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== Using Spark SQL to update data in Hive using ORC files

The example has showed up on Spark's users mailing list.

[CAUTION]

  • FIXME Offer a complete working solution in Scala
  • FIXME Load ORC files into dataframe ** val df = hiveContext.read.format("orc").load(to/path) ====

Solution was to use Hive in ORC format with partitions:

  • A table in Hive stored as an ORC file (using partitioning)
  • Using SQLContext.sql to insert data into the table
  • Using SQLContext.sql to periodically run ALTER TABLE...CONCATENATE to merge your many small files into larger files optimized for your HDFS block size ** Since the CONCATENATE command operates on files in place it is transparent to any downstream processing
  • Hive solution is just to concatenate the files ** it does not alter or change records. ** it's possible to update data in Hive using ORC format ** With transactional tables in Hive together with insert, update, delete, it does the "concatenate " for you automatically in regularly intervals. Currently this works only with tables in orc.format (stored as orc) ** Alternatively, use Hbase with Phoenix as the SQL layer on top ** Hive was originally not designed for updates, because it was.purely warehouse focused, the most recent one can do updates, deletes etc in a transactional way.

Criteria:

  • spark-streaming/spark-streaming.md[Spark Streaming] jobs are receiving a lot of small events (avg 10kb)
  • Events are stored to HDFS, e.g. for Pig jobs
  • There are a lot of small files in HDFS (several millions)

Last update: 2020-10-06