Hey everyone. I've been tasked with helping maintain our Cribl setup at work. We've been using it for about 18 months now, mostly routing logs from EC2 and Lambda to S3 and Splunk.
We're starting to push more data through it, maybe a few TB/day, and I'm hearing the senior engineers talk about "performance tuning" and "bottlenecks." I want to understand what to watch out for before we hit a wall.
From your experience, what are the real pitfalls with high-throughput workloads in Cribl? I'm thinking about things like:
* Worker auto-scaling groups in AWS not keeping up
* Specific pipeline functions that become too expensive at scale
* S3 destinations getting overwhelmed with too many small files
I tried looking at our Worker config, but I'm still learning. Is it mostly about throwing more CPU/RAM at the Workers, or are there config tweaks that make a bigger difference?
```hcl
# Our current worker setup in Terraform - is this naive?
resource "aws_autoscaling_group" "cribl_worker" {
name = "cribl-worker-asg"
min_size = 2
max_size = 10
desired_capacity = 2
vpc_zone_identifier = var.subnet_ids
# ... more config
}
```
Any gotchas with the S3 destination or buffer settings when volume gets really high?
You're right to start thinking about this before you hit the wall. The three points you listed are exactly where the friction starts at high volume.
Auto-scaling groups can struggle if your scaling metrics are just CPU/memory. You'll want to look at queue depth in the Cribl UI as a primary scaling signal, otherwise workers can fall behind even if they don't look busy. For S3, too many small files is a real cost and performance killer; tuning the buffering and file rollover settings in your Destination becomes critical.
And yes, some pipeline functions, especially heavy regex parsing or lookups, can become surprisingly expensive. It's rarely just about throwing more CPU at the workers. The config tweaks around batching, buffering, and concurrency often give you more throughput per dollar than a bigger instance size.
Agreed with the other comment about queue depth as a scaling metric. On the CPU side, I found the instance type matters more than just adding cores. Network-optimized instances helped us more than compute-optimized at high throughput, even if CPU looked idle.
The S3 small files problem hit us hard. The setting that saved us was tuning the 'Max file size' and 'Flush period' in the Destination. Letting buffers fill more before writing made a huge difference in cost and speed.
> Is it mostly about throwing more CPU/RAM at the Workers
Not in my limited experience. We got further by reducing the verbosity of the logs before they even hit a pipeline. Are you using any filtering or sampling upstream?
That Terraform snippet is a good start, but it's missing the critical pieces that will bite you. The ASG itself is just a container; the scaling policy and the metrics driving it are where the tuning happens. A simple CPU-based policy won't cut it for a streaming log pipeline.
You need a CloudWatch alarm on the `CriblWorkersQueueDepth` metric, published by the workers themselves, to trigger scaling. Set the alarm to go off well before the queue is full, maybe at a depth of a few thousand events per worker. Scaling on CPU alone means your workers are already overloaded and have started buffering.
Also, look at the `TargetGroups` attribute in that ASG spec. Are you using Network Load Balancers? For raw TCP/UDP log traffic, an NLB is far more performant and cheaper than an ALB at multi-TB daily volume. The instance type choice another comment mentioned is crucial here - `c6gn` or `m6in` instances with enhanced networking will handle the packet throughput much better than generic compute types, even with similar vCPU counts.
Mike
Absolutely on point about queue depth as the scaling signal. It's like trying to drive by looking in the rearview mirror if you only watch CPU.
One thing to add on the NLB vs. ALB point - for anyone using the HTTP/S source in Cribl, you're kinda stuck with an ALB. But if your traffic is raw syslog, TCP JSON, or even Splunk HEC, an NLB is the way to go for exactly the reasons you said. The cost difference alone at that volume is eye-watering.
Did you find a sweet spot for that CloudWatch alarm threshold? We had to experiment a bit because setting it too low caused needless scaling churn, but too high meant we were already playing catch-up.
✌️
Good point on the network-optimized instances, that's not something I'd considered at all.
On filtering upstream, what's been your most effective method? Are you using Cribl's built-in sampling in a source, or doing it in the pipeline itself before any heavy processing? I've been trying to decide where to put that logic.
The first pitfall is believing your Terraform config for the workers is the issue. It's not. The real landmine is in the scaling policy that Terraform doesn't show us. As others hinted, CPU-based scaling is a fool's errand for a log router; you're scaling reactively on symptom, not cause. You need a metric from *inside* Cribl, like queue depth.
But I'll push back slightly on the notion that tuning buffering is a silver bullet for S3 costs. It is, until you have a mandatory low-latency destination in the mix, like a real-time security analytics feed. You can't just buffer for minutes. The real cost often comes from not segmenting your data streams: your high-volume, low-value debug logs and your low-volume, critical auth logs shouldn't be on the same S3 buffering profile. Most setups just shovel everything into one destination with one settings group and then wonder why they're either bleeding money or losing fidelity.
And no, it's not about throwing CPU/RAM. It's about throwing *money* at the wrong thing. Network-optimized instances? Great, if your bottleneck is actually the network and not, say, the JavaScript runtime for your bloated pipeline functions. Have you profiled which functions are chewing cycles?
Test the migration.