def _update_cardinality(self, column):
"""
QUERY ES TO FIND CARDINALITY AND PARTITIONS FOR A SIMPLE COLUMN
"""
now = Date.now()
if column.es_index in self.index_does_not_exist:
return
if column.jx_type in STRUCT:
Log.error("not supported")
try:
if column.es_index == "meta.columns":
partitions = jx.sort([
g[column.es_column]
for g, _ in jx.groupby(self.meta.columns, column.es_column)
if g[column.es_column] != None
])
self.meta.columns.update({
"set": {
"partitions": partitions,
"count": len(self.meta.columns),
"cardinality": len(partitions),
"multi": 1,
"last_updated": now
},
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
return
if column.es_index == "meta.tables":
partitions = jx.sort([
g[column.es_column]
for g, _ in jx.groupby(self.meta.tables, column.es_column)
if g[column.es_column] != None
])
self.meta.columns.update({
"set": {
"partitions": partitions,
"count": len(self.meta.tables),
"cardinality": len(partitions),
"multi": 1,
"last_updated": now
},
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
return
es_index = column.es_index.split(".")[0]
is_text = [
cc for cc in self.meta.columns
if cc.es_column == column.es_column and cc.es_type == "text"
]
if is_text:
# text IS A MULTIVALUE STRING THAT CAN ONLY BE FILTERED
result = self.es_cluster.post("/" + es_index + "/_search",
data={
"aggs": {
"count": {
"filter": {
"match_all": {}
}
}
},
"size": 0
})
count = result.hits.total
cardinality = max(1001, count)
multi = 1001
elif column.es_column == "_id":
result = self.es_cluster.post("/" + es_index + "/_search",
data={
"query": {
"match_all": {}
},
"size": 0
})
count = cardinality = result.hits.total
multi = 1
elif column.es_type == BOOLEAN:
result = self.es_cluster.post("/" + es_index + "/_search",
data={
"aggs": {
"count":
_counting_query(column)
},
"size": 0
})
count = result.hits.total
cardinality = 2
DEBUG and Log.note("{{table}}.{{field}} has {{num}} parts",
table=column.es_index,
field=column.es_column,
num=cardinality)
self.meta.columns.update({
"set": {
"count": count,
"cardinality": cardinality,
"partitions": [False, True],
"multi": 1,
"last_updated": now
},
"clear": ["partitions"],
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
return
else:
es_query = {
"aggs": {
"count": _counting_query(column),
"_filter": {
"aggs": {
"multi": {
"max": {
"script":
"doc[" + quote(column.es_column) +
"].values.size()"
}
}
},
"filter": {
"bool": {
"should": [{
"range": {
"etl.timestamp.~n~": {
"gte": (Date.today() - WEEK)
}
}
}, {
"bool": {
"must_not": {
"exists": {
"field":
"etl.timestamp.~n~"
}
}
}
}]
}
}
}
},
"size": 0
}
result = self.es_cluster.post("/" + es_index + "/_search",
data=es_query)
agg_results = result.aggregations
count = result.hits.total
cardinality = coalesce(agg_results.count.value,
agg_results.count._nested.value,
agg_results.count.doc_count)
multi = int(coalesce(agg_results._filter.multi.value, 1))
if cardinality == None:
Log.error("logic error")
query = Data(size=0)
if column.es_column == "_id":
self.meta.columns.update({
"set": {
"count": cardinality,
"cardinality": cardinality,
"multi": 1,
"last_updated": now
},
"clear": ["partitions"],
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
return
elif cardinality > 1000 or (count >= 30 and cardinality == count
) or (count >= 1000
and cardinality / count > 0.99):
DEBUG and Log.note("{{table}}.{{field}} has {{num}} parts",
table=column.es_index,
field=column.es_column,
num=cardinality)
self.meta.columns.update({
"set": {
"count": count,
"cardinality": cardinality,
"multi": multi,
"last_updated": now
},
"clear": ["partitions"],
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
return
elif column.es_type in elasticsearch.ES_NUMERIC_TYPES and cardinality > 30:
DEBUG and Log.note("{{table}}.{{field}} has {{num}} parts",
table=column.es_index,
field=column.es_column,
num=cardinality)
self.meta.columns.update({
"set": {
"count": count,
"cardinality": cardinality,
"multi": multi,
"last_updated": now
},
"clear": ["partitions"],
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
return
elif len(column.nested_path) != 1:
query.aggs["_"] = {
"nested": {
"path": column.nested_path[0]
},
"aggs": {
"_nested": {
"terms": {
"field": column.es_column
}
}
}
}
elif cardinality == 0: # WHEN DOES THIS HAPPEN?
