def parse(*args):
try:
if len(args) == 1:
a0 = args[0]
if isinstance(a0, (datetime, date)):
output = _unix2Date(datetime2unix(a0))
elif isinstance(a0, Date):
output = _unix2Date(a0.unix)
elif isinstance(a0, (int, long, float, Decimal)):
a0 = float(a0)
if a0 > 9999999999: # WAY TOO BIG IF IT WAS A UNIX TIMESTAMP
output = _unix2Date(a0 / 1000)
else:
output = _unix2Date(a0)
elif isinstance(a0, text_type) and len(a0) in [9, 10, 12, 13] and Math.is_integer(a0):
a0 = float(a0)
if a0 > 9999999999: # WAY TOO BIG IF IT WAS A UNIX TIMESTAMP
output = _unix2Date(a0 / 1000)
else:
output = _unix2Date(a0)
elif isinstance(a0, text_type):
output = unicode2Date(a0)
else:
output = _unix2Date(datetime2unix(datetime(*args)))
else:
if isinstance(args[0], text_type):
output = unicode2Date(*args)
else:
output = _unix2Date(datetime2unix(datetime(*args)))
return output
except Exception as e:
from mo_logs import Log
Log.error("Can not convert {{args}} to Date", args=args, cause=e)
def _normalize_sort(sort=None):
"""
CONVERT SORT PARAMETERS TO A NORMAL FORM SO EASIER TO USE
"""
if sort == None:
return FlatList.EMPTY
output = FlatList()
for s in listwrap(sort):
if isinstance(s, basestring):
output.append({"value": jx_expression(s), "sort": 1})
elif isinstance(s, Expression):
output.append({"value": s, "sort": 1})
elif Math.is_integer(s):
output.append({"value": OffsetOp("offset", s), "sort": 1})
elif all(d in sort_direction
for d in s.values()) and not s.sort and not s.value:
for v, d in s.items():
output.append({
"value": jx_expression(v),
"sort": sort_direction[d]
})
else:
output.append({
"value": jx_expression(coalesce(s.value, s.field)),
"sort": coalesce(sort_direction[s.sort], 1)
})
return output
def wrap(query, schema=None):
"""
NORMALIZE QUERY SO IT CAN STILL BE JSON
"""
if isinstance(query, QueryOp) or query == None:
return query
query = wrap(query)
output = QueryOp("from", None)
output.format = query.format
from jx_python import wrap_from
output.frum = wrap_from(query["from"], schema=schema)
if not schema and isinstance(output.frum, Schema):
schema = output.frum
if not schema and hasattr(output.frum, "schema"):
schema = output.frum.schema
if query.select or isinstance(query.select, (Mapping, list)):
output.select = _normalize_selects(query.select,
query.frum,
schema=schema)
else:
if query.edges or query.groupby:
output.select = Data(name="count",
value=jx_expression("."),
aggregate="count",
default=0)
else:
output.select = _normalize_selects(".", query.frum)
if query.groupby and query.edges:
Log.error(
"You can not use both the `groupby` and `edges` clauses in the same query!"
)
elif query.edges:
output.edges = _normalize_edges(query.edges, schema=schema)
output.groupby = Null
elif query.groupby:
output.edges = Null
output.groupby = _normalize_groupby(query.groupby, schema=schema)
else:
output.edges = Null
output.groupby = Null
output.where = _normalize_where(query.where, schema=schema)
output.window = [_normalize_window(w) for w in listwrap(query.window)]
output.having = None
output.sort = _normalize_sort(query.sort)
output.limit = Math.min(MAX_LIMIT, coalesce(query.limit,
DEFAULT_LIMIT))
if not Math.is_integer(output.limit) or output.limit < 0:
Log.error("Expecting limit >= 0")
output.isLean = query.isLean
return output
def wrap(query, container, namespace):
"""
NORMALIZE QUERY SO IT CAN STILL BE JSON
"""
if isinstance(query, QueryOp) or query == None:
return query
query = wrap(query)
table = container.get_table(query['from'])
schema = table.schema
output = QueryOp(op="from",
frum=table,
format=query.format,
limit=Math.min(MAX_LIMIT,
coalesce(query.limit, DEFAULT_LIMIT)))
if query.select or isinstance(query.select, (Mapping, list)):
output.select = _normalize_selects(query.select,
query.frum,
schema=schema)
else:
if query.edges or query.groupby:
output.select = DEFAULT_SELECT
else:
output.select = _normalize_selects(".", query.frum)
if query.groupby and query.edges:
Log.error(
"You can not use both the `groupby` and `edges` clauses in the same query!"
