Structured Stream Vs Spark Steaming (DStream)?
Which is recommended for system stability. Exactly once is NOT first priority.
First priority is STABLE system.
I am I need to make a decision soon. I need help. Here is the question again.
Should I go backward and use Spark Streaming DStream based. Write our own
checkpoint and go from there. At least we never encounter these metadata
issues there.
Thanks,
Rachana
On Wednesday, June 17, 2020, 02:02:20 PM PDT, Jungtaek Lim
<kabhwan.opensou...@gmail.com> wrote:
Just in case if anyone prefers ASF projects then there are other alternative
projects in ASF as well, alphabetically, Apache Hudi [1] and Apache Iceberg
[2]. Both are recently graduated as top level projects. (DISCLAIMER: I'm not
involved in both.)
BTW it would be nice if we make the metadata implementation on file stream
source/sink be pluggable - from what I've seen, plugin approach has been
selected as the way to go whenever some part is going to be complicated and it
becomes arguable whether the part should be handled in Spark project vs should
be outside. e.g. checkpoint manager, state store provider, etc. It would open
up chances for the ecosystem to play with the challenge "without completely
re-writing the file stream source and sink", focusing on scalability for
metadata in a long run query. Alternative projects described above will still
provide more higher-level features and look attractive, but sometimes it may be
just "using a sledgehammer to crack a nut".
1. https://hudi.apache.org/2. https://iceberg.apache.org/
On Thu, Jun 18, 2020 at 2:34 AM Tathagata Das <tathagata.das1...@gmail.com>
wrote:
Hello Rachana,
Getting exactly-once semantics on files and making it scale to a very large
number of files are very hard problems to solve. While Structured Streaming +
built-in file sink solves the exactly-once guarantee that DStreams could not,
it is definitely limited in other ways (scaling in terms of files, combining
batch and streaming writes in the same place, etc). And solving this problem
requires a holistic solution that is arguably beyond the scope of the Spark
project.
There are other projects that are trying to solve this file management issue.
For example, Delta Lake (full disclosure, I am involved in it) was built to
exactly solve this problem - get exactly-once and ACID guarantees on files, but
also scale to handling millions of files. Please consider it as part of your
solution.
On Wed, Jun 17, 2020 at 9:50 AM Rachana Srivastava
<rachanasrivas...@yahoo.com.invalid> wrote:
I have written a simple spark structured steaming app to move data from Kafka
to S3. Found that in order to support exactly-once guarantee spark creates
_spark_metadata folder, which ends up growing too large as the streaming app is
SUPPOSE TO run FOREVER. But when the streaming app runs for a long time the
metadata folder grows so big that we start getting OOM errors. Only way to
resolve OOM is delete Checkpoint and Metadata folder and loose VALUABLE
customer data.
Spark open JIRAs SPARK-24295 and SPARK-29995, SPARK-30462, and SPARK-24295)
Since Spark Streaming was NOT broken like this. Is Spark Streaming a BETTER
choice?
