Spark SQL, DataFrames and Datasets Guide

• Overview
  • SQL
  • DataFrames
  • Datasets
• Getting Started
  • Starting Point: SQLContext
  • Creating DataFrames
  • DataFrame Operations
  • Running SQL Queries Programmatically
  • Creating Datasets
  • Interoperating with RDDs
    • Inferring the Schema Using Reflection
    • Programmatically Specifying the Schema
• Data Sources
  • Generic Load/Save Functions
    • Manually Specifying Options
    • Run SQL on files directly
    • Save Modes
    • Saving to Persistent Tables
  • Parquet Files
    • Loading Data Programmatically
    • Partition Discovery
    • Schema Merging
    • Hive metastore Parquet table conversion
      • Hive/Parquet Schema Reconciliation
      • Metadata Refreshing
    • Configuration
  • JSON Datasets
  • Hive Tables
    • Interacting with Different Versions of Hive Metastore
  • JDBC To Other Databases
  • Troubleshooting
• Performance Tuning
  • Caching Data In Memory
  • Other Configuration Options
• Distributed SQL Engine
  • Running the Thrift JDBC/ODBC server
  • Running the Spark SQL CLI
• Migration Guide
  • Upgrading From Spark SQL 1.5 to 1.6
  • Upgrading From Spark SQL 1.4 to 1.5
  • Upgrading from Spark SQL 1.3 to 1.4
    • DataFrame data reader/writer interface
    • DataFrame.groupBy retains grouping columns
  • Upgrading from Spark SQL 1.0-1.2 to 1.3
    • Rename of SchemaRDD to DataFrame
    • Unification of the Java and Scala APIs
    • Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)
    • Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)
    • UDF Registration Moved to sqlContext.udf (Java & Scala)
    • Python DataTypes No Longer Singletons
  • Migration Guide for Shark Users
    • Scheduling
    • Reducer number
    • Caching
  • Compatibility with Apache Hive
    • Deploying in Existing Hive Warehouses
    • Supported Hive Features
    • Unsupported Hive Functionality
• Reference
  • Data Types
  • NaN Semantics

Overview

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations. There are several ways to interact with Spark SQL including SQL, the DataFrames API and the Datasets API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between the various APIs based on which provides the most natural way to express a given transformation.

All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell, pyspark shell, or sparkR shell.

SQL

One use of Spark SQL is to execute SQL queries written using either a basic SQL syntax or HiveQL. Spark SQL can also be used to read data from an existing Hive installation. For more on how to configure this feature, please refer to the Hive Tables section. When running SQL from within another programming language the results will be returned as a DataFrame. You can also interact with the SQL interface using the command-line or over JDBC/ODBC.

DataFrames

A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs.

The DataFrame API is available in Scala, Java, Python, and R.

Datasets

A Dataset is a new experimental interface added in Spark 1.6 that tries to provide the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine. A Dataset can be constructed from JVM objects and then manipulated using functional transformations (map, flatMap, filter, etc.).

The unified Dataset API can be used both in Scala and Java. Python does not yet have support for the Dataset API, but due to its dynamic nature many of the benefits are already available (i.e. you can access the field of a row by name naturally row.columnName). Full python support will be added in a future release.

Getting Started

Starting Point: SQLContext

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

// this is used to implicitly convert an RDD to a DataFrame.
import sqlContext.implicits._

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

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

sqlContext <- sparkRSQL.init(sc)

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 Hive UDFs, 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 1.3 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.

Creating DataFrames

With a SQLContext, applications can create DataFrames from an existing RDD, from a Hive table, or from data sources.

As an example, the following creates a DataFrame based on the content of a JSON file:

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

val df = sqlContext.read.json("examples/src/main/resources/people.json")

// Displays the content of the DataFrame to stdout
df.show()

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

DataFrame df = sqlContext.read().json("examples/src/main/resources/people.json");

// Displays the content of the DataFrame to stdout
df.show();

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

df = sqlContext.read.json("examples/src/main/resources/people.json")

# Displays the content of the DataFrame to stdout
df.show()

sqlContext <- SQLContext(sc)

df <- jsonFile(sqlContext, "examples/src/main/resources/people.json")

# Displays the content of the DataFrame to stdout
showDF(df)

DataFrame Operations

DataFrames provide a domain-specific language for structured data manipulation in Scala, Java, Python and R.

