sql-programming-guide.md

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

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{: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](#hive-tables) section. When running
SQL from within another programming language the results will be returned as a [Dataset/DataFrame](#datasets-and-dataframes).
You can also interact with the SQL interface using the [command-line](#running-the-spark-sql-cli)
or over [JDBC/ODBC](#running-the-thrift-jdbcodbc-server).

## 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](#creating-datasets) from JVM objects and then
manipulated using functional transformations (`map`, `flatMap`, `filter`, etc.).
The Dataset API is available in [Scala][scala-datasets] and
[Java][java-datasets]. 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](#data-sources) such
as: structured data files, tables in Hive, external databases, or existing RDDs.
The DataFrame API is available in Scala,
Java, [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame), and [R](api/R/index.html).
In Scala and Java, a DataFrame is represented by a Dataset of `Row`s.
In [the Scala API][scala-datasets], `DataFrame` is simply a type alias of `Dataset[Row]`.
While, in [Java API][java-datasets], users need to use `Dataset<Row>` to represent a `DataFrame`.

[scala-datasets]: api/scala/index.html#org.apache.spark.sql.Dataset
[java-datasets]: api/java/index.html?org/apache/spark/sql/Dataset.html

Throughout this document, we will often refer to Scala/Java Datasets of `Row`s as DataFrames.

# Getting Started

## Starting Point: SparkSession

<div class="codetabs">
<div data-lang="scala"  markdown="1">

The entry point into all functionality in Spark is the [`SparkSession`](api/scala/index.html#org.apache.spark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder()`:

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

<div data-lang="java" markdown="1">

The entry point into all functionality in Spark is the [`SparkSession`](api/java/index.html#org.apache.spark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder()`:

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

<div data-lang="python"  markdown="1">

The entry point into all functionality in Spark is the [`SparkSession`](api/python/pyspark.sql.html#pyspark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder`:

{% include_example init_session python/sql/basic.py %}
</div>

<div data-lang="r"  markdown="1">

The entry point into all functionality in Spark is the [`SparkSession`](api/R/sparkR.session.html) 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.
</div>
</div>

`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

<div class="codetabs">
<div data-lang="scala"  markdown="1">
With a `SparkSession`, applications can create DataFrames from an [existing `RDD`](#interoperating-with-rdds),
from a Hive table, or from [Spark data sources](#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 %}
</div>

<div data-lang="java" markdown="1">
With a `SparkSession`, applications can create DataFrames from an [existing `RDD`](#interoperating-with-rdds),
from a Hive table, or from [Spark data sources](#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 %}
</div>

<div data-lang="python"  markdown="1">
With a `SparkSession`, applications can create DataFrames from an [existing `RDD`](#interoperating-with-rdds),
from a Hive table, or from [Spark data sources](#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 %}
</div>

<div data-lang="r"  markdown="1">
With a `SparkSession`, applications can create DataFrames from a local R data.frame,
from a Hive table, or from [Spark data sources](#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 %}

</div>
</div>


## Untyped Dataset Operations (aka DataFrame Operations)

DataFrames provide a domain-specific language for structured data manipulation in [Scala](api/scala/index.html#org.apache.spark.sql.Dataset), [Java](api/java/index.html?org/apache/spark/sql/Dataset.html), [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame) and [R](api/R/SparkDataFrame.html).

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:

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% 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](api/scala/index.html#org.apache.spark.sql.Dataset).

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](api/scala/index.html#org.apache.spark.sql.functions$).
</div>

<div data-lang="java" markdown="1">

{% 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](api/java/org/apache/spark/sql/Dataset.html).

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](api/java/org/apache/spark/sql/functions.html).
</div>

<div data-lang="python"  markdown="1">
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](api/python/pyspark.sql.html#pyspark.sql.DataFrame).

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](api/python/pyspark.sql.html#module-pyspark.sql.functions).

</div>

<div data-lang="r"  markdown="1">

{% 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](api/R/index.html).

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](api/R/SparkDataFrame.html).

</div>

</div>

## Running SQL Queries Programmatically

<div class="codetabs">
<div data-lang="scala"  markdown="1">
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 %}
</div>

<div data-lang="java" markdown="1">
The `sql` function on a `SparkSession` enables applications to run SQL queries programmatically and returns the result as a `Dataset<Row>`.

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

<div data-lang="python"  markdown="1">
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 %}
</div>

<div data-lang="r"  markdown="1">
The `sql` function enables applications to run SQL queries programmatically and returns the result as a `SparkDataFrame`.

{% include_example run_sql r/RSparkSQLExample.R %}

</div>
</div>


## 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`.

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example global_temp_view scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}
</div>

<div data-lang="java" markdown="1">
{% include_example global_temp_view java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}
</div>

<div data-lang="python"  markdown="1">
{% include_example global_temp_view python/sql/basic.py %}
</div>

<div data-lang="sql"  markdown="1">

{% highlight sql %}

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

SELECT * FROM global_temp.temp_view

{% endhighlight %}

</div>
</div>


## Creating Datasets

Datasets are similar to RDDs, however, instead of using Java serialization or Kryo they use
a specialized [Encoder](api/scala/index.html#org.apache.spark.sql.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.

