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https://issues.apache.org/jira/browse/HBASE-3529?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13033311#comment-13033311
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Jason Rutherglen commented on HBASE-3529:
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bq. Awesome stuff. These query times above are using the hacky (non-secure
non-checksummed) implementation of HDFS-347?
It's hackier than that. It's basically obtaining the java.io.File directly
from the FSInputStream. However it's a good baseline to benchmark against
things like HADOOP-6311 + HDFS-347. Those need to wait for HBase that works
with Hadoop 0.22/trunk anyways?
{quote}
User defines some special property on a column family that they want to be
searchable, this property would include a solr schema which specifies analyzers
and fields
{quote}
Currently there's a DocumentTransformer class which needs to be implemented to
transform column-family edits into a Lucene document. That could use the Solr
schema for example or any other separate system to tokenize the byte[]s into a
Document.
{quote}User can now perform an arbitrary lucene search over the table,
resulting in completely up-to-date results? (ie spans both memstore and flushed
data)?{quote}
I think for now we need to offer an external commit on the index, as Lucene
only has near realtime search (eg, small segments will be written out, which
will overwhelm HDFS). LUCENE-2312 will implement realtime search (eg,
searching on the RAM buffer as it's being built). The recent LUCENE-3092 could
be used in the meantime to build segments in RAM, and only flush to HDFS when
it's too RAM consuming, then we would not need to force the user to 'commit'
the index.
To answer the question, yes, though today the indexing performance will not be
as good as when LUCENE-2312 is implemented or the user will need to 'commit'
the index to search on the latest data.
Getting all of Solr work work with this system is fairly doable. Each Solr
core would map to a region. Things like replication would be disabled. The
config files would be stored in HDFS (instead of the local filesystem). For
distributed queries, we need SOLR-1431, and then to implement distributed
networking using HBase RPC instead of Solr's HTTP RPC. There are other smaller
internal things that'd need to change in Solr. I think HBase RPC is aware of
where regions live etc so I don't think we need to worry about putting failover
logic into the distributed search code?
I'm going to post additional benchmarks shortly, eg, for 100,000 and 1 mil
documents.
> 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
> Attachments: HBASE-3529.patch
>
>
> 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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