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# Core Features

This section discusses the core features of the ASDF data format, and provides examples and use cases that are specific to the Python implementation.

## Data Model

The fundamental data object in ASDF is the `tree`, which is a nested combination of basic data structures: dictionaries, lists, strings and numbers. In Python, these types correspond to `dict`, `list`, `str`, and `int`, `float`, and `complex`, respectively. The top-level tree object behaves like a Python dictionary and supports arbitrary nesting of data structures. For simple examples of creating and reading trees, see Overview.

Note

The ASDF specification imposes a maximum size of 64-bit signed integers literals in the tree (see asdf-standard:literal_integers for details and justification). Attempting to store a larger value as a YAML literal will result in a validation error.

For arbitrary precision integer support, see `IntegerType`.

Integers and floats of up to 64 bits can be stored inside of `numpy` arrays (see below).

Note

The ASDF specification does not have an immutable sequence type that maps directly to Python’s `tuple`. Following the behavior of pyyaml, asdf writes tuples as YAML sequences, which when loaded are converted to lists. If round-tripping of tuples is important to your application see Extending ASDF to write a custom extension to save and load tuples.

One of the key features of `asdf` is its ability to serialize `numpy` arrays. This is discussed in detail in Array Data.

While the core `asdf` package supports serialization of basic data types and Numpy arrays, its true power comes from its ability to be extended to support serialization of a wide range of custom data types. Details on using ASDF extensions can be found in Using extensions. Details on creating custom ASDF extensions to support custom data types can be found in Extending ASDF.

## Array Data

Much of ASDF’s power and convenience comes from its ability to represent multidimensional array data. The `asdf` Python package provides native support for `numpy` arrays.

- Array Data
  - Saving arrays
  - Sharing of data
  - Saving inline arrays
  - Saving external arrays
  - Streaming array data
  - Compression
  - Memory mapping

## Using extensions

According to Wikipedia, serialization “is the process of translating data structures or object state into a format that can be stored…and reconstructed later” [1].

The power of ASDF is that it provides the ability to store, or serialize, the state of Python objects into a human-readable data format. The state of those objects can later be restored by another program in a process called deserialization.

While ASDF is capable of serializing basic Python types and Numpy arrays out of the box, it can also be extended to serialize arbitrary custom data types. This section discusses the extension mechanism from a user’s perspective. For documentation on creating extensions, see Extensions.

Even though this particular implementation of ASDF necessarily serializes Python data types, in theory an ASDF implementation in another language could read the resulting file and reconstruct an analogous type in that language. Conversely, this implementation can read ASDF files that were written by other implementations of ASDF as long as the proper extensions are available.

- Using Extensions
  - The built-in extension
  - Custom types
  - Extensions
  - Custom types, extensions, and versioning
  - Extensions from other packages
  - Explicit use of extensions
  - Extension checking

## Schema validation

Schema validation is used to determine whether an ASDF file is well formed. All ASDF files must conform to the schemas defined by the ASDF specification. Schema validation can be run using `AsdfFile.validate` and occurs when reading ASDF files (using `asdf.open`) and writing them out (using `AsdfFile.write_to` or `AsdfFile.update`).

Schema validation also plays a role when using custom extensions (see Using extensions and Extensions). Extensions must provide schemas for the types that they serialize. When writing a file with custom types, the output is validated against the schemas corresponding to those types. If the appropriate extension is installed when reading a file with custom types, then the types will be validated against the schemas provided by the corresponding extension.

### Custom schemas

Every ASDF file is validated against the ASDF core schemas, and also against any schemas provided by custom extensions. However, it is sometimes useful for particular applications to impose additional restrictions when deciding whether a given file is valid or not.

