Skip to main content

Facebook predicts what you like to see on web page

The conventional wisdom of HTTP web page request between browser and servers is to transmit the response as whole in the form of a structured mark up language. However, this may not be how it works in today’s social networks.

Today’s smart social networks like Facebook use Hadoop and Hive driven intelligence to predict which resources of web page have a predetermined likelihood to be included in a response to a future request. Resources which includes java-scripts, style sheets, image etc are identified based on map-reduce and other computational algorithms which run on distributed systems analyzing billions of entries in the resource logs. These identified resources are stored/cached in server hash maps and the page is rendered in phase wise manner.

This method and system is described in US patent 8,108,377 Predictive resource identification and phased delivery of structured documents (Inventors: Jiang; Changhao, Wei; Xiaoliang; Assignee: Facebook, Inc. (Palo Alto, CA)) .

As the disclosure goes on to describe Hadoop and Hive usage, we find that
“…the resource logging, analyzing, filtering, predicting, and/or selecting operations discussed above can be implemented using Hive to accomplish ad hoc querying, summarization and data analysis, as well as using as incorporating statistical modules by embedding mapper and reducer scripts, such as Python or Perl scripts that implement a statistical algorithm. Other development platforms that can leverage Hadoop or other Map-Reduce execution engines can be used as well…”

Must say, this is one of the smart implementations of predictive computation which is reducing latency, limiting network load and overall leading to a better user experience. Remember, since this is patented, check with assignee before any commercial usage. 


Post a Comment

Popular posts from this blog

Offloading legacy with Hadoop

With most Fortune 500 organizations having invested in mainframes and other workload systems in the past, the rise of Big Data platforms poses newer integration challenges. The data integration and ETL players are finding fresh opportunities to solve business and IT problems within the Hadoop ecosystem.
To understand the context, challenges and opportunities, we asked a few questions to Syncsort CEO Lonne Jaffe. Syncsort provides fast, secure, enterprise-grade software spanning Big Data in Apache Hadoop to Big Iron on mainframes. At Syncsort, Lonne Jaffe is focusing on accelerating the growth of the company's high-performance Big Data offerings, both organically and through acquisition.
From mainframes to Hadoop and other platforms, Syncsort seems to have been evolving itself continuously. Where do you see Syncsort heading further?Lonne Jaffe: Syncsort is extraordinary in its ability to continuously reinvent itself. Today, we’re innovating around Apache Hadoop and other Big Data pla…

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…