SQL in io-ts, Part Two: Discriminating Unions & Expressions

September 8, 2022
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Introduction and Recap

In the first article, I introduced the SubSelect interface and its companion TSubSelect io-ts schema. In this article, we’ll continue the learning journey of implementing SQL in io-ts.

If you haven’t already, please read the first entry in this series of articles where I introduced the problem statement:

we updated our Nestjs backend by adding a new POST endpoint having a fully documented, validated, and parsed input JSON body with complete SQL support

In this article, we’ll dive deeper into the interfaces and schemas we created to describe the SQL SELECT statement. This article will focus on the ExpressionItem and all the sub-structures required to describe a general SQL expression.

In the next sections, we’ll dive deeper into each of these structures. But first, to help explain later sections, let’s explore discriminating unions, which is the type pattern chosen for ExpressionItem.

We found this pattern to be a perfect fit for describing SQL in an io-ts schema. When describing operators, for example, we found that the union of each operator’s discrete interface led to a structure that could be extended indefinitely. What’s more, adding a new operator to this structure is low effort and straightforward.

Next, I’ll describe discriminating unions and how they can be used.

Discriminating Unions

In TypeScript, discriminating union is a pattern for implementing a generalized shape with multiple concrete possibilities.

This pattern can be achieved in two ways:

  1. Pivot around a required, singleton value;
  2. Pivot around the presence of disjointed, required properties

To help explain these two, here is a contrived example of the first case:

enum UserType { admin = 'admin', public = 'public', } interface BaseUserOptions { userType: UserType; } interface AdminUserOptions extends BaseUserOptions { userType: UserType.admin; rootAccess: boolean; } interface PublicUserOptions extends BaseUserOptions { userType: UserType.public; enrollDate: string; } type UserOptions = AdminUserOptions | PublicUserOptions; const userOptions: UserOptions[] = [];

Elements of type UserType could either be admin or public. Depending on which one of the two enumerated values is present, additional properties (e.g. rootAccess and enrollDate ) can be expected.

To access the additional properties, you must first check the userType property to know which interface is to be used. This is called type narrowing. Since the type narrowing is based on the unique value found in userType, this makes userType the discriminating value, a.k.a. the discriminator.

Here is an example of working with the broader UserOptions interface:

const userOptions: UserOptions[] = someUserOptions(); for (const userOption of userOptions) switch (userOption.userType) { case UserType.admin: console.log(userOption.rootAccess); break; case UserType.public: console.log(userOption.enrollDate); break; }

In the second case, where you pivot around the presence of disjointed, required properties, the technique involves checking for the existence of properties.

Here is a contrived example of the second case:

interface AdminUserOptions { rootAccess: boolean; } interface PublicUserOptions { enrollDate: string; } type UserOptions = AdminUserOptions | PublicUserOptions;

The difference in the second construct is that AdminUserOptions and PublicUserOptions do not share any common property. But, they do have required properties of their own that do not overlap each other.

To access the data through the broader UserOptions interface, you first check for the presence of a required property. In this case, the type narrowing is functioning through checking the shape of the object, not a value of a property shared by the objects.

Here is an example of working with the second form of the broader UserOptions interface:

const userOptions: UserOptions[] = [...someUserOptions()]; for (const userOption of userOptions) { if ('rootAccess' in userOption) console.log(userOption.rootAccess); if ('enrollDate' in userOption) console.log(userOption.enrollDate); }

📝 It’s important to reiterate that the disjointed properties need to be required. If a shape is comprised of entirely optional properties, then TypeScript cannot eliminate it as a possibility just because you didn’t specify one of its properties.

Now that you’ve become more familiarized with the discriminating union pattern, let’s explore how we used this to describe the general and specific shapes that an ExpressionItem can be.

Expression Item

The ExpressionItem structure is a fundamental element of the SubSelect. This structure can be used in quite a variety of places in the schema.

For example, you can find an ExpressionItem :

  • As a SELECT column;
  • As an array of rows in a VALUES predicate;
  • As criteria for joining two tables with the ON join conditions;
  • As criteria in the WHERE, GROUP BY, HAVING and ORDER BY clauses;
  • As arguments for functions (and functions themselves are ExpressionItem );
  • As parameters to any operator such as =, LIKE, CASE, + etc.

For the sake of brevity, I want to focus on key themes when describing the different forms of the ExpressionItem .

