Splunk Search Processing Language (SPL) is the query language used to search, transform, and analyze data in Splunk. It was designed to work with time-based machine data at scale.
Within SPL, the gentimes command plays a specialized but critical role. Instead of searching indexed data, time ranges are generated synthetically. As a result, searches can be built even when no events exist.
Practically speaking, gentimes is used when timelines, baselines, or comparisons are required. For example, it is commonly applied to detect gaps, validate coverage, or power empty dashboards. Therefore, it becomes especially valuable in operational and security analytics.
Understanding the gentimes Command
The gentimes command generates a series of timestamped events. Each generated event represents a fixed time interval. Rather than relying on indexed data, time itself becomes the dataset. Because of this behavior, the command is often used as a foundation.
For instance, it enables joins, lookups, or aggregations against expected time buckets. Additionally, the command produces _time values automatically. Those timestamps can then be aligned with real data. Consequently, missing or sparse data becomes visible.
Why gentimes Matter for Your Daily Splunk Work
The value of gentimes is best understood through its outcomes. Several common use cases appear repeatedly in production environments.
- Gap detection – Time intervals can be generated and compared to indexed events, revealing missing data.
- Baseline creation – Expected time buckets can be built to support trend analysis and thresholds.
- Empty-result handling – Dashboards and reports can still render meaningful visuals without events.
Because of these benefits, search logic becomes more resilient. As a result, analytics workflows are easier to maintain.
Basic Syntax for streamstats
The syntax for gentimes is intentionally simple. However, precision is required to avoid unintended time ranges.
| gentimes start=<time> end=<time> increment=<seconds>
Usage Examples & Practical Applications
Example #1: Detecting Missing Network Traffic
This use case focuses on identifying time gaps in network traffic. The Network Traffic data model is referenced.
| gentimes start=-24h end=now increment=5m
| eval expected_time=_time
| join expected_time
[ | tstats count from datamodel=Network_Traffic.All_Traffic by _time span=5m
| eval expected_time=_time ]
| where isnull(count)
Here, five-minute intervals are generated. Then, actual traffic is joined by time. As a result, missing intervals are exposed clearly.
Example #2: Baseline Authentication Activity Over Time
This example builds a baseline timeline for authentication activity. It uses the Authentication data model from the CIM.
| gentimes start=-7d end=now increment=1h
| eval baseline_time=_time
| join baseline_time
[ | tstats count from datamodel=Authentication.Authentication by _time span=1h
| eval baseline_time=_time ]
| fillnull value=0 count
Hourly intervals are generated first. Then, authentication events are aligned to those intervals. Consequently, quiet periods become visible and measurable.
Example #3: Powering Dashboards with No Events
Dashboards often fail when no data exists. This pattern prevents empty visualizations.
| gentimes start=-1d end=now increment=1h
| timechart count
In this case, a timeline is always produced. Even without indexed events, charts still render. Therefore, user experience is improved significantly.
Conclusion
The gentimes command fills an important gap in SPL. Time can be analyzed even when data is absent. By generating synthetic events, searches become more robust. Additionally, dashboards and detections gain consistency.
- Time ranges can be analyzed independently of indexed data
- Missing or sparse data becomes easier to identify
- Dashboards remain functional under all conditions
To access more Splunk searches, check out Atlas Search Library, which is part of the Atlas Platform. Specifically, Atlas Search Library offers a curated list of optimized searches. These searches empower Splunk users without requiring SPL knowledge. Furthermore, you can create, customize, and maintain your own search library. By doing so, you ensure your users get the most from using Splunk.
