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Category Archives: cloud computing
Main takeaways from Accel’s Big Data Conference
Attended Accel Partners Big Data conference last week. It was a good event with many interesting people, a very crude estimate of distribution: 1/3 VCs/investors, 1/3 startup tech people, 1/3 big corp tech people. My personal 2 key takeaways from … Continue reading
atbr now has Apache Thrift support
atbr (large-scale and low-latency in-memory key-value pair store) now supports Apache Thrift for easier integration with other Hadoop services. Thrift Example Checkout and install atbr Prerequisite Install/compile Apache Thrift – http://thrift.apache.org/ Compile a atbr thrift server and connect using python … Continue reading
atbr – supports websocket-based sharding
atbr (large-scale and low-latency in-memory key-value pair store) now supports websocket-based sharding for parallel deployments. Websocket Sharding Example Checkout and install atbr Start 3 servers loaded with data Start shard server talking to shards Connect to shard server and lookup … Continue reading
Posted in cloud computing
Tagged atbr, dictionaries, hadoop, hashtables, in-memory, large-scale, mapreduce, python
1 Comment
atbr – large-scale in-memory hashtables (in Python)
Large-scale in-memory key-value stores are universally useful (e.g. to load and serve tsv-data created by hadoop/mapreduce jobs), in-memory key-value stores have low latency, and modern boxes have lots of memory (e.g. EC2 intances with 70GB RAM). If you look closely … Continue reading
Mapreduce & Hadoop Algorithms in Academic Papers (4th update – May 2011)
Follow @atbrox It’s been a year since I updated the mapreduce algorithms posting last time, and it has been truly an excellent year for mapreduce and hadoop – the number of commercial vendors supporting it has multiplied, e.g. with 5 … Continue reading
Posted in Atbrox, cloud computing, Hadoop and Mapreduce
16 Comments