Graylog Pros and Cons

The ability to write custom alerts is key to information security and compliance.

I am very proud of how very stable the solution is.

Message forwarding through the in-built module.

We run a containerized microservices environment. Being able to set up streams and search for errors and anomalies across hundreds of containers is why a log aggregation platform like Graylog is valuable to us.

Allowing us to set up alerts and integrate with platforms we already use, such as Slack and OpsGenie to alert users of these errors proactively, is also a very useful feature.

Real-time UDP/GELF logging and full text-based searching.

UDP is a fast and lightweight protocol, perfect for sending large volumes of logs with minimal overhead.

Storing logs in Elasticsearch means log retrieval is extremely fast, and full text search is available by default.

It is used as a log manager/SIEM. It provides visibility into the infrastructure and security related events.

The build is stable and requires little maintenance, even compared to some extremely expensive products.

We have scaled from a single machine installation (a VM with a Graylog + ES + MongoDB) to (2 Graylog + 2 ES + 3 MongoDB). This was done smoothly with a minimal impact on logging.

This had increased productivity for the dev and support teams, because we are directly notifying them.

I would like to see a default dashboard widget that shows the topology of the clusters defined for the graylog install.

I would like to see a date and time in the Graylog Grok patterns so that I can save time when searching for a log. I like how the streams and the search query work, but adding a date and time will allow me to pull out a log in a milli-second.

The biggest problem is the collector application, as we wanted to avoid using Graylog Collector Sidecar due to its architecture.

Elasticsearch recommendations for tuning could be better. Graylog doesn't have direct support for running the system inside of Kubernetes, so it can be challenging to fill in the gaps and set up containers in a way that is both performant and stable.

We ran into problems with Elasticsearch throwing a circuit-breaking exception due to field data size being too large. It turned out that the heap size directly impacted this size in a high-throughput environment, causing unexplained instability in Graylog. We were able to troubleshoot on the Elasticsearch size, but we should have been able to reference some minimum requirements for Graylog to know that our settings weren't sufficient.

Since container orchestration systems are popular and Graylog fits the niche well, perhaps they could officially support running in docker containers on Kubernetes as a StatefulSet as a use case. That way, the declarative nature of Kubernetes config files would document their best case deployment scenario-

More complex visualizations and the ability to execute custom Elasticsearch queries would be great.

With technical support, you are on your own without an enterprise license.

Dashboards, stream alerts and parsing could be improved.

Over six months, I had two similar issues where searches were performed on field "messages". It exhausted all the memory of the ES node causing an ES crash and a Graylog halt.

There should be some user groups and an auto sign-in feature.​