[jira] [Resolved] (HBASE-14477) Compaction improvements: Date tiered compaction policy

["Vladimir Rodionov (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HBASE-14477?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Vladimir Rodionov resolved HBASE-14477.
---------------------------------------
    Resolution: Duplicate

Duplicate of HBASE-15181

> Compaction improvements: Date tiered compaction policy
> ------------------------------------------------------
>
>                 Key: HBASE-14477
>                 URL: https://issues.apache.org/jira/browse/HBASE-14477
>             Project: HBase
>          Issue Type: New Feature
>            Reporter: Vladimir Rodionov
>            Assignee: Vladimir Rodionov
>             Fix For: 2.0.0
>
>
> For immutable and mostly immutable data the current SizeTiered-based compaction policy is not efficient. 
> # There is no need to compact all files into one, because, data is (mostly) immutable and we do not need to collect garbage. (performance reason will be discussed later)
> # Size-tiered compaction is not suitable for applications where most recent data is most important and prevents efficient caching of this data. 
> The idea  is pretty similar to DateTieredCompaction in Cassandra:
> http://www.datastax.com/dev/blog/datetieredcompactionstrategy
> http://www.datastax.com/dev/blog/dtcs-notes-from-the-field
> From Cassandra own blog:
> {quote}
> Since DTCS can be used with any table, it is important to know when it is a good idea, and when it is not. I’ll try to explain the spectrum and trade-offs here:
> 1. Perfect Fit: Time Series Fact Data, Deletes by Default TTL: When you ingest fact data that is ordered in time, with no deletes or overwrites. This is the standard “time series” use case.
> 2. OK Fit: Time-Ordered, with limited updates across whole data set, or only updates to recent data: When you ingest data that is (mostly) ordered in time, but revise or delete a very small proportion of the overall data across the whole timeline.
> 3. Not a Good Fit: many partial row updates or deletions over time: When you need to partially revise or delete fields for rows that you read together. Also, when you revise or delete rows within clustered reads.
> {quote}



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