Spark SQL Programming Guide

Overview

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

Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using Spark. At the core of this component is a new type of RDD, SchemaRDD. SchemaRDDs are composed 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, 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 pyspark shell.

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

Getting Started

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.

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

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

JavaSparkContext sc = ...; // An existing JavaSparkContext.
JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);

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

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

Data Sources

Spark SQL supports operating on a variety of data sources through the SchemaRDD interface. Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.

Spark SQL supports operating on a variety of data sources through the JavaSchemaRDD interface. Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.

Spark SQL supports operating on a variety of data sources through the SchemaRDD interface. Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.

RDDs

One type of table that is supported by Spark SQL is an RDD of Scala case classes. 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.

// 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.registerAsTable("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)

One type of table that is supported by Spark SQL is an RDD of JavaBeans. The BeanInfo defines the schema of the table. Currently, Spark SQL does not support JavaBeans that contain nested or contain complex types such as Lists or Arrays. You can create a JavaBean by creating a class that implements Serializable and has getters and setters for all of its fields.

public static class Person implements Serializable {
  private String name;
  private int age;

  public String getName() {
    return name;
  }

  public void setName(String name) {
    this.name = name;
  }

  public int getAge() {
    return age;
  }

  public void setAge(int age) {
    this.age = age;
  }
}

A schema can be applied to an existing RDD by calling applySchema and providing the Class object for the JavaBean.

// sc is an existing JavaSparkContext.
JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc)

// Load a text file and convert each line to a JavaBean.
JavaRDD<Person> people = sc.textFile("examples/src/main/resources/people.txt").map(
  new Function<String, Person>() {
    public Person call(String line) throws Exception {
      String[] parts = line.split(",");

      Person person = new Person();
      person.setName(parts[0]);
      person.setAge(Integer.parseInt(parts[1].trim()));

      return person;
    }
  });

// Apply a schema to an RDD of JavaBeans and register it as a table.
JavaSchemaRDD schemaPeople = sqlContext.applySchema(people, Person.class);
schemaPeople.registerAsTable("people");

// SQL can be run over RDDs that have been registered as tables.
JavaSchemaRDD 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.
List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
  public String call(Row row) {
    return "Name: " + row.getString(0);
  }
}).collect();

One type of table that is supported by Spark SQL is an RDD of dictionaries. The keys of the dictionary define the columns names of the table, and the types are inferred by looking at the first row. Any RDD of dictionaries can converted to a SchemaRDD and then registered as a table. Tables can be used in subsequent SQL statements.

# sc is an existing SparkContext.
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

# Load a text file and convert each line to a dictionary.
lines = sc.textFile("examples/src/main/resources/people.txt")
parts = lines.map(lambda l: l.split(","))
people = parts.map(lambda p: {"name": p[0], "age": int(p[1])})

# Infer the schema, and register the SchemaRDD as a table.
# In future versions of PySpark we would like to add support for registering RDDs with other
# datatypes as tables
schemaPeople = sqlContext.inferSchema(people)
schemaPeople.registerAsTable("people")

# SQL can be run over SchemaRDDs that have been registered as a table.
teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

# The results of SQL queries are RDDs and support all the normal RDD operations.
teenNames = teenagers.map(lambda p: "Name: " + p.name)
for teenName in teenNames.collect():
  print teenName

Note that Spark SQL currently uses a very basic SQL parser. Users that want a more complete dialect of SQL should look at the HiveQL support provided by HiveContext.

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. Using the data from the above example:

// 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.registerAsTable("parquetFile")
val teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
teenagers.map(t => "Name: " + t(0)).collect().foreach(println)

// sqlContext from the previous example is used in this example.

JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example.

// JavaSchemaRDDs can be saved as Parquet files, maintaining the schema information.
schemaPeople.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 JavaSchemaRDD.
JavaSchemaRDD parquetFile = sqlContext.parquetFile("people.parquet");

//Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerAsTable("parquetFile");
JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
  public String call(Row row) {
    return "Name: " + row.getString(0);
  }
}).collect();

# sqlContext from the previous example is used in this example.

schemaPeople # The SchemaRDD from the previous example.

# SchemaRDDs can be saved as Parquet files, maintaining the schema information.
schemaPeople.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.
parquetFile = sqlContext.parquetFile("people.parquet")

# Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerAsTable("parquetFile");
teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
teenNames = teenagers.map(lambda p: "Name: " + p.name)
for teenName in teenNames.collect():
  print teenName

JSON Datasets

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.
// 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.registerAsTable("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)

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

  • 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.
// sc is an existing JavaSparkContext.
JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);

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

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

// Register this JavaSchemaRDD as a table.
people.registerAsTable("people");

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

// Alternatively, a JavaSchemaRDD can be created for a JSON dataset represented by
// an RDD[String] storing one JSON object per string.
List<String> jsonData = Arrays.asList(
  "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
JavaRDD<String> anotherPeopleRDD = sc.parallelize(jsonData);
JavaSchemaRDD anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD);

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.
# sc is an existing SparkContext.
from pyspark.sql import SQLContext
sqlContext = 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.
path = "examples/src/main/resources/people.json"
# Create a SchemaRDD from the file(s) pointed to by path
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.registerAsTable("people")

# SQL statements can be run by using the sql methods provided by sqlContext.
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.
anotherPeopleRDD = sc.parallelize([
  '{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}'])
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 ‘SPARK_HIVE=true sbt/sbt 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 acccess data stored in Hive.

Configuration of Hive is done by placing your hive-site.xml file in conf/.

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 also experiment with the LocalHiveContext, which is similar to HiveContext, but creates a local copy of the metastore and warehouse automatically.

// sc is an existing SparkContext.
val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)

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

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

When working with Hive one must construct a JavaHiveContext, which inherits from JavaSQLContext, and adds support for finding tables in in the MetaStore and writing queries using HiveQL. In addition to the sql method a JavaHiveContext also provides an hql methods, which allows queries to be expressed in HiveQL.

// sc is an existing JavaSparkContext.
JavaHiveContext hiveContext = new org.apache.spark.sql.hive.api.java.HiveContext(sc);

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

// Queries are expressed in HiveQL.
Row[] results = hiveContext.hql("FROM src SELECT key, value").collect();

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. In addition to the sql method a HiveContext also provides an hql methods, which allows queries to be expressed in HiveQL.

# sc is an existing SparkContext.
from pyspark.sql import HiveContext
hiveContext = HiveContext(sc)

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

# Queries can be expressed in HiveQL.
results = hiveContext.hql("FROM src SELECT key, value").collect()

Writing Language-Integrated Relational Queries

Language-Integrated queries are 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:

// 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 the ScalaDoc.