A

• A9.com - Amazon *

  • We build Amazon's product search indices using the streaming API and pre-existing C++, Perl, and Python tools.
  • We process millions of sessions daily for analytics, using both the Java and streaming APIs.
  • Our clusters vary from 1 to 100 nodes.

• Accela Communications

  • We use a Hadoop cluster to rollup registration and view data each night.
  • Our cluster has 10 1U servers, with 4 cores, 4GB ram and 3 drives
  • Each night, we run 112 Hadoop jobs
  • It is roughly 4X faster to export the transaction tables from each of our reporting databases, transfer the data to the cluster, perform the rollups, then import back into the databases than to perform the same rollups in the database.

• Adobe

  • We use Hadoop and HBase in several areas from social services to structured data storage and processing for internal use.
  • We currently have about 30 nodes running HDFS, Hadoop and HBase in clusters ranging from 5 to 14 nodes on both production and development. We plan a deployment on an 80 nodes cluster.

  • We constantly write data to HBase and run MapReduce jobs to process then store it back to HBase or external systems.

  • Our production cluster has been running since Oct 2008.

• Able Grape - Vertical search engine for trustworthy wine information

  • We have one of the world's smaller hadoop clusters (2 nodes @ 8 CPUs/node)
  • Hadoop and Nutch used to analyze and index textual information

• Adknowledge - Ad network

  • Hadoop used to build the recommender system for behavioral targeting, plus other clickstream analytics
  • We handle 500MM clickstream events per day
  • Our clusters vary from 50 to 200 nodes, mostly on EC2.
  • Investigating use of R clusters atop Hadoop for statistical analysis and modeling at scale.

• Alibaba

  • A 15-node cluster dedicated to processing sorts of business data dumped out of database and joining them together. These data will then be fed into iSearch, our vertical search engine.
  • Each node has 8 cores, 16G RAM and 1.4T storage.

• AOL

  • We use hadoop for variety of things ranging from ETL style processing and statistics generation to running advanced algorithms for doing behavioral analysis and targeting.
  • The Cluster that we use for mainly behavioral analysis and targeting has 150 machines, Intel Xeon, dual processors, dual core, each with 16GB Ram and 800 GB hard-disk.

• Atbrox

  • We use hadoop for information extraction & search, and data analysis consulting

  • Cluster: we primarily use Amazon's Elastic Mapreduce

B

• BabaCar

  • 4 nodes cluster (32 cores, 1TB).
  • We use Hadoop for searching and analysis of millions of rental bookings.

• backdocsearch.com - search engine for chiropractic information, local chiropractors, products and schools

• Baidu - the leading Chinese language search engine

  • Hadoop used to analyze the log of search and do some mining work on web page database
  • We handle about 3000TB per week
  • Our clusters vary from 10 to 500 nodes
  • Hypertable is also supported by Baidu

• Beebler

  • 14 node cluster (each node has: 2 dual core CPUs, 2TB storage, 8GB RAM)
  • We use hadoop for matching dating profiles

• Benipal Technologies - Outsourcing, Consulting, Innovation

  • 35 Node Cluster (Core2Quad Q9400 Processor, 4-8 GB RAM, 500 GB HDD)

  • Largest Data Node with Xeon E5420*2 Processors, 64GB RAM, 3.5 TB HDD
  • Total Cluster capacity of around 20 TB on a gigabit network with failover and redundancy
  • Hadoop is used for internal data crunching, application development, testing and getting around I/O limitations

• Bixo Labs - Elastic web mining

  • The Bixolabs elastic web mining platform uses Hadoop + Cascading to quickly build scalable web mining applications.

  • We're doing a 200M page/5TB crawl as part of the public terabyte dataset project.

  • This runs as a 20 machine Elastic MapReduce cluster.

• BrainPad - Data mining and analysis

  • We use Hadoop to summarize of user's tracking data.
  • And use analyzing.

