How to Improve Your Website with Elasticsearch
We are constantly working on a variety of projects with all different scales. And among them, there are projects where the ability to make a high-quality search from a huge amount of data would be very useful. This is where Elasticsearch comes in!
When your business involves analyzing statistical data from different sources, you need to know how to collect, store, index, transform it into other data, analyze it, and so on.
Quite often the project scale is not yet large enough to implement large software platforms, and in this case, universal options based on standard SQL or NoSQL solutions will help you cope with the accumulation and processing of medium-sized data.
Such solutions, based on our practice, include Elasticsearch, which we will discuss in this article.
What is Elasticsearch and what is it for?
Let's start with a brief description of Elasticsearch. It’s a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data for lightning-fast search, relevancy, and analytics that scale with ease. It actually lets you perform and combine many types of searches — structured, unstructured, geo, metric, etc.
Elasticsearch is a fast, horizontally scalable, and very free hybrid of NoSQL database and Google. It communicates with the world via the HTTP API and receives JSON documents for indexing and storage. Storage, however, can be disabled, and in that case, there will only be a search engine that returns the IDs of once indexed documents.
Today, Elasticsearch is successfully used by eBay, Adobe, Uber, Nvidia, Blizzard, Volkswagen, SoundCloud, GitHub, Netflix, and Amazon. What is the appeal of Elasticsearch? Let's get this straight.
One of the main values in working with it is a well-organized full-text search. In fact, this is a quick search across the entire text, across the entire text's database.
Listing the main features of this service, they clearly include the following:
- An open-source search server is written in Java
- Indexing any heterogeneous data
- REST API web interface with JSON output
- Text search
- Real-time search (NRT)
- Distributed document storage without schemas, REST, and JSON
- Support for multiple languages and geo-location
If a project or organization is working with a large amount of data and documents, related and unrelated, and this company wants the search to be as useful and productive as possible, then Elasticsearch is the best solution.
What advantages can it add to the project?
Sometimes, everything is learned with comparisons, so if you compare Elasticsearch with similar systems, you come to the conclusion that Elasticsearch has one of the fastest implementations of end-to-end search.
Besides, it’s worth noting that the functionality of Elasticsearch also consists of storage for the necessary data. In terms of data storage, SQL and NoSQL databases have some advantages over Elasticsearch. And relying on this service as a database for storing data is a common mistake. You need to understand that this is primarily a database for fast and high-quality data search, and not for storing them.
If we are talking about the advantages of using Elasticsearch, then among them will be those such as:
- You can store data without a schema, and create a schema for your data.
- Manage your data records using multi-document APIs.
- Filter and query your data to provide a better understanding.
- Based on Apache Lucene and provides a RESTful API.
- Provides horizontal scalability, reliability, and multi-user capabilities for real-time indexing.
- The ability to scale both vertically and horizontally.
What projects have we used it on
Our team has several good examples of successful work with Elasticsearch, which only confirms the opinion that this service is ideal when the task is to quickly search through a huge amount of data.
For example, in the case of a project for a law firm that needed to set up a search for a library of various precedents dating back to 1870. This is, in fact, a huge repository of data from all the courts that existed then and now, containing all the cases that occurred over more than 100 years. Initially, when this project was developed, Elasticsearch had not been developed, but at the stage of refactoring, in which we were able to participate, the productivity and quality of search that we managed to achieve with this service increased significantly.
Also, in one of our current projects, this work is also being done using Elasticsearch. The amount of data is not so large in comparison with the previous case, approximately 10,000 pages of text. However, the use of Elasticsearch on this project was due to the fact that the project is associated with active communication and blogging, and the amount of data is growing exponentially. This exponential growth is guaranteed to lead to the fact that after some time the amount of data that will require searching will increase significantly.
But even at this stage, the use of cached queries to the database, in comparison with what Elasticsearch allowed to do, differs significantly in the time when search results are received. And there is every reason to think that as the amount of data on this project increases, this difference will only grow. At this stage, the project is migrating from a relational database to Elasticsearch, and we are already seeing an excellent performance increase in search queries within this project.
To sum it up
Whatever the amount of data on a particular project, the client always wants to find information as quickly as possible. In this case, those who work on the project have the task of making such a quick search possible.
From the many solutions out there, you need to choose from the best ones. Elasticsearch has earned its place on that list because no matter how quickly the amount of information grows, this service will definitely handle it!
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