Spark SQL, DataFrames and Datasets Guide
- Overview
- Getting Started
- Data Sources
- Performance Tuning
- Distributed SQL Engine
- Migration Guide
- Upgrading From Spark SQL 2.0 to 2.1
- Upgrading From Spark SQL 1.6 to 2.0
- Upgrading From Spark SQL 1.5 to 1.6
- Upgrading From Spark SQL 1.4 to 1.5
- Upgrading from Spark SQL 1.3 to 1.4
- Upgrading from Spark SQL 1.0-1.2 to 1.3
- Rename of SchemaRDD to DataFrame
- Unification of the Java and Scala APIs
- Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)
- Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)
- UDF Registration Moved to
sqlContext.udf
(Java & Scala) - Python DataTypes No Longer Singletons
- Compatibility with Apache Hive
- Reference
Overview
Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.
All of the examples on this page use sample data included in the Spark distribution and can be run in
the spark-shell
, pyspark
shell, or sparkR
shell.
SQL
One use of Spark SQL is to execute SQL queries. Spark SQL can also be used to read data from an existing Hive installation. For more on how to configure this feature, please refer to the Hive Tables section. When running SQL from within another programming language the results will be returned as a Dataset/DataFrame. You can also interact with the SQL interface using the command-line or over JDBC/ODBC.
Datasets and DataFrames
A Dataset is a distributed collection of data.
Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong
typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized
execution engine. A Dataset can be constructed from JVM objects and then
manipulated using functional transformations (map
, flatMap
, filter
, etc.).
The Dataset API is available in Scala and
Java. Python does not have the support for the Dataset API. But due to Python’s dynamic nature,
many of the benefits of the Dataset API are already available (i.e. you can access the field of a row by name naturally
row.columnName
). The case for R is similar.
A DataFrame is a Dataset organized into named columns. It is conceptually
equivalent to a table in a relational database or a data frame in R/Python, but with richer
optimizations under the hood. DataFrames can be constructed from a wide array of sources such
as: structured data files, tables in Hive, external databases, or existing RDDs.
The DataFrame API is available in Scala,
Java, Python, and R.
In Scala and Java, a DataFrame is represented by a Dataset of Row
s.
In the Scala API, DataFrame
is simply a type alias of Dataset[Row]
.
While, in Java API, users need to use Dataset<Row>
to represent a DataFrame
.
Throughout this document, we will often refer to Scala/Java Datasets of Row
s as DataFrames.
Getting Started
Starting Point: SparkSession
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
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._
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();
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()
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"))
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|
// +----+-------+
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|
// +----+-------+
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|
# +----+-------+
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|
## +----+-------+
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 Row
s 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|
// +----+-----+
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|
// +----+-----+
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|
# +----+-----+
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.
# Create the DataFrame
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
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.
Running SQL Queries Programmatically
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|
// +----+-------+
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|
// +----+-------+
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|
# +----+-------+
The sql
function enables applications to run SQL queries programmatically and returns the result as a SparkDataFrame
.
df <- sql("SELECT * FROM table")
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|
// +----+-------+
// 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|
// +----+-------+
# 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|
# +----+-------+
CREATE GLOBAL TEMPORARY VIEW temp_view AS SELECT a + 1, b * 2 FROM tbl
SELECT * FROM global_temp.temp_view
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|
// +----+-------+
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(new MapFunction<Integer, Integer>() {
@Override
public Integer call(Integer value) throws Exception {
return 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|
// +----+-------+
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 Seq
s or Array
s. This RDD can be implicitly converted to a DataFrame and then be
registered as a table. Tables can be used in subsequent SQL statements.
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.Encoder
// 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))
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(new Function<String, Person>() {
@Override
public Person call(String line) throws Exception {
String[] parts = line.split(",");
Person person = new Person();
person.setName(parts[0]);
person.setAge(Integer.parseInt(parts[1].trim()));
return person;
}
});
// Apply a schema to an RDD of JavaBeans 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(new MapFunction<Row, String>() {
@Override
public String call(Row row) throws Exception {
return "Name: " + row.getString(0);
}
}, stringEncoder);
teenagerNamesByIndexDF.show();
// +------------+
// | value|
// +------------+
// |Name: Justin|
// +------------+
// or by field name
Dataset<String> teenagerNamesByFieldDF = teenagersDF.map(new MapFunction<Row, String>() {
@Override
public String call(Row row) throws Exception {
return "Name: " + row.<String>getAs("name");
}
}, stringEncoder);
teenagerNamesByFieldDF.show();
// +------------+
// | value|
// +------------+
// |Name: Justin|
// +------------+
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
Programmatically Specifying the Schema
When case classes cannot be defined ahead of time (for example,
the structure of records is encoded in a string, or a text dataset will be parsed
and fields will be projected differently for different users),
a DataFrame
can be created programmatically with three steps.
