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https://issues.apache.org/jira/browse/HBASE-3529?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12996444#comment-12996444
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Jason Rutherglen commented on HBASE-3529:
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1) It may be more expedient for now to store the index in a dedicated
directory, and save it to HDFS periodically. However I'm not sure 'when' the
loading into HDFS would occur, eg, if HBase is always writing to HDFS then
there's no way to sync with that mechanism. Perhaps it'd need to be based on
the iterative index size changes? Ie, if the index has grown by 25% since the
last save?
2) I'd like to design the recovery logic now. It's simple to save the
timestmap into Lucene, then on recovery get the max timestamp, and iterate from
there over the HRegion for the remaining 'lost' rows/documents. What's the
most efficient way to scan over > timestamp key values?
3) We can create indexes for the entire HRegion or for the individual column
families. Perhaps this should be optional? I wonder if there are
dis/advantages from a user perspective? If interleaving postings was efficient
we could even design a system to enable parts of posting lists to be changed
per column family, where duplicate docids would be written to intermediate
in-memory indexes, and 'interleaved' during posting iteration.
> 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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