Fixed bugs

Author: tdas

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaDataConsumer.scala

@@ -82,7 +82,7 @@ private[kafka010] case class InternalKafkaConsumer(
 
   private val groupId = kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String]
 
-  private var consumer = createConsumer
+  @volatile private var consumer = createConsumer
 
   /** indicates whether this consumer is in use or not */
   @volatile var inuse = true
@@ -91,8 +91,9 @@ private[kafka010] case class InternalKafkaConsumer(
   @volatile var markedForClose = false
 
   /** Iterator to the already fetch data */
-  private var fetchedData = ju.Collections.emptyIterator[ConsumerRecord[Array[Byte], Array[Byte]]]
-  private var nextOffsetInFetchedData = UNKNOWN_OFFSET
+  @volatile private var fetchedData =
+    ju.Collections.emptyIterator[ConsumerRecord[Array[Byte], Array[Byte]]]
+  @volatile private var nextOffsetInFetchedData = UNKNOWN_OFFSET
 
   /** Create a KafkaConsumer to fetch records for `topicPartition` */
   private def createConsumer: KafkaConsumer[Array[Byte], Array[Byte]] = {
@@ -358,31 +359,44 @@ private[kafka010] object KafkaDataConsumer extends Logging {
 
   case class AvailableOffsetRange(earliest: Long, latest: Long)
 
-  private class CachedKafkaDataConsumer(val internalConsumer: InternalKafkaConsumer)
+  private case class CachedKafkaDataConsumer(internalConsumer: InternalKafkaConsumer)
     extends KafkaDataConsumer {
-    override def release(): Unit = {
-      releaseKafkaConsumer(internalConsumer.topicPartition, internalConsumer.kafkaParams)
-    }
+    override def release(): Unit = { releaseConsumer(internalConsumer) }
   }
 
-  private class NonCachedKafkaDataConsumer(val internalConsumer: InternalKafkaConsumer)
+  private case class NonCachedKafkaDataConsumer(internalConsumer: InternalKafkaConsumer)
     extends KafkaDataConsumer {
-    override def release(): Unit = {
-      internalConsumer.close()
-    }
+    override def release(): Unit = { internalConsumer.close() }
   }
 
   private case class CacheKey(groupId: String, topicPartition: TopicPartition) {
     def this(topicPartition: TopicPartition, kafkaParams: ju.Map[String, Object]) =
       this(kafkaParams.get(ConsumerConfig.GROUP_ID_CONFIG).asInstanceOf[String], topicPartition)
   }
 
+  // This cache has the following important properties.
+  // - We make a best-effort attempt to maintain the max size of the cache as configured capacity.
+  //   The capacity is not guaranteed to be maintained, especially when there are more active
+  //   tasks simultaneously using consumers than the capacity.
+  // - A cached consumer will not be cleared while it is in use. This is an invariant that
+  //   we maintain.
   private lazy val cache = {
     val conf = SparkEnv.get.conf
     val capacity = conf.getInt("spark.sql.kafkaConsumerCache.capacity", 64)
     new ju.LinkedHashMap[CacheKey, InternalKafkaConsumer](capacity, 0.75f, true) {
       override def removeEldestEntry(
         entry: ju.Map.Entry[CacheKey, InternalKafkaConsumer]): Boolean = {
+
+        // Try to remove the least-used entry if its currently not in use. This maintains the
+        // invariant mentioned in the docs above.
+        //
+        // If you cannot remove it, then the cache will keep growing. In the worst case,
+        // the cache will grow to the max number of concurrent tasks that can run in the executor,
+        // (that is, number of tasks slots) after which it will never reduce. This is unlikely to
+        // be a serious problem because an executor with more than 64 (default) tasks slots is
+        // likely running on a beefy machine that can handle a large number of simultaneously
+        // active consumers.
+
         if (entry.getValue.inuse == false && this.size > capacity) {
           logWarning(
             s"KafkaConsumer cache hitting max capacity of $capacity, " +
@@ -401,30 +415,40 @@ private[kafka010] object KafkaDataConsumer extends Logging {
     }
   }
 
