Choosing a tool for information search or storage can be a difficult task. Some tools are better at creating relations among data, some excel at quickly accessing large amounts of data, and others make it easier when attempting to search through a vast array of information. Where does ElasticSearch fit into this, and when is it the right tool for your job?
What is ElasticSearch?
Elasticsearch is an open source search and analytics engine (based on Lucene) designed to operate in real time. It was designed to be used in distributed environments by providing flexibility and scalability.
Instead of the typical full-text search setup, ElasticSearch offers ways to extend searching capabilities through the use of APIs and query DSLs. There are clients available so that it can be used with numerous programming languages, such as Ruby, PHP, JavaScript and others.
What are some advantages of ElasticSearch?
ElasticSearch has some notable features that can be helpful to an application:
Distributed approach – Indices can be divided into shards, with each shard able to have any number of replicas. Routing and rebalancing operations are done automatically when new documents are added.
Based on Lucene – Lucene is an open source library for information retrieval that may already be familiar to developers. ElasticSearch makes numerous features of the Lucene library available through its API and JSON.
An example of an index API call. Source: ElasticSearch.
Use of faceting – A faceted search is more robust than a typical text search, allowing users to apply a number of filters on the information and even have a classification system based on the data. This allows better organization of the search results and allows users to better determine what information they need to examine.
Structured search queries – While searches can still be done using a text string, more robust searches can be structured using JSON objects.
An example structured query using JSON. Source: Slant.
When is ElasticSearch the right tool?
If you are seeking a database for saving and retrieving data outside of searching, you may find a NoSQL or relational database a better fit, since they are designed for those types of queries. While ElasticSearch can serve as a NoSQL solution, it lacks , so you will need to be able to handle that limitation.
On the other hand, if you want a solution that is effective at quickly and dynamically searching through large amounts of data, then ElasticSearch is a good solution. If your application will be search-intensive, such as with GitHub, where it is used to search through 2 billion documents from all of its code repositories, then ElasticSearch is an ideal tool for the job.
Get ElasticSearch or a Database
If you want to try out ElasticSearch, one way to do so is to use a service like Morpheus, which offers databases as a service on the cloud. With Morpheus, you can easily set up one or more databases (including ElasticSearch, MongoDB, MySQL, and more). In addition, databases are deployed on a high performance infrastructure with Solid State Drives, replicated, and archived.
