Skip to main content

Indexing use cases and technical strategies

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  (Lucene

(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.

top image source:


Popular posts from this blog

Beyond NSA, the intelligence community has a big technology footprint

While all through the past few days the focus has been on NSA activities, the discussion has often veered around the technologies and products used by NSA. At the same time, a side discussion topic has been the larger technical ecosystem of intelligence units. CIA has been one of the more prolific users of Information Technology by its own admission. To that extent, CIA spinned off a venture capital firm In-Q-Tel in 1999 to invest in focused sector companies. Per Helen Coster of Fortune Magazine, In-Q-Tel (IQT) has been named “after the gadget-toting James Bond character Q”.
In-Q-Tel states on its website that “We design our strategic investments to accelerate product development and delivery for this ready-soon innovation, and specifically to help companies add capabilities needed by our customers in the Intelligence Community”. To that effect, it has made over 200 investments in early stage companies for propping up products. Being a not-for-profit group, unlike Private Venture capi…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…