def get_model(self):
client = KerberosClient(settings.DUNANT_HDFS_PATH)
MODEL_DIR = settings.DUNANT_MODEL_DIR
MOST_RECENT_MODEL = sorted(client.list(MODEL_DIR))[-1]
MODEL_PARAMETERS_PATH = f'{MODEL_DIR}/{MOST_RECENT_MODEL}/model'
MLB_PATH = f'{MODEL_PARAMETERS_PATH}/mlb_binarizer.pkl'
VECTORIZER_PATH = f'{MODEL_PARAMETERS_PATH}/vectorizer.pkl'
CLASSIFIER_PATH = f'{MODEL_PARAMETERS_PATH}/model.pkl'
# For pickle to be able to unpickle, the class must be present in the
# same import structure as when it was pickled.
# Manually setting sys.modules to mimic the expected import structure
sys.modules['models'] = classifiers
# Latin1 encoding required to convert Python2 pickle to Python3
with client.read(MLB_PATH) as r:
mlb = pickle.loads(r.read(), encoding="latin1")
with client.read(VECTORIZER_PATH) as r:
vectorizer = pickle.loads(r.read(), encoding="latin1")
with client.read(CLASSIFIER_PATH) as r:
clf = pickle.loads(r.read(), encoding="latin1")
del sys.modules['models']
return mlb, vectorizer, clf
client = KerberosClient(hdfs_url)
s3 = session.client('s3',use_ssl=False, verify=False)
counter = 0
for file_path in file_list_arr:
file_path = source_directory + file_path
status = client.status(file_path, strict=False)
if bool(status):
file_name = os.path.basename(file_path)
key_name = s3_folder_name + file_name
with client.read(file_path) as f:
s3.upload_fileobj(f, bucket_name, key_name)
client.delete(file_path, recursive=False, skip_trash=True)
counter = counter + 1
py_logger.info("File: " + file_path + " moved to s3 bucket")
py_logger.info("S3 script execution completed. No.of Files moved: " + str(counter))
#Compresses the log files which are greater than 30 days
today = date.today()
current_day = datetime.now().strftime('%d')
log_directory = log_file_path.rpartition('/')[0] + log_file_path.rpartition('/')[1]
tarFileName = log_directory + today.strftime("%d-%m-%Y") + '.tar.gz'
if current_day == "30":
import sys
from hdfs.ext.kerberos import KerberosClient
client = KerberosClient(sys.argv[1])
with client.read(sys.argv[2]) as reader:
test = reader.read()
print(test)
# encoding: utf-8 """ @Author: wanghuagang @Contact: [email protected] @Project: StudyPython @File: d1 @Date: 2019/7/15 上午10:27 @Description: """ from hdfs.ext.kerberos import KerberosClient client = KerberosClient('http://m1.node.hadoop:50070') with client.read('/tmp/fastparquet/user1.parquet') as reader: features = reader.read() print(features)
.agg(lambda x: set(x))\
.reset_index()
nb_new_documents = len(df)
if nb_new_documents == 0:
print('No new data to predict!')
sys.exit()
else:
print('{} new documents to predict.\n'.format(nb_new_documents))
X = np.array(df[TEXT_COLUMN])
print('Loading models...')
formatted_hdfs_path = "/".join(HDFS_MODEL_DIR.split('/')[5:])
most_recent_date = sorted(client.list(formatted_hdfs_path))[-1]
with client.read('{}/{}/model/mlb_binarizer.pkl'.format(
formatted_hdfs_path, most_recent_date)) as mlb_reader:
mlb = pickle.loads(mlb_reader.read())
with client.read('{}/{}/model/vectorizer.pkl'.format(
formatted_hdfs_path, most_recent_date)) as vectorizer_reader:
vectorizer = pickle.loads(vectorizer_reader.read())
with client.read('{}/{}/model/model.pkl'.format(
formatted_hdfs_path, most_recent_date)) as clf_reader:
clf = pickle.loads(clf_reader.read())
print('Predicting...')
reg_clf = RegexClassifier(RULES)
y_regex = reg_clf.predict(X)
y_regex = mlb.transform(y_regex)
X = vectorizer.transform(X)
y_logreg = clf.predict(X, threshold=THRESHOLD, max_classes=MAX_CLASSES)
class OperateHDFS:
def __init__(self, url):
'''
:param url:HDFS名称节点的主机名或IP地址,以协议为前缀,其次是namenode上的WebHDFS端口,也可以指定多个URL以分号分隔以获取高可用性支持.