Here we include some basic examples of structured data processing using DataFrames:

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

// Create the DataFrame
val df = sqlContext.read.json("examples/src/main/resources/people.json")

// Show the content of the DataFrame
df.show()
// age  name
// null Michael
// 30   Andy
// 19   Justin

// Print the schema in a tree format
df.printSchema()
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)

// Select only the "name" column
df.select("name").show()
// name
// Michael
// Andy
// Justin

// Select everybody, but increment the age by 1
df.select(df("name"), df("age") + 1).show()
// name    (age + 1)
// Michael null
// Andy    31
// Justin  20

// Select people older than 21
df.filter(df("age") > 21).show()
// age name
// 30  Andy

// Count people by age
df.groupBy("age").count().show()
// age  count
// null 1
// 19   1
// 30   1

JavaSparkContext sc // An existing SparkContext.
SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc)

// Create the DataFrame
DataFrame df = sqlContext.read().json("examples/src/main/resources/people.json");

// Show the content of the DataFrame
df.show();
// age  name
// null Michael
// 30   Andy
// 19   Justin

// Print the schema in a tree format
df.printSchema();
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)

// Select only the "name" column
df.select("name").show();
// name
// Michael
// Andy
// Justin

// Select everybody, but increment the age by 1
df.select(df.col("name"), df.col("age").plus(1)).show();
// name    (age + 1)
// Michael null
// Andy    31
// Justin  20

// Select people older than 21
df.filter(df.col("age").gt(21)).show();
// age name
// 30  Andy

// Count people by age
df.groupBy("age").count().show();
// age  count
// null 1
// 19   1
// 30   1

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

# Create the DataFrame
df = sqlContext.read.json("examples/src/main/resources/people.json")

# Show the content of the DataFrame
df.show()
## age  name
## null Michael
## 30   Andy
## 19   Justin

# Print the schema in a tree format
df.printSchema()
## root
## |-- age: long (nullable = true)
## |-- name: string (nullable = true)

# Select only the "name" column
df.select("name").show()
## name
## Michael
## Andy
## Justin

# Select everybody, but increment the age by 1
df.select(df['name'], df['age'] + 1).show()
## name    (age + 1)
## Michael null
## Andy    31
## Justin  20

# Select people older than 21
df.filter(df['age'] > 21).show()
## age name
## 30  Andy

# Count people by age
df.groupBy("age").count().show()
## age  count
## null 1
## 19   1
## 30   1

sqlContext <- sparkRSQL.init(sc)

# Create the DataFrame
df <- jsonFile(sqlContext, "examples/src/main/resources/people.json")

# Show the content of the DataFrame
showDF(df)
## age  name
## null Michael
## 30   Andy
## 19   Justin

# Print the schema in a tree format
printSchema(df)
## root
## |-- age: long (nullable = true)
## |-- name: string (nullable = true)

# Select only the "name" column
showDF(select(df, "name"))
## name
## Michael
## Andy
## Justin

# Select everybody, but increment the age by 1
showDF(select(df, df$name, df$age + 1))
## name    (age + 1)
## Michael null
## Andy    31
## Justin  20

# Select people older than 21
showDF(where(df, df$age > 21))
## age name
## 30  Andy

# Count people by age
showDF(count(groupBy(df, "age")))
## age  count
## null 1
## 19   1
## 30   1

Running SQL Queries Programmatically

The sql function on a SQLContext enables applications to run SQL queries programmatically and returns the result as a DataFrame.

val sqlContext = ... // An existing SQLContext
val df = sqlContext.sql("SELECT * FROM table")

SQLContext sqlContext = ... // An existing SQLContext
DataFrame df = sqlContext.sql("SELECT * FROM table")

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df = sqlContext.sql("SELECT * FROM table")

sqlContext <- sparkRSQL.init(sc)
df <- sql(sqlContext, "SELECT * FROM table")

Creating Datasets

Datasets are similar to RDDs, however, instead of using Java Serialization or Kryo they use a specialized Encoder to serialize the objects for processing or transmitting over the network. While both encoders and standard serialization are responsible for turning an object into bytes, encoders are code generated dynamically and use a format that allows Spark to perform many operations like filtering, sorting and hashing without deserializing the bytes back into an object.

// Encoders for most common types are automatically provided by importing sqlContext.implicits._
val ds = Seq(1, 2, 3).toDS()
ds.map(_ + 1).collect() // Returns: Array(2, 3, 4)

// Encoders are also created for case classes.
case class Person(name: String, age: Long)
val ds = Seq(Person("Andy", 32)).toDS()

// DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name.
val path = "examples/src/main/resources/people.json"
val people = sqlContext.read.json(path).as[Person]

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

Interoperating with RDDs

Spark SQL supports two different methods for converting existing RDDs into DataFrames. 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 DataFrames 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 DataFrames when the columns and their types are not known until runtime.

