Hi Stepan,
Can you better ballpark the Phoenix-Spark performance you've seen (e.g.
how much hardware do you have, how many spark executors did you use, how
many region servers)? Also, what versions of software are you using?
I don't think there are any firm guidelines on how you can solve this
problem, but you've found the tools available for you.
* You can try Phoenix+Spark to run over the Phoenix tables in place
* You can use Phoenix+Hive to offload the data into Hive for queries
If Phoenix-Spark wasn't fast enough, I'd imagine using the Phoenix-Hive
integration to query the data would be similarly not fast enough.
It's possible that the bottleneck is something we could fix in the
integration, or fix configuration of Spark and/or Phoenix. We'd need you
to help quantify this better :)
On 3/4/18 6:08 AM, Stepan Migunov wrote:
In our software we need to combine fast interactive access to the data with
quite complex data processing. I know that Phoenix intended for fast access,
but hoped that also I could be able to use Phoenix as a source for complex
processing with the Spark. Unfortunately, Phoenix + Spark shows very poor
performance. E.g., querying big (about billion records) table with distinct
takes about 2 hours. At the same time this task with Hive source takes a few
minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable
for batch processing with spark and I should duplicate data to Hive and
process it with Hive?
