# Introduction
In this PR we're introducing mainly two branded type signatures for both
`JsonbProperty` entities properties and `SerializedRelation` (jsonb
serialized property storing another entity id).
Allowing to dynamically map over them later in order to build universal
`jsonb` `serialized` relations.
## `JsonbProperty`
A branded wrapper type that marks entity properties stored as PostgreSQL
JSONB columns. It adds a phantom brand `__JsonbPropertyBrand__` to
object types while leaving primitives unchanged. The branded key is
optional and typed as never, also omitted when transpiled to
`UniversalFlat`
**Should be used at entities lvl only:**
```typescript
@Column({ type: 'jsonb', nullable: false })
gridPosition: JsonbProperty<GridPosition>;
@Column({ nullable: false, type: 'jsonb', default: [] })
publishedVersions: JsonbProperty<string[]>;
```
## `SerializedRelation`
A branded string type that marks foreign key IDs stored inside JSONB
objects. These are entity references serialized within a JSONB column
rather than being a regular database foreign key.
**Usage in jsonb property generic***
```ts
type FieldMetadataRelationSettings = {
relationType: RelationType;
onDelete?: RelationOnDeleteAction;
joinColumnName?: string | null;
junctionTargetFieldId?: SerializedRelation;
};
```
## `FormatJsonbSerializedRelation<T>`
A transformation type that processes JSONB properties for universal
entity mapping. It:
1. Detects properties with the `JsonbProperty` brand
2. Finds `SerializedRelation` properties
3. Renames them from `*Id` to `*UniversalIdentifier`
4. Removes the brand from the output type ( optional though )
```typescript
// Input: JsonbProperty<{ targetFieldMetadataId: SerializedRelation }>
// Output: { targetFieldMetadataUniversalIdentifier: SerializedRelation }
```
## Result
An example of the dynamic type mapping, through a type-test example
```ts
type SettingsTestCase = UniversalFlatFieldMetadata<
| FieldMetadataType.RELATION
| FieldMetadataType.NUMBER
| FieldMetadataType.TEXT
>['settings']
type SettingsExpectedResult =
| {
relationType: RelationType;
onDelete?: RelationOnDeleteAction | undefined;
joinColumnName?: string | null | undefined;
junctionTargetFieldUniversalIdentifier?: SerializedRelation | undefined;
}
| {
dataType?: NumberDataType | undefined;
decimals?: number | undefined;
type?: FieldNumberVariant | undefined;
}
| {
displayedMaxRows?: number | undefined;
}
| null;
type Assertions = [
Expect<Equal<SettingsTestCase, SettingsExpectedResult>>,
]
```
## Remarks
- Removed duplicated twenty-server and twenty-shared typed
- Removed class validator instances for default value that were not used
at runtime, we will refactor that to add validation across all entities
following a same pattern
In this PR :
- New field type FieldMetadataType.FILES added (as jsonb in DB)
- Backend: GraphQL types, validation, REST API schema, data processor
handling
- Frontend: field configuration in settings
- Feature flag IS_FILES_FIELD_ENABLED to gate the feature
- Integration tests for create/filter validation
- Dev seed: Feature flag enabled + FILES field on survey result object
To do in next PRs :
- Backend :
-- new workspaceFile controller (for download) & new workspaceFile
upload resolver (for upload)
-- pre-hook/post-hook to ensure fileId existence + listener to ensure
cleaning
- Frontend : FILES field display/edit in table view, record, ...
Two challenges with error messages
- always provide a useful/meaningful error message for the end user
instead of the generic one. eg: show "Wrong password" and not "An error
occured"
- avoid technical details unless error regards a technical feature. eg:
show "An error occured" and not "Invalid post-hook payload."; but do
show "Invalid issuer URL." as it occurs while configuring SSO
What this PR does
- Make userFriendlyMessage mandatory for widely used
GraphqlQueryRunnerException and CommonQueryRunnerException, so that
developers are forced to ask themselves what the error message should
be, and as it contains very wide error codes (eg: "Bad request") which
should not be mapped to just one default message
- Keep userFriendlyMessage optional for service-specific exceptions (eg:
workflowStepExecutorException), but convert the error code to
userFriendlyMessage mapper to a switch case function with a typecheck
ensuring that all codes are mapped to a message. These default messages
are still overridable where they are thrown.
