layout	displayTitle	title
global	Spark SQL, DataFrames and Datasets Guide	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`](#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 %}

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 %}

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 %}

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 %}

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.

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

For example:

{% 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.

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

For example:

{% 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.

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

For example:

{% 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.

To load a JSON file you can use:

{% 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 %}

To load a CSV file you can use:

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

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

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

{% include_example manual_load_options_csv 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" or "errorifexists"` (default)	When saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown.
`SaveMode.Append`	`"append"`	When saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data.
`SaveMode.Overwrite`	`"overwrite"`	Overwrite mode means that when saving a DataFrame to a data source, if data/table already exists, existing data is expected to be overwritten by the contents of the DataFrame.
`SaveMode.Ignore`	`"ignore"`	Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not change the existing data. This is similar to a `CREATE TABLE IF NOT EXISTS` in SQL.

Saving to Persistent Tables

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

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

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

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

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

Bucketing, Sorting and Partitioning

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

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

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

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

{% highlight sql %}

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

{% endhighlight %}

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

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

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

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

{% highlight sql %}

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

{% endhighlight %}

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

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

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

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

{% highlight sql %}

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

{% endhighlight %}

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

Parquet Files

Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons.

Loading Data Programmatically

Using the data from the above example:

{% 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. 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, date, timestamp and string type are supported. Sometimes users may not want to automatically infer the data types of the partitioning columns. For these use cases, the automatic type inference can be configured by spark.sql.sources.partitionColumnTypeInference.enabled, which is default to true. When type inference is disabled, string type will be used for the partitioning columns.

Starting from Spark 1.6.0, partition discovery only finds partitions under the given paths by default. For the above example, if users pass path/to/table/gender=male to either SparkSession.read.parquet or SparkSession.read.load, gender will not be considered as a partitioning column. If users need to specify the base path that partition discovery should start with, they can set basePath in the data source options. For example, when path/to/table/gender=male is the path of the data and users set basePath to path/to/table/, gender will be a partitioning column.

Schema Merging

Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with a simple schema, and gradually add more columns to the schema as needed. In this way, users may end up with multiple Parquet files with different but mutually compatible schemas. The Parquet data source is now able to automatically detect this case and merge schemas of all these files.

Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we turned it off by default starting from 1.5.0. You may enable it by

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

{% 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.

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

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

Fields that have the same name in both schema must have the same data type regardless of nullability. The reconciled field should have the data type of the Parquet side, so that nullability is respected.
The reconciled schema contains exactly those fields defined in Hive metastore schema.
- Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
- Any fields that only appear in the Hive metastore schema are added as nullable field in the reconciled schema.

Metadata Refreshing

Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata.

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

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

{% highlight python %}

spark is an existing SparkSession

spark.catalog.refreshTable("my_table") {% endhighlight %}

{% highlight r %} refreshTable("my_table") {% endhighlight %}

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

Configuration

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

Property Name	Default	Meaning
`spark.sql.parquet.binaryAsString`	false	Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do not differentiate between binary data and strings when writing out the Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
`spark.sql.parquet.int96AsTimestamp`	true	Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96. This flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
`spark.sql.parquet.compression.codec`	snappy	Sets the compression codec used when writing Parquet files. If either `compression` or `parquet.compression` is specified in the table-specific options/properties, the precedence would be `compression`, `parquet.compression`, `spark.sql.parquet.compression.codec`. Acceptable values include: none, uncompressed, snappy, gzip, lzo.
`spark.sql.parquet.filterPushdown`	true	Enables Parquet filter push-down optimization when set to true.
`spark.sql.hive.convertMetastoreParquet`	true	When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in support.
`spark.sql.parquet.mergeSchema`	false	When true, the Parquet data source merges schemas collected from all data files, otherwise the schema is picked from the summary file or a random data file if no summary file is available.
`spark.sql.optimizer.metadataOnly`	true	When true, enable the metadata-only query optimization that use the table's metadata to produce the partition columns instead of table scans. It applies when all the columns scanned are partition columns and the query has an aggregate operator that satisfies distinct semantics.

JSON Datasets

Spark SQL can automatically infer the schema of a JSON dataset and load it as a `Dataset[Row]`. This conversion can be done using `SparkSession.read.json()` on either a `Dataset[String]`, or a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

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

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

Spark SQL can automatically infer the schema of a JSON dataset and load it as a `Dataset`. This conversion can be done using `SparkSession.read().json()` on either a `Dataset`, or a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

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

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

Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. This conversion can be done using `SparkSession.read.json` on a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

For a regular multi-line JSON file, set the multiLine parameter to True.

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

Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. using the `read.json()` function, which loads data from a directory of JSON files where each line of the files is a JSON object.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

For a regular multi-line JSON file, set a named parameter multiLine to TRUE.

{% include_example json_dataset r/RSparkSQLExample.R %}

{% highlight sql %}

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

SELECT * FROM jsonTable

{% endhighlight %}

Hive Tables

Spark SQL also supports reading and writing data stored in Apache Hive. However, since Hive has a large number of dependencies, these dependencies are not included in the default Spark distribution. If Hive dependencies can be found on the classpath, Spark will load them automatically. Note that these Hive dependencies must also be present on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to access data stored in Hive.

Configuration of Hive is done by placing your hive-site.xml, core-site.xml (for security configuration), and hdfs-site.xml (for HDFS configuration) file in conf/.

When working with Hive, one must instantiate SparkSession with Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions. Users who do not have an existing Hive deployment can still enable Hive support. When not configured by the hive-site.xml, the context automatically creates metastore_db in the current directory and creates a directory configured by spark.sql.warehouse.dir, which defaults to the directory spark-warehouse in the current directory that the Spark application is started. Note that the hive.metastore.warehouse.dir property in hive-site.xml is deprecated since Spark 2.0.0. Instead, use spark.sql.warehouse.dir to specify the default location of database in warehouse. You may need to grant write privilege to the user who starts the Spark application.

{% include_example spark_hive scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala %}

{% include_example spark_hive java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java %}

{% include_example spark_hive python/sql/hive.py %}

When working with Hive one must instantiate SparkSession with Hive support. This adds support for finding tables in the MetaStore and writing queries using HiveQL.

{% include_example spark_hive r/RSparkSQLExample.R %}

Specifying storage format for Hive tables

When you create a Hive table, you need to define how this table should read/write data from/to file system, i.e. the "input format" and "output format". You also need to define how this table should deserialize the data to rows, or serialize rows to data, i.e. the "serde". The following options can be used to specify the storage format("serde", "input format", "output format"), e.g. CREATE TABLE src(id int) USING hive OPTIONS(fileFormat 'parquet'). By default, we will read the table files as plain text. Note that, Hive storage handler is not supported yet when creating table, you can create a table using storage handler at Hive side, and use Spark SQL to read it.