C

• Contextweb - Ad Exchange

  • We use Hadoop to store ad serving logs and use it as a source for ad optimizations, analytics, reporting and machine learning.
  • Currently we have a 50 machine cluster with 400 cores and about 140TB raw storage. Each (commodity) node has 8 cores and 16GB of RAM.

• Cooliris - Cooliris transforms your browser into a lightning fast, cinematic way to browse photos and videos, both online and on your hard drive.

  • We have a 15-node Hadoop cluster where each machine has 8 cores, 8 GB ram, and 3-4 TB of storage.
  • We use Hadoop for all of our analytics, and we use Pig to allow PMs and non-engineers the freedom to query the data in an ad-hoc manner.

• Cornell University Web Lab

  • Generating web graphs on 100 nodes (dual 2.4GHz Xeon Processor, 2 GB RAM, 72GB Hard Drive)

• CRS4

  • Computational biology applications

  • HDFS as a VM repository for virtual clusters

D

• Datagraph

  • We use Hadoop for batch-processing large RDF datasets, in particular for indexing RDF data.

  • We also use Hadoop for executing long-running offline SPARQL queries for clients.

  • We use Amazon S3 and Cassandra to store input RDF datasets and output files.

  • We've developed RDFgrid, a Ruby framework for map/reduce-based processing of RDF data.

  • We primarily use Ruby, RDF.rb and RDFgrid to process RDF data with Hadoop Streaming.

  • We primarily run Hadoop jobs on Amazon Elastic MapReduce, with cluster sizes of 1 to 20 nodes depending on the size of the dataset (hundreds of millions to billions of RDF statements).

• Deepdyve

  • Elastic cluster with 5-80 nodes
  • We use hadoop to create our indexes of deep web content and to provide a high availability and high bandwidth storage service for index shards for our search cluster.

• Detikcom - Indonesia's largest news portal

  • We use hadoop, pig and hbase to analyze search log, generate Most View News, generate top wordcloud, and analyze all of our logs
  • Currently We use 9 nodes

• DropFire

  • We generate Pig Latin scripts that describe structural and semantic conversions between data contexts
  • We use Hadoop to execute these scripts for production-level deployments
  • Eliminates the need for explicit data and schema mappings during database integration

E

• EBay

  • 532 nodes cluster (8 * 532 cores, 5.3PB).

  • Heavy usage of Java MapReduce, Pig, Hive, HBase

  • Using it for Search optimization and Research.

• Enet, 'Eleftherotypia' newspaper, Greece

  • Experimental installation - storage for logs and digital assets
  • Currently 5 nodes cluster
  • Using hadoop for log analysis/data mining/machine learning

• Enormo

  • 4 nodes cluster (32 cores, 1TB).
  • We use Hadoop to filter and index our listings, removing exact duplicates and grouping similar ones.
  • We plan to use Pig very shortly to produce statistics.

• ESPOL University (Escuela Superior Politécnica del Litoral) in Guayaquil, Ecuador

  • 4 nodes proof-of-concept cluster.
  • We use Hadoop in a Data-Intensive Computing capstone course. The course projects cover topics like information retrieval, machine learning, social network analysis, business intelligence, and network security.
  • The students use on-demand clusters launched using Amazon's EC2 and EMR services, thanks to its AWS in Education program.

• ETH Zurich Systems Group

  • We are using Hadoop in a course that we are currently teaching: "Massively Parallel Data Analysis with MapReduce". The course projects are based on real use-cases from biological data analysis.

  • Cluster hardware: 16 x (Quad-core Intel Xeon, 8GB RAM, 1.5 TB Hard-Disk)

• Eyealike - Visual Media Search Platform

  • Facial similarity and recognition across large datasets.
  • Image content based advertising and auto-tagging for social media.
  • Image based video copyright protection.

F

• Facebook

  • We use Hadoop to store copies of internal log and dimension data sources and use it as a source for reporting/analytics and machine learning.
  • Currently we have 2 major clusters:
    • A 1100-machine cluster with 8800 cores and about 12 PB raw storage.
    • A 300-machine cluster with 2400 cores and about 3 PB raw storage.
    • Each (commodity) node has 8 cores and 12 TB of storage.

