From commits-return-34132-archive-asf-public=cust-asf.ponee.io@spark.apache.org Thu Oct 18 20:59:16 2018 Return-Path: X-Original-To: archive-asf-public@cust-asf.ponee.io Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by mx-eu-01.ponee.io (Postfix) with SMTP id 25C9E180679 for ; Thu, 18 Oct 2018 20:59:13 +0200 (CEST) Received: (qmail 7480 invoked by uid 500); 18 Oct 2018 18:59:13 -0000 Mailing-List: contact commits-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list commits@spark.apache.org Received: (qmail 7460 invoked by uid 99); 18 Oct 2018 18:59:13 -0000 Received: from git1-us-west.apache.org (HELO git1-us-west.apache.org) (140.211.11.23) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 18 Oct 2018 18:59:13 +0000 Received: by git1-us-west.apache.org (ASF Mail Server at git1-us-west.apache.org, from userid 33) id 96786E004F; Thu, 18 Oct 2018 18:59:12 +0000 (UTC) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 8bit From: lixiao@apache.org To: commits@spark.apache.org Date: Thu, 18 Oct 2018 18:59:13 -0000 Message-Id: <1850df4ca01b440e98803a28c06081d8@git.apache.org> In-Reply-To: <1360ee5f55524e4d80c26b9d0e2d24ab@git.apache.org> References: <1360ee5f55524e4d80c26b9d0e2d24ab@git.apache.org> X-Mailer: ASF-Git Admin Mailer Subject: [2/4] spark git commit: [SPARK-24499][SQL][DOC] Split the page of sql-programming-guide.html to multiple separate pages http://git-wip-us.apache.org/repos/asf/spark/blob/987f3865/docs/sql-programming-guide.md ---------------------------------------------------------------------- diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index fb03ed2..42b00c9 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -4,11 +4,6 @@ displayTitle: Spark SQL, DataFrames and Datasets Guide title: Spark SQL and DataFrames --- -* This will become a table of contents (this text will be scraped). -{:toc} - -# Overview - Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations. There are several ways to @@ -24,17 +19,17 @@ the `spark-shell`, `pyspark` shell, or `sparkR` shell. One use of Spark SQL is to execute SQL queries. Spark SQL can also be used to read data from an existing Hive installation. For more on how to -configure this feature, please refer to the [Hive Tables](#hive-tables) section. When running +configure this feature, please refer to the [Hive Tables](sql-data-sources-hive-tables.html) section. When running SQL from within another programming language the results will be returned as a [Dataset/DataFrame](#datasets-and-dataframes). -You can also interact with the SQL interface using the [command-line](#running-the-spark-sql-cli) -or over [JDBC/ODBC](#running-the-thrift-jdbcodbc-server). +You can also interact with the SQL interface using the [command-line](sql-distributed-sql-engine.html#running-the-spark-sql-cli) +or over [JDBC/ODBC](#sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server). ## Datasets and DataFrames A Dataset is a distributed collection of data. Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL's optimized -execution engine. A Dataset can be [constructed](#creating-datasets) from JVM objects and then +execution engine. A Dataset can be [constructed](sql-getting-started.html#creating-datasets) from JVM objects and then manipulated using functional transformations (`map`, `flatMap`, `filter`, etc.). The Dataset API is available in [Scala][scala-datasets] and [Java][java-datasets]. Python does not have the support for the Dataset API. But due to Python's dynamic nature, @@ -43,7 +38,7 @@ many of the benefits of the Dataset API are already available (i.e. you can acce A DataFrame is a *Dataset* organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer -optimizations under the hood. DataFrames can be constructed from a wide array of [sources](#data-sources) such +optimizations under the hood. DataFrames can be constructed from a wide array of [sources](sql-data-sources.html) such as: structured data files, tables in Hive, external databases, or existing RDDs. The DataFrame API is available in Scala, Java, [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame), and [R](api/R/index.html). @@ -55,3115 +50,3 @@ While, in [Java API][java-datasets], users need to use `Dataset` to represe [java-datasets]: api/java/index.html?org/apache/spark/sql/Dataset.html Throughout this document, we will often refer to Scala/Java Datasets of `Row`s as DataFrames. - -# Getting Started - -## Starting Point: SparkSession - -
-
- -The entry point into all functionality in Spark is the [`SparkSession`](api/scala/index.html#org.apache.spark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder()`: - -{% include_example init_session scala/org/apache/spark/examples/sql/SparkSQLExample.scala %} -
- -
- -The entry point into all functionality in Spark is the [`SparkSession`](api/java/index.html#org.apache.spark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder()`: - -{% include_example init_session java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %} -
- -
- -The entry point into all functionality in Spark is the [`SparkSession`](api/python/pyspark.sql.html#pyspark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder`: - -{% include_example init_session python/sql/basic.py %} -
- -
