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import os
import posixpath
import sys
import warnings
from pyarrow.util import implements, _DEPR_MSG
from pyarrow.filesystem import FileSystem
import pyarrow._hdfsio as _hdfsio
class HadoopFileSystem(_hdfsio.HadoopFileSystem, FileSystem):
"""
DEPRECATED: FileSystem interface for HDFS cluster.
See pyarrow.hdfs.connect for full connection details
.. deprecated:: 2.0
``pyarrow.hdfs.HadoopFileSystem`` is deprecated,
please use ``pyarrow.fs.HadoopFileSystem`` instead.
"""
def __init__(self, host="default", port=0, user=None, kerb_ticket=None,
driver='libhdfs', extra_conf=None):
warnings.warn(
_DEPR_MSG.format(
"hdfs.HadoopFileSystem", "2.0.0", "fs.HadoopFileSystem"),
FutureWarning, stacklevel=2)
if driver == 'libhdfs':
_maybe_set_hadoop_classpath()
self._connect(host, port, user, kerb_ticket, extra_conf)
def __reduce__(self):
return (HadoopFileSystem, (self.host, self.port, self.user,
self.kerb_ticket, self.extra_conf))
def _isfilestore(self):
"""
Return True if this is a Unix-style file store with directories.
"""
return True
@implements(FileSystem.isdir)
def isdir(self, path):
return super().isdir(path)
@implements(FileSystem.isfile)
def isfile(self, path):
return super().isfile(path)
@implements(FileSystem.delete)
def delete(self, path, recursive=False):
return super().delete(path, recursive)
def mkdir(self, path, **kwargs):
"""
Create directory in HDFS.
Parameters
----------
path : str
Directory path to create, including any parent directories.
Notes
-----
libhdfs does not support create_parents=False, so we ignore this here
"""
return super().mkdir(path)
[docs] @implements(FileSystem.rename)
def rename(self, path, new_path):
return super().rename(path, new_path)
@implements(FileSystem.exists)
def exists(self, path):
return super().exists(path)
def ls(self, path, detail=False):
"""
Retrieve directory contents and metadata, if requested.
Parameters
----------
path : str
HDFS path to retrieve contents of.
detail : bool, default False
If False, only return list of paths.
Returns
-------
result : list of dicts (detail=True) or strings (detail=False)
"""
return super().ls(path, detail)
def walk(self, top_path):
"""
Directory tree generator for HDFS, like os.walk.
Parameters
----------
top_path : str
Root directory for tree traversal.
Returns
-------
Generator yielding 3-tuple (dirpath, dirnames, filename)
"""
contents = self.ls(top_path, detail=True)
directories, files = _libhdfs_walk_files_dirs(top_path, contents)
yield top_path, directories, files
for dirname in directories:
yield from self.walk(self._path_join(top_path, dirname))
def _maybe_set_hadoop_classpath():
import re
if re.search(r'hadoop-common[^/]+.jar', os.environ.get('CLASSPATH', '')):
return
if 'HADOOP_HOME' in os.environ:
if sys.platform != 'win32':
classpath = _derive_hadoop_classpath()
else:
hadoop_bin = '{}/bin/hadoop'.format(os.environ['HADOOP_HOME'])
classpath = _hadoop_classpath_glob(hadoop_bin)
else:
classpath = _hadoop_classpath_glob('hadoop')
os.environ['CLASSPATH'] = classpath.decode('utf-8')
def _derive_hadoop_classpath():
import subprocess
find_args = ('find', '-L', os.environ['HADOOP_HOME'], '-name', '*.jar')
find = subprocess.Popen(find_args, stdout=subprocess.PIPE)
xargs_echo = subprocess.Popen(('xargs', 'echo'),
stdin=find.stdout,
stdout=subprocess.PIPE)
jars = subprocess.check_output(('tr', "' '", "':'"),
stdin=xargs_echo.stdout)
hadoop_conf = os.environ["HADOOP_CONF_DIR"] \
if "HADOOP_CONF_DIR" in os.environ \
else os.environ["HADOOP_HOME"] + "/etc/hadoop"
return (hadoop_conf + ":").encode("utf-8") + jars
def _hadoop_classpath_glob(hadoop_bin):
import subprocess
hadoop_classpath_args = (hadoop_bin, 'classpath', '--glob')
return subprocess.check_output(hadoop_classpath_args)
def _libhdfs_walk_files_dirs(top_path, contents):
files = []
directories = []
for c in contents:
scrubbed_name = posixpath.split(c['name'])[1]
if c['kind'] == 'file':
files.append(scrubbed_name)
else:
directories.append(scrubbed_name)
return directories, files
def connect(host="default", port=0, user=None, kerb_ticket=None,
extra_conf=None):
"""
DEPRECATED: Connect to an HDFS cluster.
All parameters are optional and should only be set if the defaults need
to be overridden.
Authentication should be automatic if the HDFS cluster uses Kerberos.
However, if a username is specified, then the ticket cache will likely
be required.
.. deprecated:: 2.0
``pyarrow.hdfs.connect`` is deprecated,
please use ``pyarrow.fs.HadoopFileSystem`` instead.
Parameters
----------
host : NameNode. Set to "default" for fs.defaultFS from core-site.xml.
port : NameNode's port. Set to 0 for default or logical (HA) nodes.
user : Username when connecting to HDFS; None implies login user.
kerb_ticket : Path to Kerberos ticket cache.
extra_conf : dict, default None
extra Key/Value pairs for config; Will override any
hdfs-site.xml properties
Notes
-----
The first time you call this method, it will take longer than usual due
to JNI spin-up time.
Returns
-------
filesystem : HadoopFileSystem
"""
warnings.warn(
_DEPR_MSG.format("hdfs.connect", "2.0.0", "fs.HadoopFileSystem"),
FutureWarning, stacklevel=2
)
return _connect(
host=host, port=port, user=user, kerb_ticket=kerb_ticket,
extra_conf=extra_conf
)
def _connect(host="default", port=0, user=None, kerb_ticket=None,
extra_conf=None):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fs = HadoopFileSystem(host=host, port=port, user=user,
kerb_ticket=kerb_ticket,
extra_conf=extra_conf)
return fs
