# Features of fsspec

Here follows a brief description of some features of note of `fsspec` that provides to make it an interesting project beyond some other file-system abstractions.

## Serialisability

Coming out of the Dask stable, it was an important design decision that file-system instances be serialisable, so that they could be created in one process (e.g., the client) and used in other processes (typically the workers). These other processes may even be on other machines, so in many cases they would need to be able to re-establish credentials, ideally without passing sensitive tokens in the pickled binary data.

`fsspec` instances, generally speaking, abide by these rules, do not include locks, files and other thread-local material, and where possible, use local credentials (such as a token file) for re-establishing sessions upon de-serialisation. (While making use of cached instances, where they exist, see below).

## `OpenFile` instances

The `fsspec.core.OpenFile()` class provides a convenient way to prescribe the manner to open some file (local, remote, in a compressed store, etc.) which is portable, and can also apply any compression and text-mode to the file. These instances are also serialisable, because they do not contain any open files.

The way to work with `OpenFile` s is to isolate interaction with in a `with` context. It is the initiation of the context which actually does the work of creating file-like instances.

    of = fsspec.open(url, ...)
    # of is just a place-holder
    with of as f:
        # f is now a real file-like object holding resources
        f.read(...)

## File Buffering and random access

Most implementations create file objects which derive from `fsspec.spec.AbstractBufferedFile`, and have many behaviours in common. A subclass of `AbstractBufferedFile` provides random access for the underlying file-like data (without downloading the whole thing). This is a critical feature in the big-data access model, where each sub-task of an operation may need on a small part of a file, and does not, therefore want to be forced into downloading the whole thing.

These files offer buffering of both read and write operations, so that communication with the remote resource is limited. The size of the buffer is generally configured with the `blocksize=` kwarg at open time, although the implementation may have some minimum or maximum sizes that need to be respected.

For reading, a number of buffering schemes are available, listed in `fsspec.caching.caches` (see Read Buffering), or “none” for no buffering at all, e.g., for a simple read-ahead buffer, you can do

    fs = fsspec.filesystem(...)
    with fs.open(path, mode='rb', cache_type='readahead') as f:
        use_for_something(f)

## Transparent text-mode and compression

As mentioned above, the `OpenFile` class allows for the opening of files on a binary store, which appear to be in text mode and/or allow for a compression/decompression layer between the caller and the back-end storage system. The list of `fsspec` supported codec can be retrieved using `fsspec.available_compressions()`. From the user’s point of view, this is achieved simply by passing arguments to the `fsspec.open_files()` or `fsspec.open()` functions, and thereafter happens transparently.

## Key-value stores

File-systems are naturally like dict-like key-value mappings: each (string) path corresponds to some binary data on the storage back-end. For some use-cases, it is very convenient to be able to view some path within the file-system as a dict-like store, and the function `fsspec.get_mapper()` gives a one-stop way to return such an object. This has become useful, for example, in the context of the zarr project, which stores it array chunks in keys in any arbitrary mapping-like object.

    mapper = fsspec.get_mapper('protocol://server/path', args)
    list(mapper)
    mapper[k] = b'some data'

## PyArrow integration

pyarrow has its own internal idea of what a file-system is (`pyarrow.fs.FileSystem`), and some functions, particularly the loading of parquet, require that the target be compatible. As it happens, the design of the file-system interface in `pyarrow` is compatible with `fsspec` (this is not by accident).

At import time, `fsspec` checks for the existence of `pyarrow`, and, if `pyarrow < 2.0` is found, adds its base filesystem to the superclasses of the spec base-class. For `pyarrow >= 2.0`, `fsspec` file systems can simply be passed to `pyarrow` functions that expect `pyarrow` filesystems, and `pyarrow` will automatically wrap them.

In this manner, all `fsspec`-derived file-systems are also `pyarrow` file-systems, and can be used by `pyarrow` functions.

## Transactions

`fsspec` supports transactions, during which writing to files on a remote store are deferred (typically put into a temporary location) until the transaction is over, whereupon the whole transaction is finalised in a semi-atomic way, and all the files are moved/committed to their final destination. The implementation of the details is file-system specific (and not all support it yet), but the idea is, that all files should get written or none, to mitigate against data corruption. The feature can be used like

    fs = fsspec.filesystem(...)
    with fs.transaction:
        with fs.open('file1', 'wb') as f:
            f.write(b'some data')
        with fs.open('file2', 'wb') as f:
            f.write(b'more data')

Here, files 1 and 2 do not get moved to the target location until the transaction context finishes. If the context finishes due to an (uncaught) exception, then the files are discarded and the file target locations untouched.

The class `fsspec.spec.Transaction()` allows for fine-tuning of the operation, and every `fsspec` instance has an instance of this as an attribute `.transaction` to give access.

Note that synchronising transactions across multiple instances, perhaps across a cluster, is a harder problem to solve, and the implementation described here is only part of the solution.