Here is the top-level ExpressionItem structure:

export type ExpressionItem = | ExpressionColumnItem | ExpressionFunctionItem | ExpressionOperatorItem | ExpressionValueItem | SubSelect; export const TExpressionItem: t.Type<ExpressionItem> = t.recursion( 'ExpressionItem', () => t.union([ TExpressionColumnItem, TExpressionFunctionItem, TExpressionOperatorItem, TExpressionValueItem, TSubSelect ]), );

Each ExpressionItem possibility is a unique interface. Each of those possibilities has at least one required property that is also unique to that interface.

💡 When you use this type of interface, first provide the value for the required property of the desired shape. Once you do that, Typescript narrows the possible remaining properties to those that intersect that required property.

🤯 Notice that SubSelect is one of the options for an ExpressionItem. This means anywhere an expression can be used, it can also be a nested SubSelect.

To access the specific properties of an ExpressionItem, you first need to determine the shape of the item. Since this shape uses the second form of discriminating unions, we can discern what interface to use based on the presence of required, disjointed properties.

For example, if column is present, then it must be an ExpressionColumnItem since no other possible shape allows for a property named column. To explain this better, the next sections dive deeper into ExpressionItem and explore the possible shapes.

Expression Column Item

An ExpressionColumnItem is an interface that allows for referring to a column, with an optional correlation. A correlation qualifies the column to a specific table reference.

Here is an example of selecting a column and providing the correlation:

SELECT A.status

Defining this interface is straightforward:

export const TExpressionColumnItem = t.intersection([ t.type({ column: t.string, }), t.partial({ correlation: t.string, }), ]); export type ExpressionColumnItem = t.TypeOf<typeof TExpressionColumnItem>;

📝 The discriminator here is the column property.

Specifying a value for the column property implies that the only possible shape for ExpressionItem is ExpressionColumnItem. Therefore, the correlation property is a valid optional argument when an ExpressionItem has a column property.

Here is the previous SQL SELECT statement example encoded with the schema:

{ "select": [ { "column": "status", "correlation": "A" } ] }

Expression Value Item

An ExpressionValueItem is an interface that allows for referring to a literal value. Depending on its data type (e.g. number, string), the reference may need to be quoted or embedded as is.

The following example demonstrates a SELECT statement where a column is a literal value:

SELECT 'new' AS status

A literal value could be any one of the following primitive types: boolean , null , number, or string. To define this interface, specify value as a union of those options:

export const TExpressionValueItem = t.type({ value: t.union([ t.boolean, t.null, t.number, t.string ]), }); export type ExpressionValueItem = t.TypeOf<typeof TExpressionValueItem>;

📝 The discriminating property is the value property.

Here’s how the previous SQL SELECT statement example appears as encoded with the schema:

{ "select": [ { "alias": "status", "value": "new" } ] }

Expression Function Item

The ExpressionFunctionItem interface allows for referring to functions taking on the form of a function name, followed by a left parenthesis, zero or more comma-delimited arguments, and ending with a right parenthesis.

The example below demonstrates a SELECT statement referencing a function call:


📝 This form does not consider any function that has one or more “noise” words. For example, the TRIM function allows you to specify words like BOTH and LEFT to describe where to trim. Functions of these forms are described later with the ExpressionOperatorItem interface.

The ExpressionFunctionItem interface requires a function name and optional arguments.

🔒 For security reasons, we decided it would be best to restrict the choices for a function name. You could remove this enumeration restriction and leave it open for the database manager to validate both the function signature and required permissions to execute it.

To begin constructing this interface, we start with the available function names:

// Note: For brevity's sake, only a single function categery is shown export enum AggregateFunction { avg = 'AVG', count = 'COUNT', cube = 'CUBE', groupingSets = 'GROUPING SETS', max = 'MAX', min = 'MIN', rollUp = 'ROLLUP', sum = 'SUM', } // Note: Utils.enum returns an io-ts validator, asserting a valid enum value export const TAggregateFunction = Utils.enum<AggregateFunction>(AggregateFunction); // Note: Not an exhaustive listing export const TFunctionName = t.union([ TAggregateFunction, TConditionFunction, TConfigurationSettingFunction, TGeometryFunction, TMathFunction, TObjectManagementFunction, TStringFunction, TSubSelectFunction, ]); export type FunctionName = t.TypeOf<typeof TFunctionName>;

📝 Above, TFunctionName demonstrates an example of how to combine multiple enumerations and union them together to create a composite enumeration.

