Coralogix

My main use case for Coralogix  was that we transferred from DataDog because DataDog was expensive at the time, and we wanted something more cost-effective. The VP of R&D thought Coralogix  would be a great solution to replace DataDog. We used it for observability, logs of services, traceability of services that reach from certain endpoints, and everything related to metrics. We also used it in the queries and their Grafana  managed dashboards, so we could view all of our Kubernetes  workloads, whether it was RabbitMQ brokers panels or Kafka. We integrated it with so many things.

A specific example of how I used Coralogix in my daily work is that I opened Coralogix when developers had issues with their services, such as having 500 error codes or 400 error codes. We would observe the logs and see the logs that were transferred to Coralogix for the service, which were enriched with the data of the name of their service, the tags, and many other things that we did with the help of OpenTelemetry  agents that transferred the data to Coralogix. We also sent log groups of specific RDSs that we managed via AWS , so it was better for metrics gathering and logs observability.

The best features Coralogix offers include its very nice tool and the usage of AI, which was very useful. The AI could describe the log that was received, providing a window that explains what the log actually says, what the issues are, the impact, and many things that help to summarize the log. The console is pretty fine; you can navigate and see many things that you want.

The AI feature impacted my work by helping me to reduce my time wasted on exploring what the error means and going to Google to search for it or maybe going inside the pods of the services to see what the issue actually is. The AI summarizes the data, enriches it, and provides a better view of what I actually need to handle.

Coralogix has positively impacted my organization by handling the responsibility for the developers to track their services and see what is actually going on there in terms of logs of their services, whether it is info, debug, error, or warnings. It gave a better view of things, and you can query against things that you want to see in terms of logs. The Grafana-managed dashboards allow you to actually see the metrics of your specific workloads. I believe it provides better observability and visibility. Coralogix is a nice tool that helps developers, DevOps, and SRE track services and see their health status.

After implementing Coralogix, we noticed improvements such as integrating it with our Squadcast , which is an alerting system that actually alerts when an incident arises. We used the alerting system of Coralogix and explained via PromQL, which is a query language that helped us write alerts based on queries. When we saw a pod's CPU utilization metrics exceed the predefined limit, we wanted to alert our SRE and DevOps team so they can open and see the alert URL and log URL. Via Coralogix, we could examine the logs and see the actual issue. It was very nice to integrate it with Squadcast  and have SMS and calls when an alert is received.

This setup helped our team respond to incidents more quickly, and I think we improved our incident response time. The SMS and phone calls are received immediately; once the alert is triggered, it triggers Squadcast, which notifies me with SMS and then calls me.

Coralogix can be improved by having better documentation to help new people onboard into this platform and understand the systems, including how they can integrate their cloud provider to better understand how Coralogix and the cloud provider work in sync. If you really want to understand it, I think you need to find someone who has worked with it for at least a couple of years.

There are other improvements needed for Coralogix, such as their Helm charts and Terraform  providers. I remember back at that time, the Terraform  providers were without any support from the engineering team, and they were outdated and had many bugs.

I have been using Coralogix for at least two years.

I do not have any specific advice for others looking into using Coralogix. My overall review rating for Coralogix is eight out of ten.

My main use case for Coralogix  is to see logs. I initially used it for a logging system, so every user can see the logs. Logs are published via AWS  to Coralogix , and I set up log-related alerts and observability to analyze metrics using dashboards. These are the major areas.

When I debug our system, I filter based on the service names that I have already published from the ingress logs. I have also published some MDC tags that are visible, so I filter based on the tags or a particular message or trace ID. This is how I do log analysis. For setting up alerts, I use an alerting dashboard where I set the queries for specific logs. If a log or log-related query appears more than five times within a specific timeframe, such as the last five minutes or one hour, I can raise an alert for errors like 4xx errors.

I have mainly used Coralogix for high scale logging, and I remember the unified observability feature where log metrics and traces can be seen together. I have not used it properly recently as we have moved to a different tool, but they started using AI-powered voice anomaly detection and stream data analytics, which I found useful.

I just remember that they started setting it up, and within four or five days, we started flowing logs into the Coralogix dashboard.

Coralogix was stable most of the time, but I would not say always.