Inferring the Schema Using Reflection

// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// this is used to implicitly convert an RDD to a DataFrame.
import sqlContext.implicits._

// 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)).toDF()
people.registerTempTable("people")

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

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

// or by field name:
teenagers.map(t => "Name: " + t.getAs[String]("name")).collect().foreach(println)

// row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T]
teenagers.map(_.getValuesMap[Any](List("name", "age"))).collect().foreach(println)
// Map("name" -> "Justin", "age" -> 19)

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;
  }
}

// sc is an existing JavaSparkContext.
SQLContext sqlContext = new org.apache.spark.sql.SQLContext(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.
DataFrame schemaPeople = sqlContext.createDataFrame(people, Person.class);
schemaPeople.registerTempTable("people");

// SQL can be run over RDDs that have been registered as tables.
DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

// The results of SQL queries are DataFrames and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
List<String> teenagerNames = teenagers.javaRDD().map(new Function<Row, String>() {
  public String call(Row row) {
    return "Name: " + row.getString(0);
  }
}).collect();

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

# Load a text file and convert each line to a Row.
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 DataFrame as a table.
schemaPeople = sqlContext.createDataFrame(people)
schemaPeople.registerTempTable("people")

# SQL can be run over DataFrames 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)

Programmatically Specifying the Schema

// 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 Row.
import org.apache.spark.sql.Row;

// Import Spark SQL data types
import org.apache.spark.sql.types.{StructType,StructField,StringType};

// 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 peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema)

// Register the DataFrames as a table.
peopleDataFrame.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 DataFrames and support all the normal RDD operations.
// The columns of a row in the result can be accessed by field index or by field name.
results.map(t => "Name: " + t(0)).collect().foreach(println)

import org.apache.spark.api.java.function.Function;
// Import factory methods provided by DataTypes.
import org.apache.spark.sql.types.DataTypes;
// Import StructType and StructField
import org.apache.spark.sql.types.StructType;
import org.apache.spark.sql.types.StructField;
// Import Row.
import org.apache.spark.sql.Row;
// Import RowFactory.
import org.apache.spark.sql.RowFactory;

// sc is an existing JavaSparkContext.
SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc);

// Load a text file and convert each line to a JavaBean.
JavaRDD<String> 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<StructField> fields = new ArrayList<StructField>();
for (String fieldName: schemaString.split(" ")) {
  fields.add(DataTypes.createStructField(fieldName, DataTypes.StringType, true));
}
StructType schema = DataTypes.createStructType(fields);

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

// Apply the schema to the RDD.
DataFrame peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema);

// Register the DataFrame as a table.
peopleDataFrame.registerTempTable("people");

// SQL can be run over RDDs that have been registered as tables.
DataFrame results = sqlContext.sql("SELECT name FROM people");

// The results of SQL queries are DataFrames and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
List<String> names = results.javaRDD().map(new Function<Row, String>() {
  public String call(Row row) {
    return "Name: " + row.getString(0);
  }
}).collect();

# Import SQLContext and data types
from pyspark.sql import SQLContext
from pyspark.sql.types 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.createDataFrame(people, schema)

# Register the DataFrame as a table.
schemaPeople.registerTempTable("people")

# SQL can be run over DataFrames 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)

Data Sources

Spark SQL supports operating on a variety of data sources through the DataFrame interface. A DataFrame can be operated on as normal RDDs and can also be registered as a temporary table. Registering a DataFrame as a table allows you to run SQL queries over its data. This section describes the general methods for loading and saving data using the Spark Data Sources and then goes into specific options that are available for the built-in data sources.

Generic Load/Save Functions

In the simplest form, the default data source (parquet unless otherwise configured by spark.sql.sources.default) will be used for all operations.

val df = sqlContext.read.load("examples/src/main/resources/users.parquet")
df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")

DataFrame df = sqlContext.read().load("examples/src/main/resources/users.parquet");
df.select("name", "favorite_color").write().save("namesAndFavColors.parquet");

df = sqlContext.read.load("examples/src/main/resources/users.parquet")
df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")

df <- loadDF(sqlContext, "people.parquet")
saveDF(select(df, "name", "age"), "namesAndAges.parquet")

Manually Specifying Options

You can also manually specify the data source that will be used along with any extra options that you would like to pass to the data source. Data sources are specified by their fully qualified name (i.e., org.apache.spark.sql.parquet), but for built-in sources you can also use their short names (json, parquet, jdbc). DataFrames of any type can be converted into other types using this syntax.

val df = sqlContext.read.format("json").load("examples/src/main/resources/people.json")
df.select("name", "age").write.format("parquet").save("namesAndAges.parquet")

DataFrame df = sqlContext.read().format("json").load("examples/src/main/resources/people.json");
df.select("name", "age").write().format("parquet").save("namesAndAges.parquet");

df = sqlContext.read.load("examples/src/main/resources/people.json", format="json")
df.select("name", "age").write.save("namesAndAges.parquet", format="parquet")

df <- loadDF(sqlContext, "people.json", "json")
saveDF(select(df, "name", "age"), "namesAndAges.parquet", "parquet")

Run SQL on files directly

Instead of using read API to load a file into DataFrame and query it, you can also query that file directly with SQL.

val df = sqlContext.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")

DataFrame df = sqlContext.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");

df = sqlContext.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")

df <- sql(sqlContext, "SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")

Save Modes

Save operations can optionally take a SaveMode, that specifies how to handle existing data if present. It is important to realize that these save modes do not utilize any locking and are not atomic. Additionally, when performing a Overwrite, the data will be deleted before writing out the new data.

