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From fhue...@apache.org
Subject [30/51] [abbrv] [partial] flink git commit: [FLINK-4676] [connectors] Merge batch and streaming connectors into common Maven module.
Date Fri, 02 Dec 2016 13:35:20 GMT
http://git-wip-us.apache.org/repos/asf/flink/blob/de4fe3b7/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaConsumerTestBase.java
----------------------------------------------------------------------
diff --git a/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaConsumerTestBase.java b/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaConsumerTestBase.java
new file mode 100644
index 0000000..aa7ea49
--- /dev/null
+++ b/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaConsumerTestBase.java
@@ -0,0 +1,2006 @@
+/*
+ * 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.flink.streaming.connectors.kafka;
+
+import kafka.consumer.Consumer;
+import kafka.consumer.ConsumerConfig;
+import kafka.consumer.ConsumerIterator;
+import kafka.consumer.KafkaStream;
+import kafka.javaapi.consumer.ConsumerConnector;
+import kafka.message.MessageAndMetadata;
+import kafka.server.KafkaServer;
+import org.apache.commons.io.output.ByteArrayOutputStream;
+import org.apache.flink.api.common.ExecutionConfig;
+import org.apache.flink.api.common.JobExecutionResult;
+import org.apache.flink.api.common.functions.FlatMapFunction;
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.common.functions.RichFlatMapFunction;
+import org.apache.flink.api.common.functions.RichMapFunction;
+import org.apache.flink.api.common.restartstrategy.RestartStrategies;
+import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
+import org.apache.flink.api.common.typeinfo.TypeHint;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.java.tuple.Tuple1;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.api.java.typeutils.TypeInfoParser;
+import org.apache.flink.client.program.ProgramInvocationException;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.core.memory.DataInputView;
+import org.apache.flink.core.memory.DataInputViewStreamWrapper;
+import org.apache.flink.core.memory.DataOutputView;
+import org.apache.flink.core.memory.DataOutputViewStreamWrapper;
+import org.apache.flink.runtime.client.JobCancellationException;
+import org.apache.flink.runtime.client.JobExecutionException;
+import org.apache.flink.runtime.jobmanager.scheduler.NoResourceAvailableException;
+import org.apache.flink.runtime.state.CheckpointListener;
+import org.apache.flink.streaming.api.checkpoint.Checkpointed;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.datastream.DataStreamSink;
+import org.apache.flink.streaming.api.datastream.DataStreamSource;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks;
+import org.apache.flink.streaming.api.functions.sink.DiscardingSink;
+import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
+import org.apache.flink.streaming.api.functions.sink.SinkFunction;
+import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
+import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
+import org.apache.flink.streaming.api.functions.source.SourceFunction;
+import org.apache.flink.streaming.api.operators.AbstractStreamOperator;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.connectors.kafka.testutils.DataGenerators;
+import org.apache.flink.streaming.connectors.kafka.testutils.FailingIdentityMapper;
+import org.apache.flink.streaming.connectors.kafka.testutils.JobManagerCommunicationUtils;
+import org.apache.flink.streaming.connectors.kafka.testutils.PartitionValidatingMapper;
+import org.apache.flink.streaming.connectors.kafka.testutils.ThrottledMapper;
+import org.apache.flink.streaming.connectors.kafka.testutils.Tuple2Partitioner;
+import org.apache.flink.streaming.connectors.kafka.testutils.ValidatingExactlyOnceSink;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+import org.apache.flink.streaming.util.serialization.DeserializationSchema;
+import org.apache.flink.streaming.util.serialization.KeyedDeserializationSchema;
+import org.apache.flink.streaming.util.serialization.KeyedDeserializationSchemaWrapper;
+import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema;
+import org.apache.flink.streaming.util.serialization.KeyedSerializationSchemaWrapper;
+import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
+import org.apache.flink.streaming.util.serialization.TypeInformationKeyValueSerializationSchema;
+import org.apache.flink.streaming.util.serialization.TypeInformationSerializationSchema;
+import org.apache.flink.test.util.SuccessException;
+import org.apache.flink.testutils.junit.RetryOnException;
+import org.apache.flink.testutils.junit.RetryRule;
+import org.apache.flink.util.Collector;
+import org.apache.kafka.clients.producer.ProducerConfig;
+import org.apache.kafka.common.errors.TimeoutException;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Rule;
+
+import javax.management.MBeanServer;
+import javax.management.ObjectName;
+import java.io.ByteArrayInputStream;
+import java.io.IOException;
+import java.lang.management.ManagementFactory;
+import java.util.ArrayList;
+import java.util.BitSet;
+import java.util.Collections;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Properties;
+import java.util.Random;
+import java.util.Set;
+import java.util.UUID;
+import java.util.concurrent.atomic.AtomicReference;
+
+import static org.apache.flink.test.util.TestUtils.tryExecute;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertNull;
+import static org.junit.Assert.assertTrue;
+import static org.junit.Assert.fail;
+
+
+@SuppressWarnings("serial")
+public abstract class KafkaConsumerTestBase extends KafkaTestBase {
+	
+	@Rule
+	public RetryRule retryRule = new RetryRule();
+
+
+	// ------------------------------------------------------------------------
+	//  Common Test Preparation
+	// ------------------------------------------------------------------------
+
+	/**
+	 * Makes sure that no job is on the JobManager any more from any previous tests that use
+	 * the same mini cluster. Otherwise, missing slots may happen.
+	 */
+	@Before
+	public void ensureNoJobIsLingering() throws Exception {
+		JobManagerCommunicationUtils.waitUntilNoJobIsRunning(flink.getLeaderGateway(timeout));
+	}
+	
+	
+	// ------------------------------------------------------------------------
+	//  Suite of Tests
+	//
+	//  The tests here are all not activated (by an @Test tag), but need
+	//  to be invoked from the extending classes. That way, the classes can
+	//  select which tests to run.
+	// ------------------------------------------------------------------------
+
+	/**
+	 * Test that ensures the KafkaConsumer is properly failing if the topic doesnt exist
+	 * and a wrong broker was specified
+	 *
+	 * @throws Exception
+	 */
+	public void runFailOnNoBrokerTest() throws Exception {
+		try {
+			Properties properties = new Properties();
+
+			StreamExecutionEnvironment see = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+			see.getConfig().disableSysoutLogging();
+			see.setRestartStrategy(RestartStrategies.noRestart());
+			see.setParallelism(1);
+
+			// use wrong ports for the consumers
+			properties.setProperty("bootstrap.servers", "localhost:80");
+			properties.setProperty("zookeeper.connect", "localhost:80");
+			properties.setProperty("group.id", "test");
+			properties.setProperty("request.timeout.ms", "3000"); // let the test fail fast
+			properties.setProperty("socket.timeout.ms", "3000");
+			properties.setProperty("session.timeout.ms", "2000");
+			properties.setProperty("fetch.max.wait.ms", "2000");
+			properties.setProperty("heartbeat.interval.ms", "1000");
+			properties.putAll(secureProps);
+			FlinkKafkaConsumerBase<String> source = kafkaServer.getConsumer("doesntexist", new SimpleStringSchema(), properties);
+			DataStream<String> stream = see.addSource(source);
+			stream.print();
+			see.execute("No broker test");
+		} catch(ProgramInvocationException pie) {
+			if(kafkaServer.getVersion().equals("0.9") || kafkaServer.getVersion().equals("0.10")) {
+				assertTrue(pie.getCause() instanceof JobExecutionException);
+
+				JobExecutionException jee = (JobExecutionException) pie.getCause();
+
+				assertTrue(jee.getCause() instanceof TimeoutException);
+
+				TimeoutException te = (TimeoutException) jee.getCause();
+
+				assertEquals("Timeout expired while fetching topic metadata", te.getMessage());
+			} else {
+				assertTrue(pie.getCause() instanceof JobExecutionException);
+
+				JobExecutionException jee = (JobExecutionException) pie.getCause();
+
+				assertTrue(jee.getCause() instanceof RuntimeException);
+
+				RuntimeException re = (RuntimeException) jee.getCause();
+
+				assertTrue(re.getMessage().contains("Unable to retrieve any partitions for the requested topics [doesntexist]"));
+			}
+		}
+	}
+
+	/**
+	 * Ensures that the committed offsets to Kafka are the offsets of "the next record to process"
+	 */
+	public void runCommitOffsetsToKafka() throws Exception {
+		// 3 partitions with 50 records each (0-49, so the expected commit offset of each partition should be 50)
+		final int parallelism = 3;
+		final int recordsInEachPartition = 50;
+
+		final String topicName = writeSequence("testCommitOffsetsToKafkaTopic", recordsInEachPartition, parallelism, 1);
+
+		final StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.getConfig().disableSysoutLogging();
+		env.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+		env.setParallelism(parallelism);
+		env.enableCheckpointing(200);
+
+		DataStream<String> stream = env.addSource(kafkaServer.getConsumer(topicName, new SimpleStringSchema(), standardProps));
+		stream.addSink(new DiscardingSink<String>());
+
+		final AtomicReference<Throwable> errorRef = new AtomicReference<>();
+		final Thread runner = new Thread("runner") {
+			@Override
+			public void run() {
+				try {
+					env.execute();
+				}
+				catch (Throwable t) {
+					if (!(t.getCause() instanceof JobCancellationException)) {
+						errorRef.set(t);
+					}
+				}
+			}
+		};
+		runner.start();
+
+		final Long l50 = 50L; // the final committed offset in Kafka should be 50
+		final long deadline = 30000 + System.currentTimeMillis();
+
+		KafkaTestEnvironment.KafkaOffsetHandler kafkaOffsetHandler = kafkaServer.createOffsetHandler(standardProps);
+
+		do {
+			Long o1 = kafkaOffsetHandler.getCommittedOffset(topicName, 0);
+			Long o2 = kafkaOffsetHandler.getCommittedOffset(topicName, 1);
+			Long o3 = kafkaOffsetHandler.getCommittedOffset(topicName, 2);
+
+			if (l50.equals(o1) && l50.equals(o2) && l50.equals(o3)) {
+				break;
+			}
+
+			Thread.sleep(100);
+		}
+		while (System.currentTimeMillis() < deadline);
+
+		// cancel the job
+		JobManagerCommunicationUtils.cancelCurrentJob(flink.getLeaderGateway(timeout));
+
+		final Throwable t = errorRef.get();
+		if (t != null) {
+			throw new RuntimeException("Job failed with an exception", t);
+		}
+
+		// final check to see if offsets are correctly in Kafka
+		Long o1 = kafkaOffsetHandler.getCommittedOffset(topicName, 0);
+		Long o2 = kafkaOffsetHandler.getCommittedOffset(topicName, 1);
+		Long o3 = kafkaOffsetHandler.getCommittedOffset(topicName, 2);
+		Assert.assertEquals(Long.valueOf(50L), o1);
+		Assert.assertEquals(Long.valueOf(50L), o2);
+		Assert.assertEquals(Long.valueOf(50L), o3);
+
+		kafkaOffsetHandler.close();
+		deleteTestTopic(topicName);
+	}
+
+	/**
+	 * This test first writes a total of 300 records to a test topic, reads the first 150 so that some offsets are
+	 * committed to Kafka, and then startup the consumer again to read the remaining records starting from the committed offsets.
