## Summary
- **Remove all "core" prefixes** from the views system — the
metadata-based storage migration is complete, so `CoreView`,
`coreViewsSelector`, `getCoreViews`, etc. are now just `View`,
`viewsSelector`, `getViews`
- **Eliminate the entire converter layer** (15 files, ~850 lines
deleted) — `convertCoreViewToView` and all sub-converters were either
no-ops or trivially adding `__typename` / mapping identical enum values.
Local enums now re-export from generated GraphQL types directly (single
source of truth)
- **Unify `View` and `ViewWithRelations`** into one type —
`ViewWithRelations` is now a type alias for `View`, selectors return
data directly without conversion
### Backend
- Rename `@ObjectType('CoreView')` → `@ObjectType('View')` (and all
sub-entities)
- Rename resolver methods: `getCoreViews` → `getViews`, `createCoreView`
→ `createView`, etc.
- Rename `FIND_ALL_CORE_VIEWS_GRAPHQL_OPERATION` →
`FIND_ALL_VIEWS_GRAPHQL_OPERATION`
### Frontend
- Delete 15 converter files (`convertGqlView*ToView*`,
`convertView*ToGql`, `convertViewWithRelationsToView`)
- Re-export `ViewType`, `ViewKey`, `ViewFilterGroupLogicalOperator` from
generated enums (no more duplicate enum definitions with different
casing)
- Replace `ViewOpenRecordInType` with `ViewOpenRecordIn` from generated
- Remove `__typename` from all local view sub-types
- Remove unused `variant` from `ViewFilter`, make `displayValue` and
`definition` optional
- Rename ~45 GraphQL query/mutation files and all selectors to drop
"core" prefix
- Delete unused `viewsWithRelationsSelector`
## Fix resolver schema leaking between `/metadata` and `/graphql`
endpoints
### Summary
- Patch `@nestjs/graphql` to support a `resolverSchemaScope` option that
filters resolvers at both schema generation and runtime, preventing
cross-endpoint leaking
- Introduce `@CoreResolver()` and `@MetadataResolver()` decorators to
explicitly scope each resolver to its endpoint
- Move most resolvers (auth, billing, workspace, user, etc.) to the
metadata schema where the frontend expects them; only workflow and
timeline calendar/messaging resolvers remain on `/graphql`
- Fix frontend `SSEQuerySubscribeEffect` to use the default (metadata)
Apollo client instead of the core client
### Problem
NestJS GraphQL's module-based resolver discovery traverses transitive
imports, causing resolvers from `/metadata` modules to leak into the
`/graphql` schema and vice versa. This made the schemas unpredictable
and tightly coupled to module import order.
### Approach
- Added `resolverSchemaScope` to `GqlModuleOptions` via a patch on
`@nestjs/graphql`, filtering in both `filterResolvers()` (runtime
binding) and `getAllCtors()` (schema generation)
- Each resolver is explicitly decorated with `@CoreResolver()` or
`@MetadataResolver()`
- Organized decorator, constant, and type files under `graphql-config/`
following project conventions
Core GQL Schema: (see: no more fields!)
<img width="827" height="894" alt="image"
src="https://github.com/user-attachments/assets/668f3f0f-485e-43f0-92be-4345aeccacb6"
/>
Metadata GQL Schema (see no more getTimelineCalendarEventsFromCompany)
<img width="827" height="894" alt="image"
src="https://github.com/user-attachments/assets/443913db-e5fe-4161-b0e7-4a971cc80a71"
/>
## Overview
This PR implements **generic many-to-many relation support** through
junction tables (also known as associative entities or join tables).
This replaces the need for hardcoded taskTarget/noteTarget logic and
provides a flexible foundation for modeling complex entity
relationships.
## Architecture
### Data Model
Many-to-many relationships are implemented using a **junction object
pattern**:
```
┌─────────┐ ┌──────────────────┐ ┌─────────┐
│ Pet │──────>│ PetRocket │<──────│ Rocket │
│ │ 1:N │ (junction) │ N:1 │ │
│ rockets ├───────┤ pet : Pet ├───────┤ │
└─────────┘ │ rocket : Rocket │ └─────────┘
└──────────────────┘
```
The junction object (PetRocket) has:
- A `MANY_TO_ONE` relation to **Pet** (the source)
- A `MANY_TO_ONE` relation to **Rocket** (the target)
The source object (Pet) has a `ONE_TO_MANY` relation pointing to the
junction, with **field settings** that specify which target field to
follow.
