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 …
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What is Elasticsearch?
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
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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 Enterprises (1,001+ employees).
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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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(26-47 of 47)Companies can't remove reviews or game the system. Here's why
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.
March 01, 2018
Enthusiastic for Elasticsearch
We decided to start looking into Elasticsearch after we had good success with using lucene (the full-text search indexer that Elastic uses). We had some queries in Oracle that were running EXTREMELY slow and knew we had to do something for the customer to make their experience better. We had a few thoughts on what we could use and Elasticsearch fit what we really wanted.
- Searching, it does it well and searches are fast...real fast.
- Ease of use, we were able to get an Elasticsearch cluster up and running in a half hour and doing basic searches after that was very easy with simple requests
- Redundancy built in and stability. We haven't had any of our Elastic clusters go down intentionally, but testing out redundancy by removing nodes Elasticsearch has gone flawlessly.
- Only breaking changes between versions when they are absolutely necessary.
- Works well with .Net libraries that are supported and coded by Elastic.
- A bit more of a learning curve for complex searches, indexing more complex things.
- Some of our updates between versions haven't gone as smoothly as we would like, but in more recent versions Elastic has done a much better job at trying to allow for full uptime upgrades.
- Configuration needs to be set up to do larger searches, or more complex searches and at times while starting it wasn't obvious what configuration needed to be changed.
January 23, 2018
Need fast querying of text data? Go with Elasticsearch!
We use Elasticsearch to store data for quick querying of our various data sets via our APIs. It has allowed us to write APIs that perform much faster compared to their older versions that had complex relational queries.
We also use Elasticsearch to store log data for fast querying via Kibana.
We also use Elasticsearch to store log data for fast querying via Kibana.
- Very fast querying of data, especially text based searches.
- Nice clustering of nodes built in, to ensure a stable, redundant environment.
- Great integration with Kibana for visualizing and exploring data.
- Query syntax can be hard for developers to pick up, especially if they are used to SQL.
- Tooling leaves a lot to be desired, especially compared to the RDMS tooling that is out there.
- Updates to Elastic search data aren't the fastest, especially compared to some other nosql solutions like MongoDB
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.
November 10, 2017
ElasticSearch : a powerful and complete tool
Elastic Search is used in our organization to index Oracle Data. As there is a huge volume of data, Oracle Database is not able to respond quickly to our request. What we did is to index Oracle Data with ElasticSearch and key ElasticSearch to retrieve Data into a Web application to monitor TIBCO BW flows.
- It is built on Lucene. It allows very complex and complete text searches.
- It is an open source product and very easy to install.
- It is easily scalable. It needs few configurations to do that.
- The solution is immediately ready on the cloud.
- There's not much control over consistency of your data
- Complex searches queries are not obvious to all users. The syntax is very heavy
- Administration and monitoring of ElasticSearch are complex
October 04, 2017
Elasticsearch review
Elasticsearch is used across the whole org. It's used mainly for storing and searching application logs. We have many elastic clusters set up differently. Sometimes it's one cluster per app; sometimes it's one cluster for many apps; depending upon the volume of data being generated. Elasticsearch is used mainly for debugging purposes rather than metrics, but sometimess it's used along with Kibana to visualize metrics also.
- Elasticsearch search with its clustering solution provides a scalable logging solution. A number of query nodes, data node and master node can be added on demand to make the whole system very scalable making it possible to store and search terabytes of data.
- Elasticsearch provides logstash, file beat, and many others. It makes it really easy to ingest a log with less setup.
- Elasticsearch query language is based on Lucene and is very powerful.
- Elasticsearch is mostly free except a few features such as authentication and authorization; making it really financially economical for companies to deploy it on large scale.
- Elasticsearch doesn't have a free alerting solution. It has elastalert but it's not comparable to the paid version.
- It's lacking authentication and authorization which makes Graylog a more enticing option.
- It's lacking a mechanism to protect cluster against runoff queries. Can bring down cluster to its knees.
