Hadoop’s greatest technique for addressing big data challenges is its capability to divide and conquer with Zookeeper. After the problem has been divided, the conquering relies on the capability to employ distributed and parallel processing techniques across the Hadoop cluster.
For some big data problems, the interactive tools are unable to provide the insights or timeliness required to make business decisions. In those cases, you need to create distributed applications to solve those big data problems. Zookeeper is Hadoop’s way of coordinating all the elements of these distributed applications.
Zookeeper as a technology is actually simple, but its features are powerful. Arguably, it would be difficult, if not impossible, to create resilient, fault-tolerant distributed Hadoop applications without it. Some of the capabilities of Zookeeper are as follows:
Process synchronization: Zookeeper coordinates the starting and stopping of multiple nodes in the cluster. This ensures that all processing occurs in the intended order. When an entire process group is complete, then and only then can subsequent processing occur.
Configuration management: Zookeeper can be used to send configuration attributes to any or all nodes in the cluster. When processing is dependent on particular resources being available on all the nodes, Zookeeper ensures the consistency of the configurations.
Self-election: Zookeeper understands the makeup of the cluster and can assign a “leader” role to one of the nodes. This leader/master handles all client requests on behalf of the cluster. Should the leader node fail, another leader will be elected from the remaining nodes.
Reliable messaging: Even though workloads in Zookeeper are loosely coupled, you still have a need for communication between and among the nodes in the cluster specific to the distributed application. Zookeeper offers a publish/subscribe capability that allows the creation of a queue. This queue guarantees message delivery even in the case of a node failure.
Because Zookeeper is managing groups of nodes in service to a single distributed application, it is best implemented across racks. This is very different than the requirements for the cluster itself (within racks). The underlying reason is simple: Zookeeper needs to perform, be resilient, and be fault tolerant at a level above the cluster itself.
Remember that a Hadoop cluster is already fault tolerant, so it will heal itself. Zookeeper just needs to worry about its own fault tolerance.
The Hadoop ecosystem and the supported commercial distributions are ever-changing. New tools and technologies are introduced, existing technologies are improved, and some technologies are retired by a (hopefully better) replacement. This one of the greatest advantages of open source.
Another is the adoption of open source technologies by commercial companies. These companies enhance the products, making them better for everyone by offering support and services at a modest cost. This is how the Hadoop ecosystem has evolved and why it is a good choice for helping to solve your big data challenges.