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Indexing use cases and technical strategies

By Sachin Ghai
In this post, let us look at 3 real life indexing use cases. While Hadoop is commonly used for distributed batch index building, it is desirable to optimize the index capability in near real time. We look at some practical real life implementations where the engineers have successfully worked out their technology stack combinations using different products.

(1) Near Real Time index at eBay:

The first use case looks at eBay where HBase is used with a novel approach for building a Near Real Time search index:
- Building a full index takes hours due to data-set size
- # of items changed every minute are much less
- Identify updates in time window t1 – t2 (Timerange scan)
- Build a ‘mini index’ only on last X minutes of changes using Map-Reduce
- Mini indices are copied and consumed in near real time by query servers
- (HBase) Column Family to track last modified time
- Utilize ‘time range scan’ feature of HBase


(2) Distributed indexing strategy at Trovit:

Trovit is a search engine for classified ads of real estate, jobs, cars and vacation rentals. It seems to have arrived at a right mix of Storm, HDFS, HBase and Zookeeper for its architecture. However, particularly, with regards to distributed index strategy, it invokes:
- 2 phases indexing (2 sequential MapReduce jobs) comprising of :
-- Partial indexing: Generate lots of “micro indexes” per each monolithic or sharded index  (MapRduce + Embedded Solr + HDFS)
-- Merge: Groups all the “micro indexes” and merge them to get the production data  (Luceneà HDFS)



(3) Incremental Processing by Google’s Percolator:


The topic would have been incomplete without referring to Google’s Percolator paper which describes a technique for incremental update of index with BigTable.
A Percolator system consists of three binaries that run on every machine in the cluster: a Percolator worker, a Bigtable [9] tablet server, and a GFS [20]  chunkserver. All observers are linked into the Percolator worker, which scans the Bigtable for changed columns (“notifications”) and invokes the corresponding observers as a function call in the worker process. The observers perform transactions by sending read/write RPCs to Bigtable tablet servers, which in turn send read/write RPCs to GFS chunkservers.
Taking cue from this implementation, many variations have been worked out by engineers while leveraging Hadoop HDFS in combination with HBase, Storm and/or Hive.

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