Comparison of Hadoop with other systems

In my previous post I explained what is Hadoop and the need for Hadoop. In this post I'll compare Hadoop with other existing systems.

Hadoop vs RDBMS:

Disk latency has not improved proportionally to disk bandwidth i.e seek time has not improved proportionally to transfer time. RDBMS uses B-tree for data access which is limited by disk latency, therefore it would take large time to access majority of data. Hadoop uses MapReduce model for data access which is limited by disk bandwidth. Hence for queries involving majority of database B-tree is less effecient than MapReduce.  

RDBMS is more efficient for point queries where data is indexed to improve disk latency. Whereas Hadoop's Mapreduce is more efficient for queries involving complete data. Moreover Mapreduce suits applications in which data is written once and read many times, whereas in RDBMS dataset is continuously updated.

                                                        MapReduce                                      RDBMS

1. Size of data                              Petabytes                                         Gigabytes
2. Integrity of data                           Low                                                  High
3. Data schema                            Dynamic                                              Static
4. Access method                Interactive and Batch                                   Batch
5. Scaling                                       Linear                                             Nonlinear
6. Data structure                        Unstructured                                       Structured
7. Normalization of data              Not Required                                       Required

These difference are likely to blur in near future.

Hadoop vs Grid Computing:

Grid Computing has been doing large scale processing by dividing a job over a cluster of systems. But it is efficient for only compute intensive jobs. For data intensive jobs huge data has to be transferred over the network and the network bandwidth becomes the bottleneck. This is where Hadoop outperforms Grid Computing. Mapreduce tries to locate computations on the node where data resides thus saving network bandwidth. This is called as principle of locality which lies at the heart of MapReduce.

Moreover Mapreduce saves the programmers from writing code for node failure and handling data flow as these are handled implicitly by MapReduce.Whereas Grid Computing provides great control to handle data flow and node failures.

Thus we can say that Hadoop is not a replacement for RDBMS and both these systems can coexist simultaneously.