def mvs_file_backup(dsn, bk_dsn):
"""Create a backup data set for an MVS data set
Arguments:
dsn {str} -- The name of the data set to backup.
It could be an MVS PS/PDS/PDSE/VSAM(KSDS), etc.
bk_dsn {str} -- The name of the backup data set.
Raises:
BackupError: When backup data set exists.
BackupError: When creation of backup data set fails.
"""
dsn = _validate_data_set_name(dsn).upper()
bk_dsn = _validate_data_set_name(bk_dsn).upper()
if not bk_dsn:
hlq = Datasets.hlq()
bk_dsn = Datasets.temp_name(hlq)
cp_rc = _copy_ds(dsn, bk_dsn)
# The data set is probably a PDS or PDSE
if cp_rc == 12:
# Delete allocated backup that was created when attempting to use _copy_ds()
# Safe to delete because _copy_ds() would have raised an exception if it did
# not successfully create the backup data set, so no risk of it predating module invocation
Datasets.delete(bk_dsn)
if Datasets.move(dsn, bk_dsn) == 0:
_allocate_model(dsn, bk_dsn)
else:
raise BackupError("Unable to backup data set {0} to {1}".format(
dsn, bk_dsn))
def create_temp_data_set(LLQ,
ds_type="SEQ",
size="5M",
ds_format="FB",
lrecl=80):
"""Creates a temporary data set with the given low level qualifier.
Arguments:
LLQ {str} -- Low Level Qualifier to be used for temporary data set
ds_type {str} -- The data set type, default: Sequential
size {str} -- The size of the data set, default: 5M
format {str} -- The record format of the data set, default: FB
lrecl {int} -- The record length of the data set, default: 80
Returns:
str -- Name of the created data set
Raises:
OSError: When non-zero return code is received
from Datasets.create()
"""
chars = ascii_uppercase
HLQ2 = ''.join(choice(chars) for i in range(5))
HLQ3 = ''.join(choice(chars) for i in range(6))
temp_ds_name = "{0}.{1}.{2}.{3}".format(Datasets.hlq(), HLQ2, HLQ3,
LLQ)
rc = Datasets.create(temp_ds_name, ds_type, size, ds_format, "", lrecl)
if rc != 0:
raise OSError("Unable to create temporary data set")
return temp_ds_name
def __init__(self):
"""VIO DD type to be used in a DDStatement.
VIO uses DASD space and system I/O more efficiently than other temporary data sets.
A temporary data set will be created for use in cases where VIO is unavailable.
Defaults for VIODefinition should be sufficient.
"""
hlq = Datasets.hlq()
name = Datasets.temp_name(hlq)
super().__init__(name)
def temp_data_set(self, reclen, space_u):
"""Creates a temporary data set with the given record length and size
Arguments:
size {str} -- The size of the data set
lrecl {int} -- The record length of the data set
Returns:
str -- Name of the allocated data set
Raises:
OSError: When any exception is raised during the data set allocation
"""
size = str(space_u * 2) + "K"
hlq = Datasets.hlq()
temp_ps = Datasets.temp_name(hlq)
rc = Datasets.create(temp_ps, "SEQ", size, "VB", "", reclen)
if rc:
raise OSError(
"Failed when allocating temporary sequential data set!")
return temp_ps
def mvs_file_backup(dsn, bk_dsn=None):
"""Create a backup data set for an MVS data set
Arguments:
dsn {str} -- The name of the data set to backup.
It could be an MVS PS/PDS/PDSE/VSAM(KSDS), etc.
bk_dsn {str} -- The name of the backup data set.
Raises:
BackupError: When backup data set exists.
BackupError: When creation of backup data set fails.
