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[SPARK-49611][SQL] Introduce TVF collations() & remove the SHOW COLLATIONS command #48087

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[SPARK-49611][SQL] Introduce TVF collations() & remove the SHOW COLLATIONS command #48087

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48c614e
[SPARK-49611][SQL] Introduce TVF all_collations()
panbingkun Sep 12, 2024
089c860
fix tab
panbingkun Sep 12, 2024
98d41a2
Update sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expr…
panbingkun Sep 13, 2024
5ba5cfe
Merge branch 'master' into SPARK-49611
panbingkun Sep 13, 2024
317f105
Merge branch 'SPARK-49611' of https://github.com/panbingkun/spark int…
panbingkun Sep 13, 2024
5009a4f
remove show collations command
panbingkun Sep 13, 2024
52d7709
Merge branch 'master' into SPARK-49611
panbingkun Sep 13, 2024
cbe8e08
add some UT
panbingkun Sep 13, 2024
1141c15
Merge branch 'master' into SPARK-49611
panbingkun Sep 13, 2024
fbb1d84
improve
panbingkun Sep 13, 2024
abd58c0
update
panbingkun Sep 13, 2024
72ff1b9
update
panbingkun Sep 13, 2024
ec6c084
rename to collations
panbingkun Sep 15, 2024
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1 change: 1 addition & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
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Expand Up @@ -1158,6 +1158,7 @@ object TableFunctionRegistry {
generator[PosExplode]("posexplode"),
generator[PosExplode]("posexplode_outer", outer = true),
generator[Stack]("stack"),
generator[AllCollations]("all_collations"),
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given we have sql_keywords, shall we call it string_collations?

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Okay

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nit: To me this sounds weird. We suggest that there might exist collations for some other type, for keywords it is more likely to have other keywords (python, scala ...) @cloud-fan

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oh, not only string type? then all_collations is good

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Okay, Let's restore to all_collations.

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all_collation is fine although I'm not sure why all is necessary. But no strong feelings.

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It's all_collation, not all_collations, right?

generator[SQLKeywords]("sql_keywords"),
generator[VariantExplode]("variant_explode"),
generator[VariantExplode]("variant_explode_outer", outer = true)
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45 changes: 44 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
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Expand Up @@ -18,6 +18,7 @@
package org.apache.spark.sql.catalyst.expressions

import scala.collection.mutable
import scala.jdk.CollectionConverters.CollectionHasAsScala
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why this import?

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Because it is necessary to use from java(List) to scala(Iterable), as follows:
image


import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
Expand All @@ -28,7 +29,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen._
import org.apache.spark.sql.catalyst.expressions.codegen.Block._
import org.apache.spark.sql.catalyst.plans.logical.{FunctionSignature, InputParameter}
import org.apache.spark.sql.catalyst.trees.TreePattern.{GENERATOR, TreePattern}
import org.apache.spark.sql.catalyst.util.{ArrayData, MapData}
import org.apache.spark.sql.catalyst.util.{ArrayData, CollationFactory, MapData}
import org.apache.spark.sql.catalyst.util.SQLKeywordUtils._
import org.apache.spark.sql.errors.{QueryCompilationErrors, QueryExecutionErrors}
import org.apache.spark.sql.internal.SQLConf
Expand Down Expand Up @@ -618,3 +619,45 @@ case class SQLKeywords() extends LeafExpression with Generator with CodegenFallb

override def prettyName: String = "sql_keywords"
}

@ExpressionDescription(
usage = """_FUNC_() - Get Spark SQL all collations""",
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examples = """
Examples:
> SELECT * FROM _FUNC_() LIMIT 2;
SYSTEM BUILTIN UTF8_BINARY NULL NULL ACCENT_SENSITIVE CASE_SENSITIVE NO_PAD NULL
SYSTEM BUILTIN UTF8_LCASE NULL NULL ACCENT_SENSITIVE CASE_INSENSITIVE NO_PAD NULL
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Is this output deterministic?

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Yes, UTF8_BINARY and UTF8_LCASE have always been at the forefront
image

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It should be, listCollationMeta enforces UTF8_* collations first.

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I would replace this LIMIT by WHERE which always returns one row, to don't depend on order and how LIMIT behaves.

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Updated.

""",
since = "4.0.0",
group = "generator_funcs")
case class AllCollations() extends LeafExpression with Generator with CodegenFallback {
override def elementSchema: StructType = new StructType()
.add("COLLATION_CATALOG", StringType, nullable = false)
.add("COLLATION_SCHEMA", StringType, nullable = false)
.add("COLLATION_NAME", StringType, nullable = false)
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do we really need the COLLATION prefix? Can it just be CATALOG, SCHEMA and NAME?

