sql-programming-guide.md

layout: global
displayTitle: Spark SQL, DataFrames and Datasets Guide
title: Spark SQL and DataFrames

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

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 Rows. 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 Rows 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():

{% include_example init_session scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

The entry point into all functionality in Spark is the SparkSession class. To create a basic SparkSession, just use SparkSession.builder():

{% include_example init_session java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

The entry point into all functionality in Spark is the SparkSession class. To create a basic SparkSession, just use SparkSession.builder:

{% include_example init_session python/sql.py %}

The entry point into all functionality in Spark is the SparkSession class. To initialize a basic SparkSession, just call sparkR.session():

{% include_example init_session r/RSparkSQLExample.R %}

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:

{% include_example create_df scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

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:

{% include_example create_df java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

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:

{% highlight python %}

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() {% endhighlight %}

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:

{% include_example create_DataFrames r/RSparkSQLExample.R %}

Untyped Dataset Operations (aka DataFrame Operations)

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

As mentioned above, in Spark 2.0, DataFrames are just Dataset of Rows in Scala and Java API. These operations are also referred as "untyped transformations" in contrast to "typed transformations" come with strongly typed Scala/Java Datasets.

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

{% include_example untyped_ops scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

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.

{% include_example untyped_ops java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

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.

{% highlight python %}

spark is an existing SparkSession

Create the DataFrame

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

Show the content of the DataFrame

df.show()

age name

null Michael

30 Andy

19 Justin

Print the schema in a tree format

df.printSchema()

root

|-- age: long (nullable = true)

|-- name: string (nullable = true)

Select only the "name" column

df.select("name").show()

name

Michael

Andy

Justin

Select everybody, but increment the age by 1

df.select(df['name'], df['age'] + 1).show()

name (age + 1)

Michael null

Andy 31

Justin 20

Select people older than 21

df.filter(df['age'] > 21).show()

age name

30 Andy

Count people by age

df.groupBy("age").count().show()

age count

null 1

19 1

30 1

{% endhighlight %}

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.

{% include_example dataframe_operations r/RSparkSQLExample.R %}

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`.

{% include_example run_sql scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

The `sql` function on a `SparkSession` enables applications to run SQL queries programmatically and returns the result as a `Dataset`.

{% include_example run_sql java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

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

{% highlight python %}

spark is an existing SparkSession

df = spark.sql("SELECT * FROM table") {% endhighlight %}

The `sql` function enables applications to run SQL queries programmatically and returns the result as a `SparkDataFrame`.

{% include_example sql_query r/RSparkSQLExample.R %}

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.

{% include_example create_ds scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

{% include_example create_ds java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

Interoperating with RDDs

Spark SQL supports two different methods for converting existing RDDs into Datasets. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection based approach leads to more concise code and works well when you already know the schema while writing your Spark application.

The second method for creating Datasets is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows you to construct Datasets when the columns and their types are not known until runtime.

Inferring the Schema Using Reflection

The Scala interface for Spark SQL supports automatically converting an RDD containing case classes to a DataFrame. The case class defines the schema of the table. The names of the arguments to the case class are read using reflection and become the names of the columns. Case classes can also be nested or contain complex types such as Seqs or Arrays. This RDD can be implicitly converted to a DataFrame and then be registered as a table. Tables can be used in subsequent SQL statements.

{% include_example schema_inferring scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

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.

{% include_example schema_inferring java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

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 datase, similar to the inference that is performed on JSON files.

{% highlight python %}

spark is an existing SparkSession.

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 RDDs and support all the normal RDD operations.

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

Programmatically Specifying the Schema

When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a DataFrame can be created programmatically with three steps.

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

For example:

{% include_example programmatic_schema scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

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.

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

For example:

{% include_example programmatic_schema java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}

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.

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

For example: {% highlight python %}

Import SparkSession and data types

from pyspark.sql.types import *

spark is an existing SparkSession.

sc = spark.sparkContext

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

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

The schema is encoded in a string.

schemaString = "name age"

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

Apply the schema to the RDD.

schemaPeople = 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")

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

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

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.

{% include_example generic_load_save_functions scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example generic_load_save_functions java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% highlight python %}

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

{% endhighlight %}

{% include_example source_parquet r/RSparkSQLExample.R %}

Manually Specifying Options

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

{% include_example manual_load_options scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example manual_load_options java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% highlight python %}

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

{% endhighlight %}

{% include_example source_json r/RSparkSQLExample.R %}

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.

{% include_example direct_sql scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example direct_sql java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% highlight python %} df = spark.sql("SELECT * FROM parquet.examples/src/main/resources/users.parquet") {% endhighlight %}

{% include_example direct_query r/RSparkSQLExample.R %}

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 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.

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:

{% include_example basic_parquet_example scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example basic_parquet_example java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% highlight python %}

spark from the previous example is used in this example.

schemaPeople # The DataFrame from the previous example.

DataFrames can be saved as Parquet files, maintaining the schema information.

schemaPeople.write.parquet("people.parquet")

Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved.

The result of loading a parquet file is also a DataFrame.

parquetFile = 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") teenNames = teenagers.map(lambda p: "Name: " + p.name) for teenName in teenNames.collect(): print(teenName) {% endhighlight %}

{% include_example load_programmatically r/RSparkSQLExample.R %}

{% highlight sql %}

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

SELECT * FROM parquetTable

{% endhighlight %}

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:

{% highlight text %}

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

{% endhighlight %}

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:

{% highlight text %}

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

{% endhighlight %}

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