Kafka结构化流检查点

Question

I am trying to do structured streaming from Kafka. I am planning to store checkpoints in HDFS. I read a Cloudera blog recommending not to store checkpoints in HDFS for Spark streaming. Is it same issue for structure streaming checkpoints. https://blog.cloudera.com/blog/2017/06/offset-management-for-apache-kafka-with-apache-spark-streaming/ .

In structured streaming, If my spark program is down for certain time, how do I get latest offset from checkpoint directory and load data after that offset. I am storing checkpoints in a directory as shown below.

 df.writeStream\
        .format("text")\
        .option("path", '\files') \
        .option("checkpointLocation", 'checkpoints\chkpt') \
        .start()

Update:

This is my Structured streaming program reads a Kafka message, decompresses and writes to HDFS.

df = spark \
        .readStream \
        .format("kafka") \
        .option("kafka.bootstrap.servers", KafkaServer) \
        .option("subscribe", KafkaTopics) \
        .option("failOnDataLoss", "false")\
         .load()
Transaction_DF = df.selectExpr("CAST(value AS STRING)")
Transaction_DF.printSchema()

decomp = Transaction_DF.select(zip_extract("value").alias("decompress"))
#zip_extract is a UDF to decompress the stream

query = decomp.writeStream\
    .format("text")\
    .option("path", \Data_directory_inHDFS) \
    .option("checkpointLocation", \pathinDHFS\) \
    .start()

query.awaitTermination()

Answer 1

Storing Checkpoint on longterm storage(HDFS, AWS S3,etc.) are most preferred. I would Like to add one point here that the property "failOnDataLoss" should not be set to false as it is not best practice. Data loss is something no one would like to afford. Rest you are on the right Path.

Answer 2

In your query, try applying a checkpoint while writing results to some persistent storage like HDFS in some format like parquet. It worked good for me.

Answer 3

As I understood the artificial it recommend maintaining the offset management either in: Hbase, Kafka, HDFS or Zookeeper.

"It is worth mentioning that you can also store offsets in a storage system like HDFS. Storing offsets in HDFS is a less popular approach compared to the above options as HDFS has a higher latency compared to other systems like ZooKeeper and HBase."

you can find in Spark Documentation how to restart a query from an existing checkpoint at: http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#recovering-from-failures-with-checkpointing

Answer 4

In structured streaming, If my spark program is down for certain time, how do I get latest offset from checkpoint directory and load data after that offset.

Under your checkpointdir folder you will find a folder name 'offsets'. Folder 'offsets' maintain the next offsets to be requested from kafka. Open the latest file(latest batch file) under 'offsets' folder, the next expected offsets will be in format below

{"kafkatopicname":{"2":16810618,"1":16810853,"0":91332989}}

To load data after that offset, set below property to your spark read stream

 .option("startingOffsets", "{\""+topic+"\":{\"0\":91332989,\"1\":16810853,\"2\":16810618}}")

0,1,2 are the partitions in topic.