-  private def releaseKafkaConsumer(
-    topicPartition: TopicPartition,
-    kafkaParams: ju.Map[String, Object]): Unit = {
-    val key = new CacheKey(topicPartition, kafkaParams)
-
+  private def releaseConsumer(intConsumer: InternalKafkaConsumer): Unit = {
     synchronized {
-      val consumer = cache.get(key)
-      if (consumer != null) {
-        if (consumer.markedForClose) {
-          consumer.close()
+
+      // If it has been marked for close, then do it any way
+      if (intConsumer.inuse && intConsumer.markedForClose) intConsumer.close()
+      intConsumer.inuse = false
+
+      // Clear the consumer from the cache if this is indeed the consumer present in the cache
+      val key = new CacheKey(intConsumer.topicPartition, intConsumer.kafkaParams)
+      val cachedIntConsumer = cache.get(key)
+      if (cachedIntConsumer != null) {
+        if (cachedIntConsumer.eq(intConsumer)) {
+          // The released consumer is indeed the cached one.
           cache.remove(key)
         } else {
-          consumer.inuse = false
+          // The released consumer is not the cached one. Don't do anything.
+          // This should not happen as long as we maintain the invariant mentioned above.
+          logWarning(
+            s"Cached consumer not the same one as the one being release" +
+              s"\ncached = $cachedIntConsumer [${System.identityHashCode(cachedIntConsumer)}]" +
+              s"\nreleased = $intConsumer [${System.identityHashCode(intConsumer)}]")
         }
       } else {
-        logWarning(s"Attempting to release consumer that does not exist")
+        // The released consumer is not in the cache. Don't do anything.
+        // This should not happen as long as we maintain the invariant mentioned above.
+        logWarning(s"Attempting to release consumer that is not in the cache")
       }
     }
   }
 
-
   /**
    * Get a cached consumer for groupId, assigned to topic and partition.
    * If matching consumer doesn't already exist, will be created using kafkaParams.
+   * This will make a best effort attempt to
    */
   def acquire(
       topicPartition: TopicPartition,
@@ -433,26 +457,50 @@ private[kafka010] object KafkaDataConsumer extends Logging {
     val key = new CacheKey(topicPartition, kafkaParams)
     val existingInternalConsumer = cache.get(key)
 
-    lazy val newNonCachedConsumer =
-      new NonCachedKafkaDataConsumer(new InternalKafkaConsumer(topicPartition, kafkaParams))
+    lazy val newInternalConsumer = new InternalKafkaConsumer(topicPartition, kafkaParams)
 
     if (TaskContext.get != null && TaskContext.get.attemptNumber >= 1) {
-      // If this is reattempt at running the task, then invalidate cache and start with
-      // a new consumer
+      // If this is reattempt at running the task, then invalidate cached consumer if any and
+      // start with a new one.
       if (existingInternalConsumer != null) {
-        existingInternalConsumer.markedForClose = true
+        if (existingInternalConsumer.inuse) {
+          // Consumer exists in cache and is somehow in use. Don't close it immediately, but
+          // mark it for being closed when it is released.
+          existingInternalConsumer.markedForClose = true
+          NonCachedKafkaDataConsumer(newInternalConsumer)
+
+        } else {
+          // Consumer exists in cache and is not in use, so close it immediately and replace
+          // it with a new one.
+          existingInternalConsumer.close()
+          cache.put(key, newInternalConsumer)
+          CachedKafkaDataConsumer(newInternalConsumer)
+
+        }
+      } else {
+        // Consumer is not cached, put the new one in the cache
+        cache.put(key, newInternalConsumer)
+        CachedKafkaDataConsumer(newInternalConsumer)
+
       }
-      newNonCachedConsumer
     } else if (!useCache) {
-      newNonCachedConsumer
+      // If planner asks to not reuse consumers, then do not use it, return a new consumer
+      NonCachedKafkaDataConsumer(newInternalConsumer)
+
     } else if (existingInternalConsumer == null) {
-      newNonCachedConsumer.internalConsumer.inuse = true
-      cache.put(key, newNonCachedConsumer.internalConsumer)
-      newNonCachedConsumer
+      // If consumer is not already cached, then put a new in the cache and return it
+      newInternalConsumer.inuse = true
+      cache.put(key, newInternalConsumer)
+      CachedKafkaDataConsumer(newInternalConsumer)
+
     } else if (existingInternalConsumer.inuse) {
-      newNonCachedConsumer
+      // If consumer is already cached but is currently in use, then return a new consumer
+      NonCachedKafkaDataConsumer(newInternalConsumer)
+
     } else {
-      new CachedKafkaDataConsumer(existingInternalConsumer)
+      // If consumer is already cached and is currently not in use, then return that consumer
+      CachedKafkaDataConsumer(existingInternalConsumer)
+
     }
   }
 }

