import org.apache.spark.sql.SparkSession
val spark = SparkSession
  .builder()
  .appName("Spark SQL basic example")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()
// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.




    

    
The entry point into all functionality in Spark is the SparkSession class. To create a basic SparkSession, just use SparkSession.builder():
    
import org.apache.spark.sql.SparkSession;
SparkSession spark = SparkSession
  .builder()
  .appName("Java Spark SQL basic example")
  .config("spark.some.config.option", "some-value")
  .getOrCreate();
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
    
The entry point into all functionality in Spark is the SparkSession class. To create a basic SparkSession, just use SparkSession.builder:
    
from pyspark.sql import SparkSession
spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.
    
The entry point into all functionality in Spark is the SparkSession class. To initialize a basic SparkSession, just call sparkR.session():
    
sparkR.session(appName = "R Spark SQL basic example", sparkConfig = list(spark.some.config.option = "some-value"))
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
    
Note that when invoked for the first time, sparkR.session() initializes a global SparkSession singleton instance, and always returns a reference to this instance for successive invocations. In this way, users only need to initialize the SparkSession once, then SparkR functions like read.df will be able to access this global instance implicitly, and users don’t need to pass the SparkSession instance around.
SparkSession in Spark 2.0 provides builtin support for Hive features including the ability to
write queries using HiveQL, access to Hive UDFs, and the ability to read data from Hive tables.
To use these features, you do not need to have an existing Hive setup.
Creating DataFrames
    
With a SparkSession, applications can create DataFrames from an existing RDD,
from a Hive table, or from Spark data sources.
    
As an example, the following creates a DataFrame based on the content of a JSON file:
    
val df = spark.read.json("examples/src/main/resources/people.json")
// Displays the content of the DataFrame to stdout
df.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
    
With a SparkSession, applications can create DataFrames from an existing RDD,
from a Hive table, or from Spark data sources.
    
As an example, the following creates a DataFrame based on the content of a JSON file:
    
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
Dataset<Row> df = spark.read().json("examples/src/main/resources/people.json");
// Displays the content of the DataFrame to stdout
df.show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
    
With a SparkSession, applications can create DataFrames from an existing RDD,
from a Hive table, or from Spark data sources.
    
As an example, the following creates a DataFrame based on the content of a JSON file:
    
# spark is an existing SparkSession
df = spark.read.json("examples/src/main/resources/people.json")
# Displays the content of the DataFrame to stdout
df.show()
# +----+-------+
# | age|   name|
# +----+-------+
# |null|Michael|
# |  30|   Andy|
# |  19| Justin|
# +----+-------+
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.
    
With a SparkSession, applications can create DataFrames from a local R data.frame,
from a Hive table, or from Spark data sources.
    
As an example, the following creates a DataFrame based on the content of a JSON file:
    
df <- read.json("examples/src/main/resources/people.json")
# Displays the content of the DataFrame
head(df)
##   age    name
## 1  NA Michael
## 2  30    Andy
## 3  19  Justin
# Another method to print the first few rows and optionally truncate the printing of long values
showDF(df)
## +----+-------+
## | age|   name|
## +----+-------+
## |null|Michael|
## |  30|   Andy|
## |  19| Justin|
## +----+-------+
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
Untyped Dataset Operations (aka DataFrame Operations)
DataFrames provide a domain-specific language for structured data manipulation in Scala, Java, Python and R.
As mentioned above, in Spark 2.0, DataFrames are just Dataset of Rows in Scala and Java API. These operations are also referred as “untyped transformations” in contrast to “typed transformations” come with strongly typed Scala/Java Datasets.
Here we include some basic examples of structured data processing using Datasets:
    
// This import is needed to use the $-notation
import spark.implicits._
// Print the schema in a tree format
df.printSchema()
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)
// Select only the "name" column
df.select("name").show()
// +-------+
// |   name|
// +-------+
// |Michael|
// |   Andy|
// | Justin|
// +-------+
// Select everybody, but increment the age by 1
df.select($"name", $"age" + 1).show()
// +-------+---------+
// |   name|(age + 1)|
// +-------+---------+
// |Michael|     null|
// |   Andy|       31|
// | Justin|       20|
// +-------+---------+
// Select people older than 21
df.filter($"age" > 21).show()
// +---+----+
// |age|name|
// +---+----+
// | 30|Andy|
// +---+----+
// Count people by age
df.groupBy("age").count().show()
// +----+-----+
// | age|count|
// +----+-----+
// |  19|    1|
// |null|    1|
// |  30|    1|
// +----+-----+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
    
For a complete list of the types of operations that can be performed on a Dataset refer to the API Documentation.
    
In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.
    
// col("...") is preferable to df.col("...")
import static org.apache.spark.sql.functions.col;
// 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(col("name"), col("age").plus(1)).show();
// +-------+---------+
// |   name|(age + 1)|
// +-------+---------+
// |Michael|     null|
// |   Andy|       31|
// | Justin|       20|
// +-------+---------+
// Select people older than 21
df.filter(col("age").gt(21)).show();
// +---+----+
// |age|name|
// +---+----+
// | 30|Andy|
// +---+----+
// Count people by age
df.groupBy("age").count().show();
// +----+-----+
// | age|count|
// +----+-----+
// |  19|    1|
// |null|    1|
// |  30|    1|
// +----+-----+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
    
For a complete list of the types of operations that can be performed on a Dataset refer to the API Documentation.
    
In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.
    
In Python it’s possible to access a DataFrame’s columns either by attribute
(df.age) or by indexing (df['age']). While the former is convenient for
interactive data exploration, users are highly encouraged to use the
latter form, which is future proof and won’t break with column names that
are also attributes on the DataFrame class.
    
# spark, df are from the previous example
# 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|
# +----+-----+
# |  19|    1|
# |null|    1|
# |  30|    1|
# +----+-----+
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.




    

    
For a complete list of the types of operations that can be performed on a DataFrame refer to the API Documentation.
    
In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.
df <- read.json("examples/src/main/resources/people.json")
# Show the content of the DataFrame
head(df)
##   age    name
## 1  NA Michael
## 2  30    Andy
## 3  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
head(select(df, "name"))
##      name
## 1 Michael
## 2    Andy
## 3  Justin
# Select everybody, but increment the age by 1
head(select(df, df$name, df$age + 1))
##      name (age + 1.0)
## 1 Michael          NA
## 2    Andy          31
## 3  Justin          20
# Select people older than 21
head(where(df, df$age > 21))
##   age name
## 1  30 Andy
# Count people by age
head(count(groupBy(df, "age")))
##   age count
## 1  19     1
## 2  NA     1
## 3  30     1
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
    
For a complete list of the types of operations that can be performed on a DataFrame refer to the API Documentation.
    
In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.
    
The sql function on a SparkSession enables applications to run SQL queries programmatically and returns the result as a DataFrame.
    
// Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people")
val sqlDF = spark.sql("SELECT * FROM people")
sqlDF.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
    
The sql function on a SparkSession enables applications to run SQL queries programmatically and returns the result as a Dataset<Row>.
    
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people");
Dataset<Row> sqlDF = spark.sql("SELECT * FROM people");
sqlDF.show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
    
The sql function on a SparkSession enables applications to run SQL queries programmatically and returns the result as a DataFrame.
    
# Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people")
sqlDF = spark.sql("SELECT * FROM people")
sqlDF.show()
# +----+-------+
# | age|   name|
# +----+-------+
# |null|Michael|
# |  30|   Andy|
# |  19| Justin|
# +----+-------+
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.
    
The sql function enables applications to run SQL queries programmatically and returns the result as a SparkDataFrame.
    
df <- sql("SELECT * FROM table")
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
Global Temporary View
Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it
terminates. If you want to have a temporary view that is shared among all sessions and keep alive
until the Spark application terminates, you can create a global temporary view. Global temporary
view is tied to a system preserved database global_temp, and we must use the qualified name to
refer it, e.g. SELECT * FROM global_temp.view1.
    
// Register the DataFrame as a global temporary view
df.createGlobalTempView("people")
// Global temporary view is tied to a system preserved database `global_temp`
spark.sql("SELECT * FROM global_temp.people").show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
// Global temporary view is cross-session
spark.newSession().sql("SELECT * FROM global_temp.people").show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
    
// Register the DataFrame as a global temporary view
df.createGlobalTempView("people");
// Global temporary view is tied to a system preserved database `global_temp`
spark.sql("SELECT * FROM global_temp.people").show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
// Global temporary view is cross-session
spark.newSession().sql("SELECT * FROM global_temp.people").show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
    
# Register the DataFrame as a global temporary view
df.createGlobalTempView("people")
# Global temporary view is tied to a system preserved database `global_temp`
spark.sql("SELECT * FROM global_temp.people").show()
# +----+-------+
# | age|   name|
# +----+-------+
# |null|Michael|
# |  30|   Andy|
# |  19| Justin|
# +----+-------+
# Global temporary view is cross-session
spark.newSession().sql("SELECT * FROM global_temp.people").show()
# +----+-------+
# | age|   name|
# +----+-------+
# |null|Michael|
# |  30|   Andy|
# |  19| Justin|
# +----+-------+
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.
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.
    
