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11 files changed, 750 insertions, 50 deletions
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index b6f21a5dc6..f849716f7a 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -8,6 +8,10 @@ title: Spark SQL Programming Guide {:toc} # Overview + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + 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](api/sql/core/index.html#org.apache.spark.sql.SchemaRDD). SchemaRDDs are composed @@ -18,11 +22,27 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac **All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell.** +</div> + +<div data-lang="java" markdown="1"> +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, +[JavaSchemaRDD](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD). JavaSchemaRDDs are composed +[Row](api/sql/catalyst/index.html#org.apache.spark.sql.api.java.Row) objects along with +a schema that describes the data types of each column in the row. A JavaSchemaRDD is similar to a table +in a traditional relational database. A JavaSchemaRDD can be created from an existing RDD, parquet +file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). +</div> +</div> + *************************************************************************************************** # Getting Started -The entry point into all relational functionallity in Spark is the +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + +The entry point into all relational functionality in Spark is the [SQLContext](api/sql/core/index.html#org.apache.spark.sql.SQLContext) class, or one of its decendents. To create a basic SQLContext, all you need is a SparkContext. @@ -34,8 +54,30 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._ {% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> + +The entry point into all relational functionality in Spark is the +[JavaSQLContext](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one +of its decendents. 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 %} + +</div> + +</div> + ## Running SQL on RDDs -One type of table that is supported by Spark SQL is an RDD of Scala case classetees. The case class + +<div class="codetabs"> + +<div data-lang="scala" markdown="1"> + +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 @@ -60,7 +102,83 @@ val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") teenagers.map(t => "Name: " + t(0)).collect().foreach(println) {% endhighlight %} -**Note that Spark SQL currently uses a very basic SQL parser, and the keywords are case sensitive.** +</div> + +<div data-lang="java" markdown="1"> + +One type of table that is supported by Spark SQL is an RDD of [JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly). 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<Person> 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<String> teenagerNames = teenagers.map(new Function<Row, String>() { + public String call(Row row) { + return "Name: " + row.getString(0); + } +}).collect(); + +{% endhighlight %} + +</div> + +</div> + +**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`. @@ -70,17 +188,21 @@ Parquet is a columnar format that is supported by many other data processing sys 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: +<div class="codetabs"> + +<div data-lang="scala" markdown="1"> + {% 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. +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 SchemaRDD. +// 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. @@ -89,15 +211,43 @@ val teenagers = sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19" teenagers.collect().foreach(println) {% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> + +{% 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 %} + +</div> + +</div> + ## 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. +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) @@ -114,14 +264,17 @@ evaluated by the SQL execution engine. A full list of the functions supported c 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, it is not included in the default Spark assembly. -In order to use Hive you must first run '`sbt/sbt hive/assembly`'. This command builds a new assembly -jar that includes Hive. When this jar is present, Spark will use the Hive -assembly instead of the normal Spark assembly. Note that this Hive assembly jar must also be present +In order to use Hive you must first run '`SPARK_HIVE=true sbt/sbt assembly/assembly`'. 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/`. +<div class="codetabs"> + +<div data-lang="scala" markdown="1"> + 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`, @@ -135,9 +288,34 @@ 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._ -sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") -sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src") +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 -sql("SELECT key, value FROM src").collect().foreach(println) -{% endhighlight %}
