def get_table_info(self, table, **kwargs):
sch = SchemaForTable(table, schema=self.schemas)
key_fields = sch.key_fields()
# You can't use view from user because we need to see all the data
# to compute data required.
kwargs.pop('view', None)
all_time_df = self._get_table_info(table, view='all', **kwargs)
times = all_time_df['timestamp'].unique()
ret = {'first_time': all_time_df.timestamp.min(),
'latest_time': all_time_df.timestamp.max(),
'intervals': len(times),
'all rows': len(all_time_df),
'namespaces': self._unique_or_zero(all_time_df, 'namespace'),
'devices': self._unique_or_zero(all_time_df, 'hostname')}
return ret
class SqObject(object):
def __init__(self,
engine_name: str = 'pandas',
hostname: typing.List[str] = [],
start_time: str = '',
end_time: str = '',
view: str = 'latest',
namespace: typing.List[str] = [],
columns: typing.List[str] = ['default'],
context=None,
table: str = '',
config_file=None) -> None:
if context is None:
self.ctxt = SqContext(engine_name, config_file)
else:
self.ctxt = context
if not self.ctxt:
self.ctxt = SqContext(engine_name)
self._cfg = self.ctxt.cfg
self._schema = SchemaForTable(table, self.ctxt.schemas)
self._table = table
self._sort_fields = self._schema.key_fields()
if not namespace and self.ctxt.namespace:
self.namespace = self.ctxt.namespace
else:
self.namespace = namespace
if not hostname and self.ctxt.hostname:
self.hostname = self.ctxt.hostname
else:
self.hostname = hostname
if not start_time and self.ctxt.start_time:
self.start_time = self.ctxt.start_time
else:
self.start_time = start_time
if not end_time and self.ctxt.end_time:
self.end_time = self.ctxt.end_time
else:
self.end_time = end_time
if not view and self.ctxt.view:
self.view = self.ctxt.view
else:
self.view = view
self.columns = columns
if engine_name and engine_name != '':
self.engine = get_sqengine(engine_name, self._table)(self._table,
self)
elif self.ctxt.engine:
self.engine = get_sqengine(self.ctxt.engine,
self._table)(self._table, self)
if not self.engine:
raise ValueError('Unknown analysis engine')
self._addnl_filter = None
self._addnl_fields = []
self._valid_get_args = None
self._valid_assert_args = None
self._valid_arg_vals = None
@property
def all_schemas(self):
return self.ctxt.schemas
@property
def schema(self):
return self._schema
@property
def cfg(self):
return self._cfg
@property
def table(self):
return self._table
def _check_input_for_valid_args(
self,
good_arg_list,
**kwargs,
):
if not good_arg_list:
return
# add standard args that are always
good_arg_list = good_arg_list + (['namespace', 'addnl_fields'])
for arg in kwargs.keys():
if arg not in good_arg_list:
raise AttributeError(
f"argument {arg} not supported for this command")
def _check_input_for_valid_vals(self, good_arg_val_list, **kwargs):
'''Check if the input is valid for the arg, if possible'''
if not good_arg_val_list:
return
for arg in kwargs.keys():
if arg in good_arg_val_list:
if kwargs[arg] not in good_arg_val_list[arg]:
raise AttributeError(
f"invalid value {kwargs[arg]} for argument {arg}")
def validate_get_input(self, **kwargs):
self._check_input_for_valid_args(self._valid_get_args + ['columns'],
**kwargs)
self._check_input_for_valid_vals(self._valid_arg_vals, **kwargs)
def validate_assert_input(self, **kwargs):
self._check_input_for_valid_args(self._valid_assert_args, **kwargs)
def get(self, **kwargs) -> pd.DataFrame:
if not self._table:
raise NotImplementedError
if not self.ctxt.engine:
raise AttributeError('No analysis engine specified')
if self._addnl_filter:
kwargs['add_filter'] = self._addnl_filter
# This raises exceptions if it fails
try:
self.validate_get_input(**kwargs)
except Exception as error:
df = pd.DataFrame({'error': [f'{error}']})
return df
return self.engine.get(**kwargs)