For example, consider an application that processes digital image data. The application expects the file to contain an image, and also some metadata about how the image was created. The following example schema reflects these expectations:

    %YAML 1.1
    ---
    id: "http://example.com/schemas/your-custom-schema"
    $schema: "http://stsci.edu/schemas/yaml-schema/draft-01"

    type: object
    properties:
      image:
        description: An ndarray containing image data.
        $ref: "ndarray-1.0.0"

      metadata:
        type: object
        description: Metadata about the image
        properties:
          time:
            description: |
              A timestamp for when the image was created, in UTC.
            type: string
            format: date-time
          resolution:
            description: |
              A 2D array representing the resolution of the image (N x M).
            type: array
            items:
              type: integer
              number: 2

    required: [image, metadata]
    additionalProperties: true

This schema restricts the kinds of files that will be accepted as valid to those that contain a top-level `image` property that is an `ndarray`, and a top-level `metadata` property that contains information about the time the image was taken and the resolution of the image.

In order to use this schema for a secondary validation pass, we pass the `custom_schema` argument to either `asdf.open` or the `AsdfFile` constructor. Assume that the schema file lives in `image_schema.yaml`, and we wish to open a file called `image.asdf`. We would open the file with the following code:

    import asdf
    af = asdf.open('image.asdf', custom_schema='image_schema.yaml')

Similarly, if we wished to use this schema when creating new files:

    new_af = asdf.AsdfFile(custom_schema='image_schema.yaml')
    ...

If your custom schema is registered with ASDF in an extension, you may pass the schema URI (`http://example.com/schemas/your-custom-schema`, in this case) instead of a file path.

Note

The top-level core schemas can be found here.

## Versioning and Compatibility

There are several different versions to keep in mind when discussing ASDF:

- The software package version

- The ASDF core schemas version

- The ASDF file format version

- Individual tag, schema, and extension versions

Each ASDF file contains information about the various versions that were used to create the file. The most important of these are the ASDF core schemas version and the ASDF file format version. A particular version of the ASDF software package will explicitly provide support for specific combinations of these versions.

Tag, schema, and extension versions are also important for serializing and deserializing data types that are stored in ASDF files. A detailed discussion of these versions from a user perspective can be found in Custom types, extensions, and versioning.

Since ASDF is designed to serve as an archival format, this library is careful to maintain backwards compatibility with older versions of the ASDF core schemas, ASDF file format, and core tags. However, since deserializing custom tags requires other software packages, backwards compatibility is often contingent on the available versions of such software packages.

In general, forward compatibility with newer versions of the ASDF core schemas and ASDF file format is not supported by the software.

When creating new ASDF files, it is possible to control the version of the ASDF core schemas that is used. This can be specified by passing the `version` argument to either the `AsdfFile` constructor when the file object is created, or to the `AsdfFile.write_to` method when it is written. By default, the latest stable version of the ASDF core schemas will be used.

Warning

Take care when providing `version` to `AsdfFile.write_to` to select a version that is stable. Writing files with a `development` (unstable) version may produce files that will become unreadable as that version evolves. The default version will always be stable and is often the best choice unless you are trying to write out a file that is readable by older software (where you will want to use an older, stable version).

## External References

### Tree References

ASDF files may reference items in the tree in other ASDF files. The syntax used in the file for this is called “JSON Pointer”, but users of `asdf` can largely ignore that.

First, we’ll create a ASDF file with a couple of arrays in it:

    >>> from asdf import AsdfFile
    >>> import numpy as np

    >>> tree = {
    ...     'a': np.arange(0, 10),
    ...     'b': np.arange(10, 20)
    ... }

    >>> target = AsdfFile(tree)
    >>> target.write_to("target.asdf")

    #ASDF 1.0.0
    #ASDF_STANDARD 1.6.0
    %YAML 1.1
    %TAG ! tag:stsci.edu:asdf/
    --- !core/asdf-1.1.0
    asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
      name: asdf, version: 5.3.0}
    history:
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension._manifest.ManifestExtension
        extension_uri: asdf://asdf-format.org/core/extensions/core-1.6.0
        manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.5.0}
        software: !core/software-1.0.0 {name: asdf, version: 5.3.0}
    a: !core/ndarray-1.1.0
      source: 0
      datatype: int64
      byteorder: little
      shape: [10]
    b: !core/ndarray-1.1.0
      source: 1
      datatype: int64
      byteorder: little
      shape: [10]
    ...