Saving to Persistent Tables

When working with a HiveContext, DataFrames can also be saved as persistent tables using the saveAsTable command. Unlike the registerTempTable command, saveAsTable will materialize the contents of the dataframe and create a pointer to the data in the HiveMetastore. Persistent tables will still exist even after your Spark program has restarted, as long as you maintain your connection to the same metastore. A DataFrame for a persistent table can be created by calling the table method on a SQLContext with the name of the table.

By default saveAsTable will create a “managed table”, meaning that the location of the data will be controlled by the metastore. Managed tables will also have their data deleted automatically when a table is dropped.

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. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons.

Loading Data Programmatically

Using the data from the above example:

// sqlContext from the previous example is used in this example.
// This is used to implicitly convert an RDD to a DataFrame.
import sqlContext.implicits._

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

// The RDD is implicitly converted to a DataFrame by implicits, allowing it to be stored using Parquet.
people.write.parquet("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 DataFrame.
val parquetFile = sqlContext.read.parquet("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)

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

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

// DataFrames can be saved as Parquet files, maintaining the schema information.
schemaPeople.write().parquet("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 DataFrame.
DataFrame parquetFile = sqlContext.read().parquet("people.parquet");

// Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerTempTable("parquetFile");
DataFrame teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
List<String> teenagerNames = teenagers.javaRDD().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 DataFrame from the previous example.

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

# Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerTempTable("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)

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

schemaPeople # The DataFrame from the previous example.

# DataFrames can be saved as Parquet files, maintaining the schema information.
saveAsParquetFile(schemaPeople, "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 DataFrame.
parquetFile <- parquetFile(sqlContext, "people.parquet")

# Parquet files can also be registered as tables and then used in SQL statements.
registerTempTable(parquetFile, "parquetFile");
teenagers <- sql(sqlContext, "SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
teenNames <- map(teenagers, function(p) { paste("Name:", p$name)})
for (teenName in collect(teenNames)) {
  cat(teenName, "\n")
}

CREATE TEMPORARY TABLE parquetTable
USING org.apache.spark.sql.parquet
OPTIONS (
  path "examples/src/main/resources/people.parquet"
)

SELECT * FROM parquetTable

Partition Discovery

Table partitioning is a common optimization approach used in systems like Hive. In a partitioned table, data are usually stored in different directories, with partitioning column values encoded in the path of each partition directory. The Parquet data source is now able to discover and infer partitioning information automatically. For example, we can store all our previously used population data into a partitioned table using the following directory structure, with two extra columns, gender and country as partitioning columns:

path
└── to
    └── table
        ├── gender=male
        │   ├── ...
        │   │
        │   ├── country=US
        │   │   └── data.parquet
        │   ├── country=CN
        │   │   └── data.parquet
        │   └── ...
        └── gender=female
            ├── ...
            │
            ├── country=US
            │   └── data.parquet
            ├── country=CN
            │   └── data.parquet
            └── ...

By passing path/to/table to either SQLContext.read.parquet or SQLContext.read.load, Spark SQL will automatically extract the partitioning information from the paths. Now the schema of the returned DataFrame becomes:

root
|-- name: string (nullable = true)
|-- age: long (nullable = true)
|-- gender: string (nullable = true)
|-- country: string (nullable = true)

Notice that the data types of the partitioning columns are automatically inferred. Currently, numeric data types and string type are supported. Sometimes users may not want to automatically infer the data types of the partitioning columns. For these use cases, the automatic type inference can be configured by spark.sql.sources.partitionColumnTypeInference.enabled, which is default to true. When type inference is disabled, string type will be used for the partitioning columns.

Starting from Spark 1.6.0, partition discovery only finds partitions under the given paths by default. For the above example, if users pass path/to/table/gender=male to either SQLContext.read.parquet or SQLContext.read.load, gender will not be considered as a partitioning column. If users need to specify the base path that partition discovery should start with, they can set basePath in the data source options. For example, when path/to/table/gender=male is the path of the data and users set basePath to path/to/table/, gender will be a partitioning column.

Schema Merging

Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with a simple schema, and gradually add more columns to the schema as needed. In this way, users may end up with multiple Parquet files with different but mutually compatible schemas. The Parquet data source is now able to automatically detect this case and merge schemas of all these files.

Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we turned it off by default starting from 1.5.0. You may enable it by

1. setting data source option mergeSchema to true when reading Parquet files (as shown in the examples below), or
2. setting the global SQL option spark.sql.parquet.mergeSchema to true.

