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Forward Xu, Jark Wu

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JIRA

Jira

server	ASF JIRA
serverId	5aa69414-a9e9-3523-82ec-879b028fb15b
key	FLINK-9477

Release

1.15

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

Currently, Flink SQL does not support the SQL 2016 JSON function. and many of the message middleware data are in JSON format. For example: Kafka, Flink SQL can support JSON operation to more easily parse the data in Kafka. In addition, the data in some relational databases is also stored and ingested in JSON format.

SQL/JSON path language

The SQL/JSON path language is a query language used by certain SQL operators (JSON_VALUE, JSON_QUERY, JSON_TABLE and JSON_EXISTS, collectively known as the SQL/JSON query operators) to query JSON text. The SQL/JSON path language is not, strictly speaking, SQL, though it is embedded in these operators within SQL. Lexically and syntactically, the SQL/JSON path language adopts many features of [ECMAscript], though it is neither a subset nor a superset of [ECMAscript]. The semantics of the SQL/JSON path language are primarily SQL semantics.

The SQL/JSON path language is used by the SQL/JSON query operators in the architecture shown in this diagram:

Image Added

The SQL/JSON path language architecture

...

Non-structural errors outside of a <JSON path predicate> are always unhandled errors, resulting in an exception condition returned from the path engine to the SQL/JSON query operator.
The SQL/JSON query operators provide an ON ERROR clause to specify the behavior in case of an input conversion error, an unhandled structural error, an unhandled non-structural error, or an output conversion error.

Modes

The path engine has two modes, strict and lax. The motivation for these modes is that strict mode will be used to examine data from a strict schema perspective, for example, to look for data that diverges from an expected schema. Therefore, strict mode raises an error if the data does not strictly adhere to the requirements of a path expression. Lax mode is intended to be more forgiving, so lax mode converts errors to empty SQL/JSON sequences.

...

Three aspects of path evaluation governed by modes

	lax	strict
Automatic unnesting of arrays	Certain path steps, such as the member accessor $.key, automatically iterate over SQL/JSON sequences. To make these iterative path steps friendlier for arrays, arrays are automatically unnested prior to performing the iterative path step. This means that the user does not need to use an explicit [*] to unnest an array prior to performing an iterative path step. This facilitates the use case where a field may be either an array or a scalar.	Arrays are not automatically unnested (the user can still write [*] to unnest an array explicitly).
Automatic wrapping within an array	Subscript path steps, such as $[0] or $[*], may be applied to a non-array. To do this, the non-array is implicitly wrapped in an array prior to applying the subscript operation. This also facilitates the use case where a field may be either an array or a scalar.	There is no automatic wrapping prior to subscript path steps.
Error han- dling	Many errors related to whether data is or is not an array or scalar are handled by the two preceding features. The remain- ing errors are classified as either struc- tural or non-structural. An example of a structural error is $.name if $ has no member whose key is name. Structural errors are converted to empty SQL/JSON sequences. An example of a non-struc- tural error is divide by zero; such errors are not elided.	Errors are strictly defined in all cases

Note: that the path language mode is orthogonal to the ON ERROR clause. There are numerous use cases for having any combination of ON ERROR clauses combined with either strict or lax modes.

...

Consider the following data, stored in a table called Data:

pk	col
1	{ name: "Fred", phonetype: "work","phone#": "650-506-2051"}
2	{ name: "Molly", phones: [ { phonetype: "work","phone#": "650-506-7000" }, { phonetype: "cell","phone#": "650-555-5555" } ]
3	{ name: "Afu", phones: [ { phonetype: "cell","phone#": "88-888-8888" } ] }
4	{ name: "Justin"}
5	{ name: "U La La",phones: []}

This data has been created with a sloppy schema. If a person has just one phone (row 1), then the phonetype and phone# are members of the JSON object. If a person has more than one phone (row 2), then there is a member called phones whose value is an array holding the phone information. But sometimes a person with just one phone still has a phones array (row 3). Also, some people have no phones, which can be indicated by an absence of the phonetype and phone# members (row 4), or by the presence of a phones array whose value is empty (row 5).

