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.
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Elasticsearch Reviews
16 Reviews
Engineering
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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.
Pros
Full text search
aggregation
anomaly detection
dashboard
canvas
Cons
SIEM
Ingest API
The performance for a large cluster
business analysis
Likelihood to Recommend
It is good for delivery tracking. Customer can search for the shipment ID to get the detail of the shipment. The business analysis with excel data is not as good as PowerBI.
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.
Different forms of Elastic are being used across the company - the vanilla one, OpenDistro and OpenSearch. Licensing limbo + long-term support make people here jump from one implementation to another.
Pros
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.
Cons
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.
Likelihood to Recommend
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
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 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 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.
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.
Pros
Lightning fast
Easily scalable
Powerful feature set
Cons
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
Likelihood to Recommend
Elasticsearch is the gold standard for text-based search. Across large data sets it performs admirably, and we will certainly make it our first choice search solution in the future. For a use case where needs are simple and regular database queries might suffice, Elasticsearch may or may not provide any benefits.
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.
Pros
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.
Cons
I found the learning curve fairly difficult having a SQL background.
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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
Likelihood to Recommend
ElasticSearch is very well suited to index and search data but it not made to store data like a database.
