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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

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

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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

(26-47 of 47)
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Tarun Mangukiya | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Elasticsearch has a very fast an efficient searching process. If you've searched a heavy project, you can't just be dependent on databases. Plus, they have a REST API for everything, making it easy to use with any programming language or database.
Brett Knighton | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
If you are in a scenario where you are constantly trying to optimize queries to get better performance from your database searches, Elasticsearch is probably a product worth trying out. With the amount of data we have, doing text searches via SQL isn't even an option. If you aren't struggling with getting reasonably fast queries getting Elasticsearch up probably isn't going to be worth the hassle.
David Greenwell | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
The best situation where we have found elasticsearch to help was when you have searches and your database just isn't doing them with the speed that you want, and even where the DB is going the speed needed Elasticsearch can take some of the processing from the database(which isn't necessarily built specifically for searching) to a system that was designed for searches.

If you are doing searching, then I would suggest going with Elasticsearch.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Elasticsearch is a great solution if you want lightening quick querying of data, especially text-based querying. If you are doing a lot of writing/updating to your database, this is not the best use case and you may want to evaluate other NoSQL solutions.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
If you are building an application that requires fast retrieval, Elasticsearch would provide an excellent backend database. The distributed architecture provides high-availability and data replication natively without a large performance sacrifice. Elasticsearch also runs on minimal hardware requirements when compared to other DB solutions.
October 04, 2017

Elasticsearch review

Manish Rajkarnikar | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Elk is great for app logs and search. It comes with Kibana which is great query tool. Logstash is great. It can autodetect datatype but can be tuned if needed which is awesome. It has lots of integrations such as filesystem, syslog, kafka etc., which make setting it up a breeze. It is also sometimes used for metrics. But [I] would rather use timseries db such as influx db, prometheus for metrics. Using logs for metrics tend to be expensive and inefficient.
Devaraj Natarajan | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
I have noticed Elasticsearch is good in following scenarios:
Faster Aggregation
Full-text search features
Scalable
Great performance
Stability
Complete Ecosystems of applications

It could have been slightly better in handling indexing. (Should index all the items and create index overhead)
Better load balancing
Elasticsearch aggregations are not always precise, because of how data in the shards is placed
Colby Shores | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
ElasticSearch is hands down, the absolute best solution for logging in a virtualization environment. The Kibana front end to ElasticSearch is extremely intuitive, even computer novices can be trained on how to chain together tags in the Apache Lucene syntax to extract the data they need. Once the deploy process is nailed down and system is engineered, the logging structure can remain fairly static until the next major revision. Compared to Splunk, with an administrator well versed in the ElasticSearch suite, will save an organization upwards of 10's of thousands of dollars a year even with the caveats mentioned earlier.

As a developer looking for a quick and simple search engine which has little configuration required, ElasticSearch is fast and perfect for that solution. Literally throw JSON records in to the database and push a request to get JSON out, exceptionally straightforward.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
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.
Yasmany Cubela Medina | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Elasticsearch is a great choice for search scenarios, for architectures that heavily rely on search capabilities. It's also great for analytics using Kibana. It's also great for log aggregations and again search. It can be even used as the main database layer for critical search infrastructures. But you need to [take] care with data that may change the structure and type of fields.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
It does the thing that it was designed for (quickly searching through bulk load of data) very very well. Also, it's scalable and flexible. Don't try it for other cases. It's not a NoSQL data store where you will want to store and retrieve data. Also, don't try any complex computations. That will make the retrieval slow and the benefits will be lost.
Abdel Kamel | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Elasticsearch does one thing very well. Search and index data. Trying to go outside that realm is doable but not recommended. For example, I would not use elasticsearch as a document store. But rather treat it as a rebuildable index that can be rebuilt from a persistent database like Postgres, or MySQL.
Ivan Portugal | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Web app analytics is a great example of use for it because logging messages isn’t necessarily structured. Elasticsearch does a great job of indexing structured or unstructured data. Think of Elasticsearch and Kibana being an open source "Splunk" replacement. It may not be appropriate to use Elasticsearch for true real-time data. It is not a time series database although it may be used as one. Perhaps a better solution for time series data would be InfluxDB or Graphite, whereas Elasticsearch is more of a search engine.
Shannon Donohue | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Elasticsearch is good for any production stack for data analysis, and error monitoring and alerting. The only thing you need is an engineer who's willing to dig through log lines, write queries, and build graphs which accurately track the health of your production systems. I equate this tool to something like New Relic, where if used the right way can provide a lot of insight. If used incorrectly, it doesn't do a whole lot out of the box. It needs to be set up by someone who knows the system and cares to monitor it.
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