Source code for pyarrow.hdfs

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import os
import posixpath
import sys
import warnings

from pyarrow.util import doc, _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):
                "hdfs.HadoopFileSystem", "2.0.0", "fs.HadoopFileSystem"),
            FutureWarning, stacklevel=2)
        if driver == 'libhdfs':

        self._connect(host, port, user, kerb_ticket, extra_conf)

    def __reduce__(self):
        return (HadoopFileSystem, (, 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

    def isdir(self, path):
        return super().isdir(path)

    def isfile(self, path):
        return super().isfile(path)

[docs] @doc(FileSystem.delete) def delete(self, path, recursive=False): return super().delete(path, recursive)
[docs] 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] @doc(FileSystem.rename) def rename(self, path, new_path): return super().rename(path, new_path)
[docs] @doc(FileSystem.exists) def exists(self, path): return super().exists(path)
[docs] 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 =, 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'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
[docs]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