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The SQL is the most popular API of Apache Flink user that connects so many other engines(i.e. Apache Hive, MySQL, PostgreSQL). Flink would have better compatibility with sql query to the underlying engines it adapters to if it has the built-in support for implicit type coercion.

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Use Cases of Type Coercion

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  • the inter-operation within builtin SQL operators, like binary arithmetic operations (PLUS, MINOUS and so on), binary comparison, IN, CASE WHEN, IF, SET operator (UNION, INTERSECT ..)
  • the built in functions type coercion for input parameters
  • the SQL INSERT source that target connector row type when are are row elements type mismatch
  • the compatibility with other popular DB engines, like MySQL/Hive/PostgreSQL and so on

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Popular DB Type Conversion Rules

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Proposed Design for Flink SQL

The proposed design is a pluggable process during sql-to-rel conversion (E.G. AST node to relational  expression).

The work follow describes below mainly happens in Apache Calcite code.

How the Type Coercion Works

The validator will check the operands/return types of all kinds of operators:

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For some cases, although the validation succeed, we still need the type coercion, i.e. for expression 1 > '1', Calcite will just return false without type coercion, we do type coercion eagerly here and the expression would be coerced to 1 > cast('1' as int) whose result evaluates true.

The graph below illustrates how we coerce the operand types of a SqlCall:

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The graph below illustrates how we coerce the operand type of a SqlNode with struct type:

Image Removed

Strategies for Finding Common Type

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SQL Contexts

Expression subtype

Strategies

Set Operation

union/except/intersect

compare the data type of each branch row to find the common type of each fields pair






Arithmetic Expression

binary arithmetic:

[+, -, &, |, ^, /, %, pmod]

1. promote string operand to data type of the other numeric operand;

2. two strings would all be coerced to DECIMAL.

binary comparison:

[=, <, <=, >, >=, <>] 

1. promote string and timestamp to timestamp;

2. make 1=true and 0=false always evaluates true;

3. find common type for both operands if there is numeric type operand.




IN Expression

with subquery

compare type of LHS and RHS, find the common type, if it is struct type, find wider type for every field

without subquery

if RHS is a expr list, compare every expr to find the wider type

Special AGG Function


promote string all to decimal type


Case When Expression

case when expression

find then and else operands common wider type

Colesce FUNC

same as case when

[Date|Timestamp|String] +/- interval


promote string to timestamp



Function with Expected Inputs Type

builtin functions

look up the families registered in the operand type checker, find the family default type if rule allows it

UDF/UDAF

try to coerce based on the argument operands types of eval() func

Apache Flink SQL follows the behaviors of PostgreSQL and MS-SQL mainly, because their rules are more in line with the SQL standard.


For example, for binary arithmetic with strings, we coerce the string operand to the type of the other numeric operand: '9' / 2 (INT), we coerce '9' to int type, and the result type is also INT. While MySQL and Oracle would coerce all the string operands to DOUBLE.

Type Conversion Matrix

The table below illustrates the implicit type coercion rules for all kinds of engines:

From-To

boolean

tinyint

smallint

int

bigint

decimal

float/real

double

interval

date

time

timestamp

[var]char

[var]binary

boolean













s


tinyint



m s

m s

m s

m s

m s

m s




m

m s

m

smallint


m s


m s

m s

m s

m s

m s




m

m s

m

int


m s

m s


m s

m s

m s

m s




m

m s

m

bigint


m s

m s

m s


m s

m s

m s

o



m

m o s

m

decimal


m s

m s

m s

m s


m s

m s




m

m s

m

float/real


m s

m s

m s

m s

m s


m s





m o s

m

double


m s

m s

m s

m s

m s

m s






m o s

m

interval





o








o


date












s

m o s


time













m


timestamp


m

m

m

m

m




s



m s

m

[var]char


m o s

m o s

m o s

m o s

m o s

m o s

m o s

o

m o s

m

s


s

[var]binary


m

m

m

m

m






m

m s



c Calcite

f:

Flink

m: MS-SQL

o: Oracle

s: Spark


Apache Flink SQL follows the type coercion matrix of MS-SQL and Apache Spark for most of the cases(based on the fact that the conversion does not break the semantics), it also supports additional cases like the conversion between INTERVAL and VARCHAR.


See CalciteImplicitCasts for the details.


Appendix

The proposed design is a pluggable process during AST to relational expression conversion (E.G. AST node to relational  expression).

The work follow describes below mainly happens in Apache Calcite code.

How the Type Coercion Works

The validator will check the operands/return types of all kinds of operators:

  1. If the validation passes, the validator will just cache the data type (say RelDataType) for the SqlNode it has validated;
  2. If the validation fails, the validator will ask for the TypeCoercion component about if we can do an implicit type coercion, if the coercion rules passes, the TypeCoercion component will replace the SqlNode with a coerced one of desired type (the node may be an operand of an operator or a column of selected row);
  3. Then the TypeCoercion component would update the inferred type for the casted node and the containing operator/row column type;
  4. If the coercion rule fails again, the validator will just throw the exception as is before.


For some cases, although the validation succeed, we still need the type coercion, i.e. for expression 1 > '1', Calcite will just return false without type coercion, we do type coercion eagerly here and the expression would be coerced to 1 > cast('1' as int) whose result evaluates true.

The graph below illustrates how we coerce the operand types of a SqlCall:

Image Added

The graph below illustrates how we coerce the operand type of a SqlNode with struct type:

Image Added


Reference

[1] Mysql type conversion

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