Every vendor white paper and sales deck touts "near-zero performance impact," a phrase that should trigger immediate skepticism in anyone who has ever run a production database. When I see that, I immediately translate it to "we haven't measured it under a realistic workload, or we're defining 'near-zero' in a way that would make a politician blush."
I'm specifically looking at Carbon Black for some of our Windows-based SQL Server instances, but before I even consider a rollout, I need hard numbers. The sensor is, at its core, a filesystem filter driver, and anyone who has dealt with those knows they can turn a high-throughput OLTP disk subsystem into a congested single-lane road during rush hour. I'm not interested in anecdotes about "it feels fine." I need to see the quantified latency tax on:
* Log file writes (sequential, but latency-sensitive)
* TempDB operations (random, high-frequency)
* Checkpoint operations
* Backup I/O streams
Has anyone done a proper, controlled benchmark? I'm thinking using something like Diskspd or HammerDB with a defined workload, capturing metrics from the host, the SQL Server wait stats (WRITELOG, PAGEIOLATCH_*, etc.), and the Windows performance counters for the disk subsystem. The key is a before/after with the sensor in both passive and active modes.
I'd want to see the configuration used as well. The impact is undoubtedly tunable, but tuning for performance often means gutting the security efficacy, which is the whole point. A sample of a "performance-optimized" policy that still provides meaningful protection would be illustrative.
```yaml
# What does a 'SQL Server Optimized' policy look like?
# Example: Exclusions that go beyond the basic C:Program Files...
# - Specific process names (sqlservr.exe, ssms.exe)?
# - Exclusion of .mdf, .ldf, .ndf file extensions (risky?)
# - Directory exclusions for DATA, LOG, BACKUP volumes?
# - Sensor resource limits (CPU throttling, IOPS throttling)?
```
Without this data, we're just trading a potential security threat for a guaranteed performance and stability threat. The FinOps side of me also wants to know if this I/O latency translates to needing to scale up instances or provision higher-tier storage to maintain SLA, which is just another form of hidden cost.
So, has anyone actually put this to the test with a methodology that would hold up under peer review, or are we all just crossing our fingers and hoping the marketing material isn't completely fictional?
Trust but verify.