+	 * The test ensures that whatever offsets were committed to Kafka, the consumer correctly picks them up
+	 * and starts at the correct position.
+	 */
+	public void runStartFromKafkaCommitOffsets() throws Exception {
+		final int parallelism = 3;
+		final int recordsInEachPartition = 300;
+
+		final String topicName = writeSequence("testStartFromKafkaCommitOffsetsTopic", recordsInEachPartition, parallelism, 1);
+
+		KafkaTestEnvironment.KafkaOffsetHandler kafkaOffsetHandler = kafkaServer.createOffsetHandler(standardProps);
+
+		Long o1;
+		Long o2;
+		Long o3;
+		int attempt = 0;
+		// make sure that o1, o2, o3 are not all null before proceeding
+		do {
+			attempt++;
+			LOG.info("Attempt " + attempt + " to read records and commit some offsets to Kafka");
+
+			final StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+			env.getConfig().disableSysoutLogging();
+			env.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+			env.setParallelism(parallelism);
+			env.enableCheckpointing(20); // fast checkpoints to make sure we commit some offsets
+
+			env
+				.addSource(kafkaServer.getConsumer(topicName, new SimpleStringSchema(), standardProps))
+				.map(new ThrottledMapper<String>(50))
+				.map(new MapFunction<String, Object>() {
+					int count = 0;
+					@Override
+					public Object map(String value) throws Exception {
+						count++;
+						if (count == 150) {
+							throw new SuccessException();
+						}
+						return null;
+					}
+				})
+				.addSink(new DiscardingSink<>());
+
+			tryExecute(env, "Read some records to commit offsets to Kafka");
+
+			o1 = kafkaOffsetHandler.getCommittedOffset(topicName, 0);
+			o2 = kafkaOffsetHandler.getCommittedOffset(topicName, 1);
+			o3 = kafkaOffsetHandler.getCommittedOffset(topicName, 2);
+		} while (o1 == null && o2 == null && o3 == null && attempt < 3);
+
+		if (o1 == null && o2 == null && o3 == null) {
+			throw new RuntimeException("No offsets have been committed after 3 attempts");
+		}
+
+		LOG.info("Got final committed offsets from Kafka o1={}, o2={}, o3={}", o1, o2, o3);
+
+		final StreamExecutionEnvironment env2 = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env2.getConfig().disableSysoutLogging();
+		env2.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+		env2.setParallelism(parallelism);
+
+		// whatever offsets were committed for each partition, the consumer should pick
+		// them up and start from the correct position so that the remaining records are all read
+		HashMap<Integer, Tuple2<Integer, Integer>> partitionsToValuesCountAndStartOffset = new HashMap<>();
+		partitionsToValuesCountAndStartOffset.put(0, new Tuple2<>(
+			(o1 != null) ? (int) (recordsInEachPartition - o1) : recordsInEachPartition,
+			(o1 != null) ? o1.intValue() : 0
+		));
+		partitionsToValuesCountAndStartOffset.put(1, new Tuple2<>(
+			(o2 != null) ? (int) (recordsInEachPartition - o2) : recordsInEachPartition,
+			(o2 != null) ? o2.intValue() : 0
+		));
+		partitionsToValuesCountAndStartOffset.put(2, new Tuple2<>(
+			(o3 != null) ? (int) (recordsInEachPartition - o3) : recordsInEachPartition,
+			(o3 != null) ? o3.intValue() : 0
+		));
+
+		readSequence(env2, standardProps, topicName, partitionsToValuesCountAndStartOffset);
+
+		kafkaOffsetHandler.close();
+		deleteTestTopic(topicName);
+	}
+
+	/**
+	 * This test ensures that when the consumers retrieve some start offset from kafka (earliest, latest), that this offset
+	 * is committed to Kafka, even if some partitions are not read.
+	 *
+	 * Test:
+	 * - Create 3 partitions
+	 * - write 50 messages into each.
+	 * - Start three consumers with auto.offset.reset='latest' and wait until they committed into Kafka.
+	 * - Check if the offsets in Kafka are set to 50 for the three partitions
+	 *
+	 * See FLINK-3440 as well
+	 */
+	public void runAutoOffsetRetrievalAndCommitToKafka() throws Exception {
+		// 3 partitions with 50 records each (0-49, so the expected commit offset of each partition should be 50)
+		final int parallelism = 3;
+		final int recordsInEachPartition = 50;
+
+		final String topicName = writeSequence("testAutoOffsetRetrievalAndCommitToKafkaTopic", recordsInEachPartition, parallelism, 1);
+
+		final StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.getConfig().disableSysoutLogging();
+		env.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+		env.setParallelism(parallelism);
+		env.enableCheckpointing(200);
+
+		Properties readProps = new Properties();
+		readProps.putAll(standardProps);
+		readProps.setProperty("auto.offset.reset", "latest"); // set to reset to latest, so that partitions are initially not read
+
+		DataStream<String> stream = env.addSource(kafkaServer.getConsumer(topicName, new SimpleStringSchema(), readProps));
+		stream.addSink(new DiscardingSink<String>());
+
+		final AtomicReference<Throwable> errorRef = new AtomicReference<>();
+		final Thread runner = new Thread("runner") {
+			@Override
+			public void run() {
+				try {
+					env.execute();
+				}
+				catch (Throwable t) {
+					if (!(t.getCause() instanceof JobCancellationException)) {
+						errorRef.set(t);
+					}
+				}
+			}
+		};
+		runner.start();
+
+		KafkaTestEnvironment.KafkaOffsetHandler kafkaOffsetHandler = kafkaServer.createOffsetHandler(standardProps);
+
+		final Long l50 = 50L; // the final committed offset in Kafka should be 50
+		final long deadline = 30000 + System.currentTimeMillis();
+		do {
+			Long o1 = kafkaOffsetHandler.getCommittedOffset(topicName, 0);
+			Long o2 = kafkaOffsetHandler.getCommittedOffset(topicName, 1);
+			Long o3 = kafkaOffsetHandler.getCommittedOffset(topicName, 2);
+
+			if (l50.equals(o1) && l50.equals(o2) && l50.equals(o3)) {
+				break;
+			}
+
+			Thread.sleep(100);
+		}
+		while (System.currentTimeMillis() < deadline);
+
+		// cancel the job
+		JobManagerCommunicationUtils.cancelCurrentJob(flink.getLeaderGateway(timeout));
+
+		final Throwable t = errorRef.get();
+		if (t != null) {
+			throw new RuntimeException("Job failed with an exception", t);
+		}
+
+		// final check to see if offsets are correctly in Kafka
+		Long o1 = kafkaOffsetHandler.getCommittedOffset(topicName, 0);
+		Long o2 = kafkaOffsetHandler.getCommittedOffset(topicName, 1);
+		Long o3 = kafkaOffsetHandler.getCommittedOffset(topicName, 2);
+		Assert.assertEquals(Long.valueOf(50L), o1);
+		Assert.assertEquals(Long.valueOf(50L), o2);
+		Assert.assertEquals(Long.valueOf(50L), o3);
+
+		kafkaOffsetHandler.close();
+		deleteTestTopic(topicName);
+	}
+	
+	/**
+	 * Ensure Kafka is working on both producer and consumer side.
+	 * This executes a job that contains two Flink pipelines.
+	 *
+	 * <pre>
+	 * (generator source) --> (kafka sink)-[KAFKA-TOPIC]-(kafka source) --> (validating sink)
+	 * </pre>
+	 * 
+	 * We need to externally retry this test. We cannot let Flink's retry mechanism do it, because the Kafka producer
+	 * does not guarantee exactly-once output. Hence a recovery would introduce duplicates that
+	 * cause the test to fail.
+	 *
+	 * This test also ensures that FLINK-3156 doesn't happen again:
+	 *
+	 * The following situation caused a NPE in the FlinkKafkaConsumer
+	 *
+	 * topic-1 <-- elements are only produced into topic1.
+	 * topic-2
+	 *
+	 * Therefore, this test is consuming as well from an empty topic.