Once the possible function names are enumerated, we define the ExpressionFunctionItem interface:

export interface ExpressionFunctionItem { functionName: FunctionName; arguments?: ExpressionItem[] | ExpressionItem[][]; schemaName?: string; } export const TExpressionFunctionItem: t.Type<ExpressionFunctionItem> = t.recursion('ExpressionFunctionItem', () => t.intersection([ t.type({ functionName: TFunctionName, }), t.partial({ arguments: t.union([ t.array(TExpressionItem), t.array(t.array(TExpressionItem)) ]), schemaName: t.string, }), ]), );

📝 The discriminating property here is the functionName.

Not only is the ExpressionFunctionItem a possible ExpressionItem shape, each argument is also an ExpressionItem .

Additionally, the arguments array can be a two-dimensional array. This was done so we could support grouping sets as in the example below:

SELECT status, state, COUNT(*) FROM my_table GROUP BY GROUPING SETS ((status, state), ())

Here is the grouping sets query when encoded in the schema:

{ "select": [ { "column": "status" }, { "column": "state" }, { "functionName": "COUNT", "arguments": [{ "column": "*" }] } ], "from": [ { "operator": "FROM", "tableName": "my_table" } ], "groupBy": [ { "functionName": "GROUPING SETS", "arguments": [[{ "column": "status" }, { "column": "state" }], []] } ] }

Expression Operator Item

SQL has a wide variety of operators. Consider some examples below:

SELECT '$' || TRIM(BOTH '*' FROM currency), transactionCount + 1

Anything resembling an operator e.g. || , + , etc. is implemented as a member of the ExpressionOperatorItem. This interface uses the discriminating union pattern where each operator value is a unique value paired with its interface.

📝 When describing the ExpressionFunctionItem, I alluded that there are some functions with unique keywords and parameter requirements. Any of these types of functions are also treated as operators, even if their parsed output resembles a function. This is done so that we can use the discriminating union pattern of operators to implement specific interfaces for functions with unique requirements.

The number of operators is too great to expound upon in this article. The following snippet contains two examples of the options we’ve implemented so far, namely CASE and + :

export enum ConditionalOperator { case = 'CASE', } export const TConditionalOperator = Utils.enum<ConditionalOperator>(ConditionalOperator); export enum MathOperator { add = '+', } export const TMathOperator = Utils.enum<MathOperator>(MathOperator); export const TExpressionOperator = t.union([ TConditionalOperator, TMathOperator, ]); export type ExpressionOperator = t.TypeOf<typeof TExpressionOperator>; export const TBaseExpressionOperatorItem = t.type({ operator: TExpressionOperator, }); export type BaseExpressionOperatorItem = t.TypeOf< typeof TBaseExpressionOperatorItem >; export interface IExpressionOperatorItem { [ConditionalOperator.case]: ConditionalCaseItem; [MathOperator.add]: MathAddItem; } export type ExpressionOperatorItem = BaseExpressionOperatorItem & IExpressionOperatorItem[ExpressionOperator]; export const TExpressionOperatorItem: t.Type<ExpressionOperatorItem> = t.recursion('ExpressionOperatorItem', () => t.intersection([ TBaseExpressionOperatorItem, t.union([TConditionalCaseItem, TMathAddItem]), ]) );

The key takeaway from this snippet is the structure. We created an interface where each property name is a computed property value, derived from one of the enumerated operators. Then, we pair that with the io-ts shape to match as usual.

Here’s how the ExpressionOperatorItem structure is composed:

  1. Define logical groups of operators e.g. MathOperator and ConditionalOperator
  2. Widen the set of available operators by creating a union named ExpressionOperator
  3. Create the BaseExpressionOperatorItem base interface for all operators to extend
  4. Next, create the IExpressionOperatorItem interface, which creates a dictionary of available operator interfaces, keyed on the computed properties of each corresponding operator
  5. Finally, define the mutually recursive ExpressionOperatorItem type, which is the intersection of the BaseExpressionOperatorItem and all the possible operators shapes provided by the dictionary.

Given this structure, we were able to define a wide variety of operators. So far, we’ve implemented quite a few operators and continue to add as we need. To date we’ve implemented:

  • Comparison operators BETWEEN = > >= IN IS < <= <>
  • Conditional operator CASE
  • Logical operators AND OR NOT
  • Math operators + / ^ % * () -
  • Pattern operators LIKE ~ !~ SIMILAR TO
  • Type operator CAST

Because each operator is unique, each shape is allowed to have any criteria that may or may not overlap any other operator. This allows for unlimited extension of the structure.

The next sections describe the definitions and provides examples of the + operator and the CASE operator.