// sqlContext from the previous example is used in this example.
// This is used to implicitly convert an RDD to a DataFrame.
import sqlContext.implicits._

// Create a simple DataFrame, stored into a partition directory
val df1 = sc.makeRDD(1 to 5).map(i => (i, i * 2)).toDF("single", "double")
df1.write.parquet("data/test_table/key=1")

// Create another DataFrame in a new partition directory,
// adding a new column and dropping an existing column
val df2 = sc.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple")
df2.write.parquet("data/test_table/key=2")

// Read the partitioned table
val df3 = sqlContext.read.option("mergeSchema", "true").parquet("data/test_table")
df3.printSchema()

// The final schema consists of all 3 columns in the Parquet files together
// with the partitioning column appeared in the partition directory paths.
// root
// |-- single: int (nullable = true)
// |-- double: int (nullable = true)
// |-- triple: int (nullable = true)
// |-- key : int (nullable = true)

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

# Create a simple DataFrame, stored into a partition directory
df1 = sqlContext.createDataFrame(sc.parallelize(range(1, 6))\
                                   .map(lambda i: Row(single=i, double=i * 2)))
df1.write.parquet("data/test_table/key=1")

# Create another DataFrame in a new partition directory,
# adding a new column and dropping an existing column
df2 = sqlContext.createDataFrame(sc.parallelize(range(6, 11))
                                   .map(lambda i: Row(single=i, triple=i * 3)))
df2.write.parquet("data/test_table/key=2")

# Read the partitioned table
df3 = sqlContext.read.option("mergeSchema", "true").parquet("data/test_table")
df3.printSchema()

# The final schema consists of all 3 columns in the Parquet files together
# with the partitioning column appeared in the partition directory paths.
# root
# |-- single: int (nullable = true)
# |-- double: int (nullable = true)
# |-- triple: int (nullable = true)
# |-- key : int (nullable = true)

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

# Create a simple DataFrame, stored into a partition directory
saveDF(df1, "data/test_table/key=1", "parquet", "overwrite")

# Create another DataFrame in a new partition directory,
# adding a new column and dropping an existing column
saveDF(df2, "data/test_table/key=2", "parquet", "overwrite")

# Read the partitioned table
df3 <- loadDF(sqlContext, "data/test_table", "parquet", mergeSchema="true")
printSchema(df3)

# The final schema consists of all 3 columns in the Parquet files together
# with the partitioning column appeared in the partition directory paths.
# root
# |-- single: int (nullable = true)
# |-- double: int (nullable = true)
# |-- triple: int (nullable = true)
# |-- key : int (nullable = true)

Hive metastore Parquet table conversion

When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the spark.sql.hive.convertMetastoreParquet configuration, and is turned on by default.

Hive/Parquet Schema Reconciliation

There are two key differences between Hive and Parquet from the perspective of table schema processing.

1. Hive is case insensitive, while Parquet is not
2. Hive considers all columns nullable, while nullability in Parquet is significant

Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a Hive metastore Parquet table to a Spark SQL Parquet table. The reconciliation rules are:

1. Fields that have the same name in both schema must have the same data type regardless of nullability. The reconciled field should have the data type of the Parquet side, so that nullability is respected.

2. The reconciled schema contains exactly those fields defined in Hive metastore schema.

   • Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
   • Any fileds that only appear in the Hive metastore schema are added as nullable field in the reconciled schema.

Metadata Refreshing

Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata.

// sqlContext is an existing HiveContext
sqlContext.refreshTable("my_table")

// sqlContext is an existing HiveContext
sqlContext.refreshTable("my_table")

# sqlContext is an existing HiveContext
sqlContext.refreshTable("my_table")

REFRESH TABLE my_table;

Configuration

Configuration of Parquet can be done using the setConf method on SQLContext or by running SET key=value commands using SQL.

JSON Datasets

// 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"
val people = sqlContext.read.json(path)

// The inferred schema can be visualized using the printSchema() method.
people.printSchema()
// root
//  |-- age: integer (nullable = true)
//  |-- name: string (nullable = true)

// Register this DataFrame 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 DataFrame 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.read.json(anotherPeopleRDD)

// sc is an existing JavaSparkContext.
SQLContext 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.
DataFrame people = sqlContext.read().json("examples/src/main/resources/people.json");

// The inferred schema can be visualized using the printSchema() method.
people.printSchema();
// root
//  |-- age: integer (nullable = true)
//  |-- name: string (nullable = true)

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

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

// Alternatively, a DataFrame 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);
DataFrame anotherPeople = sqlContext.read().json(anotherPeopleRDD);

# 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.
people = sqlContext.read.json("examples/src/main/resources/people.json")

# The inferred schema can be visualized using the printSchema() method.
people.printSchema()
# root
#  |-- age: integer (nullable = true)
#  |-- name: string (nullable = true)

# Register this DataFrame as a table.
people.registerTempTable("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 DataFrame 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)

# sc is an existing SparkContext.
sqlContext <- sparkRSQL.init(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 DataFrame from the file(s) pointed to by path
people <- jsonFile(sqlContext, path)

# The inferred schema can be visualized using the printSchema() method.
printSchema(people)
# root
#  |-- age: integer (nullable = true)
#  |-- name: string (nullable = true)

# Register this DataFrame as a table.
registerTempTable(people, "people")

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

CREATE TEMPORARY TABLE jsonTable
USING org.apache.spark.sql.json
OPTIONS (
  path "examples/src/main/resources/people.json"
)

SELECT * FROM jsonTable

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. Hive support is enabled by adding the -Phive and -Phive-thriftserver flags to Spark’s build. 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, core-site.xml (for security configuration), hdfs-site.xml (for HDFS configuration) file in conf/. Please note when running the query on a YARN cluster (cluster mode), the datanucleus jars under the lib directory and hive-site.xml under conf/ directory need to be available on the driver and all executors launched by the YARN cluster. The convenient way to do this is adding them through the --jars option and --file option of the spark-submit command.