+	 *
+	 */
+	@RetryOnException(times=2, exception=kafka.common.NotLeaderForPartitionException.class)
+	public void runSimpleConcurrentProducerConsumerTopology() throws Exception {
+		final String topic = "concurrentProducerConsumerTopic_" + UUID.randomUUID().toString();
+		final String additionalEmptyTopic = "additionalEmptyTopic_" + UUID.randomUUID().toString();
+
+		final int parallelism = 3;
+		final int elementsPerPartition = 100;
+		final int totalElements = parallelism * elementsPerPartition;
+
+		createTestTopic(topic, parallelism, 2);
+		createTestTopic(additionalEmptyTopic, parallelism, 1); // create an empty topic which will remain empty all the time
+
+		final StreamExecutionEnvironment env =
+				StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(parallelism);
+		env.enableCheckpointing(500);
+		env.setRestartStrategy(RestartStrategies.noRestart()); // fail immediately
+		env.getConfig().disableSysoutLogging();
+
+		TypeInformation<Tuple2<Long, String>> longStringType = TypeInfoParser.parse("Tuple2<Long, String>");
+
+		TypeInformationSerializationSchema<Tuple2<Long, String>> sourceSchema =
+				new TypeInformationSerializationSchema<>(longStringType, env.getConfig());
+
+		TypeInformationSerializationSchema<Tuple2<Long, String>> sinkSchema =
+				new TypeInformationSerializationSchema<>(longStringType, env.getConfig());
+
+		// ----------- add producer dataflow ----------
+
+		DataStream<Tuple2<Long, String>> stream = env.addSource(new RichParallelSourceFunction<Tuple2<Long,String>>() {
+
+			private boolean running = true;
+
+			@Override
+			public void run(SourceContext<Tuple2<Long, String>> ctx) throws InterruptedException {
+				int cnt = getRuntimeContext().getIndexOfThisSubtask() * elementsPerPartition;
+				int limit = cnt + elementsPerPartition;
+
+
+				while (running && cnt < limit) {
+					ctx.collect(new Tuple2<>(1000L + cnt, "kafka-" + cnt));
+					cnt++;
+					// we delay data generation a bit so that we are sure that some checkpoints are
+					// triggered (for FLINK-3156)
+					Thread.sleep(50);
+				}
+			}
+
+			@Override
+			public void cancel() {
+				running = false;
+			}
+		});
+		Properties producerProperties = FlinkKafkaProducerBase.getPropertiesFromBrokerList(brokerConnectionStrings);
+		producerProperties.setProperty("retries", "3");
+		producerProperties.putAll(secureProps);
+		kafkaServer.produceIntoKafka(stream, topic, new KeyedSerializationSchemaWrapper<>(sinkSchema), producerProperties, null);
+
+		// ----------- add consumer dataflow ----------
+
+		List<String> topics = new ArrayList<>();
+		topics.add(topic);
+		topics.add(additionalEmptyTopic);
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		FlinkKafkaConsumerBase<Tuple2<Long, String>> source = kafkaServer.getConsumer(topics, sourceSchema, props);
+
+		DataStreamSource<Tuple2<Long, String>> consuming = env.addSource(source).setParallelism(parallelism);
+
+		consuming.addSink(new RichSinkFunction<Tuple2<Long, String>>() {
+
+			private int elCnt = 0;
+			private BitSet validator = new BitSet(totalElements);
+
+			@Override
+			public void invoke(Tuple2<Long, String> value) throws Exception {
+				String[] sp = value.f1.split("-");
+				int v = Integer.parseInt(sp[1]);
+
+				assertEquals(value.f0 - 1000, (long) v);
+
+				assertFalse("Received tuple twice", validator.get(v));
+				validator.set(v);
+				elCnt++;
+
+				if (elCnt == totalElements) {
+					// check if everything in the bitset is set to true
+					int nc;
+					if ((nc = validator.nextClearBit(0)) != totalElements) {
+						fail("The bitset was not set to 1 on all elements. Next clear:"
+								+ nc + " Set: " + validator);
+					}
+					throw new SuccessException();
+				}
+			}
+
+			@Override
+			public void close() throws Exception {
+				super.close();
+			}
+		}).setParallelism(1);
+
+		try {
+			tryExecutePropagateExceptions(env, "runSimpleConcurrentProducerConsumerTopology");
+		}
+		catch (ProgramInvocationException | JobExecutionException e) {
+			// look for NotLeaderForPartitionException
+			Throwable cause = e.getCause();
+
+			// search for nested SuccessExceptions
+			int depth = 0;
+			while (cause != null && depth++ < 20) {
+				if (cause instanceof kafka.common.NotLeaderForPartitionException) {
+					throw (Exception) cause;
+				}
+				cause = cause.getCause();
+			}
+			throw e;
+		}
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Tests the proper consumption when having a 1:1 correspondence between kafka partitions and
+	 * Flink sources.
+	 */
+	public void runOneToOneExactlyOnceTest() throws Exception {
+
+		final String topic = "oneToOneTopic";
+		final int parallelism = 5;
+		final int numElementsPerPartition = 1000;
+		final int totalElements = parallelism * numElementsPerPartition;
+		final int failAfterElements = numElementsPerPartition / 3;
+
+		createTestTopic(topic, parallelism, 1);
+
+		DataGenerators.generateRandomizedIntegerSequence(
+				StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort),
+				kafkaServer,
+				topic, parallelism, numElementsPerPartition, true);
+
+		// run the topology that fails and recovers
+
+		DeserializationSchema<Integer> schema =
+				new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.enableCheckpointing(500);
+		env.setParallelism(parallelism);
+		env.setRestartStrategy(RestartStrategies.fixedDelayRestart(1, 0));
+		env.getConfig().disableSysoutLogging();
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+
+		FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
+
+		env
+				.addSource(kafkaSource)
+				.map(new PartitionValidatingMapper(parallelism, 1))
+				.map(new FailingIdentityMapper<Integer>(failAfterElements))
+				.addSink(new ValidatingExactlyOnceSink(totalElements)).setParallelism(1);
+
+		FailingIdentityMapper.failedBefore = false;
+		tryExecute(env, "One-to-one exactly once test");
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Tests the proper consumption when having fewer Flink sources than Kafka partitions, so
+	 * one Flink source will read multiple Kafka partitions.
+	 */
+	public void runOneSourceMultiplePartitionsExactlyOnceTest() throws Exception {
+		final String topic = "oneToManyTopic";
+		final int numPartitions = 5;
+		final int numElementsPerPartition = 1000;
+		final int totalElements = numPartitions * numElementsPerPartition;
+		final int failAfterElements = numElementsPerPartition / 3;
+
+		final int parallelism = 2;
+
+		createTestTopic(topic, numPartitions, 1);
+
+		DataGenerators.generateRandomizedIntegerSequence(
+				StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort),
+				kafkaServer,
+				topic, numPartitions, numElementsPerPartition, false);
+
+		// run the topology that fails and recovers
+
+		DeserializationSchema<Integer> schema =
+				new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.enableCheckpointing(500);
+		env.setParallelism(parallelism);
+		env.setRestartStrategy(RestartStrategies.fixedDelayRestart(1, 0));
+		env.getConfig().disableSysoutLogging();
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
+
+		env
+				.addSource(kafkaSource)
+				.map(new PartitionValidatingMapper(numPartitions, 3))
+				.map(new FailingIdentityMapper<Integer>(failAfterElements))
+				.addSink(new ValidatingExactlyOnceSink(totalElements)).setParallelism(1);
+
+		FailingIdentityMapper.failedBefore = false;
+		tryExecute(env, "One-source-multi-partitions exactly once test");
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Tests the proper consumption when having more Flink sources than Kafka partitions, which means
+	 * that some Flink sources will read no partitions.
+	 */
+	public void runMultipleSourcesOnePartitionExactlyOnceTest() throws Exception {
+		final String topic = "manyToOneTopic";
+		final int numPartitions = 5;
+		final int numElementsPerPartition = 1000;
+		final int totalElements = numPartitions * numElementsPerPartition;
+		final int failAfterElements = numElementsPerPartition / 3;
+
+		final int parallelism = 8;
+
+		createTestTopic(topic, numPartitions, 1);
+
+		DataGenerators.generateRandomizedIntegerSequence(
+				StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort),
+				kafkaServer,
+				topic, numPartitions, numElementsPerPartition, true);
+
+		// run the topology that fails and recovers
+
+		DeserializationSchema<Integer> schema =
+				new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.enableCheckpointing(500);
+		env.setParallelism(parallelism);
+		// set the number of restarts to one. The failing mapper will fail once, then it's only success exceptions.
+		env.setRestartStrategy(RestartStrategies.fixedDelayRestart(1, 0));
+		env.getConfig().disableSysoutLogging();
+		env.setBufferTimeout(0);
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
+
+		env
+			.addSource(kafkaSource)
+			.map(new PartitionValidatingMapper(numPartitions, 1))
+			.map(new FailingIdentityMapper<Integer>(failAfterElements))
+			.addSink(new ValidatingExactlyOnceSink(totalElements)).setParallelism(1);
+
+		FailingIdentityMapper.failedBefore = false;
+		tryExecute(env, "multi-source-one-partitions exactly once test");
+
+
+		deleteTestTopic(topic);
+	}
+	
+	
+	/**
+	 * Tests that the source can be properly canceled when reading full partitions. 
+	 */
+	public void runCancelingOnFullInputTest() throws Exception {
+		final String topic = "cancelingOnFullTopic";
+
+		final int parallelism = 3;
+		createTestTopic(topic, parallelism, 1);
+
+		// launch a producer thread
+		DataGenerators.InfiniteStringsGenerator generator =
+				new DataGenerators.InfiniteStringsGenerator(kafkaServer, topic);
+		generator.start();
+
+		// launch a consumer asynchronously
+
+		final AtomicReference<Throwable> jobError = new AtomicReference<>();
+
+		final Runnable jobRunner = new Runnable() {
+			@Override
+			public void run() {
+				try {
+					final StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+					env.setParallelism(parallelism);
+					env.enableCheckpointing(100);
+					env.getConfig().disableSysoutLogging();
+
+					Properties props = new Properties();
+					props.putAll(standardProps);
+					props.putAll(secureProps);
+					FlinkKafkaConsumerBase<String> source = kafkaServer.getConsumer(topic, new SimpleStringSchema(), props);
+
+					env.addSource(source).addSink(new DiscardingSink<String>());
+
+					env.execute("Runner for CancelingOnFullInputTest");
+				}
+				catch (Throwable t) {
+					jobError.set(t);
+				}
+			}
+		};
+
+		Thread runnerThread = new Thread(jobRunner, "program runner thread");
+		runnerThread.start();
+
+		// wait a bit before canceling
+		Thread.sleep(2000);
+
+		Throwable failueCause = jobError.get();
+		if(failueCause != null) {
+			failueCause.printStackTrace();
+			Assert.fail("Test failed prematurely with: " + failueCause.getMessage());
+		}
+
+		// cancel
+		JobManagerCommunicationUtils.cancelCurrentJob(flink.getLeaderGateway(timeout), "Runner for CancelingOnFullInputTest");
+
+		// wait for the program to be done and validate that we failed with the right exception
+		runnerThread.join();
+
+		failueCause = jobError.get();
+		assertNotNull("program did not fail properly due to canceling", failueCause);
+		assertTrue(failueCause.getMessage().contains("Job was cancelled"));
+
+		if (generator.isAlive()) {
+			generator.shutdown();
+			generator.join();
+		}
+		else {
+			Throwable t = generator.getError();
+			if (t != null) {
+				t.printStackTrace();
+				fail("Generator failed: " + t.getMessage());
+			} else {
+				fail("Generator failed with no exception");
+			}
+		}
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Tests that the source can be properly canceled when reading empty partitions. 
