ganeshashree commented on code in PR #58034: URL: https://github.com/apache/spark/pull/58034#discussion_r4285770194
########## sql/core/src/test/scala/org/apache/spark/sql/JsonObjectSuite.scala: ########## @@ -0,0 +1,1397 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.antlr.v4.runtime.{CharStreams, CommonTokenStream} +import org.antlr.v4.runtime.atn.PredictionMode + +import org.apache.spark.SparkRuntimeException +import org.apache.spark.sql.catalyst.analysis.Star +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.DataTypeMismatch +import org.apache.spark.sql.catalyst.expressions.{Cast, CodegenObjectFactoryMode, Collate, Expression, JsonConstructorNullBehavior, JsonImplicitFormatCarrier, JsonObjectExpr, Literal} +import org.apache.spark.sql.catalyst.parser.{ParseException, SparkParserBailErrorStrategy, SqlBaseLexer, SqlBaseParser} +import org.apache.spark.sql.catalyst.plans.logical.{Filter, Join} +import org.apache.spark.sql.execution.{ProjectExec, WholeStageCodegenExec} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{ArrayType, CharType, GeographyType, GeometryType, IntegerType, MapType, StringType, StructType, VarcharType} + +/** + * End-to-end tests for the SQL:2016 `JSON_OBJECT` constructor function. + */ +class JsonObjectSuite extends QueryTest with SharedSparkSession { + import testImplicits._ + + test("in the comma form, colon-bearing arguments bind as colon members (documented precedence)") { + // `JSON_OBJECT(a:b, c:d)` matches both list forms: the colon-member list and the comma + // (MySQL-compat) list. The colon-member alternative is declared first, so it wins -- `a:b, c:d` + // reads as the two members (a -> b) and (c -> d), the SQL-standard `key : value` reading, not + // as a single comma-form pair whose key and value are `:`-extractions. + checkAnswer( + sql("SELECT json_object(a:b, c:d) FROM VALUES ('k1','v1','k2','v2') t(a,b,c,d)"), + Row("""{"k1":"v1","k2":"v2"}""")) + // Parenthesizing each argument forces the comma form (one member whose key/value are the + // `:`-extractions). The non-VARIANT extraction target then errors -- proving it parsed as one + // comma-form pair, not two colon members (which resolve cleanly above). + val e = intercept[AnalysisException] { + sql("SELECT json_object((a:b), (c:d)) FROM VALUES ('k1','v1','k2','v2') t(a,b,c,d)").collect() + } + assert(e.getCondition == "COLUMN_IS_NOT_VARIANT_TYPE") + } + + test("basic object from key-value pairs using VALUE keyword") { + checkAnswer( + sql("SELECT json_object('id' VALUE 7, 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("construct object using optional KEY keyword") { + checkAnswer( + sql("SELECT json_object(KEY 'id' VALUE 7, KEY 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("construct object using colon syntax") { + checkAnswer( + sql("SELECT json_object('id': 7, 'name': 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("colon member with a column-valued value") { + // The `key : value` member overlaps the semi-structured extraction form `expr : path` + // (`primaryExpression COLON semiStructuredExtractionPath`), which also accepts a bare + // identifier on the right. Confirm a column-valued colon member still parses as a JSON_OBJECT + // pair rather than as `'k' : v` semi-structured extraction. + checkAnswer( + sql("SELECT json_object('k': v) FROM VALUES ('x') t(v)"), + Row("""{"k":"x"}""")) + } + + test("a colon member splits at the first top-level colon after the key") { + val from = + """FROM (SELECT parse_json('{"x":1,"y":"s"}') AS v, 'kk' AS k, 'e' AS `end`, 'c' AS `case`)""" + Seq( + "json_object('k': v:x)" -> """{"k":1}""", + "json_object(k: v:x)" -> """{"kk":1}""", + "json_object('k': v:x ABSENT ON NULL)" -> """{"k":1}""", + "json_object(k: v:x RETURNING STRING)" -> """{"kk":1}""", + "json_object('a': v:x, k: v:y)" -> """{"a":1,"kk":"s"}""", + "json_object((v:y::string) : 1)" -> """{"s":1}""", + "json_object(v:y::string VALUE v:x)" -> """{"s":1}""", + "json_object(CASE WHEN v:x::int = 1 THEN 'a' END : v:y)" -> """{"a":"s"}""", + // `end` and `case` are non-reserved and usable as column names: the separator check must read + // each CASE/END token as keyword or column by its neighbour, not count it blindly. + "json_object(CASE WHEN end = 'e' AND v:x::int = 1 THEN 'a' END : v:y)" -> """{"a":"s"}""", + "json_object(case : v:x)" -> """{"c":1}""", + "json_object(array('a')[0] : v:x)" -> """{"a":1}""", + "json_object(end : v:x)" -> """{"e":1}""" + ).foreach { case (call, expected) => + checkAnswer(sql(s"SELECT $call $from"), Row(expected)) + } + checkAnswer( + spark.sql(s"SELECT json_object('k': v:x) $from", Map("v" -> "zzz")), + Row("""{"k":1}""")) + checkAnswer( + spark.sql("SELECT json_object('k' :p VALUE 1), 'a' :p", Map("p" -> "x")), + Row("""{"kx":1}""", "ax")) + } + + test("a case-named column with a postfix operator is a column, not a CASE keyword") { + // `case` is non-reserved, so `case[0]` (subscript) and `case COLLATE ...` apply a postfix + // operator to a column named `case`; neither `CASE [` nor `CASE COLLATE` begins a real CASE + // expression (`[` has no array-literal form, COLLATE needs a left operand). The separator check + // must read the token right after `case` as a postfix on an operand -- like the already-handled + // `.` -- so the top-level colon is still the member separator, not swallowed as a CASE branch. + checkAnswer( + sql("""SELECT json_object(case[0] : v:x) + |FROM (SELECT array('c') AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + checkAnswer( + sql("""SELECT json_object(case COLLATE UTF8_LCASE : v:x) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + } + + test("a colon key cannot coalesce a trailing named parameter marker") { + // `'k' :p` coalesces the marker into the key under the VALUE form, but in the colon form the + // first top-level `:` is the member separator, so a bare marker-coalesced key is a clean parse + // error (never a silent mis-split). Parenthesize the key or use the VALUE form. + val e = intercept[ParseException] { + spark.sql("SELECT json_object('k' :p : 1)", Map("p" -> "x")) + } + assert(e.getCondition == "PARSE_SYNTAX_ERROR") + checkAnswer( + spark.sql("SELECT json_object(('k' :p) : 1)", Map("p" -> "x")), Row("""{"kx":1}""")) + checkAnswer( + spark.sql("SELECT json_object('k' :p VALUE 1)", Map("p" -> "x")), Row("""{"kx":1}""")) + } + + test("a simple CASE over a named-parameter operand is a valid colon key") { + // `CASE :p WHEN ... END : value`: the `:p` operand must parse as a named-parameter marker, not + // be read as the member separator. `CASE :` is ambiguous with a `case` column before the + // separator (`case : value`), so the separator check decides it by looking ahead for the + // CASE's WHEN -- present here, so `CASE` is the keyword and `:p` stays its operand. + val from = "FROM (SELECT parse_json('{\"x\":1}') AS v)" + checkAnswer( + spark.sql( + s"SELECT json_object(CASE :p WHEN 1 THEN 'one' ELSE 'other' END : v:x) $from", + Map("p" -> 1)), + Row("""{"one":1}""")) + checkAnswer( + spark.sql( + s"SELECT json_object(CASE :p WHEN 1 THEN 'one' ELSE 'other' END : v:x) $from", + Map("p" -> 2)), + Row("""{"other":1}""")) + } + + test("a CASE-valued value does not flip a bare `case` key to the CASE keyword") { + // `case : CASE WHEN ... END`: the value's WHEN belongs to the value's CASE, not to the key, so + // the WHEN lookahead must stop at that inner CASE and keep the leading `case` a column with the + // top-level colon as the member separator. Guards the lookahead against over-triggering. + checkAnswer( + sql("""SELECT json_object(case : CASE WHEN v:x::int = 1 THEN 'y' ELSE 'z' END) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":"y"}""")) + } + + test("a `case` column inside a simple-CASE colon key is a column, not a nested CASE") { + // The simple-CASE key's own body references a column named `case` (the THEN result). The + // key-colon lookahead must not count that `case` as a nested CASE...END, or it would consume + // the real END and misread the `:p` operand marker as the member separator. + withSQLConf(SQLConf.ANSI_ENABLED.key -> "false") { + val from = "FROM (SELECT 'c' AS `case`, 'v' AS value)" + checkAnswer( + spark.sql(s"SELECT json_object(CASE :p WHEN 1 THEN case ELSE 'x' END : value) $from", + Map("p" -> 1)), + Row("""{"c":"v"}""")) + checkAnswer( + spark.sql(s"SELECT json_object(CASE :p WHEN 1 THEN case ELSE 'x' END : value) $from", + Map("p" -> 2)), + Row("""{"x":"v"}""")) + // A postfix operator on the body `case` column is the case most likely to be miscounted as a + // nested CASE...END by the key-colon lookahead. + val q = s"SELECT json_object(CASE :p WHEN 1 THEN case::string ELSE 'x' END : value) $from" + checkAnswer(spark.sql(q, Map("p" -> 1)), Row("""{"c":"v"}""")) + } + } + + test("a non-foldable key with escapable characters is JSON-escaped") { + // A dynamic (non-foldable) key is written through the generator's field-name path, which must + // escape quotes and backslashes exactly as a quoted key would. + val df = Seq(("key\"with\\chars", "v")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v)"), + Row("""{"key\"with\\chars":"v"}""")) + } + + test("a bare `when` column value does not flip a `case` colon key to the CASE keyword") { + // Outside ANSI mode `when` is a bare column; its WHEN token must not be read as the WHEN of a + // CASE keyword in the `case` key. A lone `when` value has no closing END, so `case` stays a + // column and the colon stays the member separator. + withSQLConf(SQLConf.ANSI_ENABLED.key -> "false") { + checkAnswer( + sql("""SELECT json_object(case : when) + |FROM (SELECT 'c' AS `case`, 'w' AS `when`)""".stripMargin), + Row("""{"c":"w"}""")) + } + } + + test("a case column nested in a colon key is a column, not a CASE keyword") { + // A `case` column in a CAST or parens is below the key's top level, where the token after it + // (`AS`, `+`) does not prove it is a column; without the depth guard the spurious CASE count + // hid the real separator colon (the parens already balance any colon inside). + checkAnswer( + sql("""SELECT json_object(CAST(case AS STRING) : v:x) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + checkAnswer( + sql("""SELECT json_object((case + 1)::string : v:x) + |FROM (SELECT 1 AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"2":1}""")) + } + + test("a member whose key and value are named key and value splits at VALUE") { + checkAnswer( + sql("SELECT json_object(key VALUE value + 1) FROM VALUES ('a', 1) t(key, value)"), + Row("""{"a":2}""")) + checkAnswer( + sql("SELECT json_object(KEY key VALUE value) FROM VALUES ('a', 1) t(key, value)"), + Row("""{"a":1}""")) + checkAnswer( + sql("SELECT json_object(KEY value VALUE key) FROM VALUES ('a', 'b') t(key, value)"), + Row("""{"b":"a"}""")) + checkAnswer( + sql("SELECT json_object('x' VALUE 0, key VALUE value + 1) " + + "FROM VALUES ('a', 1) t(key, value)"), + Row("""{"x":0,"a":2}""")) + } + + test("a key column continued by an operator is the key, not the optional KEY keyword") { + // `key` is non-reserved; `key + 1` continues the `key` column into the key expression rather + // than reading `key` as the optional KEY keyword with key `+ 1`. The arithmetic key is itself a + // type error (JSON object keys must be STRING), but the report names `(key + 1)` -- proof the + // `key` column is bound into the key, not dropped. Were it read as KEY, the key would be `+ 1`. + val ex = intercept[AnalysisException] { + sql("SELECT json_object(key + 1 VALUE value) FROM VALUES (10, 'x') t(key, value)") + } + assert(ex.getCondition == "DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE") + assert(ex.getMessage.contains("(key + 1)")) + } + + test("JSON_OBJECT members parse in SLL mode without the LL fallback") { + Seq( + "SELECT JSON_OBJECT('k': V)", + "SELECT JSON_OBJECT(K: V)", + "SELECT JSON_OBJECT('k': V:X, K: V:Y)", + "SELECT JSON_OBJECT(KEY 'id' VALUE 7)", + "SELECT JSON_OBJECT(KEY VALUE VALUE 1)", + // Exercises the optional-KEY `jsonOptionalKeyFollowsOperand` guard (`key + 1` as the key). + "SELECT JSON_OBJECT(key + 1 VALUE value)").foreach { query => + val parser = new SqlBaseParser( + new CommonTokenStream(new SqlBaseLexer(CharStreams.fromString(query)))) + parser.setErrorHandler(new SparkParserBailErrorStrategy()) + parser.getInterpreter.setPredictionMode(PredictionMode.SLL) + parser.singleStatement() + } + } + + test("construct object using comma-separated key-value syntax") { + checkAnswer( + sql("SELECT json_object('id', 7, 'name', 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("an odd number of arguments in the comma syntax is rejected") { + // The comma form requires paired key/value arguments; a dangling key ('name') has no value. + // JSON_OBJECT is a non-reserved keyword, so when the constructor grammar cannot match, the call + // parses as an ordinary function call and routes to the registered built-in, whose builder + // rejects the odd argument count rather than silently dropping the dangling key. + val e = intercept[AnalysisException] { + sql("SELECT json_object('id', 7, 'name')") + } + assert(e.getCondition == "WRONG_NUM_ARGS.WITHOUT_SUGGESTION") + } + + test("mixing the VALUE/colon form and the comma form is a parse error") { + // The two member-list styles are mutually exclusive grammar alternatives, so a single + // constructor cannot mix `key VALUE value` (or `key : value`) members with `key, value` ones. + Seq( + "SELECT json_object('a', 1, 'b' VALUE 2)", + "SELECT json_object('a' VALUE 1, 'b', 2)", + "SELECT json_object('a' : 1, 'b', 2)").foreach { query => + intercept[ParseException](sql(query)) + } + } + + test("construct object with