\ No newline at end of file +hql("FROM src SELECT key, value").collect().foreach(println) +{% endhighlight %} + +</div> + +<div data-lang="java" markdown="1"> + +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 %} + +</div> + +</div> diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java new file mode 100644 index 0000000000..e8e63d2745 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java @@ -0,0 +1,99 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.sql; + +import java.io.Serializable; +import java.util.List; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.VoidFunction; + +import org.apache.spark.sql.api.java.JavaSQLContext; +import org.apache.spark.sql.api.java.JavaSchemaRDD; +import org.apache.spark.sql.api.java.Row; + +public class JavaSparkSQL { + 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; + } + } + + public static void main(String[] args) throws Exception { + JavaSparkContext ctx = new JavaSparkContext("local", "JavaSparkSQL", + System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaSparkSQL.class)); + JavaSQLContext sqlCtx = new JavaSQLContext(ctx); + + // Load a text file and convert each line to a Java Bean. + JavaRDD<Person> 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 Java Beans 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<String> teenagerNames = teenagers.map(new Function<Row, String>() { + public String call(Row row) { + return "Name: " + row.getString(0); + } + }).collect(); + + // 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 teenagers2 = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala index 770cabcb31..a62cb8aa13 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala @@ -17,13 +17,13 @@ package org.apache.spark.sql +import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType} import org.apache.spark.sql.catalyst.types.BooleanType -import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext} /** * <span class="badge" style="float: right; background-color: darkblue;">ALPHA COMPONENT</span> @@ -92,23 +92,10 @@ import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext} */ class SchemaRDD( @transient val sqlContext: SQLContext, - @transient val logicalPlan: LogicalPlan) - extends RDD[Row](sqlContext.sparkContext, Nil) { + @transient protected[spark] val logicalPlan: LogicalPlan) + extends RDD[Row](sqlContext.sparkContext, Nil) with SchemaRDDLike { - /** - * A lazily computed query execution workflow. All other RDD operations are passed - * through to the RDD that is produced by this workflow. - * - * We want this to be lazy because invoking the whole query optimization pipeline can be - * expensive. - */ - @transient - protected[spark] lazy val queryExecution = sqlContext.executePlan(logicalPlan) - - override def toString = - s"""${super.toString} - |== Query Plan == - |${queryExecution.executedPlan}""".stripMargin.trim + def baseSchemaRDD = this // ========================================================================================= // RDD functions: Copy the interal row representation so we present immutable data to users. @@ -313,30 +300,11 @@ class SchemaRDD( InsertIntoTable(UnresolvedRelation(None, tableName), Map.empty, logicalPlan, overwrite)) /** - * Saves the contents of this `SchemaRDD` as a parquet file, preserving the schema. Files that - * are written out using this method can be read back in as a SchemaRDD using the ``function - * - * @group schema - */ - def saveAsParquetFile(path: String): Unit = { - sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd - } - - /** - * Registers this RDD as a temporary table using the given name. The lifetime of this temporary - * table is tied to the [[SQLContext]] that was used to create this SchemaRDD. - * - * @group schema - */ - def registerAsTable(tableName: String): Unit = { - sqlContext.registerRDDAsTable(this, tableName) - } - - /** * Returns this RDD as a SchemaRDD. * @group schema */ def toSchemaRDD = this + /** FOR INTERNAL USE ONLY */ def analyze = sqlContext.analyzer(logicalPlan) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala new file mode 100644 index 0000000000..840803a52c --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala @@ -0,0 +1,66 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import org.apache.spark.sql.catalyst.plans.logical._ + +/** + * Contains functions that are shared between all SchemaRDD types (i.e., Scala, Java) + */ +trait SchemaRDDLike { + @transient val sqlContext: SQLContext + @transient protected[spark] val logicalPlan: LogicalPlan + + private[sql] def baseSchemaRDD: SchemaRDD + + /** + * A lazily computed query execution workflow. All other RDD operations are passed + * through to the RDD that is produced by this workflow. + * + * We want this to be lazy because invoking the whole query optimization pipeline can be + * expensive. + */ + @transient + protected[spark] lazy val queryExecution = sqlContext.executePlan(logicalPlan) + + override def toString = + s"""${super.toString} + |== Query Plan == + |${queryExecution.executedPlan}""".stripMargin.trim + + + /** + * Saves the contents of this `SchemaRDD` as a parquet file, preserving the schema. Files that + * are written out using this method can be read back in as a SchemaRDD using the ``function + * + * @group schema + */ + def saveAsParquetFile(path: String): Unit = { + sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd + } + + /** + * Registers this RDD as a temporary table using the given name. The lifetime of this temporary + * table is tied to the [[SQLContext]] that was used to create this SchemaRDD. + * + * @group schema + */ + def registerAsTable(tableName: String): Unit = { + sqlContext.registerRDDAsTable(baseSchemaRDD, tableName) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala new file mode 100644 index 0000000000..7b41aa1f1b --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala @@ -0,0 +1,100 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.api.java + +import java.beans.