Solandra = Solr + Cassandra
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Lucandra - A Cassandra Backend for Lucene/Solr

By Jake Luciani -

Lucandra provides a Lucene IndexReader and IndexWriter that interfaces with Cassandra.
Solr is also supported.

Mailing List:

Useful articles:


You can see Lucandra in action here:

Bookmarks demo:
Lucandra includes a delicious like bookmarks demo to get you started. to build run the following:

1. Setup Cassandra 0.6 with storage-conf.xml in config

2. ant lucandra.jar
3. ant test -Dcassandra.port=9160 -Dcassandra.framed=false

#edit run-demo with appropriate settings (delicious clone)
4. -index bookmarks.tsv
5. -search title:linu*

Solr example:
Lucandra also supports Solr. to build run the following:

1. Setup Cassandra 0.6 with storage-conf.xml in config
2. ant lucandra.jar
3. cd solr-example; java -jar start.jar
4. cd exampledocs; ./ *.xml
5. surf to http://localhost:8983/solr/admin/


Storing an inverted index in Cassandra was the initial use-case for Cassandra at Facebook.
The Cassandra wiki discusses this:

"You can think of each super column name as a term and the columns within as the docids
with rank info and other attributes being a part of it. If you have keys as the userids
then you can have a per-user index stored in this form. This is how the per user index
 for term search is laid out for Inbox search at Facebook."

Initially we implemented Lucene support with supercolumn as described but ran into
a scaling issue when we tried to index all of wikipedia.
Turns out Cassandra keeps the supercolumn in memory for a given key.

Thankfully Cassandra added support for distributed ordered keys that
allows us to use keys to store index terms without supercolumns.

Implementation Notes

The Lucandra Cassandra config looks like this. 

<Keyspace Name="Lucandra">
    <ColumnFamily Name="TermInfo"
        KeysCached="10%" />
    <ColumnFamily Name="Documents"
        KeysCached="10%" />

* To get the most out of Lucandra you should use a OrderedPreservingParitioner.
However if you really want to use the RandomPartitioner you can but you'll loose
some of lucene's most powerful features (Wildcards,Sort,Range Queries).

* Documents Ids are currently random and autogenerated.

* Term keys and Document Keys are encoded as follows (using a random binary delimiter)

      Term Key                     col name         value
      "index_name/field/term" => { documentId , position vector }

      Document Key
      "index_name/documentId" => { fieldName , value }

The IndexReader caches terms aggressively during search and tries to avoid lots of back and forth with Cassandra.

What Works
* Real-Time indexing (documents become available almost immediately)
* No optimizing
* Search
* Sort
* Range Queries
* Delete
* Wildcards and other Lucene magic
* Faceting/Highlighting

What's Missing (for now)
* You can't walk the documents with index reader

What's Slow (for now)
* Indexes with many documents and very dense terms