Property Name	Meaning
`fileFormat`	A fileFormat is kind of a package of storage format specifications, including "serde", "input format" and "output format". Currently we support 6 fileFormats: 'sequencefile', 'rcfile', 'orc', 'parquet', 'textfile' and 'avro'.
`inputFormat, outputFormat`	These 2 options specify the name of a corresponding `InputFormat` and `OutputFormat` class as a string literal, e.g. `org.apache.hadoop.hive.ql.io.orc.OrcInputFormat`. These 2 options must be appeared in pair, and you can not specify them if you already specified the `fileFormat` option.
`serde`	This option specifies the name of a serde class. When the `fileFormat` option is specified, do not specify this option if the given `fileFormat` already include the information of serde. Currently "sequencefile", "textfile" and "rcfile" don't include the serde information and you can use this option with these 3 fileFormats.
`fieldDelim, escapeDelim, collectionDelim, mapkeyDelim, lineDelim`	These options can only be used with "textfile" fileFormat. They define how to read delimited files into rows.

All other properties defined with OPTIONS will be regarded as Hive serde properties.

Interacting with Different Versions of Hive Metastore

One of the most important pieces of Spark SQL's Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).

The following options can be used to configure the version of Hive that is used to retrieve metadata:

Property Name	Default	Meaning
`spark.sql.hive.metastore.version`	`1.2.1`	Version of the Hive metastore. Available options are `0.12.0` through `1.2.1`.
`spark.sql.hive.metastore.jars`	`builtin`	Location of the jars that should be used to instantiate the HiveMetastoreClient. This property can be one of three options: `builtin` Use Hive 1.2.1, which is bundled with the Spark assembly when `-Phive` is enabled. When this option is chosen, `spark.sql.hive.metastore.version` must be either `1.2.1` or not defined. `maven` Use Hive jars of specified version downloaded from Maven repositories. This configuration is not generally recommended for production deployments. A classpath in the standard format for the JVM. This classpath must include all of Hive and its dependencies, including the correct version of Hadoop. These jars only need to be present on the driver, but if you are running in yarn cluster mode then you must ensure they are packaged with your application.
`spark.sql.hive.metastore.sharedPrefixes`	`com.mysql.jdbc, org.postgresql, com.microsoft.sqlserver, oracle.jdbc`	A comma separated list of class prefixes that should be loaded using the classloader that is shared between Spark SQL and a specific version of Hive. An example of classes that should be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need to be shared are those that interact with classes that are already shared. For example, custom appenders that are used by log4j.
`spark.sql.hive.metastore.barrierPrefixes`	`(empty)`	A comma separated list of class prefixes that should explicitly be reloaded for each version of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a prefix that typically would be shared (i.e. `org.apache.spark.*`).

JDBC To Other Databases

Spark SQL also includes a data source that can read data from other databases using JDBC. This functionality should be preferred over using JdbcRDD. This is because the results are returned as a DataFrame and they can easily be processed in Spark SQL or joined with other data sources. The JDBC data source is also easier to use from Java or Python as it does not require the user to provide a ClassTag. (Note that this is different than the Spark SQL JDBC server, which allows other applications to run queries using Spark SQL).

To get started you will need to include the JDBC driver for your particular database on the spark classpath. For example, to connect to postgres from the Spark Shell you would run the following command:

{% highlight bash %} bin/spark-shell --driver-class-path postgresql-9.4.1207.jar --jars postgresql-9.4.1207.jar {% endhighlight %}

Tables from the remote database can be loaded as a DataFrame or Spark SQL temporary view using the Data Sources API. Users can specify the JDBC connection properties in the data source options. user and password are normally provided as connection properties for logging into the data sources. In addition to the connection properties, Spark also supports the following case-insensitive options:

Property Name	Meaning
`url`	The JDBC URL to connect to. The source-specific connection properties may be specified in the URL. e.g., `jdbc:postgresql://localhost/test?user=fred&password=secret`
`dbtable`	The JDBC table that should be read. Note that anything that is valid in a `FROM` clause of a SQL query can be used. For example, instead of a full table you could also use a subquery in parentheses.
`driver`	The class name of the JDBC driver to use to connect to this URL.
`partitionColumn, lowerBound, upperBound`	These options must all be specified if any of them is specified. In addition, `numPartitions` must be specified. They describe how to partition the table when reading in parallel from multiple workers. `partitionColumn` must be a numeric column from the table in question. Notice that `lowerBound` and `upperBound` are just used to decide the partition stride, not for filtering the rows in table. So all rows in the table will be partitioned and returned. This option applies only to reading.
`numPartitions`	The maximum number of partitions that can be used for parallelism in table reading and writing. This also determines the maximum number of concurrent JDBC connections. If the number of partitions to write exceeds this limit, we decrease it to this limit by calling `coalesce(numPartitions)` before writing.
`fetchsize`	The JDBC fetch size, which determines how many rows to fetch per round trip. This can help performance on JDBC drivers which default to low fetch size (eg. Oracle with 10 rows). This option applies only to reading.
`batchsize`	The JDBC batch size, which determines how many rows to insert per round trip. This can help performance on JDBC drivers. This option applies only to writing. It defaults to `1000`.
`isolationLevel`	The transaction isolation level, which applies to current connection. It can be one of `NONE`, `READ_COMMITTED`, `READ_UNCOMMITTED`, `REPEATABLE_READ`, or `SERIALIZABLE`, corresponding to standard transaction isolation levels defined by JDBC's Connection object, with default of `READ_UNCOMMITTED`. This option applies only to writing. Please refer the documentation in `java.sql.Connection`.
`sessionInitStatement`	After each database session is opened to the remote DB and before starting to read data, this option executes a custom SQL statement (or a PL/SQL block). Use this to implement session initialization code. Example: `option("sessionInitStatement", """BEGIN execute immediate 'alter session set "_serial_direct_read"=true'; END;""")`
`truncate`	This is a JDBC writer related option. When `SaveMode.Overwrite` is enabled, this option causes Spark to truncate an existing table instead of dropping and recreating it. This can be more efficient, and prevents the table metadata (e.g., indices) from being removed. However, it will not work in some cases, such as when the new data has a different schema. It defaults to `false`. This option applies only to writing.
`createTableOptions`	This is a JDBC writer related option. If specified, this option allows setting of database-specific table and partition options when creating a table (e.g., `CREATE TABLE t (name string) ENGINE=InnoDB.`). This option applies only to writing.
`createTableColumnTypes`	The database column data types to use instead of the defaults, when creating the table. Data type information should be specified in the same format as CREATE TABLE columns syntax (e.g: `"name CHAR(64), comments VARCHAR(1024)")`. The specified types should be valid spark sql data types. This option applies only to writing.
`customSchema`	The custom schema to use for reading data from JDBC connectors. For example, `"id DECIMAL(38, 0), name STRING"`. You can also specify partial fields, and the others use the default type mapping. For example, `"id DECIMAL(38, 0)"`. The column names should be identical to the corresponding column names of JDBC table. Users can specify the corresponding data types of Spark SQL instead of using the defaults. This option applies only to reading.

