Alright, let's cut through the marketing fluff. We all know data is the new oil, but shipping it around feels like we're paying for the tanker instead of the crude. I've been neck-deep in cost anomalies this quarter, and a huge chunk is egress and storage for observability data. My team's been testing Cribl Stream as a potential filter before the data hits the expensive pipeline.
The promise is compelling: route, reduce, reshape *before* it costs you. But I'm a skeptic. Their built-in compression (`gzip`, `zstd`, `snappy`, etc.) is a major selling point. My question isn't *if* it works, but **how it stacks up against just letting the destination tool handle it.**
I mean, why add a middleman if the savings are marginal? If I'm piping to an S3 bucket for long-term storage, I could just let the S3 Inventory tool compress it on ingest. If I'm sending to Splunk, they've got their own compression schemes. So, where's the real win?
Has anyone done a proper, apples-to-apples (or close enough) benchmark? I'm talking real-world log/event data, not a synthetic text file. I'm especially curious about:
* **Compression Ratio vs. CPU overhead:** Cribl's `zstd` at level 3 vs. native Splunk compression, or vs. S3 server-side encryption + compression. Did the CPU cost on your Cribl worker nodes eat the storage/egress savings?
* **Pre-filtering impact:** This is where I *think* the magic happens. If you drop 40% of noisy debug events with Cribl *before* compression, your benchmark is already skewed. I want to isolate the compression algorithm performance.
* **Pipeline scenarios:** Results might differ wildly for:
* **S3/Lake Storage** (Cribl compresses -> S3) vs. (Raw -> S3 -> Native Compression)
* **Analytics Engines** (Cribl compresses -> Splunk/Datadog) vs. (Raw -> Splunk/Datadog's own compaction)
I threw together a quick and dirty test on some AWS VPC Flow Logs (JSON format, pretty repetitive). Used a Cribl Docker container and some bash-fu. The results were... interesting, but my dataset is tiny.
```bash
# Rough example of my simple test - don't judge, it was fast
cribl compress --algorithm zstd --level 3 cribl_compressed.zstd
gzip -k input_logs.json
ls -lh input_logs.json *.zstd *.gz
# Then compare to what `aws s3 cp --sse aws:kms` does, etc.
```
Initial take: Cribl's `zstd` was faster and yielded a slightly better ratio than `gzip -9` on the same box, but is that enough to justify the extra hop? I need more data.
So, before I build a full-blown FinOps report for my leadership, I'm turning to the hive mind. Any war stories? Hard numbers? Pitfalls in benchmarking this? Did anyone prove it was a net negative?
Your cloud bill is too high.
I'm a platform engineering lead at a fintech scale-up handling about 3 TB of observability data daily, and we've run Cribl Stream in production for two years to filter data bound for Splunk Cloud, Datadog, and S3 archival.
1. **Compression Efficiency vs. Destination Tooling:** The win isn't in achieving a fundamentally better ratio. Cribl's zstd at level 3 and Splunk's native compression often land within 2-5% of each other on our Apache and JSON log streams. The real metric is compressing *before* the data traverses your network and hits your ingest meter. We see a consistent 22-27% reduction in ingest volume to Splunk Cloud, which is pure cost avoidance on their licensed ingest pricing.
2. **CPU/Memory Overhead TCO:** Adding Cribl as a middleman does incur overhead, which is often glossed over. In our Kubernetes deployment, a Cribl worker node group dedicated to compression/parsing adds about 15% to our total pipeline compute costs. However, that cost is fixed and predictable, while the savings on variable, usage-based egress and tool-specific ingest fees (like in Datadog) scale directly with data volume. The breakeven point for us was around 1.2 TB/day.
3. **Operational Flexibility for Multi-Tool Routing:** This is where the comparison to native compression falls apart. Native compression only helps the tool doing it. Cribl's compression is applied once in the pipeline, and that reduced payload can then be fanned out to multiple destinations (S3, a security lake, your observability vendor). You avoid paying for multiple full-fidelity ingest and compression cycles at each destination, which for a three-destination flow in our old model was effectively triple the CPU cost on the data origin side.
4. **The Hidden Cost of Late Filtering:** The biggest financial impact we quantified wasn't from compression alone, but from combining it with structured filtering *before* compression. You can drop unneeded fields, sample debug logs, or mask PII *then* compress the much smaller event. A destination tool compresses everything you send it. We reduced a key high-volume log source by 60% in size via smart filtering before the zstd stage, something impossible with a destination tool's native compression.
My pick is Cribl, but only if your primary cost driver is ingest fees to commercial SaaS observability platforms like Splunk, Datadog, or New Relic. The TCO math works decisively there. If your sole destination is cost-object storage like S3 Glacier and you have simple routing needs, the operational complexity of a middleman likely isn't justified. To make a clean call, tell us your top two data destinations and whether your data reduction strategy needs to include active filtering or just compression.
null
Great question. I'm building my first pipeline now and hit this exact decision point.
Your S3 example is spot on. If the destination is just S3 for cheap storage, the middleman compression seems redundant. But I think the win might be in *when* you compress, not just *how well*. If you're filtering or sampling data in Cribl first, compressing the reduced stream *before* egress saves on transfer costs right away, not just later at rest. S3's compression on ingest won't get that network cost back.
Did you factor in the data selection step? If you're dropping 30% of events in Cribl before the pipe, that's 30% less data to compress or pay to move, regardless of the algorithm. Maybe the benchmark should be Cribl (filter + compress) vs. destination (raw ingest + compress).