tanclary commented on code in PR #3648:
URL: https://github.com/apache/calcite/pull/3648#discussion_r1478594969
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testkit/src/main/java/org/apache/calcite/test/SqlOperatorTest.java:
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@@ -6215,6 +6215,71 @@ void checkRegexpExtract(SqlOperatorFixture f0,
FunctionAlias functionAlias) {
f.checkNull("log(10, cast(null as real))");
}
+ /** Test case for
+ * <a
href="https://issues.apache.org/jira/browse/CALCITE-6224";>[CALCITE-6224]
+ * Add LOG@ function (enabled in MYSQL, Spark library)</a>. */
+ @Test void testLog2Func() {
+ final SqlOperatorFixture f0 = fixture();
+ final Consumer<SqlOperatorFixture> consumer = f -> {
+ f.setFor(SqlLibraryOperators.LOG2);
+ f.checkScalarApprox("log2(2)", "DOUBLE NOT NULL",
+ isWithin(1.0, 0.000001));
+ f.checkScalarApprox("log2(4)", "DOUBLE NOT NULL",
+ isWithin(2.0, 0.000001));
+ f.checkScalarApprox("log2(65536)", "DOUBLE NOT NULL",
+ isWithin(16.0, 0.000001));
+ f.checkScalarApprox("log2(-2)", "DOUBLE NOT NULL",
+ "NaN");
+ f.checkScalarApprox("log2(2/3)", "DOUBLE NOT NULL",
+ "-Infinity");
+ f.checkScalarApprox("log2(2.2)", "DOUBLE NOT NULL",
+ "1.1375035237499351");
+ f.checkScalarApprox("log2(0.5)", "DOUBLE NOT NULL",
+ "-1.0");
+ f.checkScalarApprox("log2(3)", "DOUBLE NOT NULL",
+ isWithin(1.5849625007211563, 0.000001));
+ f.checkNull("log2(cast(null as real))");
+ };
+ f0.forEachLibrary(list(SqlLibrary.MYSQL, SqlLibrary.SPARK), consumer);
+ }
+
+ /** Test case for
+ * <a
href="https://issues.apache.org/jira/browse/CALCITE-6232";>[CALCITE-6232]
+ * Using fractions in LOG function does not return correct results</a>. */
+ @Test void testLogFuncByConvert() {
+ final SqlOperatorFixture f0 = fixture();
+ f0.setFor(SqlLibraryOperators.LOG, VmName.EXPAND);
+ final SqlOperatorFixture f = f0.withLibrary(SqlLibrary.BIG_QUERY);
+ f.checkScalarApprox("log(2.0/3, 2)", "DOUBLE NOT NULL",
Review Comment:
log(2/3) == log(0). Neither of these is a negative test as those are normal,
expected inputs. I found this Wiki article on negative testing to be helpful:
https://en.wikipedia.org/wiki/Negative_testing . To find a good candidate for a
negative test, imagine ALL possible inputs (of any type), and then subtract all
EXPECTED inputs. This leaves you with all UNexpected inputs.
If the dialects return different answers for the same expression, yes we
need to account for that, how do Spark and MySQL differ?
If those dialects return `null` for 0 then we should make sure we return
`null` for zero.
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