// 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: Long)
// Encoders are created for case classes
val caseClassDS = Seq(Person("Andy", 32)).toDS()
caseClassDS.show()
// +----+---+
// |name|age|
// +----+---+
// |Andy| 32|
// +----+---+
// Encoders for most common types are automatically provided by importing spark.implicits._
val primitiveDS = Seq(1, 2, 3).toDS()
primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)
// 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 peopleDS = spark.read.json(path).as[Person]
peopleDS.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.




    

    
import java.util.Arrays;
import java.util.Collections;
import java.io.Serializable;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
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;
// Create an instance of a Bean class
Person person = new Person();
person.setName("Andy");
person.setAge(32);
// Encoders are created for Java beans
Encoder<Person> personEncoder = Encoders.bean(Person.class);
Dataset<Person> javaBeanDS = spark.createDataset(
  Collections.singletonList(person),
  personEncoder
javaBeanDS.show();
// +---+----+
// |age|name|
// +---+----+
// | 32|Andy|
// +---+----+
// Encoders for most common types are provided in class Encoders
Encoder<Integer> integerEncoder = Encoders.INT();
Dataset<Integer> primitiveDS = spark.createDataset(Arrays.asList(1, 2, 3), integerEncoder);
Dataset<Integer> transformedDS = primitiveDS.map(
    (MapFunction<Integer, Integer>) value -> value + 1,
    integerEncoder);
transformedDS.collect(); // Returns [2, 3, 4]
// DataFrames can be converted to a Dataset by providing a class. Mapping based on name
String path = "examples/src/main/resources/people.json";
Dataset<Person> peopleDS = spark.read().json(path).as(personEncoder);
peopleDS.show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
Interoperating with RDDs
Spark SQL supports two different methods for converting existing RDDs into Datasets. 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 Datasets 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 Datasets when the columns and their types are not known until runtime.
Inferring the Schema Using Reflection
    
The Scala interface for Spark SQL supports automatically converting an RDD containing case classes
to a DataFrame. 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 Seqs or Arrays. This RDD can be implicitly converted to a DataFrame and then be
registered as a table. Tables can be used in subsequent SQL statements.
    
// For implicit conversions from RDDs to DataFrames
import spark.implicits._
// Create an RDD of Person objects from a text file, convert it to a Dataframe
val peopleDF = spark.sparkContext
  .textFile("examples/src/main/resources/people.txt")
  .map(_.split(","))
  .map(attributes => Person(attributes(0), attributes(1).trim.toInt))
  .toDF()
// Register the DataFrame as a temporary view
peopleDF.createOrReplaceTempView("people")
// SQL statements can be run by using the sql methods provided by Spark
val teenagersDF = spark.sql("SELECT name, age FROM people WHERE age BETWEEN 13 AND 19")
// The columns of a row in the result can be accessed by field index
teenagersDF.map(teenager => "Name: " + teenager(0)).show()
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+
// or by field name
teenagersDF.map(teenager => "Name: " + teenager.getAs[String]("name")).show()
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+
// No pre-defined encoders for Dataset[Map[K,V]], define explicitly
implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Map[String, Any]]
// Primitive types and case classes can be also defined as
// implicit val stringIntMapEncoder: Encoder[Map[String, Any]] = ExpressionEncoder()
// row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T]
teenagersDF.map(teenager => teenager.getValuesMap[Any](List("name", "age"))).collect()
// Array(Map("name" -> "Justin", "age" -> 19))
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
    
Spark SQL supports automatically converting an RDD of
JavaBeans into a DataFrame.
The BeanInfo, obtained using reflection, defines the schema of the table. Currently, Spark SQL
does not support JavaBeans that contain Map field(s). Nested JavaBeans and List or Array
fields are supported though. You can create a JavaBean by creating a class that implements
Serializable and has getters and setters for all of its fields.
    
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
// Create an RDD of Person objects from a text file
JavaRDD<Person> peopleRDD = spark.read()
  .textFile("examples/src/main/resources/people.txt")
  .javaRDD()
  .map(line -> {
    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 to get a DataFrame
Dataset<Row> peopleDF = spark.createDataFrame(peopleRDD, Person.class);
// Register the DataFrame as a temporary view
peopleDF.createOrReplaceTempView("people");
// SQL statements can be run by using the sql methods provided by spark
Dataset<Row> teenagersDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
// The columns of a row in the result can be accessed by field index
Encoder<String> stringEncoder = Encoders.STRING();
Dataset<String> teenagerNamesByIndexDF = teenagersDF.map(
    (MapFunction<Row, String>) row -> "Name: " + row.getString(0),
    stringEncoder);
teenagerNamesByIndexDF.show();
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+
// or by field name
Dataset<String> teenagerNamesByFieldDF = teenagersDF.map(
    (MapFunction<Row, String>) row -> "Name: " + row.<String>getAs("name"),
    stringEncoder);
teenagerNamesByFieldDF.show();
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.




    

    
Spark SQL can convert an RDD of Row objects to a DataFrame, 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 sampling the whole dataset, similar to the inference that is performed on JSON files.
    
from pyspark.sql import Row
sc = spark.sparkContext
# 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 = spark.createDataFrame(people)
schemaPeople.createOrReplaceTempView("people")
# SQL can be run over DataFrames that have been registered as a table.
teenagers = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
# The results of SQL queries are Dataframe objects.
# rdd returns the content as an :class:`pyspark.RDD` of :class:`Row`.
teenNames = teenagers.rdd.map(lambda p: "Name: " + p.name).collect()
for name in teenNames:
    print(name)
# Name: Justin
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.
    
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 DataFrame can be created programmatically with three steps.
      
Create an RDD of Rows from the original RDD;
      
Create the schema represented by a StructType matching the structure of
Rows in the RDD created in Step 1.
      
Apply the schema to the RDD of Rows via createDataFrame method provided
by SparkSession.
    
For example:
    
import org.apache.spark.sql.types._
// Create an RDD
val peopleRDD = spark.sparkContext.textFile("examples/src/main/resources/people.txt")
// The schema is encoded in a string
val schemaString = "name age"
// Generate the schema based on the string of schema
val fields = schemaString.split(" ")
  .map(fieldName => StructField(fieldName, StringType, nullable = true))
val schema = StructType(fields)
// Convert records of the RDD (people) to Rows
val rowRDD = peopleRDD
  .map(_.split(","))
  .map(attributes => Row(attributes(0), attributes(1).trim))
// Apply the schema to the RDD
val peopleDF = spark.createDataFrame(rowRDD, schema)
// Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")
// SQL can be run over a temporary view created using DataFrames
val results = spark.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(attributes => "Name: " + attributes(0)).show()
// +-------------+
// |        value|
// +-------------+
// |Name: Michael|
// |   Name: Andy|
// | Name: Justin|
// +-------------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
    
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 Dataset<Row> can be created programmatically with three steps.
      
Create an RDD of Rows from the original RDD;
      
Create the schema represented by a StructType matching the structure of
Rows in the RDD created in Step 1.
      
Apply the schema to the RDD of Rows via createDataFrame method provided
by SparkSession.
    
For example:
    
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
// Create an RDD
JavaRDD<String> peopleRDD = spark.sparkContext()
  .textFile("examples/src/main/resources/people.txt", 1)
  .toJavaRDD();
// 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<>();
for (String fieldName : schemaString.split(" ")) {
  StructField field = DataTypes.createStructField(fieldName, DataTypes.StringType, true);
  fields.add(field);
StructType schema = DataTypes.createStructType(fields);
// Convert records of the RDD (people) to Rows
JavaRDD<Row> rowRDD = peopleRDD.map((Function<String, Row>) record -> {
  String[] attributes = record.split(",");
  return RowFactory.create(attributes[0], attributes[1].trim());
// Apply the schema to the RDD
Dataset<Row> peopleDataFrame = spark.createDataFrame(rowRDD, schema);
// Creates a temporary view using the DataFrame
peopleDataFrame.createOrReplaceTempView("people");
// SQL can be run over a temporary view created using DataFrames
Dataset<Row> results = spark.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
Dataset<String> namesDS = results.map(
    (MapFunction<Row, String>) row -> "Name: " + row.getString(0),
    Encoders.STRING());
namesDS.show();
// +-------------+
// |        value|
// +-------------+
// |Name: Michael|
// |   Name: Andy|
// | Name: Justin|
// +-------------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.




    

    
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 DataFrame can be created programmatically with three steps.
      
Create an RDD of tuples or lists from the original RDD;
      
Create the schema represented by a StructType matching the structure of
tuples or lists in the RDD created in the step 1.
      
Apply the schema to the RDD via createDataFrame method provided by SparkSession.
    