+	 */
+	public void runCancelingOnEmptyInputTest() throws Exception {
+		final String topic = "cancelingOnEmptyInputTopic";
+
+		final int parallelism = 3;
+		createTestTopic(topic, parallelism, 1);
+
+		final AtomicReference<Throwable> error = new AtomicReference<>();
+
+		final Runnable jobRunner = new Runnable() {
+			@Override
+			public void run() {
+				try {
+					final StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+					env.setParallelism(parallelism);
+					env.enableCheckpointing(100);
+					env.getConfig().disableSysoutLogging();
+
+					Properties props = new Properties();
+					props.putAll(standardProps);
+					props.putAll(secureProps);
+					FlinkKafkaConsumerBase<String> source = kafkaServer.getConsumer(topic, new SimpleStringSchema(), props);
+
+					env.addSource(source).addSink(new DiscardingSink<String>());
+
+					env.execute("CancelingOnEmptyInputTest");
+				}
+				catch (Throwable t) {
+					LOG.error("Job Runner failed with exception", t);
+					error.set(t);
+				}
+			}
+		};
+
+		Thread runnerThread = new Thread(jobRunner, "program runner thread");
+		runnerThread.start();
+
+		// wait a bit before canceling
+		Thread.sleep(2000);
+
+		Throwable failueCause = error.get();
+		if (failueCause != null) {
+			failueCause.printStackTrace();
+			Assert.fail("Test failed prematurely with: " + failueCause.getMessage());
+		}
+		// cancel
+		JobManagerCommunicationUtils.cancelCurrentJob(flink.getLeaderGateway(timeout));
+
+		// wait for the program to be done and validate that we failed with the right exception
+		runnerThread.join();
+
+		failueCause = error.get();
+		assertNotNull("program did not fail properly due to canceling", failueCause);
+		assertTrue(failueCause.getMessage().contains("Job was cancelled"));
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Tests that the source can be properly canceled when reading full partitions. 
+	 */
+	public void runFailOnDeployTest() throws Exception {
+		final String topic = "failOnDeployTopic";
+
+		createTestTopic(topic, 2, 1);
+
+		DeserializationSchema<Integer> schema =
+				new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(12); // needs to be more that the mini cluster has slots
+		env.getConfig().disableSysoutLogging();
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
+
+		env
+				.addSource(kafkaSource)
+				.addSink(new DiscardingSink<Integer>());
+
+		try {
+			env.execute("test fail on deploy");
+			fail("this test should fail with an exception");
+		}
+		catch (ProgramInvocationException e) {
+
+			// validate that we failed due to a NoResourceAvailableException
+			Throwable cause = e.getCause();
+			int depth = 0;
+			boolean foundResourceException = false;
+
+			while (cause != null && depth++ < 20) {
+				if (cause instanceof NoResourceAvailableException) {
+					foundResourceException = true;
+					break;
+				}
+				cause = cause.getCause();
+			}
+
+			assertTrue("Wrong exception", foundResourceException);
+		}
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Test producing and consuming into multiple topics
+	 * @throws java.lang.Exception
+	 */
+	public void runProduceConsumeMultipleTopics() throws java.lang.Exception {
+		final int NUM_TOPICS = 5;
+		final int NUM_ELEMENTS = 20;
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.getConfig().disableSysoutLogging();
+		
+		// create topics with content
+		final List<String> topics = new ArrayList<>();
+		for (int i = 0; i < NUM_TOPICS; i++) {
+			final String topic = "topic-" + i;
+			topics.add(topic);
+			// create topic
+			createTestTopic(topic, i + 1 /*partitions*/, 1);
+		}
+		// run first job, producing into all topics
+		DataStream<Tuple3<Integer, Integer, String>> stream = env.addSource(new RichParallelSourceFunction<Tuple3<Integer, Integer, String>>() {
+
+			@Override
+			public void run(SourceContext<Tuple3<Integer, Integer, String>> ctx) throws Exception {
+				int partition = getRuntimeContext().getIndexOfThisSubtask();
+
+				for (int topicId = 0; topicId < NUM_TOPICS; topicId++) {
+					for (int i = 0; i < NUM_ELEMENTS; i++) {
+						ctx.collect(new Tuple3<>(partition, i, "topic-" + topicId));
+					}
+				}
+			}
+
+			@Override
+			public void cancel() {
+			}
+		});
+
+		Tuple2WithTopicSchema schema = new Tuple2WithTopicSchema(env.getConfig());
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		kafkaServer.produceIntoKafka(stream, "dummy", schema, props, null);
+
+		env.execute("Write to topics");
+
+		// run second job consuming from multiple topics
+		env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.getConfig().disableSysoutLogging();
+
+		stream = env.addSource(kafkaServer.getConsumer(topics, schema, props));
+
+		stream.flatMap(new FlatMapFunction<Tuple3<Integer, Integer, String>, Integer>() {
+			Map<String, Integer> countPerTopic = new HashMap<>(NUM_TOPICS);
+			@Override
+			public void flatMap(Tuple3<Integer, Integer, String> value, Collector<Integer> out) throws Exception {
+				Integer count = countPerTopic.get(value.f2);
+				if (count == null) {
+					count = 1;
+				} else {
+					count++;
+				}
+				countPerTopic.put(value.f2, count);
+
+				// check map:
+				for (Map.Entry<String, Integer> el: countPerTopic.entrySet()) {
+					if (el.getValue() < NUM_ELEMENTS) {
+						break; // not enough yet
+					}
+					if (el.getValue() > NUM_ELEMENTS) {
+						throw new RuntimeException("There is a failure in the test. I've read " +
+								el.getValue() + " from topic " + el.getKey());
+					}
+				}
+				// we've seen messages from all topics
+				throw new SuccessException();
+			}
+		}).setParallelism(1);
+
+		tryExecute(env, "Count elements from the topics");
+
+
+		// delete all topics again
+		for (int i = 0; i < NUM_TOPICS; i++) {
+			final String topic = "topic-" + i;
+			deleteTestTopic(topic);
+		}
+	}
+
+	/**
+	 * Serialization scheme forwarding byte[] records.
+	 */
+	private static class ByteArraySerializationSchema implements KeyedSerializationSchema<byte[]> {
+
+		@Override
+		public byte[] serializeKey(byte[] element) {
+			return null;
+		}
+
+		@Override
+		public byte[] serializeValue(byte[] element) {
+			return element;
+		}
+
+		@Override
+		public String getTargetTopic(byte[] element) {
+			return null;
+		}
+	}
+
+	private static class Tuple2WithTopicSchema implements KeyedDeserializationSchema<Tuple3<Integer, Integer, String>>,
+			KeyedSerializationSchema<Tuple3<Integer, Integer, String>> {
+
+		private final TypeSerializer<Tuple2<Integer, Integer>> ts;
+		
+		public Tuple2WithTopicSchema(ExecutionConfig ec) {
+			ts = TypeInfoParser.<Tuple2<Integer, Integer>>parse("Tuple2<Integer, Integer>").createSerializer(ec);
+		}
+
+		@Override
+		public Tuple3<Integer, Integer, String> deserialize(byte[] messageKey, byte[] message, String topic, int partition, long offset) throws IOException {
+			DataInputView in = new DataInputViewStreamWrapper(new ByteArrayInputStream(message));
+			Tuple2<Integer, Integer> t2 = ts.deserialize(in);
+			return new Tuple3<>(t2.f0, t2.f1, topic);
+		}
+
+		@Override
+		public boolean isEndOfStream(Tuple3<Integer, Integer, String> nextElement) {
+			return false;
+		}
+
+		@Override
+		public TypeInformation<Tuple3<Integer, Integer, String>> getProducedType() {
+			return TypeInfoParser.parse("Tuple3<Integer, Integer, String>");
+		}
+
+		@Override
+		public byte[] serializeKey(Tuple3<Integer, Integer, String> element) {
+			return null;
+		}
+
+		@Override
+		public byte[] serializeValue(Tuple3<Integer, Integer, String> element) {
+			ByteArrayOutputStream by = new ByteArrayOutputStream();
+			DataOutputView out = new DataOutputViewStreamWrapper(by);
+			try {
+				ts.serialize(new Tuple2<>(element.f0, element.f1), out);
+			} catch (IOException e) {
+				throw new RuntimeException("Error" ,e);
+			}
+			return by.toByteArray();
+		}
+
+		@Override
+		public String getTargetTopic(Tuple3<Integer, Integer, String> element) {
+			return element.f2;
+		}
+	}
+
+	/**
+	 * Test Flink's Kafka integration also with very big records (30MB)
+	 * see http://stackoverflow.com/questions/21020347/kafka-sending-a-15mb-message
+	 *
+	 */
+	public void runBigRecordTestTopology() throws Exception {
+
+		final String topic = "bigRecordTestTopic";
+		final int parallelism = 1; // otherwise, the kafka mini clusters may run out of heap space
+
+		createTestTopic(topic, parallelism, 1);
+
+		final TypeInformation<Tuple2<Long, byte[]>> longBytesInfo = TypeInfoParser.parse("Tuple2<Long, byte[]>");
+
+		final TypeInformationSerializationSchema<Tuple2<Long, byte[]>> serSchema =
+				new TypeInformationSerializationSchema<>(longBytesInfo, new ExecutionConfig());
+
+		final StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setRestartStrategy(RestartStrategies.noRestart());
+		env.getConfig().disableSysoutLogging();
+		env.enableCheckpointing(100);
+		env.setParallelism(parallelism);
+
+		// add consuming topology:
+		Properties consumerProps = new Properties();
+		consumerProps.putAll(standardProps);
+		consumerProps.setProperty("fetch.message.max.bytes", Integer.toString(1024 * 1024 * 14));
+		consumerProps.setProperty("max.partition.fetch.bytes", Integer.toString(1024 * 1024 * 14)); // for the new fetcher
+		consumerProps.setProperty("queued.max.message.chunks", "1");
+		consumerProps.putAll(secureProps);
+
+		FlinkKafkaConsumerBase<Tuple2<Long, byte[]>> source = kafkaServer.getConsumer(topic, serSchema, consumerProps);
+		DataStreamSource<Tuple2<Long, byte[]>> consuming = env.addSource(source);
+
+		consuming.addSink(new SinkFunction<Tuple2<Long, byte[]>>() {
+
+			private int elCnt = 0;
+
+			@Override
+			public void invoke(Tuple2<Long, byte[]> value) throws Exception {
+				elCnt++;
+				if (value.f0 == -1) {
+					// we should have seen 11 elements now.
