在 Spark Structured Streaming 中将数据内部连接到左连接 DataFrame 时丢失条目

Question

我正在尝试将数据与 DataFrame 连接起来，而 DataFrame 又是由左连接产生的。虽然在批处理中这按预期工作，但在流处理中一些条目丢失了...

下面我创建了一个“会话”的最小示例，它具有“开始”和“结束”事件以及可选的一些“元数据”。

脚本生成两个输出：sessionStartsWithMetadata 来自与“元数据”事件左连接的“开始”事件，基于 sessionId。使用“左连接”，因为即使不存在相应的元数据，我们也希望获得输出事件。

此外，DataFrame endedSessionsWithMetadata 是通过将“结束”事件连接到先前创建的 DataFrame 来创建的。这里使用了“内部连接”，因为我们只需要在会话确定结束时输出一些内容。

这段代码可以在spark-shell中执行：

import java.sql.Timestamp
import org.apache.spark.sql.execution.streaming.{MemoryStream, StreamingQueryWrapper}
import org.apache.spark.sql.streaming.StreamingQuery
import org.apache.spark.sql.{DataFrame, SQLContext}
import org.apache.spark.sql.functions.{col, expr, lit}

import spark.implicits._
implicit val sqlContext: SQLContext = spark.sqlContext

// Main data processing, regardless whether batch or stream processing
def process(
    sessionStartEvents: DataFrame,
    sessionOptionalMetadataEvents: DataFrame,
    sessionEndEvents: DataFrame
): (DataFrame, DataFrame) = {
  val sessionStartsWithMetadata: DataFrame = sessionStartEvents
    .join(
      sessionOptionalMetadataEvents,
      sessionStartEvents("sessionId") === sessionOptionalMetadataEvents("sessionId") &&
        sessionStartEvents("sessionStartTimestamp").between(
          sessionOptionalMetadataEvents("sessionOptionalMetadataTimestamp").minus(expr(s"INTERVAL 1 seconds")),
          sessionOptionalMetadataEvents("sessionOptionalMetadataTimestamp").plus(expr(s"INTERVAL 1 seconds"))
        ),
      "left" // metadata is optional
    )
    .select(
      sessionStartEvents("sessionId"),
      sessionStartEvents("sessionStartTimestamp"),
      sessionOptionalMetadataEvents("sessionOptionalMetadataTimestamp")
    )

  val endedSessionsWithMetadata = sessionStartsWithMetadata.join(
    sessionEndEvents,
    sessionStartsWithMetadata("sessionId") === sessionEndEvents("sessionId") &&
      sessionStartsWithMetadata("sessionStartTimestamp").between(
        sessionEndEvents("sessionEndTimestamp").minus(expr(s"INTERVAL 10 seconds")),
        sessionEndEvents("sessionEndTimestamp")
      )
  )

  (sessionStartsWithMetadata, endedSessionsWithMetadata)
}

def streamProcessing(
    sessionStartData: Seq[(Timestamp, Int)],
    sessionOptionalMetadata: Seq[(Timestamp, Int)],
    sessionEndData: Seq[(Timestamp, Int)]
): (StreamingQuery, StreamingQuery) = {

  val sessionStartEventsStream: MemoryStream[(Timestamp, Int)] = MemoryStream[(Timestamp, Int)]
  sessionStartEventsStream.addData(sessionStartData)

  val sessionStartEvents: DataFrame = sessionStartEventsStream
    .toDS()
    .toDF("sessionStartTimestamp", "sessionId")
    .withWatermark("sessionStartTimestamp", "1 second")

  val sessionOptionalMetadataEventsStream: MemoryStream[(Timestamp, Int)] = MemoryStream[(Timestamp, Int)]
  sessionOptionalMetadataEventsStream.addData(sessionOptionalMetadata)

  val sessionOptionalMetadataEvents: DataFrame = sessionOptionalMetadataEventsStream
    .toDS()
    .toDF("sessionOptionalMetadataTimestamp", "sessionId")
    .withWatermark("sessionOptionalMetadataTimestamp", "1 second")

  val sessionEndEventsStream: MemoryStream[(Timestamp, Int)] = MemoryStream[(Timestamp, Int)]
  sessionEndEventsStream.addData(sessionEndData)

