Top 5 Remarkable Use-Cases Of ElasticSearch In Diverse Industries
Industries that hold search engines and e-commerce with massive databases are facing issues such as product information retrieval taking too long. Nowadays, businesses are looking for alternatives to store data that promotes quick retrieval. To solve this query, ELK Stack (Elastic Stack) comes into the picture.
The ELK stack is a powerful collection of three open-source projects, ElasticSearch, Logstash, and Kibana. The ELK is a complete end-to-end log analysis solution that helps in deep searching, analyzing, and visualizing the log generated by various machines. In this blog, we will discuss one of the first open-source project of ELK, ElasticSearch in detail.
What Is ElasticSearch?
ElasticSearch is a document-oriented database that stores, retrieves, and manages the semi-structured data. To get quick retrieval of data adopting NoSQL rather than RDBMS is feasible and Elasticsearch is one such NoSQL distributed database. ElasticSearch can attain fast search responses because instead of searching the text directly, it searches an index. ElasticSearch can accommodate petabytes of data and scale up to thousands of servers. The enormous capacity of ElasticSearch results directly from its distributed architecture.
ElasticSearch has it’s own Query Domain Specific Language, which helps in specifying a query in JSON format. When we talk about real projects, it requires a search in different fields by applying some conditions and values of some predefined fields. All such complexity can be handled through a single query by using DSL.
Use-Cases Of ElasticSearch
ElasticSearch can be used in such various ways, let’s discuss a few of them-
Logging & Log Analysis
With ElasticSearch, it is easy to implement and scale logging solutions. For instance, to find an error, you need to log in to several machines and look at several log files. Now imagine that you are maintaining larger applications that are distributed across several nodes. Here the process of searching in log files can become more tedious. This is where ElasticSearch can help out. Elasticsearch gives you plenty of ways for grabbing data wherever it lives and getting it indexed.
Combining Public Data
Elasticsearch has the flexibility to take in multiple different sources of data and still keep it all manageable and searchable. For example, the Twitter Connector allows you to set up hashtags to watch on Twitter and then collect all tweets with those hashtags and analyze them in Kibana. This product was built on core Elastic Stack components.
Elasticsearch also operates well on time-series data like app events and metrics. This is another area where you can to collect a huge amount of data for common applications. Whatever technologies you use, there is a chance that ElasticSearch has the components to grab metrics and events.
Full-Text search is one of the core capabilities of ElasticSearch. ElasticSearch’s search capabilities are powerful, flexible, and include a significant number of tools to make the search easier. ElasticSearch has its query DSL as well as built-in capabilities for auto-complete. ElasticSearch is a JSON-based and RESTful search engine designed for scaling millions of events per second, providing maximum reliability.
- Wikipedia: Wikipedia, the giant search provider, uses ElasticSearch for full-text search.
- Stack Overflow: Stack Overflow, the knowledge-sharing site relies on Elasticsearch as a means to support full-text search capabilities for providing source related questions and answers.
- GitHub: GitHub, the project host, can query zillions of lines of code with the search engine.
With lots of charting options, TimeLion for time-series data, a tile service for geo-data, Kibana is powerful and easy to use visualization tool. Kibana has some visual components for every uses cases explained here. Elasticsearch + Kibana makes the best tool for visualizing data.
Benefits Of Using ElasticSearch
- Search Options: ElasticSearch offers many search options such as full-text search, customized stemming, faceted search, autocompletion, instant search, and fuzzy search. These options make searching an easy task for you.
- Scalability: Being a distributed system, ElasticSearch can quickly scale horizontally. It can extend resources and balance the loading between the nodes in a cluster.
- Document-Oriented: ElasticSearch is capable of storing complex real-world entities as structured JSON documents and indexes with a higher performance result.
- Speed: ElasticSearch can perform complex queries extremely fast. It also caches almost all of the structured queries that are used as a filter for the result set and executes them only once.
ElasticSearch stands out well in terms of its flexibility, robustness, and scalability. It offers seamless search experience to the customers. Many companies are switching to ElasticSearch as it allows you to zoom out to your data using aggregation and make sense of billions of log lines.