+					if (elCnt == 11) {
+						throw new SuccessException();
+					} else {
+						throw new RuntimeException("There have been "+elCnt+" elements");
+					}
+				}
+				if (elCnt > 10) {
+					throw new RuntimeException("More than 10 elements seen: "+elCnt);
+				}
+			}
+		});
+
+		// add producing topology
+		Properties producerProps = new Properties();
+		producerProps.setProperty("max.request.size", Integer.toString(1024 * 1024 * 15));
+		producerProps.setProperty("retries", "3");
+		producerProps.putAll(secureProps);
+		producerProps.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokerConnectionStrings);
+
+		DataStream<Tuple2<Long, byte[]>> stream = env.addSource(new RichSourceFunction<Tuple2<Long, byte[]>>() {
+
+			private boolean running;
+
+			@Override
+			public void open(Configuration parameters) throws Exception {
+				super.open(parameters);
+				running = true;
+			}
+
+			@Override
+			public void run(SourceContext<Tuple2<Long, byte[]>> ctx) throws Exception {
+				Random rnd = new Random();
+				long cnt = 0;
+				int sevenMb = 1024 * 1024 * 7;
+
+				while (running) {
+					byte[] wl = new byte[sevenMb + rnd.nextInt(sevenMb)];
+					ctx.collect(new Tuple2<>(cnt++, wl));
+
+					Thread.sleep(100);
+
+					if (cnt == 10) {
+						// signal end
+						ctx.collect(new Tuple2<>(-1L, new byte[]{1}));
+						break;
+					}
+				}
+			}
+
+			@Override
+			public void cancel() {
+				running = false;
+			}
+		});
+
+		kafkaServer.produceIntoKafka(stream, topic, new KeyedSerializationSchemaWrapper<>(serSchema), producerProps, null);
+
+		tryExecute(env, "big topology test");
+		deleteTestTopic(topic);
+	}
+
+	
+	public void runBrokerFailureTest() throws Exception {
+		final String topic = "brokerFailureTestTopic";
+
+		final int parallelism = 2;
+		final int numElementsPerPartition = 1000;
+		final int totalElements = parallelism * numElementsPerPartition;
+		final int failAfterElements = numElementsPerPartition / 3;
+
+
+		createTestTopic(topic, parallelism, 2);
+
+		DataGenerators.generateRandomizedIntegerSequence(
+				StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort),
+				kafkaServer,
+				topic, parallelism, numElementsPerPartition, true);
+
+		// find leader to shut down
+		int leaderId = kafkaServer.getLeaderToShutDown(topic);
+
+		LOG.info("Leader to shutdown {}", leaderId);
+
+
+		// run the topology (the consumers must handle the failures)
+
+		DeserializationSchema<Integer> schema =
+				new TypeInformationSerializationSchema<>(BasicTypeInfo.INT_TYPE_INFO, new ExecutionConfig());
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(parallelism);
+		env.enableCheckpointing(500);
+		env.setRestartStrategy(RestartStrategies.noRestart());
+		env.getConfig().disableSysoutLogging();
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		FlinkKafkaConsumerBase<Integer> kafkaSource = kafkaServer.getConsumer(topic, schema, props);
+
+		env
+				.addSource(kafkaSource)
+				.map(new PartitionValidatingMapper(parallelism, 1))
+				.map(new BrokerKillingMapper<Integer>(leaderId, failAfterElements))
+				.addSink(new ValidatingExactlyOnceSink(totalElements)).setParallelism(1);
+
+		BrokerKillingMapper.killedLeaderBefore = false;
+		tryExecute(env, "Broker failure once test");
+
+		// start a new broker:
+		kafkaServer.restartBroker(leaderId);
+	}
+
+	public void runKeyValueTest() throws Exception {
+		final String topic = "keyvaluetest";
+		createTestTopic(topic, 1, 1);
+		final int ELEMENT_COUNT = 5000;
+
+		// ----------- Write some data into Kafka -------------------
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(1);
+		env.setRestartStrategy(RestartStrategies.noRestart());
+		env.getConfig().disableSysoutLogging();
+
+		DataStream<Tuple2<Long, PojoValue>> kvStream = env.addSource(new SourceFunction<Tuple2<Long, PojoValue>>() {
+			@Override
+			public void run(SourceContext<Tuple2<Long, PojoValue>> ctx) throws Exception {
+				Random rnd = new Random(1337);
+				for (long i = 0; i < ELEMENT_COUNT; i++) {
+					PojoValue pojo = new PojoValue();
+					pojo.when = new Date(rnd.nextLong());
+					pojo.lon = rnd.nextLong();
+					pojo.lat = i;
+					// make every second key null to ensure proper "null" serialization
+					Long key = (i % 2 == 0) ? null : i;
+					ctx.collect(new Tuple2<>(key, pojo));
+				}
+			}
+			@Override
+			public void cancel() {
+			}
+		});
+
+		KeyedSerializationSchema<Tuple2<Long, PojoValue>> schema = new TypeInformationKeyValueSerializationSchema<>(Long.class, PojoValue.class, env.getConfig());
+		Properties producerProperties = FlinkKafkaProducerBase.getPropertiesFromBrokerList(brokerConnectionStrings);
+		producerProperties.setProperty("retries", "3");
+		kafkaServer.produceIntoKafka(kvStream, topic, schema, producerProperties, null);
+		env.execute("Write KV to Kafka");
+
+		// ----------- Read the data again -------------------
+
+		env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(1);
+		env.setRestartStrategy(RestartStrategies.noRestart());
+		env.getConfig().disableSysoutLogging();
+
+
+		KeyedDeserializationSchema<Tuple2<Long, PojoValue>> readSchema = new TypeInformationKeyValueSerializationSchema<>(Long.class, PojoValue.class, env.getConfig());
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		DataStream<Tuple2<Long, PojoValue>> fromKafka = env.addSource(kafkaServer.getConsumer(topic, readSchema, props));
+		fromKafka.flatMap(new RichFlatMapFunction<Tuple2<Long,PojoValue>, Object>() {
+			long counter = 0;
+			@Override
+			public void flatMap(Tuple2<Long, PojoValue> value, Collector<Object> out) throws Exception {
+				// the elements should be in order.
+				Assert.assertTrue("Wrong value " + value.f1.lat, value.f1.lat == counter );
+				if (value.f1.lat % 2 == 0) {
+					assertNull("key was not null", value.f0);
+				} else {
+					Assert.assertTrue("Wrong value " + value.f0, value.f0 == counter);
+				}
+				counter++;
+				if (counter == ELEMENT_COUNT) {
+					// we got the right number of elements
+					throw new SuccessException();
+				}
+			}
+		});
+
+		tryExecute(env, "Read KV from Kafka");
+
+		deleteTestTopic(topic);
+	}
+
+	public static class PojoValue {
+		public Date when;
+		public long lon;
+		public long lat;
+		public PojoValue() {}
+	}
+
+
+	/**
+	 * Test delete behavior and metrics for producer
+	 * @throws Exception
+	 */
+	public void runAllDeletesTest() throws Exception {
+		final String topic = "alldeletestest";
+		createTestTopic(topic, 1, 1);
+		final int ELEMENT_COUNT = 300;
+
+		// ----------- Write some data into Kafka -------------------
+
+		StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(1);
+		env.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+		env.getConfig().disableSysoutLogging();
+
+		DataStream<Tuple2<byte[], PojoValue>> kvStream = env.addSource(new SourceFunction<Tuple2<byte[], PojoValue>>() {
+			@Override
+			public void run(SourceContext<Tuple2<byte[], PojoValue>> ctx) throws Exception {
+				Random rnd = new Random(1337);
+				for (long i = 0; i < ELEMENT_COUNT; i++) {
+					final byte[] key = new byte[200];
+					rnd.nextBytes(key);
+					ctx.collect(new Tuple2<>(key, (PojoValue) null));
+				}
+			}
+			@Override
+			public void cancel() {
+			}
+		});
+
+		TypeInformationKeyValueSerializationSchema<byte[], PojoValue> schema = new TypeInformationKeyValueSerializationSchema<>(byte[].class, PojoValue.class, env.getConfig());
+
+		Properties producerProperties = FlinkKafkaProducerBase.getPropertiesFromBrokerList(brokerConnectionStrings);
+		producerProperties.setProperty("retries", "3");
+		producerProperties.putAll(secureProps);
+		kafkaServer.produceIntoKafka(kvStream, topic, schema, producerProperties, null);
+
+		env.execute("Write deletes to Kafka");
+
+		// ----------- Read the data again -------------------
+
+		env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env.setParallelism(1);
+		env.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+		env.getConfig().disableSysoutLogging();
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+		DataStream<Tuple2<byte[], PojoValue>> fromKafka = env.addSource(kafkaServer.getConsumer(topic, schema, props));
+
+		fromKafka.flatMap(new RichFlatMapFunction<Tuple2<byte[], PojoValue>, Object>() {
+			long counter = 0;
+			@Override
+			public void flatMap(Tuple2<byte[], PojoValue> value, Collector<Object> out) throws Exception {
+				// ensure that deleted messages are passed as nulls
+				assertNull(value.f1);
+				counter++;
+				if (counter == ELEMENT_COUNT) {
+					// we got the right number of elements
+					throw new SuccessException();
+				}
+			}
+		});
+
+		tryExecute(env, "Read deletes from Kafka");
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Test that ensures that DeserializationSchema.isEndOfStream() is properly evaluated.