- -The entry point into all functionality in Spark is the [`SparkSession`](api/R/sparkR.session.html) class. To initialize a basic `SparkSession`, just call `sparkR.session()`: - -{% include_example init_session r/RSparkSQLExample.R %} - -Note that when invoked for the first time, `sparkR.session()` initializes a global `SparkSession` singleton instance, and always returns a reference to this instance for successive invocations. In this way, users only need to initialize the `SparkSession` once, then SparkR functions like `read.df` will be able to access this global instance implicitly, and users don't need to pass the `SparkSession` instance around. -
-
- -`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](api/scala/index.html#org.apache.spark.sql.Dataset), [Java](api/java/index.html?org/apache/spark/sql/Dataset.html), [Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame) and [R](api/R/SparkDataFrame.html). - -As mentioned above, in Spark 2.0, DataFrames are just Dataset of `Row`s in Scala and Java API. These operations are also referred as "untyped transformations" in contrast to "typed transformations" come with strongly typed Scala/Java Datasets. - -Here we include some basic examples of structured data processing using Datasets: - -
-
-{% include_example untyped_ops scala/org/apache/spark/examples/sql/SparkSQLExample.scala %} - -For a complete list of the types of operations that can be performed on a Dataset refer to the [API Documentation](api/scala/index.html#org.apache.spark.sql.Dataset). - -In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/scala/index.html#org.apache.spark.sql.functions$). -
- -
- -{% include_example untyped_ops java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %} - -For a complete list of the types of operations that can be performed on a Dataset refer to the [API Documentation](api/java/org/apache/spark/sql/Dataset.html). - -In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/java/org/apache/spark/sql/functions.html). -
- -
-In Python, it's possible to access a DataFrame's columns either by attribute -(`df.age`) or by indexing (`df['age']`). While the former is convenient for -interactive data exploration, users are highly encouraged to use the -latter form, which is future proof and won't break with column names that -are also attributes on the DataFrame class. - -{% include_example untyped_ops python/sql/basic.py %} -For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/python/pyspark.sql.html#pyspark.sql.DataFrame). - -In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/python/pyspark.sql.html#module-pyspark.sql.functions). - -
- -
- -{% include_example untyped_ops r/RSparkSQLExample.R %} - -For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/R/index.html). - -In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/R/SparkDataFrame.html). - -
- -
- -## 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](api/scala/index.html#org.apache.spark.sql.Encoder) to serialize the objects -for processing or transmitting over the network. While both encoders and standard serialization are -responsible for turning an object into bytes, encoders are code generated dynamically and use a format -that allows Spark to perform many operations like filtering, sorting and hashing without deserializing -the bytes back into an object. - -
-
-{% 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 `Seq`s or `Array`s. This RDD can be implicitly converted to a DataFrame and then be -registered as a table. Tables can be used in subsequent SQL statements. - -{% include_example schema_inferring scala/org/apache/spark/examples/sql/SparkSQLExample.scala %} -
- -
- -Spark SQL supports automatically converting an RDD of -[JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly) into a DataFrame. -The `BeanInfo`, obtained using reflection, defines the schema of the table. Currently, Spark SQL -does not support JavaBeans that contain `Map` field(s). Nested JavaBeans and `List` or `Array` -fields are supported though. You can create a JavaBean by creating a class that implements -Serializable and has getters and setters for all of its fields. - -{% include_example schema_inferring java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %} -
- -
- -Spark SQL can convert an RDD of Row objects to a DataFrame, inferring the datatypes. Rows are constructed by passing a list of -key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table, -and the types are inferred by sampling the whole dataset, similar to the inference that is performed on JSON files. - -{% include_example schema_inferring python/sql/basic.py %} -
- -
- -### Programmatically Specifying the Schema - -
- -
- -When case classes cannot be defined ahead of time (for example, -the structure of records is encoded in a string, or a text dataset will be parsed -and fields will be projected differently for different users), -a `DataFrame` can be created programmatically with three steps. - -1. Create an RDD of `Row`s from the original RDD; -2. Create the schema represented by a `StructType` matching the structure of -`Row`s in the RDD created in Step 1. -3. Apply the schema to the RDD of `Row`s via `createDataFrame` method provided -by `SparkSession`. - -For example: - -{% include_example programmatic_schema scala/org/apache/spark/examples/sql/SparkSQLExample.scala %} -
- -