For example:
    
# Import data types
from pyspark.sql.types import *
sc = spark.sparkContext
# 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(","))
# Each line is converted to a tuple.
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 = spark.createDataFrame(people, schema)
# Creates a temporary view using the DataFrame
schemaPeople.createOrReplaceTempView("people")
# SQL can be run over DataFrames that have been registered as a table.
results = spark.sql("SELECT name FROM people")
results.show()
# +-------+
# |   name|
# +-------+
# |Michael|
# |   Andy|
# | Justin|
# +-------+
    
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.
The built-in DataFrames functions provide common
aggregations such as count(), countDistinct(), avg(), max(), min(), etc.
While those functions are designed for DataFrames, Spark SQL also has type-safe versions for some of them in
Scala and
Java to work with strongly typed Datasets.
Moreover, users are not limited to the predefined aggregate functions and can create their own.
Untyped User-Defined Aggregate Functions
Users have to extend the UserDefinedAggregateFunction
abstract class to implement a custom untyped aggregate function. For example, a user-defined average
can look like:
    
import org.apache.spark.sql.expressions.MutableAggregationBuffer
import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row
import org.apache.spark.sql.SparkSession
object MyAverage extends UserDefinedAggregateFunction {
  // Data types of input arguments of this aggregate function
  def inputSchema: StructType = StructType(StructField("inputColumn", LongType) :: Nil)
  // Data types of values in the aggregation buffer
  def bufferSchema: StructType = {
    StructType(StructField("sum", LongType) :: StructField("count", LongType) :: Nil)
  // The data type of the returned value
  def dataType: DataType = DoubleType
  // Whether this function always returns the same output on the identical input
  def deterministic: Boolean = true
  // Initializes the given aggregation buffer. The buffer itself is a `Row` that in addition to
  // standard methods like retrieving a value at an index (e.g., get(), getBoolean()), provides
  // the opportunity to update its values. Note that arrays and maps inside the buffer are still
  // immutable.
  def initialize(buffer: MutableAggregationBuffer): Unit = {
    buffer(0) = 0L
    buffer(1) = 0L
  // Updates the given aggregation buffer `buffer` with new input data from `input`
  def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    if (!input.isNullAt(0)) {
      buffer(0) = buffer.getLong(0) + input.getLong(0)
      buffer(1) = buffer.getLong(1) + 1
  // Merges two aggregation buffers and stores the updated buffer values back to `buffer1`
  def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
    buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0)
    buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)
  // Calculates the final result
  def evaluate(buffer: Row): Double = buffer.getLong(0).toDouble / buffer.getLong(1)
// Register the function to access it
spark.udf.register("myAverage", MyAverage)
val df = spark.read.json("examples/src/main/resources/employees.json")
df.createOrReplaceTempView("employees")
df.show()
// +-------+------+
// |   name|salary|
// +-------+------+
// |Michael|  3000|
// |   Andy|  4500|
// | Justin|  3500|
// |  Berta|  4000|
// +-------+------+
val result = spark.sql("SELECT myAverage(salary) as average_salary FROM employees")
result.show()
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/UserDefinedUntypedAggregation.scala" in the Spark repo.
    
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.expressions.MutableAggregationBuffer;
import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
import org.apache.spark.sql.types.DataType;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
public static class MyAverage extends UserDefinedAggregateFunction {
  private StructType inputSchema;
  private StructType bufferSchema;
  public MyAverage() {
    List<StructField> inputFields = new ArrayList<>();
    inputFields.add(DataTypes.createStructField("inputColumn", DataTypes.LongType, true));
    inputSchema = DataTypes.createStructType(inputFields);
    List<StructField> bufferFields = new ArrayList<>();
    bufferFields.add(DataTypes.createStructField("sum", DataTypes.LongType, true));
    bufferFields.add(DataTypes.createStructField("count", DataTypes.LongType, true));
    bufferSchema = DataTypes.createStructType(bufferFields);
  // Data types of input arguments of this aggregate function
  public StructType inputSchema() {
    return inputSchema;
  // Data types of values in the aggregation buffer
  public StructType bufferSchema() {
    return bufferSchema;
  // The data type of the returned value
  public DataType dataType() {
    return DataTypes.DoubleType;
  // Whether this function always returns the same output on the identical input
  public boolean deterministic() {
    return true;
  // Initializes the given aggregation buffer. The buffer itself is a `Row` that in addition to
  // standard methods like retrieving a value at an index (e.g., get(), getBoolean()), provides
  // the opportunity to update its values. Note that arrays and maps inside the buffer are still
  // immutable.
  public void initialize(MutableAggregationBuffer buffer) {
    buffer.update(0, 0L);
    buffer.update(1, 0L);
  // Updates the given aggregation buffer `buffer` with new input data from `input`
  public void update(MutableAggregationBuffer buffer, Row input) {
    if (!input.isNullAt(0)) {
      long updatedSum = buffer.getLong(0) + input.getLong(0);
      long updatedCount = buffer.getLong(1) + 1;
      buffer.update(0, updatedSum);
      buffer.update(1, updatedCount);
  // Merges two aggregation buffers and stores the updated buffer values back to `buffer1`
  public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
    long mergedSum = buffer1.getLong(0) + buffer2.getLong(0);
    long mergedCount = buffer1.getLong(1) + buffer2.getLong(1);
    buffer1.update(0, mergedSum);
    buffer1.update(1, mergedCount);
  // Calculates the final result
  public Double evaluate(Row buffer) {
    return ((double) buffer.getLong(0)) / buffer.getLong(1);
// Register the function to access it
spark.udf().register("myAverage", new MyAverage());
Dataset<Row> df = spark.read().json("examples/src/main/resources/employees.json");
df.createOrReplaceTempView("employees");
df.show();
// +-------+------+
// |   name|salary|
// +-------+------+
// |Michael|  3000|
// |   Andy|  4500|
// | Justin|  3500|
// |  Berta|  4000|
// +-------+------+
Dataset<Row> result = spark.sql("SELECT myAverage(salary) as average_salary FROM employees");
result.show();
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java" in the Spark repo.




    

Type-Safe User-Defined Aggregate Functions
User-defined aggregations for strongly typed Datasets revolve around the Aggregator abstract class.
For example, a type-safe user-defined average can look like:
    
import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.sql.Encoder
import org.apache.spark.sql.Encoders
import org.apache.spark.sql.SparkSession
case class Employee(name: String, salary: Long)
case class Average(var sum: Long, var count: Long)
object MyAverage extends Aggregator[Employee, Average, Double] {
  // A zero value for this aggregation. Should satisfy the property that any b + zero = b
  def zero: Average = Average(0L, 0L)
  // Combine two values to produce a new value. For performance, the function may modify `buffer`
  // and return it instead of constructing a new object
  def reduce(buffer: Average, employee: Employee): Average = {
    buffer.sum += employee.salary
    buffer.count += 1
    buffer
  // Merge two intermediate values
  def merge(b1: Average, b2: Average): Average = {
    b1.sum += b2.sum
    b1.count += b2.count
  // Transform the output of the reduction
  def finish(reduction: Average): Double = reduction.sum.toDouble / reduction.count
  // Specifies the Encoder for the intermediate value type
  def bufferEncoder: Encoder[Average] = Encoders.product
  // Specifies the Encoder for the final output value type
  def outputEncoder: Encoder[Double] = Encoders.scalaDouble
val ds = spark.read.json("examples/src/main/resources/employees.json").as[Employee]
ds.show()
// +-------+------+
// |   name|salary|
// +-------+------+
// |Michael|  3000|
// |   Andy|  4500|
// | Justin|  3500|
// |  Berta|  4000|
// +-------+------+
// Convert the function to a `TypedColumn` and give it a name
val averageSalary = MyAverage.toColumn.name("average_salary")
val result = ds.select(averageSalary)
result.show()
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/UserDefinedTypedAggregation.scala" in the Spark repo.




    

    
import java.io.Serializable;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.TypedColumn;
import org.apache.spark.sql.expressions.Aggregator;
public static class Employee implements Serializable {
  private String name;
  private long salary;
  // Constructors, getters, setters...
public static class Average implements Serializable  {
  private long sum;
  private long count;
  // Constructors, getters, setters...
public static class MyAverage extends Aggregator<Employee, Average, Double> {
  // A zero value for this aggregation. Should satisfy the property that any b + zero = b
  public Average zero() {
    return new Average(0L, 0L);
  // Combine two values to produce a new value. For performance, the function may modify `buffer`
  // and return it instead of constructing a new object
  public Average reduce(Average buffer, Employee employee) {
    long newSum = buffer.getSum() + employee.getSalary();
    long newCount = buffer.getCount() + 1;
    buffer.setSum(newSum);
    buffer.setCount(newCount);
    return buffer;
  // Merge two intermediate values
  public Average merge(Average b1, Average b2) {
    long mergedSum = b1.getSum() + b2.getSum();
    long mergedCount = b1.getCount() + b2.getCount();
    b1.setSum(mergedSum);
    b1.setCount(mergedCount);
    return b1;
  // Transform the output of the reduction
  public Double finish(Average reduction) {
    return ((double) reduction.getSum()) / reduction.getCount();
  // Specifies the Encoder for the intermediate value type
  public Encoder<Average> bufferEncoder() {
    return Encoders.bean(Average.class);
  // Specifies the Encoder for the final output value type
  public Encoder<Double> outputEncoder() {
    return Encoders.DOUBLE();
Encoder<Employee> employeeEncoder = Encoders.bean(Employee.class);
String path = "examples/src/main/resources/employees.json";
Dataset<Employee> ds = spark.read().json(path).as(employeeEncoder);
ds.show();
// +-------+------+
// |   name|salary|
// +-------+------+
// |Michael|  3000|
// |   Andy|  4500|
// | Justin|  3500|
// |  Berta|  4000|
// +-------+------+
MyAverage myAverage = new MyAverage();
// Convert the function to a `TypedColumn` and give it a name
TypedColumn<Employee, Double> averageSalary = myAverage.toColumn().name("average_salary");
Dataset<Double> result = ds.select(averageSalary);
result.show();
// +--------------+
// |average_salary|
// +--------------+
// |        3750.0|
// +--------------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java" in the Spark repo.
Data Sources
Spark SQL supports operating on a variety of data sources through the DataFrame interface.
A DataFrame can be operated on using relational transformations and can also be used to create a temporary view.
Registering a DataFrame as a temporary view 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 usersDF = spark.read.load("examples/src/main/resources/users.parquet")
usersDF.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
Dataset<Row> usersDF = spark.read().load("examples/src/main/resources/users.parquet");
usersDF.select("name", "favorite_color").write().save("namesAndFavColors.parquet");
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
df = spark.read.load("examples/src/main/resources/users.parquet")
df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
    
