Hey everyone, new here and still trying to wrap my head around all the different tools in the observability space.
We’re sending a lot of our application logs from Shopify apps and some custom services to a couple of places: our analytics platform and our support software. I keep hearing about duplicate logs causing inflated costs and messing with our basic dashboards. It seems like a waste to process and pay for the same event multiple times.
I saw that Cribl can be used for routing and filtering, but I'm really curious about using it specifically for deduplication. Is anyone here actually doing that successfully with application logs?
My main worries are:
* How do you reliably identify a duplicate? Is it based on a log message hash, a combination of fields like timestamp and user ID?
* Does it work well for high-volume streams without losing logs you actually need?
* Do you run it before sending data to your analytics, or as a filter within Cribl before the destination?
Just looking for some real-world experience before I dive in. The concept makes total sense, but the implementation details feel a bit overwhelming 😅. Any pitfalls or "I wish I'd known" tips would be amazing.
Yes, we've been running Cribl specifically for deduplication on our Kubernetes application logs for about a year. The cost savings were significant, but you have to be surgical about it.
> How do you reliably identify a duplicate?
You need to define a composite key that's meaningful for your business logic. A hash of the raw message is terrible because a single differing timestamp or pod name creates a new "unique" event. We use a combination of the log level, the normalized error message (we strip out dynamic IDs), the service name, and the user ID field. You build this key in a Cribl pipeline using Eval functions.
The main pitfall is that "high-volume without losing logs" requires careful tuning of the in-memory cache for the Dedup function. If your cache is too small for your burst rate, you'll evict entries and let duplicates through. If you set it too large, you risk OOM killing the Cribl worker. You must monitor the `cribl_dedup_cache_evictions` metric. We run it as a filter within Cribl *before* the analytics destination, but after we've parsed and enriched the events. This way the key is built from structured fields, not raw text.
My biggest "I wish I'd known" is that you absolutely must have a consistent and reliable timestamp field from your source. If your log shippers are retrying and stamping events with *their* current time, you'll never deduplicate correctly. We had to fix our Fluent Bit configuration first.
That cache warning is critical, especially for containerized deployments. People see dedup as a simple filter and ignore the memory pressure.
Your point about building the key after parsing is the only way it works long-term. If your raw log format changes, your dedup key breaks. We version the key logic in the pipeline for that reason.
One addition: you also need to consider windowed deduplication, not just infinite cache. Duplicates from a rolling restart should be caught, but the same error a week apart is not a duplicate. The Dedup function's time window is just as important as cache size.
Beep boop. Show me the data.
The time window consideration is key. We had to experiment with different durations before landing on a 24-hour window for most application logs. It caught the duplicates from deployments and cron job runs, but allowed genuine reoccurring issues from a week later to pass through.
A related caveat is that your time window should sync with your log source's own timestamp field if possible, not just Cribl's processing time. This prevents duplicates that are delayed in transit from slipping through.