Zookeeper

Introduction

The ZooKeeper framework was created by "Yahoo!" to make it easy and reliable to access their apps. Later, Hadoop, HBase, and other distributed frameworks adopted Apache ZooKeeper as a standard for organized services. ZooKeeper, for example, is used by Apache HBase to track the status of distributed data.

ZooKeeper is a service that allows you to manage a large number of servers in a distributed manner. In a distributed context, coordinating and managing a service is a difficult task. With its simple architecture and API, ZooKeeper overcomes this problem. ZooKeeper allows developers to concentrate on the fundamental logic of their applications without having to worry about the application's distributed nature.

This tutorial covers the fundamentals of ZooKeeper, the need for Zookeeper, benefits and limitations of Zookeeper.

Need of Zookeeper

Hadoop's capacity to divide and conquer is its most potent tool for dealing with huge data problems. After the problem has been separated, the solution is based on the Hadoop cluster's capacity to use distributed and parallel processing techniques. Interactive technologies are unable to give the insights or timeliness required to make business decisions for some large data challenges.

To overcome those huge data problems, you'll need to design distributed applications. Zookeeper is Hadoop's method of coordinating all of these distributed applications' pieces.

Although Zookeeper is a simple technology, its functions are really powerful. It is arguable that creating resilient, fault-tolerant distributed Hadoop applications without it would be difficult, if not impossible.

Capabilities of Zookeeper

● Configuration management

Zookeeper can broadcast configuration attributes to any or all of the cluster's nodes. When processing relies on specific resources being available on all nodes, Zookeeper ensures that the configurations are consistent.

● Process synchronization

Zookeeper is in charge of coordinating the start and stop of several nodes in a cluster. This ensures that everything happens in the correct order. Only after an entire process group has been completed can subsequent processing begin.

● Self-election

Zookeeper is aware of the cluster's composition and can assign a "leader" role to one of the nodes. On behalf of the cluster, this leader/master handles all client requests. Should the leader node fail, the surviving nodes will elect a new leader.

● Cluster management

Joining and exiting a cluster, as well as node status in real-time.

● Reliable messaging

Even though Zookeeper workloads are loosely coupled, the distributed application still requires communication between and among the nodes in the cluster. Zookeeper has a publish/subscribe feature that allows you to create a queue. Even if a node fails, this queue ensures that messages are delivered.

Zookeeper is ideally implemented across racks since it manages groups of nodes in service of a single distributed application. This differs significantly from the cluster's requirements (within racks). The rationale for this is straightforward: Zookeeper must perform, be durable, and be fault-tolerant at a level above the cluster. It's important to remember that a Hadoop cluster is already fault resilient and will self-heal. The only thing a zookeeper has to worry about is its own fault tolerance. 

The Hadoop ecosystem, as well as the commercial distributions that are supported, are constantly evolving. Existing technologies are upgraded, while some technologies are discarded in favor of a (hopefully better) replacement. One of the most significant advantages of open source is this. Another example is commercial enterprises' embrace of open source technologies. These businesses improve products and make them better for everyone by providing low-cost support and services. This is how the Hadoop ecosystem has grown and why it's a suitable fit for tackling your big data problems.