An initial API that exposes SparkSQL functionality in PySpark. A PythonRDD composed of dictionaries, with string keys and primitive values (boolean, float, int, long, string) can be converted into a SchemaRDD that supports sql queries. ``` from pyspark.context import SQLContext sqlCtx = SQLContext(sc) rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}]) srdd = sqlCtx.applySchema(rdd) sqlCtx.registerRDDAsTable(srdd, "table1") srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1") srdd2.collect() ``` The last line yields ```[{"f1" : 1, "f2" : "row1"}, {"f1" : 2, "f2": "row2"}, {"f1" : 3, "f2": "row3"}]``` Author: Ahir Reddy <ahirreddy@gmail.com> Author: Michael Armbrust <michael@databricks.com> Closes #363 from ahirreddy/pysql and squashes the following commits: 0294497 [Ahir Reddy] Updated log4j properties to supress Hive Warns 307d6e0 [Ahir Reddy] Style fix 6f7b8f6 [Ahir Reddy] Temporary fix MIMA checker. Since we now assemble Spark jar with Hive, we don't want to check the interfaces of all of our hive dependencies 3ef074a [Ahir Reddy] Updated documentation because classes moved to sql.py 29245bf [Ahir Reddy] Cache underlying SchemaRDD instead of generating and caching PythonRDD f2312c7 [Ahir Reddy] Moved everything into sql.py a19afe4 [Ahir Reddy] Doc fixes 6d658ba [Ahir Reddy] Remove the metastore directory created by the HiveContext tests in SparkSQL 521ff6d [Ahir Reddy] Trying to get spark to build with hive ab95eba [Ahir Reddy] Set SPARK_HIVE=true on jenkins ded03e7 [Ahir Reddy] Added doc test for HiveContext 22de1d4 [Ahir Reddy] Fixed maven pyrolite dependency e4da06c [Ahir Reddy] Display message if hive is not built into spark 227a0be [Michael Armbrust] Update API links. Fix Hive example. 58e2aa9 [Michael Armbrust] Build Docs for pyspark SQL Api. Minor fixes. 4285340 [Michael Armbrust] Fix building of Hive API Docs. 38a92b0 [Michael Armbrust] Add note to future non-python developers about python docs. 337b201 [Ahir Reddy] Changed com.clearspring.analytics stream version from 2.4.0 to 2.5.1 to match SBT build, and added pyrolite to maven build 40491c9 [Ahir Reddy] PR Changes + Method Visibility 1836944 [Michael Armbrust] Fix comments. e00980f [Michael Armbrust] First draft of python sql programming guide. b0192d3 [Ahir Reddy] Added Long, Double and Boolean as usable types + unit test f98a422 [Ahir Reddy] HiveContexts 79621cf [Ahir Reddy] cleaning up cruft b406ba0 [Ahir Reddy] doctest formatting 20936a5 [Ahir Reddy] Added tests and documentation e4d21b4 [Ahir Reddy] Added pyrolite dependency 79f739d [Ahir Reddy] added more tests 7515ba0 [Ahir Reddy] added more tests :) d26ec5e [Ahir Reddy] added test e9f5b8d [Ahir Reddy] adding tests 906d180 [Ahir Reddy] added todo explaining cost of creating Row object in python 251f99d [Ahir Reddy] for now only allow dictionaries as input 09b9980 [Ahir Reddy] made jrdd explicitly lazy c608947 [Ahir Reddy] SchemaRDD now has all RDD operations 725c91e [Ahir Reddy] awesome row objects 55d1c76 [Ahir Reddy] return row objects 4fe1319 [Ahir Reddy] output dictionaries correctly be079de [Ahir Reddy] returning dictionaries works cd5f79f [Ahir Reddy] Switched to using Scala SQLContext e948bd9 [Ahir Reddy] yippie 4886052 [Ahir Reddy] even better c0fb1c6 [Ahir Reddy] more working 043ca85 [Ahir Reddy] working 5496f9f [Ahir Reddy] doesn't crash b8b904b [Ahir Reddy] Added schema rdd class 67ba875 [Ahir Reddy] java to python, and python to java bcc0f23 [Ahir Reddy] Java to python ab6025d [Ahir Reddy] compiling
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layout | title |
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global | Spark SQL Programming Guide |
Spark SQL is currently an Alpha component. Therefore, the APIs may be changed in future releases.
