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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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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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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(1-17 of 17)Companies can't remove reviews or game the system. Here's why
March 31, 2024
Great search, aggregation and visualization products.
Score 9 out of 10
Vetted Review
Verified User
We use ECE platform and Elasticsearch for the delivery data to track delivery. And also use kibana for visualization of business analysis and KPI. We also ingest the log from different API and investigate when there is a trouble. We also use transform and machine learning feature to detect anomalies.
- Full text search
- aggregation
- anomaly detection
- dashboard
- canvas
- SIEM
- Ingest API
- The performance for a large cluster
- business analysis
November 09, 2021
Elasticsearch is a tricky, but great data platform
We use Elasticsearch (Elastic for short, but that includes Kibana & LogStash so the full ELK kit) for 3 major purposes:
- product data persistence - as JSON objects.
- as log storage - different components produce log files in different formats + logs from other systems like the OSes and even some networking appliances.
- as test automation results storage & reporting platform - this is an implementation we glimpsed from an old Trivago blog post.
- Data persistence & retriveval
- Data indexing
- Metrics & reporting over data thanks to its query language & Kibana visualization
- Flexibility of data sources - a lot of existing "beats" + ability to push custom data easily
- Very scalable - although a minimum of 3 nodes is advised, even a 1-node installation can work great for some use cases.
- Licensing - this is big issue with a lot of companies that try to embed Elasticsearch as a part of their products and not have to expose that explicitly or deal with licensing complications.
- Security - this is not a feature enabled by default so installations can go and be unsecure & thus exploited without anyone noticing.
- Having security turned off can be beneficial for some performance optimizations though.
- Cluster restructuring/upgrading - if you need to do a rolling cluster upgrade, node roles and data replication is handled in a complicated & tricky way so you need to have knowledge & experience to survive such an operation with your data & cluster to be operational after it.
May 17, 2021
Elasticsearch OSS Review
We are using this in conjunction with other applications such as Atlassian stack. So this is being used throughout the whole organization but is an extension to another application. This allows us to search for words/topics very quickly in projects and commits. We currently use it in a single server instance.
- Database
- Scalability
- Deployment
- Backup
- Rest API browser
- Remote management using utilities
January 13, 2021
Elasticsearch: for searches, you know!
In my organization, Elasticsearch is used as a fast and simple solution for providing search capability to text-based data and to easily perform analytics for our dashboard. Being a JSON-based response system, our APIs become simple and support multiple behaviors by translating to Elasticsearch queries. Not only does Elasticsearch act as our analytics platform, but also it serves as secondary database storage.
- Text-based searches on data
- Daily, weekly, monthly analytics on data
- Super easy scripting with painless scripting language
- Relational data query
- Sync data from SQL on table change (with hash maybe)
- Provide better tutorials for beginners
October 26, 2019
Elasticsearch is the future!
The most crucial piece of infrastructure behind my company's whole product line is Elasticsearch. Our company's big selling point is an extremely flexible data model for our customers who send us their data. We want them to be able to send us data in almost whatever shape or form they want (as long as it's valid JSON we'll take it) and yet, make it still searchable. And you know how we store that nearly-unrestricted free-form data? Elasticsearch!
- As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
- Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
- Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
- Elasticsearch paid support could be much better. Not only is it really expensive, but the reps just don't seem to be that knowledgeable and keep linking us to support documentation we've already found and read.
- I wouldn't call it missing functionality or a part that's hard to use perse, but upgrading from ES 5 to ES 6 is a PITA. Maaaan did they mess up a part of their data model so bad that when migrating, you have to restructure almost all your queries and transform almost all your data! I don't want to go into too many details here as some people may not be clued in on the concept of mapping types, but you can read more about it here https://www.elastic.co/guide/en/elasticsearch/reference/6.0/breaking-changes-6.0.html.
- This is no longer a problem in ES 6 but in versions 5 and before, reindexing is a PITA. You have to almost bring down the whole cluster to fix small problems such as missing fields or wrong types.
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 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
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
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 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.
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
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.
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.