TrustRadius Insights for Elasticsearch are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
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.
We use Elasticsearch to analyze and visualize logs from various Engineering workflows. We have clusters defined for providing Application Performance Monitoring for a variety of Engineering applications, utilizing Beats and other processes to populate the data required for monitoring and analysis. We also capture metrics (for both servers and applications).
Pros
Log and data capture, via Beats
Visualization of data
Application monitoring
Cons
Some of the cluster management functions could be more intuitive.
It would be nice if it could be used for large data sets (streaming data)
Troubleshooting could be easier.
Likelihood to Recommend
As stated before, it does a good job of providing analysis and visualization on data coming into the system, but troubleshooting could be better (when issues arise). Performance, scalability, and overall speed are good, but the trade-off is it can be resource-intensive. Overall a good tool, it just takes a bit to learn (it's not always as "intuitive" as it should be).
Elasticsearch enables an operational capacity to quickly adopt this technology and boost observability on the different platform's components (infrastructure, integration, application, and services). Elasticsearch distributed architecture to index and search data make it a robust platform to scale on the go and support operational needs.
Pros
Observability features
Machine learning for anomaly detection
Index and search high volume of data
Cons
Basic alerting features
Likelihood to Recommend
Elasticseach platform allows implementing a robust operational stuck for unified observability handling a huge volume of data with high performance and capacity to scale fast. Logstash, Beats, and APM products provide a structured framework to collect events and data being easy to deploy and configure.
We're using Elasticsearch for indexing most of our data, allowing for blazing-fast searches. We store massive time-series data volumes from thousands of IoT sensors that Elasticsearch handles brilliantly, making metrics available in realtime. We're also running dashboards and canvas in Kibana, fed from Elasticsearch, which gets updated in realtime.
Pros
Performance.
Ease of set-up.
Cons
Tuning for ingress performance can be tricky.
Merged documents can become a bottleneck.
Likelihood to Recommend
Elasticsearch really excels in search performance, so if you have massive amounts of data you need to search from, Elasticsearch is surely a great fit. I woud advise against using it as the main database or the only source of truth, because data corruption can happen in rare cases, and in that case a reindexing will have to take place.
The way we set it up usually for our customers, Elasticsearch improves developer velocity by allowing to quickly search through millions of log messages. It is usually used by the development and operations team.
Pros
Log handing
Full-text search
Cons
Easier to operate
Easier to understand its bottlenecks
Likelihood to Recommend
It is well suited for searching through logs generated by an application running in production, staging, testing or development.
VU
Verified User
Team Lead in Information Technology (11-50 employees)
Elasticsearch is used as a full-text search solution in most of my use cases. We have another analytics us -case which uses Elasticsearch for both text search and aggregation use-cases.
Pros
Extremely easy to get started and great documentation.
Excellent for full-text use cases.
Also used for analytics and Kibana UX is great for visualization.
Cons
Encountered scaling challenges with large data sets (typically in petabytes).
Performance issues for raw aggregation use-cases.
Every contract (request/response) is in JSON which is not optimal. No support for protobuffs or GRPC.
Likelihood to Recommend
Elasticsearch is great for full-text search and some aggregation use-cases. It is ideal for small to medium-sized data sets.
VU
Verified User
Professional in Information Technology (10,001+ employees)
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!
Pros
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.
Cons
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.
Likelihood to Recommend
Elasticsearch's best use case is when you want to store loosely-structured data and be able to search for it near-instantly. And you want to do that in a highly tolerant distributed system. My company doesn't use it this way but I've heard of other companies using ES to store system logs. Another company uses it to store giant store-catalogs.
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.
Pros
Search results are provided very quickly.
The search results are accurate.
Search results contain details on the accuracy of each hit.
Cons
There is a steep learning curve for this product so what is most useful for developers is good documentation including examples and sample applications.
Likelihood to Recommend
Initially, we were using Elasticsearch for just product searches. It is also becoming useful as our product repository to display all data needed for the product detail pages.
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.
Pros
Ingress and indexing.
Searching.
Aggregations.
Cons
Aggregations on top of other aggregations.
Encryption at rest.
Has a performance penalty when using inked documents.
Likelihood to Recommend
Elasticsearch is so versatile and so easy to set up that it's really a no-brainer including it in most projects as the indexing and search engine components, as well as for analytics and aggregations. It's not so well-suited to be used as the main database, as there's a minor risk of data loss.
ElasticSearch is used to store all searchable data indices from our product. We use ElasticSearch because it is extremely fast, highly available, and able to meet the demand of our product. We were using a different index-based search technology before, and it failed terribly. We migrated to ElasticSearch and have been very happy with the results.
Pros
Easy to install
Easy to use/lots of documentation
Easy to scale up as demand increases
Cons
The price point for the X-Pack plugins (ie. Security, Alerting, etc.) is a bit high, especially if you only want to do something small and simple and you don't need to leverage the full power of the plugin
Configuring the right hardware and capacity planning (when at scale) can get really tricky. In order to get the best performance, a lot of tweaking is needed, and not all of the secret tricks are documented
Getting used to ElasticSearch's query language was a bit of an adjustment. You really have to delve into defining analyzers and tokenizers in order to get application-specific results
Likelihood to Recommend
ElasticSearch is great when you need a lot of data indexed really fast, as well as when you need to retrieve a large number of documents based on a complex query. Searching is super-fast.
If you need a large data store for documents where not everything needs to be indexed, don't use JUST ElasticSearch. We use one KV database system to store all of our data and use ElasticSearch as our Index. All searches are run off of ElasticSearch, and the main data store that it pulls from is the other database.
VU
Verified User
C-Level Executive in Information Technology (1-10 employees)