// 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)

// sc is an existing JavaSparkContext.
HiveContext sqlContext = new org.apache.spark.sql.hive.HiveContext(sc.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.
Row[] results = sqlContext.sql("FROM src SELECT key, value").collect();

# sc is an existing SparkContext.
from pyspark.sql import HiveContext
sqlContext = 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 can be expressed in HiveQL.
results = sqlContext.sql("FROM src SELECT key, value").collect()

# sc is an existing SparkContext.
sqlContext <- sparkRHive.init(sc)

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

# Queries can be expressed in HiveQL.
results <- collect(sql(sqlContext, "FROM src SELECT key, value"))

Interacting with Different Versions of Hive Metastore

One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).

The following options can be used to configure the version of Hive that is used to retrieve metadata:

JDBC To Other Databases

Spark SQL also includes a data source that can read data from other databases using JDBC. This functionality should be preferred over using JdbcRDD. This is because the results are returned as a DataFrame and they can easily be processed in Spark SQL or joined with other data sources. The JDBC data source is also easier to use from Java or Python as it does not require the user to provide a ClassTag. (Note that this is different than the Spark SQL JDBC server, which allows other applications to run queries using Spark SQL).

To get started you will need to include the JDBC driver for you particular database on the spark classpath. For example, to connect to postgres from the Spark Shell you would run the following command:

SPARK_CLASSPATH=postgresql-9.3-1102-jdbc41.jar bin/spark-shell

Tables from the remote database can be loaded as a DataFrame or Spark SQL Temporary table using the Data Sources API. The following options are supported:

val jdbcDF = sqlContext.read.format("jdbc").options(
  Map("url" -> "jdbc:postgresql:dbserver",
  "dbtable" -> "schema.tablename")).load()

Map<String, String> options = new HashMap<String, String>();
options.put("url", "jdbc:postgresql:dbserver");
options.put("dbtable", "schema.tablename");

DataFrame jdbcDF = sqlContext.read().format("jdbc"). options(options).load();

df = sqlContext.read.format('jdbc').options(url='jdbc:postgresql:dbserver', dbtable='schema.tablename').load()

df <- loadDF(sqlContext, source="jdbc", url="jdbc:postgresql:dbserver", dbtable="schema.tablename")

CREATE TEMPORARY TABLE jdbcTable
USING org.apache.spark.sql.jdbc
OPTIONS (
  url "jdbc:postgresql:dbserver",
  dbtable "schema.tablename"
)

Troubleshooting

• The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java’s DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs.
• Some databases, such as H2, convert all names to upper case. You’ll need to use upper case to refer to those names in Spark SQL.

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 sqlContext.cacheTable("tableName") or dataFrame.cache(). Then Spark SQL will scan only required columns and will automatically tune compression to minimize memory usage and GC pressure. You can call sqlContext.uncacheTable("tableName") to remove the table from memory.

Configuration of in-memory caching can be done using the setConf method on SQLContext or by running SET key=value commands using SQL.

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.

Distributed SQL Engine

Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, without the need to write any code.

Running the Thrift JDBC/ODBC server

The Thrift JDBC/ODBC server implemented here corresponds to the HiveServer2 in Hive 1.2.1 You can test the JDBC server with the beeline script that comes with either Spark or Hive 1.2.1.

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

./sbin/start-thriftserver.sh

This script accepts all bin/spark-submit command line options, plus a --hiveconf option to specify Hive properties. You may run ./sbin/start-thriftserver.sh --help for a complete list of all available options. By default, the server listens on localhost:10000. You may override this behaviour via either environment variables, i.e.:

export HIVE_SERVER2_THRIFT_PORT=<listening-port>
export HIVE_SERVER2_THRIFT_BIND_HOST=<listening-host>
./sbin/start-thriftserver.sh \
  --master <master-uri> \
  ...

or system properties:

./sbin/start-thriftserver.sh \
  --hiveconf hive.server2.thrift.port=<listening-port> \
  --hiveconf hive.server2.thrift.bind.host=<listening-host> \
  --master <master-uri>
  ...

Now you can use beeline to test the Thrift JDBC/ODBC server:

./bin/beeline

Connect to the JDBC/ODBC server in beeline with:

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, core-site.xml and hdfs-site.xml files in conf/.