NULL values (default NULL ON NULL)") { + checkAnswer( + sql("SELECT json_object('id': 7, 'v': NULL)"), + Row("""{"id":7,"v":null}""")) + } + + test("construct object with explicit NULL ON NULL") { + checkAnswer( + sql("SELECT json_object('id', 7, 'v', NULL NULL ON NULL)"), + Row("""{"id":7,"v":null}""")) + } + + test("construct object with NULL values and ABSENT ON NULL") { + checkAnswer( + sql("SELECT json_object('id': 7, 'v': NULL ABSENT ON NULL)"), + Row("""{"id":7}""")) + } + + test("construct empty object") { + checkAnswer( + sql("SELECT json_object()"), + Row("{}")) + } + + test("construct object with mixed scalar types") { + checkAnswer( + sql("""SELECT json_object('int': 42, 'str': 'hello', 'bool': true, + 'float': 3.14)"""), + Row("""{"int":42,"str":"hello","bool":true,"float":3.14}""")) + } + + test("construct object with decimal type via Jackson") { + checkAnswer( + sql("""SELECT json_object('d' VALUE CAST('123.45' AS DECIMAL(5,2)))"""), + Row("""{"d":123.45}""")) + } + + test("construct object with DATE type via Jackson") { + checkAnswer( + sql("""SELECT json_object('d' VALUE DATE'2020-01-02')"""), + Row("""{"d":"2020-01-02"}""")) + } + + test("construct object with TIMESTAMP type via Jackson") { + // Note: Jackson includes timezone offset when session timezone is set + checkAnswer( + sql("""SELECT json_object('ts' VALUE TIMESTAMP'2020-01-02 10:30:00')"""), + Row("""{"ts":"2020-01-02T10:30:00.000-08:00"}""")) + } + + test("TIMESTAMP rendering follows the session time zone on direct and routed paths") { + withSQLConf(SQLConf.SESSION_LOCAL_TIMEZONE.key -> "UTC") { + val ts = "TIMESTAMP'2020-01-02 10:30:00'" + val expected = sql(s"SELECT to_json(named_struct('ts', $ts))") + checkAnswer(sql(s"SELECT json_object('ts' VALUE $ts)"), expected) + checkAnswer(sql(s"SELECT json_object('ts', $ts)"), expected) + checkAnswer(expected, Row("""{"ts":"2020-01-02T10:30:00.000Z"}""")) + } + } + + test("struct value renders like to_json") { + // A struct value must render exactly like `to_json` of the equivalent member. + checkAnswer( + sql("SELECT json_object('s' VALUE named_struct('a', 1, 'b', 'x'))"), + Row("""{"s":{"a":1,"b":"x"}}""")) + checkAnswer( + sql("SELECT json_object('s' VALUE named_struct('a', 1, 'b', 'x'))"), + sql("SELECT to_json(named_struct('s', named_struct('a', 1, 'b', 'x')))")) + } + + test("array value renders like to_json") { + checkAnswer( + sql("SELECT json_object('a' VALUE array(1, 2, 3))"), + Row("""{"a":[1,2,3]}""")) + checkAnswer( + sql("SELECT json_object('a' VALUE array(1, 2, 3))"), + sql("SELECT to_json(named_struct('a', array(1, 2, 3)))")) + } + + test("map value renders like to_json") { + checkAnswer( + sql("SELECT json_object('m' VALUE map('x', 1, 'y', 2))"), + Row("""{"m":{"x":1,"y":2}}""")) + checkAnswer( + sql("SELECT json_object('m' VALUE map('x', 1, 'y', 2))"), + sql("SELECT to_json(named_struct('m', map('x', 1, 'y', 2)))")) + } + + test("nested complex value combining struct, array and map renders like to_json") { + val value = "named_struct('arr', array(1, 2), 'm', map('k', named_struct('n', 3)))" + checkAnswer( + sql(s"SELECT json_object('c' VALUE $value)"), + sql(s"SELECT to_json(named_struct('c', $value))")) + } + + test("struct value honors spark.sql.jsonGenerator.ignoreNullFields like to_json") { + // `ON NULL` controls only top-level members; a null field *inside* a struct value follows + // spark.sql.jsonGenerator.ignoreNullFields, like `to_json`. + val value = "named_struct('a', 1, 'b', CAST(NULL AS INT))" + Seq("true", "false").foreach { ignore => + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> ignore) { + checkAnswer( + sql(s"SELECT json_object('s' VALUE $value)"), + sql(s"SELECT to_json(named_struct('s', $value))")) + } + } + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> "false") { + checkAnswer(sql(s"SELECT json_object('s' VALUE $value)"), Row("""{"s":{"a":1,"b":null}}""")) + } + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> "true") { + checkAnswer(sql(s"SELECT json_object('s' VALUE $value)"), Row("""{"s":{"a":1}}""")) + } + } + + test("top-level ON NULL and struct-internal ignoreNullFields are independent") { + // With NULL ON NULL (default) and ignoreNullFields=true, a top-level NULL member is kept as + // `null` while a null field inside a struct value is dropped. + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> "true") { + checkAnswer( + sql("""SELECT json_object('top' VALUE CAST(NULL AS INT), + 's' VALUE named_struct('a', 1, 'b', CAST(NULL AS INT)))"""), + Row("""{"top":null,"s":{"a":1}}""")) + } + } + + test("string escaping in keys") { + checkAnswer( + sql("""SELECT json_object('key"with"quotes' VALUE 1)"""), + Row("""{"key\"with\"quotes":1}""")) + } + + // For scalar string values JSON_OBJECT must escape exactly like to_json of the equivalent + // struct (both go through the same Jackson generator); assert that equivalence rather than + // hand-encoding the escaping, which is easy to get wrong across Scala/SQL/JSON layers. + test("string escaping in values matches to_json") { + checkAnswer( + sql("""SELECT json_object('msg' VALUE 'hello +world')"""), + sql("""SELECT to_json(named_struct('msg', 'hello +world'))""")) + } + + test("string escaping with backslash matches to_json") { + checkAnswer( + sql("""SELECT json_object('path' VALUE 'c:\windows')"""), + sql("""SELECT to_json(named_struct('path', 'c:\windows'))""")) + } + + test("nested JSON_OBJECT spliced raw") { + checkAnswer( + sql("""SELECT json_object('a' VALUE json_object('b' VALUE 1))"""), + Row("""{"a":{"b":1}}""")) + checkAnswer( + sql("""SELECT json_object('a', json_object('b', 1))"""), + Row("""{"a":{"b":1}}""")) + } + + test("nested JSON_OBJECT with multiple levels") { + checkAnswer( + sql("""SELECT json_object('outer' VALUE + json_object('inner' VALUE 42, 'name' VALUE 'test'))"""), + Row("""{"outer":{"inner":42,"name":"test"}}""")) + } + + test("a nested JSON_ARRAY value is spliced raw") { + checkAnswer( + sql("SELECT json_object('a' VALUE json_array(1, 2))"), + Row("""{"a":[1,2]}""")) + } + + test("JSON_OBJECT nested directly in JSON_ARRAY is spliced as an object element") { + // The inverse nesting direction: a JSON_OBJECT in a JSON_ARRAY element position stays on the + // direct grammar path (JsonArrayValueContext), so it is spliced as a JSON object rather than + // routed through resolution and emitted as a quoted string. + checkAnswer( + sql("SELECT json_array(json_object('a', 1), json_object('b', 2))"), + Row("""[{"a":1},{"b":2}]""")) + } + + test("a nested JSON_QUERY value is spliced under KEEP QUOTES and quoted under OMIT QUOTES") { + // JSON_QUERY emits JSON text under the default KEEP QUOTES, so a lexically nested JSON_QUERY is + // spliced raw: the matched object is {"x":1}, not the quoted string "{\"x\":1}". + checkAnswer( + sql("""SELECT json_object('a' VALUE json_query('{"o":{"x":1}}', '$.o'))"""), + Row("""{"a":{"x":1}}""")) + // OMIT QUOTES returns the matched scalar string's decoded content (Ada, not "Ada") -- an + // ordinary string -- so it takes the quoted path (emitsImplicitJsonText is false), never the + // invalid splice {"a":Ada}. + checkAnswer( + sql("""SELECT json_object('a' VALUE json_query('{"n":"Ada"}', '$.n' OMIT QUOTES))"""), + Row("""{"a":"Ada"}""")) + } + + test("null key error") { + val e = intercept[SparkRuntimeException] { + sql("SELECT json_object(NULL VALUE 'value')").collect() + } + // Assert the structured error contract, not just the message text. + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + assert(e.getSqlState == "22004") + } + + test("a null key is validated before a null value is omitted under ABSENT ON NULL") { + // ABSENT ON NULL omits members with a null value, but the key is validated first, so a null key + // still raises JSON_OBJECT_NULL_KEY rather than being silently dropped along with the member. + val e = intercept[SparkRuntimeException] { + sql("SELECT json_object(NULL VALUE NULL ABSENT ON NULL)").collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + assert(e.getSqlState == "22004") + } + + test("non-foldable key and value expressions") { + val df = Seq(("key1", "val1"), ("key2", "val2")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v)"), + Seq(Row("""{"key1":"val1"}"""), Row("""{"key2":"val2"}"""))) + } + + test("non-foldable with NULL value and NULL ON NULL") { + val df = Seq(("k", null), ("key", "val")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v)"), + Seq(Row("""{"k":null}"""), Row("""{"key":"val"}"""))) + } + + test("non-foldable with NULL value and ABSENT ON NULL") { + val df = Seq(("k", null), ("key", "val")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v ABSENT ON NULL)"), + Seq(Row("{}"), Row("""{"key":"val"}"""))) + } + + test("multiple keys with ABSENT ON NULL") { + checkAnswer( + sql("""SELECT json_object('a' VALUE 1, 'b' VALUE NULL, 'c' VALUE 3 + ABSENT ON NULL)"""), + Row("""{"a":1,"c":3}""")) + } + + test("duplicate keys are emitted in source order") { + checkAnswer( + sql("SELECT json_object('k' VALUE 1, 'k' VALUE 2)"), + Row("""{"k":1,"k":2}""")) + } + + test("non-string key type is rejected at analysis, not at execution") { + val ex = intercept[AnalysisException] { + sql("SELECT json_object(1 VALUE 'x')") + } + assert(ex.getMessage.contains("UNEXPECTED_INPUT_TYPE")) + } + + test("non-string key type reports the actual key argument") { + val ex = intercept[AnalysisException] { + sql("SELECT json_object('ok' VALUE 1, 2 VALUE 'bad')") + } + checkError( + exception = ex, + condition = "DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE", + sqlState = Some("42K09"), + parameters = Map( + "sqlExpr" -> "\"JSON_OBJECT(ok VALUE 1, 2 VALUE bad)\"", + "paramIndex" -> "third", + "requiredType" -> "\"STRING\"", + "inputSql" -> "\"2\"", + "inputType" -> "\"INT\""), + queryContext = Array(ExpectedContext("json_object('ok' VALUE 1, 2 VALUE 'bad')", 7, 46))) + } + + test("collated STRING RETURNING is accepted") { + // isValidReturningType must accept any StringType instance, not just the default collation. + checkAnswer( + sql("SELECT json_object('a' VALUE 1 RETURNING STRING COLLATE UTF8_LCASE)"), + Row("""{"a":1}""")) + } + + test("an invalid RETURNING type is reported under DATATYPE_MISMATCH") { + // The error is emitted as a DataTypeMismatch, so its condition must resolve under + // DATATYPE_MISMATCH -- not as a top-level INVALID_JSON_RETURNING_TYPE class. + val e = intercept[AnalysisException] { + sql("SELECT json_object('a' VALUE 1 RETURNING INT)").collect() + } + assert(e.getCondition == "DATATYPE_MISMATCH.INVALID_JSON_RETURNING_TYPE") + } + + test("a directly-constructed JsonObjectExpr with a CHAR/VARCHAR RETURNING is rejected") { + // The parser normalizes CHAR/VARCHAR RETURNING to STRING, but a raw CharType/VarcharType from + // direct Catalyst construction would advertise a length JSON_OBJECT does not enforce. + Seq(VarcharType(2), CharType(2)).foreach { returning => + val expr = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, returning) + expr.checkInputDataTypes() match { + case DataTypeMismatch(errorSubClass, _) => + assert(errorSubClass == "INVALID_JSON_RETURNING_TYPE", s"for $returning") + case other => fail(s"expected DataTypeMismatch for $returning, got $other") + } + } + } + + test("value accepts an unparenthesized predicate expression") { + // valueExpr is parsed as a full `expression`, so ordinary predicates work without parentheses. + checkAnswer(sql("SELECT json_object('isnull' VALUE 1 IS NULL)"), Row("""{"isnull":false}""")) + checkAnswer(sql("SELECT json_object('gt' : 2 > 1)"), Row("""{"gt":true}""")) + } + + test("widening the value to expression does not change documented forms") { + // Design-doc examples where a value abuts the ON NULL / RETURNING keywords must still parse and + // evaluate identically after widening valueExpression -> expression. + checkAnswer(sql("SELECT json_object('id': 7, 'v': NULL)"), Row("""{"id":7,"v":null}""")) + checkAnswer( + sql("SELECT json_object('id': 7, 'v': NULL ABSENT ON NULL)"), Row("""{"id":7}""")) + checkAnswer( + sql("SELECT json_object('id', 7, 'v', NULL ABSENT ON NULL)"), Row("""{"id":7}""")) + checkAnswer( + sql("SELECT json_object('id' VALUE 7, 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("an unsupported value type is rejected at analysis") { + // A spatial value: JacksonUtils.verifyType accepts it (it is an AtomicType) but + // JacksonGenerator cannot serialize it, so JSON_OBJECT must reject it up front, not at runtime. + val bad = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, GeometryType(4326)))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + bad.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "CANNOT_CONVERT_TO_JSON") + case other => fail(s"expected DataTypeMismatch, got $other") + } + // A spatial type nested as a MAP VALUE must still be rejected: the guard descends recursively + // into map values (unlike the top-level atomic case above), so this exercises that descent. + val badNested = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, MapType(StringType, GeometryType(4326))))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + badNested.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "CANNOT_CONVERT_TO_JSON") + case other => fail(s"expected DataTypeMismatch for a nested spatial value, got $other") + } + // The guard also descends into STRUCT fields and ARRAY elements, so a spatial type buried in + // either -- the other two recursive branches -- must be rejected just like the map-value case. + val nestedBadTypes = Seq( + new StructType().add("g", GeometryType(4326)), + ArrayType(GeographyType(4326))) + nestedBadTypes.foreach { dt => + val expr = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, dt))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + expr.