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Getting Started |
Getting Started |
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- Table of contents {:toc}
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.
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 %}
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 %}
As an example, the following creates a DataFrame based on the content of a JSON file:
{% include_example create_df python/sql/basic.py %}
As an example, the following creates a DataFrame based on the content of a JSON file:
{% include_example create_df r/RSparkSQLExample.R %}
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:
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.
{% 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.
{% include_example run_sql scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}
{% include_example run_sql java/org/apache/spark/examples/sql/JavaSparkSQLExample.java %}
{% include_example run_sql python/sql/basic.py %}
{% include_example run_sql r/RSparkSQLExample.R %}
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.
{% highlight sql %}
CREATE GLOBAL TEMPORARY VIEW temp_view AS SELECT a + 1, b * 2 FROM tbl
SELECT * FROM global_temp.temp_view
{% endhighlight %}
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.
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.
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 %}
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
StructTypematching the structure ofRows in the RDD created in Step 1. - Apply the schema to the RDD of
Rows viacreateDataFramemethod provided bySparkSession.
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
StructTypematching the structure ofRows in the RDD created in Step 1. - Apply the schema to the RDD of
Rows viacreateDataFramemethod provided bySparkSession.
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
StructTypematching the structure of tuples or lists in the RDD created in the step 1. - Apply the schema to the RDD via
createDataFramemethod provided bySparkSession.
For example:
{% include_example programmatic_schema python/sql/basic.py %}
(to be filled soon)
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.
Users have to extend the UserDefinedAggregateFunction abstract class to implement a custom untyped aggregate function. For example, a user-defined average can look like:
User-defined aggregations for strongly typed Datasets revolve around the Aggregator abstract class. For example, a type-safe user-defined average can look like: