Documentation
¶
Overview ¶
Package trends computes time-series — how many times an event happened per day (optionally unique users), the third core analysis primitive alongside funnels and retention. Deterministic and storage-agnostic.
Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func NumericProps ¶ added in v0.9.0
NumericProps returns the property names that carry at least one numeric value across the events — the columns a measure (sum/avg/p90) can aggregate. Sorted for determinism. Lets the ask bar resolve "revenue" to a real numeric property, or say honestly that none exists.
Types ¶
type Interval ¶ added in v0.9.1
type Interval string
Interval is a bucketing grain for the series.
func ParseInterval ¶ added in v0.9.1
ParseInterval maps a request string to a grain; empty = day. Unknown grains are an error, never silently day — a wrong-grain chart is a silent-wrong answer.
type Measure ¶ added in v0.9.0
type Measure string
Measure is a numeric aggregation over an event property — the money/growth questions Count can't answer: revenue (sum of "amount"), average order value (avg), p90 latency, min/max. This is the single most common "it can't do X" a new user hits on day one.
func ParseMeasure ¶ added in v0.9.0
ParseMeasure maps a string (query param, MCP arg) to a Measure, defaulting to Sum for a bare/unknown value so a caller that asks for a numeric aggregation always gets one.
type MeasurePoint ¶ added in v0.9.0
type MeasurePoint struct {
Date time.Time `json:"date"`
Value float64 `json:"value"`
N int `json:"n"`
}
MeasurePoint is one day's aggregated numeric value. N is how many events contributed — 0 marks an empty day so avg/median/p90 read as "no data", not a real zero.
type MeasureResult ¶ added in v0.9.0
type MeasureResult struct {
Event string `json:"event"`
Property string `json:"property"`
Measure Measure `json:"measure"`
Points []MeasurePoint `json:"points"`
Total float64 `json:"total"`
N int `json:"n"` // total events that carried a numeric value
}
MeasureResult is the daily numeric series plus the aggregate over the WHOLE window (so Total for avg/median/p90 is correct, not a misleading mean-of-daily-means).
func ComputeMeasure ¶ added in v0.9.0
func ComputeMeasure(events []event.Event, eventName, property string, m Measure, from, to time.Time) MeasureResult
ComputeMeasure aggregates a numeric event property per day between from and to. Events missing the property, or whose value isn't numeric, are skipped (never coerced to 0). Deterministic and storage-agnostic, same as Compute.
type Result ¶
type Result struct {
Event string `json:"event"`
Unique bool `json:"unique"` // true = distinct users, false = raw count
Points []Point `json:"points"`
Total int `json:"total"`
}
Result is the daily series for one event.
func Compute ¶
Compute returns daily counts for eventName (empty = all events) between from and to. unique=true counts distinct users per day instead of raw events. Days with no activity are filled with zero so the line/bars are continuous.
func ComputeInterval ¶ added in v0.9.1
func ComputeInterval(events []event.Event, eventName string, from, to time.Time, unique bool, iv Interval) Result
ComputeInterval is Compute with a bucketing grain. Buckets with no activity fill with zero so the series is continuous. Hourly output is capped at 31 days of buckets (744) — the guardrail every incumbent applies to keep charts readable.
func ComputeXAU ¶ added in v0.9.1
ComputeXAU plots DAU/WAU/MAU as a daily series: each point = distinct users active in the rolling half-open window (point − windowDays, point], per the TRENDS-XAU contract (mixpanel XAU semantics; posthog words it "the N days leading up to the label"). The Total echoes the LAST point (the current value) — summing rolling actives would double-count meaninglessly.
type Series ¶
type Series struct {
Value string `json:"value"`
Points []Point `json:"points"`
Total int `json:"total"`
}
Series is one line of a broken-down trend (e.g. signups from "google" over time).
func ComputeBreakdown ¶
func ComputeBreakdown(events []event.Event, eventName, property string, from, to time.Time, unique bool) []Series
ComputeBreakdown splits a trend into one series per value of property — the multi-line "signups by source over time" report. Events missing the property fall into "(none)". Series are sorted by total descending.
Source Files