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From "Sylvain Lebresne (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-8574) Gracefully degrade SELECT when there are lots of tombstones
Date Mon, 09 Mar 2015 14:01:40 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-8574?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14353000#comment-14353000
] 

Sylvain Lebresne commented on CASSANDRA-8574:
---------------------------------------------

Fyi, the short read ticket is CASSANDRA-8933. But let's please follow-up on anything related
to that issue on the issue since this is really has nothing to do with the issue of this ticket.

> Gracefully degrade SELECT when there are lots of tombstones
> -----------------------------------------------------------
>
>                 Key: CASSANDRA-8574
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-8574
>             Project: Cassandra
>          Issue Type: Improvement
>            Reporter: Jens Rantil
>             Fix For: 3.0
>
>
> *Background:* There's lots of tooling out there to do BigData analysis on Cassandra clusters.
Examples are Spark and Hadoop, which is offered by DSE. The problem with both of these so
far, is that a single partition key with too many tombstones can make the query job fail hard.
> The described scenario happens despite the user setting a rather small FetchSize. I assume
this is a common scenario if you have larger rows.
> *Proposal:* To allow a CQL SELECT to gracefully degrade to only return a smaller batch
of results if there are too many tombstones. The tombstones are ordered according to clustering
key and one should be able to page through them. Potentially:
>     SELECT * FROM mytable LIMIT 1000 TOMBSTONES;
> would page through maximum 1000 tombstones, _or_ 1000 (CQL) rows.
> I understand that this obviously would degrade performance, but it would at least yield
a result.
> *Additional comment:* I haven't dug into Cassandra code, but conceptually I guess this
would be doable. Let me know what you think.



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