high_scalability high_scalability-2012 high_scalability-2012-1262 knowledge-graph by maker-knowledge-mining
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Introduction: If you understand things best when they're formatted as a musical, this video is for you. It teaches the essentials of PostgreSQL, Riak, HBase, MongoDB, CouchDB, Neo4J and Redis in the style of My Fair Lady. And for a change, it's very SFW.
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Introduction: If you understand things best when they're formatted as a musical, this video is for you. It teaches the essentials of PostgreSQL, Riak, HBase, MongoDB, CouchDB, Neo4J and Redis in the style of My Fair Lady. And for a change, it's very SFW.
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Introduction: Update: Short presentation NYC by Bryan Fink demonstrating the riak web-shaped data storage engine Riak is another new and interesting key-value store entrant. Some of the features it offers are: Document-oriented Scalable, decentralized key-value store Standard get , put , and delete operations. Distributed, fault-tolerant storage solution. Configurable levels of consistency, availability, and partition tolerance Support for Erlang, Ruby, PHP, Javascript, Java, Python, HTTP open source and NoSQL Pluggable backends Eventing system Monitoring Inter-cluster replication Links between records that can be traversed. Map/Reduce. Functions are executed on the data node. One interesting difference is that a list keys are required to specify which values are operated on as apposed to running calculations on all values. Related Articles Hacker News Thread . More juicy details on how Riak compares to Cassandra, mongodb, couchdb, etc.
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Introduction: You may have read somewhere that Facebook has introduced a new Social Inbox integrating email, IM, SMS, text messages, on-site Facebook messages. All-in-all they need to store over 135 billion messages a month. Where do they store all that stuff? Facebook's Kannan Muthukkaruppan gives the surprise answer in The Underlying Technology of Messages : HBase . HBase beat out MySQL, Cassandra, and a few others. Why a surprise? Facebook created Cassandra and it was purpose built for an inbox type application, but they found Cassandra's eventual consistency model wasn't a good match for their new real-time Messages product. Facebook also has an extensive MySQL infrastructure , but they found performance suffered as data set and indexes grew larger. And they could have built their own, but they chose HBase. HBase is a scaleout table store supporting very high rates of row-level updates over massive amounts of data . Exactly what is needed for a Messaging system. HBase is also a colu
Introduction: This is a guest post by Doug Judd , original creator of Hypertable and the CEO of Hypertable, Inc. Hypertable delivers 2X better throughput in most tests -- HBase fails 41 and 167 billion record insert tests, overwhelmed by garbage collection -- Both systems deliver similar results for random read uniform test We recently conducted a test comparing the performance of Hypertable ( @hypertable ) version 0.9.5.5 to that of HBase ( @HBase ) version 0.90.4 (CDH3u2) running Zookeeper 3.3.4. In this post, we summarize the results and offer explanations for the discrepancies. For the full test report, see Hypertable vs. HBase II . Introduction Hypertable and HBase are both open source, scalable databases modeled after Google's proprietary Bigtable database. The primary difference between the two systems is that Hypertable is written in C++, while HBase is written in Java. We modeled this test after the one described in section 7 of the Bigtable paper and tuned both systems fo
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Introduction: Eric Pickup from Youporn.com posted on a news group that Youporn is now 100% Redis based and will soon be revealing more about their architecture at the ConFoo conference . Some stunning, but not surprising numbers were revealed: 100 million page views per day A cluster of Redis slaves are handling over 300k queries per second. Some additional nuggets: Additional Redis nodes were added because the network cards couldn't keep up with Redis. Impressed with Redis' performance. All reads come from Redis; we are maintaining MySQL just to allow us to build new sorted sets as our requirement change Most data is found in hashes with ordered sets used to know what data to show. A typical lookup would be an zInterStore on: videos:filters:released, Videos:filters:orientation:straight,Videos:filters:categories:{category_id}, Videos:ordering:rating Then perform a zRange to get the pages we want and get the list of video_ids back. Then start a pipeline and get
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