external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaDataConsumerSuite.scala

@@ -29,6 +29,7 @@ import org.apache.kafka.common.TopicPartition
 import org.apache.kafka.common.serialization.ByteArrayDeserializer
 import org.scalatest.PrivateMethodTester
 
+import org.apache.spark.{TaskContext, TaskContextImpl}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.ThreadUtils
 
@@ -59,10 +60,11 @@ class KafkaDataConsumerSuite extends SharedSQLContext with PrivateMethodTester {
     assert(e.getCause === cause)
   }
 
-  test("concurrent use of KafkaDataConsumer") {
+  test("SPARK-23623: concurrent use of KafkaDataConsumer") {
     val topic = "topic" + Random.nextInt()
+    val data = (1 to 1000).map(_.toString)
     testUtils.createTopic(topic, 1)
-    testUtils.sendMessages(topic, (1 to 1000).map(_.toString).toArray)
+    testUtils.sendMessages(topic, data.toArray)
     val topicPartition = new TopicPartition(topic, 0)
 
     import ConsumerConfig._
@@ -78,18 +80,25 @@ class KafkaDataConsumerSuite extends SharedSQLContext with PrivateMethodTester {
     val numThreads = 50
     val numConsumerUsages = 500
 
-    val threadpool = Executors.newFixedThreadPool(numThreads)
-
     @volatile var error: Throwable = null
 
     def consume(i: Int): Unit = {
+      val useCache = Random.nextBoolean
+      val taskContext = if (Random.nextBoolean) {
+        new TaskContextImpl(0, 0, 0, 0, attemptNumber = Random.nextInt(2), null, null, null)
+      } else {
+        null
+      }
+      TaskContext.setTaskContext(taskContext)
       val consumer = KafkaDataConsumer.acquire(
-        topicPartition, kafkaParams.asJava, useCache = true)
+        topicPartition, kafkaParams.asJava, useCache)
       try {
         val range = consumer.getAvailableOffsetRange()
-        for (offset <- range.earliest until range.latest) {
-          consumer.get(offset, Long.MaxValue, 10000, failOnDataLoss = false)
+        val rcvd = range.earliest until range.latest map { offset =>
+          val bytes = consumer.get(offset, Long.MaxValue, 10000, failOnDataLoss = false).value()
+          new String(bytes)
         }
+        assert(rcvd == data)
       } catch {
         case e: Throwable =>
           error = e
@@ -99,13 +108,17 @@ class KafkaDataConsumerSuite extends SharedSQLContext with PrivateMethodTester {
       }
     }
 
-    // Sub
-    val futures = (1 to numConsumerUsages).map { i =>
-      threadpool.submit(new Runnable {
-        override def run(): Unit = { consume(i) }
-      })
+    val threadpool = Executors.newFixedThreadPool(numThreads)
+    try {
+      val futures = (1 to numConsumerUsages).map { i =>
+        threadpool.submit(new Runnable {
+          override def run(): Unit = { consume(i) }
+        })
+      }
+      futures.foreach(_.get(1, TimeUnit.MINUTES))
+      assert(error == null)
+    } finally {
+      threadpool.shutdown()
     }
-    futures.foreach(_.get(1, TimeUnit.MINUTES))
-    assert(error == null)
   }
 }