{Introspector, PropertyDescriptor} + +import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} +import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, GenericRow, Row => ScalaRow} +import org.apache.spark.sql.catalyst.types._ +import org.apache.spark.sql.parquet.ParquetRelation +import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan} + +/** + * The entry point for executing Spark SQL queries from a Java program. + */ +class JavaSQLContext(sparkContext: JavaSparkContext) { + + val sqlContext = new SQLContext(sparkContext.sc) + + /** + * Executes a query expressed in SQL, returning the result as a JavaSchemaRDD + */ + def sql(sqlQuery: String): JavaSchemaRDD = { + val result = new JavaSchemaRDD(sqlContext, sqlContext.parseSql(sqlQuery)) + // We force query optimization to happen right away instead of letting it happen lazily like + // when using the query DSL. This is so DDL commands behave as expected. This is only + // generates the RDD lineage for DML queries, but do not perform any execution. + result.queryExecution.toRdd + result + } + + /** + * Applies a schema to an RDD of Java Beans. + */ + def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): JavaSchemaRDD = { + // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. + val beanInfo = Introspector.getBeanInfo(beanClass) + + val fields = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") + val schema = fields.map { property => + val dataType = property.getPropertyType match { + case c: Class[_] if c == classOf[java.lang.String] => StringType + case c: Class[_] if c == java.lang.Short.TYPE => ShortType + case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType + case c: Class[_] if c == java.lang.Long.TYPE => LongType + case c: Class[_] if c == java.lang.Double.TYPE => DoubleType + case c: Class[_] if c == java.lang.Byte.TYPE => ByteType + case c: Class[_] if c == java.lang.Float.TYPE => FloatType + case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType + } + + AttributeReference(property.getName, dataType, true)() + } + + val className = beanClass.getCanonicalName + val rowRdd = rdd.rdd.mapPartitions { iter => + // BeanInfo is not serializable so we must rediscover it remotely for each partition. + val localBeanInfo = Introspector.getBeanInfo(Class.forName(className)) + val extractors = + localBeanInfo.getPropertyDescriptors.filterNot(_.getName == "class").map(_.getReadMethod) + + iter.map { row => + new GenericRow(extractors.map(e => e.invoke(row)).toArray[Any]): ScalaRow + } + } + new JavaSchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(schema, rowRdd))) + } + + + /** + * Loads a parquet file, returning the result as a [[JavaSchemaRDD]]. + */ + def parquetFile(path: String): JavaSchemaRDD = + new JavaSchemaRDD(sqlContext, ParquetRelation("ParquetFile", path)) + + + /** + * Registers the given RDD as a temporary table in the catalog. Temporary tables exist only + * during the lifetime of this instance of SQLContext. + */ + def registerRDDAsTable(rdd: JavaSchemaRDD, tableName: String): Unit = { + sqlContext.registerRDDAsTable(rdd.baseSchemaRDD, tableName) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala new file mode 100644 index 0000000000..d43d672938 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala @@ -0,0 +1,48 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.api.java + +import org.apache.spark.api.java.{JavaRDDLike, JavaRDD} +import org.apache.spark.sql.{SQLContext, SchemaRDD, SchemaRDDLike} +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.rdd.RDD + +/** + * An RDD of [[Row]] objects that is returned as the result of a Spark SQL query. In addition to + * standard RDD operations, a JavaSchemaRDD can also be registered as a table in the JavaSQLContext + * that was used to create. Registering a JavaSchemaRDD allows its contents to be queried in + * future SQL statement. + * + * @groupname schema SchemaRDD Functions + * @groupprio schema -1 + * @groupname Ungrouped Base RDD Functions + */ +class JavaSchemaRDD( + @transient val sqlContext: SQLContext, + @transient protected[spark] val logicalPlan: LogicalPlan) + extends JavaRDDLike[Row, JavaRDD[Row]] + with SchemaRDDLike { + + private[sql] val baseSchemaRDD = new SchemaRDD(sqlContext, logicalPlan) + + override val classTag = scala.reflect.classTag[Row] + + override def wrapRDD(rdd: RDD[Row]): JavaRDD[Row] = JavaRDD.fromRDD(rdd) + + val rdd = baseSchemaRDD.map(new Row(_)) +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala new file mode 100644 index 0000000000..362fe76958 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala @@ -0,0 +1,93 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.api.java + +import org.apache.spark.sql.catalyst.expressions.