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "CANNOT_CONVERT_TO_JSON", s"for $dt") + case other => fail(s"expected DataTypeMismatch for a spatial type in $dt, got $other") + } + } + // A spatial type appearing only as a MAP KEY is fine: JacksonGenerator writes map keys via + // toString, so the value-type guard must not over-reject it. + val ok = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, MapType(GeometryType(4326), IntegerType)))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + assert(ok.checkInputDataTypes().isSuccess) + } + + test("a raw (FORMAT JSON) value that is not a string is rejected") { + // A raw-spliced value must carry JSON text (string); a non-string raw value would fail with a + // ClassCastException at eval, so reject it at analysis with the FORMAT JSON input error. + val expr = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(true), Seq(true), + JsonConstructorNullBehavior.Null, StringType) + expr.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "INVALID_JSON_FORMAT_JSON_INPUT") + case other => fail(s"expected DataTypeMismatch, got $other") + } + } + + test("SQL renders an explicit collated RETURNING and an explicit ON NULL clause") { + val collated = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), JsonConstructorNullBehavior.Null, + StringType("UTF8_LCASE")) + assert(collated.sql.contains("RETURNING STRING COLLATE UTF8_LCASE")) + // .sql always renders an explicit ON NULL clause so reparse stays on the direct grammar path. + val default = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + assert(default.sql == "JSON_OBJECT('k' VALUE 1 NULL ON NULL)") + } + + test("SQL renders a raw nested value spliced back to raw even after collation wrapping") { + val inner = JsonObjectExpr( + Seq((Literal("b"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + // Simulate the default-collation rule wrapping the raw nested value in a Cast. rawJson stays + // frozen true; the Cast hides the bare constructor, so .sql renders an explicit FORMAT JSON so + // reparse splices it raw. + val wrapped = JsonObjectExpr( + Seq((Literal("a"), Cast(inner, StringType("UTF8_LCASE")))), Seq(true), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + assert(wrapped.sql.contains("FORMAT JSON")) + checkAnswer(sql(s"SELECT ${wrapped.sql}"), Row("""{"a":{"b":1}}""")) + } + + test("emitted SQL reparses and evaluates with raw-vs-quoted semantics preserved") { + // The .sql renderings above are round-trip contracts: reparsing and evaluating them must + // reproduce the original raw-vs-quoted splicing. + // A raw nested value renders as a bare constructor and reparses back to raw splicing. + checkAnswer( + sql("SELECT JSON_OBJECT('a' VALUE JSON_OBJECT('b' VALUE 1))"), Row("""{"a":{"b":1}}""")) + // A quoted value that the optimizer inlined as an implicit-JSON expression is neutralized with + // CAST(... AS STRING); reparsing must keep it quoted rather than splicing it raw. + val inner = JsonObjectExpr( + Seq((Literal("b"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + val quoted = JsonObjectExpr( + Seq((Literal("a"), inner)), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + assert(quoted.sql == + "JSON_OBJECT('a' VALUE CAST(JSON_OBJECT('b' VALUE 1 NULL ON NULL) AS STRING) NULL ON NULL)") + checkAnswer(sql(s"SELECT ${quoted.sql}"), Row("""{"a":"{\"b\":1}"}""")) + } + + test("generated column names omit the synthetic NULL ON NULL ownership clause") { + // Canonical `.sql` appends a default NULL ON NULL so a clause-free call reparses to the builtin + // (see the shadowing test), but that marker is never reparsed for display. Unaliased column + // names must therefore not expose ownership text the user never wrote; a non-default + // ABSENT ON NULL the user did write is kept. + def colName(q: String): String = sql(q).schema.head.name + assert(colName("SELECT json_object()") == "JSON_OBJECT()") + assert(colName("SELECT json_object('id' VALUE 7)") == "JSON_OBJECT(id VALUE 7)") + assert(colName("SELECT json_object('id', 7)") == "JSON_OBJECT(id VALUE 7)") + assert(colName("SELECT json_object('a' VALUE json_object('b' VALUE 1))") == + "JSON_OBJECT(a VALUE JSON_OBJECT(b VALUE 1))") + assert(colName("SELECT json_object('v' VALUE NULL ABSENT ON NULL)") == + "JSON_OBJECT(v VALUE NULL ABSENT ON NULL)") + } + + test("ABSENT ON NULL round-trips through .sql keeping null members omitted") { + // The ABSENT renderer arm must emit `ABSENT ON NULL` (not the default clause and not nothing), + // so a canonical round trip keeps dropping null-valued members rather than retaining them. + val absent = JsonObjectExpr( + Seq((Literal("a"), Literal(1)), (Literal("b"), Literal.create(null, IntegerType))), + Seq(false, false), Seq(false, false), JsonConstructorNullBehavior.Absent, StringType) + assert(absent.sql == "JSON_OBJECT('a' VALUE 1, 'b' VALUE CAST(NULL AS INT) ABSENT ON NULL)") + checkAnswer(sql(s"SELECT ${absent.sql}"), Row("""{"a":1}""")) + } + + test("JSON_OBJECT is foldable only when it cannot throw") { + assert(JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).foldable) + // Folding a possibly-null key would surface the null-key error at optimization even for rows a + // filter/join drops. + assert(!JsonObjectExpr( + Seq((Literal.create(null, StringType), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).foldable) + assert(!JsonObjectExpr( + Seq((Literal("k"), Literal("[1]"))), Seq(true), Seq(true), + JsonConstructorNullBehavior.Null, StringType).foldable) + } + + test("a constant JSON_OBJECT is accepted as a column DEFAULT") { + withTable("t") { + sql("CREATE TABLE t (id INT, c STRING DEFAULT json_object('a' VALUE 1)) USING parquet") + sql("INSERT INTO t (id) VALUES (1)") + checkAnswer(sql("SELECT c FROM t"), Row("""{"a":1}""")) + } + } + + test("a non-foldable JSON_OBJECT projection participates in whole-stage codegen") { + // Rendering through an Invoke of the object evaluator (not CodegenFallback) keeps the enclosing + // Project eligible for whole-stage codegen; a CodegenFallback expression would exclude it. A + // column-valued member keeps the constructor from being constant-folded away. + withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false") { + val df = sql("SELECT json_object('id' VALUE id) FROM range(5)") + val fusedProjects = df.queryExecution.executedPlan.collect { + case w: WholeStageCodegenExec => w + }.flatMap(_.collect { case p: ProjectExec => p }) + assert(fusedProjects.nonEmpty, + s"expected the JSON_OBJECT Project to be fused into whole-stage codegen, but got:\n" + + df.queryExecution.executedPlan) + checkAnswer(df, (0 until 5).map(i => Row(s"""{"id":$i}"""))) + } + } + + test("a constant JSON_OBJECT is constant-folded") { + val df = sql("SELECT json_object('a' VALUE 1) AS r FROM range(2)") + assert(!df.queryExecution.optimizedPlan.exists( + _.expressions.exists(_.exists(_.isInstanceOf[JsonObjectExpr])))) + checkAnswer(df, Seq(Row("""{"a":1}"""), Row("""{"a":1}"""))) + } + + test("a null key raises JSON_OBJECT_NULL_KEY before the value is evaluated") { + // The key is checked before the value is evaluated, so a null key wins deterministically even + // when the value expression would itself throw. + // `raise_error(k)` references the column so it is neither foldable nor evaluated before the + // key null-check; if the value ran first the error would come from `raise_error`, not the key. + val e = intercept[SparkRuntimeException] { + sql("SELECT json_object(k VALUE raise_error(k)) " + + "FROM VALUES (CAST(NULL AS STRING)) t(k)").collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + assert(e.getSqlState == "22004") + } + + test("a foldable literal key is emitted on every row") { + // A foldable non-null key must be written for every row, not dropped after the first. + checkAnswer( + sql("SELECT json_object('id' VALUE a) FROM VALUES (1), (2) t(a)"), + Seq(Row("""{"id":1}"""), Row("""{"id":2}"""))) + // A foldable key that evaluates to null must still raise JSON_OBJECT_NULL_KEY per row rather + // than being silently skipped. + Seq("NULL", "CAST(NULL AS STRING)").foreach { k => + val e = intercept[SparkRuntimeException] { + sql(s"SELECT json_object($k VALUE 1)").collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY", s"for key $k") + assert(e.getSqlState == "22004", s"for key $k") + } + } + + test("CHAR/VARCHAR RETURNING is normalized to STRING regardless of preserveCharVarcharTypeInfo") { + Seq("CHAR(2)", "VARCHAR(2)").foreach { returning => + Seq("true", "false").foreach { preserve => + withSQLConf(SQLConf.PRESERVE_CHAR_VARCHAR_TYPE_INFO.key -> preserve) { + assert( + sql(s"SELECT json_object('k' VALUE 1 RETURNING $returning)").schema.head.dataType + === StringType, + s"for RETURNING $returning, preserveCharVarcharTypeInfo=$preserve") + } + } + } + } + + test("object default collation applies only when RETURNING is not explicitly collated") { + withSQLConf(SQLConf.OBJECT_LEVEL_COLLATIONS_ENABLED.key -> "true") { + withTable("t") { + sql( + """CREATE TABLE t DEFAULT COLLATION UTF8_LCASE AS + |SELECT json_object('k' VALUE 1) AS a, + | json_object('k' VALUE 1 RETURNING STRING COLLATE UTF8_BINARY) AS b""".stripMargin) + val schema = spark.table("t").schema + // Omitted RETURNING (default STRING) follows the table's default collation. + assert(schema("a").dataType === StringType("UTF8_LCASE")) + // Explicit RETURNING ... COLLATE is the user's choice and must not be overwritten. + assert(schema("b").dataType === StringType("UTF8_BINARY")) + } + } + } + + test("default collation recurses into a nested JSON_OBJECT value") { + // The rule casts each DefaultStringProducingExpression, recursing through a nested constructor + // (the flat cases above only cover a top-level constructor). This CTAS runs the default + // analyzer (single-pass included). Confirm the schema collation and that raw splicing still + // produces well-formed nested JSON at runtime. + withSQLConf(SQLConf.OBJECT_LEVEL_COLLATIONS_ENABLED.key -> "true") { + withTable("t") { + sql( + """CREATE TABLE t DEFAULT COLLATION UTF8_LCASE AS + |SELECT json_object('a' VALUE json_object('b' VALUE 1)) AS a""".stripMargin) + assert(spark.table("t").schema("a").dataType === StringType("UTF8_LCASE")) + checkAnswer(spark.table("t"), Row("""{"a":{"b":1}}""")) + } + } + } + + test("view default collation preserves an explicit collated RETURNING") { + // Exercises the CREATE VIEW resolution path (in addition to the CTAS path above): the explicit + // RETURNING collation must survive the view's default collation. Runs under dual-run (the + // default) so the single-pass resolver's view re-resolution is exercised for parity. + withSQLConf(SQLConf.OBJECT_LEVEL_COLLATIONS_ENABLED.key -> "true") { + withView("v") { + sql( + """CREATE VIEW v DEFAULT COLLATION UTF8_LCASE AS + |SELECT json_object('k' VALUE 1) AS a, + | json_object('k' VALUE 1 RETURNING STRING COLLATE UTF8_BINARY) AS b""".stripMargin) + val schema = spark.table("v").schema + assert(schema("a").dataType === StringType("UTF8_LCASE")) + assert(schema("b").dataType === StringType("UTF8_BINARY")) + } + } + } + + test("reports nullable to keep NullPropagation from skipping the null-key check") { + // The value is never null, but a possibly-null key makes JSON_OBJECT throwable, so it must + // report nullable: were it non-nullable, `NullPropagation` would fold `IS [NOT] NULL` and + // `count(...)` away and skip the eval that must raise JSON_OBJECT_NULL_KEY (see below). + assert(JsonObjectExpr( + Seq((Literal.create(null, StringType), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).nullable) + assert(sql("SELECT json_object(k VALUE 1) FROM VALUES ('a'), (NULL) t(k)") + .schema.head.nullable) + // A non-null key with a null value cannot throw, so the result is non-nullable. + assert(!JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, IntegerType))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).nullable) + assert(!sql("SELECT json_object('id' VALUE a) FROM VALUES (1), (2) t(a)") + .schema.head.nullable) + } + + test("IS NOT NULL and count still raise a null-key error") { + // NullPropagation must not rewrite these to a constant / `count(1)` and skip evaluation, which + // would swallow the null-key error on the null-key row. + Seq( + "SELECT json_object(k VALUE 1) IS NOT NULL FROM VALUES ('a'), (NULL) t(k)", + "SELECT count(json_object(k VALUE 1)) FROM VALUES ('a'), (NULL) t(k)").foreach { query => + val e = intercept[SparkRuntimeException](sql(query).collect()) + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + } + } + + test("is throwable only when a key may be null or a value needs validation") { + // A possibly-null key throws at runtime, so throwable must be true even when no child is + // itself throwable. + def obj(key: Expression, raw: Boolean = false): JsonObjectExpr = JsonObjectExpr( + Seq((key, Literal("[1]"))), Seq(raw), Seq(raw), JsonConstructorNullBehavior.Null, StringType) + assert(obj(Literal.create(null, StringType)).throwable) + assert(obj(Literal("k"), raw = true).throwable) + assert(!obj(Literal("k")).throwable) + } + + test("throwable keeps a null-key predicate above a filtering join") { + // The optimizer must not push a throwable predicate below the join (PushPredicateThroughJoin + // only pushes non-throwable conditions), so a null-key row the join eliminates is never + // evaluated and does not throw. Were the constructor not throwable, the predicate would push to + // the probe side and throw on the eliminated row. The predicate is a throwable comparison + // (`json_object(...) = literal`) that