Elastic search is an open-source search engine that is scalable, distributable and accessible through an elaborate API. TheElastic search can power extremely fast searches that support business analytics and data discovery applications.Elasticsearch is developed alongside a data-collection and log parsing engine called Logstash, and an analytics and visualization platform called Kibana. The three products are designed for use as an integrated solution, referred to as the "ELK stack"
The ElK stack is an example of the trend towards open source. ELK supports many different log management and analysis use cases including typical IT operations, customer support, website traffic, business intelligence, security events, and user behavior.
The following list shows how various companies are using ELK right now:
MEDIA AND ENTERTAINMENT:
The company chose Elastic search for its automatic sharding and replication, flexible schema, nice extension model, and ecosystem with many plugins.
Medium is one of the most popular modern blog-publishing platforms. They use ELK stack to debug production issues and detect DynamoDB hotspots.
LinkedIn uses ELK to monitor performance and security. Their ELK operations include more than 100 clusters across more than twenty teams and six data centers.
HipChat uses Elasticsearch as a search backend for horizontal scalability with processing a large amount of data and handle multiple customers as they are well known for internal and private enterprise chat service.
Swat is a popular social management system that supports large enterprise marketing teams. Swat uses ELK to store its sites’ traffic activity. This helps to control and forecast the growing cloud costs that are driven by new user demands.
Ebay is subscribed partner of Elasticsearch. They adopted Elasticsearch to handle all of their search functionalities across the business.
Tripwire is a worldwide SIEM (Security Information Event Management) leader. Tripwire uses ELK to support information packet log analysis.
EDUCATIONAL AND LEARNING
Stack Overflow uses Elasticsearch as a means to support full-text search capabilities.
The IFTTT operations team uses Elasticsearch for real-time monitoring and receiving alerts on API events.IFTTT is a free web-based service that allows users to create chains of simple conditional statements.
Elastic Search has been adopted by some major logos, including the following:
There are some use cases where Elastic Search is well-suited for performance:
- Text Search:
Elastic search is primarily used when there is a lot of text. It’s used to search any data for the best match with a specific phrase, full-text search.
Auto-complete for the search by completing a search box on partially-typed words, which are based on previous searches.
- Auto Suggest:
Allow a user to start typing a few characters and receive a list of suggested queries as they type. This reduces the number of incorrect queries, particularly because many users may be searching from a mobile device with small keyboards.
- Spell Checker:
Automatic spell correction based on whether the misspelled term exists in the index. Elastic returns a suggested query that might produce better results as hint and can be shown to user.
- Geo-location Search:
Elastic search can be used to Geo-localize product.
1. All the restaurant that serves pizza within 20 miles
2. Create a distance map for the search posted.
Faceted search is a technique for accessing information organized according to a faceted classification system, allowing users to explore a collection of information by applying multiple filters. Elastic search is used for same purpose where a user can define filters to get different results according to requirement.
Evaluate search queries of the users and provide suggestions for their searching experiences.
Migration option with recommended approach for user when looking to change current search system to elastic search.
- Logging and Analysis:
Elastic search provides analysis system:
Keep a log of search queries, store and centralize logs from various sources.
2. Analysis of time-series data ( such as social media)
- Enterprise Search:
Enterprise search includes-
1. Document Search
2. E-commerce product search
3. Blog search
4. People search
5. News search
6. Site search in all its forms.
Elastic Search is generally fantastic at providing approximate answers from data, such as scoring the results by quality. Finding approximate answers is a property that separates elastic Search from more traditional databases.
FEATURES AND HIGHLIGHTS:
· Lucene AS BASE
Lucene is the base of elastic search as elastic search is built on top of Lucene, which is an information retrieval library that let elastic to provide powerful full-text search.
· Higher performance result
Elastic Search stores real world complex entities as structured JSON documents. This document oriented approach with indexing of fields managed the complexities and results in improved performance of search system on the site.
· TEXT SEARCH
This carries implementation of many features:
1. Splitting text into words
2. Customized stemming
3. Facetted search
· FREE OF SCHEMA
Elastic Search stores JSON documents with indexing data to detect data structure, hence its schema-free.
· RESTFUL API
In Elastic Search, actions are performed by simple Restful API.
· Change LOG
With Organized Indexes - clusters and nodes, elastic search records any changes made in transaction logs and hence minimize chances of data lose in the system.
· ELASTIC SEARCH PLUG-INS
Elastic Search offers a highly useful plugin mechanism as a standard way for extending its core. Plugins enable developers to add a new functionality.
An example of few plugins are:
Big Desk, Head, HQ, Kopf, and Paramedic.
Benefits with Elastic search:
· MANAGEDS LARGE VOLUME OF DATA
Comparing to traditional SQL database management system that would take more than 10 seconds using SQL to give full-text search results on the same hardware, Elastic search will return results in under 10 milliseconds.
· Manage information via indexing
In indexing operation, Elastic search converts raw data (such as log files or message files) into internal documents and stores them in a basic data structure similar to a JSON object. Each document is a simple set of correlating keys and values: the keys are strings, and the values are one of the numerous data types—strings, numbers, dates, or lists.
· Fast and Direct access
The documents are stored in close proximity to the corresponding metadata in the index. This design greatly reduces the number of data reads and hence increases the search result response.
Distributed architecture of Elastic search enables it to scale up to thousands of servers and accommodate petabytes of data. The end user needs not to manage the complexity of distributed design as its automatic.