Fixes https://github.com/twentyhq/twenty/issues/16110
This PR implements Temporal to replace the legacy Date object, in all
features that are time zone sensitive. (around 80% of the app)
Here we define a few utils to handle Temporal primitives and obtain an
easier DX for timezone manipulation, front end and back end.
This PR deactivates the usage of timezone from the graph configuration,
because for now it's always UTC and is not really relevant, let's handle
that later.
Workflows code and backend only code that don't take user input are
using UTC time zone, the affected utils have not been refactored yet
because this PR is big enough.
# New way of filtering on date intervals
As we'll progressively rollup Temporal everywhere in the codebase and
remove `Date` JS object everywhere possible, we'll use the way to filter
that is recommended by Temporal.
This way of filtering on date intervals involves half-open intervals,
and is the preferred way to avoid edge-cases with DST and smallest time
increment edge-case.
## Filtering endOfX with DST edge-cases
Some day-light save time shifts involve having no existing hour, or even
day on certain days, for example Samoa Islands have no 30th of December
2011 : https://www.timeanddate.com/news/time/samoa-dateline.html, it
jumps from 29th to 31st, so filtering on `< next period start` makes it
easier to let the date library handle the strict inferior comparison,
than filtering on `≤ end of period` and trying to compute manually the
end of the period.
For example for Samoa Islands, is end of day `2011-12-29T23:59:59.999`
or is it `2011-12-30T23:59:59.999` ? If you say I don't need to know and
compute it, because I want everything strictly before
`2011-12-29T00:00:00 + start of next day (according to the library which
knows those edge-cases)`, then you have a 100% deterministic way of
computing date intervals in any timezone, for any day of any year.
Of course the Samoa example is an extreme one, but more common ones
involve DST shifts of 1 hour, which are still problematic on certain
days of the year.
## Computing the exact _end of period_
Having an open interval filtering, with `[included - included]` instead
of half-open `[included - excluded)`, forces to compute the open end of
an interval, which often involves taking an arbitrary unit like minute,
second, microsecond or nanosecond, which will lead to edge-case of
unhandled values.
For example, let's say my code computes endOfDay by setting the time to
`23:59:59.999`, if another library, API, or anything else, ends up
giving me a date-time with another time precision `23:59:59.999999999`
(down to the nanosecond), then this date-time will be filtered out,
while it should not.
The good deterministic way to avoid 100% of those complex bugs is to
create a half-open filter :
`≥ start of period` to `< start of next period`
For example :
`≥ 2025-01-01T00:00:00` to `< 2025-01-02T00:00:00` instead of `≥
2025-01-01T00:00:00` to `≤ 2025-01-01T23:59:59.999`
Because, `2025-01-01T00:00:00` = `2025-01-01T00:00:00.000` =
`2025-01-01T00:00:00.000000` = `2025-01-01T00:00:00.000000000` => no
risk of error in computing start of period
But `2025-01-01T23:59:59` ≠ `2025-01-01T23:59:59.999` ≠
`2025-01-01T23:59:59.999999` ≠ `2025-01-01T23:59:59.999999999` =>
existing risk of error in computing end of period
This is why an half-open interval has no risk of error in computing a
date-time interval filter.
Here is a link to this debate :
https://github.com/tc39/proposal-temporal/issues/2568
> For this reason, we recommend not calculating the exact nanosecond at
the end of the day if it's not absolutely necessary. For example, if
it's needed for <= comparisons, we recommend just changing the
comparison code. So instead of <= zdtEndOfDay your code could be <
zdtStartOfNextDay which is easier to calculate and not subject to the
issue of not knowing which unit is the right one.
>
> [Justin Grant](https://github.com/justingrant), top contributor of
Temporal
## Application to our codebase
Applying this half-open filtering paradigm to our codebase means we
would have to rename `IS_AFTER` to `IS_AFTER_OR_EQUAL` and to keep
`IS_BEFORE` (or even `IS_STRICTLY_BEFORE`) to make this half-open
interval self-explanatory everywhere in the codebase, this will avoid
any confusion.
See the relevant issue :
https://github.com/twentyhq/core-team-issues/issues/2010
In the mean time, we'll keep this operand and add this semantic in the
naming everywhere possible.