)
elif query.edges:
output.edges = _normalize_edges(query.edges,
limit=output.limit,
schema=schema)
output.groupby = Null
elif query.groupby:
output.edges = Null
output.groupby = _normalize_groupby(query.groupby,
limit=output.limit,
schema=schema)
else:
output.edges = Null
output.groupby = Null
output.where = _normalize_where(query.where, schema=schema)
output.window = [_normalize_window(w) for w in listwrap(query.window)]
output.having = None
output.sort = _normalize_sort(query.sort)
if not Math.is_integer(output.limit) or output.limit < 0:
Log.error("Expecting limit >= 0")
output.isLean = query.isLean
return output
def _convert_query(self, query):
# if not isinstance(query["from"], Container):
# Log.error('Expecting from clause to be a Container')
query = wrap(query)
output = QueryOp("from", None)
output["from"] = self._convert_from(query["from"])
output.format = query.format
if query.select:
output.select = convert_list(self._convert_select, query.select)
else:
if query.edges or query.groupby:
output.select = {"name": "count", "value": ".", "aggregate": "count", "default": 0}
else:
output.select = {"name": "__all__", "value": "*", "aggregate": "none"}
if query.groupby and query.edges:
Log.error("You can not use both the `groupby` and `edges` clauses in the same query!")
elif query.edges:
output.edges = convert_list(self._convert_edge, query.edges)
output.groupby = None
elif query.groupby:
output.edges = None
output.groupby = convert_list(self._convert_group, query.groupby)
else:
output.edges = []
output.groupby = None
output.where = self.convert(query.where)
output.window = convert_list(self._convert_window, query.window)
output.sort = self._convert_sort(query.sort)
output.limit = coalesce(query.limit, DEFAULT_LIMIT)
if not Math.is_integer(output.limit) or output.limit < 0:
Log.error("Expecting limit >= 0")
output.isLean = query.isLean
# DEPTH ANALYSIS - LOOK FOR COLUMN REFERENCES THAT MAY BE DEEPER THAN
# THE from SOURCE IS.
vars = get_all_vars(output, exclude_where=True) # WE WILL EXCLUDE where VARIABLES
for c in query.columns:
if c.name in vars and len(c.nested_path) != 1:
Log.error("This query, with variable {{var_name}} is too deep", var_name=c.name)
output.having = convert_list(self._convert_having, query.having)
return output
def wrap(query, schema=None):
"""
NORMALIZE QUERY SO IT CAN STILL BE JSON
"""
if isinstance(query, QueryOp) or query == None:
return query
query = wrap(query)
output = QueryOp("from", None)
output.format = query.format
output.frum = wrap_from(query["from"], schema=schema)
if not schema and isinstance(output.frum, Schema):
schema = output.frum
if not schema and hasattr(output.frum, "schema"):
schema = output.frum.schema
if query.select or isinstance(query.select, (Mapping, list)):
output.select = _normalize_selects(query.select, query.frum, schema=schema)
else:
if query.edges or query.groupby:
output.select = Data(name="count", value=jx_expression("."), aggregate="count", default=0)
else:
output.select = _normalize_selects(".", query.frum)
if query.groupby and query.edges:
Log.error("You can not use both the `groupby` and `edges` clauses in the same query!")
elif query.edges:
output.edges = _normalize_edges(query.edges, schema=schema)
output.groupby = Null
elif query.groupby:
output.edges = Null
output.groupby = _normalize_groupby(query.groupby, schema=schema)
else:
output.edges = Null
output.groupby = Null
output.where = _normalize_where(query.where, schema=schema)
output.window = [_normalize_window(w) for w in listwrap(query.window)]
output.having = None
output.sort = _normalize_sort(query.sort)
output.limit = Math.min(MAX_LIMIT, coalesce(query.limit, DEFAULT_LIMIT))
if not Math.is_integer(output.limit) or output.limit < 0:
Log.error("Expecting limit >= 0")
output.isLean = query.isLean
return output
def wrap(query, container, namespace):
"""
NORMALIZE QUERY SO IT CAN STILL BE JSON
"""
if isinstance(query, QueryOp) or query == None:
return query
query = wrap(query)
table = container.get_table(query['from'])
schema = table.schema
output = QueryOp(
op="from",
frum=table,
format=query.format,
limit=Math.min(MAX_LIMIT, coalesce(query.limit, DEFAULT_LIMIT))
)
if query.select or isinstance(query.select, (Mapping, list)):
output.select = _normalize_selects(query.select, query.frum, schema=schema)
else:
if query.edges or query.groupby:
output.select = DEFAULT_SELECT
else:
output.select = _normalize_selects(".", query.frum)
if query.groupby and query.edges:
Log.error("You can not use both the `groupby` and `edges` clauses in the same query!")