    • We are heavy users of both streaming as well as the Java apis. We have built a higher level data warehousing framework using these features called Hive (see the http://hadoop.apache.org/hive/). We have also developed a FUSE implementation over hdfs.

• FOX Audience Network

  • 40 machine cluster (8 cores/machine, 2TB/machine storage)
  • 70 machine cluster (8 cores/machine, 3TB/machine storage)
  • 30 machine cluster (8 cores/machine, 4TB/machine storage)
  • Use for log analysis, data mining and machine learning

• Forward3D

  • 5 machine cluster (8 cores/machine, 5TB/machine storage)
  • Existing 19 virtual machine cluster (2 cores/machine 30TB storage)

  • Predominantly Hive and Streaming API based jobs (~20,000 jobs a week) using our Ruby library, or see the canonical WordCount example.

  • Daily batch ETL with a slightly modified clojure-hadoop

  • Log analysis
  • Data mining
  • Machine learning

• Freestylers - Image retrieval engine

  • We Japanese company Freestylers use Hadoop to build the image processing environment for image-based product recommendation system mainly on Amazon EC2, from April 2009.
  • Our Hadoop environment produces the original database for fast access from our web application.
  • We also uses Hadoop to analyzing similarities of user's behavior.

G

• GIS.FCU

  • Feng Chia University
  • 3 machine cluster (4 cores, 1TB/machine)
  • storeage for sensor data

• Google

  • University Initiative to Address Internet-Scale Computing Challenges

• Gruter. Corp.

  • 30 machine cluster (4 cores, 1TB~2TB/machine storage)
  • storage for blog data and web documents

  • used for data indexing by MapReduce

  • link analyzing and Machine Learning by MapReduce

• GumGum

  • 20+ node cluster (Amazon EC2 c1.medium)

  • Nightly MapReduce jobs on Amazon Elastic MapReduce process data stored in S3

  • MapReduce jobs written in Groovy use Hadoop Java APIs

  • Image and advertising analytics

H

• Hadoop Korean User Group, a Korean Local Community Team Page.

  • 50 node cluster In the Korea university network environment.
    • Pentium 4 PC, HDFS 4TB Storage
  • Used for development projects
    • Retrieving and Analyzing Biomedical Knowledge
    • Latent Semantic Analysis, Collaborative Filtering

• Hulu

  • 13 machine cluster (8 cores/machine, 4TB/machine)
  • Log storage and analysis
  • Hbase hosting

• Hadoop Taiwan User Group

• Hipotecas y euribor

  • Evolución del euribor y valor actual
  • Simulador de hipotecas en crisis económica

• Hosting Habitat

  • We use a customised version of Hadoop and Nutch in a currently experimental 6 node/Dual Core cluster environment.
  • What we crawl are our clients Websites and from the information we gather. We fingerprint old and non updated software packages in that shared hosting environment. We can then inform our clients that they have old and non updated software running after matching a signature to a Database. With that information we know which sites would require patching as a free and courtesy service to protect the majority of users. Without the technologies of Nutch and Hadoop this would be a far harder to accomplish task.

I

• IBM

  • Blue Cloud Computing Clusters

  • University Initiative to Address Internet-Scale Computing Challenges

• ICCS

  • We are using Hadoop and Nutch to crawl Blog posts and later process them. Hadoop is also beginning to be used in our teaching and general research activities on natural language processing and machine learning.

• IIIT, Hyderabad

  • We use hadoop for Information Retrieval and Extraction research projects. Also working on map-reduce scheduling research for multi-job environments.
  • Our cluster sizes vary from 10 to 30 nodes, depending on the jobs. Heterogenous nodes with most being Quad 6600s, 4GB RAM and 1TB disk per node. Also some nodes with dual core and single core configurations.