def summarize(self,
namespace=[],
hostname=[],
query_str='') -> pd.DataFrame:
if self.columns != ["default"]:
self.summarize_df = pd.DataFrame({
'error': ['ERROR: You cannot specify columns with summarize']
})
return self.summarize_df
if not self._table:
raise NotImplementedError
if not self.ctxt.engine:
raise AttributeError('No analysis engine specified')
return self.engine.summarize(namespace=namespace,
hostname=hostname,
query_str=query_str)
def unique(self, **kwargs) -> pd.DataFrame:
if not self._table:
raise NotImplementedError
if not self.ctxt.engine:
raise AttributeError('No analysis engine specified')
columns = kwargs.pop('columns', self.columns)
return self.engine.unique(**kwargs, columns=columns)
def analyze(self, **kwargs):
raise NotImplementedError
def aver(self, **kwargs):
raise NotImplementedError
def top(self, what='', n=5, reverse=False, **kwargs) -> pd.DataFrame:
"""Get the list of top/bottom entries of "what" field"""
if "columns" in kwargs:
columns = kwargs["columns"]
del kwargs["columns"]
else:
columns = ["default"]
# if self._valid_get_args:
# self._valid_get_args += ['what', 'n', 'reverse']
# This raises exceptions if it fails
try:
self.validate_get_input(**kwargs)
except Exception as error:
df = pd.DataFrame({'error': [f'{error}']})
return df
table_schema = SchemaForTable(self._table, self.all_schemas)
columns = table_schema.get_display_fields(columns)
if what not in columns:
self._addnl_fields.append(what)
return self.engine.top(what=what, n=n, reverse=reverse, **kwargs)
def humanize_fields(self, df: pd.DataFrame, subset=None) -> pd.DataFrame:
'''Humanize the fields for human consumption.
Individual classes will implement the right transofmations. This
routine is just a placeholder for all those with nothing to modify.
'''
return df
def get_valid_df(self, table, **kwargs) -> pd.DataFrame:
if not self.ctxt.engine:
print("Specify an analysis engine using set engine command")
return pd.DataFrame(columns=["namespace", "hostname"])
sch = SchemaForTable(table, schema=self.schemas)
phy_table = sch.get_phy_table_for_table()
columns = kwargs.pop('columns', ['default'])
addnl_fields = kwargs.pop('addnl_fields', [])
view = kwargs.pop('view', self.iobj.view)
active_only = kwargs.pop('active_only', True)
fields = sch.get_display_fields(columns)
key_fields = sch.key_fields()
drop_cols = []
if columns == ['*']:
drop_cols.append('sqvers')
aug_fields = sch.get_augmented_fields()
if 'timestamp' not in fields:
fields.append('timestamp')
if 'active' not in fields + addnl_fields:
addnl_fields.append('active')
drop_cols.append('active')
# Order matters. Don't put this before the missing key fields insert
for f in aug_fields:
dep_fields = sch.get_parent_fields(f)
addnl_fields += dep_fields
for fld in key_fields:
if fld not in fields + addnl_fields:
addnl_fields.insert(0, fld)
drop_cols.append(fld)
for f in addnl_fields:
if f not in fields:
# timestamp is always the last field
fields.insert(-1, f)
if self.iobj.start_time:
try:
start_time = dateparser.parse(
self.iobj.start_time.replace('last night', 'yesterday')) \
.timestamp()*1000
except Exception as e:
print(f"ERROR: invalid time {self.iobj.start_time}: {e}")
return pd.DataFrame()
else:
start_time = ''
if self.iobj.start_time and not start_time:
# Something went wrong with our parsing
print(f"ERROR: unable to parse {self.iobj.start_time}")
return pd.DataFrame()
if self.iobj.end_time:
try:
end_time = dateparser.parse(
self.iobj.end_time.replace('last night', 'yesterday')) \
.timestamp()*1000
except Exception as e:
print(f"ERROR: invalid time {self.iobj.end_time}: {e}")
return pd.DataFrame()
else:
end_time = ''
if self.iobj.end_time and not end_time:
# Something went wrong with our parsing
print(f"ERROR: Unable to parse {self.iobj.end_time}")