exercises the pushdown path. + withTempView("t", "u") { + Seq((1, "k1"), (2, null)).toDF("id", "k").createOrReplaceTempView("t") + Seq(1).toDF("id").createOrReplaceTempView("u") + // id=2 has a null key but does not join u, so it is dropped first (the predicate stays above + // the join, so the constructor is never evaluated on the eliminated row). + checkAnswer( + sql("""SELECT t.id FROM t JOIN u ON t.id = u.id + |WHERE json_object(t.k VALUE 1) = '{"k1":1}'""".stripMargin), + Row(1)) + // With the null-key row surviving the join, evaluation still throws. + Seq(2).toDF("id").createOrReplaceTempView("u") + val e = intercept[SparkRuntimeException] { + sql("""SELECT t.id FROM t JOIN u ON t.id = u.id + |WHERE json_object(t.k VALUE 1) = '{"k1":1}'""".stripMargin).collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + } + } + + test("a non-throwing JSON_OBJECT predicate is pushed below a join") { + // Range-based views keep the filter from being folded into a local relation. + withTempView("t", "u") { + spark.range(3).selectExpr("id", "id AS x").createOrReplaceTempView("t") + spark.range(2).createOrReplaceTempView("u") + val df = sql("""SELECT t.id FROM t JOIN u ON t.id = u.id + |WHERE json_object('a' VALUE t.x) = '{"a":1}'""".stripMargin) + val join = df.queryExecution.optimizedPlan.collectFirst { case j: Join => j } + .getOrElse(fail("expected a Join in the optimized plan")) + assert(join.exists { + case f: Filter => f.condition.exists(_.isInstanceOf[JsonObjectExpr]) + case _ => false + }) + checkAnswer(df, Row(1)) + } + } + + test("raw splicing is decided from the source, not the optimized plan shape") { + // A directly-nested JSON_OBJECT carries implicit FORMAT JSON and is spliced raw. + checkAnswer( + sql("SELECT json_object('a' VALUE json_object('b' VALUE 1))"), + Row("""{"a":{"b":1}}""")) + // But a JSON_OBJECT result surfaced as a column is a plain STRING and must stay quoted -- even + // though CollapseProject may inline the inner JSON_OBJECT into the outer value position. The + // decision is frozen from the lexical argument at parse time, so the result stays + // {"a":"{\"b\":1}"}, never {"a":{"b":1}}. + val inlined = + sql("SELECT json_object('a' VALUE o) AS r FROM (SELECT json_object('b' VALUE 1) AS o) t") + checkAnswer(inlined, Row("""{"a":"{\"b\":1}"}""")) + // Referencing the alias twice blocks CollapseProject from inlining it; the result is identical, + // confirming independence from plan shape. + val notInlined = + sql("SELECT json_object('a' VALUE o) AS r, o FROM (SELECT json_object('b' VALUE 1) AS o) t") + checkAnswer(notInlined, Row("""{"a":"{\"b\":1}"}""", """{"b":1}""")) + } + + test("COLLATE on a nested JSON_OBJECT value still splices it raw") { + // COLLATE is a pass-through wrapper (collation metadata only), so a nested constructor under + // COLLATE is still spliced raw -- a no-op-looking annotation must not flip it to a quoted + // string, and the COLLATE form must agree with the bare form. + Seq("UTF8_BINARY", "UTF8_LCASE").foreach { collation => + checkAnswer( + sql(s"SELECT json_object('a' VALUE json_object('b' VALUE 1) COLLATE $collation)"), + Row("""{"a":{"b":1}}""")) + } + checkAnswer( + sql("SELECT json_object('a' VALUE json_object('b' VALUE 1) COLLATE UTF8_BINARY)"), + sql("SELECT json_object('a' VALUE json_object('b' VALUE 1))")) + } + + test("explicit CAST(... AS STRING) still cancels raw splicing") { + // In contrast to COLLATE, an explicit CAST(... AS STRING) is the documented way to quote a + // nested constructor rather than splice it raw. + checkAnswer( + sql("SELECT json_object('a' VALUE CAST(json_object('b' VALUE 1) AS STRING))"), + Row("""{"a":"{\"b\":1}"}""")) + } + + test("explicit FORMAT JSON splices a string value raw; a plain string is quoted") { + checkAnswer( + sql("""SELECT json_object('a' VALUE '{"b":1}' FORMAT JSON)"""), Row("""{"a":{"b":1}}""")) + checkAnswer( + sql("SELECT json_object('a' VALUE '[1,2]' FORMAT JSON)"), Row("""{"a":[1,2]}""")) + // FORMAT JSON applies to the colon form too, not just VALUE. + checkAnswer( + sql("""SELECT json_object('a' : '{"b":1}' FORMAT JSON)"""), Row("""{"a":{"b":1}}""")) + // Without FORMAT JSON the same string is quoted, even though its contents are valid JSON. + checkAnswer( + sql("""SELECT json_object('a' VALUE '{"b":1}')"""), Row("""{"a":"{\"b\":1}"}""")) + } + + test("per-member rawJson and validation flags stay tied to their own member") { + // Ordered object mixing a quoted string (rawJson=false), a trusted nested producer + // (rawJson=true), and an explicit FORMAT JSON string (rawJson=true, validated). Each member + // must render from its own flag, not a neighbour's. + checkAnswer( + sql("""SELECT json_object( + | 'a' VALUE '{"x":1}', + | 'b' VALUE json_object('c' VALUE 1), + | 'd' VALUE '[1,2]' FORMAT JSON)""".stripMargin), + Row("""{"a":"{\"x\":1}","b":{"c":1},"d":[1,2]}""")) + // A later malformed explicit value: validation must fire for that member (flattened arg 8), not + // be masked by the trusted/quoted earlier members. + val e = intercept[SparkRuntimeException] { + sql("""SELECT json_object( + | 'a' VALUE '{"x":1}', + | 'b' VALUE json_object('c' VALUE 1), + | 'd' VALUE '[1,2]' FORMAT JSON, + | 'e' VALUE '{bad' FORMAT JSON)""".stripMargin).collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + assert(e.getMessageParameters.get("position") == "8") + } + + test("a malformed FORMAT JSON value raises an error at runtime") { + Seq("'{bad'", "'1,2'", "''").foreach { value => + val e = intercept[SparkRuntimeException] { + sql(s"SELECT json_object('a' VALUE $value FORMAT JSON)").collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE", s"for $value") + } + // The reported position is the flattened value-argument position, not the member ordinal: the + // bad value below is the 4th argument (`a`, 1, `b`, `{bad`), not the 2nd member. + val runtimeErr = intercept[SparkRuntimeException] { + sql("SELECT json_object('a' VALUE 1, 'b' VALUE '{bad' FORMAT JSON)").collect() + } + assert(runtimeErr.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + assert(runtimeErr.getMessageParameters.get("position") == "4") + } + + test("malformed FORMAT JSON is validated per row, so a filtered-out row does not raise") { + // FORMAT JSON validation is throwable, so the constructor is not constant-folded and runs per + // row at eval time: a row the WHERE drops is never validated and must not raise. + val values = "FROM VALUES ('ok', '[1]'), ('bad', '{oops') AS t(id, j)" + checkAnswer( + sql(s"SELECT json_object('a' VALUE j FORMAT JSON) $values WHERE id = 'ok'"), + Row("""{"a":[1]}""")) + val e = intercept[SparkRuntimeException] { + sql(s"SELECT json_object('a' VALUE j FORMAT JSON) $values").collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + } + + test("FORMAT JSON on a non-string value is rejected at analysis") { + val e = intercept[AnalysisException] { + sql("SELECT json_object('a' VALUE 123 FORMAT JSON)") + } + assert(e.getCondition == "DATATYPE_MISMATCH.INVALID_JSON_FORMAT_JSON_INPUT") + // The reported position is the flattened value-argument position, not the member ordinal: the + // bad value below is the 4th argument (`a`, 1, `b`, 123), not the 2nd member. + val laterMemberErr = intercept[AnalysisException] { + sql("SELECT json_object('a' VALUE 1, 'b' VALUE 123 FORMAT JSON)") + } + assert(laterMemberErr.getCondition == "DATATYPE_MISMATCH.INVALID_JSON_FORMAT_JSON_INPUT") + assert(laterMemberErr.getMessageParameters.get("position") == "4") + } + + test("a NULL FORMAT JSON value follows ON NULL like any other null") { + checkAnswer(sql("SELECT json_object('a' VALUE NULL FORMAT JSON)"), Row("""{"a":null}""")) + checkAnswer( + sql("SELECT json_object('a' VALUE NULL FORMAT JSON ABSENT ON NULL)"), Row("{}")) + } + + test("a per-row (non-foldable) FORMAT JSON value is validated for each row") { + // A column-valued FORMAT JSON is non-foldable, so it exercises the per-row validation branch + // (not the cached foldable branch). + checkAnswer( + Seq("""{"b":1}""").toDF("j").selectExpr("json_object('a' VALUE j FORMAT JSON)"), + Row("""{"a":{"b":1}}""")) + val e = intercept[SparkRuntimeException] { + Seq("bad").toDF("j").selectExpr("json_object('a' VALUE j FORMAT JSON)").collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + } + + test("a caught FORMAT JSON error does not corrupt later rows in the same task") { + // The evaluator validates every raw value before writing to its per-task generator, so a + // malformed row that a `try_*` wrapper swallows must leave the generator clean: later rows + // reusing the same evaluator must render exactly as they would on their own. `decode(..utf-8)` + // round-trips the bytes back to the JSON string so the per-row result is checkable. + val query = + "SELECT decode(try_to_binary(json_object('a' VALUE s FORMAT JSON), 'utf-8'), 'utf-8') " + + "FROM VALUES ('1'), ('{bad'), ('2'), ('3') AS t(s)" Review Comment: Good catch. Set leafNodeDefaultParallelism to 1 so all four rows run in one task, so the test now fails if validation moves back after writeStartObject. ########## sql/core/src/test/scala/org/apache/spark/sql/JsonObjectSuite.scala: ########## @@ -0,0 +1,1397 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.antlr.v4.runtime.{CharStreams, CommonTokenStream} +import org.antlr.v4.runtime.atn.PredictionMode + +import org.apache.spark.SparkRuntimeException +import org.apache.spark.sql.catalyst.analysis.Star +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.DataTypeMismatch +import org.apache.spark.sql.catalyst.expressions.{Cast, CodegenObjectFactoryMode, Collate, Expression, JsonConstructorNullBehavior, JsonImplicitFormatCarrier, JsonObjectExpr, Literal} +import org.apache.spark.sql.catalyst.parser.{ParseException, SparkParserBailErrorStrategy, SqlBaseLexer, SqlBaseParser} +import org.apache.spark.sql.catalyst.plans.logical.{Filter, Join} +import org.apache.spark.sql.execution.{ProjectExec, WholeStageCodegenExec} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{ArrayType, CharType, GeographyType, GeometryType, IntegerType, MapType, StringType, StructType, VarcharType} + +/** + * End-to-end tests for the SQL:2016 `JSON_OBJECT` constructor function. + */ +class JsonObjectSuite extends QueryTest with SharedSparkSession { + import testImplicits._ + + test("in the comma form, colon-bearing arguments bind as colon members (documented precedence)") { + // `JSON_OBJECT(a:b, c:d)` matches both list forms: the colon-member list and the comma + // (MySQL-compat) list. The colon-member alternative is declared first, so it wins -- `a:b, c:d` + // reads as the two members (a -> b) and (c -> d), the SQL-standard `key : value` reading, not + // as a single comma-form pair whose key and value are `:`-extractions. + checkAnswer( + sql("SELECT json_object(a:b, c:d) FROM VALUES ('k1','v1','k2','v2') t(a,b,c,d)"), + Row("""{"k1":"v1","k2":"v2"}""")) + // Parenthesizing each argument forces the comma form (one member whose key/value are the + // `:`-extractions). The non-VARIANT extraction target then errors -- proving it parsed as one + // comma-form pair, not two colon members (which resolve cleanly above). + val e = intercept[AnalysisException] { + sql("SELECT json_object((a:b), (c:d)) FROM VALUES ('k1','v1','k2','v2') t(a,b,c,d)").collect() + } + assert(e.getCondition == "COLUMN_IS_NOT_VARIANT_TYPE") + } + + test("basic object from key-value pairs using VALUE keyword") { + checkAnswer( + sql("SELECT json_object('id' VALUE 7, 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("construct object using optional KEY keyword") { + checkAnswer( + sql("SELECT json_object(KEY 'id' VALUE 7, KEY 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("construct object using colon syntax") { + checkAnswer( + sql("SELECT json_object('id': 7, 'name': 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("colon member with a column-valued value") { + // The `key : value` member overlaps the semi-structured extraction form `expr : path` + // (`primaryExpression COLON semiStructuredExtractionPath`), which also accepts a bare + // identifier on the right. Confirm a column-valued colon member still parses as a JSON_OBJECT + // pair rather than as `'k' : v` semi-structured extraction. + checkAnswer( + sql("SELECT json_object('k': v) FROM VALUES ('x') t(v)"), + Row("""{"k":"x"}""")) + } + + test("a colon member splits at the first top-level colon after the key") { + val from = + """FROM (SELECT parse_json('{"x":1,"y":"s"}') AS v, 'kk' AS k, 'e' AS `end`, 'c' AS `case`)""" + Seq( + "json_object('k': v:x)" -> """{"k":1}""", + "json_object(k: v:x)" -> """{"kk":1}""", + "json_object('k': v:x ABSENT ON NULL)" -> """{"k":1}""", + "json_object(k: v:x RETURNING STRING)" -> """{"kk":1}""", + "json_object('a': v:x, k: v:y)" -> """{"a":1,"kk":"s"}""", + "json_object((v:y::string) : 1)" -> """{"s":1}""", + "json_object(v:y::string VALUE v:x)" -> """{"s":1}""", + "json_object(CASE WHEN v:x::int = 1 THEN 'a' END : v:y)" -> """{"a":"s"}""", + // `end` and `case` are non-reserved and usable as column names: the separator check must read + // each CASE/END token as keyword or column by its neighbour, not count it blindly. + "json_object(CASE WHEN end = 'e' AND v:x::int = 1 THEN 'a' END : v:y)" -> """{"a":"s"}""", + "json_object(case : v:x)" -> """{"c":1}""", + "json_object(array('a')[0] : v:x)" -> """{"a":1}""", + "json_object(end : v:x)" -> """{"e":1}""" + ).foreach { case (call, expected) => + checkAnswer(sql(s"SELECT $call $from"), Row(expected)) + } + checkAnswer( + spark.sql(s"SELECT json_object('k': v:x) $from", Map("v" -> "zzz")), + Row("""{"k":1}""")) + checkAnswer( + spark.sql("SELECT json_object('k' :p VALUE 1), 'a' :p", Map("p" -> "x")), + Row("""{"kx":1}""", "ax")) + } + + test("a case-named column with a postfix operator is a column, not a CASE keyword") { + // `case` is non-reserved, so `case[0]` (subscript) and `case COLLATE ...