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From "Todd Nine (JIRA)" <>
Subject [jira] [Commented] (CASSANDRA-2915) Lucene based Secondary Indexes
Date Mon, 29 Aug 2011 02:57:37 GMT


Todd Nine commented on CASSANDRA-2915:

I don't necessaryly think there is a 1 to 1 relationship between a column and a Lucene document
field.  In our case we have the need to index fields in more than one manner.  For instance,
we index users as straight strings (lowercased) with email, first name and last name columns.
 However we also want to tokenize the email, first and last name columns to allow our customer
support people to perform partial name matching.  I think a 1 to N mapping is required for
column to document field to allow this sort of functionality.

As far as expiration on columns, is there a system event that we can hook into to just force
a document reindex when a column expires rather than add an additional field that will need
to be sorted from?

As per Jason's previous post, I think supporting ORDER BY, GROUP BY, COUNT, LIKE etc are a
must.  Most users have become accustomed to this functionality with RDBMS.  If they cause
potential performance problems, I think this should be documented so that users have enough
information to determine if they can rely on the Lucene index or should build their own index

Lastly, this is a huge feature for the hector-jpa plugin, what can I do to help?

> Lucene based Secondary Indexes
> ------------------------------
>                 Key: CASSANDRA-2915
>                 URL:
>             Project: Cassandra
>          Issue Type: New Feature
>          Components: Core
>            Reporter: T Jake Luciani
>            Assignee: Jason Rutherglen
>              Labels: secondary_index
> Secondary indexes (of type KEYS) suffer from a number of limitations in their current
>    - Multiple IndexClauses only work when there is a subset of rows under the highest
>    - One new column family is created per index this means 10 new CFs for 10 secondary
> 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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