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

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

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

{% include_example jdbc_dataset r/RSparkSQLExample.R %}

{% highlight sql %}

CREATE TEMPORARY VIEW jdbcTable USING org.apache.spark.sql.jdbc OPTIONS ( url "jdbc:postgresql:dbserver", dbtable "schema.tablename", user 'username', password 'password' )

INSERT INTO TABLE jdbcTable SELECT * FROM resultTable {% endhighlight %}

Troubleshooting

The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java's DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs.
Some databases, such as H2, convert all names to upper case. You'll need to use upper case to refer to those names in Spark SQL.

Performance Tuning

For some workloads it is possible to improve performance by either caching data in memory, or by turning on some experimental options.

Caching Data In Memory

Spark SQL can cache tables using an in-memory columnar format by calling spark.catalog.cacheTable("tableName") or dataFrame.cache(). Then Spark SQL will scan only required columns and will automatically tune compression to minimize memory usage and GC pressure. You can call spark.catalog.uncacheTable("tableName") to remove the table from memory.

Configuration of in-memory caching can be done using the setConf method on SparkSession or by running SET key=value commands using SQL.

Property Name	Default	Meaning
`spark.sql.inMemoryColumnarStorage.compressed`	true	When set to true Spark SQL will automatically select a compression codec for each column based on statistics of the data.
`spark.sql.inMemoryColumnarStorage.batchSize`	10000	Controls the size of batches for columnar caching. Larger batch sizes can improve memory utilization and compression, but risk OOMs when caching data.

Other Configuration Options

The following options can also be used to tune the performance of query execution. It is possible that these options will be deprecated in future release as more optimizations are performed automatically.

Property Name	Default	Meaning
`spark.sql.files.maxPartitionBytes`	134217728 (128 MB)	The maximum number of bytes to pack into a single partition when reading files.
`spark.sql.files.openCostInBytes`	4194304 (4 MB)	The estimated cost to open a file, measured by the number of bytes could be scanned in the same time. This is used when putting multiple files into a partition. It is better to over estimated, then the partitions with small files will be faster than partitions with bigger files (which is scheduled first).
`spark.sql.broadcastTimeout`	300	Timeout in seconds for the broadcast wait time in broadcast joins
`spark.sql.autoBroadcastJoinThreshold`	10485760 (10 MB)	Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join. By setting this value to -1 broadcasting can be disabled. Note that currently statistics are only supported for Hive Metastore tables where the command `ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan` has been run.
`spark.sql.shuffle.partitions`	200	Configures the number of partitions to use when shuffling data for joins or aggregations.

Broadcast Hint for SQL Queries

The BROADCAST hint guides Spark to broadcast each specified table when joining them with another table or view. When Spark deciding the join methods, the broadcast hash join (i.e., BHJ) is preferred, even if the statistics is above the configuration spark.sql.autoBroadcastJoinThreshold. When both sides of a join are specified, Spark broadcasts the one having the lower statistics. Note Spark does not guarantee BHJ is always chosen, since not all cases (e.g. full outer join) support BHJ. When the broadcast nested loop join is selected, we still respect the hint.

{% highlight scala %} import org.apache.spark.sql.functions.broadcast broadcast(spark.table("src")).join(spark.table("records"), "key").show() {% endhighlight %}

{% highlight java %} import static org.apache.spark.sql.functions.broadcast; broadcast(spark.table("src")).join(spark.table("records"), "key").show(); {% endhighlight %}

{% highlight python %} from pyspark.sql.functions import broadcast broadcast(spark.table("src")).join(spark.table("records"), "key").show() {% endhighlight %}

{% highlight r %} src <- sql("SELECT * FROM src") records <- sql("SELECT * FROM records") head(join(broadcast(src), records, src$key == records$key)) {% endhighlight %}

{% highlight sql %} -- We accept BROADCAST, BROADCASTJOIN and MAPJOIN for broadcast hint SELECT /*+ BROADCAST(r) */ * FROM records r JOIN src s ON r.key = s.key {% endhighlight %}

Distributed SQL Engine

Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, without the need to write any code.

Running the Thrift JDBC/ODBC server

The Thrift JDBC/ODBC server implemented here corresponds to the HiveServer2 in Hive 1.2.1 You can test the JDBC server with the beeline script that comes with either Spark or Hive 1.2.1.

To start the JDBC/ODBC server, run the following in the Spark directory:

./sbin/start-thriftserver.sh

This script accepts all bin/spark-submit command line options, plus a --hiveconf option to specify Hive properties. You may run ./sbin/start-thriftserver.sh --help for a complete list of all available options. By default, the server listens on localhost:10000. You may override this behaviour via either environment variables, i.e.:

{% highlight bash %} export HIVE_SERVER2_THRIFT_PORT= export HIVE_SERVER2_THRIFT_BIND_HOST= ./sbin/start-thriftserver.sh
--master
... {% endhighlight %}

or system properties:

{% highlight bash %} ./sbin/start-thriftserver.sh
--hiveconf hive.server2.thrift.port=
--hiveconf hive.server2.thrift.bind.host=
--master ... {% endhighlight %}

Now you can use beeline to test the Thrift JDBC/ODBC server:

./bin/beeline

Connect to the JDBC/ODBC server in beeline with:

beeline> !connect jdbc:hive2://localhost:10000

Beeline will ask you for a username and password. In non-secure mode, simply enter the username on your machine and a blank password. For secure mode, please follow the instructions given in the beeline documentation.

Configuration of Hive is done by placing your hive-site.xml, core-site.xml and hdfs-site.xml files in conf/.

You may also use the beeline script that comes with Hive.

Thrift JDBC server also supports sending thrift RPC messages over HTTP transport. Use the following setting to enable HTTP mode as system property or in hive-site.xml file in conf/:

hive.server2.transport.mode - Set this to value: http
hive.server2.thrift.http.port - HTTP port number to listen on; default is 10001
hive.server2.http.endpoint - HTTP endpoint; default is cliservice

To test, use beeline to connect to the JDBC/ODBC server in http mode with:

beeline> !connect jdbc:hive2://<host>:<port>/<database>?hive.server2.transport.mode=http;hive.server2.thrift.http.path=<http_endpoint>

Running the Spark SQL CLI

The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute queries input from the command line. Note that the Spark SQL CLI cannot talk to the Thrift JDBC server.

To start the Spark SQL CLI, run the following in the Spark directory:

./bin/spark-sql

Configuration of Hive is done by placing your hive-site.xml, core-site.xml and hdfs-site.xml files in conf/. You may run ./bin/spark-sql --help for a complete list of all available options.

PySpark Usage Guide for Pandas with Apache Arrow

Apache Arrow in Spark

Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer data between JVM and Python processes. This currently is most beneficial to Python users that work with Pandas/NumPy data. Its usage is not automatic and might require some minor changes to configuration or code to take full advantage and ensure compatibility. This guide will give a high-level description of how to use Arrow in Spark and highlight any differences when working with Arrow-enabled data.