Then we will reference those arrays in a couple of different ways. First, we’ll load the source file in Python and use the `make_reference` method to generate a reference to array `a`. Second, we’ll work at the lower level by manually writing a JSON Pointer to array `b`, which doesn’t require loading or having access to the target file.

    >>> import asdf
    >>> from asdf import AsdfFile
    >>> ff = AsdfFile()

    >>> with asdf.open('target.asdf') as target:
    ...     ff.tree['my_ref_a'] = target.make_reference(['a'])

    >>> ff.tree['my_ref_b'] = {'$ref': 'target.asdf#b'}
    >>> ff.write_to("source.asdf")

    #ASDF 1.0.0
    #ASDF_STANDARD 1.6.0
    %YAML 1.1
    %TAG ! tag:stsci.edu:asdf/
    --- !core/asdf-1.1.0
    asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
      name: asdf, version: 5.3.0}
    history:
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension._manifest.ManifestExtension
        extension_uri: asdf://asdf-format.org/core/extensions/core-1.6.0
        manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.5.0}
        software: !core/software-1.0.0 {name: asdf, version: 5.3.0}
    my_ref_a: {$ref: target.asdf#a}
    my_ref_b: {$ref: target.asdf#b}
    ...

Calling `find_references` will look up all of the references so they can be used as if they were local to the tree. It doesn’t actually move any of the data, and keeps the references as references.

    >>> import asdf
    >>> with asdf.open('source.asdf') as ff:
    ...    ff.find_references()
    ...    assert ff.tree['my_ref_b'].shape == (10,)

On the other hand, calling `resolve_references` places all of the referenced content directly in the tree, so when we write it out again, all of the external references are gone, with the literal content in its place.

    >>> import asdf
    >>> with asdf.open('source.asdf') as ff:
    ...    ff.resolve_references()

    #ASDF 1.0.0
    #ASDF_STANDARD 1.6.0
    %YAML 1.1
    %TAG ! tag:stsci.edu:asdf/
    --- !core/asdf-1.1.0
    asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
      name: asdf, version: 5.3.0}
    history:
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension._manifest.ManifestExtension
        extension_uri: asdf://asdf-format.org/core/extensions/core-1.6.0
        manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.5.0}
        software: !core/software-1.0.0 {name: asdf, version: 5.3.0}
    my_ref_a: !core/ndarray-1.1.0
      source: 0
      datatype: int64
      byteorder: little
      shape: [10]
    my_ref_b: !core/ndarray-1.1.0
      source: 1
      datatype: int64
      byteorder: little
      shape: [10]
    ...

A similar feature provided by YAML, anchors and aliases, also provides a way to support references within the same file. These are supported by `asdf`, however the JSON Pointer approach is generally favored because:

> - It is possible to reference elements in another file

> - Elements are referenced by location in the tree, not an identifier, therefore, everything can be referenced.

Anchors and aliases are handled automatically by `asdf` when the data structure is recursive. For example here is a dictionary that is included twice in the same tree:

    >>> from asdf import AsdfFile
    >>> d = {'foo': 'bar'}
    >>> d['baz'] = d
    >>> tree = {'d': d}

    >>> ff = AsdfFile(tree)

    #ASDF 1.0.0
    #ASDF_STANDARD 1.6.0
    %YAML 1.1
    %TAG ! tag:stsci.edu:asdf/
    --- !core/asdf-1.1.0
    asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
      name: asdf, version: 5.3.0}
    history:
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension._manifest.ManifestExtension
        extension_uri: asdf://asdf-format.org/core/extensions/core-1.6.0
        manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.5.0}
        software: !core/software-1.0.0 {name: asdf, version: 5.3.0}
    d: &id001
      baz: *id001
      foo: bar
    ...

### Array References

ASDF files can refer to array data that is stored in other files using the `ExternalArrayReference` type.

