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

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort:Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban:A batch job scheduling system with a friendly UI, Azkab…

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…