df <- read.df("examples/src/main/resources/users.parquet")
write.df(select(df, "name", "favorite_color"), "namesAndFavColors.parquet")
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
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, orc, libsvm, csv, text). DataFrames loaded from any data
source type can be converted into other types using this syntax.
    
val peopleDF = spark.read.format("json").load("examples/src/main/resources/people.json")
peopleDF.select("name", "age").write.format("parquet").save("namesAndAges.parquet")
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
Dataset<Row> peopleDF =
  spark.read().format("json").load("examples/src/main/resources/people.json");
peopleDF.select("name", "age").write().format("parquet").save("namesAndAges.parquet");
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
df = spark.read.load("examples/src/main/resources/people.json", format="json")
df.select("name", "age").write.save("namesAndAges.parquet", format="parquet")
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.




    

    
df <- read.df("examples/src/main/resources/people.json", "json")
namesAndAges <- select(df, "name", "age")
write.df(namesAndAges, "namesAndAges.parquet", "parquet")
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
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 sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
Dataset<Row> sqlDF =
  spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
df = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
    
df <- sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
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 an Overwrite, the data will be deleted before writing out the
new data.
Scala/JavaAny LanguageMeaning
  SaveMode.ErrorIfExists (default)
  "error" (default)
    When saving a DataFrame to a data source, if data already exists,
    an exception is expected to be thrown.
  SaveMode.Append
  "append"
    When saving a DataFrame to a data source, if data/table already exists,
    contents of the DataFrame are expected to be appended to existing data.
  SaveMode.Overwrite
  "overwrite"
    Overwrite mode means that when saving a DataFrame to a data source,
    if data/table already exists, existing data is expected to be overwritten by the contents of
    the DataFrame.
  SaveMode.Ignore
  "ignore"
    Ignore mode means that when saving a DataFrame to a data source, if data already exists,
    the save operation is expected to not save the contents of the DataFrame and to not
    change the existing data. This is similar to a CREATE TABLE IF NOT EXISTS in SQL.
Saving to Persistent Tables
DataFrames can also be saved as persistent tables into Hive metastore using the saveAsTable
command. Notice that an existing Hive deployment is not necessary to use this feature. Spark will create a
default local Hive metastore (using Derby) for you. Unlike the createOrReplaceTempView command,
saveAsTable will materialize the contents of the DataFrame and create a pointer to the data in the
Hive metastore. 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 SparkSession with the name of the table.
For file-based data source, e.g. text, parquet, json, etc. you can specify a custom table path via the
path option, e.g. df.write.option("path", "/some/path").saveAsTable("t"). When the table is dropped,
the custom table path will not be removed and the table data is still there. If no custom table path is
specified, Spark will write data to a default table path under the warehouse directory. When the table is
dropped, the default table path will be removed too.
Starting from Spark 2.1, persistent datasource tables have per-partition metadata stored in the Hive metastore. This brings several benefits:
  
Since the metastore can return only necessary partitions for a query, discovering all the partitions on the first query to the table is no longer needed.
  
Hive DDLs such as ALTER TABLE PARTITION ... SET LOCATION are now available for tables created with the Datasource API.
Note that partition information is not gathered by default when creating external datasource tables (those with a path option). To sync the partition information in the metastore, you can invoke MSCK REPAIR TABLE.
Bucketing, Sorting and Partitioning
For file-based data source, it is also possible to bucket and sort or partition the output. 
Bucketing and sorting are applicable only to persistent tables:
    
peopleDF.write.bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed")
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
peopleDF.write().bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed");
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
df.write.bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed")
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
    
usersDF.write.partitionBy("favorite_color").format("parquet").save("namesPartByColor.parquet")
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
usersDF
  .write()
  .partitionBy("favorite_color")
  .format("parquet")
  .save("namesPartByColor.parquet");
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
df.write.partitionBy("favorite_color").format("parquet").save("namesPartByColor.parquet")
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
  .partitionBy("favorite_color")
  .bucketBy(42, "name")
  .saveAsTable("people_partitioned_bucketed")
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
peopleDF
  .write()
  .partitionBy("favorite_color")
  .bucketBy(42, "name")
  .saveAsTable("people_partitioned_bucketed");
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
df = spark.read.parquet("examples/src/main/resources/users.parquet")
    .write
    .partitionBy("favorite_color")
    .bucketBy(42, "name")
    .saveAsTable("people_partitioned_bucketed"))
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
partitionBy creates a directory structure as described in the Partition Discovery section.
Thus, it has limited applicability to columns with high cardinality. In contrast 
 bucketBy distributes
data across a fixed number of buckets and can be used when a number of unique values is unbounded.
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:
    
// Encoders for most common types are automatically provided by importing spark.implicits._
import spark.implicits._
val peopleDF = spark.read.json("examples/src/main/resources/people.json")
// DataFrames can be saved as Parquet files, maintaining the schema information
peopleDF.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 parquetFileDF = spark.read.parquet("people.parquet")
// Parquet files can also be used to create a temporary view and then used in SQL statements
parquetFileDF.createOrReplaceTempView("parquetFile")
val namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19")
namesDF.map(attributes => "Name: " + attributes(0)).show()
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
Dataset<Row> peopleDF = spark.read().json("examples/src/main/resources/people.json");
// DataFrames can be saved as Parquet files, maintaining the schema information
peopleDF.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
Dataset<Row> parquetFileDF = spark.read().parquet("people.parquet");
// Parquet files can also be used to create a temporary view and then used in SQL statements
parquetFileDF.createOrReplaceTempView("parquetFile");
Dataset<Row> namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19");
Dataset<String> namesDS = namesDF.map(
    (MapFunction<Row, String>) row -> "Name: " + row.getString(0),
    Encoders.STRING());
namesDS.show();
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.




    

    
peopleDF = spark.read.json("examples/src/main/resources/people.json")
# DataFrames can be saved as Parquet files, maintaining the schema information.
peopleDF.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 = spark.read.parquet("people.parquet")
# Parquet files can also be used to create a temporary view and then used in SQL statements.
parquetFile.createOrReplaceTempView("parquetFile")
teenagers = spark.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
teenagers.show()
# +------+
# |  name|
# +------+
# |Justin|
# +------+
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
    
df <- read.df("examples/src/main/resources/people.json", "json")
# SparkDataFrame can be saved as Parquet files, maintaining the schema information.
write.parquet(df, "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 <- read.parquet("people.parquet")
# Parquet files can also be used to create a temporary view and then used in SQL statements.
createOrReplaceTempView(parquetFile, "parquetFile")
teenagers <- sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
head(teenagers)
##     name
## 1 Justin
# We can also run custom R-UDFs on Spark DataFrames. Here we prefix all the names with "Name:"
schema <- structType(structField("name", "string"))
teenNames <- dapply(df, function(p) { cbind(paste("Name:", p$name)) }, schema)
for (teenName in collect(teenNames)$name) {
  cat(teenName, "\n")
## Name: Michael
## Name: Andy
## Name: Justin
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
    