+	 *
+	 * @throws Exception
+	 */
+	public void runEndOfStreamTest() throws Exception {
+
+		final int ELEMENT_COUNT = 300;
+		final String topic = writeSequence("testEndOfStream", ELEMENT_COUNT, 1, 1);
+
+		// read using custom schema
+		final StreamExecutionEnvironment env1 = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+		env1.setParallelism(1);
+		env1.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+		env1.getConfig().disableSysoutLogging();
+
+		Properties props = new Properties();
+		props.putAll(standardProps);
+		props.putAll(secureProps);
+
+		DataStream<Tuple2<Integer, Integer>> fromKafka = env1.addSource(kafkaServer.getConsumer(topic, new FixedNumberDeserializationSchema(ELEMENT_COUNT), props));
+		fromKafka.flatMap(new FlatMapFunction<Tuple2<Integer,Integer>, Void>() {
+			@Override
+			public void flatMap(Tuple2<Integer, Integer> value, Collector<Void> out) throws Exception {
+				// noop ;)
+			}
+		});
+
+		JobExecutionResult result = tryExecute(env1, "Consume " + ELEMENT_COUNT + " elements from Kafka");
+
+		deleteTestTopic(topic);
+	}
+
+	/**
+	 * Test metrics reporting for consumer
+	 *
+	 * @throws Exception
+	 */
+	public void runMetricsTest() throws Throwable {
+
+		// create a stream with 5 topics
+		final String topic = "metricsStream";
+		createTestTopic(topic, 5, 1);
+
+		final Tuple1<Throwable> error = new Tuple1<>(null);
+		Runnable job = new Runnable() {
+			@Override
+			public void run() {
+				try {
+					// start job writing & reading data.
+					final StreamExecutionEnvironment env1 = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+					env1.setParallelism(1);
+					env1.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+					env1.getConfig().disableSysoutLogging();
+					env1.disableOperatorChaining(); // let the source read everything into the network buffers
+
+					Properties props = new Properties();
+					props.putAll(standardProps);
+					props.putAll(secureProps);
+
+					TypeInformationSerializationSchema<Tuple2<Integer, Integer>> schema = new TypeInformationSerializationSchema<>(TypeInfoParser.<Tuple2<Integer, Integer>>parse("Tuple2<Integer, Integer>"), env1.getConfig());
+					DataStream<Tuple2<Integer, Integer>> fromKafka = env1.addSource(kafkaServer.getConsumer(topic, schema, standardProps));
+					fromKafka.flatMap(new FlatMapFunction<Tuple2<Integer, Integer>, Void>() {
+						@Override
+						public void flatMap(Tuple2<Integer, Integer> value, Collector<Void> out) throws Exception {// no op
+						}
+					});
+
+					DataStream<Tuple2<Integer, Integer>> fromGen = env1.addSource(new RichSourceFunction<Tuple2<Integer, Integer>>() {
+						boolean running = true;
+
+						@Override
+						public void run(SourceContext<Tuple2<Integer, Integer>> ctx) throws Exception {
+							int i = 0;
+							while (running) {
+								ctx.collect(Tuple2.of(i++, getRuntimeContext().getIndexOfThisSubtask()));
+								Thread.sleep(1);
+							}
+						}
+
+						@Override
+						public void cancel() {
+							running = false;
+						}
+					});
+
+					kafkaServer.produceIntoKafka(fromGen, topic, new KeyedSerializationSchemaWrapper<>(schema), standardProps, null);
+
+					env1.execute("Metrics test job");
+				} catch(Throwable t) {
+					LOG.warn("Got exception during execution", t);
+					if(!(t.getCause() instanceof JobCancellationException)) { // we'll cancel the job
+						error.f0 = t;
+					}
+				}
+			}
+		};
+		Thread jobThread = new Thread(job);
+		jobThread.start();
+
+		try {
+			// connect to JMX
+			MBeanServer mBeanServer = ManagementFactory.getPlatformMBeanServer();
+			// wait until we've found all 5 offset metrics
+			Set<ObjectName> offsetMetrics = mBeanServer.queryNames(new ObjectName("*current-offsets*:*"), null);
+			while (offsetMetrics.size() < 5) { // test will time out if metrics are not properly working
+				if (error.f0 != null) {
+					// fail test early
+					throw error.f0;
+				}
+				offsetMetrics = mBeanServer.queryNames(new ObjectName("*current-offsets*:*"), null);
+				Thread.sleep(50);
+			}
+			Assert.assertEquals(5, offsetMetrics.size());
+			// we can't rely on the consumer to have touched all the partitions already
+			// that's why we'll wait until all five partitions have a positive offset.
+			// The test will fail if we never meet the condition
+			while (true) {
+				int numPosOffsets = 0;
+				// check that offsets are correctly reported
+				for (ObjectName object : offsetMetrics) {
+					Object offset = mBeanServer.getAttribute(object, "Value");
+					if((long) offset >= 0) {
+						numPosOffsets++;
+					}
+				}
+				if (numPosOffsets == 5) {
+					break;
+				}
+				// wait for the consumer to consume on all partitions
+				Thread.sleep(50);
+			}
+
+			// check if producer metrics are also available.
+			Set<ObjectName> producerMetrics = mBeanServer.queryNames(new ObjectName("*KafkaProducer*:*"), null);
+			Assert.assertTrue("No producer metrics found", producerMetrics.size() > 30);
+
+
+			LOG.info("Found all JMX metrics. Cancelling job.");
+		} finally {
+			// cancel
+			JobManagerCommunicationUtils.cancelCurrentJob(flink.getLeaderGateway(timeout));
+		}
+
+		while (jobThread.isAlive()) {
+			Thread.sleep(50);
+		}
+		if (error.f0 != null) {
+			throw error.f0;
+		}
+
+		deleteTestTopic(topic);
+	}
+
+
+	public static class FixedNumberDeserializationSchema implements DeserializationSchema<Tuple2<Integer, Integer>> {
+		
+		final int finalCount;
+		int count = 0;
+		
+		TypeInformation<Tuple2<Integer, Integer>> ti = TypeInfoParser.parse("Tuple2<Integer, Integer>");
+		TypeSerializer<Tuple2<Integer, Integer>> ser = ti.createSerializer(new ExecutionConfig());
+
+		public FixedNumberDeserializationSchema(int finalCount) {
+			this.finalCount = finalCount;
+		}
+
+		@Override
+		public Tuple2<Integer, Integer> deserialize(byte[] message) throws IOException {
+			DataInputView in = new DataInputViewStreamWrapper(new ByteArrayInputStream(message));
+			return ser.deserialize(in);
+		}
+
+		@Override
+		public boolean isEndOfStream(Tuple2<Integer, Integer> nextElement) {
+			return ++count >= finalCount;
+		}
+
+		@Override
+		public TypeInformation<Tuple2<Integer, Integer>> getProducedType() {
+			return ti;
+		}
+	}
+
+
+	// ------------------------------------------------------------------------
+	//  Reading writing test data sets
+	// ------------------------------------------------------------------------
+
+	/**
+	 * Runs a job using the provided environment to read a sequence of records from a single Kafka topic.
+	 * The method allows to individually specify the expected starting offset and total read value count of each partition.
+	 * The job will be considered successful only if all partition read results match the start offset and value count criteria.
+	 */
+	protected void readSequence(StreamExecutionEnvironment env, Properties cc,
+								final String topicName,
+								final Map<Integer, Tuple2<Integer, Integer>> partitionsToValuesCountAndStartOffset) throws Exception {
+		final int sourceParallelism = partitionsToValuesCountAndStartOffset.keySet().size();
+
+		int finalCountTmp = 0;
+		for (Map.Entry<Integer, Tuple2<Integer, Integer>> valuesCountAndStartOffset : partitionsToValuesCountAndStartOffset.entrySet()) {
+			finalCountTmp += valuesCountAndStartOffset.getValue().f0;
+		}
+		final int finalCount = finalCountTmp;
+
+		final TypeInformation<Tuple2<Integer, Integer>> intIntTupleType = TypeInfoParser.parse("Tuple2<Integer, Integer>");
+
+		final TypeInformationSerializationSchema<Tuple2<Integer, Integer>> deser =
+			new TypeInformationSerializationSchema<>(intIntTupleType, env.getConfig());
+
+		// create the consumer
+		cc.putAll(secureProps);
+		FlinkKafkaConsumerBase<Tuple2<Integer, Integer>> consumer = kafkaServer.getConsumer(topicName, deser, cc);
+
+		DataStream<Tuple2<Integer, Integer>> source = env
+			.addSource(consumer).setParallelism(sourceParallelism)
+			.map(new ThrottledMapper<Tuple2<Integer, Integer>>(20)).setParallelism(sourceParallelism);
+
+		// verify data
+		source.flatMap(new RichFlatMapFunction<Tuple2<Integer, Integer>, Integer>() {
+
+			private HashMap<Integer, BitSet> partitionsToValueCheck;
+			private int count = 0;
+
+			@Override
+			public void open(Configuration parameters) throws Exception {
+				partitionsToValueCheck = new HashMap<>();
+				for (Integer partition : partitionsToValuesCountAndStartOffset.keySet()) {
+					partitionsToValueCheck.put(partition, new BitSet());
+				}
+			}
+
+			@Override
+			public void flatMap(Tuple2<Integer, Integer> value, Collector<Integer> out) throws Exception {
+				int partition = value.f0;
+				int val = value.f1;
+
+				BitSet bitSet = partitionsToValueCheck.get(partition);
+				if (bitSet == null) {
+					throw new RuntimeException("Got a record from an unknown partition");
+				} else {
+					bitSet.set(val - partitionsToValuesCountAndStartOffset.get(partition).f1);
+				}
+
+				count++;
+
+				LOG.info("Received message {}, total {} messages", value, count);
+
+				// verify if we've seen everything
+				if (count == finalCount) {
+					for (Map.Entry<Integer, BitSet> partitionsToValueCheck : this.partitionsToValueCheck.entrySet()) {
+						BitSet check = partitionsToValueCheck.getValue();
+						int expectedValueCount = partitionsToValuesCountAndStartOffset.get(partitionsToValueCheck.getKey()).f0;
+
+						if (check.cardinality() != expectedValueCount) {
+							throw new RuntimeException("Expected cardinality to be " + expectedValueCount +
+								", but was " + check.cardinality());
+						} else if (check.nextClearBit(0) != expectedValueCount) {
+							throw new RuntimeException("Expected next clear bit to be " + expectedValueCount +
+								", but was " + check.cardinality());
+						}
+					}
+
+					// test has passed
+					throw new SuccessException();
+				}
+			}
+
+		}).setParallelism(1);
+
+		tryExecute(env, "Read data from Kafka");
+
+		LOG.info("Successfully read sequence for verification");
+	}
+
+	/**
+	 * Variant of {@link KafkaConsumerTestBase#readSequence(StreamExecutionEnvironment, Properties, String, Map)} to
+	 * expect reading from the same start offset and the same value count for all partitions of a single Kafka topic.