• ImageShack

  • From TechCrunch:

    • Rather than put ads in or around the images it hosts, Levin is working on harnessing all the data his

      service generates about content consumption (perhaps to better target advertising on ImageShack or to syndicate that targetting data to ad networks). Like Google and Yahoo, he is deploying the open-source Hadoop software to create a massive distributed supercomputer, but he is using it to analyze all the data he is collecting.

• Information Sciences Institute (ISI)

  • Used Hadoop and 18 nodes/52 cores to plot the entire internet.

• Infochimps

  • 30 node AWS EC2 cluster (varying instance size, currently EBS-backed) managed by Chef & Poolparty running Hadoop 0.20.2+228, Pig 0.5.0+30, Azkaban 0.04, Wukong

  • Used for ETL & data analysis on terascale datasets, especially social network data (on api.infochimps.com)

• Iterend

  • using 10 node hdfs cluster to store and process retrieved data.

J

• Joost

  • Session analysis and report generation

• Journey Dynamics

  • Using Hadoop MapReduce to analyse billions of lines of GPS data to create TrafficSpeeds, our accurate traffic speed forecast product.

K

• Katta - Katta serves large Lucene indexes in a grid environment.

  • Uses Hadoop FileSytem, RPC and IO

• Koubei.com Large local community and local search at China.

  • Using Hadoop to process apache log, analyzing user's action and click flow and the links click with any specified page in site and more. Using Hadoop to process whole price data user input with map/reduce.

• Krugle

  • Source code search engine uses Hadoop and Nutch.

L

• Last.fm

  • 44 nodes
  • Dual quad-core Xeon L5520 (Nehalem) @ 2.27GHz, 16GB RAM, 4TB/node storage.
  • Used for charts calculation, log analysis, A/B testing

• Legolas Media

  • 20 dual quad-core nodes, 32GB RAM , 5x1TB
  • Used for user profile analysis, statistical analysis,cookie level reporting tools.

  • Some Hive but mainly automated Java MapReduce jobs that process ~150MM new events/day.

• Lineberger Comprehensive Cancer Center - Bioinformatics Group This is the cancer center at UNC Chapel Hill. We are using Hadoop/HBase for databasing and analyzing Next Generation Sequencing (NGS) data produced for the Cancer Genome Atlas (TCGA) project and other groups. This development is based on the SeqWare open source project which includes SeqWare Query Engine, a database and web service built on top of HBase that stores sequence data types. Our prototype cluster includes:

  • 8 dual quad core nodes running CentOS
  • total of 48TB of HDFS storage

  • HBase & Hadoop version 0.20

• LinkedIn

  • We have multiple grids divided up based upon purpose. They are composed of the following types of hardware:
    • 100 Nehalem-based nodes, with 2x4 cores, 24GB RAM, 8x1TB storage using ZFS in a JBOD configuration on Solaris.
    • 120 Westmere-based nodes, with 2x4 cores, 24GB RAM, 6x2TB storage using ext4 in a JBOD configuration on CentOS 5.5
  • We use Hadoop and Pig for discovering People You May Know and other fun facts.

• Lookery

  • We use Hadoop to process clickstream and demographic data in order to create web analytic reports.
  • Our cluster runs across Amazon's EC2 webservice and makes use of the streaming module to use Python for most operations.

• Lotame

  • Using Hadoop and Hbase for storage, log analysis, and pattern discovery/analysis.

M

• Markt24

  • We use Hadoop to filter user behaviour, recommendations and trends from externals sites
  • Using zkpython
  • Used EC2, no using many small machines (8GB Ram, 4 cores, 1TB)

• MicroCode

  • 18 node cluster (Quad-Core Intel Xeon, 1TB/node storage)
  • Financial data for search and aggregation
  • Customer Relation Management data for search and aggregation

• Media 6 Degrees

  • 20 node cluster (dual quad cores, 16GB, 6TB)
  • Used log processing, data analysis and machine learning.
  • Focus is on social graph analysis and ad optimization.
  • Use a mix of Java, Pig and Hive.