return pd.DataFrame()
table_df = self._dbeng.read(phy_table,
'pandas',
start_time=start_time,
end_time=end_time,
columns=fields,
view=view,
key_fields=key_fields,
**kwargs)
if not table_df.empty:
if view == "all" or not active_only:
table_df.drop(columns=drop_cols, inplace=True)
else:
table_df = table_df.query('active') \
.drop(columns=drop_cols)
if 'timestamp' in table_df.columns and not table_df.empty:
table_df['timestamp'] = humanize_timestamp(
table_df.timestamp,
self.cfg.get('analyzer', {}).get('timezone', None))
return table_df
def _get_combined_df(self, **kwargs):
"""OSPF has info divided across multiple tables. Get a single one"""
columns = kwargs.pop('columns', ['default'])
state = kwargs.pop('state', '')
addnl_fields = kwargs.pop('addnl_fields', self.iobj._addnl_fields)
addnl_nbr_fields = self.iobj._addnl_nbr_fields
cols = SchemaForTable('ospf', schema=self.schemas) \
.get_display_fields(columns)
if columns == ['default']:
cols.append('timestamp')
ifschema = SchemaForTable('ospfIf', schema=self.schemas)
nbrschema = SchemaForTable('ospfNbr', schema=self.schemas)
if (columns != ['default']) and (columns != ['*']):
ifkeys = ifschema.key_fields()
nbrkeys = nbrschema.key_fields()
if_flds = ifschema.fields
nbr_flds = nbrschema.fields
ifcols = ifkeys
nbrcols = nbrkeys
for fld in columns:
if fld in if_flds and fld not in ifcols:
ifcols.append(fld)
elif fld in nbr_flds and fld not in nbrcols:
nbrcols.append(fld)
else:
ifcols = ifschema.get_display_fields(columns)
nbrcols = nbrschema.get_display_fields(columns)
if state == "full":
query_str = 'adjState == "full" or adjState == "passive"'
elif state == "other":
query_str = 'adjState != "full" and adjState != "passive"'
elif state == "passive":
query_str = 'adjState == "passive"'
else:
query_str = ''
df = self.get_valid_df('ospfIf',
addnl_fields=addnl_fields,
columns=ifcols,
**kwargs)
nbr_df = self.get_valid_df('ospfNbr',
addnl_fields=addnl_nbr_fields,
columns=nbrcols,
**kwargs)
if nbr_df.empty:
return nbr_df
merge_cols = [
x for x in ['namespace', 'hostname', 'ifname']
if x in nbr_df.columns
]
# Merge the two tables
df = df.merge(nbr_df, on=merge_cols, how='left')
if columns == ['*']:
df = df.drop(columns=['area_y', 'instance_y', 'vrf_y',
'areaStub_y', 'timestamp_y']) \
.rename(columns={
'instance_x': 'instance', 'areaStub_x': 'areaStub',
'area_x': 'area', 'vrf_x': 'vrf',
'state_x': 'ifState', 'state_y': 'adjState',
'sqvers_x': 'sqvers', 'active_x': 'active',
'timestamp_x': 'timestamp'})
else:
df = df.rename(
columns={
'vrf_x': 'vrf',
'area_x': 'area',
'state_x': 'ifState',
'state_y': 'adjState',
'timestamp_x': 'timestamp'
})
df = df.drop(list(df.filter(regex='_y$')), axis=1) \
.fillna({'peerIP': '-', 'numChanges': 0,
'lastChangeTime': 0})
# Fill the adjState column with passive if passive
if 'passive' in df.columns:
df.loc[df['adjState'].isnull(), 'adjState'] = df['passive']
df.loc[df['adjState'].eq(True), 'adjState'] = 'passive'
df.loc[df['adjState'].eq(False), 'adjState'] = 'fail'
df.drop(columns=['passive'], inplace=True)
df.bfill(axis=0, inplace=True)
# Move the timestamp column to the end
if query_str:
return df.query(query_str)[cols]
return df[cols]
def get_valid_df(self, table, **kwargs) -> pd.DataFrame:
if not self.ctxt.engine:
print("Specify an analysis engine using set engine command")
return pd.DataFrame(columns=["namespace", "hostname"])
sch = SchemaForTable(table, schema=self.schemas)
phy_table = sch.get_phy_table_for_table()
columns = kwargs.pop('columns', ['default'])
addnl_fields = kwargs.pop('addnl_fields', [])
view = kwargs.pop('view', self.iobj.view)
active_only = kwargs.pop('active_only', True)
fields = sch.get_display_fields(columns)
key_fields = sch.key_fields()
