Tile-based access to SciPy/PyData data structures over the web in many formats
Project description
Tiled
Disclaimer: This is very early work, presented in the spirit of an early draft of an RFC.
Try the prototype
Install dependencies.
git clone https://github.com/bluesky/tiled
cd tiled
pip install -r requirements-demo.txt
pip install -e .
Generate example data files.
python -m tiled.generate_example_data
Run server.
uvicorn tiled.server.main:api
Make requests. The server accepts JSON and msgpack. Once the server is running,
visit http://localhost:8000/docs for documentation. (Or, see below for
example requests and responses.)
The server serves a demo catalog by default, equivalent to:
ROOT_CATALOG="tiled.examples.generic:nested_with_access_control" uvicorn tiled.server.main:api
Other catalogs can be served by changing the value of the ROOT_CATALOG
environment variable to point to a different object in any importable Python
module.
Note: Directories are created in the current directory for scratch space. If
using uvicorn's --reload option, be sure to set
--reload-dir=tiled to avoid reloading everytime a scratch file is
updated.
Requirements
- HTTP API that supports JSON and msgpack requests, with JSON and msgpack responses, as well as binary blob responses for chunked data
- Be usable from
curland languages other that Python. In contrast todask.distributedand the currentintake-serveravoid baking any Python-isms deeply into it. No pickle, no msgpack-python. - List Runs, with pagination and random access
- Search Runs, with pagination and random access on search results
- Access Run metadata cheaply, again with pagination and random access.
- Access Run data as strided C arrays in chunks.
- A Python client with rich proxy objects that do chunk-based access
transparently (like intake's
RemoteXarrayand similar). But, differently from current intake and Databroker, do not switch dask-vs-not-dask or dask-vs-another-delayed-framework at call time. Use a consistent delayed framework (or none at all) consistently within a given context. Your only option at call time should beread(). Whether that is in memory, dask, or something else should be set higher up. - Usable performance without any intrinsic caching in the server. Objects may do some internal caching for optimization, but the server will not explicitly hang on to any state between requests.
- Path toward adding state / caching in external systems (e.g. Redis, nginx)
Draft Specification
There are three user-facing objects in the system:
- DataSource -- wrapper around an array, dataframe, or other data container
- Catalog -- nestable collection of other Catalogs or DataSources
- Query -- high-level description of a search query over entries in a Catalog
- AccessPolicy -- class with methods for enforcing access control on which entries in a Catalog a given identity (user) can see
This specification proposes the Python API required to duck-type as a Catalog or DataSource as well as a sample HTTP API based on JSON API.
Python API
DataSources
-
DataSources MUST implement a
metadataattribute or property which returns a dict-like. Thismetadatais treated as user space, and no part of the server or client will rely on its contents. -
DataSources MUST implement a
containerattribute or property which returns a string of the general type that will be returned byread(), as in intake. These will be generic terms like"array", not the__qualname__of the class. It is meant to encompass the range of concrete types (cupy array, sparse array, dask array, numpy ndarray) that duck-type as a given generic container. -
DataSources MUST implement a method
structure()with no arguments which returns a description of the structure of this data. For each container (array, dataframe, etc.) there will be a specific schema for this description (TBD). For example, "array" reports machine data type, shape, and chunks. Richer structure (e.g. xarray) will include high-level structure like columns, dimensions, indexes. -
DataSources MUST implement a method
read()with no arguments which returns the data structure. -
DataSources MAY implement other methods beyond these for application-specific needs or usability.
Catalogs
-
Catalogs MUST implement the
collections.abc.Mappinginterface. That is:catalog.__getitem__ catalog.__iter__
Catalogs may omit
__len___as long as they provide__length_hint__, an estimated length that may be less expensive for Catalogs backed by databases. That is, implement one or both of these:catalog.__len__ catalog.__length_hint__
-
Catalogs MUST implement an attributes which support efficient positional lookup and slicing.
catalog.keys_indexer[i] # -> str catalog.values_indexer[i] # -> Union[Catalog, DataSource] catalog.items_indexer[i] # -> Tuple[str, Union[Catalog, Datasource]] catalog.keys_indexer[start:stop] # -> List[str] catalog.items_indexer[start:stop] # -> List[Union[Catalog, Datasource]] catalog.values_indexer[start:stop] # -> List[Tuple[str, Union[Catalog, Datasource]]]
-
The values in a Catalog MUST be other Catalogs or DataSources.
-
The keys in a Catalog MUST be non-empty strings adhering to the JSON API spec for allowed characters in resource ids.
-
Catalogs MUST implement a
searchmethod which accepts aQueryas its argument and returns another Catalog with a subset of the items. specified. -
Catalogs MUST implement a
metadataattribute or property which returns a dict-like. Thismetadatais treated as user space, and no part of the server or client will rely on its contents. -
Catalogs MAY implement other methods beyond these for application-specific needs or usability.
-
The method for initializing this object is intentionally unspecified. There will be variety.
-
[This may need revisiting. Should it be qualified? Eliminated?] The items in a Catalog MUST have an explicit and stable order.
-
[This may need revisiting. Should it be qualified? Eliminated?] The data underlying the Catalog may be updated to add items, even though the Catalog itself is a read-only view on that data. Any items added MUST be added to the end. Items may not be removed.
Queries
-
Queries MUST be dataclasses.
