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From "T Jake Luciani (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-2915) Lucene based Secondary Indexes
Date Fri, 05 Aug 2011 01:26:27 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-2915?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13079730#comment-13079730
] 

T Jake Luciani commented on CASSANDRA-2915:
-------------------------------------------

bq. Ok. Typically in distributed search one needs/wants to send the request to all of the
possible nodes that contain data pertinent to the query. Is this possible?

see CASSANDRA-1337 it's going to always need to hit all the nodes in a worst case (or if we
add support for order by in CQL)


bq. Can we simply define a class that intercepts row updates for a column family? Then that
class can implement what is needed to analyze the columns / row?

The problem is the Type class can be user defined.  So this doesn't get us very far, I was
thinking we add a new method to AbtractType class that can be set. like getLuceneAnalyzer()



> Lucene based Secondary Indexes
> ------------------------------
>
>                 Key: CASSANDRA-2915
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-2915
>             Project: Cassandra
>          Issue Type: New Feature
>          Components: Core
>            Reporter: T Jake Luciani
>              Labels: secondary_index
>             Fix For: 1.0
>
>
> Secondary indexes (of type KEYS) suffer from a number of limitations in their current
form:
>    - Multiple IndexClauses only work when there is a subset of rows under the highest
clause
>    - One new column family is created per index this means 10 new CFs for 10 secondary
indexes
> This ticket will use the Lucene library to implement secondary indexes as one index per
CF, and utilize the Lucene query engine to handle multiple index clauses. Also, by using the
Lucene we get a highly optimized file format.
> There are a few parallels we can draw between Cassandra and Lucene.
> Lucene indexes segments in memory then flushes them to disk so we can sync our memtable
flushes to lucene flushes. Lucene also has optimize() which correlates to our compaction process,
so these can be sync'd as well.
> We will also need to correlate column validators to Lucene tokenizers, so the data can
be stored properly, the big win in once this is done we can perform complex queries within
a column like wildcard searches.
> The downside of this approach is we will need to read before write since documents in
Lucene are written as complete documents. For random workloads with lot's of indexed columns
this means we need to read the document from the index, update it and write it back.

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