+	 */
+	protected void readSequence(StreamExecutionEnvironment env, Properties cc,
+								final int sourceParallelism,
+								final String topicName,
+								final int valuesCount, final int startFrom) throws Exception {
+		HashMap<Integer, Tuple2<Integer, Integer>> partitionsToValuesCountAndStartOffset = new HashMap<>();
+		for (int i = 0; i < sourceParallelism; i++) {
+			partitionsToValuesCountAndStartOffset.put(i, new Tuple2<>(valuesCount, startFrom));
+		}
+		readSequence(env, cc, topicName, partitionsToValuesCountAndStartOffset);
+	}
+
+	protected String writeSequence(
+			String baseTopicName,
+			final int numElements,
+			final int parallelism,
+			final int replicationFactor) throws Exception
+	{
+		LOG.info("\n===================================\n" +
+				"== Writing sequence of " + numElements + " into " + baseTopicName + " with p=" + parallelism + "\n" +
+				"===================================");
+
+		final TypeInformation<Tuple2<Integer, Integer>> resultType = 
+				TypeInformation.of(new TypeHint<Tuple2<Integer, Integer>>() {});
+
+		final KeyedSerializationSchema<Tuple2<Integer, Integer>> serSchema =
+				new KeyedSerializationSchemaWrapper<>(
+						new TypeInformationSerializationSchema<>(resultType, new ExecutionConfig()));
+
+		final KeyedDeserializationSchema<Tuple2<Integer, Integer>> deserSchema =
+				new KeyedDeserializationSchemaWrapper<>(
+						new TypeInformationSerializationSchema<>(resultType, new ExecutionConfig()));
+		
+		final int maxNumAttempts = 10;
+
+		for (int attempt = 1; attempt <= maxNumAttempts; attempt++) {
+			
+			final String topicName = baseTopicName + '-' + attempt;
+			
+			LOG.info("Writing attempt #1");
+			
+			// -------- Write the Sequence --------
+
+			createTestTopic(topicName, parallelism, replicationFactor);
+
+			StreamExecutionEnvironment writeEnv = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+			writeEnv.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+			writeEnv.getConfig().disableSysoutLogging();
+			
+			DataStream<Tuple2<Integer, Integer>> stream = writeEnv.addSource(new RichParallelSourceFunction<Tuple2<Integer, Integer>>() {
+	
+				private boolean running = true;
+	
+				@Override
+				public void run(SourceContext<Tuple2<Integer, Integer>> ctx) throws Exception {
+					int cnt = 0;
+					int partition = getRuntimeContext().getIndexOfThisSubtask();
+	
+					while (running && cnt < numElements) {
+						ctx.collect(new Tuple2<>(partition, cnt));
+						cnt++;
+					}
+				}
+	
+				@Override
+				public void cancel() {
+					running = false;
+				}
+			}).setParallelism(parallelism);
+	
+			// the producer must not produce duplicates
+			Properties producerProperties = FlinkKafkaProducerBase.getPropertiesFromBrokerList(brokerConnectionStrings);
+			producerProperties.setProperty("retries", "0");
+			producerProperties.putAll(secureProps);
+			
+			kafkaServer.produceIntoKafka(stream, topicName, serSchema, producerProperties, new Tuple2Partitioner(parallelism))
+					.setParallelism(parallelism);
+
+			try {
+				writeEnv.execute("Write sequence");
+			}
+			catch (Exception e) {
+				LOG.error("Write attempt failed, trying again", e);
+				deleteTestTopic(topicName);
+				JobManagerCommunicationUtils.waitUntilNoJobIsRunning(flink.getLeaderGateway(timeout));
+				continue;
+			}
+			
+			LOG.info("Finished writing sequence");
+
+			// -------- Validate the Sequence --------
+			
+			// we need to validate the sequence, because kafka's producers are not exactly once
+			LOG.info("Validating sequence");
+
+			JobManagerCommunicationUtils.waitUntilNoJobIsRunning(flink.getLeaderGateway(timeout));
+			
+			final StreamExecutionEnvironment readEnv = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+			readEnv.getConfig().setRestartStrategy(RestartStrategies.noRestart());
+			readEnv.getConfig().disableSysoutLogging();
+			readEnv.setParallelism(parallelism);
+			
+			Properties readProps = (Properties) standardProps.clone();
+			readProps.setProperty("group.id", "flink-tests-validator");
+			readProps.putAll(secureProps);
+			FlinkKafkaConsumerBase<Tuple2<Integer, Integer>> consumer = kafkaServer.getConsumer(topicName, deserSchema, readProps);
+
+			readEnv
+					.addSource(consumer)
+					.map(new RichMapFunction<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>>() {
+						
+						private final int totalCount = parallelism * numElements;
+						private int count = 0;
+						
+						@Override
+						public Tuple2<Integer, Integer> map(Tuple2<Integer, Integer> value) throws Exception {
+							if (++count == totalCount) {
+								throw new SuccessException();
+							} else {
+								return value;
+							}
+						}
+					}).setParallelism(1)
+					.addSink(new DiscardingSink<Tuple2<Integer, Integer>>()).setParallelism(1);
+			
+			final AtomicReference<Throwable> errorRef = new AtomicReference<>();
+			
+			Thread runner = new Thread() {
+				@Override
+				public void run() {
+					try {
+						tryExecute(readEnv, "sequence validation");
+					} catch (Throwable t) {
+						errorRef.set(t);
+					}
+				}
+			};
+			runner.start();
+			
+			final long deadline = System.currentTimeMillis() + 10000;
+			long delay;
+			while (runner.isAlive() && (delay = deadline - System.currentTimeMillis()) > 0) {
+				runner.join(delay);
+			}
+			
+			boolean success;
+			
+			if (runner.isAlive()) {
+				// did not finish in time, maybe the producer dropped one or more records and
+				// the validation did not reach the exit point
+				success = false;
+				JobManagerCommunicationUtils.cancelCurrentJob(flink.getLeaderGateway(timeout));
+			}
+			else {
+				Throwable error = errorRef.get();
+				if (error != null) {
+					success = false;
+					LOG.info("Attempt " + attempt + " failed with exception", error);
+				}
+				else {
+					success = true;
+				}
+			}
+
+			JobManagerCommunicationUtils.waitUntilNoJobIsRunning(flink.getLeaderGateway(timeout));
+			
+			if (success) {
+				// everything is good!
+				return topicName;
+			}
+			else {
+				deleteTestTopic(topicName);
+				// fall through the loop
+			}
+		}
+		
+		throw new Exception("Could not write a valid sequence to Kafka after " + maxNumAttempts + " attempts");
+	}
+
+	// ------------------------------------------------------------------------
+	//  Debugging utilities
+	// ------------------------------------------------------------------------
+
+	/**
+	 * Read topic to list, only using Kafka code.
+	 */
+	private static List<MessageAndMetadata<byte[], byte[]>> readTopicToList(String topicName, ConsumerConfig config, final int stopAfter) {
+		ConsumerConnector consumerConnector = Consumer.createJavaConsumerConnector(config);
+		// we request only one stream per consumer instance. Kafka will make sure that each consumer group
+		// will see each message only once.