...

The solution to this use case is the following query:

SELECT D.pk, JT.name,

COALESCE (JT."phone#", JT."phones.phone#") AS "phone#",

COALESCE (JT."phonetype", JT."phones.phonetype#") AS "phonetype"

FROM Data AS D,

JSON_TABLE (D.col, 'lax $'

COLUMNS (

name VARCHAR(30) PATH 'lax $.name',

"phone#" VARCHAR(30) PATH 'lax $.phone#',

"phonetype" VARCHAR(30) PATH 'lax $.phonetype',

NESTED COLUMNS PATH 'lax $.phones[*]' (

) )

) AS JT

Above, two output columns of the JSON_TABLE have been underlined, and two others are boxed. To understand this query, note the following:

Row 1 has phone# and phonetype as “bare” members of the outermost object. These two members will be picked up by the underlined columns called “phone#” and “phonetype”. The NESTED COLUMNS clause has a path that will find no rows. The default plan for NESTED COLUMNS is an outer join. Thus, there will be effectively a dummy row created with null values for the boxed columns. In the SELECT list, each COALESCE operator is used to choose the non-null values from an underlined column and the corresponding boxed column.
Rows 2 and 3 do not have bare phone# and phonetype; instead they have an array called phones. In these rows, the underlined columns have paths that will find empty sequences, defaulting to the null value. The NESTED COLUMNS clause is used to iterate over the phones array, producing values for the boxed columns, and again, the COALESCE operators in the SELECT list retain the non-null values.
Row 4 has no phone data at all. In this case, the underlined columns have paths that will find nothing (defaulting to null values). The NESTED COLUMNS clause also has a path that finds an empty sequence. Using the default outer join logic, this means that the boxed columns will also be null. The COALESCE operators must coalesce two null values, resulting in null.
Row 5 has a phones array, but it is empty. This case is processed similarly to rows 2 and 3: the underlined columns are null because their paths are empty. The NESTED COLUMNS clause is used, but the array is empty, so this is an outer join with an empty table. Thus, the boxed columns also come up null, and the COALESCE operators combine these nulls to get null. The end result is the same as row 4.

Proposed Changes

We detail the implementation to support JSON functions in Flink SQL with this section.This article is mainly based on flink-table-planner-blink.

SQL/JSON functions

This document mainly implements FLINK SQL JSON FUNCTION based on the SQL JSON FUNCTION already implemented in Jira CALCITE-2867.

...

path is a character string containing a JSON path expression; mode flag strict or lax should be specified in the beginning of path.

Query Functions

Use in SQL:

SQL SYNTAX	DESCRIPTION	RETURN TYPE
JSON_EXISTS(jsonValue, path [ { TRUE \| FALSE \| UNKNOWN \| ERROR } ON ERROR ] )	Whether a jsonValue satisfies a search criterion described using JSON path expression path.	BOOLEAN
JSON_VALUE(jsonValue, path [ RETURNING type ] [ { ERROR \| NULL \| DEFAULT expr } ON EMPTY ] [ { ERROR \| NULL \| DEFAULT expr } ON ERROR ] )	Extract an SQL scalar from a jsonValue using JSON path expression path.

JSON_QUERY(

if no "RETURNING type" clause, STRING is returned. If a "RETURNING type" clause is included, an attempt is made to cast to the specified type. For example: json_value ('{"foo": 100}', 'lax $ .foo' returning integer null on empty) will return an integer type.
JSON_QUERY(jsonValue, path [ { WITHOUT [ ARRAY ] \| WITH [ CONDITIONAL \| UNCONDITIONAL ] [ ARRAY ] } WRAPPER ] [ { ERROR \| NULL \| EMPTY ARRAY \| EMPTY OBJECT } ON EMPTY ] [ { ERROR \| NULL \| EMPTY ARRAY \| EMPTY OBJECT } ON ERROR ] )	Extract a JSON object or JSON array from jsonValue using the path JSON path expression.	STRING

Note: The ON ERROR and ON EMPTY clauses define the fallback behavior of the function when an error is thrown or a null value is about to be returned.