- This will become a table of contents (this text will be scraped). {:toc}
Overview
Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of RDD, SchemaRDD. SchemaRDDs are composed Row objects along with a schema that describes the data types of each column in the row. A SchemaRDD is similar to a table in a traditional relational database. A SchemaRDD can be created from an existing RDD, parquet file, or by running HiveQL against data stored in Apache Hive.
All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell
.
Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using Spark. At the core of this component is a new type of RDD, SchemaRDD. SchemaRDDs are composed Row objects along with a schema that describes the data types of each column in the row. A SchemaRDD is similar to a table in a traditional relational database. A SchemaRDD can be created from an existing RDD, parquet file, or by running HiveQL against data stored in Apache Hive.
All of the examples on this page use sample data included in the Spark distribution and can be run in the pyspark
shell.
Getting Started
The entry point into all relational functionality in Spark is the SQLContext class, or one of its descendants. To create a basic SQLContext, all you need is a SparkContext.
{% highlight scala %} val sc: SparkContext // An existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// Importing the SQL context gives access to all the public SQL functions and implicit conversions. import sqlContext._ {% endhighlight %}
The entry point into all relational functionality in Spark is the JavaSQLContext class, or one of its descendants. To create a basic JavaSQLContext, all you need is a JavaSparkContext.
{% highlight java %} JavaSparkContext ctx = ...; // An existing JavaSparkContext. JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx); {% endhighlight %}
The entry point into all relational functionality in Spark is the SQLContext class, or one of its decedents. To create a basic SQLContext, all you need is a SparkContext.
{% highlight python %} from pyspark.sql import SQLContext sqlCtx = SQLContext(sc) {% endhighlight %}
Running SQL on RDDs
One type of table that is supported by Spark SQL is an RDD of Scala case classes. 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 Sequences or Arrays. This RDD can be implicitly converted to a SchemaRDD and then be registered as a table. Tables can be used in subsequent SQL statements.
{% highlight scala %} val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._
// Define the schema using a case class. case class Person(name: String, age: Int)
// Create an RDD of Person objects and register it as a table. val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt)) people.registerAsTable("people")
// SQL statements can be run by using the sql methods provided by sqlContext. val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. teenagers.map(t => "Name: " + t(0)).collect().foreach(println) {% endhighlight %}
One type of table that is supported by Spark SQL is an RDD of JavaBeans. The BeanInfo defines the schema of the table. Currently, Spark SQL does not support JavaBeans that contain nested or contain complex types such as Lists or Arrays. You can create a JavaBean by creating a class that implements Serializable and has getters and setters for all of its fields.
{% highlight java %}
public static class Person implements Serializable { private String name; private int age;
String getName() { return name; }
void setName(String name) { this.name = name; }
int getAge() { return age; }
void setAge(int age) { this.age = age; } }
{% endhighlight %}
A schema can be applied to an existing RDD by calling applySchema
and providing the Class object
for the JavaBean.
{% highlight java %} JavaSQLContext ctx = new org.apache.spark.sql.api.java.JavaSQLContext(sc)
// Load a text file and convert each line to a JavaBean. JavaRDD people = ctx.textFile("examples/src/main/resources/people.txt").map( new Function<String, Person>() { public Person call(String line) throws Exception { String[] parts = line.split(",");
Person person = new Person();
person.setName(parts[0]);
person.setAge(Integer.parseInt(parts[1].trim()));
return person;
}
});
// Apply a schema to an RDD of JavaBeans and register it as a table. JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class); schemaPeople.registerAsTable("people");
// SQL can be run over RDDs that have been registered as tables. JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. List teenagerNames = teenagers.map(new Function<Row, String>() { public String call(Row row) { return "Name: " + row.getString(0); } }).collect();
{% endhighlight %}
One type of table that is supported by Spark SQL is an RDD of dictionaries. The keys of the dictionary define the columns names of the table, and the types are inferred by looking at the first row. Any RDD of dictionaries can converted to a SchemaRDD and then registered as a table. Tables can be used in subsequent SQL statements.
{% highlight python %}
Load a text file and convert each line to a dictionary.
lines = sc.textFile("examples/src/main/resources/people.txt") parts = lines.map(lambda l: l.split(",")) people = parts.map(lambda p: {"name": p[0], "age": int(p[1])})
Infer the schema, and register the SchemaRDD as a table.