- -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` can be created programmatically with three steps. - -1. Create an RDD of `Row`s from the original RDD; -2. Create the schema represented by a `StructType` matching the structure of -`Row`s in the RDD created in Step 1. -3. Apply the schema to the RDD of `Row`s via `createDataFrame` method provided -by `SparkSession`. - -For example: - -{% include_example programmatic_schema java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %} -
- -
- -When a dictionary of kwargs cannot be defined ahead of time (for example, -the structure of records is encoded in a string, or a text dataset will be parsed and -fields will be projected differently for different users), -a `DataFrame` can be created programmatically with three steps. - -1. Create an RDD of tuples or lists from the original RDD; -2. Create the schema represented by a `StructType` matching the structure of -tuples or lists in the RDD created in the step 1. -3. Apply the schema to the RDD via `createDataFrame` method provided by `SparkSession`. - -For example: - -{% include_example programmatic_schema python/sql/basic.py %} -
- -
- -## Aggregations - -The [built-in DataFrames functions](api/scala/index.html#org.apache.spark.sql.functions$) provide common -aggregations such as `count()`, `countDistinct()`, `avg()`, `max()`, `min()`, etc. -While those functions are designed for DataFrames, Spark SQL also has type-safe versions for some of them in -[Scala](api/scala/index.html#org.apache.spark.sql.expressions.scalalang.typed$) and -[Java](api/java/org/apache/spark/sql/expressions/javalang/typed.html) to work with strongly typed Datasets. -Moreover, users are not limited to the predefined aggregate functions and can create their own. - -### Untyped User-Defined Aggregate Functions -Users have to extend the [UserDefinedAggregateFunction](api/scala/index.html#org.apache.spark.sql.expressions.UserDefinedAggregateFunction) -abstract class to implement a custom untyped aggregate function. For example, a user-defined average -can look like: - -
-
-{% 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](api/scala/index.html#org.apache.spark.sql.expressions.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/JavaAny LanguageMeaning
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](#partition-discovery) section. -Thus, it has limited applicability to columns with high cardinality. In contrast - `bucketBy` distributes -data across a fixed number of buckets and can be used when a number of unique values is unbounded. - -## Parquet Files - -[Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems. -Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema -of the original data. When writing Parquet files, all columns are automatically converted to be nullable for -compatibility reasons. - -### Loading Data Programmatically - -Using the data from the above example: - -
- -
-{% 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 - -1. setting data source option `mergeSchema` to `true` when reading Parquet files (as shown in the - examples below), or -2. setting the global SQL option `spark.sql.parquet.mergeSchema` to `true`. - -
- -
-{% include_example schema_merging scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %} -
- -
-{% include_example schema_merging java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %} -
- -
- -{% include_example schema_merging python/sql/datasource.py %} -
- -
- -{% include_example schema_merging r/RSparkSQLExample.R %} - -
- -
- -### Hive metastore Parquet table conversion - -When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own -Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the -`spark.sql.hive.convertMetastoreParquet` configuration, and is turned on by default. - -#### Hive/Parquet Schema Reconciliation - -There are two key differences between Hive and Parquet from the perspective of table schema -processing. - -1. Hive is case insensitive, while Parquet is not -1. Hive considers all columns nullable, while nullability in Parquet is significant - -Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a -Hive metastore Parquet table to a Spark SQL Parquet table. The reconciliation rules are: - -1. Fields that have the same name in both schema must have the same data type regardless of - nullability. The reconciled field should have the data type of the Parquet side, so that - nullability is respected. - -1. The reconciled schema contains exactly those fields defined in Hive metastore schema. - - - Any fields that only appear in the Parquet schema are dropped in the reconciled schema. - - Any fields that only appear in the Hive metastore schema are added as nullable field in the - reconciled schema. - -#### Metadata Refreshing - -Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table -conversion is enabled, metadata of those converted tables are also cached. If these tables are -updated by Hive or other external tools, you need to refresh them manually to ensure consistent -metadata. - -
- -
- -{% 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 NameDefaultMeaning
spark.sql.parquet.binaryAsStringfalse - 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.int96AsTimestamptrue - 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.codecsnappy - 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, brotli, lz4, zstd. - Note that `zstd` requires `ZStandardCodec` to be installed before Hadoop 2.9.0, `brotli` requires - `BrotliCodec` to be installed. -
spark.sql.parquet.filterPushdowntrueEnables Parquet filter push-down optimization when set to true.
