Overview
What is Elasticsearch?
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Learn from top reviewers
Elasticsearch is your way to go!
Great search, aggregation and visualization products.
Elasticsearch Overall Review
Elasticsearch is a tricky, but great data platform
Elasticsearch Observability Enables an Outstanding Capacity To Transform IT Operations
Search begets Search - Navigating your data progressively
Elasticsearch OSS Review
Elasticsearch Review
Elasticsearch: for searches, you know!
Elasticsearch: Open-source, Fast, Excellent!
Elasticsearch helps you find the information you need!
Brilliant search powerhouse
Elastisys simplified understanding our customers' production workloads
Elasticsearch is a great product!
Reliable and affordable solution which is figuring as a industry pattern for managing huge data searching.
Awards
Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards
Reviewer Pros & Cons
Pricing
Standard
$16.00
Gold
$19.00
Platinum
$22.00
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
How to create data views and gain insights on Elastic
Setting Up a Search Box to Your Website or Application with Elasticsearch
ChatGPT and Elasticsearch: OpenAI meets private data setup walkthrough
Product Details
- About
- Tech Details
- FAQs
What is Elasticsearch?
Elasticsearch now features generative AI search capabilities. Elasticsearch Relevance Engineâ„¢ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
- Applying advanced relevance ranking features including BM25f, a critical component of hybrid search
- Creating, storing, and searching dense embeddings using Elastic’s vector database
- Processing text using a wide range of natural language processing (NLP) tasks and models
- Letting developers manage and use their own transformer models in Elastic for business specific context
- Integrating with third-party transformer models such as OpenAI’s GPT-3 and 4 via API to retrieve intuitive summarization of content based on the customer’s data stores consolidated within Elasticsearch deployments
- Enabling ML-powered search without training or maintaining a model using Elastic’s out-of-the-box Learned Sparse Encoder model to deliver highly relevant, semantic search across a variety of domains
- Combining sparse and dense retrieval using Reciprocal Rank Fusion (RRF), a hybrid ranking method that gives developers control to optimize their AI search engine to their unique mix of natural language and keyword query types
- Integrating with third-party tooling such as LangChain to help build sophisticated data pipelines and generative AI applications
Elasticsearch Video
Elasticsearch Technical Details
Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
---|---|
Operating Systems | Unspecified |
Mobile Application | No |
Frequently Asked Questions
Comparisons
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Reviews and Ratings
(206)Community Insights
- Business Problems Solved
- Pros
- Cons
Elasticsearch has become an essential tool for users across various industries and domains. Its distributed architecture enables efficient searching of large datasets, even with partial text matches and across multiple fields. This capability makes it invaluable for tasks such as logging and analysis in cloud environments, where managing hundreds or thousands of servers is a necessity. Elasticsearch's fast and powerful search capabilities find application in B2B and B2C eCommerce websites, allowing users to search by various criteria like title, artist, genre, price range, and availability date. It serves as a reliable solution for tracking logs, incidents, analytics, and code quality. Additionally, Elasticsearch's ability to index and search large sets of data facilitates the creation of reporting dashboards. The product's built-in data replication features ensure data availability and easy retrieval while its scalability supports operational needs. It also enables tokenized free text search in audio transcripts as well as indexing and analyzing HTTP Request Response messages to detect security threats. With its wide range of use cases spanning from web search engines to scientific journals and complex data indexing, Elasticsearch proves to be an indispensable tool for organizations seeking efficient data storage solutions.
Highly Scalable Solution: Elasticsearch has been consistently praised by users for its highly scalable nature. It is able to handle storing and retrieving large numbers of documents, offering redundancy and distributed storage across multiple hosts with minimal configuration required.
Extensive Search Capabilities: Users highly praise Elasticsearch for its extensive search capabilities, especially in terms of full-text search. They find it easy to search and filter through millions of documents efficiently, even on large datasets, thanks to its fast search speeds.
Valuable Aggregations and Facets: Elasticsearch's support for aggregations and facets is highlighted as a valuable feature by users. They appreciate the ability to progressively add search criteria to refine their searches and uncover trends in their data.
Configuration Process: Users have encountered difficulties when implementing custom functions and have found the configuration process to be lacking. Some reviewers have mentioned challenges in integrating different elements of the program, incomplete documentation, and misleading forums.
Query Editor Limitations: Users have experienced issues with the query editor and noted that certain queries are not supported in the IntelliSense feature. Several users expressed frustration with inadequate documentation, hard-to-debug problems, and the complexities involved in tuning for ingress performance.
Learning Curve: Users have found the learning curve to be challenging, particularly for those with a background in SQL. Many reviewers mentioned a steep learning curve, extensive documentation requirements, and complexities related to mapping and data type conversion.
Reviews
(1-25 of 44)Great search, aggregation and visualization products.
Elasticsearch Overall Review
Elasticsearch is a tricky, but great data platform
- Apache Solr and MongoDB
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
Elasticsearch Observability Enables an Outstanding Capacity To Transform IT Operations
With Elasticsarch, specialized support teams can easily view all the relevant information by using real-time dashboards, and can immediately start the initial analysis to isolate and mitigate issues.
Search begets Search - Navigating your data progressively
Elasticsearch Review
Elasticsearch: for searches, you know!
Elasticsearch: Open-source, Fast, Excellent!
Elasticsearch helps you find the information you need!
Brilliant search powerhouse
Elastisys simplified understanding our customers' production workloads
Elasticsearch is a great product!
- Google Search Appliance, Amazon Elastic File System (EFS) and MS SharePoint
- SharePoint seems antiquated.
- Amazon Elastic File System is hard to find things.
- Google Search Appliance does ranking poorly.
Reliable and affordable solution which is figuring as a industry pattern for managing huge data searching.
Win quickly with Elasticsearch
- Apache Druid and InfluxDB
Elasticsearch is the future!
Lucene: Elasticsearch is built using Lucene instances for each index (the ES code essentially just glues together tons of Lucene instances), so it's not a fair comparison. But I suppose if you wanted the flexible data-model and you don't need the system to be distributed and highly available and parallel, Lucene would be a good choice.
Very useful for eCommerce
An amazing search engine
Elasticsearch, centralized logs and anomaly detection, easily deployed.
ElasticSearch handles a large number of requests quickly and easily
Redis is great at SET operations on large sets of data and quick in-memory operations. We actually use Redis for a small subset of tasks in our product that wasn't appropriate to perform on ElasticSearch. In this case, it was much faster and cheaper to use Redis.
Elasticsearch: A Great Lab / Development Platform for Data Architects and DevOps
- Logstash, Redis, Jenkins, Ansible, Puppet Enterprise (formerly Puppet Data Center Automation), Chef and Loggly