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Elasticsearch

Elasticsearch

Overview

What is Elasticsearch?

Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.

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Recent Reviews

TrustRadius Insights

Elasticsearch has become an essential tool for users across various industries and domains. Its distributed architecture enables efficient …
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Awards

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Reviewer Pros & Cons

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Pricing

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Standard

$16.00

Cloud
per month

Gold

$19.00

Cloud
per month

Platinum

$22.00

Cloud
per month

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Demos

How to create data views and gain insights on Elastic

YouTube

Setting Up a Search Box to Your Website or Application with Elasticsearch

YouTube

ChatGPT and Elasticsearch: OpenAI meets private data setup walkthrough

YouTube
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Product Details

What is Elasticsearch?

Elasticsearch is 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 data for fast search, fine‑tuned relevancy, and analytics that scale.

Elasticsearch now features generative AI search capabilities. Elasticsearch Relevance Engine™ (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.

ESRE combines AI with Elastic’s text search to give developers a full suite of sophisticated retrieval algorithms and the ability to integrate with large language models (LLMs). It is accessed through a single, unified API.

The Elasticsearch Relevance Engine’s configurable capabilities can be used to help improve relevance by:

  • 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

What is Elasticsearch?

Elasticsearch Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.

Reviewers rate Support Rating highest, with a score of 7.8.

The most common users of Elasticsearch are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(205)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

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.

Attribute Ratings

Reviews

(1-1 of 1)
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Score 10 out of 10
Vetted Review
Verified User
Incentivized
We have used Elasticsearch for indexing both large and small documents for rapid searching and retrieval. Our other services analyze the documents we index in Elasticsearch to look for interesting information that can help us and our customers make informed decisions.

We also enjoy leveraging the built-in data replication features to keep our data as available and easily retrievable as possible.
  • Indexing. Elasticsearch can index thousands of documents per second.
  • Searching. Elasticsearch provides plenty of options for querying your data to get just the right information back.
  • Scalability. Elasticsearch has built-in features for replicating data and distributing load, so you don't have to invest a ton of time and effort into third-party or customized clustering and/or sharding solutions.
  • Backup. Elasticsearch has built-in options for backing up your data. If you're dealing with a large cluster, backing things up can get rather interesting from a storage perspective, but Elasticsearch has worked very well for us thus far.
  • Recovery. If part of your cluster goes offline, Elasticsearch generally does a decent job of staying online and recovering from the outage. Occasionally you'll lose nodes that house all copies of a given set of shards (which isn't fun), but Elasticsearch still handles that situation as well as can be expected.
  • Elasticsearch can struggle if you're trying to create too many new indexes at the same time.
As the name implies, when you need to search thousands, millions, or billions text-based documents for keywords, Elasticsearch is great. The way it indexes and internally analyzes the content of your documents is very powerful. Assuming you have enough servers in your cluster with fast enough storage, querying those documents becomes a breeze.
  • We would not be able to do what we do without Elasticsearch. Our business revolves around it.
We first started out experimenting with PostgreSQL's fulltext searching capabilities for our project. As our dataset grew, PostgreSQL began to slow down too much for our purposes. The simple fact that Elasticsearch has built-in clustering and replication was enough for us to switch away from PostgreSQL very early on in our development efforts.
  • Indexing. Simply toss documents at the REST API, and Elasticsearch will figure out what to do.
  • Querying. There are tons of options for querying your data.
  • Scrolling through hundreds or thousands of results can be a bother.
No
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching.

If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
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