I am experiencing an issue to start spark streaming on a really big kafka topic, there are around 150 million data in this topic already and the topic is growing super fast.
When I tried to start spark streaming and read data from the beginning of this topic by setting kafka parameter ("auto.offset.reset" -> "smallest"), it always try to finish all 150 million data processing in the first batch and return a "java.lang.OutOfMemoryError: GC overhead limit exceeded" error. There isn't a lot calculation in this spark stream app though.
Can I have a way to process the history data in this topic in first several batches but not all in first batch?
Bunch of thanks in advance!
James
You can control spark kafka-input reading rate with following spark configuration spark.streaming.kafka.maxRatePerPartition .
You can configure this by giving how many docs you want to process per batch.
sparkConf.set("spark.streaming.kafka.maxRatePerPartition","<docs-count>")
Above config process <docs-count>*<batch_interval> records per batch.
You can find more info about above config here.
Related
I have a Spark Structured Streaming app that reads from Kafka and writes to Elasticsearch and S3. I have enabled checkpointing to a S3 bucket as well (app runs AWS EMR). I saw that in S3 bucket that over time the commits get less frequently and there is always growing delay in the data.
So I want to make Spark to process always to process batches with same amount of data each batch. I tried to set the ".option("maxOffsetsPerTrigger", 100)" but the batch size didnt become smaller, still huge amount of time between commits.
As I understood that we just tell spark how much data consume from kafka per poll and that spark just polls multiple times and then writes, so no limitations in the batch size.
I also tried to use continuous mode but the submit failed, i guess cuz of the output sink / foreachbatch doesnt support it.
any ideas are welcome, i will try everything ^^
actually the each offset contained so much data that I had to limit the max offsets per trigger to 50, and had to delete the old checkpoint folder, I read somewhere that it tries to finish first batch with the offset in the checkpoint, and then turns on the max offset per trigger
Suppose we have batch jobs producing records into kafka and we have a kafka connect cluster consuming records and moving them to HDFS. We want the ability to run batch jobs later on the same data but we want to ensure that batch jobs see the whole records generated by producers. What is a good design for this?
You can run any MapReduce, Spark, Hive, etc query on the data, and you will get all records that have been thus far been written to HDFS. It will not see data that has not been consumed by the Sink from the producers, but this has nothing to do with Connect or HDFS, that is a pure Kafka limitation.
Worth pointing out that Apache Pinot is a better place to combine Kafka streaming data and have batch query support.
I am using spark 2.1 and Kafka 0.10.1.
I want to process the data by reading the entire data of specific topics in Kafka on a daily basis.
For spark streaming, I know that createDirectStream only needs to include a list of topics and some configuration information as arguments.
However, I realized that createRDD would have to include all of the topic, partitions, and offset information.
I want to make batch processing as convenient as streaming in spark.
Is it possible?
I suggest you to read this text from Cloudera.
This example show you how to get from Kafka the data just one time. That you will persist the offsets in a postgres due to the ACID archtecture.
So I hope that will solve your problem.
I'm writing a Spark (v1.6.0) batch job which reads from a Kafka topic.
For this I can use org.apache.spark.streaming.kafka.KafkaUtils#createRDD however,
I need to set the offsets for all the partitions and also need to store them somewhere (ZK? HDFS?) to know from where to start the next batch job.
What is the right approach to read from Kafka in a batch job?
I'm also thinking about writing a streaming job instead, which reads from auto.offset.reset=smallest and saves the checkpoint
to HDFS and then in the next run it starts from that.
But in this case how can I just fetch once and stop streaming after the first batch?
createRDD is the right approach for reading a batch from kafka.
To query for info about the latest / earliest available offsets, look at KafkaCluster.scala methods getLatestLeaderOffsets and getEarliestLeaderOffsets. That file was private, but should be public in the latest versions of spark.
I have one Kafka partition, and one sparkStreaming application. One server with 10 cores. When the spark streaming get one message from Kafka, the subsequent process will take 5 seconds(this is my code). So I found sparkStreaming read Kafka message very slow, I'm guessing that when spark read out one message then it will wait until the message was processed, so the reading and processing are synchronized.
I was wondering can I make the spark reading asynchronously? So the reading from Kafka won't be dragged by the subsequent processing. Then the spark will very quickly consume data from Kafka. And then I can focus on the slow data process inside spark. btw, I'm using foreachRDD function.
you can increase the number of partitions in kafka, it should improve the parallelism , also you can try with "Direct kafka receiver" which really improve the performance when your app is reading from kafka