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example create_ds scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}
</div>

<div data-lang="java" markdown="1">
{% include_example create_ds java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}
</div>
</div>

## 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
<div class="codetabs">

<div data-lang="scala"  markdown="1">

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.

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

<div data-lang="java"  markdown="1">

Spark SQL supports automatically converting an RDD of
[JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly) 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 %}
</div>

<div data-lang="python"  markdown="1">

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 %}
</div>

</div>

### Programmatically Specifying the Schema

<div class="codetabs">

<div data-lang="scala"  markdown="1">

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

For example:

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

<div data-lang="java"  markdown="1">

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

For example:

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

<div data-lang="python"  markdown="1">

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 %}
</div>

</div>

## Aggregations

The [built-in DataFrames functions](api/scala/index.html#org.apache.spark.sql.functions$) provide common
aggregations such as `count()`, `countDistinct()`, `avg()`, `max()`, `min()`, etc.
While those functions are designed for DataFrames, Spark SQL also has type-safe versions for some of them in
[Scala](api/scala/index.html#org.apache.spark.sql.expressions.scalalang.typed$) and
[Java](api/java/org/apache/spark/sql/expressions/javalang/typed.html) 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](api/scala/index.html#org.apache.spark.sql.expressions.UserDefinedAggregateFunction)
abstract class to implement a custom untyped aggregate function. For example, a user-defined average
can look like:

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example untyped_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedUntypedAggregation.scala%}
</div>
<div data-lang="java"  markdown="1">
{% include_example untyped_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java%}
</div>
</div>

### Type-Safe User-Defined Aggregate Functions

User-defined aggregations for strongly typed Datasets revolve around the [Aggregator](api/scala/index.html#org.apache.spark.sql.expressions.Aggregator) abstract class.
For example, a type-safe user-defined average can look like:

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example typed_custom_aggregation scala/org/apache/spark/examples/sql/UserDefinedTypedAggregation.scala%}
</div>
<div data-lang="java"  markdown="1">
{% include_example typed_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java%}
</div>
</div>

# 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.

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example generic_load_save_functions scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example generic_load_save_functions java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">

{% include_example generic_load_save_functions python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">

{% include_example generic_load_save_functions r/RSparkSQLExample.R %}

</div>
</div>

### 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.

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example manual_load_options scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example manual_load_options java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">
{% include_example manual_load_options python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">
{% include_example manual_load_options r/RSparkSQLExample.R %}
</div>
</div>

### 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.

<div class="codetabs">
<div data-lang="scala"  markdown="1">
{% include_example direct_sql scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example direct_sql java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">
{% include_example direct_sql python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">
{% include_example direct_sql r/RSparkSQLExample.R %}

</div>
</div>

### 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.

<table class="table">
<tr><th>Scala/Java</th><th>Any Language</th><th>Meaning</th></tr>
<tr>
  <td><code>SaveMode.ErrorIfExists</code> (default)</td>
  <td><code>"error" or "errorifexists"</code> (default)</td>
  <td>
    When saving a DataFrame to a data source, if data already exists,
    an exception is expected to be thrown.
  </td>
</tr>
<tr>
  <td><code>SaveMode.Append</code></td>
  <td><code>"append"</code></td>
  <td>
    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.
  </td>
</tr>
<tr>
  <td><code>SaveMode.Overwrite</code></td>
  <td><code>"overwrite"</code></td>
  <td>
    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.
  </td>
</tr>
<tr>
  <td><code>SaveMode.Ignore</code></td>
  <td><code>"ignore"</code></td>
  <td>
    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 <code>CREATE TABLE IF NOT EXISTS</code> in SQL.
  </td>
</tr>
</table>

### Saving to Persistent Tables

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

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

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

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

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

### Bucketing, Sorting and Partitioning

For file-based data source, it is also possible to bucket and sort or partition the output. 
Bucketing and sorting are applicable only to persistent tables:

<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example write_sorting_and_bucketing scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example write_sorting_and_bucketing java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">
{% include_example write_sorting_and_bucketing python/sql/datasource.py %}
</div>

<div data-lang="sql"  markdown="1">

{% highlight sql %}

CREATE TABLE users_bucketed_by_name(
  name STRING,
  favorite_color STRING,
  favorite_numbers array<integer>
) USING parquet 
CLUSTERED BY(name) INTO 42 BUCKETS;

{% endhighlight %}

</div>

</div>

while partitioning can be used with both `save` and `saveAsTable` when using the Dataset APIs.