• MobileAnalytic.TV

  • We use Hadoop to develop MapReduce algorithms:

    • Information retrival and analytics

    • Machine generated content - documents, text, audio, & video

    • Natural Language Processing
  • Project portfolio includes:
    • Natural Language Processing
    • Mobile Social Network Hacking
    • Web Crawlers/Page scrapping
    • Text to Speech

    • Machine generated Audio & Video with remuxing

    • Automatic PDF creation & IR

  • 2 node cluster (Windows Vista/CYGWIN, & CentOS) for developing MapReduce programs.

• MyLife

  • 18 node cluster (Quad-Core AMD Opteron 2347, 1TB/node storage)
  • Powers data for search and aggregation

• MetrixCloud - provides commercial support, installation, and hosting of Hadoop Clusters. Contact Us.

N

• NAVTEQ Media Solutions

  • We use Hadoop/Mahout to process user interactions with advertisements to optimize ad selection.

• Neptune

  • Another Bigtable cloning project using Hadoop to store large structured data set.
  • 200 nodes(each node has: 2 dual core CPUs, 2TB storage, 4GB RAM)

• NetSeer -

  • Up to 1000 instances on Amazon EC2

  • Data storage in Amazon S3

  • 50 node cluster in Coloc
  • Used for crawling, processing, serving and log analysis

• The New York Times

  • Large scale image conversions

  • Used EC2 to run hadoop on a large virtual cluster

• Ning

  • We use Hadoop to store and process our log files

  • We rely on Apache Pig for reporting, analytics, Cascading for machine learning, and on a proprietary JavaScript API for ad-hoc queries

  • We use commodity hardware, with 8 cores and 16 GB of RAM per machine

O

P

• PARC - Used Hadoop to analyze Wikipedia conflicts paper.

• Performable - Web Analytics Software

  • We use Hadoop to process web clickstream, marketing, CRM, & email data in order to create multi-channel analytic reports.

  • Our cluster runs on Amazon's EC2 webservice and makes use of Python for most of our codebase.

• Pharm2Phork Project - Agricultural Traceability

  • Using Hadoop on EC2 to process observation messages generated by RFID/Barcode readers as items move through supply chain.
  • Analysis of BPEL generated log files for monitoring and tuning of workflow processes.

• Powerset / Microsoft - Natural Language Search

  • up to 400 instances on Amazon EC2

  • data storage in Amazon S3

  • Microsoft is now contributing to HBase, a Hadoop subproject ( announcement).

• Pressflip - Personalized Persistent Search

  • Using Hadoop on EC2 to process documents from a continuous web crawl and distributed training of support vector machines
  • Using HDFS for large archival data storage

• Pronux

  • 4 nodes cluster (32 cores, 1TB).
  • We use Hadoop for searching and analysis of millions of bookkeeping postings
  • Also used as a proof of concept cluster for a cloud based ERP system

• PSG Tech, Coimbatore, India

  • Multiple alignment of protein sequences helps to determine evolutionary linkages and to predict molecular structures. The dynamic nature of the algorithm coupled with data and compute parallelism of hadoop data grids improves the accuracy and speed of sequence alignment. Parallelism at the sequence and block level reduces the time complexity of MSA problems. Scalable nature of Hadoop makes it apt to solve large scale alignment problems.
  • Our cluster size varies from 5 to 10 nodes. Cluster nodes vary from 2950 Quad Core Rack Server, with 2x6MB Cache and 4 x 500 GB SATA Hard Drive to E7200 / E7400 processors with 4 GB RAM and 160 GB HDD.

Q

• Quantcast

  • 3000 cores, 3500TB. 1PB+ processing each day.
  • Hadoop scheduler with fully custom data path / sorter
  • Significant contributions to KFS filesystem

R

• Rackspace

  • 30 node cluster (Dual-Core, 4-8GB RAM, 1.5TB/node storage)

    • Parses and indexes logs from email hosting system for search: http://blog.racklabs.com/?p=66

• Rakuten - Japan's online shopping mall

  • 13 node cluster
  • We use Hadoop to analyze logs and mine data for recommender system and so on.

