SLO Error Budget — 28-Day Burndown Export (csv)
A day-by-day burndown of the availability SLO defined in the OpenSLO fixture: achieved ratio, budget consumed and remaining, and one-hour and six-hour burn rates. Consumption is monotonic across the window, which is the invariant a burn-rate chart must not violate.
date,slo,window_days,target,achieved,budget_total_minutes,budget_consumed_minutes,budget_remaining_pct,burn_rate_1h,burn_rate_6h
2026-02-18,checkout-availability,28,0.999,0.99801,40.3,2.9,92.9,1.99,1.63
2026-02-19,checkout-availability,28,0.999,0.99925,40.3,3.9,90.2,0.75,0.61
2026-02-20,checkout-availability,28,0.999,0.99903,40.3,5.3,86.8,0.97,0.80
2026-02-21,checkout-availability,28,0.999,0.99777,40.3,8.5,78.8,2.23,1.83
2026-02-22,checkout-availability,28,0.999,0.99880,40.3,10.3,74.5,1.20,0.98
2026-02-23,checkout-availability,28,0.999,0.99751,40.3,13.9,65.6,2.49,2.04
2026-02-24,checkout-availability,28,0.999,0.99879,40.3,15.6,61.3,1.21,0.99
2026-02-25,checkout-availability,28,0.999,0.99910,40.3,16.9,58.1,0.90,0.74
2026-02-26,checkout-availability,28,0.999,0.99698,40.3,21.3,47.3,3.02,2.48
2026-02-27,checkout-availability,28,0.999,0.99740,40.3,25.0,38.0,2.60,2.13
2026-02-28,checkout-availability,28,0.999,0.99707,40.3,29.2,27.5,2.93,2.40
2026-03-01,checkout-availability,28,0.999,0.99791,40.3,32.2,20.1,2.09,1.71
2026-03-02,checkout-availability,28,0.999,0.99752,40.3,35.8,11.2,2.48,2.03
2026-03-03,checkout-availability,28,0.999,0.99801,40.3,38.7,4.1,1.99,1.63
2026-03-04,checkout-availability,28,0.999,0.99821,40.3,41.2,0.0,1.79,1.47
2026-03-05,checkout-availability,28,0.999,0.99890,40.3,42.8,0.0,1.10,0.90
2026-03-06,checkout-availability,28,0.999,0.99815,40.3,45.5,0.0,1.85,1.52
2026-03-07,checkout-availability,28,0.999,0.99860,40.3,47.5,0.0,1.40,1.15
2026-03-08,checkout-availability,28,0.999,0.99828,40.3,50.0,0.0,1.72,1.41
2026-03-09,checkout-availability,28,0.999,0.99736,40.3,53.8,0.0,2.64,2.16
2026-03-10,checkout-availability,28,0.999,0.99975,40.3,54.1,0.0,0.25,0.20
2026-03-11,checkout-availability,28,0.999,0.99917,40.3,55.3,0.0,0.83,0.68
2026-03-12,checkout-availability,28,0.999,0.99945,40.3,56.1,0.0,0.55,0.45
2026-03-13,checkout-availability,28,0.999,0.99735,40.3,59.9,0.0,2.65,2.17
2026-03-14,checkout-availability,28,0.999,0.99875,40.3,61.7,0.0,1.25,1.02
2026-03-15,checkout-availability,28,0.999,0.99919,40.3,62.9,0.0,0.81,0.66
2026-03-16,checkout-availability,28,0.999,0.99779,40.3,66.1,0.0,2.21,1.81
2026-03-17,checkout-availability,28,0.999,0.99943,40.3,66.9,0.0,0.57,0.47
Specifications
- Rows
- 28
- Columns
- 10
- Window Days
- 28
- Target
- 0.999
- Budget Total Minutes
- 40.3
- Monotonic Consumption
- true
- Seed
- 7511
- Matches Slo
- checkout-availability
Testing contract
Expected to pass- Scenario
- Chart the burndown and evaluate a multi-window burn-rate alert.
- Expected result
- 28 rows load, budget_consumed_minutes never decreases, and budget_remaining_pct reaches zero only if consumption exceeds the 40.3-minute total for a 99.9% target.
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“SLO Error Budget — 28-Day Burndown Export (csv)” is a deterministic Novus Examples fixture for Observability, CSV parsing, Time-series data. Structured and plain-text telemetry with known timestamps, levels, request identifiers, and error states for testing log ingestion, correlation, dashboards, and alert pipelines.
Documented properties for this file: seed 7511 · 28 rows · 10 columns. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Telemetry fixtures use fixed trace IDs, span IDs, and timestamps so ingestion is reproducible run to run. Point your collector, parser, or query layer at the file and assert the documented span tree, metric families, or severity mix; service and host names are invented.
Code examples
import pandas as pd
df = pd.read_csv("slo-error-budget.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/observability.py. Free for any use, no attribution required — license.