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

Thrift JDBC server also supports sending thrift RPC messages over HTTP transport. Use the following setting to enable HTTP mode as system property or in hive-site.xml file in conf/:

hive.server2.transport.mode - Set this to value: http
hive.server2.thrift.http.port - HTTP port number fo listen on; default is 10001
hive.server2.http.endpoint - HTTP endpoint; default is cliservice

To test, use beeline to connect to the JDBC/ODBC server in http mode with:

beeline> !connect jdbc:hive2://<host>:<port>/<database>?hive.server2.transport.mode=http;hive.server2.thrift.http.path=<http_endpoint>

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:

./bin/spark-sql

Configuration of Hive is done by placing your hive-site.xml, core-site.xml and hdfs-site.xml files in conf/. You may run ./bin/spark-sql --help for a complete list of all available options.

Migration Guide

Upgrading From Spark SQL 1.5 to 1.6

• From Spark 1.6, by default the Thrift server runs in multi-session mode. Which means each JDBC/ODBC connection owns a copy of their own SQL configuration and temporary function registry. Cached tables are still shared though. If you prefer to run the Thrift server in the old single-session mode, please set option spark.sql.hive.thriftServer.singleSession to true. You may either add this option to spark-defaults.conf, or pass it to start-thriftserver.sh via --conf:

./sbin/start-thriftserver.sh \
     --conf spark.sql.hive.thriftServer.singleSession=true \
     ...

• From Spark 1.6, LongType casts to TimestampType expect seconds instead of microseconds. This change was made to match the behavior of Hive 1.2 for more consistent type casting to TimestampType from numeric types. See SPARK-11724 for details.

Upgrading From Spark SQL 1.4 to 1.5

• Optimized execution using manually managed memory (Tungsten) is now enabled by default, along with code generation for expression evaluation. These features can both be disabled by setting spark.sql.tungsten.enabled to false.
• Parquet schema merging is no longer enabled by default. It can be re-enabled by setting spark.sql.parquet.mergeSchema to true.
• Resolution of strings to columns in python now supports using dots (.) to qualify the column or access nested values. For example df['table.column.nestedField']. However, this means that if your column name contains any dots you must now escape them using backticks (e.g., table.`column.with.dots`.nested).
• In-memory columnar storage partition pruning is on by default. It can be disabled by setting spark.sql.inMemoryColumnarStorage.partitionPruning to false.
• Unlimited precision decimal columns are no longer supported, instead Spark SQL enforces a maximum precision of 38. When inferring schema from BigDecimal objects, a precision of (38, 18) is now used. When no precision is specified in DDL then the default remains Decimal(10, 0).
• Timestamps are now stored at a precision of 1us, rather than 1ns
• In the sql dialect, floating point numbers are now parsed as decimal. HiveQL parsing remains unchanged.
• The canonical name of SQL/DataFrame functions are now lower case (e.g. sum vs SUM).
• It has been determined that using the DirectOutputCommitter when speculation is enabled is unsafe and thus this output committer will not be used when speculation is on, independent of configuration.
• JSON data source will not automatically load new files that are created by other applications (i.e. files that are not inserted to the dataset through Spark SQL). For a JSON persistent table (i.e. the metadata of the table is stored in Hive Metastore), users can use REFRESH TABLE SQL command or HiveContext’s refreshTable method to include those new files to the table. For a DataFrame representing a JSON dataset, users need to recreate the DataFrame and the new DataFrame will include new files.

Upgrading from Spark SQL 1.3 to 1.4

DataFrame data reader/writer interface

Based on user feedback, we created a new, more fluid API for reading data in (SQLContext.read) and writing data out (DataFrame.write), and deprecated the old APIs (e.g. SQLContext.parquetFile, SQLContext.jsonFile).

See the API docs for SQLContext.read ( Scala, Java, Python ) and DataFrame.write ( Scala, Java, Python ) more information.

DataFrame.groupBy retains grouping columns

Based on user feedback, we changed the default behavior of DataFrame.groupBy().agg() to retain the grouping columns in the resulting DataFrame. To keep the behavior in 1.3, set spark.sql.retainGroupColumns to false.

// In 1.3.x, in order for the grouping column "department" to show up,
// it must be included explicitly as part of the agg function call.
df.groupBy("department").agg($"department", max("age"), sum("expense"))

// In 1.4+, grouping column "department" is included automatically.
df.groupBy("department").agg(max("age"), sum("expense"))

// Revert to 1.3 behavior (not retaining grouping column) by:
sqlContext.setConf("spark.sql.retainGroupColumns", "false")

// In 1.3.x, in order for the grouping column "department" to show up,
// it must be included explicitly as part of the agg function call.
df.groupBy("department").agg(col("department"), max("age"), sum("expense"));

// In 1.4+, grouping column "department" is included automatically.
df.groupBy("department").agg(max("age"), sum("expense"));

// Revert to 1.3 behavior (not retaining grouping column) by:
sqlContext.setConf("spark.sql.retainGroupColumns", "false");

import pyspark.sql.functions as func

# In 1.3.x, in order for the grouping column "department" to show up,
# it must be included explicitly as part of the agg function call.
df.groupBy("department").agg(df["department"], func.max("age"), func.sum("expense"))