drop_cols = []
if 'timestamp' not in fields:
fields.append('timestamp')
if 'active' not in fields + addnl_fields:
addnl_fields.append('active')
drop_cols.append('active')
for fld in key_fields:
if fld not in fields + addnl_fields:
addnl_fields.insert(0, fld)
drop_cols.append(fld)
for f in addnl_fields:
if f not in fields:
# timestamp is always the last field
fields.insert(-1, f)
for dt in [self.iobj.start_time, self.iobj.end_time]:
if dt:
try:
parse(dt)
except (ValueError, ParserError) as e:
print(f"invalid time {dt}: {e}")
return pd.DataFrame()
table_df = self.ctxt.engine.get_table_df(
self.cfg,
table=phy_table,
start_time=self.iobj.start_time,
end_time=self.iobj.end_time,
columns=fields,
view=view,
key_fields=key_fields,
**kwargs)
if not table_df.empty:
if view == 'latest' and active_only:
table_df = table_df.query('active') \
.drop(columns=drop_cols)
else:
table_df.drop(columns=drop_cols, inplace=True)
if 'timestamp' in table_df.columns:
table_df['timestamp'] = pd.to_datetime(
table_df.timestamp.astype(str), unit="ms")
return table_df
def _get_combined_df(self, **kwargs):
"""OSPF has info divided across multiple tables. Get a single one"""
columns = kwargs.pop('columns', ['default'])
state = kwargs.pop('state', '')
addnl_fields = kwargs.pop('addnl_fields', self.iobj._addnl_fields)
addnl_nbr_fields = self.iobj._addnl_nbr_fields
user_query = kwargs.pop('query_str', '')
cols = SchemaForTable('ospf', schema=self.schemas) \
.get_display_fields(columns)
if columns == ['default']:
cols.append('timestamp')
ifschema = SchemaForTable('ospfIf', schema=self.schemas)
nbrschema = SchemaForTable('ospfNbr', schema=self.schemas)
if (columns != ['default']) and (columns != ['*']):
ifkeys = ifschema.key_fields()
nbrkeys = nbrschema.key_fields()
if_flds = ifschema.fields
nbr_flds = nbrschema.fields
ifcols = ifkeys
nbrcols = nbrkeys
for fld in columns:
if fld in if_flds and fld not in ifcols:
ifcols.append(fld)
elif fld in nbr_flds and fld not in nbrcols:
nbrcols.append(fld)
else:
ifcols = ifschema.get_display_fields(columns)
nbrcols = nbrschema.get_display_fields(columns)
if state == "full":
query_str = 'adjState == "full" or adjState == "passive"'
elif state == "other":
query_str = 'adjState != "full" and adjState != "passive"'
elif state == "passive":
query_str = 'adjState == "passive"'
else:
query_str = ''
df = self.get_valid_df('ospfIf',
addnl_fields=addnl_fields,
columns=ifcols,
**kwargs)
nbr_df = self.get_valid_df('ospfNbr',
addnl_fields=addnl_nbr_fields,
columns=nbrcols,
**kwargs)
if nbr_df.empty:
return df
merge_cols = [
x for x in ['namespace', 'hostname', 'ifname']
if x in nbr_df.columns
]
# Merge the two tables
df = df.merge(nbr_df, on=merge_cols, how='left')
# This is because some NOS have the ipAddress in nbr table and some in
# interface table. Nbr table wins over interface table if present
if 'ipAddress_y' in df:
df['ipAddress'] = np.where(df['ipAddress_y'] == "",
df['ipAddress_x'], df['ipAddress_y'])
df['ipAddress'] = np.where(df['ipAddress'], df['ipAddress'],
df['ipAddress_x'])
if columns == ['*']:
df = df.drop(columns=['area_y', 'instance_y', 'vrf_y',
'ipAddress_x', 'ipAddress_y', 'areaStub_y',
'timestamp_y'], errors='ignore') \
.rename(columns={
'instance_x': 'instance', 'areaStub_x': 'areaStub',
'area_x': 'area', 'vrf_x': 'vrf',
'state_x': 'ifState', 'state_y': 'adjState',
'sqvers_x': 'sqvers', 'active_x': 'active',
'timestamp_x': 'timestamp'})
else:
df = df.rename(
columns={
'vrf_x': 'vrf',
'area_x': 'area',
'state_x': 'ifState',
'state_y': 'adjState',
'timestamp_x': 'timestamp'
})
df = df.drop(list(df.filter(regex='_y$')), axis=1) \
.drop('ipAddress_x', axis=1, errors='ignore') \
.fillna({'peerIP': '-', 'numChanges': 0,
'lastChangeTime': 0})
