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From "Jonathan Ellis (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-3620) Proposal for distributed deletes - fully automatic "Reaper Model" rather than GCSeconds and manual repairs
Date Tue, 29 Jan 2013 18:15:13 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-3620?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13565615#comment-13565615
] 

Jonathan Ellis commented on CASSANDRA-3620:
-------------------------------------------

bq. we could say "if you use batch, use it on all nodes"

Also fine with this. :)
                
> Proposal for distributed deletes - fully automatic "Reaper Model" rather than GCSeconds
and manual repairs
> ----------------------------------------------------------------------------------------------------------
>
>                 Key: CASSANDRA-3620
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-3620
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Dominic Williams
>              Labels: GCSeconds,, deletes,, distributed_deletes,, merkle_trees, repair,
>             Fix For: 2.0
>
>   Original Estimate: 504h
>  Remaining Estimate: 504h
>
> Proposal for an improved system for handling distributed deletes, which removes the requirement
to regularly run repair processes to maintain performance and data integrity. 
> h2. The Problem
> There are various issues with repair:
> * Repair is expensive to run
> * Repair jobs are often made more expensive than they should be by other issues (nodes
dropping requests, hinted handoff not working, downtime etc)
> * Repair processes can often fail and need restarting, for example in cloud environments
where network issues make a node disappear from the ring for a brief moment
> * When you fail to run repair within GCSeconds, either by error or because of issues
with Cassandra, data written to a node that did not see a later delete can reappear (and a
node might miss a delete for several reasons including being down or simply dropping requests
during load shedding)
> * If you cannot run repair and have to increase GCSeconds to prevent deleted data reappearing,
in some cases the growing tombstone overhead can significantly degrade performance
> Because of the foregoing, in high throughput environments it can be very difficult to
make repair a cron job. It can be preferable to keep a terminal open and run repair jobs one
by one, making sure they succeed and keeping and eye on overall load to reduce system impact.
This isn't desirable, and problems are exacerbated when there are lots of column families
in a database or it is necessary to run a column family with a low GCSeconds to reduce tombstone
load (because there are many write/deletes to that column family). The database owner must
run repair within the GCSeconds window, or increase GCSeconds, to avoid potentially losing
delete operations. 
> It would be much better if there was no ongoing requirement to run repair to ensure deletes
aren't lost, and no GCSeconds window. Ideally repair would be an optional maintenance utility
used in special cases, or to ensure ONE reads get consistent data. 
> h2. "Reaper Model" Proposal
> # Tombstones do not expire, and there is no GCSeconds
> # Tombstones have associated ACK lists, which record the replicas that have acknowledged
them
> # Tombstones are deleted (or marked for compaction) when they have been acknowledged
by all replicas
> # When a tombstone is deleted, it is added to a "relic" index. The relic index makes
it possible for a reaper to acknowledge a tombstone after it is deleted
> # The ACK lists and relic index are held in memory for speed
> # Background "reaper" threads constantly stream ACK requests to other nodes, and stream
back ACK responses back to requests they have received (throttling their usage of CPU and
bandwidth so as not to affect performance)
> # If a reaper receives a request to ACK a tombstone that does not exist, it creates the
tombstone and adds an ACK for the requestor, and replies with an ACK. This is the worst that
can happen, and does not cause data corruption. 
> ADDENDUM
> The proposal to hold the ACK and relic lists in memory was added after the first posting.
Please see comments for full reasons. Furthermore, a proposal for enhancements to repair was
posted to comments, which would cause tombstones to be scavenged when repair completes (the
author had assumed this was the case anyway, but it seems at time of writing they are only
scavenged during compaction on GCSeconds timeout). The proposals are not exclusive and this
proposal is extended to include the possible enhancements to repair described.
> NOTES
> * If a node goes down for a prolonged period, the worst that can happen is that some
tombstones are recreated across the cluster when it restarts, which does not corrupt data
(and this will only occur with a very small number of tombstones)
> * The system is simple to implement and predictable 
> * With the reaper model, repair would become an optional process for optimizing the database
to increase the consistency seen by ConsistencyLevel.ONE reads, and for fixing up nodes, for
example after an sstable was lost
> h3. Planned Benefits
> * Reaper threads can utilize "spare" cycles to constantly scavenge tombstones in the
background thereby greatly reducing tombstone load, improving query performance, reducing
the system resources needed by processes such as compaction, and making performance generally
more predictable 
> * The reaper model means that GCSeconds is no longer necessary, which removes the threat
of data corruption if repair can't be run successfully within that period (for example if
repair can't be run because of a new adopter's lack of Cassandra expertise, a cron script
failing, or Cassandra bugs or other technical issues)
> * Reaper threads are fully automatic, work in the background and perform finely grained
operations where interruption has little effect. This is much better for database administrators
than having to manually run and manage repair, whether for the purposes of preventing data
corruption or for optimizing performance, which in addition to wasting operator time also
often creates load spikes and has to be restarted after failure.  

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