The ARRAY WRAPPER clause defines how to represent a JSON array result in JSON_QUERY function. The following examples compare the wrapper behaviors.

Use in TableApi:

jsonExists

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
STRING.jsonExists(

path)

(Expression path [,JsonExistsBehavior behavior])

Whether a jsonValue satisfies a search criterion described using JSON path expression path

.Table API currently only support path string parameter, doesn't support ON ERROR clause.

. The JsonExistsBehavior is built as follows: behavior = JsonExistsBehavior.TrueOnError ("abc").

BOOLEAN

jsonValue

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
STRING.jsonValue(Expression path[,JsonValueBehavior behavior])	Extract an SQL scalar from a jsonValue using JSON path expression path.

Table API currently only support path string parameter, doesn't support ON ERROR clause.

The JsonValueBehavior is built as follows:

behavior = JsonValueBehavior.defaultOnEmpty ("abc"). NullOnError(). or

behavior = JsonValueBehavior.returnInt().defaultOnEmpty ("abc"). NullOnError().

if no "RETURNING type" clause, STRING is returned. If a "RETURNING type" clause is included, an attempt is made to cast to the specified type. For example: json_value ('{"foo": 100}', 'lax $ .foo' returning integer null on empty) will return an integer type.

jsonQuery

STRING.jsonQuery(path

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
STRING.jsonQuery(Expression path [,JsonQueryBehavior behavior]

)	Extract a JSON object or JSON array from jsonValue using the

path JSON path expression.Table API currently only support path string parameter, doesn't support ON ERROR clause.

path JSON path expression.

The JsonQueryBehavior is built as follows:

behavior = JsonQueryBehavior.withoutArray().defaultOnEmpty ("abc"). NullOnError ().

STRING

Example Data:

{"a": "[1,2]", "b": [1,2], "c": "hi"}

Comparison:

OPERATOR	$.A	$.B	$.C
JSON_EXISTS	true	true	true
JSON_VALUE	[1, 2]	error	hi
JSON QUERY WITHOUT ARRAY WRAPPER	error	[1, 2]	error
JSON QUERY WITH UNCONDITIONAL ARRAY WRAPPER	[ “[1,2]” ]	[ [1,2] ]	[ “hi” ]
JSON QUERY WITH CONDITIONAL ARRAY WRAPPER	[ “[1,2]” ]	[1,2]	[

“hi” ]

Constructor Functions

Use in SQL:

“hi” ]

Constructor Functions

Use in SQL:

SQL SYNTAX	DESCRIPTION	RETURN TYPE
JSON_OBJECT( { [ KEY ] name VALUE value [ FORMAT JSON ] \| name : value [ FORMAT JSON ] } * [ { NULL \| ABSENT } ON NULL ] )	Construct JSON object using a series of key (name) value (value) pairs.	STRING
JSON_OBJECTAGG

SQL SYNTAX

DESCRIPTION

JSON_OBJECT

( { [ KEY ] name VALUE value [ FORMAT JSON ] \| name : value [ FORMAT JSON ] } [ { NULL \| ABSENT } ON NULL ] )	Aggregate function to construct a JSON object using a key (name) value (value) pair.	STRING
JSON_ARRAY( { value [ FORMAT JSON ] } * [ { NULL \| ABSENT } ON NULL ] )	Construct a JSON

object

array using a series of

key (name) value

values (value)

pairs

.	STRING
JSON_

OBJECTAGG( { [ KEY ] name VALUE

ARRAYAGG( value [ FORMAT JSON ]

| name : value [ FORMAT JSON ] }

[ ORDER BY orderItem [, orderItem ]* ] [ { NULL | ABSENT } ON NULL ] )

Aggregate function to construct a JSON

object

array using a

key (name)

value (value)

pair.JSON_ARRAY( { value [ FORMAT JSON ] } * [ { NULL | ABSENT } ON

.

