[SPARK-17346][SQL][test-maven]Add Kafka source for Structured Streaming (branch 2.0)

core/src/main/scala/org/apache/spark/util/UninterruptibleThread.scala

@@ -89,13 +89,6 @@ private[spark] class UninterruptibleThread(name: String) extends Thread(name) {
     }
   }
 
-  /**
-   * Tests whether `interrupt()` has been called.
-   */
-  override def isInterrupted: Boolean = {
-    super.isInterrupted || uninterruptibleLock.synchronized { shouldInterruptThread }
-  }
-
   /**
    * Interrupt `this` thread if possible. If `this` is in the uninterruptible status, it won't be
    * interrupted until it enters into the interruptible status.

dev/run-tests.py

@@ -110,7 +110,7 @@ def determine_modules_to_test(changed_modules):
     ['graphx', 'examples']
     >>> x = [x.name for x in determine_modules_to_test([modules.sql])]
     >>> x # doctest: +NORMALIZE_WHITESPACE
-    ['sql', 'hive', 'mllib', 'examples', 'hive-thriftserver',
+    ['sql', 'hive', 'mllib', 'sql-kafka-0-10', 'examples', 'hive-thriftserver',
      'pyspark-sql', 'sparkr', 'pyspark-mllib', 'pyspark-ml']
     """
     modules_to_test = set()

dev/sparktestsupport/modules.py

@@ -158,6 +158,18 @@ def __hash__(self):
 )
 
 
+sql_kafka = Module(
+    name="sql-kafka-0-10",
+    dependencies=[sql],
+    source_file_regexes=[
+        "external/kafka-0-10-sql",
+    ],
+    sbt_test_goals=[
+        "sql-kafka-0-10/test",
+    ]
+)
+
+
 sketch = Module(
     name="sketch",
     dependencies=[tags],

docs/structured-streaming-kafka-integration.md

@@ -0,0 +1,239 @@
+---
+layout: global
+title: Structured Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher)
+---
+
+Structured Streaming integration for Kafka 0.10 to poll data from Kafka.
+
+### Linking
+For Scala/Java applications using SBT/Maven project definitions, link your application with the following artifact:
+
+    groupId = org.apache.spark
+    artifactId = spark-sql-kafka-0-10_{{site.SCALA_BINARY_VERSION}}
+    version = {{site.SPARK_VERSION_SHORT}}
+
+For Python applications, you need to add this above library and its dependencies when deploying your
+application. See the [Deploying](#deploying) subsection below.
+
+### Creating a Kafka Source Stream
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+    // Subscribe to 1 topic
+    val ds1 = spark
+      .readStream
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribe", "topic1")
+      .load()
+    ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+      .as[(String, String)]
+
+    // Subscribe to multiple topics
+    val ds2 = spark
+      .readStream
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribe", "topic1,topic2")
+      .load()
+    ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+      .as[(String, String)]
+
+    // Subscribe to a pattern
+    val ds3 = spark
+      .readStream
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribePattern", "topic.*")
+      .load()
+    ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+      .as[(String, String)]
+
+</div>
+<div data-lang="java" markdown="1">
+
+    // Subscribe to 1 topic
+    Dataset<Row> ds1 = spark
+      .readStream()
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribe", "topic1")
+      .load()
+    ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+
+    // Subscribe to multiple topics
+    Dataset<Row> ds2 = spark
+      .readStream()
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribe", "topic1,topic2")
+      .load()
+    ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+
+    // Subscribe to a pattern
+    Dataset<Row> ds3 = spark
+      .readStream()
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribePattern", "topic.*")
+      .load()
+    ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+
+</div>
+<div data-lang="python" markdown="1">
+
+    # Subscribe to 1 topic
+    ds1 = spark
+      .readStream()
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribe", "topic1")
+      .load()
+    ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+
+    # Subscribe to multiple topics
+    ds2 = spark
+      .readStream
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribe", "topic1,topic2")
+      .load()
+    ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+
+    # Subscribe to a pattern
+    ds3 = spark
+      .readStream()
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
+      .option("subscribePattern", "topic.*")
+      .load()
+    ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
+
+</div>
+</div>
+
+Each row in the source has the following schema:
+<table class="table">
+<tr><th>Column</th><th>Type</th></tr>
+<tr>
+  <td>key</td>
+  <td>binary</td>
+</tr>
+<tr>
+  <td>value</td>
+  <td>binary</td>
+</tr>
+<tr>
+  <td>topic</td>
+  <td>string</td>
+</tr>
+<tr>
+  <td>partition</td>
+  <td>int</td>
+</tr>
+<tr>
+  <td>offset</td>
+  <td>long</td>
+</tr>
+<tr>
+  <td>timestamp</td>
+  <td>long</td>
+</tr>
+<tr>
+  <td>timestampType</td>
+  <td>int</td>
+</tr>
+</table>
+
+The following options must be set for the Kafka source.
+
+<table class="table">
+<tr><th>Option</th><th>value</th><th>meaning</th></tr>
+<tr>
+  <td>subscribe</td>
+  <td>A comma-separated list of topics</td>
+  <td>The topic list to subscribe. Only one of "subscribe" and "subscribePattern" options can be
+  specified for Kafka source.</td>
+</tr>
+<tr>
+  <td>subscribePattern</td>
+  <td>Java regex string</td>
+  <td>The pattern used to subscribe the topic. Only one of "subscribe" and "subscribePattern"
+  options can be specified for Kafka source.</td>
+</tr>
+<tr>
+  <td>kafka.bootstrap.servers</td>
+  <td>A comma-separated list of host:port</td>
+  <td>The Kafka "bootstrap.servers" configuration.</td>
+</tr>
+</table>
+
+The following configurations are optional:
+
+<table class="table">
+<tr><th>Option</th><th>value</th><th>default</th><th>meaning</th></tr>
+<tr>
+  <td>startingOffset</td>
+  <td>["earliest", "latest"]</td>
+  <td>"latest"</td>
+  <td>The start point when a query is started, either "earliest" which is from the earliest offset, 
+  or "latest" which is just from the latest offset. Note: This only applies when a new Streaming q
+  uery is started, and that resuming will always pick up from where the query left off.</td>
+</tr>
+<tr>
+  <td>failOnDataLoss</td>
+  <td>[true, false]</td>
+  <td>true</td>
+  <td>Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or 
+  offsets are out of range). This may be a false alarm. You can disable it when it doesn't work
+  as you expected.</td>
+</tr>
+<tr>
+  <td>kafkaConsumer.pollTimeoutMs</td>
+  <td>long</td>
+  <td>512</td>
+  <td>The timeout in milliseconds to poll data from Kafka in executors.</td>
+</tr>
+<tr>
+  <td>fetchOffset.numRetries</td>
+  <td>int</td>
+  <td>3</td>
+  <td>Number of times to retry before giving up fatch Kafka latest offsets.</td>
+</tr>
+<tr>
+  <td>fetchOffset.retryIntervalMs</td>
+  <td>long</td>
+  <td>10</td>
+  <td>milliseconds to wait before retrying to fetch Kafka offsets</td>
+</tr>
+</table>
+
+Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, 
+`stream.option("kafka.bootstrap.servers", "host:port")`. For possible kafkaParams, see 
+[Kafka consumer config docs](http://kafka.apache.org/documentation.html#newconsumerconfigs).
+
+Note that the following Kafka params cannot be set and the Kafka source will throw an exception:
+- **group.id**: Kafka source will create a unique group id for each query automatically.
+- **auto.offset.reset**: Set the source option `startingOffset` to `earliest` or `latest` to specify
+ where to start instead. Structured Streaming manages which offsets are consumed internally, rather 
+ than rely on the kafka Consumer to do it. This will ensure that no data is missed when when new 
+ topics/partitions are dynamically subscribed. Note that `startingOffset` only applies when a new
+ Streaming query is started, and that resuming will always pick up from where the query left off.
+- **key.deserializer**: Keys are always deserialized as byte arrays with ByteArrayDeserializer. Use 
+ DataFrame operations to explicitly deserialize the keys.
+- **value.deserializer**: Values are always deserialized as byte arrays with ByteArrayDeserializer. 
+ Use DataFrame operations to explicitly deserialize the values.
+- **enable.auto.commit**: Kafka source doesn't commit any offset.
+- **interceptor.classes**: Kafka source always read keys and values as byte arrays. It's not safe to
+ use ConsumerInterceptor as it may break the query.
+
+### Deploying
+
+As with any Spark applications, `spark-submit` is used to launch your application. `spark-sql-kafka-0-10_{{site.SCALA_BINARY_VERSION}}`
+and its dependencies can be directly added to `spark-submit` using `--packages`, such as,
+
+    ./bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ...
+
+See [Application Submission Guide](submitting-applications.html) for more details about submitting
+applications with external dependencies.