September 15, 2017
Developer's Elasticsearch Review
Elasticsearch is currently in our organization for multiple use cases. With the data volume growing huge and rapidly, we push the data into an Elasticsearch cluster setup. We collect logs from multiple systems and push into E C using logstash and few other message brokers system. We collect telemetry from multiple systems and run algorithms to analyze the data.
- Indexing
- Text analysis
- Time series data handling
- Connector to other big data software
- Plugins to visualize the data other than Kibana
- Better query editor
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.
June 13, 2017
Powerful and resilient database
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.
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.
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
April 05, 2017
Incredible ROI, Easy to set up
We use Elasticsearch to index large sets of data for an extremely fast and searchable database for reporting dashboards. We use Elasticsearch for several of our client projects as well as internal projects here at Xertigo.
- Searching for data across many database tables.
- JSON Response makes it easy to implement on different platforms.
- Plenty of documentation.
- Searching by dates seems a bit complicated.
- Attributes across indexes need to be the same type. Can be very cumbersome.
April 05, 2017
Elasticsearch - Great tool for search applications
We use ElasticSearch for the search functionality in our application. We have a lot of data to search from and ElasticSearch makes it ridiculously fast by tokenizing the content. It enables us to do free text search in a large blob of audio transcripts that we have.
- Easy to scale - It's designed to be used across distributed environments. Indexes can be divided into shards, with each shard able to have any number of replicas.
- Search queries can be structured as JSON objects (in addition to text strings) that enables complex and robust searches.
- If your application needs an effective solution for dynamic searching, I think ElasticSearch is the way to go.
- If you want to store or retrieve data outside of searching, you may want to try a different solution since ElasticSearch's capabilities are limited.
- If you want to do large or complex computations with the data, ElasticSearch isn't really good at that.
- ElasticSearch shouldn't be the primary source of data because data backups and durability are not high priority.
April 04, 2017
Find things with Elasticsearch
The first use case is log aggregation. We have a number of micro-services running, some of them in Docker, and we use the ELK to ensure we have easy access to our most recent logs. This proves invaluable for fault detection and diagnosis and is used primarily by engineers. Another use case in a customer-centric search index. Each of our customers is described by a set of data points that come from various sources and are indexed in Elasticsearch. The index is later used by marketing, customer service, and other departments to get quick insights on our customer base.
- Flexible and advanced search.
- Ease of creating time-based indices and automatic archiving of old indices.
- Quick data ingestion.
- Configuration. Looking forward to seeing Elasticsearch detecting hardware specs and self-adjusting its config.
- The lack of _changes streams. They were promised to appear in 2.0...
- The price of the hosted solution could be lower.
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
We use Elasticsearch as a search provider for our ecommerce software. Our search, category and navigation pages are rendered from ElasticSearch.
- More relevant search results. There are lot of in build algorithms that are part of Elasticsearch. Using these algorithms improved search results.
- Decrease in the page load time since read operation is very fast.
- Easy to implement when compared to other software.
- Installation and configuration of Elasticsearch on windows server is not straight forward.
- Completion suggester algorithm in Elasticsearch (v 2.0) saves information in memory. So any deletes/updates are not reflected immediately unless a flush command is executed. Execution of flush command is not advised by Elasticsearch team.
- Elasticsearch Nest API code is not updated to match with Elasticsearch release version. So we have to write our own implementation.
June 10, 2016
Elastic - ruler in the index world
We use Elasticsearch in our Web-Payment Fraud and Security Solution. We index every Http Request Response message of our customers' eBanking applications to analyze for fraud/malware/security threats. We then provide flexible and robust analytics on their data including free text search, reporting and real time data insights.
- Free text search. Query String Query is totally awesome and allows complex search in real time.
- Very scalable and highly configurable, there is no scalability problem we couldn't solve.
- Aggregations are great for analytics and we utilize them in our proprietary reporting tool.
- Aggregations scalability - elastic search doesn't do a very good job in protecting its cluster from bad queries. Circuit breakers are good, but to completely protect ourselves we had to implement our own mechanisms.
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