CREATE TEMPORARY VIEW 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. All built-in file sources (including Text/CSV/JSON/ORC/Parquet)
are 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:
└── 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 SparkSession.read.parquet or SparkSession.read.load, Spark SQL
will automatically extract the partitioning information from the paths.
Now the schema of the returned DataFrame becomes:
|-- 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, date, timestamp 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
SparkSession.read.parquet or SparkSession.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
  
setting data source option mergeSchema to true when reading Parquet files (as shown in the
examples below), or
  
setting the global SQL option spark.sql.parquet.mergeSchema to true.
// Create a simple DataFrame, store into a partition directory
val squaresDF = spark.sparkContext.makeRDD(1 to 5).map(i => (i, i * i)).toDF("value", "square")
squaresDF.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 cubesDF = spark.sparkContext.makeRDD(6 to 10).map(i => (i, i * i * i)).toDF("value", "cube")
cubesDF.write.parquet("data/test_table/key=2")
// Read the partitioned table
val mergedDF = spark.read.option("mergeSchema", "true").parquet("data/test_table")
mergedDF.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
//  |-- value: int (nullable = true)
//  |-- square: int (nullable = true)
//  |-- cube: int (nullable = true)
//  |-- key: int (nullable = true)
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
public static class Square implements Serializable {
  private int value;
  private int square;
  // Getters and setters...
public static class Cube implements Serializable {
  private int value;
  private int cube;
  // Getters and setters...
List<Square> squares = new ArrayList<>();
for (int value = 1; value <= 5; value++) {
  Square square = new Square();
  square.setValue(value);
  square.setSquare(value * value);
  squares.add(square);
// Create a simple DataFrame, store into a partition directory
Dataset<Row> squaresDF = spark.createDataFrame(squares, Square.class);
squaresDF.write().parquet("data/test_table/key=1");
List<Cube> cubes = new ArrayList<>();
for (int value = 6; value <= 10; value++) {
  Cube cube = new Cube();
  cube.setValue(value);
  cube.setCube(value * value * value);
  cubes.add(cube);
// Create another DataFrame in a new partition directory,
// adding a new column and dropping an existing column
Dataset<Row> cubesDF = spark.createDataFrame(cubes, Cube.class);
cubesDF.write().parquet("data/test_table/key=2");
// Read the partitioned table
Dataset<Row> mergedDF = spark.read().option("mergeSchema", true).parquet("data/test_table");
mergedDF.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
//  |-- value: int (nullable = true)
//  |-- square: int (nullable = true)
//  |-- cube: int (nullable = true)
//  |-- key: int (nullable = true)
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.




    

# spark is from the previous example.
# Create a simple DataFrame, stored into a partition directory
sc = spark.sparkContext
squaresDF = spark.createDataFrame(sc.parallelize(range(1, 6))
                                  .map(lambda i: Row(single=i, double=i ** 2)))
squaresDF.write.parquet("data/test_table/key=1")
# Create another DataFrame in a new partition directory,
# adding a new column and dropping an existing column
cubesDF = spark.createDataFrame(sc.parallelize(range(6, 11))
                                .map(lambda i: Row(single=i, triple=i ** 3)))
cubesDF.write.parquet("data/test_table/key=2")
# Read the partitioned table
mergedDF = spark.read.option("mergeSchema", "true").parquet("data/test_table")
mergedDF.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
#  |-- double: long (nullable = true)
#  |-- single: long (nullable = true)
#  |-- triple: long (nullable = true)
#  |-- key: integer (nullable = true)
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
    
df1 <- createDataFrame(data.frame(single=c(12, 29), double=c(19, 23)))
df2 <- createDataFrame(data.frame(double=c(19, 23), triple=c(23, 18)))
# Create a simple DataFrame, stored into a partition directory
write.df(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
write.df(df2, "data/test_table/key=2", "parquet", "overwrite")
# Read the partitioned table
df3 <- read.df("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: double (nullable = true)
##  |-- double: double (nullable = true)
##  |-- triple: double (nullable = true)
##  |-- key: integer (nullable = true)
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
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.
  
Hive is case insensitive, while Parquet is not
  
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:
    
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.
    
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 fields 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.
Configuration
Configuration of Parquet can be done using the setConf method on SparkSession or by running
SET key=value commands using SQL.
Property NameDefaultMeaning
  spark.sql.parquet.binaryAsString
  false
    Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do
    not differentiate between binary data and strings when writing out the Parquet schema. This
    flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
  spark.sql.parquet.int96AsTimestamp
    Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96. This
    flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
  spark.sql.parquet.cacheMetadata
    Turns on caching of Parquet schema metadata. Can speed up querying of static data.
  spark.sql.parquet.compression.codec
  snappy
    Sets the compression codec use when writing Parquet files. Acceptable values include:
    uncompressed, snappy, gzip, lzo.
  spark.sql.parquet.filterPushdown
  Enables Parquet filter push-down optimization when set to true.
  spark.sql.hive.convertMetastoreParquet
    When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in
    support.
  spark.sql.parquet.mergeSchema
  false
      When true, the Parquet data source merges schemas collected from all data files, otherwise the
      schema is picked from the summary file or a random data file if no summary file is available.
      When true, enable the metadata-only query optimization that use the table's metadata to
      produce the partition columns instead of table scans. It applies when all the columns scanned
      are partition columns and the query has an aggregate operator that satisfies distinct
      semantics.
    
Spark SQL can automatically infer the schema of a JSON dataset and load it as a Dataset[Row].
This conversion can be done using SparkSession.read.json() on either a Dataset[String],
or a JSON file.
    
Note that the file that is offered as a json file is not a typical JSON file. Each
line must contain a separate, self-contained valid JSON object. For more information, please see
JSON Lines text format, also called newline-delimited JSON.
    
For a regular multi-line JSON file, set the multiLine option to true.
    
// Primitive types (Int, String, etc) and Product types (case classes) encoders are
// supported by importing this when creating a Dataset.
import spark.implicits._
// 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 peopleDF = spark.read.json(path)
// The inferred schema can be visualized using the printSchema() method
peopleDF.printSchema()
// root
//  |-- age: long (nullable = true)
//  |-- name: string (nullable = true)
// Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")
// SQL statements can be run by using the sql methods provided by spark
val teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()
// +------+
// |  name|
// +------+
// |Justin|
// +------+
// Alternatively, a DataFrame can be created for a JSON dataset represented by
// a Dataset[String] storing one JSON object per string
val otherPeopleDataset = spark.createDataset(
  """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val otherPeople = spark.read.json(otherPeopleDataset)
otherPeople.show()
// +---------------+----+
// |        address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
    
Spark SQL can automatically infer the schema of a JSON dataset and load it as a Dataset<Row>.
This conversion can be done using SparkSession.read().json() on either a Dataset<String>,
or a JSON file.
    
Note that the file that is offered as a json file is not a typical JSON file. Each
line must contain a separate, self-contained valid JSON object. For more information, please see
JSON Lines text format, also called newline-delimited JSON.
    
For a regular multi-line JSON file, set the multiLine option to true.
    
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files
Dataset<Row> people = spark.read().json("examples/src/main/resources/people.json");
// The inferred schema can be visualized using the printSchema() method
people.printSchema();
// root
//  |-- age: long (nullable = true)
//  |-- name: string (nullable = true)
// Creates a temporary view using the DataFrame
people.createOrReplaceTempView("people");
// SQL statements can be run by using the sql methods provided by spark
Dataset<Row> namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
namesDF.show();
// +------+
// |  name|
// +------+
// |Justin|
// +------+
// Alternatively, a DataFrame can be created for a JSON dataset represented by
// a Dataset<String> storing one JSON object per string.
List<String> jsonData = Arrays.asList(
        "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
Dataset<String> anotherPeopleDataset = spark.createDataset(jsonData, Encoders.STRING());
Dataset<Row> anotherPeople = spark.read().json(anotherPeopleDataset);
anotherPeople.show();
// +---------------+----+
// |        address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.




    

    
Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame.
This conversion can be done using SparkSession.read.json on a JSON file.
    
Note that the file that is offered as a json file is not a typical JSON file. Each
line must contain a separate, self-contained valid JSON object. For more information, please see
JSON Lines text format, also called newline-delimited JSON.
    
For a regular multi-line JSON file, set the multiLine parameter to True.
    
# spark is from the previous example.
sc = spark.sparkContext
# 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"
peopleDF = spark.read.json(path)
# The inferred schema can be visualized using the printSchema() method
peopleDF.printSchema()
# root
#  |-- age: long (nullable = true)
#  |-- name: string (nullable = true)
# Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")
# SQL statements can be run by using the sql methods provided by spark
teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()
# +------+
# |  name|
# +------+
# |Justin|
# +------+
# Alternatively, a DataFrame can be created for a JSON dataset represented by
# an RDD[String] storing one JSON object per string
jsonStrings = ['{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}']
otherPeopleRDD = sc.parallelize(jsonStrings)
otherPeople = spark.read.json(otherPeopleRDD)
otherPeople.show()
# +---------------+----+
# |        address|name|
# +---------------+----+
# |[Columbus,Ohio]| Yin|
# +---------------+----+
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
    
Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. using
the read.json() function, which loads data from a directory of JSON files where each line of the
files is a JSON object.
    
Note that the file that is offered as a json file is not a typical JSON file. Each
line must contain a separate, self-contained valid JSON object. For more information, please see
JSON Lines text format, also called newline-delimited JSON.
    
For a regular multi-line JSON file, set a named parameter multiLine to TRUE.
    