# Fill the adjState column with passive if passive
if 'passive' in df.columns:
df.loc[df['adjState'].isnull(), 'adjState'] = df['passive']
df.loc[df['adjState'].eq(True), 'adjState'] = 'passive'
df.loc[df['adjState'].eq(False), 'adjState'] = 'fail'
df.drop(columns=['passive'], inplace=True)
df.bfill(axis=0, inplace=True)
if 'peerHostname' in columns or (columns in [['*'], ['default']]):
nfdf = df.query('adjState != "full"').reset_index()
nfdf['peerHostname'] = ''
newdf = df.query('adjState == "full"').reset_index() \
.drop('peerHostname', axis=1, errors='ignore')
if not newdf.empty:
newdf['matchIP'] = newdf.ipAddress.str.split('/').str[0]
newdf = newdf.merge(newdf[['namespace', 'hostname', 'vrf',
'matchIP']],
left_on=['namespace', 'vrf', 'peerIP'],
right_on=['namespace', 'vrf', 'matchIP'],
suffixes=["", "_y"]) \
.rename(columns={'hostname_y': 'peerHostname'}) \
.drop_duplicates(subset=['namespace', 'hostname',
'vrf', 'ifname']) \
.drop(columns=['matchIP', 'matchIP_y'], errors='ignore')
if newdf.empty:
newdf = df.query('adjState == "full"').reset_index()
newdf['peerHostname'] = ''
final_df = pd.concat([nfdf, newdf])
else:
final_df = df
else:
final_df = df
if query_str:
final_df = final_df.query(query_str).reset_index(drop=True)
if user_query and not final_df.empty:
final_df = self._handle_user_query_str(final_df, user_query)
# Move the timestamp column to the end
return final_df[cols]
class SqObject(object):
def __init__(self,
engine_name: str = '',
hostname: typing.List[str] = [],
start_time: str = '',
end_time: str = '',
view: str = 'latest',
namespace: typing.List[str] = [],
columns: typing.List[str] = ['default'],
context=None,
table: str = '',
config_file=None) -> None:
if context is None:
self.ctxt = SqContext(engine_name, config_file)
else:
self.ctxt = context
if not self.ctxt:
self.ctxt = SqContext(engine_name)
self._cfg = self.ctxt.cfg
self._schema = SchemaForTable(table, self.ctxt.schemas)
self._table = table
self._sort_fields = self._schema.key_fields()
if not namespace and self.ctxt.namespace:
self.namespace = self.ctxt.namespace
else:
self.namespace = namespace
if not hostname and self.ctxt.hostname:
self.hostname = self.ctxt.hostname
else:
self.hostname = hostname
if not start_time and self.ctxt.start_time:
self.start_time = self.ctxt.start_time
else:
self.start_time = start_time
if not end_time and self.ctxt.end_time:
self.end_time = self.ctxt.end_time
else:
self.end_time = end_time
if not view and self.ctxt.view:
self.view = self.ctxt.view
else:
self.view = view
self.columns = columns
if engine_name and engine_name != '':
self.engine = get_sqengine(engine_name)
else:
self.engine = self.ctxt.engine
if self._table:
self.engine_obj = self.engine.get_object(self._table, self)
else:
self.engine_obj = None
self._addnl_filter = None
self._addnl_fields = []
@property
def all_schemas(self):
return self.ctxt.schemas
@property
def schema(self):
return self._schema
@property
def cfg(self):
return self._cfg
@property
def table(self):
return self._table
def validate_input(self, **kwargs):
"""Dummy validate input"""
return
def get(self, **kwargs) -> pd.DataFrame:
if not self._table:
raise NotImplementedError
if not self.ctxt.engine:
raise AttributeError('No analysis engine specified')
if self._addnl_filter:
kwargs['add_filter'] = self._addnl_filter
# This raises exceptions if it fails
try:
self.validate_input(**kwargs)
except Exception as error:
df = pd.DataFrame({'error': [f'{error}']})
return df
return self.engine_obj.get(**kwargs)
def summarize(self, namespace='') -> pd.DataFrame:
if not self._table:
raise NotImplementedError
if not self.ctxt.engine:
raise AttributeError('No analysis engine specified')
return self.engine_obj.summarize(namespace=namespace)