STRING

Use in TableApi:

jsonObject

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
jsonObject(ANY1, ANY2, …[,SqlJsonConstructorNullClause.NULL_ON_NULL])	Construct

a

JSON

array

object using a series of

values

key (name) value (value)

.

JSON_ARRAYAGG( value [ FORMAT JSON ] [ ORDER BY orderItem [, orderItem ]* ] [ { NULL | ABSENT } ON NULL ] )

Aggregate function to construct a JSON array using a value (value).

pairs.Table API currently only supports JSON string operations.

This means NULL ON NULL.

STRING

jsonObject(ANY1, ANY2, …[,SqlJsonConstructorNullClause.ABSENT_ON_NULL])

This means ABSENT ON NULL.

STRING

jsonObjectAggUse in TableApi:

JAVA/PYTHON/SCALA

DESCRIPTION

jsonObject

RETURN TYPE

jsonObjectAgg(ANY1,ANY2[,

...)Construct

SqlJsonConstructorNullClause.NULL_ON_NULL])

Aggregate function to construct a JSON object using a

series of

key (name) value (value)

pairs.Table API currently only supports JSON string operations.

jsonObjectAgg(ANY1,ANY2)

Aggregate function to construct a JSON object using a key (name) value (value) pair.Table API currently only supports JSON string operations.

pair.This means NULL ON NULL.	STRING
jsonObjectAgg(ANY1,ANY2[,SqlJsonConstructorNullClause.ABSENT_ON_NULL])	This means ABSENT ON NULL.	STRING

jsonArray

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
jsonArray(ANY1, ANY2, ...[,SqlJsonConstructorNullClause.NULL_ON_NULL])	Construct a JSON array using a series of values (value). This means NULL ON NULL.	STRING
jsonArray(ANY1, ANY2,

...)

Construct a JSON array using a series of values (value). Table API currently only supports JSON string operations.

...[,SqlJsonConstructorNullClause.ABSENT_ON_NULL])

This means ABSENT ON NULL.

STRING

jsonArrayAgg

jsonArrayAgg(ANY

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
jsonArrayAgg(ANY[,SqlJsonConstructorNullClause.NULL_ON_NULL]

)	Aggregate function to construct a JSON array using a value (value).

Table API currently only supports JSON string operations.

This means NULL ON NULL.	STRING
jsonArrayAgg(ANY[,SqlJsonConstructorNullClause.ABSENT_ON_NULL])	This means ABSENT ON NULL.	STRING

Note: The flag FORMAT JSON indicates the value is formatted as JSON character string. When FORMAT JSON is used, the value should be de-parse from JSON character string to a SQL structured value.

...

If ORDER BY clause is provided, JSON_ARRAYAGG sorts the input rows into the specified order before performing aggregation.

Comparison:

OPERATOR	RESULT
JSON_OBJECT('id', 87, 'name', 'carrot')	{"id": 87, "name": "carrot"}
JSON_ARRAY(1, "abc", NULL, TRUE)	[1, "abc", null, true]

Simple Data Table T1:

o_id	attribute	value
2	color	red
2	fabric	silk
3	color	green
3	shape	square

SELECT o_id, JSON_OBJECTAGG(attribute, value) FROM t1 GROUP BY o_id;

OPERATOR	o_id	AFTER AGG
JSON_OBJECTAGG(attribute, value)	2	{"color": "red", "fabric": "silk"}
JSON_OBJECTAGG(attribute, value)	3	{"color": "green", "shape": "square"}

SELECT o_id, JSON_ARRAYAGG(attribute) AS attributes FROM t1 GROUP BY o_id;

OPERATOR	o_id	AFTER AGG
JSON_ARRAYAGG(attribute)	2	["color", "fabric"]
JSON_ARRAYAGG(attribute)	3	["color", "shape"]

Comparison Operators

Applications will frequently want to ensure that the data they expect to consume as JSON data is, indeed, JSON data. The IS JSON predicate determines whether the value of a specified string does or does not conform to the structural rules for JSON. The syntax of the IS JSON predicate is:

