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Posted to commits@cassandra.apache.org by "C. Scott Andreas (JIRA)" <ji...@apache.org> on 2018/11/18 18:20:01 UTC
[jira] [Updated] (CASSANDRA-8574) Gracefully degrade SELECT when
there are lots of tombstones
[ https://issues.apache.org/jira/browse/CASSANDRA-8574?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
C. Scott Andreas updated CASSANDRA-8574:
----------------------------------------
Component/s: CQL
> 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
> Components: CQL
> Reporter: Jens Rantil
> Priority: Major
> Fix For: 4.x
>
>
> *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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