## Description
This is the first step in making use of Logical Models with document databases such as MongoDB. As part of schema introspection, a data connector agent can supply a set of custom types that can be used to describe the schema for columns within the tables of the database (or _fields_ within a _document collection_ in MongoDB terminology).
Previously, we were storing these custom types as `TableObjectType`s within the `TableCoreInfo` for each table.
In this PR we
- replace the `TableObjectTypes` with `LogicalModel` types
- store these directly within the `DBObjectsIntrospection` instead of within the `TableCoreInfo` for each table. (The custom types are shared at the source level so there was no reason to have a separate set of types for each table.)
- When building the `SourceInfo`, we combine the `LogicalModel`s from `DBObjectsIntrospection` with `LogicalModel`s from the user's metadata to create the set of `LogicalModels` in the `SourceInfo` within the `SchemaCache`. I.e. we combine the set of types obtained by database introspection with the set of types specified by the user in the metadata. If two types have the same name, we use the type defined in the metadata.
## Limitations and future work
- Provide a way for the user to associate a meta-data defined `LogicalModel` with a table instead of requiring one to be provided by DB introspection
- Provide a way for the user to edit the `LogicalModel` types provided by introspection and add them to the metadata.
- Allow a `LogicalModel` object type to describe and entire table rather than just individual columns.
- Better handling for "unknown" types, e.g. if the type of a collection (or part of a collection) is unknown we should treat it as a JSON scalar value. This may also involve adding an `_everything` field which returns the full document as a JSON scalar.
PR-URL: https://github.com/hasura/graphql-engine-mono/pull/9345
GitOrigin-RevId: 5cec72fc1be1380d8600f7be547bbf71aad770bd
## Description
This change adds support for querying into nested arrays in Data Connector agents that support such a concept (currently MongoDB).
### DC API changes
- New API type `ColumnType` which allows representing the type of a "column" as either a scalar type, an object reference or an array of `ColumnType`s. This recursive definition allows arbitrary nesting of arrays of types.
- The `type` fields in the API types `ColumnInfo` and `ColumnInsertSchema` now take a `ColumnType` instead of a `ScalarType`.
- To ensure backwards compatibility, a `ColumnType` representing a scalar serialises and deserialises to the same representation as `ScalarType`.
- In queries, the `Field` type now has a new constructor `NestedArrayField`. This contains a nested `Field` along with optional `limit`, `offset`, `where` and `order_by` arguments. (These optional arguments are not yet used by either HGE or the MongoDB agent.)
### MongoDB Haskell agent changes
- The `/schema` endpoint will now recognise arrays within the JSON validation schema and generate corresponding arrays in the DC schema.
- The `/query` endpoint will now handle `NestedArrayField`s within queries (although it does not yet handle `limit`, `offset`, `where` and `order_by`).
### HGE server changes
- The `Backend` type class adds a new type family `XNestedArrays b` to enable nested arrays on a per-backend basis (currently enabled only for the `DataConnector` backend.
- Within `RawColumnInfo` the column type is now represented by a new type `RawColumnType b` which mirrors the shape of the DC API `ColumnType`, but uses `XNestedObjects b` and `XNestedArrays b` type families to allow turning nested object and array supports on or off for a particular backend. In the `DataConnector` backend `API.CustomType` is converted into `RawColumnInfo 'DataConnector` while building the schema.
- In the next stage of schema building, the `RawColumnInfo` is converted into a `StructuredColumnInfo` which allows us to represent the three different types of columns: scalar, object and array. TODO: the `StructuredColumnInfo` looks very similar to the Logical Model types. The main difference is that it uses the `XNestedObjects` and `XNestedArrays` type families. We should be able to combine these two representations.
- The `StructuredColumnInfo` is then placed into a `FIColumn` `FieldInfo`. This involved some refactoring of `FieldInfo` as I had previously split out `FINestedObject` into a separate constructor. However it works out better to represent all "column" fields (i.e. scalar, object and array) using `FIColumn` as this make it easier to implement permission checking correctly. This is the reason the `StructuredColumnInfo` was needed.
- Next, the `FieldInfo` are used to generate `FieldParser`s. We add a new constructor to `AnnFieldG` for `AFNestedArray`. An `AFNestedArray` field parser can contain either a simple array selection or an array aggregate. Simple array `FieldParsers` are currently limited to subfield selection. We will add support for limit, offset, where and order_by in a future PR. We also don't yet generate array aggregate `FieldParsers.
- The new `AFNestedArray` field is handled by the `QueryPlan` module in the `DataConnector` backend. There we generate an `API.NestedArrayField` from the AFNestedArray. We also handle nested arrays when reshaping the response from the DC agent.
## Limitations
- Support for limit, offset, filter (where) and order_by is not yet fully implemented, although it should not be hard to add this
- Support for aggregations on nested arrays is not yet fully implemented
- Permissions involving nested arrays (and objects) not yet implemented
- This should be integrated with Logical Model types, but that will happen in a separate PR
PR-URL: https://github.com/hasura/graphql-engine-mono/pull/9149
GitOrigin-RevId: 0e7b71a994fc1d2ca1ef73bfe7b96e95b5328531