Simple Math Operator Example

The MathAddItem operator provides a simple example of implementing the + SQL operator in JSON.

Here is an example usage of SQL + :

SELECT A.item_count + 1;

Here is the interface:

export const TMathExpressionItem = t.intersection([ TBaseExpressionOperatorItem, t.type({ operator: TMathOperator, }), ]); export type MathExpressionItem = t.TypeOf<typeof TMathExpressionItem>; export interface MathAddItem extends MathExpressionItem { source: ExpressionItem; operator: MathOperator.add; target?: ExpressionItem; } export const TMathAddItem: t.Type<MathAddItem> = t.recursion('MathAddItem', () => t.intersection([ TMathExpressionItem, t.type({ source: TExpressionItem, operator: t.literal(MathOperator.add), }), t.partial({ target: TExpressionItem, }), ]), );

📝 The operator property is the discriminator and, in the case of MathAddItem, this is the literal value '+'.

The source and target properties are two addends, both of which are ExpressionItem.

This means we can sum any of the following items: columns, values, functions (whose parameters are also expressions), nested sub-selects, and nested operators, and any combination thereof.

Here is the simple math example SQL statement above, as encoded in the schema:

{ "select": [ { "source": { "correlation": "A", "column": "item_count" }, "operator": "+", "target": { "value": 1 } } ] }

Complex Case Statement Example

In the more complex ConditionalCaseItem operator, we build up the representation of an SQL CASE statement in two parts.

Consider this example of a SELECT statement containing a CASE statement:


In order to support this type of structure, we created an interface to define the shape of the WHEN. This structure consists of a search condition paired with the result of the statement when that condition evaluates to true.

To complete the overall structure of the CASE statement, we composed a larger structure having the discriminating operator, a collection of when items, and an optional else expression.

Here is the complete CASE statement interface:

export interface ConditionalCaseWhenItem { where: ExpressionItem; then: ExpressionItem; } export const TConditionalCaseWhenItem: t.Type<ConditionalCaseWhenItem> = t.recursion('ConditionalCaseWhenItem', () => t.type({ where: TExpressionItem, then: TExpressionItem, }), ); export interface ConditionalCaseItem extends BaseExpressionOperatorItem { operator: ConditionalOperator.case; when: ConditionalCaseWhenItem[]; else?: ExpressionItem; } export const TConditionalCaseItem: t.Type<ConditionalCaseItem> = t.recursion('ConditionalCaseItem', () => t.intersection([ TBaseExpressionOperatorItem, t.type({ operator: t.literal(ConditionalOperator.case), when: t.array(TConditionalCaseWhenItem), }), t.partial({ else: TExpressionItem, }), ]), );

📝 The operator property is the discriminator and the value is CASE.

The previous CASE example, encoded in the schema, looks like this:

{ "select": [ { "operator": "CASE", "when": [ { "where": { "source": { "column": "status" }, "operator": "IS", "target": { "value": null } }, "then": { "value": 0 } }, { "where": { "source": { "functionName": "UPPER", "arguments": [{ "column": "status" }] }, "operator": "IN", "values": [{ "value": "OPEN" }, { "value": "REMODEL" }] }, "then": { "value": 1 } } ], "else": { "value": 2 } } ] }

What’s Next?

In this article, I explained how we encoded general SQL expression using a number of io-ts schemas. The ExpressionItem is highly extensible and supports a wide variety of shapes. Some of the shapes, such as ExpressionValueItem, are simple and straightforward. While other more complex shapes, such as ExpressionOperatorItem pivot around a common operator and provide an entire dictionary of possibilities.

Moreover, the ExpressionItem has a mutually recursive relationship with the SubSelect. This means that the definition of ExpressionItem relies on SubSelect and vice versa. Specifically, one of the possible shapes of an ExpressionItem is the SubSelect. Moreover, many parts of the SubSelect allow for the specification of ExpressionItem.

In the next article in the series, we’ll explore even more SubSelect shapes, such as how to join tables with the FromItem and how to filter results with the WhereItem. We’ll even go into SQL set processing support with UNION and INTERSECT. Finally, we’ll discuss the WithItem, which provides support for common-table expressions. At the end, we’ll have described a schema with complete FullSelect support.

In later articles, we’ll dive deep into the SQL parser responsible for converting this complex schema into an SQL SELECT statement.

But we’re not done there. Remember, all of this is in a Nestjs application. We have yet to discuss the Nestjs DTOs and the beautiful Swagger documentation, all of which is covered later on.

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