Clustering

Introduction> Introduction # Elasticsearch is a popular open source search server that is used for real-time distributed search and analysis of data. When used for anything other than development, Elasticsearch should be deployed across multiple servers as a cluster, for the best performance, stability, and scalability.

Introduction> Introduction # Redis is an open source key-value data store, using an in-memory storage model with optional disk writes for persistence. It features transactions, pub/sub, and automatic failover, among other functionality.

Introduction> Introduction # Consul is a distributed, highly available, datacenter-aware, service discovery and configuration system. It can be used to present services and nodes in a flexible and powerful interface that allows clients to always have an up-to-date view of the infrastructure they are a part of.

Introduction> Introduction # CoreOS allows you to easily run services in Docker containers across a cluster of machines. The procedure for doing so usually involves starting one or multiple instances of a service and then registering each instance with etcd, CoreOS’s distributed key-value store.

Introduction> Introduction # Hadoop is a Java-based programming framework that supports the processing and storage of extremely large datasets on a cluster of inexpensive machines. It was the first major open source project in the big data playing field and is sponsored by the Apache Software Foundation.

Introduction> Introduction # Docker Swarm is the Docker-native solution for deploying a cluster of Docker hosts. You can use it to quickly deploy a cluster of Docker hosts running either on your local machine or on supported cloud platforms.

Introduction> Introduction # One of the technologies that makes CoreOS possible is etcd, a globally distributed key-value store. This service is used by the individual CoreOS machines to form a cluster and as a platform to store globally-accessible data.

Introduction> Introduction # Big data is a blanket term for the non-traditional strategies and technologies needed to gather, organize, process, and gather insights from large datasets. While the problem of working with data that exceeds the computing power or storage of a single computer is not new, the pervasiveness, scale, and value of this type of computing has greatly expanded in recent years.