'''
# 实例化HDFS web client using Kerberos authentication
self.client = KerberosClient(url)
def file_list(self, file_path):
'''
:param file_path: HDFS远程目录路径
:return: 返回一个远程目录中包含的所有文件
'''
file_detail = self.client.list(hdfs_path=file_path)
return file_detail
def file_read(self, file_path):
'''
从HDFS中读取文件
:param file_path: HDFS远程文件路径
:return:
'''
lines = []
with self.client.read(hdfs_path=file_path,
encoding='utf-8',
delimiter=r'\n') as reader:
# content = file.read()
# print(content)
for item in reader:
lines.append(item.strip())
return lines
def file_create_write(self, file_path, data_write):
'''
在HDFS中创建新文件并写入内容
:param file_path: HDFS远程文件路径
:param data_write: 写入到文件的数据
:return:
'''
self.client.write(hdfs_path=file_path,
data=data_write,
encoding='utf-8')
def file_append_write(self, file_path, data_append):
'''
在HDFS中已存在的文件中追加写入内容，文件必须已存在
:param file_path: HDFS远程文件路径
:param data_append: 追加到文件的数据
:return:
'''
self.client.write(hdfs_path=file_path,
data=data_append,
encoding='utf-8',
append=True)
def file_rename(self, src_file_path, dst_file_path):
'''
重命名/移动文件或文件夹
:param src_file_path: 源文件路径
:param dst_file_path: 目的文件路径
:return:
'''
self.client.rename(hdfs_src_path=src_file_path,
hdfs_dst_path=dst_file_path)
def mkdir(self, file_path):
'''
在HDFS中创建远程目录，必要时递归创建
:param file_path: 需要新建的文件夹路径(包含名字)
:return:
'''
self.client.makedirs(hdfs_path=file_path)
def upload_files(self, file_path, local_path):
'''
上传文件或目录到HDFS
:param file_path:HDFS目标路径。如果它已经存在并且是一个目录，文件将被上传其中。
:param local_path:文件或文件夹的本地路径。 如果是文件夹，则将上传其中的所有文件（请注意，这意味着没有文件的文件夹将不会远程创建）
:return:hdfs_path_return:成功后，此方法将返回远程上传路径。
'''
hdfs_path_return = self.client.upload(hdfs_path=file_path,
local_path=local_path)
return hdfs_path_return
def download_files(self, file_path, local_path):
'''
从HDFS下载一个文件或文件夹并将其保存在本地
:param file_path:HDFS上要下载的文件或文件夹的路径。 如果是文件夹，则将下载该文件夹下的所有文件
:param local_path:本地路径。 如果它已经存在并且是目录，则文件将在其中下载。
:return: local_path_return:成功后，此方法将返回本地下载路径
'''
local_path_return = self.client.download(hdfs_path=file_path,
local_path=local_path)
return local_path_return
def delete_files(self, file_path):
'''
从HDFS中删除文件或目录
:param file_path: HDFS中需要删除的文件或目录的路径
:return:如果删除成功，则此函数返回“ True”，如果先前在“ hdfs_path”处不存在文件或目录，则返回“ False”。
'''
# recursive：递归删除文件和目录。 默认情况下，如果尝试删除非空目录，则此方法将引发HdfsError。
# skip_trash：设置为false时，已删除的路径将被移至相应的垃圾文件夹，而不是被删除。 这需要Hadoop 2.9+且在集群上启用trash
return self.client.delete(hdfs_path=file_path,
recursive=False,
skip_trash=True)
def set_files_permission(self, file_path):
'''
更改文件的权限
:param file_path: 需要更改权限的文件路径
:return:
'''
# permission：文件的新八进制权限字符串
self.client.set_permission(hdfs_path=file_path, permission=None)