External files need not be ASDF files: ASDF is completely agnostic as to the format of the external file. The ASDF external array reference does not define how the external data file will be resolved; in fact it does not even check for the existence of the external file. It simply provides a way for ASDF files to refer to arrays that exist in external files.

Creating an external array reference is simple. Only four pieces of information are required:

- The name of the external file. Since ASDF does not itself resolve the file or check for its existence, the format of the name is not important. In most cases the name will be a path relative to the ASDF file itself, or a URI for a network resource.

- The data type of the array data. This is a string representing any valid `numpy.dtype`.

- The shape of the data array. This is a tuple representing the dimensions of the array data.

- The array data `target`. This is either an integer or a string that indicates to the user something about how the data array should be accessed in the external file. For example, if there are multiple data arrays in the external file, the `target` might be an integer index. Or if the external file is an ASDF file, the `target` might be a string indicating the key to use in the external file’s tree. The value and format of the `target` field is completely arbitrary since ASDF will not use it itself.

As an example, we will create a reference to an external CSV file. We will assume that one of the rows of the CSV file contains the array data we care about:

    >>> import asdf

    >>> csv_data_row = 10 # The row of the CSV file containing the data we want
    >>> csv_row_size = 100 # The size of the array
    >>> extref = asdf.ExternalArrayReference('data.csv', csv_data_row, "int64", (csv_row_size,))

    >>> tree = {'csv_data': extref}
    >>> af = asdf.AsdfFile(tree)

    #ASDF 1.0.0
    #ASDF_STANDARD 1.6.0
    %YAML 1.1
    %TAG ! tag:stsci.edu:asdf/
    --- !core/asdf-1.1.0
    asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
      name: asdf, version: 5.3.0}
    history:
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension._manifest.ManifestExtension
        extension_uri: asdf://asdf-format.org/core/extensions/core-1.6.0
        manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.5.0}
        software: !core/software-1.0.0 {name: asdf, version: 5.3.0}
    csv_data: !core/externalarray-1.0.0
      datatype: int64
      fileuri: data.csv
      shape: [100]
      target: 10
    ...

When reading a file containing external references, the user is responsible for using the information in the `ExternalArrayReference` type to open the external file and retrieve the associated array data.

## Saving history entries

`asdf` has a convenience method for notating the history of transformations that have been performed on a file.

Given a `AsdfFile` object, call `add_history_entry`, given a description of the change and optionally a description of the software (i.e. your software, not `asdf`) that performed the operation.

    >>> from asdf import AsdfFile
    >>> import numpy as np

    >>> tree = {
    ...    'a': np.random.rand(32, 32)
    ... }

    >>> ff = AsdfFile(tree)
    >>> ff.add_history_entry(
    ...    "Initial random numbers",
    ...    {'name': 'asdf examples',
    ...     'author': 'John Q. Public',
    ...     'homepage': 'http://github.com/asdf-format/asdf',
    ...     'version': '0.1'})

    #ASDF 1.0.0
    #ASDF_STANDARD 1.6.0
    %YAML 1.1
    %TAG ! tag:stsci.edu:asdf/
    --- !core/asdf-1.1.0
    asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
      name: asdf, version: 5.3.0}
    history:
      entries:
      - !core/history_entry-1.0.0
        description: Initial random numbers
        software: !core/software-1.0.0 {author: John Q. Public, homepage: 'http://github.com/asdf-format/asdf',
          name: asdf examples, version: '0.1'}
        time: 2026-06-01 16:19:08+00:00
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension._manifest.ManifestExtension
        extension_uri: asdf://asdf-format.org/core/extensions/core-1.6.0
        manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.5.0}
        software: !core/software-1.0.0 {name: asdf, version: 5.3.0}
    a: !core/ndarray-1.1.0
      source: 0
      datatype: float64
      byteorder: little
      shape: [32, 32]
    ...

`asdf` automatically saves history metadata about the extensions that were used to create the file. This information is used when opening files to determine if the proper extensions are installed (see Extension checking for more details).