+		Map<String,Integer> topicCountMap = Collections.singletonMap(topicName, 1);
+		Map<String, List<KafkaStream<byte[], byte[]>>> streams = consumerConnector.createMessageStreams(topicCountMap);
+		if (streams.size() != 1) {
+			throw new RuntimeException("Expected only one message stream but got "+streams.size());
+		}
+		List<KafkaStream<byte[], byte[]>> kafkaStreams = streams.get(topicName);
+		if (kafkaStreams == null) {
+			throw new RuntimeException("Requested stream not available. Available streams: "+streams.toString());
+		}
+		if (kafkaStreams.size() != 1) {
+			throw new RuntimeException("Requested 1 stream from Kafka, bot got "+kafkaStreams.size()+" streams");
+		}
+		LOG.info("Opening Consumer instance for topic '{}' on group '{}'", topicName, config.groupId());
+		ConsumerIterator<byte[], byte[]> iteratorToRead = kafkaStreams.get(0).iterator();
+
+		List<MessageAndMetadata<byte[], byte[]>> result = new ArrayList<>();
+		int read = 0;
+		while(iteratorToRead.hasNext()) {
+			read++;
+			result.add(iteratorToRead.next());
+			if (read == stopAfter) {
+				LOG.info("Read "+read+" elements");
+				return result;
+			}
+		}
+		return result;
+	}
+
+	private static void printTopic(String topicName, ConsumerConfig config,
+								DeserializationSchema<?> deserializationSchema,
+								int stopAfter) throws IOException {
+
+		List<MessageAndMetadata<byte[], byte[]>> contents = readTopicToList(topicName, config, stopAfter);
+		LOG.info("Printing contents of topic {} in consumer grouo {}", topicName, config.groupId());
+
+		for (MessageAndMetadata<byte[], byte[]> message: contents) {
+			Object out = deserializationSchema.deserialize(message.message());
+			LOG.info("Message: partition: {} offset: {} msg: {}", message.partition(), message.offset(), out.toString());
+		}
+	}
+
+	private static void printTopic(String topicName, int elements,DeserializationSchema<?> deserializer) 
+			throws IOException
+	{
+		// write the sequence to log for debugging purposes
+		Properties newProps = new Properties(standardProps);
+		newProps.setProperty("group.id", "topic-printer"+ UUID.randomUUID().toString());
+		newProps.setProperty("auto.offset.reset", "smallest");
+		newProps.setProperty("zookeeper.connect", standardProps.getProperty("zookeeper.connect"));
+		newProps.putAll(secureProps);
+
+		ConsumerConfig printerConfig = new ConsumerConfig(newProps);
+		printTopic(topicName, printerConfig, deserializer, elements);
+	}
+
+
+	public static class BrokerKillingMapper<T> extends RichMapFunction<T,T>
+			implements Checkpointed<Integer>, CheckpointListener {
+
+		private static final long serialVersionUID = 6334389850158707313L;
+
+		public static volatile boolean killedLeaderBefore;
+		public static volatile boolean hasBeenCheckpointedBeforeFailure;
+		
+		private final int shutdownBrokerId;
+		private final int failCount;
+		private int numElementsTotal;
+
+		private boolean failer;
+		private boolean hasBeenCheckpointed;
+
+
+		public BrokerKillingMapper(int shutdownBrokerId, int failCount) {
+			this.shutdownBrokerId = shutdownBrokerId;
+			this.failCount = failCount;
+		}
+
+		@Override
+		public void open(Configuration parameters) {
+			failer = getRuntimeContext().getIndexOfThisSubtask() == 0;
+		}
+
+		@Override
+		public T map(T value) throws Exception {
+			numElementsTotal++;
+			
+			if (!killedLeaderBefore) {
+				Thread.sleep(10);
+				
+				if (failer && numElementsTotal >= failCount) {
+					// shut down a Kafka broker
+					KafkaServer toShutDown = null;
+					for (KafkaServer server : kafkaServer.getBrokers()) {
+
+						if (kafkaServer.getBrokerId(server) == shutdownBrokerId) {
+							toShutDown = server;
+							break;
+						}
+					}
+	
+					if (toShutDown == null) {
+						StringBuilder listOfBrokers = new StringBuilder();
+						for (KafkaServer server : kafkaServer.getBrokers()) {
+							listOfBrokers.append(kafkaServer.getBrokerId(server));
+							listOfBrokers.append(" ; ");
+						}
+						
+						throw new Exception("Cannot find broker to shut down: " + shutdownBrokerId
+								+ " ; available brokers: " + listOfBrokers.toString());
+					}
+					else {
+						hasBeenCheckpointedBeforeFailure = hasBeenCheckpointed;
+						killedLeaderBefore = true;
+						toShutDown.shutdown();
+					}
+				}
+			}
+			return value;
+		}
+
+		@Override
+		public void notifyCheckpointComplete(long checkpointId) {
+			hasBeenCheckpointed = true;
+		}
+
+		@Override
+		public Integer snapshotState(long checkpointId, long checkpointTimestamp) {
+			return numElementsTotal;
+		}
+
+		@Override
+		public void restoreState(Integer state) {
+			this.numElementsTotal = state;
+		}
+	}
+}

http://git-wip-us.apache.org/repos/asf/flink/blob/de4fe3b7/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaProducerTestBase.java
----------------------------------------------------------------------
diff --git a/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaProducerTestBase.java b/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaProducerTestBase.java
new file mode 100644
index 0000000..c925c8f
--- /dev/null
+++ b/flink-connectors/flink-connector-kafka-base/src/test/java/org/apache/flink/streaming/connectors/kafka/KafkaProducerTestBase.java
@@ -0,0 +1,193 @@
+/*
+ * 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.flink.streaming.connectors.kafka;
+
+import org.apache.flink.api.common.functions.RichMapFunction;
+import org.apache.flink.api.common.restartstrategy.RestartStrategies;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.api.java.typeutils.TypeInfoParser;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.streaming.api.functions.sink.SinkFunction;
+import org.apache.flink.streaming.api.functions.source.SourceFunction;
+import org.apache.flink.streaming.connectors.kafka.partitioner.KafkaPartitioner;
+import org.apache.flink.streaming.util.serialization.KeyedSerializationSchemaWrapper;
+import org.apache.flink.streaming.util.serialization.TypeInformationSerializationSchema;
+import org.apache.flink.test.util.SuccessException;
+
+
+import java.io.Serializable;
+import java.util.Properties;
+
+import static org.apache.flink.test.util.TestUtils.tryExecute;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.fail;
+
+@SuppressWarnings("serial")
+public abstract class KafkaProducerTestBase extends KafkaTestBase {
+
+
+	/**
+	 * 
+	 * <pre>
+	 *             +------> (sink) --+--> [KAFKA-1] --> (source) -> (map) --+
+	 *            /                  |                                       \
+	 *           /                   |                                        \
+	 * (source) ----------> (sink) --+--> [KAFKA-2] --> (source) -> (map) -----+-> (sink)
+	 *           \                   |                                        /
+	 *            \                  |                                       /
+	 *             +------> (sink) --+--> [KAFKA-3] --> (source) -> (map) --+
+	 * </pre>
+	 * 
+	 * The mapper validates that the values come consistently from the correct Kafka partition.
+	 * 
+	 * The final sink validates that there are no duplicates and that all partitions are present.
+	 */
+	public void runCustomPartitioningTest() {
+		try {
+			LOG.info("Starting KafkaProducerITCase.testCustomPartitioning()");
+
+			final String topic = "customPartitioningTestTopic";
+			final int parallelism = 3;
+			
+			createTestTopic(topic, parallelism, 1);
+
+			TypeInformation<Tuple2<Long, String>> longStringInfo = TypeInfoParser.parse("Tuple2<Long, String>");
+
+			StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("localhost", flinkPort);
+			env.setRestartStrategy(RestartStrategies.noRestart());
+			env.getConfig().disableSysoutLogging();
+
+			TypeInformationSerializationSchema<Tuple2<Long, String>> serSchema =
+					new TypeInformationSerializationSchema<>(longStringInfo, env.getConfig());
+
+			TypeInformationSerializationSchema<Tuple2<Long, String>> deserSchema =
+					new TypeInformationSerializationSchema<>(longStringInfo, env.getConfig());
+
+			// ------ producing topology ---------
+			
+			// source has DOP 1 to make sure it generates no duplicates
+			DataStream<Tuple2<Long, String>> stream = env.addSource(new SourceFunction<Tuple2<Long, String>>() {
+
+				private boolean running = true;
+
+				@Override
+				public void run(SourceContext<Tuple2<Long, String>> ctx) throws Exception {
+					long cnt = 0;
+					while (running) {
+						ctx.collect(new Tuple2<Long, String>(cnt, "kafka-" + cnt));
+						cnt++;
+					}
+				}
+
+				@Override
+				public void cancel() {
+					running = false;
+				}
+			})
+			.setParallelism(1);
+
+			Properties props = new Properties();
+			props.putAll(FlinkKafkaProducerBase.getPropertiesFromBrokerList(brokerConnectionStrings));
+			props.putAll(secureProps);
+			
+			// sink partitions into 
+			kafkaServer.produceIntoKafka(stream, topic,
+					new KeyedSerializationSchemaWrapper<>(serSchema),
+					props,
+					new CustomPartitioner(parallelism)).setParallelism(parallelism);
+
+			// ------ consuming topology ---------
+
+			Properties consumerProps = new Properties();
+			consumerProps.putAll(standardProps);
+			consumerProps.putAll(secureProps);
+			FlinkKafkaConsumerBase<Tuple2<Long, String>> source = kafkaServer.getConsumer(topic, deserSchema, consumerProps);
+			
+			env.addSource(source).setParallelism(parallelism)
+
+					// mapper that validates partitioning and maps to partition
+					.map(new RichMapFunction<Tuple2<Long, String>, Integer>() {
+						
+						private int ourPartition = -1;
+						@Override
+						public Integer map(Tuple2<Long, String> value) {
+							int partition = value.f0.intValue() % parallelism;
+							if (ourPartition != -1) {
+								assertEquals("inconsistent partitioning", ourPartition, partition);
+							} else {
+								ourPartition = partition;
+							}
+							return partition;
+						}
+					}).setParallelism(parallelism)
+					
+					.addSink(new SinkFunction<Integer>() {
+						
+						private int[] valuesPerPartition = new int[parallelism];
+						
+						@Override
+						public void invoke(Integer value) throws Exception {
+							valuesPerPartition[value]++;
+							
+							boolean missing = false;
+							for (int i : valuesPerPartition) {
+								if (i < 100) {
+									missing = true;
+									break;
+								}
+							}
+							if (!missing) {
+								throw new SuccessException();
+							}
+						}
+					}).setParallelism(1);
+			
+			tryExecute(env, "custom partitioning test");
+
+			deleteTestTopic(topic);
+			
+			LOG.info("Finished KafkaProducerITCase.testCustomPartitioning()");
+		}
+		catch (Exception e) {
+			e.printStackTrace();
+			fail(e.getMessage());
+		}
+	}
+
+	// ------------------------------------------------------------------------
+
+	public static class CustomPartitioner extends KafkaPartitioner<Tuple2<Long, String>> implements Serializable {
+
+		private final int expectedPartitions;
+
+		public CustomPartitioner(int expectedPartitions) {
+			this.expectedPartitions = expectedPartitions;
+		}
+
+
+		@Override
+		public int partition(Tuple2<Long, String> next, byte[] serializedKey, byte[] serializedValue, int numPartitions) {
+			assertEquals(expectedPartitions, numPartitions);
+
+			return (int) (next.f0 % numPartitions);
+		}
+	}
+}


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