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I agree with it because sql_keywords doesn't seem to have a prefix either.
image

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@panbingkun panbingkun Sep 13, 2024 •
edited
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Also, I noticed an issue. Do we need to register it as a SQL built-in function? I noticed that some functions in FunctionRegistry are both TVF and SQL built-in function, eg:

spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala

Lines 370 to 371 in 9fc58aa

expression[Inline]("inline"),
expressionGeneratorOuter[Inline]("inline_outer"),

spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala

Lines 1155 to 1156 in 9fc58aa

generator[Inline]("inline"),
generator[Inline]("inline_outer", outer = true),

so we can use it as follows:

SELECT all_collations();

or

SELECT * FROM all_collations();

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Using generator functions in SELECT is not recommended. We only keep them for backward compatibility.

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Thank you, I see.

.add("LANGUAGE", StringType)
.add("COUNTRY", StringType)
.add("ACCENT_SENSITIVITY", StringType, nullable = false)
.add("CASE_SENSITIVITY", StringType, nullable = false)
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.add("PAD_ATTRIBUTE", StringType, nullable = false)
.add("ICU_VERSION", StringType)

override def eval(input: InternalRow): IterableOnce[InternalRow] = {
CollationFactory.listCollations().asScala.map(CollationFactory.loadCollationMeta).map { m =>
InternalRow(
UTF8String.fromString(m.catalog),
UTF8String.fromString(m.schema),
UTF8String.fromString(m.collationName),
if (m.language != null) UTF8String.fromString(m.language) else null,
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fromString() does null-checking, not need to check this additionally, see:

  public static UTF8String fromString(String str) {
    return str == null ? null : fromBytes(str.getBytes(StandardCharsets.UTF_8));
  }

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Updated.

if (m.country != null) UTF8String.fromString(m.country) else null,
UTF8String.fromString(
if (m.accentSensitivity) "ACCENT_SENSITIVE" else "ACCENT_INSENSITIVE"),
UTF8String.fromString(
if (m.caseSensitivity) "CASE_SENSITIVE" else "CASE_INSENSITIVE"),
UTF8String.fromString(m.padAttribute),
if (m.icuVersion != null) UTF8String.fromString(m.icuVersion) else null)
}
}

override def prettyName: String = "all_collations"
}
42 changes: 42 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/CollationSuite.scala
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Expand Up @@ -1666,4 +1666,46 @@ class CollationSuite extends DatasourceV2SQLBase with AdaptiveSparkPlanHelper {
Row("SYSTEM", "BUILTIN", "zh_Hant_HKG_CI_AI", "Chinese", "Hong Kong SAR China",
"ACCENT_INSENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0")))
}

test("TVF all_collations()") {
assert(sql("SELECT * FROM all_collations()").collect().length >= 562)

// verify that the output ordering is as expected (UTF8_BINARY, UTF8_LCASE, etc.)
val df = sql("SELECT * FROM all_collations() limit 10")
checkAnswer(df,
Seq(Row("SYSTEM", "BUILTIN", "UTF8_BINARY", null, null,
"ACCENT_SENSITIVE", "CASE_SENSITIVE", "NO_PAD", null),
Row("SYSTEM", "BUILTIN", "UTF8_LCASE", null, null,
"ACCENT_SENSITIVE", "CASE_INSENSITIVE", "NO_PAD", null),
Row("SYSTEM", "BUILTIN", "UNICODE", "", "",
"ACCENT_SENSITIVE", "CASE_SENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "UNICODE_AI", "", "",
"ACCENT_SENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "UNICODE_CI", "", "",
"ACCENT_INSENSITIVE", "CASE_SENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "UNICODE_CI_AI", "", "",
"ACCENT_INSENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "af", "Afrikaans", "",
"ACCENT_SENSITIVE", "CASE_SENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "af_AI", "Afrikaans", "",
"ACCENT_SENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "af_CI", "Afrikaans", "",
"ACCENT_INSENSITIVE", "CASE_SENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "af_CI_AI", "Afrikaans", "",
"ACCENT_INSENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0")))

checkAnswer(sql("SELECT * FROM all_collations() WHERE COLLATION_NAME LIKE '%UTF8_BINARY%'"),
Row("SYSTEM", "BUILTIN", "UTF8_BINARY", null, null,
"ACCENT_SENSITIVE", "CASE_SENSITIVE", "NO_PAD", null))

checkAnswer(sql("SELECT * FROM all_collations() WHERE COLLATION_NAME LIKE '%zh_Hant_HKG%'"),
Seq(Row("SYSTEM", "BUILTIN", "zh_Hant_HKG", "Chinese", "Hong Kong SAR China",
"ACCENT_SENSITIVE", "CASE_SENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "zh_Hant_HKG_AI", "Chinese", "Hong Kong SAR China",
"ACCENT_SENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "zh_Hant_HKG_CI", "Chinese", "Hong Kong SAR China",
"ACCENT_INSENSITIVE", "CASE_SENSITIVE", "NO_PAD", "75.1.0.0"),
Row("SYSTEM", "BUILTIN", "zh_Hant_HKG_CI_AI", "Chinese", "Hong Kong SAR China",
"ACCENT_INSENSITIVE", "CASE_INSENSITIVE", "NO_PAD", "75.1.0.0")))
}
}