Ensure PyArrow Installed

If you install PySpark using pip, then PyArrow can be brought in as an extra dependency of the SQL module with the command pip install pyspark[sql]. Otherwise, you must ensure that PyArrow is installed and available on all cluster nodes. The current supported version is 0.8.0. You can install using pip or conda from the conda-forge channel. See PyArrow installation for details.

Enabling for Conversion to/from Pandas

Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame using the call toPandas() and when creating a Spark DataFrame from a Pandas DataFrame with createDataFrame(pandas_df). To use Arrow when executing these calls, users need to first set the Spark configuration 'spark.sql.execution.arrow.enabled' to 'true'. This is disabled by default.

{% include_example dataframe_with_arrow python/sql/arrow.py %}

Using the above optimizations with Arrow will produce the same results as when Arrow is not enabled. Note that even with Arrow, toPandas() results in the collection of all records in the DataFrame to the driver program and should be done on a small subset of the data. Not all Spark data types are currently supported and an error can be raised if a column has an unsupported type, see Supported Types. If an error occurs during createDataFrame(), Spark will fall back to create the DataFrame without Arrow.

Pandas UDFs (a.k.a. Vectorized UDFs)

Pandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and Pandas to work with the data. A Pandas UDF is defined using the keyword pandas_udf as a decorator or to wrap the function, no additional configuration is required. Currently, there are two types of Pandas UDF: Scalar and Grouped Map.

Scalar

Scalar Pandas UDFs are used for vectorizing scalar operations. They can be used with functions such as select and withColumn. The Python function should take pandas.Series as inputs and return a pandas.Series of the same length. Internally, Spark will execute a Pandas UDF by splitting columns into batches and calling the function for each batch as a subset of the data, then concatenating the results together.

The following example shows how to create a scalar Pandas UDF that computes the product of 2 columns.

{% include_example scalar_pandas_udf python/sql/arrow.py %}

Grouped Map

Grouped map Pandas UDFs are used with groupBy().apply() which implements the "split-apply-combine" pattern. Split-apply-combine consists of three steps:

Split the data into groups by using DataFrame.groupBy.
Apply a function on each group. The input and output of the function are both pandas.DataFrame. The input data contains all the rows and columns for each group.
Combine the results into a new DataFrame.

To use groupBy().apply(), the user needs to define the following:

A Python function that defines the computation for each group.
A StructType object or a string that defines the schema of the output DataFrame.

Note that all data for a group will be loaded into memory before the function is applied. This can lead to out of memory exceptons, especially if the group sizes are skewed. The configuration for maxRecordsPerBatch is not applied on groups and it is up to the user to ensure that the grouped data will fit into the available memory.

The following example shows how to use groupby().apply() to subtract the mean from each value in the group.

{% include_example grouped_map_pandas_udf python/sql/arrow.py %}

For detailed usage, please see pyspark.sql.functions.pandas_udf and pyspark.sql.GroupedData.apply.

Usage Notes

Supported SQL Types

Currently, all Spark SQL data types are supported by Arrow-based conversion except MapType, ArrayType of TimestampType, and nested StructType.

Setting Arrow Batch Size

Data partitions in Spark are converted into Arrow record batches, which can temporarily lead to high memory usage in the JVM. To avoid possible out of memory exceptions, the size of the Arrow record batches can be adjusted by setting the conf "spark.sql.execution.arrow.maxRecordsPerBatch" to an integer that will determine the maximum number of rows for each batch. The default value is 10,000 records per batch. If the number of columns is large, the value should be adjusted accordingly. Using this limit, each data partition will be made into 1 or more record batches for processing.

Timestamp with Time Zone Semantics

Spark internally stores timestamps as UTC values, and timestamp data that is brought in without a specified time zone is converted as local time to UTC with microsecond resolution. When timestamp data is exported or displayed in Spark, the session time zone is used to localize the timestamp values. The session time zone is set with the configuration 'spark.sql.session.timeZone' and will default to the JVM system local time zone if not set. Pandas uses a datetime64 type with nanosecond resolution, datetime64[ns], with optional time zone on a per-column basis.

When timestamp data is transferred from Spark to Pandas it will be converted to nanoseconds and each column will be converted to the Spark session time zone then localized to that time zone, which removes the time zone and displays values as local time. This will occur when calling toPandas() or pandas_udf with timestamp columns.

When timestamp data is transferred from Pandas to Spark, it will be converted to UTC microseconds. This occurs when calling createDataFrame with a Pandas DataFrame or when returning a timestamp from a pandas_udf. These conversions are done automatically to ensure Spark will have data in the expected format, so it is not necessary to do any of these conversions yourself. Any nanosecond values will be truncated.

Note that a standard UDF (non-Pandas) will load timestamp data as Python datetime objects, which is different than a Pandas timestamp. It is recommended to use Pandas time series functionality when working with timestamps in pandas_udfs to get the best performance, see here for details.

Migration Guide

Upgrading From Spark SQL 2.2 to 2.3

Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named _corrupt_record by default). For example, spark.read.schema(schema).json(file).filter($"_corrupt_record".isNotNull).count() and spark.read.schema(schema).json(file).select("_corrupt_record").show(). Instead, you can cache or save the parsed results and then send the same query. For example, val df = spark.read.schema(schema).json(file).cache() and then df.filter($"_corrupt_record".isNotNull).count().
The percentile_approx function previously accepted numeric type input and output double type results. Now it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.
Since Spark 2.3, the Join/Filter's deterministic predicates that are after the first non-deterministic predicates are also pushed down/through the child operators, if possible. In prior Spark versions, these filters are not eligible for predicate pushdown.

Partition column inference previously found incorrect common type for different inferred types, for example, previously it ended up with double type as the common type for double type and date type. Now it finds the correct common type for such conflicts. The conflict resolution follows the table below:

InputA \ InputB	NullType	IntegerType	LongType	DecimalType(38,0)*	DoubleType	DateType	TimestampType	StringType
NullType	NullType	IntegerType	LongType	DecimalType(38,0)	DoubleType	DateType	TimestampType	StringType
IntegerType	IntegerType	IntegerType	LongType	DecimalType(38,0)	DoubleType	StringType	StringType	StringType
LongType	LongType	LongType	LongType	DecimalType(38,0)	StringType	StringType	StringType	StringType
DecimalType(38,0)*	DecimalType(38,0)	DecimalType(38,0)	DecimalType(38,0)	DecimalType(38,0)	StringType	StringType	StringType	StringType
DoubleType	DoubleType	DoubleType	StringType	StringType	DoubleType	StringType	StringType	StringType
DateType	DateType	StringType	StringType	StringType	StringType	DateType	TimestampType	StringType
TimestampType	TimestampType	StringType	StringType	StringType	StringType	TimestampType	TimestampType	StringType
StringType	StringType	StringType	StringType	StringType	StringType	StringType	StringType	StringType

Note that, for DecimalType(38,0)*, the table above intentionally does not cover all other combinations of scales and precisions because currently we only infer decimal type like BigInteger/BigInt. For example, 1.1 is inferred as double type.