elif query.edges:
output.edges = _normalize_edges(query.edges, limit=output.limit, schema=schema)
output.groupby = Null
elif query.groupby:
output.edges = Null
output.groupby = _normalize_groupby(query.groupby, limit=output.limit, schema=schema)
else:
output.edges = Null
output.groupby = Null
output.where = _normalize_where(query.where, schema=schema)
output.window = [_normalize_window(w) for w in listwrap(query.window)]
output.having = None
output.sort = _normalize_sort(query.sort)
if not Math.is_integer(output.limit) or output.limit < 0:
Log.error("Expecting limit >= 0")
output.isLean = query.isLean
return output
def normalize_sort(sort=None):
"""
CONVERT SORT PARAMETERS TO A NORMAL FORM SO EASIER TO USE
"""
if not sort:
return Null
output = FlatList()
for s in listwrap(sort):
if isinstance(s, basestring) or Math.is_integer(s):
output.append({"value": s, "sort": 1})
elif not s.field and not s.value and s.sort==None:
#ASSUME {name: sort} FORM
for n, v in s.items():
output.append({"value": n, "sort": sort_direction[v]})
else:
output.append({"value": coalesce(s.field, s.value), "sort": coalesce(sort_direction[s.sort], 1)})
return wrap(output)
def normalize_sort(sort=None):
"""
CONVERT SORT PARAMETERS TO A NORMAL FORM SO EASIER TO USE
"""
if not sort:
return Null
output = FlatList()
for s in listwrap(sort):
if isinstance(s, basestring) or Math.is_integer(s):
output.append({"value": s, "sort": 1})
elif not s.field and not s.value and s.sort == None:
#ASSUME {name: sort} FORM
for n, v in s.items():
output.append({"value": n, "sort": sort_direction[v]})
else:
output.append({
"value": coalesce(s.field, s.value),
"sort": coalesce(sort_direction[s.sort], 1)
})
return wrap(output)
def _normalize_sort(sort=None):
"""
CONVERT SORT PARAMETERS TO A NORMAL FORM SO EASIER TO USE
"""
if sort==None:
return FlatList.EMPTY
output = FlatList()
for s in listwrap(sort):
if isinstance(s, basestring):
output.append({"value": jx_expression(s), "sort": 1})
elif isinstance(s, Expression):
output.append({"value": s, "sort": 1})
elif Math.is_integer(s):
output.append({"value": OffsetOp("offset", s), "sort": 1})
elif all(d in sort_direction for d in s.values()) and not s.sort and not s.value:
for v, d in s.items():
output.append({"value": jx_expression(v), "sort": sort_direction[d]})
else:
output.append({"value": jx_expression(coalesce(s.value, s.field)), "sort": coalesce(sort_direction[s.sort], 1)})
return output
def parse(*args):
try:
if len(args) == 1:
a0 = args[0]
if isinstance(a0, (datetime, date)):
output = _unix2Date(datetime2unix(a0))
elif isinstance(a0, Date):
output = _unix2Date(a0.unix)
elif isinstance(a0, (int, long, float, Decimal)):
a0 = float(a0)
if a0 > 9999999999: # WAY TOO BIG IF IT WAS A UNIX TIMESTAMP
output = _unix2Date(a0 / 1000)
else:
output = _unix2Date(a0)
elif isinstance(a0, text_type) and len(a0) in [
9, 10, 12, 13
] and Math.is_integer(a0):
a0 = float(a0)
if a0 > 9999999999: # WAY TOO BIG IF IT WAS A UNIX TIMESTAMP
output = _unix2Date(a0 / 1000)
else:
output = _unix2Date(a0)
elif isinstance(a0, text_type):
output = unicode2Date(a0)
else:
output = _unix2Date(datetime2unix(datetime(*args)))
else:
if isinstance(args[0], text_type):
output = unicode2Date(*args)
else:
output = _unix2Date(datetime2unix(datetime(*args)))
return output
except Exception as e:
from mo_logs import Log
Log.error("Can not convert {{args}} to Date", args=args, cause=e)
def _convert_query(self, query):
# if not isinstance(query["from"], Container):
# Log.error('Expecting from clause to be a Container')
query = wrap(query)
output = QueryOp("from", None)
output["from"] = self._convert_from(query["from"])
output.format = query.format
if query.select:
output.select = convert_list(self._convert_select, query.select)
else:
if query.edges or query.groupby:
output.select = {
"name": "count",
"value": ".",
"aggregate": "count",
"default": 0
}
else:
output.select = {
"name": "__all__",
"value": "*",
"aggregate": "none"
}
if query.groupby and query.edges:
Log.error(
"You can not use both the `groupby` and `edges` clauses in the same query!"
)
elif query.edges:
output.edges = convert_list(self._convert_edge, query.edges)
output.groupby = None
elif query.groupby:
output.edges = None
output.groupby = convert_list(self._convert_group, query.groupby)
else:
output.edges = []
output.groupby = None
output.where = self.convert(query.where)
output.window = convert_list(self._convert_window, query.window)
output.sort = self._convert_sort(query.sort)
output.limit = coalesce(query.limit, DEFAULT_LIMIT)
if not Math.is_integer(output.limit) or output.limit < 0:
Log.error("Expecting limit >= 0")
output.isLean = query.isLean
# DEPTH ANALYSIS - LOOK FOR COLUMN REFERENCES THAT MAY BE DEEPER THAN
# THE from SOURCE IS.
vars = get_all_vars(
output, exclude_where=True) # WE WILL EXCLUDE where VARIABLES
for c in query.columns:
if c.name in vars and len(c.nested_path) != 1:
Log.error("This query, with variable {{var_name}} is too deep",
var_name=c.name)
output.having = convert_list(self._convert_having, query.having)
return output
def __init__(self, op, var):
Expression.__init__(self, "offset", None)
if not Math.is_integer(var):
Log.error("Expecting an integer")
self.var = var
