With the increasingly growing volume of data, the techniques to manage big data are needed in many areas. Open source community and many companies have attempted developing solutions to deal with big data.
Recently, Prof. Daniel Abadi, who is an Assistant Professor of Computer Science at Yale University, announced HadoopDB release and the paper published in VLDB’09. HadoopDB is an open source analytical database, being developed by him and his students. The paper states that HadoopDB is a hybrid of both MapReduce and parallel database and it takes the best features from both.
Actually, MapReduce has made controversial issues from a database point of view. Formerly, there was some debates. Representatively, Prof. David Dewitt, who is well known as a great master of (parallel) database, critiqued that MapReduce is a major step backwards. On the other hand, proponents of MapReduce argue that MapReduce outperforms parallel database in respect of scalability, fault tolerance, and flexibility to unstructured data.
This paper concludes that HadoopDB is close to the performance of parallel databases while it is similar score on fault tolerance and feasibility in heterogeneous systems as Hadoop.
In sum, HadoopDB is a hybrid system of MapReduce and parallel DBMS. It is quite interesting achievement. I respect their decision to release HadoopDB as open source because their achievement will more broadly contribute to Hadoop and data analytical database. Still, I do not read this paper completely, and sooner I will discuss HadoopDB in detail.
Some interesting points:
- They carried out experiments on a 100 node of amazon EC2 cluster.
- They try to deal with semantic web data (i.e., RDF) by HadoopDB.
- HadoopDB is a full open source project.
- HadoopDB isn’t well suited for real-time data yet.
- I can participate in his presentation at the session at VLDB.