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import base64
import urllib
import requests
from fsspec import AbstractFileSystem
from fsspec.spec import AbstractBufferedFile
class DatabricksException(Exception):
"""
Helper class for exceptions raised in this module.
"""
def __init__(self, error_code, message):
"""Create a new DatabricksException"""
super().__init__(message)
self.error_code = error_code
self.message = message
class DatabricksFileSystem(AbstractFileSystem):
"""
Get access to the Databricks filesystem implementation over HTTP.
Can be used inside and outside of a databricks cluster.
"""
def __init__(self, instance, token, **kwargs):
"""
Create a new DatabricksFileSystem.
Parameters
----------
instance: str
The instance URL of the databricks cluster.
For example for an Azure databricks cluster, this
has the form adb-<some-number>.<two digits>.azuredatabricks.net.
token: str
Your personal token. Find out more
here: https://docs.databricks.com/dev-tools/api/latest/authentication.html
"""
self.instance = instance
self.token = token
self.session = requests.Session()
self.session.headers.update({"Authorization": f"Bearer {self.token}"})
super().__init__(**kwargs)
def ls(self, path, detail=True):
"""
List the contents of the given path.
Parameters
----------
path: str
Absolute path
detail: bool
Return not only the list of filenames,
but also additional information on file sizes
and types.
"""
out = self._ls_from_cache(path)
if not out:
try:
r = self._send_to_api(
method="get", endpoint="list", json={"path": path}
)
except DatabricksException as e:
if e.error_code == "RESOURCE_DOES_NOT_EXIST":
raise FileNotFoundError(e.message)
raise e
files = r["files"]
out = [
{
"name": o["path"],
"type": "directory" if o["is_dir"] else "file",
"size": o["file_size"],
}
for o in files
]
self.dircache[path] = out
if detail:
return out
return [o["name"] for o in out]
def makedirs(self, path, exist_ok=True):
"""
Create a given absolute path and all of its parents.
Parameters
----------
path: str
Absolute path to create
exist_ok: bool
If false, checks if the folder
exists before creating it (and raises an
Exception if this is the case)
"""
if not exist_ok:
try:
# If the following succeeds, the path is already present
self._send_to_api(
method="get", endpoint="get-status", json={"path": path}
)
raise FileExistsError(f"Path {path} already exists")
except DatabricksException as e:
if e.error_code == "RESOURCE_DOES_NOT_EXIST":
pass
try:
self._send_to_api(method="post", endpoint="mkdirs", json={"path": path})
except DatabricksException as e:
if e.error_code == "RESOURCE_ALREADY_EXISTS":
raise FileExistsError(e.message)
raise e
self.invalidate_cache(self._parent(path))
def mkdir(self, path, create_parents=True, **kwargs):
"""
Create a given absolute path and all of its parents.
Parameters
----------
path: str
Absolute path to create
create_parents: bool
Whether to create all parents or not.
"False" is not implemented so far.
"""
if not create_parents:
raise NotImplementedError
self.mkdirs(path, **kwargs)
def rm(self, path, recursive=False):
"""
Remove the file or folder at the given absolute path.
Parameters
----------
path: str
Absolute path what to remove
recursive: bool
Recursively delete all files in a folder.
"""
try:
self._send_to_api(
method="post",
endpoint="delete",
json={"path": path, "recursive": recursive},
)
except DatabricksException as e:
# This is not really an exception, it just means
# not everything was deleted so far
if e.error_code == "PARTIAL_DELETE":
self.rm(path=path, recursive=recursive)
elif e.error_code == "IO_ERROR":
# Using the same exception as the os module would use here
raise OSError(e.message)
raise e
self.invalidate_cache(self._parent(path))
def mv(self, source_path, destination_path, recursive=False, maxdepth=None):
"""
Move a source to a destination path.
A note from the original [databricks API manual]
(https://docs.databricks.com/dev-tools/api/latest/dbfs.html#move).
When moving a large number of files the API call will time out after
approximately 60s, potentially resulting in partially moved data.
Therefore, for operations that move more than 10k files, we strongly
discourage using the DBFS REST API.
Parameters
----------
source_path: str
From where to move (absolute path)
destination_path: str
To where to move (absolute path)
recursive: bool
Not implemented to far.
maxdepth:
Not implemented to far.
"""
if recursive:
raise NotImplementedError
if maxdepth:
raise NotImplementedError
try:
self._send_to_api(
method="post",
endpoint="move",
json={"source_path": source_path, "destination_path": destination_path},
)
except DatabricksException as e:
if e.error_code == "RESOURCE_DOES_NOT_EXIST":
raise FileNotFoundError(e.message)
elif e.error_code == "RESOURCE_ALREADY_EXISTS":
raise FileExistsError(e.message)
raise e
self.invalidate_cache(self._parent(source_path))
self.invalidate_cache(self._parent(destination_path))
def _open(self, path, mode="rb", block_size="default", **kwargs):
"""
Overwrite the base class method to make sure to create a DBFile.
All arguments are copied from the base method.
Only the default blocksize is allowed.
"""
return DatabricksFile(self, path, mode=mode, block_size=block_size, **kwargs)
def _send_to_api(self, method, endpoint, json):
"""
Send the given json to the DBFS API
using a get or post request (specified by the argument `method`).
