q1482

R-SET GOLD-ONLY not-a-function-of-the-data

debit_card_specializing · mini_dev_pg-00000-of-00001 from https://huggingface.co/datasets/birdsql/bird_mini_dev/resolve/f65faf4ae3b638c1fa6df1d3370c8d92c8366301/data/mini_dev_pg-00000-of-00001.json (commit f65faf4a, downloaded 2026-09-14)

The question

Which of the three segments—SME, LAM and KAM—has the biggest and lowest percentage increases in consumption paid in EUR between 2012 and 2013?

the hint the set supplies: Increase or Decrease = consumption for 2013 - consumption for 2012; Percentage of Increase = (Increase or Decrease / consumption for 2013) * 100%; The first 4 strings of the Date values in the yearmonth table can represent year

This question was audited without a prediction beside it, so there is nothing to compare the gold with. The probes below read the gold alone.

The statements

gold

SELECT CAST((SUM(CASE WHEN T1.Segment = 'SME' AND T2.Date LIKE '2013%' THEN T2.Consumption ELSE 0 END) - SUM(CASE WHEN T1.Segment = 'SME' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END)) AS REAL) * 100 / NULLIF(SUM(CASE WHEN T1.Segment = 'SME' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END), 0), CAST(SUM(CASE WHEN T1.Segment = 'LAM' AND T2.Date LIKE '2013%' THEN T2.Consumption ELSE 0 END) - SUM(CASE WHEN T1.Segment = 'LAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END) AS REAL) * 100 / NULLIF(SUM(CASE WHEN T1.Segment = 'LAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END), 0), CAST(SUM(CASE WHEN T1.Segment = 'KAM' AND T2.Date LIKE '2013%' THEN T2.Consumption ELSE 0 END) - SUM(CASE WHEN T1.Segment = 'KAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END) AS REAL) * 100 / NULLIF(SUM(CASE WHEN T1.Segment = 'KAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END), 0) FROM customers AS T1 INNER JOIN yearmonth AS T2 ON T1.CustomerID = T2.CustomerID

this statement states no ordering of its own

sha256:6954ce039c20be170f7c1fbc3ccf90d5848078406bcd0aa2be3a437673a926eb

The results

gold, 1 row

from evidence-gold.json, 1 row

?column?float8 ?column?float8 ?column?float8
545.397464234613 681.5838032367947 708.1116371784611

The probes

A smell is a mechanical reason to read this gold statement again. It is a heuristic: it does not state that the statement is wrong, and a maintainer decides.

ordering-over-numeric-text not applicable

this statement orders by a text column holding only numbers, and ordering it as a number gives a different answer, so the gold may be sorting 9.5 above 10

the statement states no top level ORDER BY

what it measured 
{
  "heuristic": true,
  "reason": "the statement states no top level ORDER BY"
}

arbitrary-cut not applicable

this statement cuts its result at a LIMIT that does not decide which rows come back, so a different but equally correct statement can return other rows and score zero

the statement states no LIMIT

what it measured 
{
  "heuristic": true,
  "reason": "the statement states no LIMIT"
}

not-a-function-of-the-data fired

rerun over the same rows in another physical order this statement gives another answer, so its result depends on how the rows are stored and not only on the data