## Mount anything with FUSE

Any path of any file-system can be mapped to a local directory using fusepy and `fsspec.fuse.run()`. This feature is experimental, but basic file listing with details, and read/write should generally be available to the extent that the remote file-system provides enough information. Naturally, if a file-system is read-only, then write operations will fail - but they will tend to fail late and with obscure error messages such as “bad address”.

Some specific quirks of some file-systems may cause confusion for FUSE. For example, it is possible for a given path on s3 to be both a valid key (i.e., containing binary data, like a file) and a valid prefix (i.e., can be listed to find subkeys, like a directory). Since this breaks the assumptions of a normal file-system, it may not be possible to reach all paths on the remote.

## Instance Caching

In a file-system implementation class is marked as cachable (attribute `.cachable`), then its instances will get stored in a class attribute, to enable quick look-up instead of needing to regenerate potentially expensive connections and sessions. They key in the cache is a tokenisation of the arguments to create the instance. The cache itself (attribute `._cache`) is currently a simple dict, but could in the future be LRU, or something more complicated, to fine-tune instance lifetimes.

Since files can hold on to write caches and read buffers, the instance cache may cause excessive memory usage in some situations; but normally, files’ `close` methods will be called, discarding the data. Only when there is also an unfinalised transaction or captured traceback might this be anticipated becoming a problem.

To disable instance caching, i.e., get a fresh instance which is not in the cache even for a cachable class, pass `skip_instance_cache=True`.

## Listings Caching

For some implementations, getting file listings (i.e., `ls` and anything that depends on it) is expensive. These implementations use dict-like instances of `fsspec.dircache.DirCache` to manage the listings.

The cache allows for time-based expiry of entries with the `listings_expiry_time` parameter, or LRU expiry with the `max_paths` parameter. These can be set on any implementation instance that uses listings caching; or to skip the caching altogether, use `use_listings_cache=False`. That would be appropriate when the target location is known to be volatile because it is being written to from other sources.

When the `fsspec` instance writes to the backend, the method `invalidate_cache` is called, so that subsequent listing of the given paths will force a refresh. In addition, some methods like `ls` have a `refresh` parameter to force fetching the listing again.

## URL chaining

Some implementations proxy or otherwise make use of another filesystem implementation, such as locally caching remote files, i.e., finding out what files exist using the remote implementation, but actually opening the local copies upon access. Other examples include reading from a Dask worker which can see file-systems the client cannot, and accessing a zip file which is being read from another backend.

In such cases, you can specify the parameters exactly as specified in the implementation docstrings, for the dask case something like

    of = fsspec.open('dask://bucket/key', target_protocol='s3', target_options={'anon': True})

As a shorthand, particularly useful where you have multiple hops, is to “chain” the URLs with the special separator `"::"`. The arguments to be passed on to each of the implementations referenced are keyed by the protocol names included in the URL. Here is the equivalent to the line above:

    of = fsspec.open('dask::s3://bucket/key', s3={'anon': True})

A couple of more complicates cases:

    of = fsspec.open_files('zip://*.csv::simplecache::gcs://bucket/afile.zip',
                           simplecache={'cache_storage': '/stored/zip/files'},
                           gcs={'project': 'my-project'})

reads a zip-file from google, stores it locally, and gives access to the contained CSV files. Conversely,

    of = fsspec.open_files('simplecache::zip://*.csv::gcs://bucket/afile.zip',
                           simplecache={'cache_storage': '/stored/csv/files'},
                           gcs={'project': 'my-project'})

reads the same zip-file, but extracts the CSV files and stores them locally in the cache.

For developers: this “chaining” methods works by formatting the arguments passed to `open_*` into `target_protocol` (a simple string) and `target_options` (a dict) and also optionally `fo` (target path, if a specific file is required). In order for an implementation to chain successfully like this, it must look for exactly those named arguments. Implementations that require access to the target path of their nested targets should inherit from `ChainedFileSystem`, which will trigger pass-through of the nested path automatically.

## Caching Files Locally

`fsspec` allows you to access data on remote file systems, that is its purpose. However, such access can often be rather slow compared to local storage, so as well as buffering (see above), the option exists to copy files locally when you first access them, and thereafter to use the local data. This local cache of data might be temporary (i.e., attached to the process and discarded when the process ends) or at some specific location in your local storage.

Two mechanisms are provided, and both involve wrapping a `target` filesystem. The following example creates a file-based cache.

    fs = fsspec.filesystem("filecache", target_protocol='s3', target_options={'anon': True},
                           cache_storage='/tmp/files/')

Each time you open a remote file on S3, it will first copy it to a local temporary directory, and then all further access will use the local file. Since we specify a particular local location, the files will persist and can be reused from future sessions, although you can also set policies to have cached files expire after some time, or to check the remote file system on each open, to see if the target file has changed since it was copied.

With the top-level functions `open`, `open_local` and `open_files`, you can use the same set of kwargs as the example above, or you can chain the URL - the following would be the equivalent

    of = fsspec.open("filecache::s3://bucket/key",
                     s3={'anon': True}, filecache={'cache_storage':'/tmp/files'})

With the “blockcache” variant, data is downloaded block-wise: only the specific parts of the remote file which are accessed. This means that the local copy of the file might end up being much smaller than the remote one, if only certain parts of it are required.