Parameters
----------
method: str
Which http method to use for communication; "get" or "post".
endpoint: str
Where to send the request to (last part of the API URL)
json: dict
Dictionary of information to send
"""
if method == "post":
session_call = self.session.post
elif method == "get":
session_call = self.session.get
else:
raise ValueError(f"Do not understand method {method}")
url = urllib.parse.urljoin(f"https://{self.instance}/api/2.0/dbfs/", endpoint)
r = session_call(url, json=json)
# The DBFS API will return a json, also in case of an exception.
# We want to preserve this information as good as possible.
try:
r.raise_for_status()
except requests.HTTPError as e:
# try to extract json error message
# if that fails, fall back to the original exception
try:
exception_json = e.response.json()
except Exception:
raise e
raise DatabricksException(**exception_json)
return r.json()
def _create_handle(self, path, overwrite=True):
"""
Internal function to create a handle, which can be used to
write blocks of a file to DBFS.
A handle has a unique identifier which needs to be passed
whenever written during this transaction.
The handle is active for 10 minutes - after that a new
write transaction needs to be created.
Make sure to close the handle after you are finished.
Parameters
----------
path: str
Absolute path for this file.
overwrite: bool
If a file already exist at this location, either overwrite
it or raise an exception.
"""
try:
r = self._send_to_api(
method="post",
endpoint="create",
json={"path": path, "overwrite": overwrite},
)
return r["handle"]
except DatabricksException as e:
if e.error_code == "RESOURCE_ALREADY_EXISTS":
raise FileExistsError(e.message)
raise e
def _close_handle(self, handle):
"""
Close a handle, which was opened by :func:`_create_handle`.
Parameters
----------
handle: str
Which handle to close.
"""
try:
self._send_to_api(method="post", endpoint="close", json={"handle": handle})
except DatabricksException as e:
if e.error_code == "RESOURCE_DOES_NOT_EXIST":
raise FileNotFoundError(e.message)
raise e
def _add_data(self, handle, data):
"""
Upload data to an already opened file handle
(opened by :func:`_create_handle`).
The maximal allowed data size is 1MB after
conversion to base64.
Remember to close the handle when you are finished.
Parameters
----------
handle: str
Which handle to upload data to.
data: bytes
Block of data to add to the handle.
"""
data = base64.b64encode(data).decode()
try:
self._send_to_api(
method="post",
endpoint="add-block",
json={"handle": handle, "data": data},
)
except DatabricksException as e:
if e.error_code == "RESOURCE_DOES_NOT_EXIST":
raise FileNotFoundError(e.message)
elif e.error_code == "MAX_BLOCK_SIZE_EXCEEDED":
raise ValueError(e.message)
raise e
def _get_data(self, path, start, end):
"""
Download data in bytes from a given absolute path in a block
from [start, start+length].
The maximum number of allowed bytes to read is 1MB.
Parameters
----------
path: str
Absolute path to download data from
start: int
Start position of the block
end: int
End position of the block
"""
try:
r = self._send_to_api(
method="get",
endpoint="read",
json={"path": path, "offset": start, "length": end - start},
)
return base64.b64decode(r["data"])
except DatabricksException as e:
if e.error_code == "RESOURCE_DOES_NOT_EXIST":
raise FileNotFoundError(e.message)
elif e.error_code in ["INVALID_PARAMETER_VALUE", "MAX_READ_SIZE_EXCEEDED"]:
raise ValueError(e.message)
raise e
def invalidate_cache(self, path=None):
if path is None:
self.dircache.clear()
else:
self.dircache.pop(path, None)
super().invalidate_cache(path)
class DatabricksFile(AbstractBufferedFile):
"""
Helper class for files referenced in the DatabricksFileSystem.
"""
DEFAULT_BLOCK_SIZE = 1 * 2**20 # only allowed block size
def __init__(
self,
fs,
path,
mode="rb",
block_size="default",
autocommit=True,
cache_type="readahead",
cache_options=None,
**kwargs,
):
"""
Create a new instance of the DatabricksFile.
The blocksize needs to be the default one.
"""
if block_size is None or block_size == "default":
block_size = self.DEFAULT_BLOCK_SIZE
assert (
block_size == self.DEFAULT_BLOCK_SIZE
), f"Only the default block size is allowed, not {block_size}"
super().__init__(
fs,
path,
mode=mode,
block_size=block_size,
autocommit=autocommit,
cache_type=cache_type,
cache_options=cache_options or {},
**kwargs,
)
def _initiate_upload(self):
"""Internal function to start a file upload"""
self.handle = self.fs._create_handle(self.path)
def _upload_chunk(self, final=False):
"""Internal function to add a chunk of data to a started upload"""
self.buffer.seek(0)
data = self.buffer.getvalue()
data_chunks = [
data[start:end] for start, end in self._to_sized_blocks(len(data))
]
for data_chunk in data_chunks:
self.fs._add_data(handle=self.handle, data=data_chunk)
if final:
self.fs._close_handle(handle=self.handle)
return True
def _fetch_range(self, start, end):
"""Internal function to download a block of data"""
return_buffer = b""
length = end - start
for chunk_start, chunk_end in self._to_sized_blocks(length, start):
return_buffer += self.fs._get_data(
path=self.path, start=chunk_start, end=chunk_end
)
return return_buffer
def _to_sized_blocks(self, length, start=0):
"""Helper function to split a range from 0 to total_length into bloksizes"""
end = start + length
for data_chunk in range(start, end, self.blocksize):
data_start = data_chunk
data_end = min(end, data_chunk + self.blocksize)
yield data_start, data_end
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