from smells.json, 1 row

545.4000427186699 681.5866605734918 708.1088457267458
what it measured 
{
  "heuristic": true,
  "rule": "R-SET",
  "baseline_result_hash": "sha256:6954ce039c20be170f7c1fbc3ccf90d5848078406bcd0aa2be3a437673a926eb",
  "baseline_result": {
    "columns": [
      {
        "name": "?column?",
        "declared_type": "float8"
      },
      {
        "name": "?column?",
        "declared_type": "float8"
      },
      {
        "name": "?column?",
        "declared_type": "float8"
      }
    ],
    "row_count": 1,
    "truncated": false,
    "rows_shown": 1,
    "rows": [
      [
        {
          "type": "dec",
          "value": "545.397464234613"
        },
        {
          "type": "dec",
          "value": "681.5838032367947"
        },
        {
          "type": "dec",
          "value": "708.1116371784611"
        }
      ]
    ],
    "result_hash": "sha256:6954ce039c20be170f7c1fbc3ccf90d5848078406bcd0aa2be3a437673a926eb"
  },
  "planner_statistics": {
    "customers": {
      "last_analyze": "2026-09-14 12:50:59.475079+00",
      "last_autoanalyze": null,
      "n_mod_since_analyze": 0
    },
    "yearmonth": {
      "last_analyze": "2026-09-14 12:51:05.044978+00",
      "last_autoanalyze": null,
      "n_mod_since_analyze": 0
    }
  },
  "shuffle": {
    "seed": "1",
    "row_limit": 300000,
    "tables": [
      "customers",
      "yearmonth"
    ],
    "tables_not_shuffled": [
      "yearmonth"
    ],
    "tables_skipped_for_size": {
      "laptimes": 400524,
      "legalities": 427907,
      "posthistory": 303155,
      "trans": 1056320,
      "yearmonth": 383282
    },
    "tables_not_reached_by_a_copy": {}
  },
  "shuffled_copies": {
    "run": true,
    "verdict": "not_equal",
    "differs": true,
    "result_hash": "sha256:852b9566b3810beedce16530fed501e16d4a317afb6090414d28098a65a57c71",
    "result": {
      "columns": [
        {
          "name": "?column?",
          "declared_type": "float8"
        },
        {
          "name": "?column?",
          "declared_type": "float8"
        },
        {
          "name": "?column?",
          "declared_type": "float8"
        }
      ],
      "row_count": 1,
      "truncated": false,
      "rows_shown": 1,
      "rows": [
        [
          {
            "type": "dec",
            "value": "545.4000427186699"
          },
          {
            "type": "dec",
            "value": "681.5866605734918"
          },
          {
            "type": "dec",
            "value": "708.1088457267458"
          }
        ]
      ],
      "result_hash": "sha256:852b9566b3810beedce16530fed501e16d4a317afb6090414d28098a65a57c71"
    }
  },
  "plan_variant": {
    "run": false,
    "reason": "the plan variant was not asked for"
  }
}

duplicate-full-row not applicable

this statement returns the same whole row more than once and never says DISTINCT, so a statement answering the same question once per row disagrees on multiplicity alone

what it measured 
{
  "heuristic": true,
  "rows": 1,
  "distinct_rows": 1,
  "repeated_rows": 0,
  "largest_repeat": 1,
  "result_bounded": false,
  "distinct_stated": false,
  "set_operation": false,
  "not_applicable": "a result of fewer than two rows has nothing to repeat"
}

The evidence records

gold: evidence-gold.json

SELECT CAST((SUM(CASE WHEN T1.Segment = 'SME' AND T2.Date LIKE '2013%' THEN T2.Consumption ELSE 0 END) - SUM(CASE WHEN T1.Segment = 'SME' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END)) AS REAL) * 100 / NULLIF(SUM(CASE WHEN T1.Segment = 'SME' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END), 0), CAST(SUM(CASE WHEN T1.Segment = 'LAM' AND T2.Date LIKE '2013%' THEN T2.Consumption ELSE 0 END) - SUM(CASE WHEN T1.Segment = 'LAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END) AS REAL) * 100 / NULLIF(SUM(CASE WHEN T1.Segment = 'LAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END), 0), CAST(SUM(CASE WHEN T1.Segment = 'KAM' AND T2.Date LIKE '2013%' THEN T2.Consumption ELSE 0 END) - SUM(CASE WHEN T1.Segment = 'KAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END) AS REAL) * 100 / NULLIF(SUM(CASE WHEN T1.Segment = 'KAM' AND T2.Date LIKE '2012%' THEN T2.Consumption ELSE 0 END), 0) FROM customers AS T1 INNER JOIN yearmonth AS T2 ON T1.CustomerID = T2.CustomerID
statement read from
data/questions/mini_dev_pg-00000-of-00001.json
digest
sha256:7fa740ef9225389cff6c34432120e8325d0ca3008d73db1ae38731234bc10da7
origin
https://huggingface.co/datasets/birdsql/bird_mini_dev/resolve/f65faf4ae3b638c1fa6df1d3370c8d92c8366301/data/mini_dev_pg-00000-of-00001.json (commit f65faf4a, downloaded 2026-09-14), 2026-01-18T08:44:25Z