- Create an RDD of
Row
s from the original RDD; - Create the schema represented by a
StructType
matching the structure ofRow
s in the RDD created in Step 1. - Apply the schema to the RDD of
Row
s viacreateDataFrame
method provided bySparkSession
.
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|
// +-------------+
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
Row
s from the original RDD; - Create the schema represented by a
StructType
matching the structure ofRow
s in the RDD created in Step 1. - Apply the schema to the RDD of
Row
s viacreateDataFrame
method provided bySparkSession
.
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(new Function<String, Row>() {
@Override
public Row call(String record) throws Exception {
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(new MapFunction<Row, String>() {
@Override
public String call(Row row) throws Exception {
return "Name: " + row.getString(0);
}
}, Encoders.STRING());
namesDS.show();
// +-------------+
// | value|
// +-------------+
// |Name: Michael|
// | Name: Andy|
// | Name: Justin|
// +-------------+
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 bySparkSession
.
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|
# +-------+
Aggregations
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|
// +--------------+
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|
// +--------------+
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
b1
}
// 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|
// +--------------+
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|
// +--------------+
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")
Dataset<Row> usersDF = spark.read().load("examples/src/main/resources/users.parquet");
usersDF.select("name", "favorite_color").write().save("namesAndFavColors.parquet");
df = spark.read.load("examples/src/main/resources/users.parquet")
df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
df <- read.df("examples/src/main/resources/users.parquet")
write.df(select(df, "name", "favorite_color"), "namesAndFavColors.parquet")
Manually Specifying Options
You can also manually specify the data source that will be used along with any extra options
that you would like to pass to the data source. Data sources are specified by their fully qualified
name (i.e., org.apache.spark.sql.parquet
), but for built-in sources you can also use their short
names (json
, parquet
, jdbc
, 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")
Dataset<Row> peopleDF =
spark.read().format("json").load("examples/src/main/resources/people.json");
peopleDF.select("name", "age").write().format("parquet").save("namesAndAges.parquet");
df = spark.read.load("examples/src/main/resources/people.json", format="json")
df.select("name", "age").write.save("namesAndAges.parquet", format="parquet")
df <- read.df("examples/src/main/resources/people.json", "json")
namesAndAges <- select(df, "name", "age")
write.df(namesAndAges, "namesAndAges.parquet", "parquet")
Run SQL on files directly
Instead of using read API to load a file into DataFrame and query it, you can also query that file directly with SQL.
val sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
Dataset<Row> sqlDF =
spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");
df = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
df <- sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
Save Modes
Save operations can optionally take a SaveMode
, that specifies how to handle existing data if
present. It is important to realize that these save modes do not utilize any locking and are not
atomic. Additionally, when performing an Overwrite
, the data will be deleted before writing out the
new data.
Scala/Java | Any Language | Meaning |
---|---|---|
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.
By default saveAsTable
will create a “managed table”, meaning that the location of the data will
be controlled by the metastore. Managed tables will also have their data deleted automatically
when a table is dropped.
Currently, saveAsTable
does not expose an API supporting the creation of an “external table” from a DataFrame
.
However, this functionality can be achieved by providing a path
option to the DataFrameWriter
with path
as the key
and location of the external table as its value (a string) when saving the table with saveAsTable
. When an External table
is dropped only its metadata is removed.
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
.
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|
// +------------+
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
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(new MapFunction<Row, String>() {
public String call(Row row) {
return "Name: " + row.getString(0);
}
}, Encoders.STRING());
namesDS.show();
// +------------+
// | value|
// +------------+
// |Name: Justin|
// +------------+
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|
# +------+
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
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. The Parquet data source is now able to discover and infer
partitioning information automatically. For example, we can store all our previously used
population data into a partitioned table using the following directory structure, with two extra
columns, gender
and country
as partitioning columns:
path
└── to
└── table
├── gender=male
│ ├── ...
│ │
│ ├── country=US
│ │ └── data.parquet
│ ├── country=CN
│ │ └── data.parquet
│ └── ...
└── gender=female
├── ...
│
├── country=US
│ └── data.parquet
├── country=CN
│ └── data.parquet
└── ...
