Adobe - 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 are using HBase in several areas from social services to structured data and processing for internal use. 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.
Flurry provides mobile application analytics. We use HBase and Hadoop for all of our analytics processing, and serve all of our live requests directly out of HBase on our 16-node production cluster with billions of rows over several tables.
Drawn to Scale Consulting consults on HBase, Hadoop, Distributed Search, and Scalable architectures.
GumGum is an analytics and monetization platform for online content. We've developed usage-based licensing models that make the best content in the world accessible to publishers of all sizes. We use HBase 0.20.0 on a 4-node Amazon EC2 cluster to record visits to advertisers in our ad network. Our production cluster has been running since July 2009.
Mahalo, "...the world's first human-powered search engine". All the markup that powers the wiki is stored in HBase. It's been in use for a few months now. MediaWiki - the same software that power Wikipedia - has version/revision control. Mahalo's in-house editors produce a lot of revisions per day, which was not working well in a RDBMS. An hbase-based solution for this was built and tested, and the data migrated out of MySQL and into HBase. Right now it's at something like 6 million items in HBase. The upload tool runs every hour from a shell script to back up that data, and on 6 nodes takes about 5-10 minutes to run - and does not slow down production at all.
Meetup is on a mission to help the world’s people self-organize into local groups. We use Hadoop and HBase to power a site-wide, real-time activity feed system for all of our members and groups. Group activity is written directly to HBase, and indexed per member, with the member's custom feed served directly from HBase for incoming requests. We're running HBase 0.20.0 on a 11 node cluster.
Ning uses HBase to store and serve the results of processing user events and log files, which allows us to provide near-real time analytics and reporting. We use a small cluster of commodity machines with 4 cores and 16GB of RAM per machine to handle all our analytics and reporting needs.
Openplaces is a search engine for travel that uses HBase to store terabytes of web pages and travel-related entity records (countries, cities, hotels, etc.). We have dozens of MapReduce jobs that crunch data on a daily basis. We use a 20-node cluster for development, a 40-node cluster for offline production processing and an EC2 cluster for the live web site.
Powerset (a Microsoft company) uses HBase to store raw documents. We have a ~110 node hadoop cluster running DFS, mapreduce, and hbase. In our wikipedia hbase table, we have one row for each wikipedia page (~2.5M pages and climbing). We use this as input to our indexing jobs, which are run in hadoop mapreduce. Uploading the entire wikipedia dump to our cluster takes a couple hours. Scanning the table inside mapreduce is very fast -- the latency is in the noise compared to everything else we do.
Runa Inc. offers a SaaS that enables online merchants to offer dynamic per-consumer, per-product promotions embedded in their website. To implement this we collect the click streams of all their visitors to determine along with the rules of the merchant what promotion to offer the visitor at different points of their browsing the Merchant website. So we have lots of data and have to do lots of off-line and real-time analytics. HBase is the core for us. We also use Clojure and our own open sourced distributed processing framework, Swarmiji. The HBase Community has been key to our forward movement with HBase. We're looking for experienced developers to join us to help make things go even faster!
SocialMedia uses HBase to store and process user events which allows us to provide near-realtime user metrics and reporting. HBase forms the heart of our Advertising Network data storage and management system. We use HBase as a data source and sink for both realtime request cycle queries and as a backend for mapreduce analysis.
Streamy is a recently launched realtime social news site. We use HBase for all of our data storage, query, and analysis needs, replacing an existing SQL-based system. This includes hundreds of millions of documents, sparse matrices, logs, and everything else once done in the relational system. We perform significant in-memory caching of query results similar to a traditional Memcached/SQL setup as well as other external components to perform joining and sorting. We also run thousands of daily MapReduce jobs using HBase tables for log analysis, attention data processing, and feed crawling. HBase has helped us scale and distribute in ways we could not otherwise, and the community has provided consistent and invaluable assistance.
Stumbleupon and Su.pr use HBase as a real time data storage and analytics platform. Serving directly out of HBase, various site features and statistics are kept up to date in a real time fashion. We also use HBase a map-reduce data source to overcome traditional query speed limits in MySQL.
SubRecord Project is an Open Source project that is using HBase as a repository of records (persisted map-like data) for the aspects it provides like logging, tracing or metrics. HBase and Lucene index both constitute a repo/storage for this platform.
Shopping Engine at Tokenizer is a web crawler; it uses HBase to store URLs and Outlinks (AnchorText + LinkedURL): more than a billion. It was initially designed as Nutch-Hadoop extension, then (due to very specific 'shopping' scenario) moved to SOLR + MySQL(InnoDB) (ten thousands queries per second), and now - to HBase. HBase is significantly faster due to: no need for huge transaction logs, column-oriented design exactly matches 'lazy' business logic, data compression, MapReduce support. Number of mutable 'indexes' (term from RDBMS) significantly reduced due to the fact that each 'row::column' structure is physically sorted by 'row'. MySQL InnoDB engine is best DB choice for highly-concurrent updates. However, necessity to flash a block of data to harddrive even if we changed only few bytes is obvious bottleneck. HBase greatly helps: not-so-popular in modern DBMS 'delete-insert', 'mutable primary key', and 'natural primary key' patterns become a big advantage with HBase.
Trend Micro Advanced Threats Research is running Hadoop 0.18.1 and HBase 0.18.0. Our application is a web crawling application with concurrent batch content analysis of various kinds. All of the workflow components are implemented as subclasses of TableMap and/or TableReduce on a cluster of 25 nodes. We see a constant rate of 2500 requests/sec or greater, peaking periodically near 100K/sec when some of the batch scan tasks run.
Veoh Networks uses HBase to store and process visitor(human) and entity(non-human) profiles which are used for behavioral targeting, demographic detection, and personalization services. Our site reads this data in real-time (heavily cached) and submits updates via various batch map/reduce jobs. With 25 million unique visitors a month storing this data in a traditional RDBMS is not an option. We currently have a 24 node Hadoop/HBase cluster and our profiling system is sharing this cluster with our other Hadoop data pipeline processes.
VideoSurf - "The video search engine that has taught computers to see". We're using Hbase to persist various large graphs of data and other statistics. Hbase was a real win for us because it let us store substantially larger datasets without the need for manually partitioning the data and it's column-oriented nature allowed us to create schemas that were substantially more efficient for storing and retrieving data.
Visible Technologies - We use Hadoop, HBase, Katta, and more to collect, parse, store, and search hundreds of millions of Social Media content. We get incredibly fast throughput and very low latency on commodity hardware. HBase enables our business to exist.
WorldLingo - The WorldLingo Multilingual Archive. We use HBase to store millions of documents that we scan using Map/Reduce jobs to machine translate them into all or selected target languages from our set of available machine translation languages. We currently store 12 million documents but plan to eventually reach the 450 million mark. HBase allows us to scale out as we need to grow our storage capacities. Combined with Hadoop to keep the data replicated and therefore fail-safe we have the backbone our service can rely on now and in the future. WorldLingo is using HBase since December 2007 and is along with a few others one of the longest running HBase installation. Currently we are running the latest HBase 0.20 and serving directly from it: MultilingualArchive.
Yahoo! uses HBase to store document fingerprint for detecting near-duplications. We have a cluster of few nodes that runs HDFS, mapreduce, and HBase. The table contains millions of rows. We use this for querying duplicated documents with realtime traffic.