result_hash sha256:6954ce039c20be170f7c1fbc3ccf90d5848078406bcd0aa2be3a437673a926eb recomputed from this JSON: match

record_hash sha256:1f21baa0d89f8838f875b67a829407f6f5e8772abe74e5704dc21e1d7e0b313d recomputed from this JSON: match

the result this record holds, 1 row

from evidence-gold.json, 1 row

?column?float8 ?column?float8 ?column?float8
545.397464234613 681.5838032367947 708.1116371784611
what ran, and where
run
audit-2e8f77d1-fb99-4c4c-8a42-65a40c5d382c
executed at
2026-09-14T12:51:14.666264+00:00
data as of
2026-09-14T12:51:05.775721+00:00
backend at checkout
PostgreSQL 16.15 (Debian 16.15-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit | server=172.17.0.2/32:5432 | database=bird
backend that answered
PostgreSQL 16.15 (Debian 16.15-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit | server=172.17.0.2/32:5432 | database=bird
database role
auditor
replay rule
R-SET
question set version
sha256:7fa740ef9225389cff6c34432120e8325d0ca3008d73db1ae38731234bc10da7
validator
audit:libpg_query-parse
checks run
parses_as_exactly_one_statement, the_one_statement_is_a_select, no_placeholder_without_a_bound_parameter
statement timeout
30000 ms
rows
1 row
the session it ran under
engine
postgresql
time_zone
Etc/UTC
date_style
ISO, MDY
interval_style
postgres
extra_float_digits
1
database_collation
en_US.utf8
work_mem
4096
hash_mem_multiplier
2

recorded beside them

statement_timeout
0
server_version
16.15 (Debian 16.15-1.pgdg13+2)
server_version_num
160015
transaction_read_only
on
max_parallel_workers_per_gather
2
server_encoding
UTF8
search_path
public
datlocprovider
c
daticulocale
datcollversion
2.41
the rendering and the data
version
attestql/audit/4
numeric_scale
6
timestamp_format
%Y-%m-%dT%H:%M:%S.%fZ
timezone
UTC
null_rendering
NULL
encoding
utf-8
schema digest
sha256:808947361d5e98fdfcaf192086e97e103446adf3e974573c0f50c4f2e5242320
source file sha256
sha256:31b1da211849d24a57c9af7636da46a5b82fc8a3ca1542bb3ebd8775e9a31cec
rows in public.customers
32461
rows in public.yearmonth
383282

Running these again

gold

re-run this statement read-only against PostgreSQL 16.15 (Debian 16.15-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit | server=172.17.0.2/32:5432 | database=bird under the session settings and over the data this record's fixture digest names, and compare the two results under R-SET

This question's run

run
audit-2e8f77d1-fb99-4c4c-8a42-65a40c5d382c
server
PostgreSQL 16.15 (Debian 16.15-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit | server=172.17.0.2/32:5432 | database=bird
question set
mini_dev_pg-00000-of-00001 from https://huggingface.co/datasets/birdsql/bird_mini_dev/resolve/f65faf4ae3b638c1fa6df1d3370c8d92c8366301/data/mini_dev_pg-00000-of-00001.json (commit f65faf4a, downloaded 2026-09-14)
replay rule
R-SET

the run this question belongs to

The JSON this page was rendered from