Great search, aggregation and visualization products.
We use ECE platform and Elasticsearch for the delivery data to track delivery. And also use kibana for visualization of business analysis …
Starting at $16 per month
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What is Elasticsearch?
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
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Standard
$16.00
Cloud
per month
Gold
$19.00
Cloud
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Platinum
$22.00
Cloud
per month
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- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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How to create data views and gain insights on Elastic
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Setting Up a Search Box to Your Website or Application with Elasticsearch
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ChatGPT and Elasticsearch: OpenAI meets private data setup walkthrough
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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.
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 Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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 Mid-sized Companies (51-1,000 employees).
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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!
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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.
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Reviews
(1-18 of 18)Companies can't remove reviews or game the system. Here's why
We use Elasticsearch to Index and make available for Search and Navigation our proprietary data on the M&A landscape. It drives dashboards and alerts to allow users to monitor trends and the latest events that occur in our dataset. It aligns our research group with our bankers. We marry it to Couchbase and MS SQL-Server.
- Indexing text data
- Aggregations allow users to progressively add search criteria to refine their searches
- Find trends in our data with Aggregations
- Integrate text Search our taxonomy Search
- Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
- Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
- Schema changes require complete reindexing of an index
April 01, 2021
Elasticsearch Review
It is used in our custom software application for advanced searching and filtering capabilities for our users.
- It allows extremely fast search and filtering on large datasets
- It has a very powerful aggregation engine that can allow for tons of customizable analytics and reports.
- The documentation could be a bit more detailed and have more examples, especially for advanced functionality.
- The ability to update/change existing live field mappings would be nice.
- The ingest pipeline structure is a bit more complicated and confusing than previous implementations for using things like attachment plug-ins.
October 09, 2019
Very useful for eCommerce
Elasticsearch is used on our B2B and B2C eCommerce websites to provide fast and powerful search capabilities for products. Search by title, artist, or various facets like genre, price-range and availability-date results in a list of products that the user can then drill down or continue searching within the result list. Within the organization, Elasticsearch is used by the programmers in the IT department.
- Search results are provided very quickly.
- The search results are accurate.
- Search results contain details on the accuracy of each hit.
- There is a steep learning curve for this product so what is most useful for developers is good documentation including examples and sample applications.
June 06, 2019
An amazing search engine
Elasticsearch is being used for multiple purposes in multiple projects: centralized log management, APM, Metrics Collection as a TSDB, and as a replacement for traditional OLAP databases. It provides a high-performance indexing and search engine, which has become an invaluable tool addressing hard problems that would otherwise be very difficult to solve.
- Ingress and indexing.
- Searching.
- Aggregations.
- Aggregations on top of other aggregations.
- Encryption at rest.
- Has a performance penalty when using inked documents.
Elasticsearch has been a big help for us. We used to work with Application Performance Management Tools that need another layer of visualization and data treatment, and with Elasticsearch we have delivered better insights for our customers.
We use Elasticsearch at our Technology & Services Department to address these issues for our customers:
- Customized Dashboards.
- Anomaly Detection.
- Metrics Predictability.
We use Elasticsearch at our Technology & Services Department to address these issues for our customers:
- Customized Dashboards.
- Anomaly Detection.
- Metrics Predictability.
- Anomaly detection. It can find patterns over a wide variety of metrics and values.
- Behind the walls, Elasticsearch has a robust distributed architecture to support queries and data processing, and it is easy to maintain and scale.
- Elasticsearch has a new Elastic Cloud SaaS solution which is very easy to deploy, set up, and scale with all features and more.
- Elasticsearch has an important security layer to separate access to data and dashboards.
- If you want to explode Elasticsearch's capabilities, you need to have a medium-high SQL and Database knowledge.
- The user interface is heavy in Java requirements, and sometimes you can get some lag displaying heavy results for heavy queries.
- It will be helpful if you can construct Logstash queries with a drag&drop based user interface.
January 10, 2019
The Best Available
It provides a distributed, multitenant-capable, full-text search engine with an HTTP web interface and schema-free JSON
documents. We use this in our IT department, but also resell it as part of a predictive AIOps platform that offers automation for many of the tedious tasks that data center staff struggle with every day.
- Search
- Correlation
- Analysis
- Big data
- Pagination
- Presentation
- Mapping
October 15, 2018
The gold standard for text-based search
We use Elasticsearch to power a web search engine that allows users of our web platform to search for products, content, and more. With Elasticsearch we were able to quickly and effectively develop and deploy a search solution that is fast, scalable, and was a breeze for our developers to implement.
- Lightning fast
- Easily scalable
- Powerful feature set
- Additional complexities when in need of frequent & rapid updates to the Elasticsearch data set
- New syntax can be confusing, particularly with advanced features and more powerful queries
Elasticsearch is being used for multiple purposes at Iconscout. Starting from a search engine to viewing detailed analytics. We're even using it for logging of the server. It helps us to query through the millions of data easily and efficiently.
- Fast Search through millions of data
- Uses a very limited storage to store the data - high compression
- Easy to get started & configure
- Their documentation needs a lot of imporvement
- Difficult to understand query language
- New updates are difficult to adopt
April 13, 2018
Find more faster with Elasticsearch
We use Elasticsearch to efficiently search large pools of data. Elasticsearch gives us the ability to have blazing fast searches even when doing partial text matches on multiple fields.
- The best solution we've found for blazing fast searches, especially text-based.
- Easy to add nodes for data redundancy.
- Good documentation makes getting up and running easy.
- I found the learning curve fairly difficult having a SQL background.
January 18, 2018
Elasticsearch for Log Management
We utilize Elasticsearch (with Kibana and Logstash) to provide log management services internally and as an offering to our IT clients. This helps clients meet compliance regulations requiring log review and SIEM implementation without paying the premium at other high-end products. In essence, Elasticsearch allows us and our clients on the platform to gain greater visibility into their applications and endpoints.