def unique(self, **kwargs) -> pd.DataFrame:
if not self._table:
raise NotImplementedError
if not self.ctxt.engine:
raise AttributeError('No analysis engine specified')
return self.engine_obj.unique(**kwargs)
def analyze(self, **kwargs):
raise NotImplementedError
def aver(self, **kwargs):
raise NotImplementedError
def top(self, what='', n=5, reverse=False, **kwargs) -> pd.DataFrame:
"""Get the list of top/bottom entries of "what" field"""
if "columns" in kwargs:
columns = kwargs["columns"]
del kwargs["columns"]
else:
columns = ["default"]
table_schema = SchemaForTable(self._table, self.all_schemas)
columns = table_schema.get_display_fields(columns)
if what not in columns:
self._addnl_fields.append(what)
return self.engine_obj.top(what=what, n=n, reverse=reverse, **kwargs)
def get_valid_df(self, table, **kwargs) -> pd.DataFrame:
if not self.ctxt.engine:
print("Specify an analysis engine using set engine command")
return pd.DataFrame(columns=["namespace", "hostname"])
sch = SchemaForTable(table, schema=self.schemas)
phy_table = sch.get_phy_table_for_table()
columns = kwargs.pop('columns', ['default'])
addnl_fields = kwargs.pop('addnl_fields', [])
view = kwargs.pop('view', self.iobj.view)
active_only = kwargs.pop('active_only', True)
query_str = kwargs.pop('query_str', '')
# The REST API provides the query_str enclosed in ". Strip that
if query_str:
if query_str.startswith('"') and query_str.endswith('"'):
query_str = query_str[1:-1]
fields = sch.get_display_fields(columns)
key_fields = sch.key_fields()
drop_cols = []
if columns == ['*']:
drop_cols.append('sqvers')
if 'timestamp' not in fields:
fields.append('timestamp')
if 'active' not in fields+addnl_fields:
addnl_fields.append('active')
drop_cols.append('active')
for fld in key_fields:
if fld not in fields+addnl_fields:
addnl_fields.insert(0, fld)
drop_cols.append(fld)
for f in addnl_fields:
if f not in fields:
# timestamp is always the last field
fields.insert(-1, f)
if self.iobj.start_time:
try:
start_time = dateparser.parse(
self.iobj.start_time.replace('last night', 'yesterday')) \
.timestamp()*1000
except Exception as e:
print(f"ERROR: invalid time {self.iobj.start_time}: {e}")
return pd.DataFrame()
else:
start_time = ''
if self.iobj.start_time and not start_time:
# Something went wrong with our parsing
print(f"ERROR: unable to parse {self.iobj.start_time}")
return pd.DataFrame()
if self.iobj.end_time:
try:
end_time = dateparser.parse(
self.iobj.end_time.replace('last night', 'yesterday')) \
.timestamp()*1000
except Exception as e:
print(f"ERROR: invalid time {self.iobj.end_time}: {e}")
return pd.DataFrame()
else:
end_time = ''
if self.iobj.end_time and not end_time:
# Something went wrong with our parsing
print(f"ERROR: Unable to parse {self.iobj.end_time}")
return pd.DataFrame()
table_df = self._dbeng.read(
phy_table,
'pandas',
start_time=start_time,
end_time=end_time,
columns=fields,
view=view,
key_fields=key_fields,
**kwargs
)
if not table_df.empty:
if view == 'latest' and active_only:
table_df = table_df.query('active') \
.drop(columns=drop_cols)
else:
table_df.drop(columns=drop_cols, inplace=True)
if 'timestamp' in table_df.columns:
table_df['timestamp'] = humanize_timestamp(
table_df.timestamp, self.cfg.get('analyzer', {})
.get('timezone', None))
if query_str:
return table_df.query(query_str)
else:
return table_df
def get_table_df(self, cfg, schemas, **kwargs) -> pd.DataFrame:
"""Use Pandas instead of Spark to retrieve the data"""
MAX_FILECNT_TO_READ_FOLDER = 10000
self.cfg = cfg
table = kwargs.pop("table")
start = kwargs.pop("start_time")
end = kwargs.pop("end_time")
view = kwargs.pop("view")
sort_fields = kwargs.pop("sort_fields")
ign_key_fields = kwargs.pop("ign_key", [])
addnl_fields = kwargs.pop("addnl_fields", [])