"""
dsn = _validate_data_set_name(dsn).upper()
if is_member(dsn):
if not bk_dsn:
bk_dsn = extract_dsname(dsn) + "({0})".format(temp_member_name())
bk_dsn = _validate_data_set_name(bk_dsn).upper()
if Datasets.copy(dsn, bk_dsn) != 0:
raise BackupError("Unable to backup {0} to {1}".format(
dsn, bk_dsn))
else:
if not bk_dsn:
bk_dsn = Datasets.temp_name(Datasets.hlq())
bk_dsn = _validate_data_set_name(bk_dsn).upper()
cp_rc = _copy_ds(dsn, bk_dsn)
if cp_rc == 12: # The data set is probably a PDS or PDSE
# Delete allocated backup that was created when attempting to use _copy_ds()
# Safe to delete because _copy_ds() would have raised an exception if it did
# not successfully create the backup data set, so no risk of it predating module invocation
Datasets.delete(bk_dsn)
_allocate_model(bk_dsn, dsn)
rc, out, err = _copy_pds(dsn, bk_dsn)
if rc != 0:
raise BackupError(
"Unable to backup data set {0} to {1}".format(dsn, bk_dsn))
return bk_dsn
import mysql.connector
#Import the Z Open Automation Utilities libraries we need
from zoautil_py import MVSCmd, Datasets, Jobs
from zoautil_py.types import DDStatement
# Import datetime, needed so we can format the report
from datetime import datetime
# Import os, needed to get the environment variables
import os
import string
import time
#Take the contents of this data set and read it into content variables
USERID = os.getenv('USER')
hlq = Datasets.hlq()
print("running jobs for ", hlq)
damember = "%s.OUTPUT.SDSFDAS" % hlq
da_contents = Datasets.read(damember)
#ckmember="%s.OUTPUT.SDSFCKS" % hlq
#hc_contents = Datasets.read(ckmember)
now = datetime.now()
formatted_date = now.strftime('%Y-%m-%d %H:%M:%S')
#Submit the job
jclmember = "%s.JCL(SDSFJCL)" % hlq
jobid = Jobs.submit(dataset=jclmember)
while True:
js = Jobs.list(job_id=jobid)[0]
if (js["return"] != "?"):
break
else:
print(js)
def run_module():
# ********************************************************** #
# Module initialization #
# ********************************************************** #
module = AnsibleModule(argument_spec=dict(
src=dict(required=True, type="str"),
dest=dict(required=True, type="path"),
fail_on_missing=dict(required=False, default=True, type="bool"),
flat=dict(required=False, default=True, type="bool"),
is_binary=dict(required=False, default=False, type="bool"),
use_qualifier=dict(required=False, default=False, type="bool"),
validate_checksum=dict(required=False, default=True, type="bool"),
encoding=dict(required=False, type="dict"),
))
src = module.params.get("src")
if module.params.get("use_qualifier"):
module.params["src"] = Datasets.hlq() + "." + src
# ********************************************************** #
# Verify paramater validity #
# ********************************************************** #
arg_def = dict(
src=dict(arg_type="data_set_or_path", required=True),
dest=dict(arg_type="path", required=True),
fail_on_missing=dict(arg_type="bool", required=False, default=True),
is_binary=dict(arg_type="bool", required=False, default=False),
use_qualifier=dict(arg_type="bool", required=False, default=False),
)
if module.params.get("encoding"):
module.params.update(
dict(
from_encoding=module.params.get("encoding").get("from"),
to_encoding=module.params.get("encoding").get("to"),
))
arg_def.update(
dict(
from_encoding=dict(arg_type="encoding"),
to_encoding=dict(arg_type="encoding"),
))
fetch_handler = FetchHandler(module)
try:
parser = better_arg_parser.BetterArgParser(arg_def)
parsed_args = parser.parse_args(module.params)
except ValueError as err:
module.fail_json(msg="Parameter verification failed", stderr=str(err))
src = parsed_args.get("src")
b_src = to_bytes(src)
fail_on_missing = boolean(parsed_args.get("fail_on_missing"))
use_qualifier = boolean(parsed_args.get("use_qualifier"))
is_binary = boolean(parsed_args.get("is_binary"))
encoding = module.params.get("encoding")
# ********************************************************** #
# Check for data set existence and determine its type #
# ********************************************************** #
res_args = dict()
_fetch_member = "(" in src and src.endswith(")")
ds_name = src if not _fetch_member else src[:src.find("(")]
try:
ds_utils = data_set.DataSetUtils(ds_name)
if not ds_utils.exists():
if fail_on_missing:
module.fail_json(msg=("The source '{0}' does not exist or is "
"uncataloged".format(ds_name)))
module.exit_json(
note=("Source '{0}' was not found. No data was fetched".format(
ds_name)))
ds_type = ds_utils.ds_type()
if not ds_type:
module.fail_json(msg="Unable to determine data set type")
except Exception as err:
module.fail_json(msg="Error while gathering data set information",
stderr=str(err))
# ********************************************************** #
# Fetch a sequential data set #
# ********************************************************** #
if ds_type == "PS":
file_path = fetch_handler._fetch_mvs_data(src, is_binary, encoding)
res_args["remote_path"] = file_path
# ********************************************************** #
# Fetch a partitioned data set or one of its members #
# ********************************************************** #
elif ds_type == "PO":
if _fetch_member:
member_name = src[src.find("(") + 1:src.find(")")]
if not ds_utils.member_exists(member_name):
module.fail_json(
msg=("The data set member '{0}' was not found inside data "
"set '{1}'").format(member_name, ds_name))
file_path = fetch_handler._fetch_mvs_data(src, is_binary, encoding)
res_args["remote_path"] = file_path
else:
res_args["remote_path"] = fetch_handler._fetch_pdse(
src, is_binary, encoding)
# ********************************************************** #
# Fetch a USS file #
# ********************************************************** #
elif ds_type == "USS":
if not os.access(b_src, os.R_OK):
module.fail_json(
msg="File '{0}' does not have appropriate read permission".
format(src))
file_path = fetch_handler._fetch_uss_file(src, is_binary, encoding)
res_args["remote_path"] = file_path
# ********************************************************** #
# Fetch a VSAM data set #
# ********************************************************** #
elif ds_type == "VSAM":
file_path = fetch_handler._fetch_vsam(src, is_binary, encoding)
res_args["remote_path"] = file_path
res_args["file"] = ds_name
res_args["ds_type"] = ds_type
module.exit_json(**res_args)