By passing path/to/table
to either 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:
root
|-- name: string (nullable = true)
|-- age: long (nullable = true)
|-- gender: string (nullable = true)
|-- country: string (nullable = true)
Notice that the data types of the partitioning columns are automatically inferred. Currently,
numeric data types and string type are supported. Sometimes users may not want to automatically
infer the data types of the partitioning columns. For these use cases, the automatic type inference
can be configured by spark.sql.sources.partitionColumnTypeInference.enabled
, which is default to
true
. When type inference is disabled, string type will be used for the partitioning columns.
Starting from Spark 1.6.0, partition discovery only finds partitions under the given paths
by default. For the above example, if users pass path/to/table/gender=male
to either
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
totrue
when reading Parquet files (as shown in the examples below), or - setting the global SQL option
spark.sql.parquet.mergeSchema
totrue
.
// This is used to implicitly convert an RDD to a DataFrame.
import spark.implicits._
// 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)
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)
from pyspark.sql import Row
# 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)
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)
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.
// spark is an existing SparkSession
spark.catalog.refreshTable("my_table")
// spark is an existing SparkSession
spark.catalog().refreshTable("my_table");
# spark is an existing SparkSession
spark.catalog.refreshTable("my_table")
REFRESH TABLE my_table;
Configuration
Configuration of Parquet can be done using the setConf
method on SparkSession
or by running
SET key=value
commands using SQL.
Property Name | Default | Meaning |
---|---|---|
spark.sql.parquet.binaryAsString |
false | Some other Parquet-producing systems, in particular Impala, 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 |
true | 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 |
true | 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 |
true | Enables Parquet filter push-down optimization when set to true. |
spark.sql.hive.convertMetastoreParquet |
true | 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. |
spark.sql.optimizer.metadataOnly |
true |
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. |
JSON Datasets
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 an RDD of 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. As a consequence, a regular multi-line JSON file will most often fail.
// 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
// an RDD[String] storing one JSON object per string
val otherPeopleRDD = spark.sparkContext.makeRDD(
"""{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val otherPeople = spark.read.json(otherPeopleRDD)
otherPeople.show()
// +---------------+----+
// | address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+
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 an RDD of 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. As a consequence, a regular multi-line JSON file will most often fail.
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
// an RDD[String] storing one JSON object per string.
List<String> jsonData = Arrays.asList(
"{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
JavaRDD<String> anotherPeopleRDD =
new JavaSparkContext(spark.sparkContext()).parallelize(jsonData);
Dataset anotherPeople = spark.read().json(anotherPeopleRDD);
anotherPeople.show();
// +---------------+----+
// | address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+
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. As a consequence, a regular multi-line JSON file will most often fail.
# 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|
# +---------------+----+
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. As a consequence, a regular multi-line JSON file will most often fail.
# 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
CREATE TEMPORARY VIEW jsonTable
USING org.apache.spark.sql.json
OPTIONS (
path "examples/src/main/resources/people.json"
)
SELECT * FROM jsonTable
Hive Tables
Spark SQL also supports reading and writing data stored in Apache Hive. However, since Hive has a large number of dependencies, 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.Row
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 = "spark-warehouse"
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)")
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 DaraFrames 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|
// ...
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 = "spark-warehouse";
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)");
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 DaraFrames are of type Row, which lets you to access each column by ordinal.
Dataset<String> stringsDS = sqlDF.map(new MapFunction<Row, String>() {
@Override
public String call(Row row) throws Exception {
return "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|
// ...
from os.path import expanduser, join
from pyspark.sql import SparkSession
from pyspark.sql import Row
# warehouse_location points to the default location for managed databases and tables
warehouse_location = '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)")
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 DaraFrames 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|
# ...
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)")
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"))
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 Name | Default | Meaning |
---|---|---|
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:
-Phive is
enabled. When this option is chosen, spark.sql.hive.metastore.version must be
either 1.2.1 or not defined.
|
spark.sql.hive.metastore.sharedPrefixes |
com.mysql.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. |
spark.sql.hive.metastore.barrierPrefixes |
(empty) |
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. |
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 Name | Meaning |
---|---|
url |
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.
|
driver |
The class name of the JDBC driver to use to connect to this URL. |
partitionColumn, lowerBound, upperBound, numPartitions |
These options must all be specified if any of them is 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.
|
fetchsize |
The JDBC fetch size, which determines how many rows to fetch per round trip. This can help performance on JDBC drivers which default to low fetch size (eg. Oracle with 10 rows). This option applies only to reading. |
batchsize |
The JDBC batch size, which determines how many rows to insert per round trip. This can help performance on JDBC drivers. This option applies only to writing. It defaults to 1000 .
