Github user marmbrus commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15102#discussion_r79676581
  
    --- Diff: 
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala
 ---
    @@ -0,0 +1,446 @@
    +/*
    + * 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, ConsumerConfig, 
KafkaConsumer}
    +import 
org.apache.kafka.clients.consumer.internals.NoOpConsumerRebalanceListener
    +import org.apache.kafka.common.TopicPartition
    +import org.apache.kafka.common.serialization.ByteArrayDeserializer
    +
    +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.sources.{DataSourceRegister, 
StreamSourceProvider}
    +import org.apache.spark.sql.types._
    +import org.apache.spark.SparkContext
    +
    +/**
    + * 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[Array[Byte], Array[Byte]],
    +    executorKafkaParams: ju.Map[String, Object],
    +    sourceOptions: Map[String, String])
    +  extends Source with Logging {
    +
    +  @transient private val consumer = consumerStrategy.createConsumer()
    +  @transient private val sc = sqlContext.sparkContext
    +  @transient 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 = {
    +    logDebug(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
    +    }
    +
    +    // 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))
    +      }
    +    }
    +    val sortedTopicPartitions = 
untilPartitionOffsets.keySet.toSeq.sorted(topicPartitionOrdering)
    +    val sortedExecutors = getSortedExecutorList(sc)
    +    val numExecutors = sortedExecutors.size
    +    logDebug("Sorted executors: " + sortedExecutors.mkString(", "))
    +    val offsetRanges = sortedTopicPartitions.flatMap { tp =>
    +      fromPartitionOffsets.get(tp).map { fromOffset =>
    +        val untilOffset = untilPartitionOffsets(tp)
    +        val preferredLoc = if (numExecutors > 0) {
    +          Some(sortedExecutors(positiveMod(tp.hashCode, numExecutors)))
    +        } else None
    +        KafkaSourceRDD.OffsetRange(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[Array[Byte], Array[Byte]](
    +      sc, executorKafkaParams, offsetRanges, sourceOptions).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: " + 
offsetRanges.sortBy(_.topicPartition.toString).mkString(", "))
    +    sqlContext.createDataFrame(rdd, schema)
    +  }
    +
    +  /** Stop this source and free any resources it has allocated. */
    +  override def stop(): Unit = synchronized {
    +    consumer.close()
    +  }
    +
    +  override def toString(): String = s"KafkaSource[$consumerStrategy]"
    +
    +  private def fetchPartitionOffsets(seekToLatest: Boolean): 
Map[TopicPartition, Long] = {
    +    synchronized {
    +      logTrace("\tPolling")
    +      consumer.poll(0)
    +      val partitions = consumer.assignment()
    +      consumer.pause(partitions)
    +      logDebug(s"\tPartitioned assigned to consumer: $partitions")
    +      if (seekToLatest) {
    +        consumer.seekToEnd(partitions)
    +        logDebug("\tSeeked to the end")
    +      }
    +      logTrace("Getting positions")
    +      val partitionToOffsets = partitions.asScala.map(p => p -> 
consumer.position(p))
    +      logDebug(s"Got positions $partitionToOffsets")
    +      partitionToOffsets.toMap
    +    }
    +  }
    +
    +  private def positiveMod(a: Long, b: Int): Int = ((a % b).toInt + b) % b
    +}
    +
    +/** Companion object for the [[KafkaSource]]. */
    +private[kafka010] object KafkaSource {
    +
    +  def kafkaSchema: StructType = StructType(Seq(
    +    StructField("checksum", LongType),
    +    StructField("key", BinaryType),
    +    StructField("offset", LongType),
    +    StructField("partition", IntegerType),
    +    StructField("serializedKeySize", IntegerType),
    +    StructField("serializedValueSize", IntegerType),
    +    StructField("timestamp", LongType),
    +    StructField("timestampType", IntegerType),
    +    StructField("topic", StringType),
    +    StructField("value", BinaryType)
    +  ))
    +
    +  sealed trait ConsumerStrategy[K, V] {
    --- End diff --
    
    I disagree that we should expose half-baked external API's in the MVP.  
Specifically, it sounds like doing so would allow users to setup configurations 
that would violate the correctness guarantees.  This is not to say that we 
should never expose these, but instead that I would value stability, simplicity 
and correctness over extra functionality.


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