Whereas “filecache” works for all file system implementations, and provides a real local file for other libraries to use, “blockcache” has restrictions: that you have a storage/OS combination which supports sparse files, that the backend implementation uses files which derive `from AbstractBufferedFile`, and that the library you pass the resultant object to accepts generic python file-like objects. You should not mix block- and file-caches in the same directory. “simplecache” is the same as “filecache”, except without the options for cache expiry and to check the original source - it can be used where the target can be considered static, and particularly where a large number of target files are expected (because no metadata is written to disc). Only “simplecache” is guaranteed thread/process-safe.

## Remote Write Caching

You can cache files to local files to send to remote using the “simplecache” protocol. The following example demonstrates how this might look

    with fsspec.open('simplecache::s3://mybucket/myfile', 'wb',
                     s3={"profile": "writer"}) as f:
        f.write(b"some data")

This will open a local file for writing, and when this file is closed, it will be uploaded to the target URL, in this case on S3. The file-like object `f` can be passed to any library expecting to write to a file. Note that we pass parameters to `S3FileSystem` using the key `"s3"`, the same as the name of the protocol.

## File Selector (GUI)

The module `fsspec.gui` contains a graphical file selector interface. It is built using panel, which must be installed in order to use the GUI. Upon instantiation, you can provide the initial URL location (which can be returned to with the “🏠” button), arguments and filters.

Clicking on a directory will descend into it, and selecting a file will mark it as the output of the interface. You can select any of the known protocols, but should provide any required arguments in the “kwargs” box (as a dictionary) and any absolute URL location before clicking “⇨” to go to that location. If using file filters, they will appear as a list of checkboxes; only those file-endings selected will be shown (or if none are selected, all files are shown).

The interface provides the following outputs:

1. `.urlpath`: the currently selected item (if any)

2. `.storage_options`: the value of the kwargs box

3. `.fs`: the current filesystem instance

4. `.open_file()`: produces an `OpenFile` instance for the current selection

## Configuration

You can set default keyword arguments to pass to any fsspec backend by editing config files, providing environment variables, or editing the contents of the dictionary `fsspec.config.conf`.

Files are stored in the directory pointed to by `FSSPEC_CONFIG_DIR`, `"~/.config/fsspec/` by default. All *.ini and *.json files will be loaded and parsed from their respective formats and fed into the config dict at import time. For example, if there is a file “~/.config/fsspec/conf.json” containing

    {"file": {"auto_mkdir": true}}

then any instance of the file system whose protocol is “file” (i.e., `LocalFileSystem`) with be passed the kwargs `auto_mkdir=True` unless the user supplies the kwarg themselves.

For instance:

    import fsspec
    fs = fsspec.filesystem("file")
    assert fs.auto_mkdir == True
    fs = fsspec.filesystem("file", auto_mkdir=False)
    assert fs.auto_mkdir == False

Obviously, you should only define default values that are appropriate for a given file system implementation. INI files only support string values.

Alternatively, you can provide overrides with environment variables of the style `FSSPEC_{protocol}=<json_dict_value>` and `FSSPEC_{protocol}_{kwargname}=<string_value>`.

Configuration is determined in the following order, with later items winning:

1. ini and json files in the config directory (`FSSPEC_CONFIG_DIRECTORY` or `$HOME/.config/fsspec/`), sorted lexically by filename

2. `FSSPEC_{protocol}` environment variables

3. `FSSPEC_{protocol}_{kwargname}` environment variables

4. the contents of `fsspec.config.conf`, which can be edited at runtime

5. kwargs explicitly passed, whether with `fsspec.open`, `fsspec.filesystem` or directly instantiating the implementation class.

## Asynchronous

Some implementations, those deriving from `fsspec.asyn.AsyncFileSystem`, have async/coroutine implementations of some file operations. The async methods have names beginning with `_`, and listed in the `asyn` module; synchronous or blocking functions are automatically generated, which will operate via an event loop in another thread, by default.

See Async for modes of operation and how to implement such file systems.

## Callbacks

Some methods support a `callback=` argument, which is the entry point to providing feedback on transfers to the user or any other logging service. This feature is new and experimental and supported by varying amounts in the backends.

See the docstrings in the callbacks module for further details. `fsspec.callbacks.TqdmCallback` can be used to display a progress bar using tqdm.

## Exclusive write

Some backends support writing to a file only if it doesn’t already exist. This may be implemented for the following methods: - pipe_file (with argument `mode=='create'`) - put_file (with argument `mode=='create'`) - open (with argument `mode="xb"`) Since some writes will be achieved in blocks, the timing of when the check is done is not defined - it may be at the start or at the completion of the operation, depending on the backend.

If using exclusive mode on a file that does already exist, a `FileExistsError` will be raised.

This feature is currently included on a trial basis and may change in the future.