|
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.
|
createTableOptions |
This is a JDBC writer related option. If specified, this option allows setting of database-specific table and partition options when creating a table (e.g., CREATE TABLE t (name string) ENGINE=InnoDB. ). 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)
// 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);
# 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"})
# 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")
CREATE TEMPORARY VIEW jdbcTable
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:postgresql:dbserver",
dbtable "schema.tablename",
user 'username',
password 'password'
)
INSERT INTO TABLE jdbcTable
SELECT * FROM resultTable
Troubleshooting
- The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java’s DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs.
- Some databases, such as H2, convert all names to upper case. You’ll need to use upper case to refer to those names in Spark SQL.
Performance Tuning
For some workloads it is possible to improve performance by either caching data in memory, or by turning on some experimental options.
Caching Data In Memory
Spark SQL can cache tables using an in-memory columnar format by calling 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 Name | Default | Meaning |
---|---|---|
spark.sql.inMemoryColumnarStorage.compressed |
true | 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 Name | Default | Meaning |
---|---|---|
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 |
300 |
Timeout in seconds for the broadcast wait time in broadcast joins |
spark.sql.autoBroadcastJoinThreshold |
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 |
200 | 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 2.0 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 issuingDESCRIBE FORMATTED
on the table.
- Legacy datasource tables can be migrated to this format via the
- 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.
- In prior Spark versions
Upgrading From Spark SQL 1.6 to 2.0
-
SparkSession
is now the new entry point of Spark that replaces the oldSQLContext
andHiveContext
. Note that the old SQLContext and HiveContext are kept for backward compatibility. A newcatalog
interface is accessible fromSparkSession
- existing API on databases and tables access such aslistTables
,createExternalTable
,dropTempView
,cacheTable
are moved here. -
Dataset API and DataFrame API are unified. In Scala,
DataFrame
becomes a type alias forDataset[Row]
, while Java API users must replaceDataFrame
withDataset<Row>
. Both the typed transformations (e.g.,map
,filter
, andgroupByKey
) and untyped transformations (e.g.,select
andgroupBy
) 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 byunion
- Dataset and DataFrame API
explode
has been deprecated, alternatively, usefunctions.explode()
withselect
orflatMap
-
Dataset and DataFrame API
registerTempTable
has been deprecated and replaced bycreateOrReplaceTempView
- Changes to
CREATE TABLE ... LOCATION
behavior for Hive tables.- From Spark 2.0,
CREATE TABLE ... LOCATION
is equivalent toCREATE 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 2.0,
Upgrading From Spark SQL 1.5 to 1.6
- From Spark 1.6, by default the Thrift server runs in multi-session mode. Which means each JDBC/ODBC
connection owns a copy of their own SQL configuration and temporary function registry. Cached
tables are still shared though. If you prefer to run the Thrift server in the old single-session
mode, please set option
spark.sql.hive.thriftServer.singleSession
totrue
. You may either add this option tospark-defaults.conf
, or pass it tostart-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
tofalse
. - Parquet schema merging is no longer enabled by default. It can be re-enabled by setting
spark.sql.parquet.mergeSchema
totrue
. - Resolution of strings to columns in python now supports using dots (
.
) to qualify the column or access nested values. For exampledf['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
tofalse
. - 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 remainsDecimal(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 orHiveContext
’srefreshTable
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 RDD
s into DataFrame
s 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 Product
s (i.e.,
case classes or tuples) with a method toDF
, instead of applying automatically.
When using function inside of the DSL (now replaced with the DataFrame
API) users used to import
org.apache.spark.sql.catalyst.dsl
. Instead the public dataframe functions API should be used:
import org.apache.spark.sql.functions._
.
Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)
Spark 1.3 removes the type aliases that were present in the base sql package for DataType
. Users
should instead import the classes in org.apache.spark.sql.types
UDF Registration Moved to sqlContext.udf
(Java & Scala)
Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been
moved into the udf object in SQLContext
.
sqlContext.udf.register("strLen", (s: String) => s.length())
sqlContext.udf().register("strLen", (String s) -> s.length(), DataTypes.IntegerType);
Python UDF registration is unchanged.
Python DataTypes No Longer Singletons
When using DataTypes in Python you will need to construct them (i.e. StringType()
) instead of
referencing a singleton.