spark.sql.hive.convertMetastoreParquettrue - When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in - support. -
spark.sql.parquet.mergeSchemafalse -

- 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.metadataOnlytrue -

- 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. -

-
spark.sql.parquet.writeLegacyFormatfalse - If true, data will be written in a way of Spark 1.4 and earlier. For example, decimal values - will be written in Apache Parquet's fixed-length byte array format, which other systems such as - Apache Hive and Apache Impala use. If false, the newer format in Parquet will be used. For - example, decimals will be written in int-based format. If Parquet output is intended for use - with systems that do not support this newer format, set to true. -
- -## ORC Files - -Since Spark 2.3, Spark supports a vectorized ORC reader with a new ORC file format for ORC files. -To do that, the following configurations are newly added. The vectorized reader is used for the -native ORC tables (e.g., the ones created using the clause `USING ORC`) when `spark.sql.orc.impl` -is set to `native` and `spark.sql.orc.enableVectorizedReader` is set to `true`. For the Hive ORC -serde tables (e.g., the ones created using the clause `USING HIVE OPTIONS (fileFormat 'ORC')`), -the vectorized reader is used when `spark.sql.hive.convertMetastoreOrc` is also set to `true`. - - - - - - - - - - - - - -
Property NameDefaultMeaning
spark.sql.orc.implnativeThe name of ORC implementation. It can be one of native and hive. native means the native ORC support that is built on Apache ORC 1.4. `hive` means the ORC library in Hive 1.2.1.
spark.sql.orc.enableVectorizedReadertrueEnables vectorized orc decoding in native implementation. If false, a new non-vectorized ORC reader is used in native implementation. For hive implementation, this is ignored.
- -## 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](http://jsonlines.org/). - -For a regular multi-line JSON file, set the `multiLine` option to `true`. - -{% include_example json_dataset scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %} -
- -
-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](http://jsonlines.org/). - -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](http://jsonlines.org/). - -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](http://jsonlines.org/). - -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](http://hive.apache.org/). -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 NameMeaning
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 NameDefaultMeaning
spark.sql.hive.metastore.version1.2.1 - Version of the Hive metastore. Available - options are 0.12.0 through 2.3.3. -
spark.sql.hive.metastore.jarsbuiltin - Location of the jars that should be used to instantiate the HiveMetastoreClient. This - property can be one of three options: -
    -
  1. builtin
    2. - 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. -
  2. maven
    3. - Use Hive jars of specified version downloaded from Maven repositories. This configuration - is not generally recommended for production deployments. -
  3. 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.
    4. -
-
spark.sql.hive.metastore.sharedPrefixescom.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](api/scala/index.html#org.apache.spark.rdd.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 NameMeaning
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 from or written into. Note that when using it in the read - path 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. It is not - allowed to specify `dbtable` and `query` options at the same time. -
query - A query that will be used to read data into Spark. The specified query will be parenthesized and used - as a subquery in the FROM clause. Spark will also assign an alias to the subquery clause. - As an example, spark will issue a query of the following form to the JDBC Source.

- SELECT <columns> FROM (<user_specified_query>) spark_gen_alias

- Below are couple of restrictions while using this option.
-
    -
  1. It is not allowed to specify `dbtable` and `query` options at the same time.
    2. -
  2. It is not allowed to spcify `query` and `partitionColumn` options at the same time. When specifying - `partitionColumn` option is required, the subquery can be specified using `dbtable` option instead and - partition columns can be qualified using the subquery alias provided as part of `dbtable`.