In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as toPandas, createDataFrame from Pandas DataFrame, etc.
In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration spark.sql.execution.pandas.respectSessionTimeZone to False. See SPARK-22395 for details.
In PySpark, na.fill() or fillna also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame.
Since Spark 2.3, when either broadcast hash join or broadcast nested loop join is applicable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint. For details, see the section Broadcast Hint and SPARK-22489.
Since Spark 2.3, when all inputs are binary, functions.concat() returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set spark.sql.function.concatBinaryAsString to true.
Since Spark 2.3, when all inputs are binary, SQL elt() returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set spark.sql.function.eltOutputAsString to true.
Since Spark 2.3, by default arithmetic operations between decimals return a rounded value if an exact representation is not possible (instead of returning NULL). This is compliant with SQL ANSI 2011 specification and Hive's new behavior introduced in Hive 2.2 (HIVE-15331). This involves the following changes
- The rules to determine the result type of an arithmetic operation have been updated. In particular, if the precision / scale needed are out of the range of available values, the scale is reduced up to 6, in order to prevent the truncation of the integer part of the decimals. All the arithmetic operations are affected by the change, ie. addition (+), subtraction (-), multiplication (*), division (/), remainder (%) and positive module (pmod).
- Literal values used in SQL operations are converted to DECIMAL with the exact precision and scale needed by them.
- The configuration spark.sql.decimalOperations.allowPrecisionLoss has been introduced. It defaults to true, which means the new behavior described here; if set to false, Spark uses previous rules, ie. it doesn't adjust the needed scale to represent the values and it returns NULL if an exact representation of the value is not possible.

Upgrading From Spark SQL 2.1 to 2.2

Spark 2.1.1 introduced a new configuration key: spark.sql.hive.caseSensitiveInferenceMode. It had a default setting of NEVER_INFER, which kept behavior identical to 2.1.0. However, Spark 2.2.0 changes this setting's default value to INFER_AND_SAVE to restore compatibility with reading Hive metastore tables whose underlying file schema have mixed-case column names. With the INFER_AND_SAVE configuration value, on first access Spark will perform schema inference on any Hive metastore table for which it has not already saved an inferred schema. Note that schema inference can be a very time consuming operation for tables with thousands of partitions. If compatibility with mixed-case column names is not a concern, you can safely set spark.sql.hive.caseSensitiveInferenceMode to NEVER_INFER to avoid the initial overhead of schema inference. Note that with the new default INFER_AND_SAVE setting, the results of the schema inference are saved as a metastore key for future use. Therefore, the initial schema inference occurs only at a table's first access.

Upgrading From Spark SQL 2.0 to 2.1

Datasource tables now store partition metadata in the Hive metastore. This means that Hive DDLs such as ALTER TABLE PARTITION ... SET LOCATION are now available for tables created with the Datasource API.
- Legacy datasource tables can be migrated to this format via the MSCK REPAIR TABLE command. Migrating legacy tables is recommended to take advantage of Hive DDL support and improved planning performance.
- To determine if a table has been migrated, look for the PartitionProvider: Catalog attribute when issuing DESCRIBE FORMATTED on the table.
Changes to INSERT OVERWRITE TABLE ... PARTITION ... behavior for Datasource tables.
- In prior Spark versions INSERT OVERWRITE overwrote the entire Datasource table, even when given a partition specification. Now only partitions matching the specification are overwritten.
- Note that this still differs from the behavior of Hive tables, which is to overwrite only partitions overlapping with newly inserted data.

Upgrading From Spark SQL 1.6 to 2.0

SparkSession is now the new entry point of Spark that replaces the old SQLContext and HiveContext. Note that the old SQLContext and HiveContext are kept for backward compatibility. A new catalog interface is accessible from SparkSession - existing API on databases and tables access such as listTables, createExternalTable, dropTempView, cacheTable are moved here.
Dataset API and DataFrame API are unified. In Scala, DataFrame becomes a type alias for Dataset[Row], while Java API users must replace DataFrame with Dataset<Row>. Both the typed transformations (e.g., map, filter, and groupByKey) and untyped transformations (e.g., select and groupBy) are available on the Dataset class. Since compile-time type-safety in Python and R is not a language feature, the concept of Dataset does not apply to these languages’ APIs. Instead, DataFrame remains the primary programming abstraction, which is analogous to the single-node data frame notion in these languages.
Dataset and DataFrame API unionAll has been deprecated and replaced by union
Dataset and DataFrame API explode has been deprecated, alternatively, use functions.explode() with select or flatMap
Dataset and DataFrame API registerTempTable has been deprecated and replaced by createOrReplaceTempView
Changes to CREATE TABLE ... LOCATION behavior for Hive tables.
- From Spark 2.0, CREATE TABLE ... LOCATION is equivalent to CREATE EXTERNAL TABLE ... LOCATION in order to prevent accidental dropping the existing data in the user-provided locations. That means, a Hive table created in Spark SQL with the user-specified location is always a Hive external table. Dropping external tables will not remove the data. Users are not allowed to specify the location for Hive managed tables. Note that this is different from the Hive behavior.
- As a result, DROP TABLE statements on those tables will not remove the data.
spark.sql.parquet.cacheMetadata is no longer used. See SPARK-13664 for details.

Upgrading From Spark SQL 1.5 to 1.6

From Spark 1.6, by default the Thrift server runs in multi-session mode. Which means each JDBC/ODBC connection owns a copy of their own SQL configuration and temporary function registry. Cached tables are still shared though. If you prefer to run the Thrift server in the old single-session mode, please set option spark.sql.hive.thriftServer.singleSession to true. You may either add this option to spark-defaults.conf, or pass it to start-thriftserver.sh via --conf:

{% highlight bash %} ./sbin/start-thriftserver.sh
--conf spark.sql.hive.thriftServer.singleSession=true
... {% endhighlight %}
Since 1.6.1, withColumn method in sparkR supports adding a new column to or replacing existing columns of the same name of a DataFrame.
From Spark 1.6, LongType casts to TimestampType expect seconds instead of microseconds. This change was made to match the behavior of Hive 1.2 for more consistent type casting to TimestampType from numeric types. See SPARK-11724 for details.

Upgrading From Spark SQL 1.4 to 1.5

Optimized execution using manually managed memory (Tungsten) is now enabled by default, along with code generation for expression evaluation. These features can both be disabled by setting spark.sql.tungsten.enabled to false.
Parquet schema merging is no longer enabled by default. It can be re-enabled by setting spark.sql.parquet.mergeSchema to true.
Resolution of strings to columns in python now supports using dots (.) to qualify the column or access nested values. For example df['table.column.nestedField']. However, this means that if your column name contains any dots you must now escape them using backticks (e.g., table.`column.with.dots`.nested).
In-memory columnar storage partition pruning is on by default. It can be disabled by setting spark.sql.inMemoryColumnarStorage.partitionPruning to false.
Unlimited precision decimal columns are no longer supported, instead Spark SQL enforces a maximum precision of 38. When inferring schema from BigDecimal objects, a precision of (38, 18) is now used. When no precision is specified in DDL then the default remains Decimal(10, 0).
Timestamps are now stored at a precision of 1us, rather than 1ns
In the sql dialect, floating point numbers are now parsed as decimal. HiveQL parsing remains unchanged.
The canonical name of SQL/DataFrame functions are now lower case (e.g., sum vs SUM).
JSON data source will not automatically load new files that are created by other applications (i.e. files that are not inserted to the dataset through Spark SQL). For a JSON persistent table (i.e. the metadata of the table is stored in Hive Metastore), users can use REFRESH TABLE SQL command or HiveContext's refreshTable method to include those new files to the table. For a DataFrame representing a JSON dataset, users need to recreate the DataFrame and the new DataFrame will include new files.
DataFrame.withColumn method in pySpark supports adding a new column or replacing existing columns of the same name.