Compatibility with Apache Hive
Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently Hive SerDes and UDFs are based on Hive 1.2.1, and Spark SQL can be connected to different versions of Hive Metastore (from 0.12.0 to 1.2.1. Also see [Interacting with Different Versions of Hive Metastore] (#interacting-with-different-versions-of-hive-metastore)).
Deploying in Existing Hive Warehouses
The Spark SQL Thrift JDBC server is designed to be “out of the box” compatible with existing Hive installations. You do not need to modify your existing Hive Metastore or change the data placement or partitioning of your tables.
Supported Hive Features
Spark SQL supports the vast majority of Hive features, such as:
- Hive query statements, including:
SELECT
GROUP BY
ORDER BY
CLUSTER BY
SORT BY
- All Hive operators, including:
- Relational operators (
=
,⇔
,==
,<>
,<
,>
,>=
,<=
, etc) - Arithmetic operators (
+
,-
,*
,/
,%
, etc) - Logical operators (
AND
,&&
,OR
,||
, etc) - Complex type constructors
- Mathematical functions (
sign
,ln
,cos
, etc) - String functions (
instr
,length
,printf
, etc)
- Relational operators (
- User defined functions (UDF)
- User defined aggregation functions (UDAF)
- User defined serialization formats (SerDes)
- Window functions
- Joins
JOIN
{LEFT|RIGHT|FULL} OUTER JOIN
LEFT SEMI JOIN
CROSS JOIN
- Unions
- Sub-queries
SELECT col FROM ( SELECT a + b AS col from t1) t2
- Sampling
- Explain
- Partitioned tables including dynamic partition insertion
- View
- All Hive DDL Functions, including:
CREATE TABLE
CREATE TABLE AS SELECT
ALTER TABLE
- Most Hive Data types, including:
TINYINT
SMALLINT
INT
BIGINT
BOOLEAN
FLOAT
DOUBLE
STRING
BINARY
TIMESTAMP
DATE
ARRAY<>
MAP<>
STRUCT<>
Unsupported Hive Functionality
Below is a list of Hive features that we don’t support yet. Most of these features are rarely used in Hive deployments.
Major Hive Features
- Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL doesn’t support buckets yet.
Esoteric Hive Features
UNION
type- Unique join
- Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at the moment and only supports populating the sizeInBytes field of the hive metastore.
Hive Input/Output Formats
- File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
- Hadoop archive
Hive Optimizations
A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are less important due to Spark SQL’s in-memory computational model. Others are slotted for future releases of Spark SQL.
- Block level bitmap indexes and virtual columns (used to build indexes)
- Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
need to control the degree of parallelism post-shuffle using “
SET spark.sql.shuffle.partitions=[num_tasks];
”. - Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still launches tasks to compute the result.
- Skew data flag: Spark SQL does not follow the skew data flags in Hive.
STREAMTABLE
hint in join: Spark SQL does not follow theSTREAMTABLE
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
to127
.ShortType
: Represents 2-byte signed integer numbers. The range of numbers is from-32768
to32767
.IntegerType
: Represents 4-byte signed integer numbers. The range of numbers is from-2147483648
to2147483647
.LongType
: Represents 8-byte signed integer numbers. The range of numbers is from-9223372036854775808
to9223372036854775807
.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 byjava.math.BigDecimal
. ABigDecimal
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 ofelementType
.containsNull
is used to indicate if elements in aArrayType
value can havenull
values.MapType(keyType, valueType, valueContainsNull)
: Represents values comprising a set of key-value pairs. The data type of keys are described bykeyType
and the data type of values are described byvalueType
. For aMapType
value, keys are not allowed to havenull
values.valueContainsNull
is used to indicate if values of aMapType
value can havenull
values.StructType(fields)
: Represents values with the structure described by a sequence ofStructField
s (fields
).StructField(name, dataType, nullable)
: Represents a field in aStructType
. The name of a field is indicated byname
. The data type of a field is indicated bydataType
.nullable
is used to indicate if values of this fields can havenull
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._
Data type | Value type in Scala | API to access or create a data type |
---|---|---|
ByteType | Byte | ByteType |
ShortType | Short | ShortType |
IntegerType | Int | IntegerType |
LongType | Long | LongType |
FloatType | Float | FloatType |
DoubleType | Double | DoubleType |
DecimalType | 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 |
long 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 | bool | 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 | dict |
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. |
Data type | Value type in R | API to access or create a data type |
---|---|---|
ByteType |
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 | raw | "binary" |
BooleanType | logical | "bool" |
TimestampType | POSIXct | "timestamp" |
DateType | Date | "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.