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Standard Deviation of Volume Ingestion for Alerting Historical Volatility (Z-Score Method)


The SPL 

splunk index=_internal host=*.splunkcloud.com source=*license_usage.log* type="Usage" earliest=-15m@m | bin _time span=15m | stats sum(b) AS byte_sum by idx, _time | join type=inner idx [| inputlookup avg_index_bytes_15m.csv] | eval z_score=(byte_sum - average)/std | eval currentGB=round((byte_sum/1024/1024/1024), 3) | eval averageGB=round((average/1024/1024/1024), 3) | eval stdGB=round((std/1024/1024/1024), 3) | table _time, idx, currentGB, averageGB, z_score, stdGB | where z_score < -3 | sort 0 - z_score | rename idx AS Index, currentGB AS "GB Indexed over Past 15 Minutes", averageGB AS "Average GB Indexed per 15 Minutes", stdGB AS "Standard Deviation", z_score AS "Z-Score" | search "Standard Deviation">1

Ultra-Detailed, Pipe-by-Pipe Breakdown

Below, every stage shows: what it does, why it's done, and what fields/rows look like afterwards.

1. Initial event search

splunk index=_internal host=*.splunkcloud.com source=*license_usage.log* type="Usage" earliest=-15m@m 

What it does:
- Pulls events from Splunk’s internal index (_internal) where the source is the license usage log (*license_usage.log*) and the event type is "Usage".
- Limits time to the last 15 minutes (earliest=-15m@m) rounded to the minute boundary.
- host=*.splunkcloud.com narrows results to Splunk Cloud license hosts.

Why: These events include per-index byte counts (b) that Splunk uses to track license usage. You want to know how many bytes each index ingested in the last 15 minutes.

Key fields coming out of this stage (per event): _time, host, source, type, idx (index name), b (bytes indexed), plus internal metadata.

2. Bucket times into 15‑minute bins

splunk | bin _time span=15m 

What it does: Rounds _time down to the nearest 15-minute boundary so multiple events in the same period share the same timestamp.

Why: Prepares data for grouping by a consistent time bucket; ensures one row per index per 15-minute window after aggregation.

Output change: _time values become aligned (e.g. 2025-07-23 14:00:00).

3. Aggregate bytes by index and time

splunk | stats sum(b) AS byte_sum by idx, _time 

What it does: For each (idx, _time) pair, sums all b values to produce byte_sum.

Why: You need one consolidated metric: total bytes an index ingested during that 15-minute window.

Output fields now: _time, idx, byte_sum (one row per index per bin). All other original fields are dropped unless included in by or aggregated.

4. Join with historical baseline lookup

splunk | join type=inner idx [| inputlookup avg_index_bytes_15m.csv] 

What it does:
- Loads a CSV lookup (avg_index_bytes_15m.csv) that (presumably) contains historical stats per index: e.g. idx, average, std.
- Performs an inner join on idx. Only indexes present in both the live data and the lookup survive.

Why: To compare the current 15‑minute volume against a precomputed baseline mean (average) and standard deviation (std) for that same index.

After join you typically have: _time, idx, byte_sum, average, std (and any extra fields in the lookup).

Tip: You could swap join for lookup for efficiency:
splunk | lookup avg_index_bytes_15m.csv idx OUTPUT average std Functionally the same if idx is unique in the lookup.

5. Compute the z-score

splunk | eval z_score=(byte_sum - average)/std 

What it does: Calculates how far (in standard deviations) today’s 15‑minute byte total deviates from the historical mean.

Why: A z-score is a quick anomaly metric. Negative means below average; a large negative (e.g. < -3) is statistically rare under normal distribution assumptions.

Math refresher:
- z = (current - mean) / std_dev
- Example: (0.768 GB - 5.905 GB) / 1.315 GB ≈ -3.91

Output fields now include: z_score.

6. Convert bytes to GB for readability

splunk | eval currentGB=round((byte_sum/1024/1024/1024), 3) | eval averageGB=round((average/1024/1024/1024), 3) | eval stdGB=round((std/1024/1024/1024), 3) 

What it does: Creates human-friendly gigabyte versions of the raw byte metrics, rounded to three decimals.

Why: Easier for humans to interpret and include in alerts/reports.

Output now has: currentGB, averageGB, stdGB in addition to the raw byte fields.

7. Keep only the fields you care about

splunk | table _time, idx, currentGB, averageGB, z_score, stdGB 

What it does: Drops everything but the main fields you plan to show or alert on.

Why: Clean, tidy output (also reduces later processing overhead).

8. Filter to “significant” negative anomalies

splunk | where z_score < -3 

What it does: Keeps rows where the current volume is more than 3 standard deviations below the mean. (Nothing above average or slight dips.)

Why: A common statistical threshold: ~0.13% of a normal distribution lies below -3σ, so it signals “rare” events.

Effect: All normal or mild drops are removed; only “big” drops remain.

9. Sort by z-score (descending)

splunk | sort 0 - z_score 

What it does: Sorts results by z_score descending. Since all are negative, the least negative (closest to zero) appears first.

Why: Presentation choice. If you want most extreme at top, use sort 0 z_score instead (ascending).

10. Rename columns for final presentation

splunk | rename idx AS Index, currentGB AS "GB Indexed over Past 15 Minutes", averageGB AS "Average GB Indexed per 15 Minutes", stdGB AS "Standard Deviation", z_score AS "Z-Score" 

What it does: Makes headers human-readable (nice for dashboards/emails).