Upgrading from Spark SQL 1.3 to 1.4

DataFrame data reader/writer interface

Based on user feedback, we created a new, more fluid API for reading data in (SQLContext.read) and writing data out (DataFrame.write), and deprecated the old APIs (e.g., SQLContext.parquetFile, SQLContext.jsonFile).

See the API docs for SQLContext.read ( Scala, Java, Python ) and DataFrame.write ( Scala, Java, Python ) more information.

DataFrame.groupBy retains grouping columns

Based on user feedback, we changed the default behavior of DataFrame.groupBy().agg() to retain the grouping columns in the resulting DataFrame. To keep the behavior in 1.3, set spark.sql.retainGroupColumns to false.

{% highlight scala %}

// In 1.3.x, in order for the grouping column "department" to show up, // it must be included explicitly as part of the agg function call. df.groupBy("department").agg($"department", max("age"), sum("expense"))

// In 1.4+, grouping column "department" is included automatically. df.groupBy("department").agg(max("age"), sum("expense"))

// Revert to 1.3 behavior (not retaining grouping column) by: sqlContext.setConf("spark.sql.retainGroupColumns", "false")

{% endhighlight %}

{% highlight java %}

// In 1.3.x, in order for the grouping column "department" to show up, // it must be included explicitly as part of the agg function call. df.groupBy("department").agg(col("department"), max("age"), sum("expense"));

// In 1.4+, grouping column "department" is included automatically. df.groupBy("department").agg(max("age"), sum("expense"));

// Revert to 1.3 behavior (not retaining grouping column) by: sqlContext.setConf("spark.sql.retainGroupColumns", "false");

{% endhighlight %}

{% highlight python %}

import pyspark.sql.functions as func

In 1.3.x, in order for the grouping column "department" to show up,

it must be included explicitly as part of the agg function call.

df.groupBy("department").agg(df["department"], func.max("age"), func.sum("expense"))

In 1.4+, grouping column "department" is included automatically.

df.groupBy("department").agg(func.max("age"), func.sum("expense"))

Revert to 1.3.x behavior (not retaining grouping column) by:

sqlContext.setConf("spark.sql.retainGroupColumns", "false")

{% endhighlight %}

Behavior change on DataFrame.withColumn

Prior to 1.4, DataFrame.withColumn() supports adding a column only. The column will always be added as a new column with its specified name in the result DataFrame even if there may be any existing columns of the same name. Since 1.4, DataFrame.withColumn() supports adding a column of a different name from names of all existing columns or replacing existing columns of the same name.

Note that this change is only for Scala API, not for PySpark and SparkR.

Upgrading from Spark SQL 1.0-1.2 to 1.3

In Spark 1.3 we removed the "Alpha" label from Spark SQL and as part of this did a cleanup of the available APIs. From Spark 1.3 onwards, Spark SQL will provide binary compatibility with other releases in the 1.X series. This compatibility guarantee excludes APIs that are explicitly marked as unstable (i.e., DeveloperAPI or Experimental).

Rename of SchemaRDD to DataFrame

The largest change that users will notice when upgrading to Spark SQL 1.3 is that SchemaRDD has been renamed to DataFrame. This is primarily because DataFrames no longer inherit from RDD directly, but instead provide most of the functionality that RDDs provide though their own implementation. DataFrames can still be converted to RDDs by calling the .rdd method.

In Scala there is a type alias from SchemaRDD to DataFrame to provide source compatibility for some use cases. It is still recommended that users update their code to use DataFrame instead. Java and Python users will need to update their code.

Unification of the Java and Scala APIs

Prior to Spark 1.3 there were separate Java compatible classes (JavaSQLContext and JavaSchemaRDD) that mirrored the Scala API. In Spark 1.3 the Java API and Scala API have been unified. Users of either language should use SQLContext and DataFrame. In general these classes try to use types that are usable from both languages (i.e. Array instead of language specific collections). In some cases where no common type exists (e.g., for passing in closures or Maps) function overloading is used instead.

Additionally the Java specific types API has been removed. Users of both Scala and Java should use the classes present in org.apache.spark.sql.types to describe schema programmatically.

Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)

Many of the code examples prior to Spark 1.3 started with import sqlContext._, which brought all of the functions from sqlContext into scope. In Spark 1.3 we have isolated the implicit conversions for converting RDDs into DataFrames into an object inside of the SQLContext. Users should now write import sqlContext.implicits._.

Additionally, the implicit conversions now only augment RDDs that are composed of Products (i.e., case classes or tuples) with a method toDF, instead of applying automatically.

When using function inside of the DSL (now replaced with the DataFrame API) users used to import org.apache.spark.sql.catalyst.dsl. Instead the public dataframe functions API should be used: import org.apache.spark.sql.functions._.

Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)

Spark 1.3 removes the type aliases that were present in the base sql package for DataType. Users should instead import the classes in org.apache.spark.sql.types

UDF Registration Moved to `sqlContext.udf` (Java & Scala)

Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been moved into the udf object in SQLContext.

{% highlight scala %}

sqlContext.udf.register("strLen", (s: String) => s.length())

{% endhighlight %}

{% highlight java %}

sqlContext.udf().register("strLen", (String s) -> s.length(), DataTypes.IntegerType);

{% endhighlight %}

Python UDF registration is unchanged.

Python DataTypes No Longer Singletons

When using DataTypes in Python you will need to construct them (i.e. StringType()) instead of referencing a singleton.

Compatibility with Apache Hive

Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently Hive SerDes and UDFs are based on Hive 1.2.1, and Spark SQL can be connected to different versions of Hive Metastore (from 0.12.0 to 2.1.1. Also see [Interacting with Different Versions of Hive Metastore] (#interacting-with-different-versions-of-hive-metastore)).

Deploying in Existing Hive Warehouses

The Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive installations. You do not need to modify your existing Hive Metastore or change the data placement or partitioning of your tables.