Footnotes

[1]

https://en.wikipedia.org/wiki/Serialization

## Rendering ASDF trees

The `asdf.info` function prints a representation of an ASDF tree to stdout. For example:

    >>> asdf.info("path/to/some/file.asdf")
    root (AsdfObject)
    ├─asdf_library (Software)
    │ ├─author (str): The ASDF Developers
    │ ├─homepage (str): http://github.com/asdf-format/asdf
    │ ├─name (str): asdf
    │ └─version (str): 2.5.1
    ├─history (dict)
    │ └─extensions (list) ...
    └─data (dict)
      └─example_key (str): example value

The first argument may be a `str` or `pathlib.Path` filesystem path, or an `AsdfFile` or sub-node of an ASDF tree.

By default, `asdf.info` limits the number of lines, and line length, of the displayed tree. The `max_rows` parameter controls the number of lines, and `max_cols` controls the line length. Set either to `None` to disable that limit.

An integer `max_rows` will be interpreted as an overall limit on the number of displayed lines. If `max_rows` is a tuple, then each member limits lines per node at the depth corresponding to its tuple index. For example, to show all top-level nodes and 5 of each’s children:

    >>> asdf.info("file.asdf", max_rows=(None, 5))

If the attribute is described in a schema, the info functionality will see if it has an associated title and if it does, display it as a comment on the same line. This provides a way for users to see more information about the attribute, similar to the way FITS header comments are used.

The `AsdfFile.info` method behaves similarly to `asdf.info`, rendering the tree of the associated `AsdfFile`.

Normally `asdf.info` will not show the contents of asdf nodes turned into Python custom objects, but if that object supports a special method, you may see the contents of such objects. See Making converted object’s contents visible to info and search for how to implement such support for `asdf.info` and `asdf.search`.

## Searching the ASDF tree

The `AsdfFile` search interface provides a way to interactively discover the locations and values of nodes within the ASDF tree. We can search for nodes by key/index, type, or value.

### Basic usage

Initiate a search by calling `AsdfFile.search` on an open file:

    >>> af.search()
    root (AsdfObject)
    ├─asdf_library (Software)
    │ ├─author (str): The ASDF Developers
    │ ├─homepage (str): http://github.com/asdf-format/asdf
    │ ├─name (str): asdf
    │ └─version (str): 2.5.1
    ├─history (dict)
    │ └─extensions (list) ...
    └─data (dict)
      └─example_key (str): example value

    >>> af.search("example")
    root (AsdfObject)
    └─data (dict)
      └─example_key (str): example value

The search returns an `AsdfSearchResult` object that displays in the Python console as a rendered tree. For single-node search results, the `AsdfSearchResult.path` property contains the Python code required to reference that node directly:

    >>> af.search("example").path
    "root['data']['example_key']"

While the `AsdfSearchResult.node` property contains the actual value of the node:

    >>> af.search("example").node
    'example value'

For searches with multiple matching nodes, use the `AsdfSearchResult.paths` and `AsdfSearchResult.nodes` properties instead:

    >>> af.search("duplicate_key").paths
    ["root['data']['duplicate_key']", "root['other_data']['duplicate_key']"]
    >>> af.search("duplicate_key").nodes
    ["value 1", "value 2"]

To replace matching nodes with a new value, use the `AsdfSearchResult.replace` method:

    >>> af.search("example").replace("replacement value")
    >>> af.search("example").node
    'replacement value'

The first argument to `AsdfFile.search` searches by dict key or list/tuple index. We can also search by type, value, or any combination thereof:

    >>> af.search("foo")  # Find nodes with key containing the string 'foo'
    >>> af.search(type_=int)  # Find nodes that are instances of int
    >>> af.search(value=10)  # Find nodes whose value is equal to 10
    >>> af.search(
    ...     "foo", type_=int, value=10
    ... )  # Find the intersection of the above

### Chaining searches

The return value of `AsdfFile.search`, `asdf.search.AsdfSearchResult`, has its own search method, so it’s possible to chain searches together. This is useful when you need to see intermediate results before deciding how to further narrow the search.

