Valentin,
> Can you please create a separate ticket for the strategy implementation then?
Done.
https://issues.apache.org/jira/browse/IGNITE-7077
> Any idea on how long will it take?
I think it will take 2-4 weeks to implement such a strategy.
I try my best to make a ready to review PR before the end of the year.
30.11.2017 02:13, Valentin Kulichenko пишет:
Nikolay,
Can you please create a separate ticket for the strategy implementation
then? Any idea on how long will it take?
As for querying a partition, both SqlQuery and SqlFieldQuery allow to
specify set of partitions to work with (see setPartitions method). I think
that should be enough.
-Val
On Wed, Nov 29, 2017 at 3:39 AM, Vladimir Ozerov <voze...@gridgain.com>
wrote:
Hi Nikolay,
No, it is not possible to get this info from public API, neither we planned
to expose it. See IGNITE-4509 and commit *fbf0e353* to get better
understanding on how this was implemented.
Vladimir.
On Wed, Nov 29, 2017 at 2:01 PM, Николай Ижиков <nizhikov....@gmail.com>
wrote:
Hello, Vladimir.
partition pruning is already implemented in Ignite, so there is no need
to do this on your own.
Spark work with partitioned data set.
It is required to provide data partition information to Spark from custom
Data Source(Ignite).
Can I get information about pruned partitions throw some public API?
Is there a plan or ticket to implement such API?
2017-11-29 10:34 GMT+03:00 Vladimir Ozerov <voze...@gridgain.com>:
Nikolay,
Regarding p3. - partition pruning is already implemented in Ignite, so
there is no need to do this on your own.
On Wed, Nov 29, 2017 at 3:23 AM, Valentin Kulichenko <
valentin.kuliche...@gmail.com> wrote:
Nikolay,
Custom strategy allows to fully process the AST generated by Spark
and
convert it to Ignite SQL, so there will be no execution on Spark side
at
all. This is what we are trying to achieve here. Basically, one will
be
able to use DataFrame API to execute queries directly on Ignite. Does
it
make sense to you?
I would recommend you to take a look at MemSQL implementation which
does
similar stuff: https://github.com/memsql/memsql-spark-connector
Note that this approach will work only if all relations included in
AST
are
Ignite tables. Otherwise, strategy should return null so that Spark
falls
back to its regular mode. Ignite will be used as regular data source
in
this case, and probably it's possible to implement some optimizations
here
as well. However, I never investigated this and it seems like another
separate discussion.
-Val
On Tue, Nov 28, 2017 at 9:54 AM, Николай Ижиков <
nizhikov....@gmail.com>
wrote:
Hello, guys.
I have implemented basic support of Spark Data Frame API [1], [2]
for
Ignite.
Spark provides API for a custom strategy to optimize queries from
spark
to
underlying data source(Ignite).
The goal of optimization(obvious, just to be on the same page):
Minimize data transfer between Spark and Ignite.
Speedup query execution.
I see 3 ways to optimize queries:
1. *Join Reduce* If one make some query that join two or
more
Ignite tables, we have to pass all join info to Ignite and transfer
to
Spark only result of table join.
To implement it we have to extend current implementation
with
new
RelationProvider that can generate all kind of joins for two or
more
tables.
We should add some tests, also.
The question is - how join result should be partitioned?
2. *Order by* If one make some query to Ignite table with
order
by
clause we can execute sorting on Ignite side.
But it seems that currently Spark doesn’t have any way to
tell
that partitions already sorted.
3. *Key filter* If one make query with `WHERE key = XXX` or
`WHERE
key IN (X, Y, Z)`, we can reduce number of partitions.
And query only partitions that store certain key values.
Is this kind of optimization already built in Ignite or I
should
implement it by myself?
May be, there is any other way to make queries run faster?
[1] https://spark.apache.org/docs/latest/sql-programming-guide.
html
[2] https://github.com/apache/ignite/pull/2742
--
Nikolay Izhikov
nizhikov....@gmail.com