` apply a postfix + // operator to a column named `case`; neither `CASE [` nor `CASE COLLATE` begins a real CASE + // expression (`[` has no array-literal form, COLLATE needs a left operand). The separator check + // must read the token right after `case` as a postfix on an operand -- like the already-handled + // `.` -- so the top-level colon is still the member separator, not swallowed as a CASE branch. + checkAnswer( + sql("""SELECT json_object(case[0] : v:x) + |FROM (SELECT array('c') AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + checkAnswer( + sql("""SELECT json_object(case COLLATE UTF8_LCASE : v:x) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + } + + test("a colon key cannot coalesce a trailing named parameter marker") { + // `'k' :p` coalesces the marker into the key under the VALUE form, but in the colon form the + // first top-level `:` is the member separator, so a bare marker-coalesced key is a clean parse + // error (never a silent mis-split). Parenthesize the key or use the VALUE form. + val e = intercept[ParseException] { + spark.sql("SELECT json_object('k' :p : 1)", Map("p" -> "x")) + } + assert(e.getCondition == "PARSE_SYNTAX_ERROR") + checkAnswer( + spark.sql("SELECT json_object(('k' :p) : 1)", Map("p" -> "x")), Row("""{"kx":1}""")) + checkAnswer( + spark.sql("SELECT json_object('k' :p VALUE 1)", Map("p" -> "x")), Row("""{"kx":1}""")) + } + + test("a simple CASE over a named-parameter operand is a valid colon key") { + // `CASE :p WHEN ... END : value`: the `:p` operand must parse as a named-parameter marker, not + // be read as the member separator. `CASE :` is ambiguous with a `case` column before the + // separator (`case : value`), so the separator check decides it by looking ahead for the + // CASE's WHEN -- present here, so `CASE` is the keyword and `:p` stays its operand. + val from = "FROM (SELECT parse_json('{\"x\":1}') AS v)" + checkAnswer( + spark.sql( + s"SELECT json_object(CASE :p WHEN 1 THEN 'one' ELSE 'other' END : v:x) $from", + Map("p" -> 1)), + Row("""{"one":1}""")) + checkAnswer( + spark.sql( + s"SELECT json_object(CASE :p WHEN 1 THEN 'one' ELSE 'other' END : v:x) $from", + Map("p" -> 2)), + Row("""{"other":1}""")) + } + + test("a CASE-valued value does not flip a bare `case` key to the CASE keyword") { + // `case : CASE WHEN ... END`: the value's WHEN belongs to the value's CASE, not to the key, so + // the WHEN lookahead must stop at that inner CASE and keep the leading `case` a column with the + // top-level colon as the member separator. Guards the lookahead against over-triggering. + checkAnswer( + sql("""SELECT json_object(case : CASE WHEN v:x::int = 1 THEN 'y' ELSE 'z' END) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":"y"}""")) + } + + test("a `case` column inside a simple-CASE colon key is a column, not a nested CASE") { + // The simple-CASE key's own body references a column named `case` (the THEN result). The + // key-colon lookahead must not count that `case` as a nested CASE...END, or it would consume + // the real END and misread the `:p` operand marker as the member separator. + withSQLConf(SQLConf.ANSI_ENABLED.key -> "false") { + val from = "FROM (SELECT 'c' AS `case`, 'v' AS value)" + checkAnswer( + spark.sql(s"SELECT json_object(CASE :p WHEN 1 THEN case ELSE 'x' END : value) $from", + Map("p" -> 1)), + Row("""{"c":"v"}""")) + checkAnswer( + spark.sql(s"SELECT json_object(CASE :p WHEN 1 THEN case ELSE 'x' END : value) $from", + Map("p" -> 2)), + Row("""{"x":"v"}""")) + // A postfix operator on the body `case` column is the case most likely to be miscounted as a + // nested CASE...END by the key-colon lookahead. + val q = s"SELECT json_object(CASE :p WHEN 1 THEN case::string ELSE 'x' END : value) $from" + checkAnswer(spark.sql(q, Map("p" -> 1)), Row("""{"c":"v"}""")) + } + } + + test("a non-foldable key with escapable characters is JSON-escaped") { + // A dynamic (non-foldable) key is written through the generator's field-name path, which must + // escape quotes and backslashes exactly as a quoted key would. + val df = Seq(("key\"with\\chars", "v")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v)"), + Row("""{"key\"with\\chars":"v"}""")) + } + + test("a bare `when` column value does not flip a `case` colon key to the CASE keyword") { + // Outside ANSI mode `when` is a bare column; its WHEN token must not be read as the WHEN of a + // CASE keyword in the `case` key. A lone `when` value has no closing END, so `case` stays a + // column and the colon stays the member separator. + withSQLConf(SQLConf.ANSI_ENABLED.key -> "false") { + checkAnswer( + sql("""SELECT json_object(case : when) + |FROM (SELECT 'c' AS `case`, 'w' AS `when`)""".stripMargin), + Row("""{"c":"w"}""")) + } + } + + test("a case column nested in a colon key is a column, not a CASE keyword") { + // A `case` column in a CAST or parens is below the key's top level, where the token after it + // (`AS`, `+`) does not prove it is a column; without the depth guard the spurious CASE count + // hid the real separator colon (the parens already balance any colon inside). + checkAnswer( + sql("""SELECT json_object(CAST(case AS STRING) : v:x) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + checkAnswer( + sql("""SELECT json_object((case + 1)::string : v:x) + |FROM (SELECT 1 AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"2":1}""")) + } + + test("a member whose key and value are named key and value splits at VALUE") { + checkAnswer( + sql("SELECT json_object(key VALUE value + 1) FROM VALUES ('a', 1) t(key, value)"), + Row("""{"a":2}""")) + checkAnswer( + sql("SELECT json_object(KEY key VALUE value) FROM VALUES ('a', 1) t(key, value)"), + Row("""{"a":1}""")) + checkAnswer( + sql("SELECT json_object(KEY value VALUE key) FROM VALUES ('a', 'b') t(key, value)"), + Row("""{"b":"a"}""")) + checkAnswer( + sql("SELECT json_object('x' VALUE 0, key VALUE value + 1) " + + "FROM VALUES ('a', 1) t(key, value)"), + Row("""{"x":0,"a":2}""")) + } + + test("a key column continued by an operator is the key, not the optional KEY keyword") { + // `key` is non-reserved; `key + 1` continues the `key` column into the key expression rather + // than reading `key` as the optional KEY keyword with key `+ 1`. The arithmetic key is itself a + // type error (JSON object keys must be STRING), but the report names `(key + 1)` -- proof the + // `key` column is bound into the key, not dropped. Were it read as KEY, the key would be `+ 1`. + val ex = intercept[AnalysisException] { + sql("SELECT json_object(key + 1 VALUE value) FROM VALUES (10, 'x') t(key, value)") + } + assert(ex.getCondition == "DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE") + assert(ex.getMessage.contains("(key + 1)")) + } + + test("JSON_OBJECT members parse in SLL mode without the LL fallback") { + Seq( + "SELECT JSON_OBJECT('k': V)", + "SELECT JSON_OBJECT(K: V)", + "SELECT JSON_OBJECT('k': V:X, K: V:Y)", + "SELECT JSON_OBJECT(KEY 'id' VALUE 7)", + "SELECT JSON_OBJECT(KEY VALUE VALUE 1)", + // Exercises the optional-KEY `jsonOptionalKeyFollowsOperand` guard (`key + 1` as the key). + "SELECT JSON_OBJECT(key + 1 VALUE value)").foreach { query => + val parser = new SqlBaseParser( + new CommonTokenStream(new SqlBaseLexer(CharStreams.fromString(query)))) + parser.setErrorHandler(new SparkParserBailErrorStrategy()) + parser.getInterpreter.setPredictionMode(PredictionMode.SLL) + parser.singleStatement() + } + } + + test("construct object using comma-separated key-value syntax") { + checkAnswer( + sql("SELECT json_object('id', 7, 'name', 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("an odd number of arguments in the comma syntax is rejected") { + // The comma form requires paired key/value arguments; a dangling key ('name') has no value. + // JSON_OBJECT is a non-reserved keyword, so when the constructor grammar cannot match, the call + // parses as an ordinary function call and routes to the registered built-in, whose builder + // rejects the odd argument count rather than silently dropping the dangling key. + val e = intercept[AnalysisException] { + sql("SELECT json_object('id', 7, 'name')") + } + assert(e.getCondition == "WRONG_NUM_ARGS.WITHOUT_SUGGESTION") + } + + test("mixing the VALUE/colon form and the comma form is a parse error") { + // The two member-list styles are mutually exclusive grammar alternatives, so a single + // constructor cannot mix `key VALUE value` (or `key : value`) members with `key, value` ones. + Seq( + "SELECT json_object('a', 1, 'b' VALUE 2)", + "SELECT json_object('a' VALUE 1, 'b', 2)", + "SELECT json_object('a' : 1, 'b', 2)").foreach { query => + intercept[ParseException](sql(query)) + } + } + + test("construct object with NULL values (default NULL ON NULL)") { + checkAnswer( + sql("SELECT json_object('id': 7, 'v': NULL)"), + Row("""{"id":7,"v":null}""")) + } + + test("construct object with explicit NULL ON NULL") { + checkAnswer( + sql("SELECT json_object('id', 7, 'v', NULL NULL ON NULL)"), + Row("""{"id":7,"v":null}""")) + } + + test("construct object with NULL values and ABSENT ON NULL") { + checkAnswer( + sql("SELECT json_object('id': 7, 'v': NULL ABSENT ON NULL)"), + Row("""{"id":7}""")) + } + + test("construct empty object") { + checkAnswer( + sql("SELECT json_object()"), + Row("{}")) + } + + test("construct object with mixed scalar types") { + checkAnswer( + sql("""SELECT json_object('int': 42, 'str': 'hello', 'bool': true, + 'float': 3.14)"""), + Row("""{"int":42,"str":"hello","bool":true,"float":3.14}""")) + } + + test("construct object with decimal type via Jackson") { + checkAnswer( + sql("""SELECT json_object('d' VALUE CAST('123.45' AS DECIMAL(5,2)))"""), + Row("""{"d":123.45}""")) + } + + test("construct object with DATE type via Jackson") { + checkAnswer( + sql("""SELECT json_object('d' VALUE DATE'2020-01-02')"""), + Row("""{"d":"2020-01-02"}""")) + } + + test("construct object with TIMESTAMP type via Jackson") { + // Note: Jackson includes timezone offset when session timezone is set + checkAnswer( + sql("""SELECT json_object('ts' VALUE TIMESTAMP'2020-01-02 10:30:00')"""), + Row("""{"ts":"2020-01-02T10:30:00.000-08:00"}""")) + } + + test("TIMESTAMP rendering follows the session time zone on direct and routed paths") { + withSQLConf(SQLConf.SESSION_LOCAL_TIMEZONE.key -> "UTC") { + val ts = "TIMESTAMP'2020-01-02 10:30:00'" + val expected = sql(s"SELECT to_json(named_struct('ts', $ts))") + checkAnswer(sql(s"SELECT json_object('ts' VALUE $ts)"), expected) + checkAnswer(sql(s"SELECT json_object('ts', $ts)"), expected) + checkAnswer(expected, Row("""{"ts":"2020-01-02T10:30:00.000Z"}""")) + } + } + + test("struct value renders like to_json") { + // A struct value must render exactly like `to_json` of the equivalent member. + checkAnswer( + sql("SELECT json_object('s' VALUE named_struct('a', 1, 'b', 'x'))"), + Row("""{"s":{"a":1,"b":"x"}}""")) + checkAnswer( + sql("SELECT json_object('s' VALUE named_struct('a', 1, 'b', 'x'))"), + sql("SELECT to_json(named_struct('s', named_struct('a', 1, 'b', 'x')))")) + } + + test("array value renders like to_json") { + checkAnswer( + sql("SELECT json_object('a' VALUE array(1, 2, 3))"), + Row("""{"a":[1,2,3]}""")) + checkAnswer( + sql("SELECT json_object('a' VALUE array(1, 2, 3))"), + sql("SELECT to_json(named_struct('a', array(1, 2, 3)))")) + } + + test("map value renders like to_json") { + checkAnswer( + sql("SELECT json_object('m' VALUE map('x', 1, 'y', 2))"), + Row("""{"m":{"x":1,"y":2}}""")) + checkAnswer( + sql("SELECT json_object('m' VALUE map('x', 1, 'y', 2))"), + sql("SELECT to_json(named_struct('m', map('x', 1, 'y', 2)))")) + } + + test("nested complex value combining struct, array and map renders like to_json") { + val value = "named_struct('arr', array(1, 2), 'm', map('k', named_struct('n', 3)))" + checkAnswer( + sql(s"SELECT json_object('c' VALUE $value)"), + sql(s"SELECT to_json(named_struct('c', $value))")) + } + + test("struct value honors spark.sql.jsonGenerator.ignoreNullFields like to_json") { + // `ON NULL` controls only top-level members; a null field *inside* a struct value follows + // spark.sql.jsonGenerator.ignoreNullFields, like `to_json`. + val value = "named_struct('a', 1, 'b', CAST(NULL AS INT))" + Seq("true", "false").foreach { ignore => + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> ignore) { + checkAnswer( + sql(s"SELECT json_object('s' VALUE $value)"), + sql(s"SELECT to_json(named_struct('s', $value))")) + } + } + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> "false") { + checkAnswer(sql(s"SELECT json_object('s' VALUE $value)"), Row("""{"s":{"a":1,"b":null}}""")) + } + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> "true") { + checkAnswer(sql(s"SELECT json_object('s' VALUE $value)"), Row("""{"s":{"a":1}}""")) + } + } + + test("top-level ON NULL and struct-internal ignoreNullFields are independent") { + // With NULL ON NULL (default) and ignoreNullFields=true, a top-level NULL member is kept as + // `null` while a null field inside a struct value is dropped. + withSQLConf(SQLConf.JSON_GENERATOR_IGNORE_NULL_FIELDS.key -> "true") { + checkAnswer( + sql("""SELECT json_object('top' VALUE CAST(NULL AS INT), + 's' VALUE named_struct('a', 1, 'b', CAST(NULL AS INT)))"""), + Row("""{"top":null,"s":{"a":1}}""")) + } + } + + test("string escaping in keys") { + checkAnswer( + sql("""SELECT json_object('key"with"quotes' VALUE 