# In 1.4+, grouping column "department" is included automatically.
df.groupBy("department").agg(func.max("age"), func.sum("expense"))

# Revert to 1.3.x behavior (not retaining grouping column) by:
sqlContext.setConf("spark.sql.retainGroupColumns", "false")

Upgrading from Spark SQL 1.0-1.2 to 1.3

In Spark 1.3 we removed the “Alpha” label from Spark SQL and as part of this did a cleanup of the available APIs. From Spark 1.3 onwards, Spark SQL will provide binary compatibility with other releases in the 1.X series. This compatibility guarantee excludes APIs that are explicitly marked as unstable (i.e., DeveloperAPI or Experimental).

Rename of SchemaRDD to DataFrame

The largest change that users will notice when upgrading to Spark SQL 1.3 is that SchemaRDD has been renamed to DataFrame. This is primarily because DataFrames no longer inherit from RDD directly, but instead provide most of the functionality that RDDs provide though their own implementation. DataFrames can still be converted to RDDs by calling the .rdd method.

In Scala there is a type alias from SchemaRDD to DataFrame to provide source compatibility for some use cases. It is still recommended that users update their code to use DataFrame instead. Java and Python users will need to update their code.

Unification of the Java and Scala APIs

Prior to Spark 1.3 there were separate Java compatible classes (JavaSQLContext and JavaSchemaRDD) that mirrored the Scala API. In Spark 1.3 the Java API and Scala API have been unified. Users of either language should use SQLContext and DataFrame. In general theses classes try to use types that are usable from both languages (i.e. Array instead of language specific collections). In some cases where no common type exists (e.g., for passing in closures or Maps) function overloading is used instead.

Additionally the Java specific types API has been removed. Users of both Scala and Java should use the classes present in org.apache.spark.sql.types to describe schema programmatically.

Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)

Many of the code examples prior to Spark 1.3 started with import sqlContext._, which brought all of the functions from sqlContext into scope. In Spark 1.3 we have isolated the implicit conversions for converting RDDs into DataFrames into an object inside of the SQLContext. Users should now write import sqlContext.implicits._.

Additionally, the implicit conversions now only augment RDDs that are composed of Products (i.e., case classes or tuples) with a method toDF, instead of applying automatically.

When using function inside of the DSL (now replaced with the DataFrame API) users used to import org.apache.spark.sql.catalyst.dsl. Instead the public dataframe functions API should be used: import org.apache.spark.sql.functions._.

Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)

Spark 1.3 removes the type aliases that were present in the base sql package for DataType. Users should instead import the classes in org.apache.spark.sql.types

UDF Registration Moved to sqlContext.udf (Java & Scala)

Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been moved into the udf object in SQLContext.

sqlContext.udf.register("strLen", (s: String) => s.length())

sqlContext.udf().register("strLen", (String s) -> s.length(), DataTypes.IntegerType);

Python UDF registration is unchanged.

Python DataTypes No Longer Singletons

When using DataTypes in Python you will need to construct them (i.e. StringType()) instead of referencing a singleton.

Migration Guide for Shark Users

Scheduling

To set a Fair Scheduler pool for a JDBC client session, users can set the spark.sql.thriftserver.scheduler.pool variable:

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:

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:

CACHE TABLE logs_last_month;
UNCACHE TABLE logs_last_month;

NOTE: CACHE TABLE tbl is now eager by default not lazy. Don’t need to trigger cache materialization manually anymore.

Spark SQL newly introduced a statement to let user control table caching whether or not lazy since Spark 1.2.0:

CACHE [LAZY] TABLE [AS SELECT] ...

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 Hive SerDes and UDFs are based on Hive 1.2.1, and Spark SQL can be connected to different versions of Hive Metastore (from 0.12.0 to 1.2.1. Also see [Interacting with Different Versions of Hive Metastore] (#interacting-with-different-versions-of-hive-metastore)).

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)
• Window functions
• 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 including dynamic partition insertion
• View
• 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
  • DATE
  • 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

• Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL doesn’t support buckets yet.

Esoteric Hive Features

• UNION type
• Unique join
• 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 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.

Reference

Data Types

Spark SQL and DataFrames support the following data types:

• 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: Represents arbitrary-precision signed decimal numbers. Backed internally by java.math.BigDecimal. A BigDecimal consists of an arbitrary precision integer unscaled value and a 32-bit integer scale.
• 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.
  • DateType: Represents values comprising values of fields year, month, day.
• 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 StructFields (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.

import  org.apache.spark.sql.types._

from pyspark.sql.types import *

NaN Semantics

There is specially handling for not-a-number (NaN) when dealing with float or double types that does not exactly match standard floating point semantics. Specifically:

• NaN = NaN returns true.
• In aggregations all NaN values are grouped together.
• NaN is treated as a normal value in join keys.
• NaN values go last when in ascending order, larger than any other numeric value.