Time-series edge fixtures

Time-series — Irregular Intervals (CSV)

Sensor readings at irregular minute gaps — tests time-series resampling and gap detection.

File: CSV · Timeseries · 8 rows
Use case: Time-series dataData import+1· Conversion set

Time-series — Irregular Intervals (JSON)

JSON twin of irregular-interval sensor readings for charting library tests.

File: JSON · Timeseries · 8 records
Use case: Time-series dataData import+1· Conversion set

Time-series — DST Spring Forward Gap (CSV)

Hourly load data skipping the 03:00 hour on DST spring-forward day — tests timezone/DST gap handling.

File: CSV · Timeseries · 4 rows
Use case: Time-series dataData import+1

Time-series — DST Fall Back Duplicate Hour (CSV)

Load readings with a repeated 01:00 local hour on fall-back DST — tests ambiguous timestamp handling.

File: CSV · Timeseries · 5 rows
Use case: Time-series dataData import+1

Time-series — Duplicate Timestamp Keys (CSV)

Two rows share the same timestamp key — tests deduplication and last-write-wins policies.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+2· Conversion set

Time-series — Duplicate Timestamp Keys (JSON)

JSON array with duplicate timestamp keys for ingestion pipeline tests.

File: JSON · Timeseries · 3 records
Use case: Time-series dataData import+1· Conversion set

Time-series — Gap Filled

Series with an explicit null value at a missing sample point.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Out Of Order

Timestamps out of chronological order — tests sort-on-ingest behaviour.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Mixed Tz

Same nominal instant expressed in Z, -05:00, and +09:00 offsets.

File: JSON · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Nan Gaps

CSV with NaN string and empty cells representing missing measurements.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Millisecond

Sub-second millisecond timestamps for precision parsing tests.

File: JSON · Timeseries · 2 rows
Use case: Time-series dataData import+1

Time-series — Business Hours

Hourly order counts weekdays only (09–16 UTC) across three SAMPLE days.

File: CSV · Timeseries · 24 rows
Use case: Time-series dataData import+1

Time-series — Weekly Agg

Pre-aggregated weekly totals JSON for rollup/chart tests.

File: JSON · Timeseries · 1 row
Use case: Time-series dataData import+1

Time-series — Leap Day

Readings spanning Feb 29 leap day — calendar edge case.

File: CSV · Timeseries · 2 rows
Use case: Time-series dataData import+1

Time-series — Epoch Boundary

Timestamps at Unix epoch and classic 32-bit Y2038 boundary.

File: JSON · Timeseries · 2 rows
Use case: Time-series dataData import+1

Time-series — Sparse Events (JSONL)

Sparse event log in JSON Lines — two events hours apart for event-stream ingestion tests.

File: JSONL · Timeseries · 2 records
Use case: Time-series dataData import+2

Fintech FX Rates Time-series (JSON)

Daily USD/EUR SAMPLE rates for FX chart and conversion tests.

File: JSON · Fintech
Use case: Time-series dataJSON parsing

Prometheus Exposition — Cumulative Histogram (prom)

A classic Prometheus histogram: eleven finite le buckets plus +Inf, with monotonically non-decreasing cumulative counts and matching _sum and _count series. The fixture for bucket ordering, cumulative arithmetic and histogram_quantile interpolation.

File: PROM · Metrics
Use case: ObservabilityTime-series data+1· Conversion set

Prometheus Exposition — Summary with and without Quantiles (prom)

Two summary families — one exposing four client-side quantiles including quantile="1" for the observed maximum, and one exposing only _sum and _count, which is legal and common. Summaries cannot be re-aggregated across instances, and this fixture is where that gets tested.

File: PROM · Metrics
Use case: ObservabilityTime-series data+1· Conversion set

Prometheus Exposition — Explicit and Skewed Timestamps (prom)

Samples with explicit millisecond timestamps alongside samples without any, including one an hour stale and one an hour in the future. Optional timestamps are the part of the exposition format most parsers get wrong, and out-of-window samples are what a scraper must reject rather than backfill.

File: PROM · Metrics
Use case: ObservabilityTime-series data+1· Conversion set

OTLP Metrics — Explicit-Bucket Histogram (json)

An OTLP explicit-bucket histogram over the same latency distribution as the Prometheus fixture — but with per-bucket counts rather than cumulative ones, and one more bucket count than bounds. Converting between the two is where most OTLP-to-Prometheus bridges get the arithmetic wrong.

File: JSON · Metrics
Use case: ObservabilityJSON parsing+1· Conversion set

OTLP Metrics — Base-2 Exponential Histogram (json)

An exponential histogram at scale 3 — eight buckets per power of two — with a negative offset, a zero bucket and its threshold. Bucket i covers (base^(offset+i), base^(offset+i+1)], and getting that indexing wrong silently shifts every percentile, which is what this fixture is for.

File: JSON · Metrics
Use case: ObservabilityJSON parsing+1· Conversion set

OTLP Metrics — Counter Reset on Process Restart (json)

A monotonic counter that drops from 94,880 to 1,120 when its process restarts, with startTimeUnixNano changing at exactly that point to mark the reset. Rate calculations that subtract consecutive values without checking the start timestamp produce a large negative rate here.

File: JSON · Metrics
Use case: ObservabilityTime-series data+1· Conversion set

Structured Log — Out-of-Order Timestamps (jsonl)

Records whose ts field moves backwards seven times while emitted_seq stays monotonic — what a buffered shipper produces when several workers flush concurrently. The fixture for sort-on-ingest, late-arrival windows and any index that assumes append order equals time order.

File: JSONL · Logs
Use case: ObservabilityLog parsing+1· Conversion set