Supported Hive Features

Spark SQL supports the vast majority of Hive features, such as:

Hive query statements, including:
- SELECT
- GROUP BY
- ORDER BY
- CLUSTER BY
- SORT BY
All Hive operators, including:
- Relational operators (=, ⇔, ==, <>, <, >, >=, <=, etc)
- Arithmetic operators (+, -, *, /, %, etc)
- Logical operators (AND, &&, OR, ||, etc)
- Complex type constructors
- Mathematical functions (sign, ln, cos, etc)
- String functions (instr, length, printf, etc)
User defined functions (UDF)
User defined aggregation functions (UDAF)
User defined serialization formats (SerDes)
Window functions
Joins
- JOIN
- {LEFT|RIGHT|FULL} OUTER JOIN
- LEFT SEMI JOIN
- CROSS JOIN
Unions
Sub-queries
- SELECT col FROM ( SELECT a + b AS col from t1) t2
Sampling
Explain
Partitioned tables including dynamic partition insertion
View
All Hive DDL Functions, including:
- CREATE TABLE
- CREATE TABLE AS SELECT
- ALTER TABLE
Most Hive Data types, including:
- TINYINT
- SMALLINT
- INT
- BIGINT
- BOOLEAN
- FLOAT
- DOUBLE
- STRING
- BINARY
- TIMESTAMP
- DATE
- ARRAY<>
- MAP<>
- STRUCT<>

Unsupported Hive Functionality

Below is a list of Hive features that we don't support yet. Most of these features are rarely used in Hive deployments.

Major Hive Features

Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL doesn't support buckets yet.

Esoteric Hive Features

UNION type
Unique join
Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at the moment and only supports populating the sizeInBytes field of the hive metastore.

Hive Input/Output Formats

File format for CLI: For results showing back to the CLI, Spark SQL only supports TextOutputFormat.
Hadoop archive

Hive Optimizations

A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are less important due to Spark SQL's in-memory computational model. Others are slotted for future releases of Spark SQL.

Block level bitmap indexes and virtual columns (used to build indexes)
Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you need to control the degree of parallelism post-shuffle using "SET spark.sql.shuffle.partitions=[num_tasks];".
Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still launches tasks to compute the result.
Skew data flag: Spark SQL does not follow the skew data flags in Hive.
STREAMTABLE hint in join: Spark SQL does not follow the STREAMTABLE hint.
Merge multiple small files for query results: if the result output contains multiple small files, Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS metadata. Spark SQL does not support that.

Hive UDF/UDTF/UDAF

Not all the APIs of the Hive UDF/UDTF/UDAF are supported by Spark SQL. Below are the unsupported APIs:

getRequiredJars and getRequiredFiles (UDF and GenericUDF) are functions to automatically include additional resources required by this UDF.
initialize(StructObjectInspector) in GenericUDTF is not supported yet. Spark SQL currently uses a deprecated interface initialize(ObjectInspector[]) only.
configure (GenericUDF, GenericUDTF, and GenericUDAFEvaluator) is a function to initialize functions with MapredContext, which is inapplicable to Spark.
close (GenericUDF and GenericUDAFEvaluator) is a function to release associated resources. Spark SQL does not call this function when tasks finish.
reset (GenericUDAFEvaluator) is a function to re-initialize aggregation for reusing the same aggregation. Spark SQL currently does not support the reuse of aggregation.
getWindowingEvaluator (GenericUDAFEvaluator) is a function to optimize aggregation by evaluating an aggregate over a fixed window.

Incompatible Hive UDF

Below are the scenarios in which Hive and Spark generate different results:

SQRT(n) If n < 0, Hive returns null, Spark SQL returns NaN.
ACOS(n) If n < -1 or n > 1, Hive returns null, Spark SQL returns NaN.
ASIN(n) If n < -1 or n > 1, Hive returns null, Spark SQL returns NaN.

Reference

Data Types

Spark SQL and DataFrames support the following data types:

Numeric types
- ByteType: Represents 1-byte signed integer numbers. The range of numbers is from -128 to 127.
- ShortType: Represents 2-byte signed integer numbers. The range of numbers is from -32768 to 32767.
- IntegerType: Represents 4-byte signed integer numbers. The range of numbers is from -2147483648 to 2147483647.
- LongType: Represents 8-byte signed integer numbers. The range of numbers is from -9223372036854775808 to 9223372036854775807.
- FloatType: Represents 4-byte single-precision floating point numbers.
- DoubleType: Represents 8-byte double-precision floating point numbers.
- DecimalType: Represents arbitrary-precision signed decimal numbers. Backed internally by java.math.BigDecimal. A BigDecimal consists of an arbitrary precision integer unscaled value and a 32-bit integer scale.
String type
- StringType: Represents character string values.
Binary type
- BinaryType: Represents byte sequence values.
Boolean type
- BooleanType: Represents boolean values.
Datetime type
- TimestampType: Represents values comprising values of fields year, month, day, hour, minute, and second.
- DateType: Represents values comprising values of fields year, month, day.
Complex types
- ArrayType(elementType, containsNull): Represents values comprising a sequence of elements with the type of elementType. containsNull is used to indicate if elements in a ArrayType value can have null values.
- MapType(keyType, valueType, valueContainsNull): Represents values comprising a set of key-value pairs. The data type of keys are described by keyType and the data type of values are described by valueType. For a MapType value, keys are not allowed to have null values. valueContainsNull is used to indicate if values of a MapType value can have null values.
- StructType(fields): Represents values with the structure described by a sequence of StructFields (fields).
  - StructField(name, dataType, nullable): Represents a field in a StructType. The name of a field is indicated by name. The data type of a field is indicated by dataType. nullable is used to indicate if values of this fields can have null values.

All data types of Spark SQL are located in the package org.apache.spark.sql.types. You can access them by doing

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

Data type	Value type in Scala	API to access or create a data type
ByteType	Byte	ByteType
ShortType	Short	ShortType
IntegerType	Int	IntegerType
LongType	Long	LongType
FloatType	Float	FloatType
DoubleType	Double	DoubleType
DecimalType	java.math.BigDecimal	DecimalType
StringType	String	StringType
BinaryType	Array[Byte]	BinaryType
BooleanType	Boolean	BooleanType
TimestampType	java.sql.Timestamp	TimestampType
DateType	java.sql.Date	DateType
ArrayType	scala.collection.Seq	ArrayType(elementType, [containsNull]) Note: The default value of containsNull is true.
MapType	scala.collection.Map	MapType(keyType, valueType, [valueContainsNull]) Note: The default value of valueContainsNull is true.
StructType	org.apache.spark.sql.Row	StructType(fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed.
StructField	The value type in Scala of the data type of this field (For example, Int for a StructField with the data type IntegerType)	StructField(name, dataType, [nullable]) Note: The default value of nullable is true.

All data types of Spark SQL are located in the package of org.apache.spark.sql.types. To access or create a data type, please use factory methods provided in org.apache.spark.sql.types.DataTypes.