• Rapleaf

  • 80 node cluster (each node has: 2 quad core CPUs, 4TB storage, 16GB RAM)
  • We use hadoop to process data relating to people on the web
  • We also involved with Cascading to help simplify how our data flows through various processing stages

• Redpoll

  • Hardware: 35 nodes (2*4cpu 10TB disk 16GB RAM each)
  • We intend to parallelize some traditional classification, clustering algorithms like Naive Bayes, K-Means, EM so that can deal with large-scale data sets.

S

• SARA, Netherlands

  • SARA has initiated a Proof-of-Concept project to evaluate the Hadoop software stack for scientific use.

• Search Wikia

  • A project to help develop open source social search tools. We run a 125 node hadoop cluster.

• SEDNS - Security Enhanced DNS Group

  • We are gathering world wide DNS data in order to discover content distribution networks and

    configuration issues utilizing Hadoop DFS and MapRed.

• SLC Security Services LLC

  • 18 node cluster (each node has: 4 dual core CPUs, 1TB storage, 4GB RAM, RedHat OS)

  • We use Hadoop for our high speed data mining applications

• Sling Media

  • We have a core Analytics group that is using a 10-Node cluster running RedHat OS

  • Hadoop is used as an infrastructure to run MapReduce (MR) algorithms on a number of raw data

  • Raw data ingest happens hourly. Raw data comes from hardware and software systems out in the field
  • Ingested and processed data is stored into a relational DB and rolled up using Hive/Pig
  • Plan to implement Mahout to build recommendation engine

• Socialmedia.com

  • 14 node cluster (each node has: 2 dual core CPUs, 2TB storage, 8GB RAM)
  • We use hadoop to process log data and perform on-demand analytics

• Spadac.com

  • We are developing the MrGeo (Map/Reduce Geospatial) application to allow our users to bring cloud computing to geospatial processing.

  • We use HDFS and MapReduce to store, process, and index geospatial imagery and vector data.

  • MrGeo is soon to be open sourced as well.

• Stampede Data Solutions (Stampedehost.com)

  • Hosted Hadoop data warehouse solution provider

T

• Taragana - Web 2.0 Product development and outsourcing services

  • We are using 16 consumer grade computers to create the cluster, connected by 100 Mbps network.
  • Used for testing ideas for blog and other data mining.

• The Lydia News Analysis Project - Stony Brook University

  • We are using Hadoop on 17-node and 103-node clusters of dual-core nodes to process and extract statistics from over 1000 U.S. daily newspapers as well as historical archives of the New York Times and other sources.

• Tailsweep - Ad network for blogs and social media

  • 8 node cluster (Xeon Quad Core 2.4GHz, 8GB RAM, 500GB/node Raid 1 storage)
  • Used as a proof of concept cluster
  • Handling i.e. data mining and blog crawling

• Technical analysis and Stock Research

  • Generating stock analysis on 23 nodes (dual 2.4GHz Xeon, 2 GB RAM, 36GB Hard Drive)

• Telefonica Research

  • We use Hadoop in our data mining and user modeling, multimedia, and internet research groups.
  • 6 node cluster with 96 total cores, 8GB RAM and 2 TB storage per machine.

• Tianya

  • We use Hadoop for log analysis.

• Twitter

  • We use Hadoop to store and process tweets, log files, and many other types of data generated across Twitter. We use Cloudera's CDH2 distribution of Hadoop, and store all data as compressed LZO files.

  • We use both Scala and Java to access Hadoop's MapReduce APIs

  • We use Pig heavily for both scheduled and ad-hoc jobs, due to its ability to accomplish a lot with few statements.

