In this lesson, you will learn how to manage storage costs and query performance by downsampling high-cardinality eBPF trace data before it hits your time-series database.
The Cardinality Challenge
When using eBPF to trace kernel-level events—such as per-file descriptor latency or specific memory access patterns—the volume of unique metric "labels" (cardinality) can explode. For example, if you attach a label for every unique thread_id or inode currently active on a high-concurrency system, you create a "cardinality bomb." This leads to massive memory overhead in your metrics backend and renders long-term trend analysis computationally expensive.
Downsampling Strategies
To keep data manageable, we apply aggregation strategies within the eBPF program itself or in the local collection agent.
1. Label Pruning (Bucketization)
Instead of tracking raw values, convert continuous variables into discrete buckets. If you are tracking context_switch durations, don't store the exact nanosecond value as a tag; use a log-based histogram:
// Instead of tag: {duration: 1234ns}, use a bucket label
u64 bucket = bpf_log2l(duration);
// Update a histogram map indexed by process_name, excluding thread_id
__sync_fetch_and_add(&latency_hist[bucket], 1);
2. Time-Windowed Aggregation
High-frequency events like signal delivery or thread affinity shifts shouldn't be ingested as individual events. Instead, maintain a local per-CPU buffer that aggregates metrics (e.g., sum and count) over a fixed interval (like 10 seconds). Only the summarized "flush" is exported to the database. By pre-aggregating, you trade precision for massive storage efficiency.
3. Adaptive Sampling
If your system detects an influx of events (e.g., during an interrupt storm), shift to a statistical sampling model. Only record a fraction of events (e.g., 1 out of every 100) and scale the counter value proportionally. This keeps the performance footprint low during catastrophic spikes while preserving the relative trend of the metric.
By shifting the work of cardinality management to the eBPF program (or the local sidecar agent), you protect your monitoring infrastructure from the high-velocity data generated by deep kernel tracing.
Key Takeaway: To handle high-cardinality eBPF data, shift from exporting raw event tags to exporting aggregated histograms and pruned, time-windowed summaries.