1)"""), + Row("""{"key\"with\"quotes":1}""")) + } + + // For scalar string values JSON_OBJECT must escape exactly like to_json of the equivalent + // struct (both go through the same Jackson generator); assert that equivalence rather than + // hand-encoding the escaping, which is easy to get wrong across Scala/SQL/JSON layers. + test("string escaping in values matches to_json") { + checkAnswer( + sql("""SELECT json_object('msg' VALUE 'hello +world')"""), + sql("""SELECT to_json(named_struct('msg', 'hello +world'))""")) + } + + test("string escaping with backslash matches to_json") { + checkAnswer( + sql("""SELECT json_object('path' VALUE 'c:\windows')"""), + sql("""SELECT to_json(named_struct('path', 'c:\windows'))""")) + } + + test("nested JSON_OBJECT spliced raw") { + checkAnswer( + sql("""SELECT json_object('a' VALUE json_object('b' VALUE 1))"""), + Row("""{"a":{"b":1}}""")) + checkAnswer( + sql("""SELECT json_object('a', json_object('b', 1))"""), + Row("""{"a":{"b":1}}""")) + } + + test("nested JSON_OBJECT with multiple levels") { + checkAnswer( + sql("""SELECT json_object('outer' VALUE + json_object('inner' VALUE 42, 'name' VALUE 'test'))"""), + Row("""{"outer":{"inner":42,"name":"test"}}""")) + } + + test("a nested JSON_ARRAY value is spliced raw") { + checkAnswer( + sql("SELECT json_object('a' VALUE json_array(1, 2))"), + Row("""{"a":[1,2]}""")) + } + + test("JSON_OBJECT nested directly in JSON_ARRAY is spliced as an object element") { + // The inverse nesting direction: a JSON_OBJECT in a JSON_ARRAY element position stays on the + // direct grammar path (JsonArrayValueContext), so it is spliced as a JSON object rather than + // routed through resolution and emitted as a quoted string. + checkAnswer( + sql("SELECT json_array(json_object('a', 1), json_object('b', 2))"), + Row("""[{"a":1},{"b":2}]""")) + } + + test("a nested JSON_QUERY value is spliced under KEEP QUOTES and quoted under OMIT QUOTES") { + // JSON_QUERY emits JSON text under the default KEEP QUOTES, so a lexically nested JSON_QUERY is + // spliced raw: the matched object is {"x":1}, not the quoted string "{\"x\":1}". + checkAnswer( + sql("""SELECT json_object('a' VALUE json_query('{"o":{"x":1}}', '$.o'))"""), + Row("""{"a":{"x":1}}""")) + // OMIT QUOTES returns the matched scalar string's decoded content (Ada, not "Ada") -- an + // ordinary string -- so it takes the quoted path (emitsImplicitJsonText is false), never the + // invalid splice {"a":Ada}. + checkAnswer( + sql("""SELECT json_object('a' VALUE json_query('{"n":"Ada"}', '$.n' OMIT QUOTES))"""), + Row("""{"a":"Ada"}""")) + } + + test("null key error") { + val e = intercept[SparkRuntimeException] { + sql("SELECT json_object(NULL VALUE 'value')").collect() + } + // Assert the structured error contract, not just the message text. + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + assert(e.getSqlState == "22004") + } + + test("a null key is validated before a null value is omitted under ABSENT ON NULL") { + // ABSENT ON NULL omits members with a null value, but the key is validated first, so a null key + // still raises JSON_OBJECT_NULL_KEY rather than being silently dropped along with the member. + val e = intercept[SparkRuntimeException] { + sql("SELECT json_object(NULL VALUE NULL ABSENT ON NULL)").collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + assert(e.getSqlState == "22004") + } + + test("non-foldable key and value expressions") { + val df = Seq(("key1", "val1"), ("key2", "val2")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v)"), + Seq(Row("""{"key1":"val1"}"""), Row("""{"key2":"val2"}"""))) + } + + test("non-foldable with NULL value and NULL ON NULL") { + val df = Seq(("k", null), ("key", "val")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v)"), + Seq(Row("""{"k":null}"""), Row("""{"key":"val"}"""))) + } + + test("non-foldable with NULL value and ABSENT ON NULL") { + val df = Seq(("k", null), ("key", "val")).toDF("k", "v") + checkAnswer( + df.selectExpr("json_object(k VALUE v ABSENT ON NULL)"), + Seq(Row("{}"), Row("""{"key":"val"}"""))) + } + + test("multiple keys with ABSENT ON NULL") { + checkAnswer( + sql("""SELECT json_object('a' VALUE 1, 'b' VALUE NULL, 'c' VALUE 3 + ABSENT ON NULL)"""), + Row("""{"a":1,"c":3}""")) + } + + test("duplicate keys are emitted in source order") { + checkAnswer( + sql("SELECT json_object('k' VALUE 1, 'k' VALUE 2)"), + Row("""{"k":1,"k":2}""")) + } + + test("non-string key type is rejected at analysis, not at execution") { + val ex = intercept[AnalysisException] { + sql("SELECT json_object(1 VALUE 'x')") + } + assert(ex.getMessage.contains("UNEXPECTED_INPUT_TYPE")) + } + + test("non-string key type reports the actual key argument") { + val ex = intercept[AnalysisException] { + sql("SELECT json_object('ok' VALUE 1, 2 VALUE 'bad')") + } + checkError( + exception = ex, + condition = "DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE", + sqlState = Some("42K09"), + parameters = Map( + "sqlExpr" -> "\"JSON_OBJECT(ok VALUE 1, 2 VALUE bad)\"", + "paramIndex" -> "third", + "requiredType" -> "\"STRING\"", + "inputSql" -> "\"2\"", + "inputType" -> "\"INT\""), + queryContext = Array(ExpectedContext("json_object('ok' VALUE 1, 2 VALUE 'bad')", 7, 46))) + } + + test("collated STRING RETURNING is accepted") { + // isValidReturningType must accept any StringType instance, not just the default collation. + checkAnswer( + sql("SELECT json_object('a' VALUE 1 RETURNING STRING COLLATE UTF8_LCASE)"), + Row("""{"a":1}""")) + } + + test("an invalid RETURNING type is reported under DATATYPE_MISMATCH") { + // The error is emitted as a DataTypeMismatch, so its condition must resolve under + // DATATYPE_MISMATCH -- not as a top-level INVALID_JSON_RETURNING_TYPE class. + val e = intercept[AnalysisException] { + sql("SELECT json_object('a' VALUE 1 RETURNING INT)").collect() + } + assert(e.getCondition == "DATATYPE_MISMATCH.INVALID_JSON_RETURNING_TYPE") + } + + test("a directly-constructed JsonObjectExpr with a CHAR/VARCHAR RETURNING is rejected") { + // The parser normalizes CHAR/VARCHAR RETURNING to STRING, but a raw CharType/VarcharType from + // direct Catalyst construction would advertise a length JSON_OBJECT does not enforce. + Seq(VarcharType(2), CharType(2)).foreach { returning => + val expr = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, returning) + expr.checkInputDataTypes() match { + case DataTypeMismatch(errorSubClass, _) => + assert(errorSubClass == "INVALID_JSON_RETURNING_TYPE", s"for $returning") + case other => fail(s"expected DataTypeMismatch for $returning, got $other") + } + } + } + + test("value accepts an unparenthesized predicate expression") { + // valueExpr is parsed as a full `expression`, so ordinary predicates work without parentheses. + checkAnswer(sql("SELECT json_object('isnull' VALUE 1 IS NULL)"), Row("""{"isnull":false}""")) + checkAnswer(sql("SELECT json_object('gt' : 2 > 1)"), Row("""{"gt":true}""")) + } + + test("widening the value to expression does not change documented forms") { + // Design-doc examples where a value abuts the ON NULL / RETURNING keywords must still parse and + // evaluate identically after widening valueExpression -> expression. + checkAnswer(sql("SELECT json_object('id': 7, 'v': NULL)"), Row("""{"id":7,"v":null}""")) + checkAnswer( + sql("SELECT json_object('id': 7, 'v': NULL ABSENT ON NULL)"), Row("""{"id":7}""")) + checkAnswer( + sql("SELECT json_object('id', 7, 'v', NULL ABSENT ON NULL)"), Row("""{"id":7}""")) + checkAnswer( + sql("SELECT json_object('id' VALUE 7, 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("an unsupported value type is rejected at analysis") { + // A spatial value: JacksonUtils.verifyType accepts it (it is an AtomicType) but + // JacksonGenerator cannot serialize it, so JSON_OBJECT must reject it up front, not at runtime. + val bad = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, GeometryType(4326)))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + bad.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "CANNOT_CONVERT_TO_JSON") + case other => fail(s"expected DataTypeMismatch, got $other") + } + // A spatial type nested as a MAP VALUE must still be rejected: the guard descends recursively + // into map values (unlike the top-level atomic case above), so this exercises that descent. + val badNested = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, MapType(StringType, GeometryType(4326))))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + badNested.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "CANNOT_CONVERT_TO_JSON") + case other => fail(s"expected DataTypeMismatch for a nested spatial value, got $other") + } + // The guard also descends into STRUCT fields and ARRAY elements, so a spatial type buried in + // either -- the other two recursive branches -- must be rejected just like the map-value case. + val nestedBadTypes = Seq( + new StructType().add("g", GeometryType(4326)), + ArrayType(GeographyType(4326))) + nestedBadTypes.foreach { dt => + val expr = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, dt))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + expr.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "CANNOT_CONVERT_TO_JSON", s"for $dt") + case other => fail(s"expected DataTypeMismatch for a spatial type in $dt, got $other") + } + } + // A spatial type appearing only as a MAP KEY is fine: JacksonGenerator writes map keys via + // toString, so the value-type guard must not over-reject it. + val ok = JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, MapType(GeometryType(4326), IntegerType)))), + Seq(false), Seq(false), JsonConstructorNullBehavior.Null, StringType) + assert(ok.checkInputDataTypes().isSuccess) + } + + test("a raw (FORMAT JSON) value that is not a string is rejected") { + // A raw-spliced value must carry JSON text (string); a non-string raw value would fail with a + // ClassCastException at eval, so reject it at analysis with the FORMAT JSON input error. + val expr = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(true), Seq(true), + JsonConstructorNullBehavior.Null, StringType) + expr.checkInputDataTypes() match { + case DataTypeMismatch(sub, _) => assert(sub == "INVALID_JSON_FORMAT_JSON_INPUT") + case other => fail(s"expected DataTypeMismatch, got $other") + } + } + + test("SQL renders an explicit collated RETURNING and an explicit ON NULL clause") { + val collated = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), JsonConstructorNullBehavior.Null, + StringType("UTF8_LCASE")) + assert(collated.sql.contains("RETURNING STRING COLLATE UTF8_LCASE")) + // .sql always renders an explicit ON NULL clause so reparse stays on the direct grammar path. + val default = JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + assert(default.sql == "JSON_OBJECT('k' VALUE 1 NULL ON NULL)") + } + + test("SQL renders a raw nested value spliced back to raw even after collation wrapping") { + val inner = JsonObjectExpr( + Seq((Literal("b"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + // Simulate the default-collation rule wrapping the raw nested value in a Cast. rawJson stays + // frozen true; the Cast hides the bare constructor, so .sql renders an explicit FORMAT JSON so + // reparse splices it raw. + val wrapped = JsonObjectExpr( + Seq((Literal("a"), Cast(inner, StringType("UTF8_LCASE")))), Seq(true), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + assert(wrapped.sql.contains("FORMAT JSON")) + checkAnswer(sql(s"SELECT ${wrapped.sql}"), Row("""{"a":{"b":1}}""")) + } + + test("emitted SQL reparses and evaluates with raw-vs-quoted semantics preserved") { + // The .sql renderings above are round-trip contracts: reparsing and evaluating them must + // reproduce the original raw-vs-quoted splicing. + // A raw nested value renders as a bare constructor and reparses back to raw splicing. + checkAnswer( + sql("SELECT JSON_OBJECT('a' VALUE JSON_OBJECT('b' VALUE 1))"), Row("""{"a":{"b":1}}""")) + // A quoted value that the optimizer inlined as an implicit-JSON expression is neutralized with + // CAST(... AS STRING); reparsing must keep it quoted rather than splicing it raw. + val inner = JsonObjectExpr( + Seq((Literal("b"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + val quoted = JsonObjectExpr( + Seq((Literal("a"), inner)), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType) + assert(quoted.sql == + "JSON_OBJECT('a' VALUE CAST(JSON_OBJECT('b' VALUE 1 NULL ON NULL) AS STRING) NULL ON NULL)") + checkAnswer(sql(s"SELECT ${quoted.sql}"), Row("""{"a":"{\"b\":1}"}""")) + } + + test("generated column names omit the synthetic NULL ON NULL ownership clause") { + // Canonical `.sql` appends a default NULL ON NULL so a clause-free call reparses to the builtin + // (see the shadowing test), but that marker is never reparsed for display. Unaliased column + // names must therefore not expose ownership text the user never wrote; a non-default + // ABSENT ON NULL the user did write is kept. + def colName(q: String): String = sql(q).schema.head.name + assert(colName("SELECT json_object()") == "JSON_OBJECT()") + assert(colName("SELECT json_object('id' VALUE 7)") == "JSON_OBJECT(id VALUE 7)") + assert(colName("SELECT json_object('id', 7)") == "JSON_OBJECT(id VALUE 7)") + assert(colName("SELECT json_object('a' VALUE json_object('b' VALUE 1))") == + "JSON_OBJECT(a VALUE JSON_OBJECT(b VALUE 1))") + assert(colName("SELECT json_object('v' VALUE NULL ABSENT ON NULL)") == + "JSON_OBJECT(v VALUE NULL ABSENT ON NULL)") + } + + test("ABSENT ON NULL round-trips through .sql keeping null members omitted") { + // The ABSENT renderer arm must emit `ABSENT ON NULL` (not the default clause and not nothing), + // so