### Field Settings Schema
Junction configuration is stored in `FieldMetadataRelationSettings`:
```typescript
{
relationType: "ONE_TO_MANY",
// Points to the target field on the junction object
junctionTargetFieldId?: string; // For regular relations
junctionTargetMorphId?: string; // For polymorphic relations
}
```
**Two configuration modes:**
1. **`junctionTargetFieldId`** - References a specific `RELATION` field
on the junction
2. **`junctionTargetMorphId`** - References a `morphId` group for
polymorphic targets (e.g., link to Person OR Company)
### GraphQL Query Generation
When a junction relation is detected, the GraphQL fields are generated
to fetch the nested target:
```graphql
query GetPetWithRockets {
pet(id: "...") {
rockets { # ONE_TO_MANY to junction
id
rocket { # Target field on junction
id
name
__typename
}
}
}
}
```
For polymorphic junction targets:
```graphql
caretakerPerson { id, name }
caretakerCompany { id, name }
```
## Frontend Architecture
### Display Flow
1. **Detection**: `hasJunctionConfig()` checks if field has junction
settings
2. **Config Resolution**: `getJunctionConfig()` resolves junction object
metadata and target fields
3. **Record Extraction**: `extractTargetRecordsFromJunction()` extracts
target records from junction records
4. **Rendering**: Target records displayed as chips (not junction
records)
### Edit Flow
1. **Picker Opening**: Initializes the multi-record picker with:
- Searchable object types (derived from junction target fields)
- Pre-selected items (extracted from existing junction records)
2. **Selection Handling**: Manages create/delete of junction records:
- **Select**: Creates new junction record with source + target IDs
- **Deselect**: Finds and deletes the junction record
- **Optimistic Updates**: Manually updates Recoil store before API call
### Key Trade-offs
| Decision | Trade-off |
|----------|-----------|
| Junction records managed manually | More control over optimistic
updates, but requires manual cache management |
| Settings stored per-field | Flexible (same junction can power
different views), but requires UI to configure |
| Polymorphic via morphId groups | Supports N target types, but adds
query complexity |
| Feature flag gated | Safe rollout, but requires flag management |
## Backend Changes
- **Validation**: Junction target field must exist and be a valid
`MANY_TO_ONE` relation
- **Settings**: Extended `FieldMetadataRelationSettings` type with
junction fields
- **Dev Seeder**: Added sample junction objects (PetRocket,
EmploymentHistory, PetCareAgreement) for testing
## How to Test
1. Enable the `IS_JUNCTION_RELATIONS_ENABLED` feature flag
2. Create objects with junction pattern (Pet → PetRocket → Rocket)
3. Configure the junction target in field settings (advanced mode)
4. Verify:
- Display shows target objects (Rockets), not junction records
(PetRockets)
- Picker allows selecting/deselecting targets
- Changes persist correctly
https://github.com/user-attachments/assets/d04f057a-228c-4de8-af48-76bb2d72cac1
---------
Co-authored-by: Charles Bochet <charles@twenty.com>
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
```
Validations :
- relations count (in common api)
- oneToMany relation nested count (in common)
- requested fields count (in gql)
- root resolver count (in gql)
- root resolver duplicates (in gql)
- specific complexity for metadata / nesting count (in gql)
## 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/1600.
Two remarks
- This `limit` variable does not reduce postgre's work at it still needs
to scan the whole table. It did not seem possible to me to optimize this
as we cannot foresee which dimensions will be used by the user, and an
optimization could only result from an index on the dimension(s) (e.g.:
group companies by addressCity limit 50 can be optimized if we have an
index on companies.addressCity + we had a default orderBy on
adressCity). But this will still optimize the FE which at the moment
receives all groups and truncates the result.
- I have not done the work on the FE as the addition of limit is a
breaking change, and will break until the workspaces' schema is rebuilt,
so we need to flush the cache. I think this could be acceptable as the
feature is in the lab but I preferred not doing it yet as it would have
no impact since in the BE I added a default limit to 50 groups, and I
expect more FE work will be done to allow the user to choose their own
limit
# Introduction
A while ago we migrated view from workspace to metadata
Their standard objects workspace entities declaration remained we can
now remove them
## Deprecating commands before 1.5
The view migration command from workspace to metadata was introduced in
`1.5.0`. Removing the `baseWorkspaceEntity` make this command obsolete.
If tomorrow twenty handles auto upgrade in latest and a user having an
instance in `1.3.0` starts auto-upgrading he won't be able to migrate
his views ( that's why we should not support upgrade before 1.5 anymore
here )
We will have the same use case with FavoritesFolders
Charles Bochet