Data type	Value type in Java	API to access or create a data type
ByteType	byte or Byte	DataTypes.ByteType
ShortType	short or Short	DataTypes.ShortType
IntegerType	int or Integer	DataTypes.IntegerType
LongType	long or Long	DataTypes.LongType
FloatType	float or Float	DataTypes.FloatType
DoubleType	double or Double	DataTypes.DoubleType
DecimalType	java.math.BigDecimal	DataTypes.createDecimalType() DataTypes.createDecimalType(precision, scale).
StringType	String	DataTypes.StringType
BinaryType	byte[]	DataTypes.BinaryType
BooleanType	boolean or Boolean	DataTypes.BooleanType
TimestampType	java.sql.Timestamp	DataTypes.TimestampType
DateType	java.sql.Date	DataTypes.DateType
ArrayType	java.util.List	DataTypes.createArrayType(elementType) Note: The value of containsNull will be true DataTypes.createArrayType(elementType, containsNull).
MapType	java.util.Map	DataTypes.createMapType(keyType, valueType) Note: The value of valueContainsNull will be true. DataTypes.createMapType(keyType, valueType, valueContainsNull)
StructType	org.apache.spark.sql.Row	DataTypes.createStructType(fields) Note: fields is a List or an array of StructFields. Also, two fields with the same name are not allowed.
StructField	The value type in Java of the data type of this field (For example, int for a StructField with the data type IntegerType)	DataTypes.createStructField(name, dataType, nullable)

All data types of Spark SQL are located in the package of pyspark.sql.types. You can access them by doing {% highlight python %} from pyspark.sql.types import * {% endhighlight %}

Data type	Value type in Python	API to access or create a data type
ByteType	int or long Note: Numbers will be converted to 1-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -128 to 127.	ByteType()
ShortType	int or long Note: Numbers will be converted to 2-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -32768 to 32767.	ShortType()
IntegerType	int or long	IntegerType()
LongType	long Note: Numbers will be converted to 8-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -9223372036854775808 to 9223372036854775807. Otherwise, please convert data to decimal.Decimal and use DecimalType.	LongType()
FloatType	float Note: Numbers will be converted to 4-byte single-precision floating point numbers at runtime.	FloatType()
DoubleType	float	DoubleType()
DecimalType	decimal.Decimal	DecimalType()
StringType	string	StringType()
BinaryType	bytearray	BinaryType()
BooleanType	bool	BooleanType()
TimestampType	datetime.datetime	TimestampType()
DateType	datetime.date	DateType()
ArrayType	list, tuple, or array	ArrayType(elementType, [containsNull]) Note: The default value of containsNull is True.
MapType	dict	MapType(keyType, valueType, [valueContainsNull]) Note: The default value of valueContainsNull is True.
StructType	list or tuple	StructType(fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed.
StructField	The value type in Python of the data type of this field (For example, Int for a StructField with the data type IntegerType)	StructField(name, dataType, [nullable]) Note: The default value of nullable is True.

Data type	Value type in R	API to access or create a data type
ByteType	integer Note: Numbers will be converted to 1-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -128 to 127.	"byte"
ShortType	integer Note: Numbers will be converted to 2-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -32768 to 32767.	"short"
IntegerType	integer	"integer"
LongType	integer Note: Numbers will be converted to 8-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -9223372036854775808 to 9223372036854775807. Otherwise, please convert data to decimal.Decimal and use DecimalType.	"long"
FloatType	numeric Note: Numbers will be converted to 4-byte single-precision floating point numbers at runtime.	"float"
DoubleType	numeric	"double"
DecimalType	Not supported	Not supported
StringType	character	"string"
BinaryType	raw	"binary"
BooleanType	logical	"bool"
TimestampType	POSIXct	"timestamp"
DateType	Date	"date"
ArrayType	vector or list	list(type="array", elementType=elementType, containsNull=[containsNull]) Note: The default value of containsNull is TRUE.
MapType	environment	list(type="map", keyType=keyType, valueType=valueType, valueContainsNull=[valueContainsNull]) Note: The default value of valueContainsNull is TRUE.
StructType	named list	list(type="struct", fields=fields) Note: fields is a Seq of StructFields. Also, two fields with the same name are not allowed.
StructField	The value type in R of the data type of this field (For example, integer for a StructField with the data type IntegerType)	list(name=name, type=dataType, nullable=[nullable]) Note: The default value of nullable is TRUE.

NaN Semantics

There is specially handling for not-a-number (NaN) when dealing with float or double types that does not exactly match standard floating point semantics. Specifically:

NaN = NaN returns true.
In aggregations all NaN values are grouped together.
NaN is treated as a normal value in join keys.
NaN values go last when in ascending order, larger than any other numeric value.

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Overview

SQL

Datasets and DataFrames

Getting Started

Starting Point: SparkSession

Creating DataFrames

Untyped Dataset Operations (aka DataFrame Operations)

Running SQL Queries Programmatically

Global Temporary View

Creating Datasets

Interoperating with RDDs

Inferring the Schema Using Reflection

Programmatically Specifying the Schema

Aggregations

Untyped User-Defined Aggregate Functions

Type-Safe User-Defined Aggregate Functions

Data Sources

Generic Load/Save Functions

Manually Specifying Options

Run SQL on files directly

Save Modes

Saving to Persistent Tables

Bucketing, Sorting and Partitioning

Parquet Files

Loading Data Programmatically

Partition Discovery

Schema Merging

Hive metastore Parquet table conversion

Hive/Parquet Schema Reconciliation

Metadata Refreshing

spark is an existing SparkSession

Configuration

JSON Datasets

Hive Tables

Specifying storage format for Hive tables

Interacting with Different Versions of Hive Metastore

JDBC To Other Databases

Troubleshooting

Performance Tuning

Caching Data In Memory

Other Configuration Options

Broadcast Hint for SQL Queries

Distributed SQL Engine

Running the Thrift JDBC/ODBC server

Running the Spark SQL CLI

PySpark Usage Guide for Pandas with Apache Arrow

Apache Arrow in Spark

Ensure PyArrow Installed

Enabling for Conversion to/from Pandas

Pandas UDFs (a.k.a. Vectorized UDFs)

Scalar

Grouped Map

Usage Notes

Supported SQL Types

Setting Arrow Batch Size

Timestamp with Time Zone Semantics

Migration Guide

Upgrading From Spark SQL 2.2 to 2.3

Upgrading From Spark SQL 2.1 to 2.2

Upgrading From Spark SQL 2.0 to 2.1

Upgrading From Spark SQL 1.6 to 2.0

Upgrading From Spark SQL 1.5 to 1.6

Upgrading From Spark SQL 1.4 to 1.5

Upgrading from Spark SQL 1.3 to 1.4

DataFrame data reader/writer interface

DataFrame.groupBy retains grouping columns

In 1.3.x, in order for the grouping column "department" to show up,

it must be included explicitly as part of the agg function call.

In 1.4+, grouping column "department" is included automatically.

Revert to 1.3.x behavior (not retaining grouping column) by:

Behavior change on DataFrame.withColumn

Upgrading from Spark SQL 1.0-1.2 to 1.3

Rename of SchemaRDD to DataFrame

Unification of the Java and Scala APIs

UDF Registration Moved to `sqlContext.udf` (Java & Scala)