Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/15102#discussion_r80584097
--- Diff:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala
---
@@ -0,0 +1,344 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.kafka010
+
+import java.{util => ju}
+
+import scala.collection.JavaConverters._
+
+import org.apache.kafka.clients.consumer.{Consumer, KafkaConsumer}
+import
org.apache.kafka.clients.consumer.internals.NoOpConsumerRebalanceListener
+import org.apache.kafka.common.TopicPartition
+
+import org.apache.spark.SparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.scheduler.ExecutorCacheTaskLocation
+import org.apache.spark.sql._
+import org.apache.spark.sql.execution.streaming._
+import org.apache.spark.sql.kafka010.KafkaSource._
+import org.apache.spark.sql.types._
+
+/**
+ * A [[Source]] that uses Kafka's own [[KafkaConsumer]] API to reads data
from Kafka. The design
+ * for this source is as follows.
+ *
+ * - The [[KafkaSourceOffset]] is the custom [[Offset]] defined for this
source that contains
+ * a map of TopicPartition -> offset. Note that this offset is 1 +
(available offset). For
+ * example if the last record in a Kafka topic "t", partition 2 is
offset 5, then
+ * KafkaSourceOffset will contain TopicPartition("t", 2) -> 6. This is
done keep it consistent
+ * with the semantics of `KafkaConsumer.position()`.
+ *
+ * - The [[ConsumerStrategy]] class defines which Kafka topics and
partitions should be read
+ * by this source. These strategies directly correspond to the different
consumption options
+ * in . This class is designed to return a configured
+ * [[KafkaConsumer]] that is used by the [[KafkaSource]] to query for
the offsets.
+ * See the docs on
[[org.apache.spark.sql.kafka010.KafkaSource.ConsumerStrategy]] for
+ * more details.
+ *
+ * - The [[KafkaSource]] written to do the following.
+ *
+ * - As soon as the source is created, the pre-configured KafkaConsumer
returned by the
+ * [[ConsumerStrategy]] is used to query the initial offsets that this
source should
+ * start reading from. This used to create the first batch.
+ *
+ * - `getOffset()` uses the KafkaConsumer to query the latest available
offsets, which are
+ * returned as a [[KafkaSourceOffset]].
+ *
+ * - `getBatch()` returns a DF that reads from the 'start offset' until
the 'end offset' in
+ * for each partition. The end offset is excluded to be consistent
with the semantics of
+ * [[KafkaSourceOffset]] and `KafkaConsumer.position()`.
+ *
+ * - The DF returned is based on [[KafkaSourceRDD]] which is constructed
such that the
+ * data from Kafka topic + partition is consistently read by the same
executors across
+ * batches, and cached KafkaConsumers in the executors can be reused
efficiently. See the
+ * docs on [[KafkaSourceRDD]] for more details.
+ */
+private[kafka010] case class KafkaSource(
+ sqlContext: SQLContext,
+ consumerStrategy: ConsumerStrategy,
+ executorKafkaParams: ju.Map[String, Object],
+ sourceOptions: Map[String, String])
+ extends Source with Logging {
+
+ private val consumer = consumerStrategy.createConsumer()
+ private val sc = sqlContext.sparkContext
+ private val initialPartitionOffsets = fetchPartitionOffsets(seekToLatest
= false)
+ logInfo(s"Initial offsets: $initialPartitionOffsets")
+
+ override def schema: StructType = KafkaSource.kafkaSchema
+
+ /** Returns the maximum available offset for this source. */
+ override def getOffset: Option[Offset] = {
+ val offset = KafkaSourceOffset(fetchPartitionOffsets(seekToLatest =
true))
+ logInfo(s"GetOffset: $offset")
+ Some(offset)
+ }
+
+ /** Returns the data that is between the offsets [`start`, `end`), i.e.
