Time-series interface¶

The tsdb feature (eg-tsdb) gives a native, redb-backed time-series store with the analytic primitives a time-series workload needs — including ASOF joins, which DataFusion 43 lacks. It is pure-Rust and shares the engine's one durability model.

Status snapshot: the store and time-ops are supported as native functions, now bound into the unified planner (Op::Window execution, EG-KG.query.streaming-execution) with per-point retention trim (EG-KG.temporal.bucket-cutoff-trim). A columnar segment layout backs analytical/SQL-window scans (EG-089), and a PromQL /api/v1/query HTTP API (EG-172) serves the metric series. TimescaleDB hypertables + continuous aggregates (EG-117) layer on over pgwire. See the capability matrix.

The store¶

SeriesStore keeps columnar chunks under a composite redb key (series_id, bucket_start):

append_batch amortizes the fsync across a group of points in one transaction;
range does a bounded scan over a [from, to) window;
evict_before applies retention, trimming straddling buckets at the boundary (per-point trim, EG-KG.temporal.bucket-cutoff-trim), not just whole-bucket eviction.

A columnar (struct-of-arrays) segment layout (EG-089) backs analytical scans over the tsdb/table store — the same layout the SQL window functions read (see sql).

Time operations (native, no DataFusion)¶

Function	What it does
`time_bucket`	aligned buckets `(ts/w)*w` with avg / min / max / sum / count / first / last
`asof_join_backward`	ASOF (as-of-or-before) join with tolerance, O(L+R) merge
`gap_fill_locf`	last-observation-carried-forward over a fixed grid
`ohlc_bars`	open / high / low / close + volume bars
`downsample`	rollup to a coarser resolution
`decay_weighted_mean`	Ebbinghaus decay-weighted mean (shared `eg_core::decay` curve)
`series_ewma`, `series_rolling_zscore`	EWMA and rolling z-score (reuse the finance kernels)

Unified-planner binding (EG-KG.query.streaming-execution)¶

The time-ops are wired behind the planner's Op::Window (with the eg-plan→eg-tsdb dependency edge + cost-based reorder), so a temporal aggregate composes inline in a UQL pipeline with graph/vector/SQL stages. The bi-temporal Op::AsOf point-in-time filter also executes today:

MATCH (:Reading)
  |> AS OF VALID @1700000000
  |> LIMIT 100

PromQL / Prometheus HTTP API (EG-172, feature `promql` on the `obs` listener)¶

A PromQL evaluator (instant/range vectors, selectors, rate/sum/avg/max/min/histogram functions, binary ops) over the eg-tsdb metric series is exposed as a Prometheus-compatible HTTP API on the obs listener (EPISTEMIC_GRAPH_OBS_ADDR, default 127.0.0.1:5080):

curl -s 'http://127.0.0.1:5080/api/v1/query?query=up'
curl -s 'http://127.0.0.1:5080/api/v1/query_range?query=rate(http_requests[5m])&start=…&end=…&step=15s'

Point a Grafana Prometheus data source at the obs listener. This is one leg of the full observability surface (logs + PromQL + traces) — see observability.

TimescaleDB compatibility (EG-117, over pgwire)¶

CREATE EXTENSION timescaledb lights up create_hypertable(), time_bucket() gap-fill, and CREATE MATERIALIZED VIEW … WITH (timescaledb.continuous) continuous aggregates, lowered onto this time-series store + Op::Window — see sql.