Weekend reading material



  1. redishttp://code.google.com/p/redis/ : Redis is a key-value database. It is similar to memcached but the dataset is not volatile, and values can be strings, exactly like in memcached, but also lists and sets with atomic operations to push/pop elements.
  2. HBasehttp://hadoop.apache.org/hbase/ : HBase is the Hadoop database. Its an open-source, distributed, column-oriented store modeled after the Google paper, Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Hadoop.
  3. Sherpahttp://research.yahoo.com/node/2139
  4. BigTablehttp://labs.google.com/papers/bigtable-osdi06.pdf
  5. voldemort – It is basically just a big, distributed, persistent, fault-tolerant hash table. For applications that can use an O/R mapper like active-record or hibernate this will provide horizontal scalability and much higher availability but at great loss of convenience. For large applications under internet-type scalability pressure, a system may likely consists of a number of functionally partitioned services or apis, which may manage storage resources across multiple data centers using storage systems which may themselves be horizontally partitioned. For applications in this space, arbitrary in-database joins are already impossible since all the data is not available in any single database. A typical pattern is to introduce a caching layer which will require hashtable semantics anyway. For these applications Voldemort offers a number of advantages
  6. Dynamo – A highly available key-value storage system that some of Amazon’s core services use to provide an “always-on” experience.  To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
  7. Cassandra – Cassandra is a highly scalable, eventually consistent, distributed, structured key-value store. Cassandra brings together the distributed systems technologies from Dynamo and the data model from Google’s BigTable. Like Dynamo, Cassandra is eventually consistent. Like BigTable, Cassandra provides a ColumnFamily-based data model richer than typical key/value systems.
  8. Hypertable – : Hypertable is an open source project based on published best practices and our own experience in solving large-scale data-intensive tasks.
  9. HDFS – The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets.


  1. Eventually Consistent 
  2. Bunch of Links at bytepawn
  3. Fallacies of Distributed Computing