# 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 <- read.json(path)
# The inferred schema can be visualized using the printSchema() method.
printSchema(people)
## root
##  |-- age: long (nullable = true)
##  |-- name: string (nullable = true)
# Register this DataFrame as a table.
createOrReplaceTempView(people, "people")
# SQL statements can be run by using the sql methods.
teenagers <- sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
head(teenagers)
##     name
## 1 Justin
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
USING org.apache.spark.sql.json
OPTIONS (
  path "examples/src/main/resources/people.json"
SELECT * FROM jsonTable
Spark SQL also supports reading and writing data stored in Apache Hive.
However, since Hive has a large number of dependencies, these dependencies are not included in the
default Spark distribution. If Hive dependencies can be found on the classpath, Spark will load them
automatically. Note that these Hive dependencies 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),
and hdfs-site.xml (for HDFS configuration) file in conf/.
When working with Hive, one must instantiate SparkSession with Hive support, including
connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions.
Users who do not have an existing Hive deployment can still enable Hive support. When not configured
by the hive-site.xml, the context automatically creates metastore_db in the current directory and
creates a directory configured by spark.sql.warehouse.dir, which defaults to the directory
spark-warehouse in the current directory that the Spark application is started. Note that
the hive.metastore.warehouse.dir property in hive-site.xml is deprecated since Spark 2.0.0.
Instead, use spark.sql.warehouse.dir to specify the default location of database in warehouse.
You may need to grant write privilege to the user who starts the Spark application.
import org.apache.spark.sql.SparkSession
case class Record(key: Int, value: String)
// warehouseLocation points to the default location for managed databases and tables
val warehouseLocation = new File("spark-warehouse").getAbsolutePath
val spark = SparkSession
  .builder()
  .appName("Spark Hive Example")
  .config("spark.sql.warehouse.dir", warehouseLocation)
  .enableHiveSupport()
  .getOrCreate()
import spark.implicits._
import spark.sql
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
// Queries are expressed in HiveQL
sql("SELECT * FROM src").show()
// +---+-------+
// |key|  value|
// +---+-------+
// |238|val_238|
// | 86| val_86|
// |311|val_311|
// ...
// Aggregation queries are also supported.
sql("SELECT COUNT(*) FROM src").show()
// +--------+
// |count(1)|
// +--------+
// |    500 |
// +--------+
// The results of SQL queries are themselves DataFrames and support all normal functions.
val sqlDF = sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")
// The items in DataFrames are of type Row, which allows you to access each column by ordinal.
val stringsDS = sqlDF.map {
  case Row(key: Int, value: String) => s"Key: $key, Value: $value"
stringsDS.show()
// +--------------------+
// |               value|
// +--------------------+
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// ...
// You can also use DataFrames to create temporary views within a SparkSession.
val recordsDF = spark.createDataFrame((1 to 100).map(i => Record(i, s"val_$i")))
recordsDF.createOrReplaceTempView("records")
// Queries can then join DataFrame data with data stored in Hive.
sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()
// +---+------+---+------+
// |key| value|key| value|
// +---+------+---+------+
// |  2| val_2|  2| val_2|
// |  4| val_4|  4| val_4|
// |  5| val_5|  5| val_5|
// ...
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala" in the Spark repo.




    

    
import java.io.File;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
public static class Record implements Serializable {
  private int key;
  private String value;
  public int getKey() {
    return key;
  public void setKey(int key) {
    this.key = key;
  public String getValue() {
    return value;
  public void setValue(String value) {
    this.value = value;
// warehouseLocation points to the default location for managed databases and tables
String warehouseLocation = new File("spark-warehouse").getAbsolutePath();
SparkSession spark = SparkSession
  .builder()
  .appName("Java Spark Hive Example")
  .config("spark.sql.warehouse.dir", warehouseLocation)
  .enableHiveSupport()
  .getOrCreate();
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive");
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
// Queries are expressed in HiveQL
spark.sql("SELECT * FROM src").show();
// +---+-------+
// |key|  value|
// +---+-------+
// |238|val_238|
// | 86| val_86|
// |311|val_311|
// ...
// Aggregation queries are also supported.
spark.sql("SELECT COUNT(*) FROM src").show();
// +--------+
// |count(1)|
// +--------+
// |    500 |
// +--------+
// The results of SQL queries are themselves DataFrames and support all normal functions.
Dataset<Row> sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key");
// The items in DataFrames are of type Row, which lets you to access each column by ordinal.
Dataset<String> stringsDS = sqlDF.map(
    (MapFunction<Row, String>) row -> "Key: " + row.get(0) + ", Value: " + row.get(1),
    Encoders.STRING());
stringsDS.show();
// +--------------------+
// |               value|
// +--------------------+
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// ...
// You can also use DataFrames to create temporary views within a SparkSession.
List<Record> records = new ArrayList<>();
for (int key = 1; key < 100; key++) {
  Record record = new Record();
  record.setKey(key);
  record.setValue("val_" + key);
  records.add(record);
Dataset<Row> recordsDF = spark.createDataFrame(records, Record.class);
recordsDF.createOrReplaceTempView("records");
// Queries can then join DataFrames data with data stored in Hive.
spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show();
// +---+------+---+------+
// |key| value|key| value|
// +---+------+---+------+
// |  2| val_2|  2| val_2|
// |  2| val_2|  2| val_2|
// |  4| val_4|  4| val_4|
// ...
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java" in the Spark repo.
    
from os.path import expanduser, join, abspath
from pyspark.sql import SparkSession
from pyspark.sql import Row
# warehouse_location points to the default location for managed databases and tables
warehouse_location = abspath('spark-warehouse')
spark = SparkSession \
    .builder \
    .appName("Python Spark SQL Hive integration example") \
    .config("spark.sql.warehouse.dir", warehouse_location) \
    .enableHiveSupport() \
    .getOrCreate()
# spark is an existing SparkSession
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
# Queries are expressed in HiveQL
spark.sql("SELECT * FROM src").show()
# +---+-------+
# |key|  value|
# +---+-------+
# |238|val_238|
# | 86| val_86|
# |311|val_311|
# ...
# Aggregation queries are also supported.
spark.sql("SELECT COUNT(*) FROM src").show()
# +--------+
# |count(1)|
# +--------+
# |    500 |
# +--------+
# The results of SQL queries are themselves DataFrames and support all normal functions.
sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")
# The items in DataFrames are of type Row, which allows you to access each column by ordinal.
stringsDS = sqlDF.rdd.map(lambda row: "Key: %d, Value: %s" % (row.key, row.value))
for record in stringsDS.collect():
    print(record)
# Key: 0, Value: val_0
# Key: 0, Value: val_0
# Key: 0, Value: val_0
# ...
# You can also use DataFrames to create temporary views within a SparkSession.
Record = Row("key", "value")
recordsDF = spark.createDataFrame([Record(i, "val_" + str(i)) for i in range(1, 101)])
recordsDF.createOrReplaceTempView("records")
# Queries can then join DataFrame data with data stored in Hive.
spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()
# +---+------+---+------+
# |key| value|key| value|
# +---+------+---+------+
# |  2| val_2|  2| val_2|
# |  4| val_4|  4| val_4|
# |  5| val_5|  5| val_5|
# ...
    
Find full example code at "examples/src/main/python/sql/hive.py" in the Spark repo.
    
When working with Hive one must instantiate SparkSession with Hive support. This
adds support for finding tables in the MetaStore and writing queries using HiveQL.
    
# enableHiveSupport defaults to TRUE
sparkR.session(enableHiveSupport = TRUE)
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
# Queries can be expressed in HiveQL.
results <- collect(sql("FROM src SELECT key, value"))
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
Specifying storage format for Hive tables
When you create a Hive table, you need to define how this table should read/write data from/to file system,
i.e. the “input format” and “output format”. You also need to define how this table should deserialize the data
to rows, or serialize rows to data, i.e. the “serde”. The following options can be used to specify the storage
format(“serde”, “input format”, “output format”), e.g. CREATE TABLE src(id int) USING hive OPTIONS(fileFormat 'parquet').
By default, we will read the table files as plain text. Note that, Hive storage handler is not supported yet when
creating table, you can create a table using storage handler at Hive side, and use Spark SQL to read it.
  Property NameMeaning
    fileFormat
      A fileFormat is kind of a package of storage format specifications, including "serde", "input format" and
      "output format". Currently we support 6 fileFormats: 'sequencefile', 'rcfile', 'orc', 'parquet', 'textfile' and 'avro'.
    inputFormat, outputFormat
      These 2 options specify the name of a corresponding `InputFormat` and `OutputFormat` class as a string literal,
      e.g. `org.apache.hadoop.hive.ql.io.orc.OrcInputFormat`. These 2 options must be appeared in pair, and you can not
      specify them if you already specified the `fileFormat` option.
    serde
      This option specifies the name of a serde class. When the `fileFormat` option is specified, do not specify this option
      if the given `fileFormat` already include the information of serde. Currently "sequencefile", "textfile" and "rcfile"
      don't include the serde information and you can use this option with these 3 fileFormats.
All other properties defined with OPTIONS will be regarded as Hive serde properties.
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:
  Property NameDefaultMeaning
    spark.sql.hive.metastore.version
    1.2.1
      Version of the Hive metastore. Available
      options are 0.12.0 through 1.2.1.
    spark.sql.hive.metastore.jars
    builtin
      Location of the jars that should be used to instantiate the HiveMetastoreClient. This
      property can be one of three options:
        
builtin
        Use Hive 1.2.1, which is bundled with the Spark assembly when -Phive is
        enabled. When this option is chosen, spark.sql.hive.metastore.version must be
        either 1.2.1 or not defined.
        
maven
        Use Hive jars of specified version downloaded from Maven repositories. This configuration
        is not generally recommended for production deployments.
        