Use in SQL:

SQL SYNTAX	DESCRIPTION	RETURN TYPE
jsonValue IS JSON [ VALUE ]	Whether jsonValue is a JSON value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS NOT JSON [ VALUE ]	Whether jsonValue is not a JSON value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS JSON SCALAR	Whether jsonValue is a JSON scalar value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS NOT JSON SCALAR	Whether jsonValue is not a JSON scalar value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS JSON OBJECT	Whether jsonValue is a JSON object.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS NOT JSON OBJECT	Whether jsonValue is not a JSON object.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS JSON ARRAY	Whether jsonValue is a JSON array.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
jsonValue IS NOT JSON ARRAY	Whether jsonValue is not a JSON array.Table API functions currently only support JSON strings as input parameters.	BOOLEAN

Use in TableApi:

JAVA/PYTHON/SCALA	DESCRIPTION	RETURN TYPE
ANY.isJsonValue()	Whether jsonValue is a JSON value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isNotJsonValue()	Whether jsonValue is not a JSON value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isJsonScalar()	Whether jsonValue is a JSON scalar value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isNotJsonScalar()	Whether jsonValue is not a JSON scalar value.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isJsonObject()	Whether jsonValue is a JSON object.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isNotJsonObject()	Whether jsonValue is not a JSON object.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isJsonArray()	Whether jsonValue is a JSON array.Table API functions currently only support JSON strings as input parameters.	BOOLEAN
ANY.isNotJsonArray()	Whether jsonValue is not a JSON array.Table API functions currently only support JSON strings as input parameters.	BOOLEAN

The test example:

tester.checkBoolean("'{}' is json value", true);

tester.checkBoolean("'{]' is json value", false);

tester.checkBoolean("'{}' is json object", true);

tester.checkBoolean("'[]' is json object", false);

tester.checkBoolean("'{}' is json array", false);

tester.checkBoolean("'[]' is json array", true);

tester.checkBoolean("'100' is json scalar", true);

tester.checkBoolean("'[]' is json scalar", false);

tester.checkBoolean("'{}' is not json value", false);

tester.checkBoolean("'{]' is not json value", true);

tester.checkBoolean("'{}' is not json object", false);

tester.checkBoolean("'[]' is not json object", true);

tester.checkBoolean("'{}' is not json array", true);

tester.checkBoolean("'[]' is not json array", false);

tester.checkBoolean("'100' is not json scalar", false);

tester.checkBoolean("'[]' is not json scalar", true);

Implementation Details

Table Api interface proposal

I suggest that the first step about the table api does not implement the passing syntax. Start with a simple implementation and later implement further functionality if further functionality is required.

...

BuiltInFunctionDefinitions.java

// json functions

public static final BuiltInFunctionDefinition JSON_EXISTS =

new BuiltInFunctionDefinition.Builder()

.name("json_exists")

.kind(SCALAR)

.outputTypeStrategy(TypeStrategies.MISSING)

.build();

expressionDsl.scala

/**

* Returns a boolean of json_exists.

* E.g. json_exists("{\"foo\":\"bar\"}".345,'strict $.foo') to true.

*/

def jsonExists(path: Expression): Expression = unresolvedCall(JSON_EXISTS, expr, path)

DirectConvertRule.java

// json functions

DEFINITION_OPERATOR_MAP.put(BuiltInFunctionDefinitions.JSON_EXISTS, FlinkSqlOperatorTable.JSON_EXISTS);

FlinkSqlOperatorTable.java

// JSON FUNCTIONS

public static final SqlFunction JSON_EXISTS = SqlStdOperatorTable.JSON_EXISTS;

BuiltInMethods.scala

val JSON_EXISTS = Types.lookupMethod(classOf[JsonFunctions], "jsonExists",

classOf[String], classOf[String])

FunctionGenerator.scala

addSqlFunctionMethod(

JSON_EXISTS,

Seq(VARCHAR, VARCHAR),

BuiltInMethods.JSON_EXISTS)