for f in ['active', 'timestamp']:
if f not in addnl_fields:
addnl_fields.append(f)
sch = SchemaForTable(table, schema=schemas)
phy_table = sch.get_phy_table_for_table()
folder = self._get_table_directory(phy_table)
# Restrict to a single DC if thats whats asked
if "namespace" in kwargs:
v = kwargs["namespace"]
if v:
if not isinstance(v, list):
folder += "/namespace={}/".format(v)
fcnt = self.get_filecnt(folder)
if fcnt == 0:
return pd.DataFrame()
# We are going to hard code use_get_files until we have some autoamted testing
use_get_files = False
# use_get_files = (
# (fcnt > MAX_FILECNT_TO_READ_FOLDER and view == "latest") or
# start or end
# )
if use_get_files:
# Switch to more efficient method when there are lotsa files
# Reduce I/O since that is the worst drag
key_fields = []
if len(kwargs.get("namespace", [])) > 1:
del kwargs["namespace"]
files = get_latest_files(folder, start, end, view)
else:
# ign_key_fields contains key fields that are not partition cols
key_fields = [i for i in sch.key_fields()
if i not in ign_key_fields]
filters = self.build_pa_filters(start, end, key_fields, **kwargs)
if "columns" in kwargs:
columns = kwargs["columns"]
del kwargs["columns"]
else:
columns = ["default"]
fields = sch.get_display_fields(columns)
for f in addnl_fields:
if f not in fields:
fields.append(f)
# Create the filter to select only specified columns
query_str = ""
prefix = ""
addnl_filter = kwargs.pop('add_filter', None)
for f, v in kwargs.items():
if not v or f in key_fields or f in ["groupby"]:
continue
if isinstance(v, str):
if v.startswith('!'):
v = v[1:]
op = '!='
else:
op = '=='
query_str += "{} {}{}'{}' ".format(prefix, f, op, v)
prefix = "and"
else:
query_str += "{} {}=={} ".format(prefix, f, v)
prefix = "and"
# Add the ignored fields back to key fields to ensure we
# do the drop_duplicates correctly below incl reading reqd cols
key_fields.extend(ign_key_fields)
# Handle the case where key fields are missing from display fields
fldset = set(fields)
kfldset = set(key_fields)
add_flds = kfldset.difference(fldset)
if add_flds:
fields.extend(list(add_flds))
if addnl_filter:
# This is for special cases that are specific to an object
if not query_str:
query_str = addnl_filter
else:
query_str += ' and {}'.format(addnl_filter)
# Restore the folder to what it needs to be
folder = self._get_table_directory(phy_table)
if use_get_files:
if not query_str:
query_str = "active == True"
pdf_list = []
with Executor(max_workers=8) as exe:
jobs = [
exe.submit(self.read_pq_file, f, fields, query_str)
for f in files
]
pdf_list = [job.result() for job in jobs]
if pdf_list:
final_df = pd.concat(pdf_list)
else:
final_df = pd.DataFrame(columns=fields)
elif view == "latest":
if not query_str:
# Make up a dummy query string to avoid if/then/else
query_str = "timestamp != 0"
try:
final_df = (
pa.ParquetDataset(
folder, filters=filters or None, validate_schema=False
)
.read(columns=fields)
.to_pandas(split_blocks=True, self_destruct=True)
.query(query_str)
.drop_duplicates(subset=key_fields, keep="last")
.query("active == True")
)
except pa.lib.ArrowInvalid:
return pd.DataFrame(columns=fields)
else:
if not query_str:
# Make up a dummy query string to avoid if/then/else
query_str = 'timestamp != "0"'
try:
final_df = (
pa.ParquetDataset(
folder, filters=filters or None, validate_schema=False
)
.read(columns=fields)
.to_pandas()
.query(query_str)
)
except pa.lib.ArrowInvalid:
return pd.DataFrame(columns=fields)
if 'active' not in columns:
final_df.drop(columns=['active'], axis=1, inplace=True)
fields.remove('active')
final_df = df_timestamp_to_datetime(final_df)
if sort_fields and all(x in sort_fields for x in fields):
return final_df[fields].sort_values(by=sort_fields)
else:
return final_df[fields]