The only improvement I remember is that the cost aspect is a bit more tedious. My company often did not store older logs due to the high cost. There were multiple tiers for querying high-priority logs and archiving low-priority ones. Sometimes, while filtering based on tags, it broke, but it was usually fixed. It would also be beneficial if I could see Kafka-related logs in the system, as we were using a separate system for Kafka.

I remember the OpenTelemetry  configuration was not working properly in Coralogix. I am not sure if it is working now or not.

I have around ten years of experience working in my current field.

Coralogix was stable most of the time, but I would not say always.

Customer support was good. There is a chat option on the UI for immediate connection with the team, and if assistance is needed, they set up Zoom calls to help you. I would rate customer support nine out of ten.

I have worked on multiple logging systems, and I would say Coralogix was the best among those. I find Coralogix to be one of the best tools. Currently, I am using Kibana, which is not as user-friendly and lacks fast aggregations compared to Coralogix, which worked really well.

I remember that they started setting it up, and within four or five days, we started flowing logs into the Coralogix dashboard. The fast onboarding process allowed users to start working on it without issues, enabling everyone to see centralized alerts and logs during any issues with upstream or downstream services.

I cannot specify a return on investment, as every company assesses these things based on their tools. However, I find Coralogix to be one of the best tools.

Cost was the only major reason for switching from a different solution.

Coralogix is one place for all logs and analysis. I have not used such a tool. Coralogix is way ahead of its time compared to others I have seen in different companies. My company often did not store older logs due to the high cost. There were multiple tiers for querying high-priority logs and archiving low-priority ones.

Coralogix is a tool you will not regret using.

My main use case with Coralogix  has been to troubleshoot, narrow down the problem, understand the logs, and identify errors.

For troubleshooting or analyzing logs, we usually employ two methods. The first method is sorting by time, where we trigger an action and see in real-time if it appears or not. The second one involves using services. We know that some actions will trigger specific services, so we filter it by services and, after triggering it, we check if there is an error or a log trace to see in which service the error is happening.

The best features of Coralogix  are the real-time logging and the filtering, which are what we use the most.

The real-time log feature helps me in my day-to-day work when I am trying to test some APIs; I expect to see the logs in real-time as I trigger them. The filtering helps us understand where the bottlenecks and deadlocks are, so when we send a request to a specific service, we filter it and monitor it. This way, we narrow down the problem; if it passes the services successfully, we know the issue lies elsewhere, not in that service.

Coralogix has positively impacted our organization by providing us with a clearer data flow, which allows us to analyze data better and find errors easier using the smart logs it offers.

Coralogix has many features, but we usually use only these two, and the syntax has not been so straightforward. It was a bit difficult to write specific queries, so I have templates of specific queries where I just change the ID or the service. It is nice to see the whole picture, but it requires getting used to the program.

Coralogix can be improved by simplifying it. Perhaps, with AI now, it could allow users to write in plain text and then create the query automatically. The main pain issue for me with Coralogix was that the syntax was a little tricky. Although Coralogix has so many features, I mainly used only a few due to difficulties in knowing how to write them. It was also chained, making it a little tricky. Therefore, I created templates for easier usage. The potential of Coralogix is huge, but we used it this way since no one knew how to use it correctly. We once had a session explaining all the features, but we took very little from it as it was difficult for us. With many microservices, it was sometimes tricky to understand where the data goes. I often activated multiple services to track the flow and would sometimes need extra time to grasp it. I believe now, with AI, it can be improved, allowing for easier queries created from plain text.

Coralogix is stable.

The scalability is very fine as we add more and more microservices.

The customer support at Coralogix is great. We have used them several times, and when we had trouble finding something, they guided us on how to redefine our query to obtain the results we needed.

I would rate the customer support a ten, as they helped us effectively.

Before choosing Coralogix, I evaluated other options, including Splunk, which I find very similar to Coralogix. However, I believe Coralogix is more powerful than Splunk.

My experience with pricing, setup cost, and licensing has been transparent since I am only the engineer using it.

I can confidently say that I experienced faster troubleshooting after using Coralogix.

When I did not find the problem in my Azure  logs, which are often trivial issues, I typically use the heavy cannon like Coralogix. It sometimes took me time to investigate, but I usually find solutions there. For me, it was not time-saving but more a solution-saver, which is very important.

We are using different types of logs concurrently, such as Azure .

I would rate Coralogix an eight.