Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/15102#discussion_r81663920
--- Diff:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala
---
@@ -0,0 +1,366 @@
+/*
+ * 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 scala.util.control.NonFatal
+
+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.
+ *
+ * Zero data lost is not guaranteed when topics are deleted. If zero data
lost is critical, the user
+ * must make sure all messages in a topic have been processed when
deleting a topic.
+ */
+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))
+ logDebug(s"GetOffset:
${offset.partitionToOffsets.toSeq.map(_.toString).sorted}")
+ 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]
+ }
+ if (newPartitionOffsets.keySet != newPartitions) {
+ // We cannot get from offsets for some partitions. It means they got
deleted.
+ val deletedPartitions =
newPartitions.diff(newPartitionOffsets.keySet)
+ logWarning(s"Partitions removed: ${deletedPartitions}, some data may
have been missed")
+ }
+ 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.filter { tp =>
+ newPartitionOffsets.contains(tp) || fromPartitionOffsets.contains(tp)
+ }.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 =>
--- End diff --
> I'm lost on what the point of sorting topic partitions is, if you're
still just using topicpartition hashcode as an index into the executors.
Removed the sorting
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