<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example write_partitioning scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example write_partitioning java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">
{% include_example write_partitioning python/sql/datasource.py %}
</div>

<div data-lang="sql"  markdown="1">

{% highlight sql %}

CREATE TABLE users_by_favorite_color(
  name STRING, 
  favorite_color STRING,
  favorite_numbers array<integer>
) USING csv PARTITIONED BY(favorite_color);

{% endhighlight %}

</div>

</div>

It is possible to use both partitioning and bucketing for a single table:

<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example write_partition_and_bucket scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example write_partition_and_bucket java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">
{% include_example write_partition_and_bucket python/sql/datasource.py %}
</div>

<div data-lang="sql"  markdown="1">

{% highlight sql %}

CREATE TABLE users_bucketed_and_partitioned(
  name STRING,
  favorite_color STRING,
  favorite_numbers array<integer>
) USING parquet 
PARTITIONED BY (favorite_color)
CLUSTERED BY(name) SORTED BY (favorite_numbers) INTO 42 BUCKETS;

{% endhighlight %}

</div>

</div>

`partitionBy` creates a directory structure as described in the [Partition Discovery](#partition-discovery) section.
Thus, it has limited applicability to columns with high cardinality. In contrast 
 `bucketBy` distributes
data across a fixed number of buckets and can be used when a number of unique values is unbounded.

## Parquet Files

[Parquet](http://parquet.io) 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:

<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example basic_parquet_example scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example basic_parquet_example java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">

{% include_example basic_parquet_example python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">

{% include_example basic_parquet_example r/RSparkSQLExample.R %}

</div>

<div data-lang="sql"  markdown="1">

{% highlight sql %}

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

SELECT * FROM parquetTable

{% endhighlight %}

</div>

</div>

### Partition Discovery

Table partitioning is a common optimization approach used in systems like Hive. In a partitioned
table, data are usually stored in different directories, with partitioning column values encoded in
the path of each partition directory. All built-in file sources (including Text/CSV/JSON/ORC/Parquet)
are able to discover and infer partitioning information automatically.
For example, we can store all our previously used
population data into a partitioned table using the following directory structure, with two extra
columns, `gender` and `country` as partitioning columns:

{% 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`.

<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example schema_merging scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example schema_merging java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">

{% include_example schema_merging python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">

{% include_example schema_merging r/RSparkSQLExample.R %}

</div>

</div>

### 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
1. Hive considers all columns nullable, while nullability in Parquet is significant

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

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

1. 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.

<div class="codetabs">

<div data-lang="scala"  markdown="1">

{% highlight scala %}
// spark is an existing SparkSession
spark.catalog.refreshTable("my_table")
{% endhighlight %}

</div>

<div data-lang="java"  markdown="1">

{% highlight java %}
// spark is an existing SparkSession
spark.catalog().refreshTable("my_table");
{% endhighlight %}

</div>

<div data-lang="python"  markdown="1">

{% highlight python %}
# spark is an existing SparkSession
spark.catalog.refreshTable("my_table")
{% endhighlight %}

</div>

<div data-lang="sql"  markdown="1">

{% highlight sql %}
REFRESH TABLE my_table;
{% endhighlight %}

</div>

</div>

### Configuration

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

<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.sql.parquet.binaryAsString</code></td>
  <td>false</td>
  <td>
    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.
  </td>
</tr>
<tr>
  <td><code>spark.sql.parquet.int96AsTimestamp</code></td>
  <td>true</td>
  <td>
    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.
  </td>
</tr>
<tr>
  <td><code>spark.sql.parquet.compression.codec</code></td>
  <td>snappy</td>
  <td>
    Sets the compression codec use when writing Parquet files. Acceptable values include:
    uncompressed, snappy, gzip, lzo.
  </td>
</tr>
<tr>
  <td><code>spark.sql.parquet.filterPushdown</code></td>
  <td>true</td>
  <td>Enables Parquet filter push-down optimization when set to true.</td>
</tr>
<tr>
  <td><code>spark.sql.hive.convertMetastoreParquet</code></td>
  <td>true</td>
  <td>
    When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in
    support.
  </td>
</tr>
<tr>
  <td><code>spark.sql.parquet.mergeSchema</code></td>
  <td>false</td>
  <td>
    <p>
      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.
    </p>
  </td>
</tr>
<tr>
  <td><code>spark.sql.optimizer.metadataOnly</code></td>
  <td>true</td>
  <td>
    <p>
      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.
    </p>
  </td>
</tr>
</table>

## JSON Datasets
<div class="codetabs">

<div data-lang="scala"  markdown="1">
Spark SQL can automatically infer the schema of a JSON dataset and load it as a `Dataset[Row]`.
This conversion can be done using `SparkSession.read.json()` on either a `Dataset[String]`,
or a JSON file.

Note that the file that is offered as _a json file_ is not a typical JSON file. Each
line must contain a separate, self-contained valid JSON object. For more information, please see
[JSON Lines text format, also called newline-delimited JSON](http://jsonlines.org/).

For a regular multi-line JSON file, set the `multiLine` option to `true`.

{% include_example json_dataset scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
Spark SQL can automatically infer the schema of a JSON dataset and load it as a `Dataset<Row>`.
This conversion can be done using `SparkSession.read().json()` on either a `Dataset<String>`,
or a JSON file.

Note that the file that is offered as _a json file_ is not a typical JSON file. Each
line must contain a separate, self-contained valid JSON object. For more information, please see
[JSON Lines text format, also called newline-delimited JSON](http://jsonlines.org/).

For a regular multi-line JSON file, set the `multiLine` option to `true`.