Load Test — Latency Percentiles by Scenario (CSV)
The percentile table a performance report renders as a chart, with mean, p50, p95, p99 and max for each of the four scenarios. The same numbers appear inside the JSON summary in this group, so a chart built from either source can be diffed against the other.
scenario,requests,mean_ms,p50_ms,p95_ms,p99_ms,max_ms,error_rate_pct
GET /v1/cart,24000,41.2,68.4,118.6,264.9,812.4,0.12
POST /v1/cart/items,12000,88.1,142.7,231.4,498.2,1204.0,0.41
GET /v1/checkout/session,9600,32.9,51.6,92.2,188.7,604.1,0.08
POST /v1/checkout/pay,3200,214.6,356.8,588.3,1102.4,2410.9,1.87
Specifications
- Seed
- 61200
- Rows
- 4
- Columns
- 8
- Delimiter
- ,
- Header
- true
- Percentiles
- p50, p95, p99
- Slowest Scenario
- POST /v1/checkout/pay
- Line Endings
- LF
Testing contract
Reference control- Scenario
- Chart latency percentiles and check them against the JSON summary twin.
- Expected result
- Every value in this table matches the corresponding scenario entry in load-test-summary.json, and p50 <= p95 <= p99 <= max holds on all four rows.
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
“Load Test — Latency Percentiles by Scenario (CSV)” is a deterministic Novus Examples fixture for Performance testing, CSV parsing, Conversion testing. Documented size, row-count, duration, and resolution ladders for measuring parser, renderer, converter, and upload performance without relying on private production data.
Documented properties for this file: seed 61200 · 4 rows · 8 columns · LF. 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.
Test and coverage reports document their totals (suites, cases, passes, failures, skips, covered lines) in the spec table. Point your CI dashboard, coverage gate, or report converter at the file and assert those counts survive; format twins carry identical numbers so a conversion can be scored exactly.
Code examples
import pandas as pd
df = pd.read_csv("latency-percentiles.csv")
print(df.head())
print(df.dtypes)Related files
- csvCoverage — Per-File Table (CSV)The same coverage as a flat table with a TOTAL row, for spreadsheets, trend charts and diffing two runs without an XML parser. The TOTAL row is the arithmetic sum of the four file rows, so it doubles as a checksum on any tool that regenerates it. Every file in this group describes the same four-file source tree and reports 127/140 lines, 17/24 branch outcomes and 18/20 functions, so a converter can be diffed against a known answer.
- csvFlaky Detection — Per-Test Failure-Rate Summary (CSV)The aggregate answer key for the 40-build history in this group: runs, passes, failures and failure rate per test, with each test labelled stable or flaky. Use it to score a detector rather than eyeballing 240 JSONL records.
- csvFlaky Run — Network Dependency, Flake-Rate Table (CSV)The answer key for the network dependency family: the flake rate a detector should derive from the five build reports next to it. Exactly one of the six tests is unstable, at a rate of 2/5; the other five are stable at 0.00.
- csvFlaky Run — Order Dependent, Flake-Rate Table (CSV)The answer key for the order dependent family: the flake rate a detector should derive from the five build reports next to it. Exactly one of the six tests is unstable, at a rate of 2/5; the other five are stable at 0.00.
- csvFlaky Run — Quarantined After Two Failures, Flake-Rate Table (CSV)The answer key for the quarantined after two failures family: the flake rate a detector should derive from the five build reports next to it. Exactly one of the six tests is unstable, at a rate of 5/5; the other five are stable at 0.00.
- csvFlaky Run — Timing Race, Flake-Rate Table (CSV)The answer key for the timing race family: the flake rate a detector should derive from the five build reports next to it. Exactly one of the six tests is unstable, at a rate of 2/5; the other five are stable at 0.00.
Generated by generation/testing.py. Free for any use, no attribution required — license.