    - Example:
    - - spark.read.format("jdbc")
    -    .option("dbtable", "(select c1, c2 from t1) as subq")
    -    .option("partitionColumn", "subq.c1"
    -    .load() -
    3. -
-
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, date, or timestamp 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. -
queryTimeout - The number of seconds the driver will wait for a Statement object to execute to the given - number of seconds. Zero means there is no limit. In the write path, this option depends on - how JDBC drivers implement the API setQueryTimeout, e.g., the h2 JDBC driver - checks the timeout of each query instead of an entire JDBC batch. - It defaults to 0. -
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. -
cascadeTruncate - This is a JDBC writer related option. If enabled and supported by the JDBC database (PostgreSQL and Oracle at the moment), this options allows execution of a TRUNCATE TABLE t CASCADE (in the case of PostgreSQL a TRUNCATE TABLE ONLY t CASCADE is executed to prevent inadvertently truncating descendant tables). This will affect other tables, and thus should be used with care. This option applies only to writing. It defaults to the default cascading truncate behaviour of the JDBC database in question, specified in the isCascadeTruncate in each JDBCDialect. -
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. -
pushDownPredicate - The option to enable or disable predicate push-down into the JDBC data source. The default value is true, in which case Spark will push down filters to the JDBC data source as much as possible. Otherwise, if set to false, no filter will be pushed down to the JDBC data source and thus all filters will be handled by Spark. Predicate push-down is usually turned off when the predicate filtering is performed faster by Spark than by the JDBC data source. -
- -
- -
-{% 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 %} - -
-
- -## Avro Files -See the [Apache Avro Data Source Guide](avro-data-source-guide.html). - -## 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. - * Users can specify vendor-specific JDBC connection properties in the data source options to do special treatment. For example, `spark.read.format("jdbc").option("url", oracleJdbcUrl).option("oracle.jdbc.mapDateToTimestamp", "false")`. `oracle.jdbc.mapDateToTimestamp` defaults to true, users often need to disable this flag to avoid Oracle date being resolved as timestamp. - -# 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 NameDefaultMeaning
spark.sql.inMemoryColumnarStorage.compressedtrue - When set to true Spark SQL will automatically select a compression codec for each column based - on statistics of the data. -
spark.sql.inMemoryColumnarStorage.batchSize10000 - 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 NameDefaultMeaning
spark.sql.files.maxPartitionBytes134217728 (128 MB) - The maximum number of bytes to pack into a single partition when reading files. -
spark.sql.files.openCostInBytes4194304 (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.broadcastTimeout300 -

- Timeout in seconds for the broadcast wait time in broadcast joins -

-
spark.sql.autoBroadcastJoinThreshold10485760 (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.partitions200 - 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`](https://cwiki.apache.org/confluence/display/Hive/Setting+Up+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](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients). - -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://:/?hive.server2.transport.mode=http;hive.server2.thrift.http.path= - - -## 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](https://arrow.apache.org/docs/python/install.html) 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. - -In addition, optimizations enabled by 'spark.sql.execution.arrow.enabled' could fallback automatically -to non-Arrow optimization implementation if an error occurs before the actual computation within Spark. -This can be controlled by 'spark.sql.execution.arrow.fallback.enabled'. - -
-
-{% 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 SQL Types](#supported-sql-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`. - -The column labels of the returned `pandas.DataFrame` must either match the field names in the -defined output schema if specified as strings, or match the field data types by position if not -strings, e.g. integer indices. See [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html#pandas.DataFrame) -on how to label columns when constructing a `pandas.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 exceptions, especially if the group sizes are skewed. The configuration for -[maxRecordsPerBatch](#setting-arrow-batch-size) 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`](api/python/pyspark.sql.html#pyspark.sql.functions.pandas_udf) and -[`pyspark.sql.GroupedData.apply`](api/python/pyspark.sql.html#pyspark.sql.GroupedData.apply). - -### Grouped Aggregate - -Grouped aggregate Pandas UDFs are similar to Spark aggregate functions. Grouped aggregate Pandas UDFs are used with `groupBy().agg()` and -[`pyspark.sql.Window`](api/python/pyspark.sql.html#pyspark.sql.Window). It defines an aggregation from one or more `pandas.Series` -to a scalar value, where each `pandas.Series` represents a column within the group or window. - -Note that this type of UDF does not support partial aggregation and all data for a group or window will be loaded into memory. Also, -only unbounded window is supported with Grouped aggregate Pandas UDFs currently. - -The following example shows how to use this type of UDF to compute mean with groupBy and window operations: - -
-