Why: Stakeholders don’t need to see cryptic field names.

11. Final filter on Std Dev (variance floor)

splunk | search "Standard Deviation">1 

What it does: After the rename, “Standard Deviation” refers to stdGB. This line keeps only rows where stdGB > 1 GB.

Why: Prevents noisy alerts from low-volume, low-variance indexes where even tiny dips produce a big z-score. You only care about big, variance-rich indexes.

Example That Keeps Triggering (False Positive Case)

_time: 2025-07-23 14:00:00 Index: network-firewall-palo_alto GB Indexed over Past 15 Minutes: 0.768 Average GB Indexed per 15 Minutes: 5.905 Z-Score: -3.906902640760295 Standard Deviation: 1.315

  • It passed all filters: z < -3 and stdGB (~1.315) > 1.
  • But this drop could be perfectly normal (maintenance window, lull, delayed forwarder, weekend, etc.).

Why You’re Seeing False Positives

  1. Seasonality ignored: One global average for an index hides periodic patterns (night vs day, weekday vs weekend). Off-hours will always look “low” compared to the all-hours average.
  2. Baseline drift / stale lookup: If ingestion patterns changed (new sources, retirements), the saved average/std might no longer represent current reality.
  3. Single-bucket sensitivity: One late/dropped batch in a 15‑min window triggers an alert, even if the next bucket catches up.
  4. Statistical vs practical significance: A -3.1 z-score could reflect a small absolute change that isn’t operationally important.

Tuning Ideas to Cut Noise

A. Add Time Context to the Baseline

  • Recompute averages and stddev per time slice, e.g. by:
  • weekday/weekend (boolean)
  • biz_hours vs off_hours
  • Even hour_of_day (0–23) or (0–95 for 15‑min buckets)
  • Store these keys in your lookup (avg_index_bytes_15m.csv) and add them to both your live search and lookup join.
  • Compare Wednesday 14:00 against historical Wednesday 14:00, not against 24/7 global average.

B. Tighten the Threshold(s)

  • Change where z_score < -3 to < -3.5, < -4, etc.
  • Add an absolute delta: e.g. currentGB < averageGB * 0.5 or averageGB - currentGB > 2 GB.

C. Minimum Baseline Requirements

  • Already filtering on stdGB > 1. Consider also averageGB > 1 (or a larger value) to ensure you only alert on high-volume indexes.
  • If you see noise around std between 1–2 GB, bump that to >2 GB.

D. Persistence Check (multi-bucket confirmation)

  • Search a longer window (e.g. 45–60 min), compute z-scores per 15‑min bin, and alert only if 2+ consecutive bins are anomalous.
    Example pattern: splunk earliest=-60m@m latest=now | bin _time span=15m | stats sum(b) AS byte_sum by idx _time | lookup avg_index_bytes_15m.csv idx OUTPUT average std weekday weekend biz_hours | eval z=(byte_sum-average)/std | eval currentGB=round(byte_sum/1024/1024/1024,3), averageGB=round(average/1024/1024/1024,3), stdGB=round(std/1024/1024/1024,3) | where z < -3 AND stdGB>1 AND averageGB>1 | streamstats count(eval(z < -3)) AS consecutive_low BY idx | where consecutive_low>=2 (There are many ways to implement this; streamstats, eventstats, or even transaction can be used.)

E. Keep the Baseline Fresh

  • Schedule a daily job to rebuild avg_index_bytes_15m.csv from the last N days (30 is common).
  • Optionally, use median & MAD instead of mean & std for robustness to outliers.

F. Performance/Clarity Tweaks

  • Prefer lookup over join where possible.
  • Consider adding comments in your SPL (with # on a new line) to remind future-you what each stage does.

Sample Query

index=_internal source=*license_usage.log* type="Usage" earliest=-60m@m | bin _time span=15m | stats sum(b) AS byte_sum by idx _time | lookup avg_index_bytes_15m.csv idx OUTPUT average std weekday weekend biz_hours | eval z_score=(byte_sum-average)/std | eval currentGB=round(byte_sum/1024/1024/1024,3), averageGB=round(average/1024/1024/1024,3), stdGB=round(std/1024/1024/1024,3) | where z_score < -3 AND currentGB < averageGB*0.6 AND stdGB > 1 AND averageGB > 1 | streamstats count(eval(z_score < -3)) AS low_run BY idx | where low_run >= 2 | table _time idx currentGB averageGB stdGB z_score | rename idx AS Index currentGB AS "GB Indexed (15m)" averageGB AS "Avg GB (15m)" stdGB AS "Std Dev (GB)" z_score AS "Z-Score" | sort 0 "Z-Score"

Note: Adjust thresholds (0.6, >1, >=2, etc.) to match your tolerance for noise and business risk.

Key Takeaways

  • Know the math: z-score = (current − average) ÷ std. Big negative = big drop.
  • Context beats raw stats: Segment your baseline (time-of-day, weekday/weekend) to avoid “expected lows” being flagged.
  • Multiple gates reduce noise: z-score + absolute delta + std/avg floors + persistence check.
  • Refresh baselines regularly: Keep that CSV accurate and reflective of current behavior.
  • Iterate: Tune, observe alert volume, tweak thresholds again.

Happy tuning and fewer false positives!

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