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From "Jason Rutherglen (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HBASE-3529) Add search to HBase
Date Sat, 26 Feb 2011 05:54:21 GMT

    [ https://issues.apache.org/jira/browse/HBASE-3529?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12999722#comment-12999722
] 

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

@Stack I didn't see any patches at HDFS-237.  I'd be curious to learn what the security issues
are, I guess they're articulated in HDFS-347 as solvable by transferring file descriptors,
though I'm not sure why the user running the Hadoop Java process should not be accessing certain
local files?  Also, maybe there are higher level synchronization issues to be aware of (eg,
HDFS-1605)?  I'm sure much of this can be changed, though it may require a separate call 'path'
and classes to avoid any extraneous synchronization.  I do like this approach of making core
changes to HDFS which'll benefit HBase and this issue, then also streamlines the Lucene integration
(ie, there'll be no need for replicating the index back into HDFS from local disk), which'll
reduce the aggregate complexity and testing.  

> 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
>
> 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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