Certain products that involve search engines with large databases and e-commerce are now faced with a problem that says product information retrieval is taking too long. The problem results in bad user experience and therefore turns off your potential customers.
The lag in search is caused by a relational database that is used in designing the product – the data is situated within several tables and to successfully collect useful user information, it must be taken from across each table simultaneously. Relational base becomes so slow when collecting search result from database queries as well as when collecting huge data. Businesses are now searching for alternate ways of storing their data that allows quick retrieval, and this is where NoSQL comes into play. Elasticsearch or ES is one of such NoSQL databases for distributions. ES depends on flexible data models to update and build customer’s profile as well as meeting the low latency needed engagement in real-time and the demanding workloads.
What is so important about Elasticsearch? Elasticsearch is a database that is designed to retrieve, store as well as manage semi-structured or document-oriented data. When using ES, your data is stored in JSON document type, and you can easily query to retrieve them. It uses certain defaults to index your data otherwise; you have to indicate mappings based on your needs. ES utilizes Lucene Standard Analyzer to index for high precision and auto type guessing.
All futures of ES is exposed as Rest API:
- Search API: is usually used to submit queries
- Index API: is usually used for documenting the index
- Get API: is usually used to retrieve documents
- Put mapping API: Is usually used to define mapping and override the default choice.
Elasticsearch comes with its own query domain-specific language where you have to specify your query in JSON format. Depending on your needs, you can as well nest other queries. You require different weights, search on different fields by using some conditions, value of some predefined fields, recent documents and many more for real-world projects. Every single one of the complexity can be fully expressed using just one query. The DSL query is very powerful, and it is designed to take care of real-world query complexity using just one single query. Elasticsearch APIs are related directly to Lucene, and they also use the same name with Lucene operations.
Elasticsearch user possesses delightfully in-depth use cases, this includes: maximizing index throughput and appending little log-line documents to index website-scale collections of huge documents. Lots of time, we have more than a way to query or index documents, and through the use of Elasticsearch, we can handle it much better. ES pave the way for bid data developments as it is evolving rapidly, but it isn’t new. Still, the main product is consistent as well as helping you achieve a fast performance with your search results from search engines.