sql-programming-guide.md

layout: global
title: Spark SQL Programming Guide

• This will become a table of contents (this text will be scraped). {:toc}

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 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 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 of 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, a 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 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 pyspark shell.

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

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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.

{% highlight scala %} 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 {% endhighlight %}

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 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.

{% highlight java %} JavaSparkContext sc = ...; // An existing JavaSparkContext. JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc); {% endhighlight %}

In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict super set 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 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.

{% highlight python %} from pyspark.sql import SQLContext sqlContext = SQLContext(sc) {% endhighlight %}

In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict super set 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

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.

{% highlight scala %} // 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) {% endhighlight %}

Spark SQL supports automatically converting an RDD of JavaBeans into a Schema RDD. The BeanInfo, obtained using reflection, 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.

{% highlight java %}

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; } }

{% endhighlight %}

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

{% highlight java %} // 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 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.registerTempTable("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 teenagerNames = teenagers.map(new Function<Row, String>() { public String call(Row row) { return "Name: " + row.getString(0); } }).collect();

{% endhighlight %}

Spark SQL can convert an RDD of Row objects to a SchemaRDD, inferring the datatypes. Rows are constructed by passing a list of key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table, and the types are inferred by looking at the first row. Since we currently only look at the first row, it is important that there is no missing data in the first row of the RDD. In future versions we plan to more completely infer the schema by looking at more data, similar to the inference that is performed on JSON files.

{% highlight python %}

sc is an existing SparkContext.

from pyspark.sql import SQLContext, Row 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: Row(name=p[0], age=int(p[1])))

Infer the schema, and register the SchemaRDD as a table.

schemaPeople = sqlContext.inferSchema(people) schemaPeople.registerTempTable("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 {% endhighlight %}

Programmatically Specifying the Schema

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.

1. Create an RDD of Rows from the original RDD;
2. Create the schema represented by a StructType matching the structure of Rows in the RDD created in Step 1.
3. Apply the schema to the RDD of Rows via applySchema method provided by SQLContext.

For example: {% highlight scala %} // 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) {% endhighlight %}

When JavaBean 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.

1. Create an RDD of Rows from the original RDD;
2. Create the schema represented by a StructType matching the structure of Rows in the RDD created in Step 1.
3. Apply the schema to the RDD of Rows via applySchema method provided by JavaSQLContext.

For example: {% highlight java %} // Import factory methods provided by DataType. import org.apache.spark.sql.api.java.DataType // Import StructType and StructField import org.apache.spark.sql.api.java.StructType import org.apache.spark.sql.api.java.StructField // Import Row. import org.apache.spark.sql.api.java.Row

// 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 people = sc.textFile("examples/src/main/resources/people.txt");

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

// Generate the schema based on the string of schema List fields = new ArrayList(); for (String fieldName: schemaString.split(" ")) { fields.add(DataType.createStructField(fieldName, DataType.StringType, true)); } StructType schema = DataType.createStructType(fields);

// Convert records of the RDD (people) to Rows. JavaRDD rowRDD = people.map( new Function<String, Row>() { public Row call(String record) throws Exception { String[] fields = record.split(","); return Row.create(fields[0], fields[1].trim()); } });

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

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

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

{% endhighlight %}

When a dictionary of kwargs 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.

1. Create an RDD of tuples or lists from the original RDD;
2. Create the schema represented by a StructType matching the structure of tuples or lists in the RDD created in the step 1.
3. Apply the schema to the RDD via applySchema method provided by SQLContext.

For example: {% highlight python %}

Import SQLContext and data types

from pyspark.sql import *

sc is an existing SparkContext.

sqlContext = SQLContext(sc)

Load a text file and convert each line to a tuple.

lines = sc.textFile("examples/src/main/resources/people.txt") parts = lines.map(lambda l: l.split(",")) people = parts.map(lambda p: (p[0], p[1].strip()))

The schema is encoded in a string.

schemaString = "name age"

fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()] schema = StructType(fields)

Apply the schema to the RDD.

schemaPeople = sqlContext.applySchema(people, schema)

Register the SchemaRDD as a table.

schemaPeople.registerTempTable("people")

SQL can be run over SchemaRDDs that have been registered as a table.

results = sqlContext.sql("SELECT name FROM people")

The results of SQL queries are RDDs and support all the normal RDD operations.

names = results.map(lambda p: "Name: " + p.name) for name in names.collect(): print name {% endhighlight %}

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.