I work in a regulated industry where quick production alerts are crucial. Our team uses Splunk, but it's gotten so bloated that alerts can be delayed by up to 15 minutes, which is detrimental since our support team no longer trusts it. Frustrated by this issue, I've started creating my own real-time alerting system as a side project. I'm aiming for something fast, lightweight, and self-hosted. I've already learned quite a bit, like implementing passkey login for fun. I'm reaching out to see if any of you have built your own monitoring or alerting tools to replace cumbersome enterprise solutions like Splunk. What has your experience been like, and what have you learned in the process? I'm committed to improving my project long-term, so any insights would be great!
6 Answers
It’s often not the tool itself but rather how the data is managed. When you've already got issues using a renowned solution like Splunk, building your own can lead to even more struggles. You should consider using parsers in front of Splunk to lessen the load and avoid querying every log for alerts.
Have you reached out to Splunk's support about these issues? If everyone in your organization is using it, it might become overloaded. Your current approach won't work if alerts are always at a lag.
That’s right! High volumes can lead to frustrations, especially if smaller teams are struggling but their needs aren't prioritized.
If you're going to self-host an alerting system, make sure to have some form of synthetic monitoring externally. If your system goes down, you're going to want to know about it right away!
That's a good point! Decentralized alerting might help, but the key is to ensure it remains operational even when things go south. It’s a tough yet important goal to aim for.
Honestly, if you’re facing delays with Splunk, it sounds like a mismanaged instance. How huge is your setup? If it's bogging down, you might be pushing more logs than it can handle, especially in big enterprise environments with massive log volumes.
Right, I've seen teams get bogged down because everything's centralized in one instance—even the cost adds up!
Definitely sounds like a case of overwhelming Splunk. Maybe restructuring how logs are sent could ease some of that burden.
About nine years ago, I set up a Graylog cluster that could handle over 150k messages a second. I found using tools that send logs to both Graylog and Splunk really helpful for instant alerts. With large data, Splunk just isn't worth the cost.
If you're managing the entire stack, the speediest way to handle alerts is through bash scripting right on the machines. For critical log conditions, writing scripts that greps logs can be super effective.
Absolutely! Setting up filters for unnecessary logs can help a lot with costs and efficiency.