docs/structured-streaming-programming-guide.md

@@ -418,10 +418,15 @@ Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, as
 Streaming DataFrames can be created through the `DataStreamReader` interface 
 ([Scala](api/scala/index.html#org.apache.spark.sql.streaming.DataStreamReader)/
 [Java](api/java/org/apache/spark/sql/streaming/DataStreamReader.html)/
-[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamReader) docs) returned by `SparkSession.readStream()`. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc. In Spark 2.0, there are a few built-in sources.
+[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamReader) docs) returned by `SparkSession.readStream()`. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc.
+
+#### Data Sources
+In Spark 2.0, there are a few built-in sources.
 
   - **File source** - Reads files written in a directory as a stream of data. Supported file formats are text, csv, json, parquet. See the docs of the DataStreamReader interface for a more up-to-date list, and supported options for each file format. Note that the files must be atomically placed in the given directory, which in most file systems, can be achieved by file move operations.
 
+  - **Kafka source** - Poll data from Kafka. It's compatible with Kafka broker versions 0.10.0 or higher. See the [Kafka Integration Guide](structured-streaming-kafka-integration.html) for more details.
+
   - **Socket source (for testing)** - Reads UTF8 text data from a socket connection. The listening server socket is at the driver. Note that this should be used only for testing as this does not provide end-to-end fault-tolerance guarantees. 
 
 Here are some examples.

external/kafka-0-10-sql/pom.xml

@@ -0,0 +1,96 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ 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.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent_2.11</artifactId>
+    <version>2.0.2-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
+  <properties>
+    <sbt.project.name>sql-kafka-0-10</sbt.project.name>
+  </properties>
+  <packaging>jar</packaging>
+  <name>Kafka 0.10 Source for Structured Streaming</name>
+  <url>http://spark.apache.org/</url>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-sql_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-core_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-catalyst_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-sql_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.kafka</groupId>
+      <artifactId>kafka-clients</artifactId>
+      <version>0.10.0.1</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.kafka</groupId>
+      <artifactId>kafka_${scala.binary.version}</artifactId>
+      <version>0.10.0.1</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.sf.jopt-simple</groupId>
+      <artifactId>jopt-simple</artifactId>
+      <version>3.2</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scalacheck</groupId>
+      <artifactId>scalacheck_${scala.binary.version}</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-tags_${scala.binary.version}</artifactId>
+    </dependency>
+  </dependencies>
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+  </build>
+</project>

external/kafka-0-10-sql/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister

@@ -0,0 +1 @@
+org.apache.spark.sql.kafka010.KafkaSourceProvider