Elastic Observability Reviews

The product is always evolving and producing new tools to ensure that various technologies are monitored and integrated. Furthermore, the open community allows users to find and share solutions for a variety of use cases. Open source code, community support, frequent iterations, and new feature releases are all part of the package. Application call water flow, Drilldown Bundling capabilities with rest of the Elastic Search Stack Open Source providing availability is the best feature of this product.

Constant advancements necessitate regular configuration changes/maintenance; however, the documentation's quality helps to mitigate this.

The boards are handy and show all the required information at once

The query language is proprietary and difficult to master

Elastic Search is easy to install
Easy to use as there are loads of documentations present online
Easy to scale up when the need arises
Uses REST FULL API which is light weight.

monitoring of Elastic Search are complex like wise administration
Installation of elastic search on windows OS is not straight forward

Logstash does a great job of ingesting data, transforming it and then storing in a 'stash' like elasticsearch. It can virtually ingest any data as long as you have the time and patience to set it all up. We use it as part of the process of collecting all logs from a multitude of sources using filebeat (generally application and system logs) on different operating systems, shipping to logstash then transforming and storing that in elasticsearch and having Kibana for dashboards and analysis. It works great for this use case.

It can be tricky to set up as it is mostly just a command line tool with configuration files. The recent log4j issues also make you think again about all this log shipping, transformation and ingesting in a different way. If there is similar vulnerabilities in the future, it could end up happening way down in your process in non-public components due to the way logs are shipped and processed down the line.

I've been using Elasticsearch since early days, with very different things in mind.

I started with simple text search - with some additional tweaks, stemming and other cool features it helped us drive enormous traffic to our website. I can't imaging pulling it off so easily with any other tool.

Every day I use it for web server log analytics. Search and great visualizations make it an absolute essential in work my toolset.

We also run a lot of algorithm analytics on top of our Elasticsearch cluster.

If you're looking for managed options check AWS Elasticsearch Service, or the recently introduced Elastic Cloud.

My only concern with Elasticsearch is that it might get expensive to run pretty quickly. But with a certain amount of effort put into optimization it's gonna be worth it.

This is the most awesome software stack for data analysis and searching. We stream data to the elastic search and index it so we can search it and analyse what we receive on the fly. It does the job extremely well. On top of that, the software comes as a managed service on cloud providers which means it comes with almost no maintenance overhead.

Nothing really comes to mind. The only thing is Json is mandatory data format unlike the Apache Solr which is a competitor.

Free!
API driven
Scalable multi-node architecture.
Easy to understand JSON
Tight integration with logstash and the rest of their stack

The steep initial learning curve
Their own log format is often difficult to parse
Some features are only available in the paid product.

A consolidated UI to search and find the patterns in log file. Speed of search return is also very good. The way this product manages the files at the backend, it does conserve a lot of space considering the amound of data it stores.

Search pattern bar could be more user friendly. When the load increases, serach bar is the first to show impact and starts to deviate from an efficient behaviour. Cursor keeps going away during such times making it difficult to tweak the attern.

Elasticsearch has turned into a solution that you'd typically integrate into another to a fully customizable standalone solution. It has everything in one spot now, with hardly a need to use 3rd party plugins/extensions.

The licensing models are very confusing. There is a big push to get on their hosted SaaS offering but that itself is also not very clear.

Adding elasticsearch to most projects is a great way to have high performant full text or json document search. The common use case is for searching through log files (ELK or EFK stack) which is now the canonical way to handle logging for most distributed systems such as Kubernetes. Installing elasticsearch is not trivial but has been made much simpler in recent years due to being able to run ES in containers (i.e. helm charts). We use ES to accelerate general search, as well as being a core component in our logging stack.

There are many different configuration options available so there is still some ramp time required. There's also the issue of Elastic changing the licensing from Apache 2.0 to SSPL and Elastic. While this is probably not unexpected due to Amazon's predatory behavior when it comes to OSS and AWS, it doesn't help customers and the community to have two different projects.

We are using elastic search mostly with kibana and logstash to build a full stack for log evalutaion and analysis and it's really cool. It works like a charme and it allows you to use a powerfull and compleate search engine .

Elasticsearch it' complete and powerfull but as cons i see heavy resource requirements. It's also quite complex to learn and require a long path to be able to be able to build complex task.

It's user friendly, considering the fact it has a complete stack (ELK) associated with it, which not only stores data, but collects log(Logstash) and it's capable of showing those data in it's visualization tool(Kibana).

I have not come across anything associated with this too that I did not like.

I love how fast is it. It's possible find text and object in a very rapid way. The ELK suite allow to create a monitoring system in a quite easy way. The beats very useful tool to collect information that come form a lot of different system

The elastic syntax is not so easy to learn. but with new release the sql format should make it easier.
Some times when you performs particular expensive operation elastic fail with strange error, however with a custom configuration is it possibile solve them.

I loved how easy this was to spin up and get going in our environment with minimal research, trial, and error.

I think to cluster Elasticsearch was my least favorite part about the implementation. there were a lot of "nitty gritty" config details to be ironed out

*Very big community base
*Elasticsearch is open-source
*Very powerful REST API
*Well documented
*Easy to install, just a few commands
*You do not need knowledge of databases
*Elasticsearch can be integrated to 3rd-Party software.

*For some of premium features (included in X-pack) you must buy subscription, which cost too much money.
*If you want subscription, least number of licensed nodes must be more than 3.
*

It's a great tool for managing application and server logs at large scale. Combined with Kibana as part of the ELK stack it is very powerful and extremely useful.

It can be difficult to understand at first when using it and when setting it up, but once configured correctly it does a great job.

Elasticsearch has a lot of in build advantages, like scalability by sharding, aggregations, schema flexibility and full text search options to name a few.

Elasticsearch's query DSL is kind of less flexible and less common than other solutions available. Almost everything is indexed by default which is a pain and causes overhead.

I'm new to elasticsearch but I've used it to build an admin panel that reads my server logs (nginx, postfix ... ) and show detailed informations with logstash and kibana. It's very easy and quick to setup. I see a lot more use cases now that I've made the first step into ES, like converting a big mysql database into ES.

I don't like how the documentation is made, sometimes I get lost. Other than that I'm very satisfied with ElastickSearch and how it performs. There is a huge difference between it and a normal database.

Easy to use.
indexing helps in increasing scalability.
good online tutorial.
few extensions helps in further personlization.

Understanding the tool for a beginner would be a challenging task.
Too many features with only a few to be used for a concentrated user.

It's easy to use and when you combine it with ElasticSearch, it even more powerful

I cannot think in a Con per se, but I guess that at first you need some time to familiarize with the tool

Logs are very important source of information. Elastic Search makes it easy by converting them into index form in form of documents. Put,post, get methods are releif for search.

The process and organisation is complex for begineers to understand. Require other took support to work in full capability.

It is a very useful software for storing and searching data. It also logs data for the user. Service provides a complex way at first to store data. Once you understand it, it is easy to use.

The only thing i dislike is the learning curve. it to be able to use the full service, it is a bit of a learning curve.