Introduction to Effect Schema
Welcome to the documentation for effect/Schema
, a module for defining and using schemas to validate and transform data in TypeScript.
The effect/Schema
module allows you to define a Schema<Type, Encoded, Requirements>
that provides a blueprint for describing the structure and data types of your data. Once defined, you can leverage this schema to perform a range of operations, including:
Operation | Description |
---|---|
Decoding | Transforming data from an input type Encoded to an output type Type . |
Encoding | Converting data from an output type Type back to an input type Encoded . |
Asserting | Verifying that a value adheres to the schema’s output type Type . |
Arbitraries | Generate arbitraries for fast-check testing. |
JSON Schemas | Create JSON Schemas based on defined schemas. |
Equivalence | Create Equivalence based on defined schemas. |
Pretty printing | Support pretty printing for data structures. |
- TypeScript 5.4 or newer.
- The
strict
flag enabled in yourtsconfig.json
file. - (Optional) The
exactOptionalPropertyTypes
flag enabled in yourtsconfig.json
file.
The effect/Schema
module takes advantage of the exactOptionalPropertyTypes
option of tsconfig.json
. This option affects how optional properties are typed (to learn more about this option, you can refer to the official TypeScript documentation).
Example (With exactOptionalPropertyTypes
Enabled)
Here, notice that the type of name
is “exact” (string
), which means the type checker will catch any attempt to assign an invalid value (like undefined
).
Example (With exactOptionalPropertyTypes
Disabled)
If, for some reason, you can’t enable the exactOptionalPropertyTypes
option (perhaps due to conflicts with other third-party libraries), you can still use effect/Schema
. However, there will be a mismatch between the types and the runtime behavior:
In this case, the type of name
is widened to string | undefined
, which means the type checker won’t catch the invalid value (undefined
). However, during decoding, you’ll encounter an error, indicating that undefined
is not allowed.
The Schema
type represents an immutable value that describes the structure of your data.
Here is the general form of a Schema
:
The Schema
type has three type parameters with the following meanings:
Parameter | Description |
---|---|
Type | Represents the type of value that a schema can succeed with during decoding. |
Encoded | Represents the type of value that a schema can succeed with during encoding. By default, it’s equal to Type if not explicitly provided. |
Requirements | Similar to the Effect type, it represents the contextual data required by the schema to execute both decoding and encoding. If this type parameter is never (default if not explicitly provided), it means the schema has no requirements. |
Examples
Schema<string>
(defaulted toSchema<string, string, never>
) represents a schema that decodes tostring
, encodes tostring
, and has no requirements.Schema<number, string>
(defaulted toSchema<number, string, never>
) represents a schema that decodes tonumber
fromstring
, encodes anumber
to astring
, and has no requirements.
Immutability. Schema
values are immutable, and every function in the effect/Schema
mpodule produces a new Schema
value.
Modeling Data Structure. These values do not perform any actions themselves, they simply model or describe the structure of your data.
Interpretation by Compilers. A Schema
can be interpreted by various “compilers” into specific operations, depending on the compiler type (decoding, encoding, pretty printing, arbitraries, etc…).
When working with data in TypeScript, you often need to handle data coming from or being sent to external systems. This data may not always match the format or types you expect, especially when dealing with user input, data from APIs, or data stored in different formats. To handle these discrepancies, we use decoding and encoding.
Term | Description |
---|---|
Decoding | Used for parsing data from external sources where you have no control over the data format. |
Encoding | Used when sending data out to external sources, converting it to a format that is expected by those sources. |
For instance, when working with forms in the frontend, you often receive untyped data in the form of strings. This data can be tampered with and does not natively support arrays or booleans. Decoding helps you validate and parse this data into more useful types like numbers, dates, and arrays. Encoding allows you to convert these types back into the string format expected by forms.
Below is a diagram that shows the relationship between encoding and decoding using a Schema<A, I, R>
:
We’ll break down these concepts using an example with a Schema<Date, string, never>
. This schema serves as a tool to transform a string
into a Date
and vice versa.
When we talk about “encoding,” we are referring to the process of changing a Date
into a string
. To put it simply, it’s the act of converting data from one format to another.
Conversely, “decoding” entails transforming a string
back into a Date
. It’s essentially the reverse operation of encoding, where data is returned to its original form.
Decoding from unknown
involves two key steps:
-
Checking: Initially, we verify that the input data (which is of the
unknown
type) matches the expected structure. In our specific case, this means ensuring that the input is indeed astring
. -
Decoding: Following the successful check, we proceed to convert the
string
into aDate
. This process completes the decoding operation, where the data is both validated and transformed.
Encoding from unknown
involves two key steps:
-
Checking: Initially, we verify that the input data (which is of the
unknown
type) matches the expected structure. In our specific case, this means ensuring that the input is indeed aDate
. -
Encoding: Following the successful check, we proceed to convert the
Date
into astring
. This process completes the encoding operation, where the data is both validated and transformed.
When working with schemas, there’s an important rule to keep in mind: your schemas should be crafted in a way that when you perform both encoding and decoding operations, you should end up with the original value.
In simpler terms, if you encode a value and then immediately decode it, the result should match the original value you started with. This rule ensures that your data remains consistent and reliable throughout the encoding and decoding process.