end is exclusive. */
+ override def getBatch(start: Option[Offset], end: Offset): DataFrame = {
+ logInfo(s"GetBatch called with start = $start, end = $end")
+ val untilPartitionOffsets = KafkaSourceOffset.getPartitionOffsets(end)
+ val fromPartitionOffsets = start match {
+ case Some(prevBatchEndOffset) =>
+ KafkaSourceOffset.getPartitionOffsets(prevBatchEndOffset)
+ case None =>
+ initialPartitionOffsets
+ }
+
+ // Find the new partitions, and get their earliest offsets
+ val newPartitions =
untilPartitionOffsets.keySet.diff(fromPartitionOffsets.keySet)
+ val newPartitionOffsets = if (newPartitions.nonEmpty) {
+ fetchNewPartitionEarliestOffsets(newPartitions.toSeq)
+ } else {
+ Map.empty[TopicPartition, Long]
+ }
+ logInfo(s"Partitions added: $newPartitionOffsets")
+ newPartitionOffsets.filter(_._2 != 0).foreach { case (p, o) =>
+ logWarning(s"Added partition $p starts from $o instead of 0, some
data may have been missed")
+ }
+
+ val deletedPartitions =
fromPartitionOffsets.keySet.diff(untilPartitionOffsets.keySet)
+ logWarning(s"Partitions removed: $deletedPartitions, some data may
have been missed")
+
+ // Sort the partitions and current list of executors to consistently
assign each partition
+ // to the executor. This allows cached KafkaConsumers in the executors
to be re-used to
+ // read the same partition in every batch.
+ val topicPartitionOrdering = new Ordering[TopicPartition] {
+ override def compare(l: TopicPartition, r: TopicPartition): Int = {
+ implicitly[Ordering[(String, Long)]].compare(
+ (l.topic, l.partition),
+ (r.topic, r.partition))
+ }
+ }
+
+ // Use the until partitions to calculate offset ranges to ignore
partitions that have
+ // been deleted
+ val sortedTopicPartitions =
untilPartitionOffsets.keySet.toSeq.sorted(topicPartitionOrdering)
+ logDebug("Sorted topicPartitions: " +
sortedTopicPartitions.mkString(", "))
+
+ val sortedExecutors = getSortedExecutorList(sc)
+ val numExecutors = sortedExecutors.length
+ logDebug("Sorted executors: " + sortedExecutors.mkString(", "))
+
+ // Calculate offset ranges
+ val offsetRanges = sortedTopicPartitions.map { tp =>
+ val fromOffset = fromPartitionOffsets.get(tp).getOrElse {
+ newPartitionOffsets.getOrElse(tp, {
+ // This should not happen since newPartitionOffsets contains all
partitions not in
+ // fromPartitionOffsets
+ throw new IllegalStateException(s"$tp doesn't have a from
offset")
+ })
+ }
+ val untilOffset = untilPartitionOffsets(tp)
+ val preferredLoc = if (numExecutors > 0) {
+ Some(sortedExecutors(positiveMod(tp.hashCode, numExecutors)))
+ } else None
+ KafkaSourceRDDOffsetRange(tp, fromOffset, untilOffset, preferredLoc)
+ }.toArray
+
+ // Create a RDD that reads from Kafka and get the (key, value) pair as
byte arrays.
+ val rdd = new KafkaSourceRDD(
+ sc, executorKafkaParams, offsetRanges).map { cr =>
+ Row(cr.checksum, cr.key, cr.offset, cr.partition,
cr.serializedKeySize,
+ cr.serializedValueSize, cr.timestamp, cr.timestampType.id,
cr.topic, cr.value)
+ }
+
+ logInfo("GetBatch generating RDD of offset range: " +
+ offsetRanges.sortBy(_.topicPartition.toString).mkString(", "))
+ sqlContext.createDataFrame(rdd, schema)
--- End diff --
We don't have to fix this here, but this is probably really slow. We
should be using the internal APIs here. I might open a PR against this branch
to encode directly into tungsten format.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
