Elastic Search Reviews

Elasticsearch helps us to store the data in key value pairs and, based on that, we can produce visualisations in Kibana. It helps us to analyse the logs based on the location, user, and other log parameters.

It needs email notification, similar to what Logentries has. Because of the notification issue, we moved to Logentries, as it provides a simple way to receive notification whenever a server encounters an error or unexpected conditions (which we have defined using RegEx).

No stability issues.

Not really any scalability issues, but we have set up a cron job to delete old logs so that we don’t hit the disk space issues. It would be helpful if such a feature were added to the UI, where old logs could be deleted automatically. (Don’t know if this feature is already there).

ELK documentation is very good, so we have never needed to contact technical support.

We used Logentries. Because of open source we moved to ELK, considering it as part of a cost-cutting strategy and evaluation. But due to the lack of a notification feature, we again moved to Logentries.

Slightly complex, especially when you are configuring a machine which is on a separate IP, rather than on a single machine. In my case, Elasticsearch, Kibana and Logstash were on different machines. Along with that, we added a proxy server (nginx) ahead of the Kibana server. We used the proxy server for user authentication so that only known users would be able to access the Kibana dashboard. ELK's free version doesn't have user authentication, and this forced us to go with this alternative. We have four machines in total.

When we were using the Amazon Elasticsearch Service we had one cluster of Elasticsearch which, by default, gave us the Kibana dashboard. We just added a proxy server for user authentication.

Graylog, Fluentd.

I give it a seven out of 10. They don't provide user authentication and authorisation (shield) as a part of their open source version.

Nice to implement, they have nicely written documentation.

• Fast search
• Relevant results returned
• Autocomplete/fuzzy search

• Search 
• To implement the main requirements regarding my support portal.

Machine learning on search.

No issues. It is very stable.

No issues. It is easy to scale with the cluster node model.

I would rate technical support with a nine out of 10. Support is very reactive and proactive.

We did have a previous solution. It was a monster to install and configure.

The initial setup was straightforward.

The pricing and license model are clear: node-based model. 

We evaluated Solr.

The search product is a product exposing API. Therefore, be comfortable in developing your own front-end to integrate the Search API.

We use ELK primarily for enterprise monitoring and analytics through log ingestion. The data collected in Elasticsearch will be used for near real-time monitoring, analytics, and machine learning.

All new applications have been onboarded and used by the application teams. The initial feedback has been positive, and its capabilities seem to be a descent fit for our needs.

ELK being an open source certainly provided a platform for our organization to get involved. 

X-Pack provides good features, like authorization and alerts. An X-Pack license is more affordable than Splunk.

Logstash has been a challenge and needs improvements in data ingestion reconciliation. The Kibana Cross Cluster feature is long awaited and I hope 6.0 will address it without issues.

ELK has been considered as an alternative to Splunk to reduce licensing costs.

ELK provides 

• good end to end log monitoring
• Logstash grok pattern
• elasticserach indexing
• easily configurable Kibana dashboards
• feature rich and appealing Kibana UI, as its USP.

The product offers a powerful, cost effective solution for proprietary log management and is easy to understand and start with. This saves cost as well as enhances usability.

The open source version should ship basic security versions with it. Alerting is an important feature which is not available in the open source stack.

About two years.

Stability issues can be seen with Logstash and can be addressed using Kafka. Also, data collections may not be 100% optimal using Filebeat and could be further improved.

The product is highly scalable, especially on the database side (elasticserach).

Have worked on the open source version and, hence, I don't have much information about tech support.

I have not worked with another solution.

The setup is straightforward for PoC, but becomes complex while scaling it or deploying it for production.

Yes, I evaluated GrayLog and Fluentd, but ELK was more feature rich.

The ELK open source stack is rich in features but lacks the essential security features.

ELK is one of the best open source alternatives for log management. If alerting and security features can be built by the team internally, on top of the stack, then the product is for sure going to add a lot of value to business.

We are using ELK Elasticsearch in a database. We use both Logstash and Kibana. Kibana is used for monitoring where the data is coming from.

The solution has good security features. I have been happy with the dashboards and interface.

There are some features lacking in ELK Elasticsearch.

I have been using ELK Elasticsearch for approximately two years.

We had some stability issues where we could not access the application.

We have approximately five people in my organization using ELK Elasticsearch.

All the installations were directly set up on the local servers.

The solution is free.

Elasticsearch is open source.

I rate ELK Elasticsearch an eight out of ten.

My organization works in the healthcare industry and we use this product as our database.

When we have questions about our data then we use Elasticsearch to make queries.

The most valuable feature is that I can push data to Elasticsearch using Logstash.

Technical support should be faster.

I have been using Elasticsearch for about one year.

This is a stable product.

Elasticsearch is scalable, although we only have about five users and they are not constant. We do plan to increase our usage in the future.

The initial installation and setup were straightforward. It will take a few minutes to deploy.

Our in-house team was responsible for the deployment.

This product is open-source and can be used free of charge.

I also use Kibana, which is integrated with Elasticsearch. Kibana is for visualization and we can also customize Elasticsearch using Kibana.

In summary, Elasticsearch is a very useful product that I can quickly recommend.

I would rate this solution an eight out of ten.

We use the product for log management. 

The products comes with REST APIs. 

Elastic Search needs to improve authentication. It also needs to work on the Kibana visualization dashboard. 

I have been using the product for six years. 

I rate the product's stability a nine out of ten. 

I rate Elastic Search's scalability a ten out of ten. 

The technical team needs to improve their response time. 

Positive

The tool's deployment is easy. It took us one day to deploy a seven-node Elastic Search cluster. 

Elastic Search is open-source, but you need to pay for support, which is expensive. 

The solution suits medium to large companies better. I rate it a nine out of ten.