a canonical round trip keeps dropping null-valued members rather than retaining them. + val absent = JsonObjectExpr( + Seq((Literal("a"), Literal(1)), (Literal("b"), Literal.create(null, IntegerType))), + Seq(false, false), Seq(false, false), JsonConstructorNullBehavior.Absent, StringType) + assert(absent.sql == "JSON_OBJECT('a' VALUE 1, 'b' VALUE CAST(NULL AS INT) ABSENT ON NULL)") + checkAnswer(sql(s"SELECT ${absent.sql}"), Row("""{"a":1}""")) + } + + test("JSON_OBJECT is foldable only when it cannot throw") { + assert(JsonObjectExpr( + Seq((Literal("k"), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).foldable) + // Folding a possibly-null key would surface the null-key error at optimization even for rows a + // filter/join drops. + assert(!JsonObjectExpr( + Seq((Literal.create(null, StringType), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).foldable) + assert(!JsonObjectExpr( + Seq((Literal("k"), Literal("[1]"))), Seq(true), Seq(true), + JsonConstructorNullBehavior.Null, StringType).foldable) + } + + test("a constant JSON_OBJECT is accepted as a column DEFAULT") { + withTable("t") { + sql("CREATE TABLE t (id INT, c STRING DEFAULT json_object('a' VALUE 1)) USING parquet") + sql("INSERT INTO t (id) VALUES (1)") + checkAnswer(sql("SELECT c FROM t"), Row("""{"a":1}""")) + } + } + + test("a non-foldable JSON_OBJECT projection participates in whole-stage codegen") { + // Rendering through an Invoke of the object evaluator (not CodegenFallback) keeps the enclosing + // Project eligible for whole-stage codegen; a CodegenFallback expression would exclude it. A + // column-valued member keeps the constructor from being constant-folded away. + withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false") { + val df = sql("SELECT json_object('id' VALUE id) FROM range(5)") + val fusedProjects = df.queryExecution.executedPlan.collect { + case w: WholeStageCodegenExec => w + }.flatMap(_.collect { case p: ProjectExec => p }) + assert(fusedProjects.nonEmpty, + s"expected the JSON_OBJECT Project to be fused into whole-stage codegen, but got:\n" + + df.queryExecution.executedPlan) + checkAnswer(df, (0 until 5).map(i => Row(s"""{"id":$i}"""))) + } + } + + test("a constant JSON_OBJECT is constant-folded") { + val df = sql("SELECT json_object('a' VALUE 1) AS r FROM range(2)") + assert(!df.queryExecution.optimizedPlan.exists( + _.expressions.exists(_.exists(_.isInstanceOf[JsonObjectExpr])))) + checkAnswer(df, Seq(Row("""{"a":1}"""), Row("""{"a":1}"""))) + } + + test("a null key raises JSON_OBJECT_NULL_KEY before the value is evaluated") { + // The key is checked before the value is evaluated, so a null key wins deterministically even + // when the value expression would itself throw. + // `raise_error(k)` references the column so it is neither foldable nor evaluated before the + // key null-check; if the value ran first the error would come from `raise_error`, not the key. + val e = intercept[SparkRuntimeException] { + sql("SELECT json_object(k VALUE raise_error(k)) " + + "FROM VALUES (CAST(NULL AS STRING)) t(k)").collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + assert(e.getSqlState == "22004") + } + + test("a foldable literal key is emitted on every row") { + // A foldable non-null key must be written for every row, not dropped after the first. + checkAnswer( + sql("SELECT json_object('id' VALUE a) FROM VALUES (1), (2) t(a)"), + Seq(Row("""{"id":1}"""), Row("""{"id":2}"""))) + // A foldable key that evaluates to null must still raise JSON_OBJECT_NULL_KEY per row rather + // than being silently skipped. + Seq("NULL", "CAST(NULL AS STRING)").foreach { k => + val e = intercept[SparkRuntimeException] { + sql(s"SELECT json_object($k VALUE 1)").collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY", s"for key $k") + assert(e.getSqlState == "22004", s"for key $k") + } + } + + test("CHAR/VARCHAR RETURNING is normalized to STRING regardless of preserveCharVarcharTypeInfo") { + Seq("CHAR(2)", "VARCHAR(2)").foreach { returning => + Seq("true", "false").foreach { preserve => + withSQLConf(SQLConf.PRESERVE_CHAR_VARCHAR_TYPE_INFO.key -> preserve) { + assert( + sql(s"SELECT json_object('k' VALUE 1 RETURNING $returning)").schema.head.dataType + === StringType, + s"for RETURNING $returning, preserveCharVarcharTypeInfo=$preserve") + } + } + } + } + + test("object default collation applies only when RETURNING is not explicitly collated") { + withSQLConf(SQLConf.OBJECT_LEVEL_COLLATIONS_ENABLED.key -> "true") { + withTable("t") { + sql( + """CREATE TABLE t DEFAULT COLLATION UTF8_LCASE AS + |SELECT json_object('k' VALUE 1) AS a, + | json_object('k' VALUE 1 RETURNING STRING COLLATE UTF8_BINARY) AS b""".stripMargin) + val schema = spark.table("t").schema + // Omitted RETURNING (default STRING) follows the table's default collation. + assert(schema("a").dataType === StringType("UTF8_LCASE")) + // Explicit RETURNING ... COLLATE is the user's choice and must not be overwritten. + assert(schema("b").dataType === StringType("UTF8_BINARY")) + } + } + } + + test("default collation recurses into a nested JSON_OBJECT value") { + // The rule casts each DefaultStringProducingExpression, recursing through a nested constructor + // (the flat cases above only cover a top-level constructor). This CTAS runs the default + // analyzer (single-pass included). Confirm the schema collation and that raw splicing still + // produces well-formed nested JSON at runtime. + withSQLConf(SQLConf.OBJECT_LEVEL_COLLATIONS_ENABLED.key -> "true") { + withTable("t") { + sql( + """CREATE TABLE t DEFAULT COLLATION UTF8_LCASE AS + |SELECT json_object('a' VALUE json_object('b' VALUE 1)) AS a""".stripMargin) + assert(spark.table("t").schema("a").dataType === StringType("UTF8_LCASE")) + checkAnswer(spark.table("t"), Row("""{"a":{"b":1}}""")) + } + } + } + + test("view default collation preserves an explicit collated RETURNING") { + // Exercises the CREATE VIEW resolution path (in addition to the CTAS path above): the explicit + // RETURNING collation must survive the view's default collation. Runs under dual-run (the + // default) so the single-pass resolver's view re-resolution is exercised for parity. + withSQLConf(SQLConf.OBJECT_LEVEL_COLLATIONS_ENABLED.key -> "true") { + withView("v") { + sql( + """CREATE VIEW v DEFAULT COLLATION UTF8_LCASE AS + |SELECT json_object('k' VALUE 1) AS a, + | json_object('k' VALUE 1 RETURNING STRING COLLATE UTF8_BINARY) AS b""".stripMargin) + val schema = spark.table("v").schema + assert(schema("a").dataType === StringType("UTF8_LCASE")) + assert(schema("b").dataType === StringType("UTF8_BINARY")) + } + } + } + + test("reports nullable to keep NullPropagation from skipping the null-key check") { + // The value is never null, but a possibly-null key makes JSON_OBJECT throwable, so it must + // report nullable: were it non-nullable, `NullPropagation` would fold `IS [NOT] NULL` and + // `count(...)` away and skip the eval that must raise JSON_OBJECT_NULL_KEY (see below). + assert(JsonObjectExpr( + Seq((Literal.create(null, StringType), Literal(1))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).nullable) + assert(sql("SELECT json_object(k VALUE 1) FROM VALUES ('a'), (NULL) t(k)") + .schema.head.nullable) + // A non-null key with a null value cannot throw, so the result is non-nullable. + assert(!JsonObjectExpr( + Seq((Literal("k"), Literal.create(null, IntegerType))), Seq(false), Seq(false), + JsonConstructorNullBehavior.Null, StringType).nullable) + assert(!sql("SELECT json_object('id' VALUE a) FROM VALUES (1), (2) t(a)") + .schema.head.nullable) + } + + test("IS NOT NULL and count still raise a null-key error") { + // NullPropagation must not rewrite these to a constant / `count(1)` and skip evaluation, which + // would swallow the null-key error on the null-key row. + Seq( + "SELECT json_object(k VALUE 1) IS NOT NULL FROM VALUES ('a'), (NULL) t(k)", + "SELECT count(json_object(k VALUE 1)) FROM VALUES ('a'), (NULL) t(k)").foreach { query => + val e = intercept[SparkRuntimeException](sql(query).collect()) + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + } + } + + test("is throwable only when a key may be null or a value needs validation") { + // A possibly-null key throws at runtime, so throwable must be true even when no child is + // itself throwable. + def obj(key: Expression, raw: Boolean = false): JsonObjectExpr = JsonObjectExpr( + Seq((key, Literal("[1]"))), Seq(raw), Seq(raw), JsonConstructorNullBehavior.Null, StringType) + assert(obj(Literal.create(null, StringType)).throwable) + assert(obj(Literal("k"), raw = true).throwable) + assert(!obj(Literal("k")).throwable) + } + + test("throwable keeps a null-key predicate above a filtering join") { + // The optimizer must not push a throwable predicate below the join (PushPredicateThroughJoin + // only pushes non-throwable conditions), so a null-key row the join eliminates is never + // evaluated and does not throw. Were the constructor not throwable, the predicate would push to + // the probe side and throw on the eliminated row. The predicate is a throwable comparison + // (`json_object(...) = literal`) that exercises the pushdown path. + withTempView("t", "u") { + Seq((1, "k1"), (2, null)).toDF("id", "k").createOrReplaceTempView("t") + Seq(1).toDF("id").createOrReplaceTempView("u") + // id=2 has a null key but does not join u, so it is dropped first (the predicate stays above + // the join, so the constructor is never evaluated on the eliminated row). + checkAnswer( + sql("""SELECT t.id FROM t JOIN u ON t.id = u.id + |WHERE json_object(t.k VALUE 1) = '{"k1":1}'""".stripMargin), + Row(1)) + // With the null-key row surviving the join, evaluation still throws. + Seq(2).toDF("id").createOrReplaceTempView("u") + val e = intercept[SparkRuntimeException] { + sql("""SELECT t.id FROM t JOIN u ON t.id = u.id + |WHERE json_object(t.k VALUE 1) = '{"k1":1}'""".stripMargin).collect() + } + assert(e.getCondition == "JSON_OBJECT_NULL_KEY") + } + } + + test("a non-throwing JSON_OBJECT predicate is pushed below a join") { + // Range-based views keep the filter from being folded into a local relation. + withTempView("t", "u") { + spark.range(3).selectExpr("id", "id AS x").createOrReplaceTempView("t") + spark.range(2).createOrReplaceTempView("u") + val df = sql("""SELECT t.id FROM t JOIN u ON t.id = u.id + |WHERE json_object('a' VALUE t.x) = '{"a":1}'""".stripMargin) + val join = df.queryExecution.optimizedPlan.collectFirst { case j: Join => j } + .getOrElse(fail("expected a Join in the optimized plan")) + assert(join.exists { + case f: Filter => f.condition.exists(_.isInstanceOf[JsonObjectExpr]) + case _ => false + }) + checkAnswer(df, Row(1)) + } + } + + test("raw splicing is decided from the source, not the optimized plan shape") { + // A directly-nested JSON_OBJECT carries implicit FORMAT JSON and is spliced raw. + checkAnswer( + sql("SELECT json_object('a' VALUE json_object('b' VALUE 1))"), + Row("""{"a":{"b":1}}""")) + // But a JSON_OBJECT result surfaced as a column is a plain STRING and must stay quoted -- even + // though CollapseProject may inline the inner JSON_OBJECT into the outer value position. The + // decision is frozen from the lexical argument at parse time, so the result stays + // {"a":"{\"b\":1}"}, never {"a":{"b":1}}. + val inlined = + sql("SELECT json_object('a' VALUE o) AS r FROM (SELECT json_object('b' VALUE 1) AS o) t") + checkAnswer(inlined, Row("""{"a":"{\"b\":1}"}""")) + // Referencing the alias twice blocks CollapseProject from inlining it; the result is identical, + // confirming independence from plan shape. + val notInlined = + sql("SELECT json_object('a' VALUE o) AS r, o FROM (SELECT json_object('b' VALUE 1) AS o) t") + checkAnswer(notInlined, Row("""{"a":"{\"b\":1}"}""", """{"b":1}""")) + } + + test("COLLATE on a nested JSON_OBJECT value still splices it raw") { + // COLLATE is a pass-through wrapper (collation metadata only), so a nested constructor under + // COLLATE is still spliced raw -- a no-op-looking annotation must not flip it to a quoted + // string, and the COLLATE form must agree with the bare form. + Seq("UTF8_BINARY", "UTF8_LCASE").foreach { collation => + checkAnswer( + sql(s"SELECT json_object('a' VALUE json_object('b' VALUE 1) COLLATE $collation)"), + Row("""{"a":{"b":1}}""")) + } + checkAnswer( + sql("SELECT json_object('a' VALUE json_object('b' VALUE 1) COLLATE UTF8_BINARY)"), + sql("SELECT json_object('a' VALUE json_object('b' VALUE 1))")) + } + + test("explicit CAST(... AS STRING) still cancels raw splicing") { + // In contrast to COLLATE, an explicit CAST(... AS STRING) is the documented way to quote a + // nested constructor rather than splice it raw. + checkAnswer( + sql("SELECT json_object('a' VALUE CAST(json_object('b' VALUE 1) AS STRING))"), + Row("""{"a":"{\"b\":1}"}""")) + } + + test("explicit FORMAT JSON splices a string value raw; a plain string is quoted") { + checkAnswer( + sql("""SELECT json_object('a' VALUE '{"b":1}' FORMAT JSON)"""), Row("""{"a":{"b":1}}""")) + checkAnswer( + sql("SELECT json_object('a' VALUE '[1,2]' FORMAT JSON)"), Row("""{"a":[1,2]}""")) + // FORMAT JSON applies to the colon form too, not just VALUE. + checkAnswer( + sql("""SELECT json_object('a' : '{"b":1}' FORMAT JSON)"""), Row("""{"a":{"b":1}}""")) + // Without FORMAT JSON the same string is quoted, even though its contents are valid JSON. + checkAnswer( + sql("""SELECT json_object('a' VALUE '{"b":1}')"""), Row("""{"a":"{\"b\":1}"}""")) + } + + test("per-member rawJson and validation flags stay tied to their own member") { + // Ordered object mixing a quoted string (rawJson=false), a trusted nested producer + // (rawJson=true), and an explicit FORMAT JSON string (rawJson=true, validated). Each member + // must render from its own flag, not a neighbour's. + checkAnswer( + sql("""SELECT json_object( + | 'a' VALUE '{"x":1}', + | 'b' VALUE json_object('c' VALUE 1), + | 'd' VALUE '[1,2]' FORMAT JSON)""".stripMargin), + Row("""{"a":"{\"x\":1}","b":{"c":1},"d":[1,2]}""")) + // A later malformed explicit value: validation must fire for that member (flattened arg 8), not + // be masked by the trusted/quoted earlier members. + val e = intercept[SparkRuntimeException] { + sql("""SELECT json_object( + | 'a' VALUE '{"x":1}', + | 'b' VALUE json_object('c' VALUE 1), + | 'd' VALUE '[1,2]' FORMAT JSON, + | 'e' VALUE '{bad' FORMAT JSON)""".stripMargin).collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + assert(e.getMessageParameters.get("position") == "8") + } + + test("a malformed FORMAT JSON value raises an error at runtime") { + Seq("'{bad'", "'1,2'", "''").foreach { value => + val e = intercept[SparkRuntimeException] { + sql(s"SELECT json_object('a' VALUE $value FORMAT JSON)").collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE", s"for $value") + } + // The reported position is the flattened value-argument position, not the member ordinal: the + // bad value below is the 4th argument (`a`, 1, `b`, `{bad`), not the 2nd member. + val runtimeErr = intercept[SparkRuntimeException] { + sql("SELECT json_object('a' VALUE 1, 'b' VALUE '{bad' FORMAT JSON)").collect() + } + assert(runtimeErr.