  val sessionEndEvents: DataFrame = sessionEndEventsStream
    .toDS()
    .toDF("sessionEndTimestamp", "sessionId")
    .withWatermark("sessionEndTimestamp", "1 second")

  val (sessionStartsWithMetadata, endedSessionsWithMetadata) =
    process(sessionStartEvents, sessionOptionalMetadataEvents, sessionEndEvents)

  val sessionStartsWithMetadataQuery = sessionStartsWithMetadata
    .select(lit("sessionStartsWithMetadata"), col("*")) // Add label to see which query's output it is
    .writeStream
    .outputMode("append")
    .format("console")
    .option("truncate", "false")
    .option("numRows", "1000")
    .start()

  val endedSessionsWithMetadataQuery = endedSessionsWithMetadata
    .select(lit("endedSessionsWithMetadata"), col("*")) // Add label to see which query's output it is
    .writeStream
    .outputMode("append")
    .format("console")
    .option("truncate", "false")
    .option("numRows", "1000")
    .start()

  (sessionStartsWithMetadataQuery, endedSessionsWithMetadataQuery)
}

def batchProcessing(
    sessionStartData: Seq[(Timestamp, Int)],
    sessionOptionalMetadata: Seq[(Timestamp, Int)],
    sessionEndData: Seq[(Timestamp, Int)]
): Unit = {

  val sessionStartEvents = spark.createDataset(sessionStartData).toDF("sessionStartTimestamp", "sessionId")
  val sessionOptionalMetadataEvents = spark.createDataset(sessionOptionalMetadata).toDF("sessionOptionalMetadataTimestamp", "sessionId")
  val sessionEndEvents = spark.createDataset(sessionEndData).toDF("sessionEndTimestamp", "sessionId")

  val (sessionStartsWithMetadata, endedSessionsWithMetadata) =
    process(sessionStartEvents, sessionOptionalMetadataEvents, sessionEndEvents)

  println("sessionStartsWithMetadata")
  sessionStartsWithMetadata.show(100, truncate = false)

  println("endedSessionsWithMetadata")
  endedSessionsWithMetadata.show(100, truncate = false)
}


// Data is represented as tuples of (eventTime, sessionId)...
val sessionStartData = Vector(
  (new Timestamp(1), 0),
  (new Timestamp(2000), 1),
  (new Timestamp(2000), 2),
  (new Timestamp(20000), 10)
)

val sessionOptionalMetadata = Vector(
  (new Timestamp(1), 0),
  // session `1` has no metadata
  (new Timestamp(2000), 2),
  (new Timestamp(20000), 10)
)

val sessionEndData = Vector(
  (new Timestamp(10000), 0),
  (new Timestamp(11000), 1),
  (new Timestamp(12000), 2),
  (new Timestamp(30000), 10)
)

batchProcessing(sessionStartData, sessionOptionalMetadata, sessionEndData)

val (sessionStartsWithMetadataQuery, endedSessionsWithMetadataQuery) =
  streamProcessing(sessionStartData, sessionOptionalMetadata, sessionEndData)

在 ID 为 1 的示例会话中没有元数据，因此相应的元数据列为 null。

加入数据的主要功能在def process(…)中实现，使用批数据和流数据调用。

在批处理版本中，输出符合预期：

sessionStartsWithMetadata
+---------+-----------------------+--------------------------------+            
|sessionId|sessionStartTimestamp  |sessionOptionalMetadataTimestamp|
+---------+-----------------------+--------------------------------+
|0        |1970-01-01 01:00:00.001|1970-01-01 01:00:00.001         |
|1        |1970-01-01 01:00:02    |null                            | ← has no metadata ✔
|2        |1970-01-01 01:00:02    |1970-01-01 01:00:02             |
|10       |1970-01-01 01:00:20    |1970-01-01 01:00:20             |
+---------+-----------------------+--------------------------------+

endedSessionsWithMetadata
+---------+-----------------------+--------------------------------+-------------------+---------+
|sessionId|sessionStartTimestamp  |sessionOptionalMetadataTimestamp|sessionEndTimestamp|sessionId|
+---------+-----------------------+--------------------------------+-------------------+---------+
|0        |1970-01-01 01:00:00.001|1970-01-01 01:00:00.001         |1970-01-01 01:00:10|0        |
|1        |1970-01-01 01:00:02    |null                            |1970-01-01 01:00:11|1        |  ← has no metadata ✔
|2        |1970-01-01 01:00:02    |1970-01-01 01:00:02             |1970-01-01 01:00:12|2        |
|10       |1970-01-01 01:00:20    |1970-01-01 01:00:20             |1970-01-01 01:00:30|10       |
+---------+-----------------------+--------------------------------+-------------------+---------+