StringCallGen.scala

case JSON_EXISTS if operands.size == 2 &&

isCharacterString(operands.head.resultType) &&

isCharacterString(operands(1).resultType) =>

methodGen(BuiltInMethods.JSON_EXISTS)

PlannerExpressionConverter.scala

case JSON_EXISTS =>

assert(args.size == 2)

JsonExists(args.head, args.last)

jsonExpressions.scala

package org.apache.flink.table.planner.expressions

import org.apache.flink.api.common.typeinfo.BasicTypeInfo._

import org.apache.flink.api.common.typeinfo.TypeInformation

import org.apache.flink.table.planner.typeutils.TypeInfoCheckUtils

import org.apache.flink.table.planner.validate.{ValidationFailure, ValidationResult}

case class JsonExists(child: PlannerExpression, path: PlannerExpression)

extends PlannerExpression with InputTypeSpec {

override private[flink] def resultType: TypeInformation[_] = BOOLEAN_TYPE_INFO

override private[flink] def children: Seq[PlannerExpression] =

Seq(child, path)

override private[flink] def expectedTypes: Seq[TypeInformation[_]] =

Seq(STRING_TYPE_INFO, STRING_TYPE_INFO)

override def toString: String = s"json_exists(${children.mkString(",")})"

override private[flink] def validateInput(): ValidationResult = {

if (child != null && path != null) {

if (!TypeInfoCheckUtils.isString(child.resultType) ||

!TypeInfoCheckUtils.isString(path.resultType)) {

ValidationFailure(s"json_exists num requires int, get " +

s"$child : ${child.resultType}, $path : ${path.resultType}")

}

TypeInfoCheckUtils.assertNumericExpr(BOOLEAN_TYPE_INFO, s"json_exists base :$child")

}

ScalarTypesTestBase.scala

testData.setField(55, "{\"foo\":\"bar\"}")

/* 55 */ Types.STRING)

ScalarFunctionsTest.scala

//-------------------------------------------------------------------

// JSON functions

//-------------------------------------------------------------------

@Test

def testJsonExists(): Unit = {

testAllApis(

'f55.jsonExists("strict $.foo"),

"f55.json_exists('strict $.foo')",

"json_exists(f55, 'strict $.foo')",

"true")

}

JsonITCase.scala

/**

* tests for Json Table Api

*/

@RunWith(classOf[Parameterized])

class JsonITCase(mode: StateBackendMode) extends StreamingWithStateTestBase(mode) {

// input data

val data = List(

("{\"foo\":\"bar\"}"),

("flink")

)

@Test

def testJsonExists(): Unit = {

val stream = env.fromCollection(data)

val table = stream.toTable(tEnv, 'str)

val resultTable = table

.select('str, 'str.jsonExists("strict $.foo"))

val sink = new TestingAppendSink

resultTable.toAppendStream[Row].addSink(sink)

env.execute()

val expected = mutable.MutableList("flink,false", "{\"foo\":\"bar\"},true")

assertEquals(expected.sorted, sink.getAppendResults.sorted)

}

Migration Plan and Compatibility

It is a new feature for Flink SQL, there is no migration needed.The implementation sequence is as follows:

...

3.Implement Constructor Functions.

References

[1] A more detailed JSON standard description can be viewed: c067367_ISO_IEC_TR_19075-6_2017

...

[3] https://calcite.apache.org/docs/reference.html#json-functions

Document

https://docs.google.com/document/d/1JfaFYIFOAY8P2pFhOYNCQ9RTzwF4l85_bnTvImOLKMk/edit#heading=h.76mb88ca6yjp

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Versions Compared

Old Version 1

New Version Current

Key

Motivation

SQL/JSON path language

Modes

Proposed Changes

SQL/JSON functions

Query Functions

Constructor Functions

Constructor Functions

Comparison Operators

Implementation Details

Table Api interface proposal

Migration Plan and Compatibility

References

Document