- Elasticsearch has a great ecosystem and user base.
- Elasticsearch is easy to use and set up (once you have the basic training).
- The document/searching focused feature of the database is perfect for log management (or any searching) application.
- I wish many of the features in the X-Pack were native.
November 14, 2017
Excelent choice for data analytics and search engine
Elasticsearch helped us to provide comprehensive reports, and frequent queries on our data (millions of rows), provided us a performance that we could not achieve before (though we have only 40 concurrent users at most) We also consolidate data from many sources within our company, and elasticsearch made it easy for us to do data analyzing, to have many useful insights of our data; things that we could never do (so easily) in the past.
- Comprehensive reports and queries
- Data analytics
- A better way to provide custom functions. I struggled with implementing the PercentileExc (exlusive) funtion, the one that Excel provided, because the business users requested it.
- Better IntelliSense in development console, when the query is complex, I often lost the IntelliSense feature. The “exists” query is not supported by IntelliSense.
August 31, 2017
ElasticSearch is a simple straightforward search engine that literally anyone can get started with!
We use Elasticsearch as the storage/search component of our logging infrastructure (ElasticStack). Once we have broken apart the individual variable components of each log as their own variable type using Logstash, we store those records in to Elasticsearch. Kibana queries Elasticsearch to display the resulting data. We also utilize Elasticsearch to display the cluster status for each of our markets across our entire web cluster using an internal reporting tool we wrote.
- Effortless to set up. Literally set the memory thresholds for Java and start throwing JSON formatted records in to the database, it "Just Works". Even clustering is automated as the cluster finds other ElasticSearch servers on the network and assigns each a name.
- Very simple to use interface either through it's RESTFUL API (ala Curl) or via its speedy protocol on port 9300. Once records are added, the very easy to use Apache Lucene syntax is supported to extract data.
- It's search capabilities are fast on huge datasets, even on very modest hardware. Our organization operates in the hundreds of servers taking thousands of requests a second, each with it's own log w/ a 2 week retention. The ElasticSearch server we recently decommissioned was Pentium 4 Netburst class Xeon, it rarely skipped a beat.
- Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
- The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
- This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.
April 13, 2017
One of the best search engines out there
Elasticsearch its a critical piece of our platform. We rely on it not only for searching of our documents (that is 80% of our business goal and most used feature) but for tracking logs and analytics with Kibana. Elasticsearch allows us to build this amazing search component that gets the user the refinement they want so they can find easily and quickly the results they are looking for. Monitoring our logs is almost that important; we track incidents and code quality among others.
- Search
- Organize data
- Scale
- Mapping and data type auto conversion
June 18, 2016
Elasticsearch is the way to go!
We used Elasticsearch to build and search a complex index of tv shows, actors, seasons, episodes etc... Using Elasticsearch we can derive information very quickly about what season belongs to which tv show. This allowed us to dynamically build a tree like data structure on the fly without any performance degradation.
- Fuzzy query matching
- Indexing and Sharding data
- High availability and cluster managment
- A better user interface
- Better integration with AWS
June 10, 2016
Stretch Your Ambitions With ElasticSearch
We use ElasticSearch for multiple projects across our company, everything from development proof-of-concept efforts to large production systems supporting real-time data ingestion and multiple simultaneous users. ElasticSearch is our go-to data storage solution for anything requiring a responsive web interface. While it's full text search capabilities are its most often touted feature, we get more use out of its rapid search aggregations (formerly facets) and its scalability for large data sets.
- Store large numbers of documents in a redundant, distributed fashion across multiple hosts. It handles sharding out of the box with a minimal amount of configuration.
- Extensive search capabilities, particularly full text search. It also supports aggregations/facets and geospatial searching.
- Native REST API is great for web applicaitons.
- The online documentation is very difficult to use, both as a teaching tool and as a quick reference. The search syntax is arcane and not particularly "human friendly" and examples from the documentation are often insufficiently detailed to apply directly.
- ElasticSearch is touted as "schemaless" when in fact mappings (aka schemas) are required for all but the most basic use cases.
June 07, 2016
Elasticsearch - go for it!
We get a lot of scientific journals in pdf format. Windows only allows title search. Some scientists use Mendeley but there is a licensing cost involved. We implemented Elasticsearch to help the scientists to search by author or look for keywords in the title or in the content. And we have provided options to look for an exact match as well as partial match.
- The snippet that we get back before and after the search words is very helpful for the scientists to get the right content.
- At my previous job with a simple 3 node cluster, Elasticsearch did not do a good job in electing a new master, when the master node went down. Many times, I had to stop and restart all the nodes to make it function again. I have heard implementation models where customers had dedicated multiple nodes just for master.
The oil and gas web application is heavily used for monitoring active wells. We need app-specific analytics based on user behavior and error context. Elasticsearch is used to collect arbitrary information during production. Kibana is used to view these messages in an effort to "fix" the app before the user is able to submit a ticket (proactive feature and defect resolution).
- It indexes anything. Just use structured logging to begin sending messages to it.
- Kibana, the UI for it, allows you to easily build dashboards with real-time widgets.
- The REST API for Elasticsearch is well-written, should you choose to incorporate the data on your own custom application.
May 10, 2016
How Elasticsearch changed our culture
We use Elasticsearch in tandem with Logstash and Kibana, in order to graph trends through log line analysis. The tool has become invaluable as we can peer into data on a deeper level, and set up alerts if there is a high frequency of errors. This becomes useful to study how changes positively or negatively impact production.
- Consolidate data
- Searchable
- Graphable
- Kibana GUI could use some work, better than Logstash though
- URL shortening was just released
- Graph coloring was just released