但是当相同的处理是运行作为流处理时 endedSessionsWithMetadata 的输出不包含没有元数据的会话 1 的条目：

-------------------------------------------                                     
Batch: 0 ("start event")
-------------------------------------------
+-------------------------+---------+-----------------------+--------------------------------+
|sessionStartsWithMetadata|sessionId|sessionStartTimestamp  |sessionOptionalMetadataTimestamp|
+-------------------------+---------+-----------------------+--------------------------------+
|sessionStartsWithMetadata|10       |1970-01-01 01:00:20    |1970-01-01 01:00:20             |
|sessionStartsWithMetadata|2        |1970-01-01 01:00:02    |1970-01-01 01:00:02             |
|sessionStartsWithMetadata|0        |1970-01-01 01:00:00.001|1970-01-01 01:00:00.001         |
+-------------------------+---------+-----------------------+--------------------------------+

-------------------------------------------                                     
Batch: 0 ("end event")
-------------------------------------------
+-------------------------+---------+-----------------------+--------------------------------+-------------------+---------+
|endedSessionsWithMetadata|sessionId|sessionStartTimestamp  |sessionOptionalMetadataTimestamp|sessionEndTimestamp|sessionId|
+-------------------------+---------+-----------------------+--------------------------------+-------------------+---------+
|endedSessionsWithMetadata|10       |1970-01-01 01:00:20    |1970-01-01 01:00:20             |1970-01-01 01:00:30|10       |
|endedSessionsWithMetadata|2        |1970-01-01 01:00:02    |1970-01-01 01:00:02             |1970-01-01 01:00:12|2        |
|endedSessionsWithMetadata|0        |1970-01-01 01:00:00.001|1970-01-01 01:00:00.001         |1970-01-01 01:00:10|0        |
+-------------------------+---------+-----------------------+--------------------------------+-------------------+---------+

-------------------------------------------                                     
Batch: 1 ("start event")
-------------------------------------------
+-------------------------+---------+---------------------+--------------------------------+
|sessionStartsWithMetadata|sessionId|sessionStartTimestamp|sessionOptionalMetadataTimestamp|
+-------------------------+---------+---------------------+--------------------------------+
|sessionStartsWithMetadata|1        |1970-01-01 01:00:02  |null                            | ← has no metadata ✔
+-------------------------+---------+---------------------+--------------------------------+

-------------------------------------------                                     
Batch: 1 ("end event")
-------------------------------------------
+-------------------------+---------+---------------------+--------------------------------+-------------------+---------+
|endedSessionsWithMetadata|sessionId|sessionStartTimestamp|sessionOptionalMetadataTimestamp|sessionEndTimestamp|sessionId|
+-------------------------+---------+---------------------+--------------------------------+-------------------+---------+
+-------------------------+---------+---------------------+--------------------------------+-------------------+---------+
  ↳ ✘ here I would have expected a line with sessionId=1, that has "start" and "end" information, but no "metadata" ✘

谁能解释为什么在流处理中不存在没有“元数据”(sessionId=1) 的“结束”事件？我需要做什么才能让它出现在输出中？

非常感谢！

Answer 1

经过大量测试，环顾四周并re-reading手册：

It must be a bug in Spark.

I note also this post in circulation: https://lists.apache.org/thread.html/cc6489a19316e7382661d305fabd8c21915e5faf6a928b4869ac2b4a@%3Cdev.spark.apache.org%3E and whilst global vs chained stream-stream joins are understood, this point imo to an issue for this type of processing.

I ran on Spark Databricks 3.x to no avail.

在 Spark Structured Streaming 中将数据内部连接到左连接 DataFrame 时丢失条目

Losing entries when inner-joining data to a left-joined DataFrame in Spark Structured Streaming

scala

apache-spark

apache-spark-sql

spark-structured-streaming