-
They MAY have any attributes. There are no required attributes.
Access Policy
An AcessPolicy is tightly coupled to:
- How the metadata related to data management are stored --- "Which type of database? Which collection / table in the database? Which field / column in that table?"
- The particulars of a given access management system that encodes who can see what --- "Given an authenticated identity, what API do I use to check what it is allowed to see?"
We envision a custom AccessPolicy will be needed for every combination of
(1) and (2). It is injected into a given Catalog at __init__ time.
An AccessPolicy gets two opportunities to restrict the entries that a caller
can see. First, a Catalog gives it the opportunity to modify a query before it
is processed in order to restirct the set of results marshalled from storage.
Second, it may filter the query results before they are returned to the user.
Depending on the storage and the complexity of the data access rules, one or
both of these modes may be used by a given AccessPolicy.
- An AccessPolicy MUST implement a method
modify_query(query: Query, authenticated_identity: str) -> Query. This MAY filter the result at the database level so that less data is marshalled from storage. - An AccessPolicy MUST implement a method
filter_results(catalog, authenticated_identity: str) -> Catalog - An AccessPolicy MUST implement a method
check_compatibility(catalog) -> boolwhich can be used by the Catalog to verify at its__init___time that theAccessPolicyit has been given understands how to modify its queries.
Extension points
The prototype uses several user-configurable registries for extensibility of various features.
- MIME types for data For each container type (e.g. "array") there is a
registry mapping MIME type (e.g.
application/octet-stream,application/json) to a function that can encode a block of data from that container. - Query types The server and client use a registry of associates each
Querywith a string name. Additional queries can be registered. - Query support for a given Catalog type In the method
Catalog.search, a Catalog needs to translate the generic description encoded by aQueryinto a concrete filtering operation on its particular storage backend. Thus, custom Queries also need to registered by Catalogs. It is not necessary for every Catalog to understand every type of Query.
JSON API
Examples from the prototype....
List entries in the root catalog, paginated.
GET /entries?page[offset]=2&page[limit]=2
{
"data": [
{
"attributes": {
"count": 3,
"metadata": {
"animal": "dog",
"fruit": "orange"
}
},
"id": "medium",
"type": "catalog"
},
{
"attributes": {
"count": 3,
"metadata": {
"animal": "penguin",
"fruit": "grape"
}
},
"id": "large",
"type": "catalog"
}
],
"error": null,
"links": {
"first": "/?page[offset]=0&page[limit]=2",
"last": "/?page[offset]=2&page[limit]=2",
"next": null,
"prev": "/?page[offset]=0&page[limit]=2",
"self": "/?page[offset]=2&page[limit]=2"
},
"meta": {
"count": 4
}
}
Search the full text of the metadata of the entries in the root catalog.
GET /search?filter[fulltext][condition][text]=dog
{
"data": [
{
"attributes": {
"count": 3,
"metadata": {
"animal": "dog",
"fruit": "orange"
}
},
"id": "medium",
"type": "catalog"
}
],
"error": null,
"links": {
"first": "/?page[offset]=0&page[limit]=10",
"last": "/?page[offset]=0&page[limit]=10",
"next": null,
"prev": null,
"self": "/?page[offset]=0&page[limit]=10"
},
"meta": {
"count": 1
}
}
View the metadata of a sub-Catalog.
GET /metadata/tiny
{
"data": {
"attributes": {
"count": 3,
"metadata": {
"animal": "bird",
"fruit": "apple"
}
},
"id": "tiny",
"type": "catalog"
},
"error": null,
"links": null,
"meta": null
}
List the entries of a sub-Catalog. In this case, these are "array" DataSources. We are given their machine datatype, shape, and chunk strucutre
GET /entries/tiny
{
"data": [
{
"attributes": {
"metadata": {},
"structure": {
"chunks": [
[
3
],
[
3
]
],
"dtype": {
"endianness": "little",
"itemsize": 8,
"kind": "f"
},
"shape": [
3,
3
]
}
},
"id": "ones",
"type": "datasource"
},
{
"attributes": {
"metadata": {},
"structure": {
"chunks": [
[
3
],
[
3
]
],
"dtype": {
"endianness": "little",
"itemsize": 8,
"kind": "f"
},
"shape": [
3,
3
]
}
},
"id": "twos",
"type": "datasource"
},
{
"attributes": {
"metadata": {},
"structure": {
"chunks": [
[
3
],
[
3
]
],
"dtype": {
"endianness": "little",
"itemsize": 8,
"kind": "f"
},
"shape": [
3,
3
]
}
},
"id": "threes",
"type": "datasource"
}
],
"error": null,
"links": {
"first": "/?page[offset]=0&page[limit]=10",
"last": "/?page[offset]=0&page[limit]=10",
"next": null,
"prev": null,
"self": "/?page[offset]=0&page[limit]=10"
},
"meta": {
"count": 3
}
}
Get a chunk of data from the array as JSON (using a small array as an example) and as binary (using a large array as an example).
GET /blob/array/tiny/threes?block=0,0 Accept:application/json
[
[
3.0,
3.0,
3.0
],
[
3.0,
3.0,
3.0
],
[
3.0,
3.0,
3.0
]
]
GET /blob/array/large/threes?block=0,0 Accept:application/octet-stream
<50000000 bytes of binary data>
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