xudong963 commented on code in PR #4505:
URL: https://github.com/apache/arrow-datafusion/pull/4505#discussion_r1039000530
##########
datafusion/core/tests/sqllogictests/test_files/aggregate.slt:
##########
@@ -274,187 +271,367 @@ SELECT approx_distinct(c9) AS a, approx_distinct(c9) AS
b FROM aggregate_test_10
----
100 100
-# TODO: csv_query_approx_percentile_cont
+## This test executes the APPROX_PERCENTILE_CONT aggregation against the test
+## data, asserting the estimated quantiles are ±5% their actual values.
+##
+## Actual quantiles calculated with:
+##
+## ```r
+## read_csv("./testing/data/csv/aggregate_test_100.csv") |>
+## select_if(is.numeric) |>
+## summarise_all(~ quantile(., c(0.1, 0.5, 0.9)))
+## ```
+##
+## Giving:
+##
+## ```text
+## c2 c3 c4 c5 c6 c7 c8 c9
c10 c11 c12
+## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
<dbl> <dbl> <dbl>
+## 1 1 -95.3 -22925. -1882606710 -7.25e18 18.9 2671. 472608672.
1.83e18 0.109 0.0714
+## 2 3 15.5 4599 377164262 1.13e18 134. 30634 2365817608.
9.30e18 0.491 0.551
+## 3 5 102. 25334. 1991374996. 7.37e18 231 57518. 3776538487.
1.61e19 0.834 0.946
+## ```
+##
+## Column `c12` is omitted due to a large relative error (~10%) due to the
small
+## float values.
+
+#csv_query_approx_percentile_cont (c2)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c2, 0.1) AS DOUBLE) / 1.0) < 0.05)
AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c2, 0.5) AS DOUBLE) / 3.0) < 0.05)
AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c2, 0.9) AS DOUBLE) / 5.0) < 0.05)
AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c3)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c3, 0.1) AS DOUBLE) / -95.3) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c3, 0.5) AS DOUBLE) / 15.5) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c3, 0.9) AS DOUBLE) / 102.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c4)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c4, 0.1) AS DOUBLE) / -22925.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c4, 0.5) AS DOUBLE) / 4599.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c4, 0.9) AS DOUBLE) / 25334.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c5)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c5, 0.1) AS DOUBLE) /
-1882606710.0) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c5, 0.5) AS DOUBLE) / 377164262.0)
< 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c5, 0.9) AS DOUBLE) /
1991374996.0) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c6)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c6, 0.1) AS DOUBLE) /
-7250000000000000000) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c6, 0.5) AS DOUBLE) /
1130000000000000000) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c6, 0.9) AS DOUBLE) /
7370000000000000000) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c7)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c7, 0.1) AS DOUBLE) / 18.9) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c7, 0.5) AS DOUBLE) / 134.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c7, 0.9) AS DOUBLE) / 231.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c8)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c8, 0.1) AS DOUBLE) / 2671.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c8, 0.5) AS DOUBLE) / 30634.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c8, 0.9) AS DOUBLE) / 57518.0) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c9)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c9, 0.1) AS DOUBLE) / 472608672.0)
< 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c9, 0.5) AS DOUBLE) /
2365817608.0) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c9, 0.9) AS DOUBLE) /
3776538487.0) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c10)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c10, 0.1) AS DOUBLE) /
1830000000000000000) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c10, 0.5) AS DOUBLE) /
9300000000000000000) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c10, 0.9) AS DOUBLE) /
16100000000000000000) < 0.05) AS q FROM aggregate_test_100
+----
+true
+
+# csv_query_approx_percentile_cont (c11)
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c11, 0.1) AS DOUBLE) / 0.109) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c11, 0.5) AS DOUBLE) / 0.491) <
0.05) AS q FROM aggregate_test_100
+----
+true
+
+query T
+SELECT (ABS(1 - CAST(approx_percentile_cont(c11, 0.9) AS DOUBLE) / 0.834) <
0.05) AS q FROM aggregate_test_100
+----
+true
-# TODO: fix decimal places
# csv_query_cube_avg
-# query TIR
-# SELECT c1, c2, AVG(c3) FROM aggregate_test_100_by_sql GROUP BY CUBE (c1, c2)
ORDER BY c1, c2
-# ----
-# a 1 -17.6
-# a 2 -15.333333333333334
-# a 3 -4.5
-# a 4 -32
-# a 5 -32
-# a -18.333333333333332
-# b 1 31.666666666666668
-# b 2 25.5
-# b 3 -42
-# b 4 -44.6
-# b 5 -0.2
-# b -5.842105263157895
-# c 1 47.5
-# c 2 -55.57142857142857
-# c 3 47.5
-# c 4 -10.75
-# c 5 12
-# c -1.3333333333333333
-# d 1 -8.142857142857142
-# d 2 109.33333333333333
-# d 3 41.333333333333336
-# d 4 54
-# d 5 -49.5
-# d 25.444444444444443
-# e 1 75.66666666666667
-# e 2 37.8
-# e 3 48
-# e 4 37.285714285714285
-# e 5 -11
-# e 40.333333333333336
-# 1 16.681818181818183
-# 2 8.363636363636363
-# 3 20.789473684210527
-# 4 1.2608695652173914
-# 5 -13.857142857142858
-# 7.81
-
-# TODO: fix decimal places
+query TIR
+SELECT c1, c2, AVG(c3) FROM aggregate_test_100_by_sql GROUP BY CUBE (c1, c2)
ORDER BY c1, c2
+----
Review Comment:
Simplifying during migration or in separate PR both ok to me. -- Seems
during migration can reduce our workload
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
