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/basic.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:

{% include_example create_df python/sql/basic.py %}

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_df 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.

{% include_example untyped_ops python/sql/basic.py %} 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 untyped_ops 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`.

{% include_example run_sql python/sql/basic.py %}

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

{% include_example run_sql r/RSparkSQLExample.R %}

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.

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

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

{% include_example global_temp_view python/sql/basic.py %}

{% highlight sql %}

CREATE GLOBAL TEMPORARY VIEW temp_view AS SELECT a + 1, b * 2 FROM tbl

SELECT * FROM global_temp.temp_view

{% endhighlight %}

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

{% include_example schema_inferring python/sql/basic.py %}

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:

{% include_example programmatic_schema python/sql/basic.py %}

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:

{% include_example untyped_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedUntypedAggregation.scala%}

{% include_example untyped_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java%}

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:

{% include_example typed_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedTypedAggregation.scala%}

{% include_example typed_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java%}

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 %}

{% include_example generic_load_save_functions python/sql/datasource.py %}

{% include_example generic_load_save_functions 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, orc, libsvm, csv, text). 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 %}

{% include_example manual_load_options python/sql/datasource.py %}

{% include_example manual_load_options 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 %}

{% include_example direct_sql python/sql/datasource.py %}

{% include_example direct_sql 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 that an existing Hive deployment is not necessary to use this feature. Spark will create a default local Hive metastore (using Derby) for you. Unlike the createOrReplaceTempView command, saveAsTable will materialize the contents of the DataFrame and create a pointer to the data in the Hive metastore. Persistent tables will still exist even after your Spark program has restarted, as long as you maintain your connection to the same metastore. A DataFrame for a persistent table can be created by calling the table method on a SparkSession with the name of the table.

For file-based data source, e.g. text, parquet, json, etc. you can specify a custom table path via the path option, e.g. df.write.option("path", "/some/path").saveAsTable("t"). When the table is dropped, the custom table path will not be removed and the table data is still there. If no custom table path is specifed, Spark will write data to a default table path under the warehouse directory. When the table is dropped, the default table path will be removed too.

Starting from Spark 2.1, persistent datasource tables have per-partition metadata stored in the Hive metastore. This brings several benefits:

• Since the metastore can return only necessary partitions for a query, discovering all the partitions on the first query to the table is no longer needed.
• Hive DDLs such as ALTER TABLE PARTITION ... SET LOCATION are now available for tables created with the Datasource API.

Note that partition information is not gathered by default when creating external datasource tables (those with a path option). To sync the partition information in the metastore, you can invoke MSCK REPAIR TABLE.

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 %}

{% include_example basic_parquet_example python/sql/datasource.py %}

{% include_example basic_parquet_example 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

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

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

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

{% include_example schema_merging python/sql/datasource.py %}

{% include_example schema_merging r/RSparkSQLExample.R %}

Hive metastore Parquet table conversion

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

Hive/Parquet Schema Reconciliation

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

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