-{% include_example grouped_agg_pandas_udf python/sql/arrow.py %} -
-
- -For detailed usage, please see [`pyspark.sql.functions.pandas_udf`](api/python/pyspark.sql.html#pyspark.sql.functions.pandas_udf) - -## Usage Notes - -### Supported SQL Types - -Currently, all Spark SQL data types are supported by Arrow-based conversion except `BinaryType`, `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_udf`s to get the best performance, see -[here](https://pandas.pydata.org/pandas-docs/stable/timeseries.html) for details. - -# Migration Guide - -## Upgrading From Spark SQL 2.4 to 3.0 - - - In PySpark, when creating a `SparkSession` with `SparkSession.builder.getOrCreate()`, if there is an existing `SparkContext`, the builder was trying to update the `SparkConf` of the existing `SparkContext` with configurations specified to the builder, but the `SparkContext` is shared by all `SparkSession`s, so we should not update them. Since 3.0, the builder come to not update the configurations. This is the same behavior as Java/Scala API in 2.3 and above. If you want to update them, you need to update them prior to creating a `SparkSession`. - -## Upgrading From Spark SQL 2.3 to 2.4 - - - In Spark version 2.3 and earlier, the second parameter to array_contains function is implicitly promoted to the element type of first array type parameter. This type promotion can be lossy and may cause `array_contains` function to return wrong result. This problem has been addressed in 2.4 by employing a safer type promotion mechanism. This can cause some change in behavior and are illustrated in the table below. - - - - - - - - - - - - - - - - - - - - - - - - - -
- Query - - Result Spark 2.3 or Prior - - Result Spark 2.4 - - Remarks -
- SELECT
array_contains(array(1), 1.34D); - 		- true - - false - - In Spark 2.4, left and right parameters are promoted to array(double) and double type respectively. -
- SELECT
array_contains(array(1), '1'); - 		- true - - AnalysisException is thrown since integer type can not be promoted to string type in a loss-less manner. - - Users can use explict cast -
- SELECT
array_contains(array(1), 'anystring'); - 		- null - - AnalysisException is thrown since integer type can not be promoted to string type in a loss-less manner. - - Users can use explict cast -
- - - Since Spark 2.4, when there is a struct field in front of the IN operator before a subquery, the inner query must contain a struct field as well. In previous versions, instead, the fields of the struct were compared to the output of the inner query. Eg. if `a` is a `struct(a string, b int)`, in Spark 2.4 `a in (select (1 as a, 'a' as b) from range(1))` is a valid query, while `a in (select 1, 'a' from range(1))` is not. In previous version it was the opposite. - - In versions 2.2.1+ and 2.3, if `spark.sql.caseSensitive` is set to true, then the `CURRENT_DATE` and `CURRENT_TIMESTAMP` functions incorrectly became case-sensitive and would resolve to columns (unless typed in lower case). In Spark 2.4 this has been fixed and the functions are no longer case-sensitive. - - Since Spark 2.4, Spark will evaluate the set operations referenced in a query by following a precedence rule as per the SQL standard. If the order is not specified by parentheses, set operations are performed from left to right with the exception that all INTERSECT operations are performed before any UNION, EXCEPT or MINUS operations. The old behaviour of giving equal precedence to all the set operations are preserved under a newly added configuration `spark.sql.legacy.setopsPrecedence.enabled` with a default value of `false`. When this property is set to `true`, spark will evaluate the set operators from left to right as they appear in the query given no explicit ordering is enforced by usage of parenthesis. - - Since Spark 2.4, Spark will display table description column Last Access value as UNKNOWN when the value was Jan 01 1970. - - Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader for ORC files by default. To do that, `spark.sql.orc.impl` and `spark.sql.orc.filterPushdown` change their default values to `native` and `true` respectively. - - In PySpark, when Arrow optimization is enabled, previously `toPandas` just failed when Arrow optimization is unable to be used whereas `createDataFrame` from Pandas DataFrame allowed the fallback to non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas DataFrame allow the fallback by default, which can be switched off by `spark.sql.execution.arrow.fallback.enabled`. - - Since Spark 2.4, writing an empty dataframe to a directory launches at least one write task, even if physically the dataframe has no partition. This introduces a small behavior change that for self-describing file formats like Parquet and Orc, Spark creates a metadata-only file in the target directory when writing a 0-partition dataframe, so that schema inference can still work if users read that directory later. The new behavior is more reasonable and more consistent regarding writing empty dataframe. - - Since Spark 2.4, expression IDs in UDF arguments do not appear in column names. For example, an column name in Spark 2.4 is not `UDF:f(col0 AS colA#28)` but ``UDF:f(col0 AS `colA`)``. - - Since Spark 2.4, writing a dataframe with an empty or nested empty schema using any file formats (parquet, orc, json, text, csv etc.) is not allowed. An exception is thrown when attempting to write dataframes with empty schema. - - Since Spark 2.4, Spark compares a DATE type with a TIMESTAMP type after promotes both sides to TIMESTAMP. To set `false` to `spark.sql.legacy.compareDateTimestampInT --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscribe@spark.apache.org For additional commands, e-mail: commits-help@spark.apache.org