In future versions of PySpark we would like to add support for registering RDDs with other
datatypes as tables
peopleTable = sqlCtx.inferSchema(people) peopleTable.registerAsTable("people")
SQL can be run over SchemaRDDs that have been registered as a table.
teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
The results of SQL queries are RDDs and support all the normal RDD operations.
teenNames = teenagers.map(lambda p: "Name: " + p.name) {% endhighlight %}
Note that Spark SQL currently uses a very basic SQL parser.
Users that want a more complete dialect of SQL should look at the HiveQL support provided by
HiveContext
.
Using Parquet
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. Using the data from the above example:
{% highlight scala %} val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._
val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.
// The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using parquet. people.saveAsParquetFile("people.parquet")
// Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a JavaSchemaRDD. val parquetFile = sqlContext.parquetFile("people.parquet")
//Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerAsTable("parquetFile") val teenagers = sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19") teenagers.collect().foreach(println) {% endhighlight %}
{% highlight java %}
JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example.
// JavaSchemaRDDs can be saved as parquet files, maintaining the schema information. schemaPeople.saveAsParquetFile("people.parquet");
// Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a JavaSchemaRDD. JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
//Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerAsTable("parquetFile"); JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
{% endhighlight %}
{% highlight python %}
peopleTable # The SchemaRDD from the previous example.
SchemaRDDs can be saved as parquet files, maintaining the schema information.
peopleTable.saveAsParquetFile("people.parquet")
Read in the parquet file created above. Parquet files are self-describing so the schema is preserved.
The result of loading a parquet file is also a SchemaRDD.
parquetFile = sqlCtx.parquetFile("people.parquet")
Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerAsTable("parquetFile"); teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
{% endhighlight %}
Writing Language-Integrated Relational Queries
Language-Integrated queries are currently only supported in Scala.
Spark SQL also supports a domain specific language for writing queries. Once again, using the data from the above examples:
{% highlight scala %} val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._ val people: RDD[Person] = ... // An RDD of case class objects, from the first example.
// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19' val teenagers = people.where('age >= 10).where('age <= 19).select('name) {% endhighlight %}
The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers
prefixed with a tick ('
). Implicit conversions turn these symbols into expressions that are
evaluated by the SQL execution engine. A full list of the functions supported can be found in the
ScalaDoc.
Hive Support
Spark SQL also supports reading and writing data stored in Apache Hive.
However, since Hive has a large number of dependencies, it is not included in the default Spark assembly.
In order to use Hive you must first run 'SPARK_HIVE=true sbt/sbt assembly/assembly
' (or use -Phive
for maven).
This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar 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 acccess data stored in Hive.
Configuration of Hive is done by placing your hive-site.xml
file in conf/
.
When working with Hive one must construct a HiveContext
, which inherits from SQLContext
, and
adds support for finding tables in in the MetaStore and writing queries using HiveQL. Users who do
not have an existing Hive deployment can also experiment with the LocalHiveContext
,
which is similar to HiveContext
, but creates a local copy of the metastore
and warehouse
automatically.
{% highlight scala %} val sc: SparkContext // An existing SparkContext. val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
// Importing the SQL context gives access to all the public SQL functions and implicit conversions. import hiveContext._
hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
// Queries are expressed in HiveQL hql("FROM src SELECT key, value").collect().foreach(println) {% endhighlight %}
When working with Hive one must construct a JavaHiveContext
, which inherits from JavaSQLContext
, and
adds support for finding tables in in the MetaStore and writing queries using HiveQL. In addition to
the sql
method a JavaHiveContext
also provides an hql
methods, which allows queries to be
expressed in HiveQL.
{% highlight java %} JavaSparkContext ctx = ...; // An existing JavaSparkContext. JavaHiveContext hiveCtx = new org.apache.spark.sql.hive.api.java.HiveContext(ctx);
hiveCtx.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"); hiveCtx.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
// Queries are expressed in HiveQL. Row[] results = hiveCtx.hql("FROM src SELECT key, value").collect();
{% endhighlight %}
When working with Hive one must construct a HiveContext
, which inherits from SQLContext
, and
adds support for finding tables in in the MetaStore and writing queries using HiveQL. In addition to
the sql
method a HiveContext
also provides an hql
methods, which allows queries to be
expressed in HiveQL.
{% highlight python %}
from pyspark.sql import HiveContext hiveCtx = HiveContext(sc)
hiveCtx.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") hiveCtx.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
Queries can be expressed in HiveQL.
results = hiveCtx.hql("FROM src SELECT key, value").collect()
{% endhighlight %}