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https://issues.apache.org/jira/browse/CASSANDRA-2915?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13079712#comment-13079712
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T Jake Luciani commented on CASSANDRA-2915:
-------------------------------------------

Todd: once CASSANDRA-2982 is done we can get started. I'm trying to focus on 
that right now.  In the meantime we need to think of how to link lucene 
analyzers to column_metadata.

Jason: This currently works by executing the query locally if that does not 
have enough results it moves on to the next node. since the ring is split we 
know the range of keys to restrict the search to. this avoids dups


> 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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