    >>> af.search()  # See an overview of the entire ASDF tree
    >>> af.search().search(type_="NDArrayType")  # Find only ndarrays
    >>> af.search().search(type_="NDArrayType").search(
    ...     "err"
    ... )  # Only ndarrays with 'err' in the key

### Descending into child nodes

Another way to narrow the search is to use the index operator to descend into a child node of the current tree root:

    >>> af.search()["data"]  # Restrict search to the 'data' child
    >>> af.search()["data"].search(
    ...     type_=int
    ... )  # Find integer descendants of 'data'

### Regular expression searches

Any string argument to search is interpreted as a regular expression. For example, we can search for nodes whose keys start with a particular string:

    >>> af.search("foo")  # Find nodes with 'foo' anywhere in the key
    >>> af.search("^foo")  # Find only nodes whose keys start with 'foo'

Note that all node keys (even list indices) will be converted to string before the regular expression is matched:

    >>> af.search("^7$")  # Returns all nodes with key '7' or index 7

When the `type_` argument is a string, the search compares against the fully-qualified class name of each node:

    >>> af.search(
    ...     type_="asdf.tags.core.Software"
    ... )  # Find instances of ASDF's Software type
    >>> af.search(type_="^asdf\.")  # Find all ASDF objects

When the `value` argument is a string, the search compares against the string representation of each node’s value.

    >>> af.search(
    ...     value="^[0-9]{4}-[0-9]{2}-[0-9]{2}$"
    ... )  # Find values that look like dates

### Arbitrary search criteria

If `key`, `type_`, and `value` aren’t sufficient, we can also provide a callback function to search by arbitrary criteria. The `filter_` parameter accepts a callable that receives the node under consideration, and returns `True` to keep it or `False` to reject it from the search results. For example, to search for NDArrayType with a particular shape:

    >>> af.search(type_="NDArrayType", filter_=lambda n: n.shape[0] == 1024)

### Formatting search results

The `AsdfSearchResult` object displays its content as a rendered tree with reasonable defaults for maximum number of lines and columns displayed. To change those values, we call `AsdfSearchResult.format`:

    >>> af.search(type_=float)  # Displays limited rows
    >>> af.search(type_=float).format(max_rows=None)  # Show all matching rows

Like `AsdfSearchResult.search`, calls to format may be chained:

    >>> af.search("time").format(max_rows=10).search(type_=str).format(
    ...     max_rows=None
    ... )

## Searching Schema information

In some cases, one may wish to include information and/or documentation about an object defined by a tagged schema within the schema itself. It can be useful to directly access this information relative to a given ASDF file. For example one may wish to examine:

- The `title` of a value to get a short description of it.

- The `description` of a value to get the longer description of it.

In other cases, it may be useful to store general descriptive information such as specific archival information about a given value in the file so that an archive can easily ingest the file into the archive, such as what is done with the `archive_catalog` information in the rad schemas for the Nancy Grace Roman Space Telescope.

The `AsdfFile.schema_info` method provides a way to access this information. This method returns a nested tree of dictionaries which contains tuples consisting of the information from the schema requested together with the value stored in the ASDF file itself.

One needs to provide a `key`, which corresponds to the keyword the information is stored under inside the schema; by default this is `description`. One can also provide a `path` in the form of a dot-separated string of the keys in the ASDF file that lead to the value(s) of interest. For example:

    >>> af.schema_info("archive_catalog", "foo.bar")
    {'thing1': {'archive_catalog': 'Thing 1 Archive catalog information'},
     'thing2': {'archive_catalog': 'Thing 2 Archive catalog information'}}

Or one can provide a `path` as an `asdf.search.AsdfSearchResult` object:

    >>> af.schema_info("archive_catalog", af.search("bar"))
    {'thing1': {'archive_catalog': 'Thing 1 Archive catalog information'},
     'thing2': {'archive_catalog': 'Thing 2 Archive catalog information'}}

Note

There is also the `asdf.search.AsdfSearchResult.schema_info` method, which can be directly called on an `asdf.search.AsdfSearchResult` object, instead of passing the search through `AsdfFile.schema_info`.