from the above examples:

[code]// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// Importing the SQL context gives access to all the public SQL functions and implicit conversions.
import sqlContext._
val people: RDD[Person] = ... // An RDD of case class objects, from the first example.

// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19'
val teenagers = people.where('age >= 10).where('age <= 19).select('name)
teenagers.map(t => "Name: " + t(0)).collect().foreach(println)

The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers prefixed with a tick (

'

). Implicit
conversions turn these symbols into expressions that are evaluated by the SQL execution engine. A full list of the functions supported can be found in theScalaDoc.

Spark SQL DataType Reference

Numeric types

ByteType

: Represents 1-byte signed integer numbers. The range of numbers is from

-128

to

127

.

ShortType

: Represents 2-byte signed integer numbers. The range of numbers is from

-32768

to

32767

.

IntegerType

: Represents 4-byte signed integer numbers. The range of numbers is from

-2147483648

to

2147483647

.

LongType

: Represents 8-byte signed integer numbers. The range of numbers is from

-9223372036854775808

to

9223372036854775807

.

FloatType

: Represents 4-byte single-precision floating point numbers.

DoubleType

: Represents 8-byte double-precision floating point numbers.

DecimalType

:

String type

StringType

: Represents character string values.

Binary type

BinaryType

: Represents byte sequence values.

Boolean type

BooleanType

: Represents boolean values.

Datetime type

TimestampType

: Represents values comprising values of fields year, month, day, hour, minute, and second.

Complex types

ArrayType(elementType, containsNull)

: Represents values comprising a sequence of elements with the type of

elementType

.

containsNull

is
used to indicate if elements in a

ArrayType

value can have

null

values.

MapType(keyType, valueType, valueContainsNull)

: Represents values comprising a set of key-value pairs. The data type of keys
are described by

keyType

and the data type of values are described by

valueType

.
For a

MapType

value, keys are not allowed to have

null

values.

valueContainsNull

is
used to indicate if values of a

MapType

value can have

null

values.

StructType(fields)

: Represents values with the structure described by a sequence of

StructField

s
(

fields

).

StructField(name, dataType, nullable)

: Represents a field in a

StructType

.
The name of a field is indicated by

name

. The data type of a field is indicated by

dataType

.

nullable

is
used to indicate if values of this fields can have

null

values.

Scala
Java
Python

All data types of Spark SQL are located in the package

org.apache.spark.sql

. You can
access them by doing

[code]import  org.apache.spark.sql._

Data type	Value type in Scala	API to access or create a data type
ByteType	Byte	ByteType
ShortType	Short	ShortType
IntegerType	Int	IntegerType
LongType	Long	LongType
FloatType	Float	FloatType
DoubleType	Double	DoubleType
DecimalType	scala.math.sql.BigDecimal	DecimalType
StringType	String	StringType
BinaryType	Array[Byte]	BinaryType
BooleanType	Boolean	BooleanType
TimestampType	java.sql.Timestamp	TimestampType
ArrayType	scala.collection.Seq	ArrayType(elementType, [containsNull]) Note: The default value of containsNull is false.
MapType	scala.collection.Map	MapType(keyType, valueType, [valueContainsNull]) Note: The default value of valueContainsNull is true.
StructType	org.apache.spark.sql.Row	StructType(fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed.
StructField	The value type in Scala of the data type of this field (For example, Int for a StructField with the data type IntegerType)	StructField(name, dataType, nullable)