## Example with a different user timezone
Example on a graph grouped by week in timezone Pacific/Samoa, on a
computer running on Europe/Paris :
<img width="342" height="511" alt="image"
src="https://github.com/user-attachments/assets/9e7d5121-ecc4-4233-835b-f59293fbd8c8"
/>
Then the associated data in the table view, with our **half-open
date-time filter** :
<img width="804" height="262" alt="image"
src="https://github.com/user-attachments/assets/28efe1d7-d2fc-4aec-b521-bada7f980447"
/>
And the associated SQL query result to see how DATE_TRUNC in Postgres
applies its internal start of week logic :
<img width="709" height="220" alt="image"
src="https://github.com/user-attachments/assets/4d0542e1-eaae-4b4b-afa9-5005f48ffdca"
/>
The associated SQL query without parameters to test in your SQL client :
```SQL
SELECT "opportunity"."closeDate" as "close_date", TO_CHAR(DATE_TRUNC('week', "opportunity"."closeDate", 'Pacific/Samoa') AT TIME ZONE 'Pacific/Samoa', 'YYYY-MM-DD') AS "DATE_TRUNC by week start in timezone Pacific/Samoa", "opportunity"."name" FROM "workspace_1wgvd1injqtife6y4rvfbu3h5"."opportunity" "opportunity" ORDER BY "opportunity"."closeDate" ASC NULLS LAST
```
# Date picker simplification (not in this PR)
Our DatePicker component, which is wrapping `react-datepicker` library
component, is now exposing plain dates as string instead of Date object.
The Date object is still used internally to manage the library
component, but since the date picker calendar is only manipulating plain
dates, there is no need to add timezone management to it, and no need to
expose a handleChange with Date object.
The timezone management relies on date time inputs now.
The modification has been made in a previous PR :
https://github.com/twentyhq/twenty/issues/15377 but it's good to
reference it here.
# Calendar feature refactor
Calendar feature has been refactored to rely on Temporal.PlainDate as
much as possible, while leaving some date-fns utils to avoid re-coding
them.
Since the trick is to use utils to convert back and from Date object in
exec env reliably, we can do it everywhere we need to interface legacy
Date object utils and Temporal related code.
## TimeZone is now shown on Calendar :
<img width="894" height="958" alt="image"
src="https://github.com/user-attachments/assets/231f8107-fad6-4786-b532-456692c20f1d"
/>
## Month picker has been refactored
<img width="503" height="266" alt="image"
src="https://github.com/user-attachments/assets/cb90bc34-6c4d-436d-93bc-4b6fb00de7f5"
/>
Since the days weren't useful, the picker has been refactored to remove
the days.
# Miscellaneous
- Fixed a bug with drag and drop edge-case with 2 items in a list.
# Improvements
## Lots of chained operations
It would be nice to create small utils to avoid repeated chained
operations, but that is how Temporal is designed, a very small set of
primitive operations that allow to compose everything needed. Maybe
we'll have wrappers on top of Temporal in the coming years.
## Creation of Temporal objects is throwing errors
If the input is badly formatted Temporal will throw, we might want to
adopt a global strategy to avoid that.
Example :
```ts
const newPlainDate = Temporal.PlainDate.from('bad-string'); // Will throw
```
## Context
Deprecating the old objectMetadataMap type in favour of split flat
entities to match with our new caching.
In the long run, trying to achieve:
- Better performance through caching
- Consistent data access patterns across the codebase
- Reduced database queries
Now that everything is based on flat entities, which are cached, we can
finish the refactoring of workspace context cache which should already
improve performances.
Then the last step will be to consume that new cache in the new global
datasource to get rid of the many workspace datasources stored in the
server
closes https://github.com/twentyhq/core-team-issues/issues/1629
To do before requesting review :
- filter update
Migration to come in an other PR
Strat :
1/ Null transformation
- [x] Transform NULL equivalent value to NULL in field validation in
common api - pre-query - with feature flag
- [ ] Same logic in ORM (Not done, complex to handle feature flag here)
- [x] Transform NULL value to equivalent in data formatting in ORM -
post-query
2/ Migration (in other PR) for fieldMetadata not nullable with default
defaultValue (empty string, ...)
- [ ] Remove NOT NULL db constraint
- [ ] Update record value to NULL
- [ ] Update field metadata : isNullable:true
- [ ] Update uniqueIndex whereClause (also for standard uniqueIndex)
- [ ] Activate feature flag
3/ Update metadata creation
- [x] No more default default value
- [x] Update standard field nullability
- [x] Remove index default whereClause for standard field
4/ Update filter
- [x] When filtering on NULL or empty string, be sure all records are
returned (the one with NULL + the one with "")
5/ Test
- [ ] Strat. to do
Etienne