  • We employ committers on Pig, Avro, Hive, and Cassandra, and contribute much of our internal Hadoop work to opensource (see hadoop-lzo)

  • For more on our use of hadoop, see the following presentations: Hadoop and Pig at Twitter and Protocol Buffers and Hadoop at Twitter

• Tynt

  • We use Hadoop to assemble web publishers' summaries of what users are copying from their websites, and to analyze user engagement on the web.
  • We use Pig and custom Java map-reduce code, as well as chukwa.
  • We have 94 nodes (752 cores) in our clusters, as of July 2010, but the number grows regularly.

U

• Universidad Distrital Francisco Jose de Caldas (Grupo GICOGE/Grupo Linux UD GLUD/Grupo GIGA

  • 5 node low-profile cluster. We use Hadoop to support the research project: Territorial Intelligence System of Bogota City.

• University of Glasgow - Terrier Team

  • 30 nodes cluster (Xeon Quad Core 2.4GHz, 4GB RAM, 1TB/node storage).

    We use Hadoop to facilitate information retrieval research & experimentation, particularly for TREC, using the Terrier IR platform. The open source release of Terrier includes large-scale distributed indexing using Hadoop Map Reduce.

• University of Maryland

  • We are one of six universities participating in IBM/Google's academic cloud computing initiative. Ongoing research and teaching efforts include projects in machine translation, language modeling, bioinformatics, email analysis, and image processing.

• University of Nebraska Lincoln, Research Computing Facility

  • We currently run one medium-sized Hadoop cluster (200TB) to store and serve up physics data for the computing portion of the Compact Muon Solenoid (CMS) experiment. This requires a filesystem which can download data at multiple Gbps and process data at an even higher rate locally. Additionally, several of our students are involved in research projects on Hadoop.

V

• Veoh

  • We use a small Hadoop cluster to reduce usage data for internal metrics, for search indexing and for recommendation data.

• Visible Measures Corporation uses Hadoop as a component in our Scalable Data Pipeline, which ultimately powers VisibleSuite and other products. We use Hadoop to aggregate, store, and analyze data related to in-stream viewing behavior of Internet video audiences. Our current grid contains more than 128 CPU cores and in excess of 100 terabytes of storage, and we plan to grow that substantially during 2008.

• VK Solutions

  • We use a small Hadoop cluster in the scope of our general research activities at VK Labs to get a faster data access from web applications.

  • We also use Hadoop for filtering and indexing listing, processing log analysis, and for recommendation data.

W

• Web Alliance

  • We use Hadoop for our internal search engine optimization (SEO) tools. It allows us to store, index, search data in a much faster way.
  • We also use it for logs analysis and trends prediction.

• WorldLingo

  • Hardware: 44 servers (each server has: 2 dual core CPUs, 2TB storage, 8GB RAM)
  • Each server runs Xen with one Hadoop/HBase instance and another instance with web or application servers, giving us 88 usable virtual machines.
  • We run two separate Hadoop/HBase clusters with 22 nodes each.
  • Hadoop is primarily used to run HBase and Map/Reduce jobs scanning over the HBase tables to perform specific tasks.
  • HBase is used as a scalable and fast storage back end for millions of documents.
  • Currently we store 12million documents with a target of 450million in the near future.

X

Y

• Yahoo!

  • More than 100,000 CPUs in >36,000 computers running Hadoop

  • Our biggest cluster: 4000 nodes (2*4cpu boxes w 4*1TB disk & 16GB RAM)

    • Used to support research for Ad Systems and Web Search
    • Also used to do scaling tests to support development of Hadoop on larger clusters

  • Our Blog - Learn more about how we use Hadoop.

  • >60% of Hadoop Jobs within Yahoo are Pig jobs.

Z

• Zvents

  • 10 node cluster (Dual-Core AMD Opteron 2210, 4GB RAM, 1TB/node storage)
  • Run Naive Bayes classifiers in parallel over crawl data to discover event information

When applicable, please include details about your cluster hardware and size.

From:http://wiki.apache.org/hadoop/PoweredBy