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https://issues.apache.org/jira/browse/CASSANDRA-1337?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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David Alves updated CASSANDRA-1337:
-----------------------------------
Attachment: 1337.patch
Clean rehash that addressed Sylvain's (very helpful comments) including
implementing for the CQL3 case. It estimated concurrency factor the following
ways:
- Primary Indexes + Thrift - divides cfs by RF
- 2ndary indexes + Thrift - uses the mean col count of the most selective index
to estimate the number of keys
- CQL3 + IdentityFilter - uses the estimated keys + mean col count to estimate
cols per node
- CQL3 + Names filter - assumes cols with names are present and uses estimated
keys to calculate cols per node
- CQL3 - Other filters - as sylvain mentioned because we have no idea on the
selectivity of the col filter we cannot estimate how many cols will be returned
per node so we revert to concurrecy factor = 1.
Reimplemented parallel the parallel execution part to make it a lot cleaner IMO
(previous implementation was adapting sequential execution which made it
difficult to read)
cql_test.py dtest is failing in the same place as trunk ,need to look into it
to make sure Sylvain's dtest passes
> parallelize fetching rows for low-cardinality indexes
> -----------------------------------------------------
>
> Key: CASSANDRA-1337
> URL: https://issues.apache.org/jira/browse/CASSANDRA-1337
> Project: Cassandra
> Issue Type: Improvement
> Reporter: Jonathan Ellis
> Assignee: David Alves
> Priority: Minor
> Fix For: 1.2.1
>
> Attachments: 1137-bugfix.patch, 1337.patch,
> ASF.LICENSE.NOT.GRANTED--0001-CASSANDRA-1337-scan-concurrently-depending-on-num-rows.txt,
> CASSANDRA-1337.patch
>
> Original Estimate: 8h
> Remaining Estimate: 8h
>
> currently, we read the indexed rows from the first node (in partitioner
> order); if that does not have enough matching rows, we read the rows from the
> next, and so forth.
> we should use the statistics fom CASSANDRA-1155 to query multiple nodes in
> parallel, such that we have a high chance of getting enough rows w/o having
> to do another round of queries (but, if our estimate is incorrect, we do need
> to loop and do more rounds until we have enough data or we have fetched from
> each node).
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