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From "Jason Rutherglen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-3529) Add search to HBase
Date Fri, 27 May 2011 22:01:49 GMT

    [ https://issues.apache.org/jira/browse/HBASE-3529?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13040465#comment-13040465
] 

Jason Rutherglen commented on HBASE-3529:
-----------------------------------------

In discussing with J-D (thanks!), we can place logic in the Lucene
Coprocessor preOpen method to find out if any of the blocks of the Lucene
files in HDFS are not local (by asking the NameNode), then we can:

1) Rewrite, partially optimize, or fully optimize the index, thereby
rewriting the index files which causes them to 'go local'.

2) Extend the default placement policy and balancer to skip 'balancing'
Lucene files, because we want them to stay local.

3) Use HDFS-2004 to manually move non-local blocks to the local DataNode.

Where #3 is more complex and will likely be much more time consuming.

This functionality is important as it could currently be considered the only
'blocker' on putting HBase search into a test/production environment.

> Add search to HBase
> -------------------
>
>                 Key: HBASE-3529
>                 URL: https://issues.apache.org/jira/browse/HBASE-3529
>             Project: HBase
>          Issue Type: Improvement
>    Affects Versions: 0.90.0
>            Reporter: Jason Rutherglen
>         Attachments: HBASE-3529.patch
>
>
> Using the Apache Lucene library we can add freetext search to HBase.  The advantages
of this are:
> * HBase is highly scalable and distributed
> * HBase is realtime
> * Lucene is a fast inverted index and will soon be realtime (see LUCENE-2312)
> * Lucene offers many types of queries not currently available in HBase (eg, AND, OR,
NOT, phrase, etc)
> * It's easier to build scalable realtime systems on top of already architecturally sound,
scalable realtime data system, eg, HBase.
> * Scaling realtime search will be as simple as scaling HBase.
> Phase 1 - Indexing:
> * Integrate Lucene into HBase such that an index mirrors a given region.  This means
cascading add, update, and deletes between a Lucene index and an HBase region (and vice versa).
> * Define meta-data to mark a region as indexed, and use a Solr schema to allow the user
to define the fields and analyzers.
> * Integrate with the HLog to ensure that index recovery can occur properly (eg, on region
server failure)
> * Mirror region splits with indexes (use Lucene's IndexSplitter?)
> * When a region is written to HDFS, also write the corresponding Lucene index to HDFS.
> * A row key will be the ID of a given Lucene document.  The Lucene docstore will explicitly
not be used because the document/row data is stored in HBase.  We will need to solve what
the best data structure for efficiently mapping a docid -> row key is.  It could be a docstore,
field cache, column stride fields, or some other mechanism.
> * Write unit tests for the above
> Phase 2 - Queries:
> * Enable distributed Lucene queries
> * Regions that have Lucene indexes are inherently available and may be searched on, meaning
there's no need for a separate search related system in Zookeeper.
> * Integrate search with HBase's RPC mechanism

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