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + assert(runtimeErr.getMessageParameters.get("position") == "4") + } + + test("malformed FORMAT JSON is validated per row, so a filtered-out row does not raise") { + // FORMAT JSON validation is throwable, so the constructor is not constant-folded and runs per + // row at eval time: a row the WHERE drops is never validated and must not raise. + val values = "FROM VALUES ('ok', '[1]'), ('bad', '{oops') AS t(id, j)" + checkAnswer( + sql(s"SELECT json_object('a' VALUE j FORMAT JSON) $values WHERE id = 'ok'"), + Row("""{"a":[1]}""")) + val e = intercept[SparkRuntimeException] { + sql(s"SELECT json_object('a' VALUE j FORMAT JSON) $values").collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + } + + test("FORMAT JSON on a non-string value is rejected at analysis") { + val e = intercept[AnalysisException] { + sql("SELECT json_object('a' VALUE 123 FORMAT JSON)") + } + assert(e.getCondition == "DATATYPE_MISMATCH.INVALID_JSON_FORMAT_JSON_INPUT") + // The reported position is the flattened value-argument position, not the member ordinal: the + // bad value below is the 4th argument (`a`, 1, `b`, 123), not the 2nd member. + val laterMemberErr = intercept[AnalysisException] { + sql("SELECT json_object('a' VALUE 1, 'b' VALUE 123 FORMAT JSON)") + } + assert(laterMemberErr.getCondition == "DATATYPE_MISMATCH.INVALID_JSON_FORMAT_JSON_INPUT") + assert(laterMemberErr.getMessageParameters.get("position") == "4") + } + + test("a NULL FORMAT JSON value follows ON NULL like any other null") { + checkAnswer(sql("SELECT json_object('a' VALUE NULL FORMAT JSON)"), Row("""{"a":null}""")) + checkAnswer( + sql("SELECT json_object('a' VALUE NULL FORMAT JSON ABSENT ON NULL)"), Row("{}")) + } + + test("a per-row (non-foldable) FORMAT JSON value is validated for each row") { + // A column-valued FORMAT JSON is non-foldable, so it exercises the per-row validation branch + // (not the cached foldable branch). + checkAnswer( + Seq("""{"b":1}""").toDF("j").selectExpr("json_object('a' VALUE j FORMAT JSON)"), + Row("""{"a":{"b":1}}""")) + val e = intercept[SparkRuntimeException] { + Seq("bad").toDF("j").selectExpr("json_object('a' VALUE j FORMAT JSON)").collect() + } + assert(e.getCondition == "INVALID_JSON_FORMAT_JSON_VALUE") + } + + test("a caught FORMAT JSON error does not corrupt later rows in the same task") { + // The evaluator validates every raw value before writing to its per-task generator, so a + // malformed row that a `try_*` wrapper swallows must leave the generator clean: later rows + // reusing the same evaluator must render exactly as they would on their own. `decode(..utf-8)` + // round-trips the bytes back to the JSON string so the per-row result is checkable. + val query = + "SELECT decode(try_to_binary(json_object('a' VALUE s FORMAT JSON), 'utf-8'), 'utf-8') " + + "FROM VALUES ('1'), ('{bad'), ('2'), ('3') AS t(s)" + val expected = Seq(Row("""{"a":1}"""), Row(null), Row("""{"a":2}"""), Row("""{"a":3}""")) + val modes = Seq(CodegenObjectFactoryMode.NO_CODEGEN, CodegenObjectFactoryMode.CODEGEN_ONLY) + modes.foreach { mode => + withSQLConf( + SQLConf.CODEGEN_FACTORY_MODE.key -> mode.toString, + SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> + (mode == CodegenObjectFactoryMode.CODEGEN_ONLY).toString) { + checkAnswer(sql(query), expected) + } + } + } + + test("each foldable FORMAT JSON member splices its own validated text across rows") { + // Two constant FORMAT JSON members at different positions share the validated-text cache. A + // column-valued member keeps the object from being constant-folded, so rows after the first + // read the cache: a fill or read at the wrong index would splice the other member's text. + checkAnswer( + sql("SELECT json_object('a' VALUE '[1]' FORMAT JSON, 'b' VALUE x, " + + "'c' VALUE '{\"d\":2}' FORMAT JSON) FROM VALUES (1), (2) t(x)"), Review Comment: Done. Set leafNodeDefaultParallelism to 1 so both rows share a task and the second takes the cache-read branch. ########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala: ########## @@ -2273,3 +2334,337 @@ case class JsonTypeof(child: Expression) override protected def withNewChildInternal(newChild: Expression): JsonTypeof = copy(child = newChild) } + +/** + * Guards a JSON_OBJECT key: returns the key, or raises JSON_OBJECT_NULL_KEY when it is null. Wraps + * each key inside `JsonObjectExpr.replacement` so that, as the carrier struct is evaluated left to + * right, a null key fails before its member's value is evaluated (the key-before-value error + * order). Internal, so it is `NonSQLExpression`; it never appears in `JsonObjectExpr.sql`. + */ +case class JsonObjectKeyGuard(child: Expression) extends UnaryExpression with NonSQLExpression { + override def dataType: DataType = child.dataType + // Can throw, so it is not foldable (mirrors AssertNotNull). + override def foldable: Boolean = false + // Raises on a null key, so the result is never null. + override def nullable: Boolean = false + + override def eval(input: InternalRow): Any = { + val value = child.eval(input) + if (value == null) { Review Comment: Added a JsonExpressionsSuite case (checkErrorInExpression, so interpreted and codegen) covering a null literal key, a nullable bound key, and a null key ahead of a throwing value. ########## sql/core/src/test/scala/org/apache/spark/sql/JsonObjectSuite.scala: ########## @@ -0,0 +1,1397 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.antlr.v4.runtime.{CharStreams, CommonTokenStream} +import org.antlr.v4.runtime.atn.PredictionMode + +import org.apache.spark.SparkRuntimeException +import org.apache.spark.sql.catalyst.analysis.Star +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.DataTypeMismatch +import org.apache.spark.sql.catalyst.expressions.{Cast, CodegenObjectFactoryMode, Collate, Expression, JsonConstructorNullBehavior, JsonImplicitFormatCarrier, JsonObjectExpr, Literal} +import org.apache.spark.sql.catalyst.parser.{ParseException, SparkParserBailErrorStrategy, SqlBaseLexer, SqlBaseParser} +import org.apache.spark.sql.catalyst.plans.logical.{Filter, Join} +import org.apache.spark.sql.execution.{ProjectExec, WholeStageCodegenExec} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{ArrayType, CharType, GeographyType, GeometryType, IntegerType, MapType, StringType, StructType, VarcharType} + +/** + * End-to-end tests for the SQL:2016 `JSON_OBJECT` constructor function. + */ +class JsonObjectSuite extends QueryTest with SharedSparkSession { + import testImplicits._ + + test("in the comma form, colon-bearing arguments bind as colon members (documented precedence)") { + // `JSON_OBJECT(a:b, c:d)` matches both list forms: the colon-member list and the comma + // (MySQL-compat) list. The colon-member alternative is declared first, so it wins -- `a:b, c:d` + // reads as the two members (a -> b) and (c -> d), the SQL-standard `key : value` reading, not + // as a single comma-form pair whose key and value are `:`-extractions. + checkAnswer( + sql("SELECT json_object(a:b, c:d) FROM VALUES ('k1','v1','k2','v2') t(a,b,c,d)"), + Row("""{"k1":"v1","k2":"v2"}""")) + // Parenthesizing each argument forces the comma form (one member whose key/value are the + // `:`-extractions). The non-VARIANT extraction target then errors -- proving it parsed as one + // comma-form pair, not two colon members (which resolve cleanly above). + val e = intercept[AnalysisException] { + sql("SELECT json_object((a:b), (c:d)) FROM VALUES ('k1','v1','k2','v2') t(a,b,c,d)").collect() + } + assert(e.getCondition == "COLUMN_IS_NOT_VARIANT_TYPE") + } + + test("basic object from key-value pairs using VALUE keyword") { + checkAnswer( + sql("SELECT json_object('id' VALUE 7, 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("construct object using optional KEY keyword") { + checkAnswer( + sql("SELECT json_object(KEY 'id' VALUE 7, KEY 'name' VALUE 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("construct object using colon syntax") { + checkAnswer( + sql("SELECT json_object('id': 7, 'name': 'Ada')"), + Row("""{"id":7,"name":"Ada"}""")) + } + + test("colon member with a column-valued value") { + // The `key : value` member overlaps the semi-structured extraction form `expr : path` + // (`primaryExpression COLON semiStructuredExtractionPath`), which also accepts a bare + // identifier on the right. Confirm a column-valued colon member still parses as a JSON_OBJECT + // pair rather than as `'k' : v` semi-structured extraction. + checkAnswer( + sql("SELECT json_object('k': v) FROM VALUES ('x') t(v)"), + Row("""{"k":"x"}""")) + } + + test("a colon member splits at the first top-level colon after the key") { + val from = + """FROM (SELECT parse_json('{"x":1,"y":"s"}') AS v, 'kk' AS k, 'e' AS `end`, 'c' AS `case`)""" + Seq( + "json_object('k': v:x)" -> """{"k":1}""", + "json_object(k: v:x)" -> """{"kk":1}""", + "json_object('k': v:x ABSENT ON NULL)" -> """{"k":1}""", + "json_object(k: v:x RETURNING STRING)" -> """{"kk":1}""", + "json_object('a': v:x, k: v:y)" -> """{"a":1,"kk":"s"}""", + "json_object((v:y::string) : 1)" -> """{"s":1}""", + "json_object(v:y::string VALUE v:x)" -> """{"s":1}""", + "json_object(CASE WHEN v:x::int = 1 THEN 'a' END : v:y)" -> """{"a":"s"}""", + // `end` and `case` are non-reserved and usable as column names: the separator check must read + // each CASE/END token as keyword or column by its neighbour, not count it blindly. + "json_object(CASE WHEN end = 'e' AND v:x::int = 1 THEN 'a' END : v:y)" -> """{"a":"s"}""", + "json_object(case : v:x)" -> """{"c":1}""", + "json_object(array('a')[0] : v:x)" -> """{"a":1}""", + "json_object(end : v:x)" -> """{"e":1}""" + ).foreach { case (call, expected) => + checkAnswer(sql(s"SELECT $call $from"), Row(expected)) + } + checkAnswer( + spark.sql(s"SELECT json_object('k': v:x) $from", Map("v" -> "zzz")), + Row("""{"k":1}""")) + checkAnswer( + spark.sql("SELECT json_object('k' :p VALUE 1), 'a' :p", Map("p" -> "x")), + Row("""{"kx":1}""", "ax")) + } + + test("a case-named column with a postfix operator is a column, not a CASE keyword") { + // `case` is non-reserved, so `case[0]` (subscript) and `case COLLATE ...` apply a postfix + // operator to a column named `case`; neither `CASE [` nor `CASE COLLATE` begins a real CASE + // expression (`[` has no array-literal form, COLLATE needs a left operand). The separator check + // must read the token right after `case` as a postfix on an operand -- like the already-handled + // `.` -- so the top-level colon is still the member separator, not swallowed as a CASE branch. + checkAnswer( + sql("""SELECT json_object(case[0] : v:x) + |FROM (SELECT array('c') AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + checkAnswer( + sql("""SELECT json_object(case COLLATE UTF8_LCASE : v:x) + |FROM (SELECT 'c' AS `case`, parse_json('{"x":1}') AS v)""".stripMargin), + Row("""{"c":1}""")) + } + + test("a colon key cannot coalesce a trailing named parameter marker") { + // `'k' :p` coalesces the marker into the key under the VALUE form, but in the colon form the + // first top-level `:` is the member separator, so a bare marker-coalesced key is a clean parse + // error (never a silent mis-split). Parenthesize the key or use the VALUE form. + val e = intercept[ParseException] { + spark.sql("SELECT json_object('k' :p : 1)", Map("p" -> "x")) + } + assert(e.getCondition == "PARSE_SYNTAX_ERROR") + checkAnswer( + spark.sql("SELECT json_object(('k' :p) : 1)", Map("p" -> "x")), Row("""{"kx":1}""")) + checkAnswer( + spark.sql("SELECT json_object('k' :p VALUE 1)", Map("p" -> "x")), Row("""{"kx":1}""")) + } + + test("a simple CASE over a named-parameter operand is a valid colon key") { + // `CASE :p WHEN ... END : value`: the `:p` operand must parse as a named-parameter marker, not + // be read as the member separator. `CASE :` is ambiguous with a `case` column before the + // separator (`case : value`), so the separator check decides it by looking ahead for the Review Comment: Done. Reworded to a complete CASE body, a top-level WHEN closed by its END. -- This is an automated message from the Apache Git Service. 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