重新启动Spark结构化流作业会消耗数百万个Kafka消息并消亡

Question

We have a Spark Streaming Application running on Spark 2.3.3

Basically, it opens a Kafka Stream:

  kafka_stream = spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "mykafka:9092") \
  .option("subscribe", "mytopic") \
  .load()

The kafka topic has 2 partitions. After that, there are some basic filtering operations, some Python UDFs and an explode() on a column, like:

   stream = apply_operations(kafka_stream)

where apply_operations does all the work on the data. In the end, we would like to write the stream to a sink, ie:

   stream.writeStream \
   .format("our.java.sink.Class") \
   .option("some-option", "value") \
   .trigger(processingTime='15 seconds') \
   .start()

To let this stream operation run forever, we apply:

   spark.streams.awaitAnyTermination()

In the end.

So far, so good. Everything runs for days. But due to a network problem, the job died for a few days, and there are now millions of messages in the kafka stream waiting to be catched up.

When we restart the streaming data job using spark-submit, the first batch will be too large and will take ages to be completed. We thought there might be a way to limit the size of the first batch with some parameter, but we did not find anything that helped.

We tried:

• spark.streaming.backpressure.enabled=true along with spark.streaming.backpressure.initialRate=2000 and spark.streaming.kafka.maxRatePerPartition=1000 and spark.streaming.receiver.maxrate=2000

• setting spark.streaming.backpressure.pid.minrate to a lower value did not had an effect either

• setting the option("maxOffsetsPerTrigger", 10000) did not had an effect as well

Now, after we restart the pipeline, sooner or later the whole Spark Job will crash again. We cannot simply widen the memory or the cores to be used for the spark job.

Is there anything we missed to control the amount of events beeing processed in one stream-batch?

Answer 1

You wrote in the comments that you are using spark-streaming-kafka-0-8_2.11 and that api version is not able to handle maxOffsetPerTrigger (or any other mechanism to reduce the number of consumed messages as far as I know) as it was only implemented for the newer api spark-streaming-kafka-0-10_2.11 . This newer api also works with your kafka version 0.10.2.2 according to the documentation .