A classpath in the standard format for the JVM. This classpath must include all of Hive
        and its dependencies, including the correct version of Hadoop. These jars only need to be
        present on the driver, but if you are running in yarn cluster mode then you must ensure
        they are packaged with your application.
    spark.sql.hive.metastore.sharedPrefixes
    com.mysql.jdbc,
org.postgresql,
com.microsoft.sqlserver,
oracle.jdbc
        A comma separated list of class prefixes that should be loaded using the classloader that is
        shared between Spark SQL and a specific version of Hive. An example of classes that should
        be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need
        to be shared are those that interact with classes that are already shared. For example,
        custom appenders that are used by log4j.
        A comma separated list of class prefixes that should explicitly be reloaded for each version
        of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a
        prefix that typically would be shared (i.e. org.apache.spark.*).
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:
bin/spark-shell --driver-class-path postgresql-9.4.1207.jar --jars postgresql-9.4.1207.jar
Tables from the remote database can be loaded as a DataFrame or Spark SQL temporary view using
the Data Sources API. Users can specify the JDBC connection properties in the data source options.
user and password are normally provided as connection properties for
logging into the data sources. In addition to the connection properties, Spark also supports
the following case-insensitive options:
  Property NameMeaning
      The JDBC URL to connect to. The source-specific connection properties may be specified in the URL. e.g., jdbc:postgresql://localhost/test?user=fred&password=secret
    dbtable
      The JDBC table that should be read. Note that anything that is valid in a FROM clause of
      a SQL query can be used. For example, instead of a full table you could also use a
      subquery in parentheses.
    partitionColumn, lowerBound, upperBound
      These options must all be specified if any of them is specified. In addition,
      numPartitions must be specified. They describe how to partition the table when
      reading in parallel from multiple workers.
      partitionColumn must be a numeric column from the table in question. Notice
      that lowerBound and upperBound are just used to decide the
      partition stride, not for filtering the rows in table. So all rows in the table will be
      partitioned and returned. This option applies only to reading.
     numPartitions
       The maximum number of partitions that can be used for parallelism in table reading and
       writing. This also determines the maximum number of concurrent JDBC connections.
       If the number of partitions to write exceeds this limit, we decrease it to this limit by
       calling coalesce(numPartitions) before writing.
     isolationLevel
       The transaction isolation level, which applies to current connection. It can be one of NONE, READ_COMMITTED, READ_UNCOMMITTED, REPEATABLE_READ, or SERIALIZABLE, corresponding to standard transaction isolation levels defined by JDBC's Connection object, with default of READ_UNCOMMITTED. This option applies only to writing. Please refer the documentation in java.sql.Connection.
    truncate
     This is a JDBC writer related option. When SaveMode.Overwrite is enabled, this option causes Spark to truncate an existing table instead of dropping and recreating it. This can be more efficient, and prevents the table metadata (e.g., indices) from being removed. However, it will not work in some cases, such as when the new data has a different schema. It defaults to false. This option applies only to writing.
    createTableColumnTypes
     The database column data types to use instead of the defaults, when creating the table. Data type information should be specified in the same format as CREATE TABLE columns syntax (e.g: "name CHAR(64), comments VARCHAR(1024)"). The specified types should be valid spark sql data types. This option applies only to writing.
    
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
// Loading data from a JDBC source
val jdbcDF = spark.read
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .load()
val connectionProperties = new Properties()
connectionProperties.put("user", "username")
connectionProperties.put("password", "password")
val jdbcDF2 = spark.read
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// Saving data to a JDBC source
jdbcDF.write
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .save()
jdbcDF2.write
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// Specifying create table column data types on write
jdbcDF.write
  .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.




    

    
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
// Loading data from a JDBC source
Dataset<Row> jdbcDF = spark.read()
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .load();
Properties connectionProperties = new Properties();
connectionProperties.put("user", "username");
connectionProperties.put("password", "password");
Dataset<Row> jdbcDF2 = spark.read()
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
// Saving data to a JDBC source
jdbcDF.write()
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .save();
jdbcDF2.write()
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
// Specifying create table column data types on write
jdbcDF.write()
  .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
    
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
    
# Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
# Loading data from a JDBC source
jdbcDF = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:postgresql:dbserver") \
    .option("dbtable", "schema.tablename") \
    .option("user", "username") \
    .option("password", "password") \
    .load()
jdbcDF2 = spark.read \
    .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
          properties={"user": "username", "password": "password"})
# Saving data to a JDBC source
jdbcDF.write \
    .format("jdbc") \
    .option("url", "jdbc:postgresql:dbserver") \
    .option("dbtable", "schema.tablename") \
    .option("user", "username") \
    .option("password", "password") \
    .save()
jdbcDF2.write \
    .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
          properties={"user": "username", "password": "password"})
# Specifying create table column data types on write
jdbcDF.write \
    .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)") \
    .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
          properties={"user": "username", "password": "password"})
    
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.




    

    
# Loading data from a JDBC source
df <- read.jdbc("jdbc:postgresql:dbserver", "schema.tablename", user = "username", password = "password")
# Saving data to a JDBC source
write.jdbc(df, "jdbc:postgresql:dbserver", "schema.tablename", user = "username", password = "password")
    
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
  
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 spark.catalog.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 spark.catalog.uncacheTable("tableName") to remove the table from memory.
Configuration of in-memory caching can be done using the setConf method on SparkSession or by running
SET key=value commands using SQL.
Property NameDefaultMeaning
  spark.sql.inMemoryColumnarStorage.compressed
    When set to true Spark SQL will automatically select a compression codec for each column based
    on statistics of the data.
  spark.sql.inMemoryColumnarStorage.batchSize
  10000
    Controls the size of batches for columnar caching. Larger batch sizes can improve memory utilization
    and compression, but risk OOMs when caching data.
Other Configuration Options
The following options can also be used to tune the performance of query execution. It is possible
that these options will be deprecated in future release as more optimizations are performed automatically.
  Property NameDefaultMeaning
    spark.sql.files.maxPartitionBytes
    134217728 (128 MB)
      The maximum number of bytes to pack into a single partition when reading files.
    spark.sql.files.openCostInBytes
    4194304 (4 MB)
      The estimated cost to open a file, measured by the number of bytes could be scanned in the same
      time. This is used when putting multiple files into a partition. It is better to over estimated,
      then the partitions with small files will be faster than partitions with bigger files (which is
      scheduled first).
    spark.sql.broadcastTimeout
      Timeout in seconds for the broadcast wait time in broadcast joins
    10485760 (10 MB)
      Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when
      performing a join. By setting this value to -1 broadcasting can be disabled. Note that currently
      statistics are only supported for Hive Metastore tables where the command
      ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan has been run.
    spark.sql.shuffle.partitions
      Configures the number of partitions to use when shuffling data for joins or aggregations.
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 to 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 2.1 to 2.2
  
Spark 2.1.1 introduced a new configuration key: spark.sql.hive.caseSensitiveInferenceMode. It had a default setting of NEVER_INFER, which kept behavior identical to 2.1.0. However, Spark 2.2.0 changes this setting’s default value to INFER_AND_SAVE to restore compatibility with reading Hive metastore tables whose underlying file schema have mixed-case column names. With the INFER_AND_SAVE configuration value, on first access Spark will perform schema inference on any Hive metastore table for which it has not already saved an inferred schema. Note that schema inference can be a very time consuming operation for tables with thousands of partitions. If compatibility with mixed-case column names is not a concern, you can safely set spark.sql.hive.caseSensitiveInferenceMode to NEVER_INFER to avoid the initial overhead of schema inference. Note that with the new default INFER_AND_SAVE setting, the results of the schema inference are saved as a metastore key for future use. Therefore, the initial schema inference occurs only at a table’s first access.
Upgrading From Spark SQL 2.0 to 2.1
  
Datasource tables now store partition metadata in the Hive metastore. This means that Hive DDLs such as ALTER TABLE PARTITION ... SET LOCATION are now available for tables created with the Datasource API.
      
Legacy datasource tables can be migrated to this format via the MSCK REPAIR TABLE command. Migrating legacy tables is recommended to take advantage of Hive DDL support and improved planning performance.
      
To determine if a table has been migrated, look for the PartitionProvider: Catalog attribute when issuing DESCRIBE FORMATTED on the table.
  
Changes to INSERT OVERWRITE TABLE ... PARTITION ... behavior for Datasource tables.
      
In prior Spark versions INSERT OVERWRITE overwrote the entire Datasource table, even when given a partition specification. Now only partitions matching the specification are overwritten.
      
Note that this still differs from the behavior of Hive tables, which is to overwrite only partitions overlapping with newly inserted data.
    
SparkSession is now the new entry point of Spark that replaces the old SQLContext and
HiveContext. Note that the old SQLContext and HiveContext are kept for backward compatibility. A new catalog interface is accessible from SparkSession - existing API on databases and tables access such as listTables, createExternalTable, dropTempView, cacheTable are moved here.
    