{Row => ScalaRow} + +/** + * A result row from a SparkSQL query. + */ +class Row(row: ScalaRow) extends Serializable { + + /** Returns the number of columns present in this Row. */ + def length: Int = row.length + + /** Returns the value of column `i`. */ + def get(i: Int): Any = + row(i) + + /** Returns true if value at column `i` is NULL. */ + def isNullAt(i: Int) = get(i) == null + + /** + * Returns the value of column `i` as an int. This function will throw an exception if the value + * is at `i` is not an integer, or if it is null. + */ + def getInt(i: Int): Int = + row.getInt(i) + + /** + * Returns the value of column `i` as a long. This function will throw an exception if the value + * is at `i` is not a long, or if it is null. + */ + def getLong(i: Int): Long = + row.getLong(i) + + /** + * Returns the value of column `i` as a double. This function will throw an exception if the + * value is at `i` is not a double, or if it is null. + */ + def getDouble(i: Int): Double = + row.getDouble(i) + + /** + * Returns the value of column `i` as a bool. This function will throw an exception if the value + * is at `i` is not a boolean, or if it is null. + */ + def getBoolean(i: Int): Boolean = + row.getBoolean(i) + + /** + * Returns the value of column `i` as a short. This function will throw an exception if the value + * is at `i` is not a short, or if it is null. + */ + def getShort(i: Int): Short = + row.getShort(i) + + /** + * Returns the value of column `i` as a byte. This function will throw an exception if the value + * is at `i` is not a byte, or if it is null. + */ + def getByte(i: Int): Byte = + row.getByte(i) + + /** + * Returns the value of column `i` as a float. This function will throw an exception if the value + * is at `i` is not a float, or if it is null. + */ + def getFloat(i: Int): Float = + row.getFloat(i) + + /** + * Returns the value of column `i` as a String. This function will throw an exception if the + * value is at `i` is not a String. + */ + def getString(i: Int): String = + row.getString(i) +} + diff --git a/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala new file mode 100644 index 0000000000..def0e046a3 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.api.java + +import scala.beans.BeanProperty + +import org.scalatest.FunSuite + +import org.apache.spark.api.java.JavaSparkContext +import org.apache.spark.sql.test.TestSQLContext + +// Implicits +import scala.collection.JavaConversions._ + +class PersonBean extends Serializable { + @BeanProperty + var name: String = _ + + @BeanProperty + var age: Int = _ +} + +class JavaSQLSuite extends FunSuite { + val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext) + val javaSqlCtx = new JavaSQLContext(javaCtx) + + test("schema from JavaBeans") { + val person = new PersonBean + person.setName("Michael") + person.setAge(29) + + val rdd = javaCtx.parallelize(person :: Nil) + val schemaRDD = javaSqlCtx.applySchema(rdd, classOf[PersonBean]) + + schemaRDD.registerAsTable("people") + javaSqlCtx.sql("SELECT * FROM people").collect() + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index 46febbfad0..ff8eaacded 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -71,6 +71,18 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { override def executePlan(plan: LogicalPlan): this.QueryExecution = new this.QueryExecution { val logical = plan } + /** + * Executes a query expressed in HiveQL using Spark, returning the result as a SchemaRDD. + */ + def hql(hqlQuery: String): SchemaRDD = { + val result = new SchemaRDD(this, HiveQl.parseSql(hqlQuery)) + // We force query optimization to happen right away instead of letting it happen lazily like + // when using the query DSL. This is so DDL commands behave as expected. This is only + // generates the RDD lineage for DML queries, but do not perform any execution. + result.queryExecution.toRdd + result + } + // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. @transient protected val outputBuffer = new java.io.OutputStream { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala new file mode 100644 index 0000000000..6df76fa825 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala @@ -0,0 +1,42 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.api.java + +import org.apache.spark.api.java.JavaSparkContext +import org.apache.spark.sql.api.java.{JavaSQLContext, JavaSchemaRDD} +import org.apache.spark.sql.hive.{HiveContext, HiveQl} + +/** + * The entry point for executing Spark SQL queries from a Java program. + */ +class JavaHiveContext(sparkContext: JavaSparkContext) extends JavaSQLContext(sparkContext) { + + override val sqlContext = new HiveContext(sparkContext) + + /** + * Executes a query expressed in HiveQL, returning the result as a JavaSchemaRDD. + */ + def hql(hqlQuery: String): JavaSchemaRDD = { + val result = new JavaSchemaRDD(sqlContext, HiveQl.parseSql(hqlQuery)) + // We force query optimization to happen right away instead of letting it happen lazily like + // when using the query DSL. This is so DDL commands behave as expected. This is only + // generates the RDD lineage for DML queries, but do not perform any execution. + result.queryExecution.toRdd + result + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala new file mode 100644 index 0000000000..8137f99b22 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.api.java + +import org.scalatest.FunSuite + +import org.apache.spark.api.java.JavaSparkContext +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.hive.TestHive + +// Implicits +import scala.collection.JavaConversions._ + +class JavaHiveSQLSuite extends FunSuite { + ignore("SELECT * FROM src") { + val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext) + // There is a little trickery here to avoid instantiating two HiveContexts in the same JVM + val javaSqlCtx = new JavaHiveContext(javaCtx) { + override val sqlContext = TestHive + } + + assert( + javaSqlCtx.hql("SELECT * FROM src").collect().map(_.getInt(0)) === + TestHive.sql("SELECT * FROM src").collect().map(_.getInt(0)).toSeq) + } +} |