Dataset API and DataFrame API are unified. In Scala, DataFrame becomes a type alias for
Dataset[Row], while Java API users must replace DataFrame with Dataset<Row>. Both the typed
transformations (e.g., map, filter, and groupByKey) and untyped transformations (e.g.,
select and groupBy) are available on the Dataset class. Since compile-time type-safety in
Python and R is not a language feature, the concept of Dataset does not apply to these languages’
APIs. Instead, DataFrame remains the primary programing abstraction, which is analogous to the
single-node data frame notion in these languages.
  
Dataset and DataFrame API unionAll has been deprecated and replaced by union
  
Dataset and DataFrame API explode has been deprecated, alternatively, use functions.explode() with select or flatMap
    
Dataset and DataFrame API registerTempTable has been deprecated and replaced by createOrReplaceTempView
  
Changes to CREATE TABLE ... LOCATION behavior for Hive tables.
      
From Spark 2.0, CREATE TABLE ... LOCATION is equivalent to CREATE EXTERNAL TABLE ... LOCATION
in order to prevent accidental dropping the existing data in the user-provided locations.
That means, a Hive table created in Spark SQL with the user-specified location is always a Hive external table.
Dropping external tables will not remove the data. Users are not allowed to specify the location for Hive managed tables.
Note that this is different from the Hive behavior.
      
As a result, DROP TABLE statements on those tables will not remove the data.
  
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 \
    
Since 1.6.1, withColumn method in sparkR supports adding a new column to or replacing existing columns
of the same name of a DataFrame.




    

    
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).
  
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.
  
DataFrame.withColumn method in pySpark supports adding a new column or replacing existing columns of the same name.
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")
Behavior change on DataFrame.withColumn
Prior to 1.4, DataFrame.withColumn() supports adding a column only. The column will always be added
as a new column with its specified name in the result DataFrame even if there may be any existing
columns of the same name. Since 1.4, DataFrame.withColumn() supports adding a column of a different
name from names of all existing columns or replacing existing columns of the same name.
Note that this change is only for Scala API, not for PySpark and SparkR.
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.
Python DataTypes No Longer Singletons
When using DataTypes in Python you will need to construct them (i.e. StringType()) instead of
referencing a singleton.
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 2.1.1. 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
      
{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
  
All Hive DDL Functions, including:
      
CREATE TABLE
      
CREATE TABLE AS SELECT
      
ALTER TABLE
  
Most Hive Data types, including:
      
TINYINT
      
SMALLINT
      
BIGINT
      
BOOLEAN
      
FLOAT
      
DOUBLE
      
STRING
      
BINARY
      
TIMESTAMP
      
ARRAY<>
      
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.
    
All data types of Spark SQL are located in the package org.apache.spark.sql.types.
You can access them by doing
    
import org.apache.spark.sql.types._
    
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
  Data type
  Value type in Scala
  API to access or create a data type
   ByteType 
  ByteType
   ShortType 
   Short 
  ShortType
   IntegerType 
  IntegerType
   LongType 
  LongType
   FloatType 
   Float 
  FloatType
   DoubleType 
   Double 
  DoubleType
   DecimalType 
   java.math.BigDecimal 
  DecimalType
   StringType 
   String 
  StringType
   BinaryType 
   Array[Byte] 
  BinaryType
   BooleanType 
   Boolean 
  BooleanType
   TimestampType 
   java.sql.Timestamp 
  TimestampType
   DateType 
   java.sql.Date 
  DateType
   ArrayType 
   scala.collection.Seq 
  ArrayType(elementType, [containsNull])

  Note: The default value of containsNull is true.
   MapType 
   scala.collection.Map 
  MapType(keyType, valueType, [valueContainsNull])

  Note: The default value of valueContainsNull is true.
   StructType 
   org.apache.spark.sql.Row 
  StructType(fields)

  Note: fields is a Seq of StructFields. Also, two fields with the same
  name are not allowed.
   StructField 
   The value type in Scala of the data type of this field
  (For example, Int for a StructField with the data type IntegerType) 
  StructField(name, dataType, [nullable])

  Note: The default value of nullable is true.
    
All data types of Spark SQL are located in the package of
org.apache.spark.sql.types. To access or create a data type,
please use factory methods provided in
org.apache.spark.sql.types.DataTypes.
  Data type
  Value type in Java
  API to access or create a data type
   ByteType 
   byte or Byte 
  DataTypes.ByteType
   ShortType 
   short or Short 
  DataTypes.ShortType
   IntegerType 
   int or Integer 
  DataTypes.IntegerType
   LongType 
   long or Long 
  DataTypes.LongType
   FloatType 
   float or Float 
  DataTypes.FloatType
   DoubleType 
   double or Double 
  DataTypes.DoubleType
   DecimalType 
   java.math.BigDecimal 
  DataTypes.createDecimalType()

  DataTypes.createDecimalType(precision, scale).
   StringType 
   String 
  DataTypes.StringType
   BinaryType 
   byte[] 
  DataTypes.BinaryType
   BooleanType 
   boolean or Boolean 
  DataTypes.BooleanType
   TimestampType 
   java.sql.Timestamp 
  DataTypes.TimestampType
   DateType 
   java.sql.Date 
  DataTypes.DateType
   ArrayType 
   java.util.List 
  DataTypes.createArrayType(elementType)

  Note: The value of containsNull will be true

  DataTypes.createArrayType(elementType, containsNull).
   MapType 
   java.util.Map 
  DataTypes.createMapType(keyType, valueType)

  Note: The value of valueContainsNull will be true.

  DataTypes.createMapType(keyType, valueType, valueContainsNull)

   StructType 
   org.apache.spark.sql.Row 
  DataTypes.createStructType(fields)

  Note: fields is a List or an array of StructFields.
  Also, two fields with the same name are not allowed.
   StructField 
   The value type in Java of the data type of this field
  (For example, int for a StructField with the data type IntegerType) 
  DataTypes.createStructField(name, dataType, nullable)
    
All data types of Spark SQL are located in the package of pyspark.sql.types.
You can access them by doing
    
from pyspark.sql.types import *
  Data type
  Value type in Python
  API to access or create a data type
   ByteType 
  int or long 

  Note: Numbers will be converted to 1-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -128 to 127.
  ByteType()
   ShortType 
  int or long 

  Note: Numbers will be converted to 2-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -32768 to 32767.
  ShortType()
   IntegerType 
   int or long 
  IntegerType()
   LongType 
  Note: Numbers will be converted to 8-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of
  -9223372036854775808 to 9223372036854775807.
  Otherwise, please convert data to decimal.Decimal and use DecimalType.
  LongType()
   FloatType 
  float 

  Note: Numbers will be converted to 4-byte single-precision floating
  point numbers at runtime.
  FloatType()
   DoubleType 
   float 
  DoubleType()
   DecimalType 
   decimal.Decimal 
  DecimalType()
   StringType 
   string 
  StringType()
   BinaryType 
   bytearray 
  BinaryType()
   BooleanType 
  BooleanType()
   TimestampType 
   datetime.datetime 
  TimestampType()
   DateType 
   datetime.date 
  DateType()
   ArrayType 
   list, tuple, or array 
  ArrayType(elementType, [containsNull])

  Note: The default value of containsNull is True.
   MapType 
  MapType(keyType, valueType, [valueContainsNull])

  Note: The default value of valueContainsNull is True.
   StructType 
   list or tuple 
  StructType(fields)

  Note: fields is a Seq of StructFields. Also, two fields with the same
  name are not allowed.
   StructField 
   The value type in Python of the data type of this field
  (For example, Int for a StructField with the data type IntegerType) 
  StructField(name, dataType, [nullable])

  Note: The default value of nullable is True.
  integer 

  Note: Numbers will be converted to 1-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -128 to 127.
  "byte"
   ShortType 
  integer 

  Note: Numbers will be converted to 2-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of -32768 to 32767.
  "short"
   IntegerType 
   integer 
  "integer"
   LongType 
  integer 

  Note: Numbers will be converted to 8-byte signed integer numbers at runtime.
  Please make sure that numbers are within the range of
  -9223372036854775808 to 9223372036854775807.
  Otherwise, please convert data to decimal.Decimal and use DecimalType.
  "long"
   FloatType 
  numeric 

  Note: Numbers will be converted to 4-byte single-precision floating
  point numbers at runtime.
  "float"
   DoubleType 
   numeric 
  "double"
   DecimalType 
   Not supported 
   Not supported
   StringType 
   character 
  "string"
   BinaryType 
  "binary"
   BooleanType 
   logical 
  "bool"
   TimestampType 
   POSIXct 
  "timestamp"
   DateType 
  "date"
   ArrayType 
   vector or list 
  list(type="array", elementType=elementType, containsNull=[containsNull])

  Note: The default value of containsNull is TRUE.
   MapType 
   environment 
  list(type="map", keyType=keyType, valueType=valueType, valueContainsNull=[valueContainsNull])

  Note: The default value of valueContainsNull is TRUE.
   StructType 
   named list
  list(type="struct", fields=fields)

  Note: fields is a Seq of StructFields. Also, two fields with the same
  name are not allowed.
   StructField 
   The value type in R of the data type of this field
  (For example, integer for a StructField with the data type IntegerType) 
  list(name=name, type=dataType, nullable=[nullable])

  Note: The default value of nullable is TRUE.
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.