query.aggs["_"] = {"terms": {"field": column.es_column}}
else:
query.aggs["_"] = {
"terms": {
"field": column.es_column,
"size": cardinality
}
}
result = self.es_cluster.post("/" + es_index + "/_search",
data=query)
aggs = result.aggregations._
if aggs._nested:
parts = jx.sort(aggs._nested.buckets.key)
else:
parts = jx.sort(aggs.buckets.key)
DEBUG and Log.note(
"update metadata for {{column.es_index}}.{{column.es_column}} (id={{id}}) at {{time}}",
id=id(column),
column=column,
time=now)
self.meta.columns.update({
"set": {
"count": count,
"cardinality": cardinality,
"multi": multi,
"partitions": parts,
"last_updated": now
},
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
except Exception as e:
# CAN NOT IMPORT: THE TEST MODULES SETS UP LOGGING
# from tests.test_jx import TEST_TABLE
e = Except.wrap(e)
TEST_TABLE = "testdata"
is_missing_index = any(
w in e for w in
["IndexMissingException", "index_not_found_exception"])
is_test_table = column.es_index.startswith(
(TEST_TABLE_PREFIX, TEST_TABLE))
if is_missing_index:
# WE EXPECT TEST TABLES TO DISAPPEAR
Log.warning("Missing index {{col.es_index}}",
col=column,
cause=e)
self.meta.columns.update({
"clear": ".",
"where": {
"eq": {
"es_index": column.es_index
}
}
})
self.index_does_not_exist.add(column.es_index)
elif "No field found for" in e:
self.meta.columns.update({
"clear": ".",
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
Log.warning(
"Could not get column {{col.es_index}}.{{col.es_column}} info",
col=column,
cause=e)
else:
self.meta.columns.update({
"set": {
"last_updated": now
},
"clear": [
"count",
"cardinality",
"multi",
"partitions",
],
"where": {
"eq": {
"es_index": column.es_index,
"es_column": column.es_column
}
}
})
Log.warning(
"Could not get {{col.es_index}}.{{col.es_column}} info",
col=column,
cause=e)
def Parts2Term(self, domain):
"""
TERMS ARE ALWAYS ESCAPED SO THEY CAN BE COMPOUNDED WITH PIPE (|)
CONVERT AN ARRAY OF PARTS{name, esfilter} TO AN MVEL EXPRESSION
RETURN expression, function PAIR, WHERE
expression - MVEL EXPRESSION
function - TAKES RESULT OF expression AND RETURNS PART
"""
fields = domain.dimension.fields
term = []
if len(split_field(self.fromData.name)) == 1 and fields:
if isinstance(fields, Mapping):
# CONVERT UNORDERED FIELD DEFS
jx_fields, es_fields = transpose(
*[(k, fields[k]) for k in sorted(fields.keys())])
else:
jx_fields, es_fields = transpose(
*[(i, e) for i, e in enumerate(fields)])
# NO LOOPS BECAUSE QUERY IS SHALLOW
# DOMAIN IS FROM A DIMENSION, USE IT'S FIELD DEFS TO PULL
if len(es_fields) == 1:
def fromTerm(term):
return domain.getPartByKey(term)
return Data(head="",
body='getDocValue(' +
quote(domain.dimension.fields[0]) + ')'), fromTerm
else:
def fromTerm(term):
terms = [
convert.pipe2value(t)
for t in convert.pipe2value(term).split("|")
]
candidate = dict(zip(jx_fields, terms))
for p in domain.partitions:
for k, t in candidate.items():
if p.value[k] != t:
break
else:
return p
if domain.type in ["uid", "default"]:
part = {"value": candidate}
domain.partitions.append(part)
return part
else:
return Null
for f in es_fields:
term.append('Value2Pipe(getDocValue(' + quote(f) + '))')
return Data(head="",
body='Value2Pipe(' + ('+"|"+'.join(term)) +
')'), fromTerm
else:
for v in domain.partitions:
term.append("if (" +
_where(v.esfilter, lambda x: self._translate(x)) +
") " + value2MVEL(domain.getKey(v)) + "; else ")
term.append(value2MVEL(domain.getKey(domain.NULL)))
func_name = "_temp" + UID()
return self.register_function("+\"|\"+".join(term))
def _set_op(self, query, frum):
# GET LIST OF COLUMNS
frum_path = split_field(frum)
primary_nested_path = join_field(frum_path[1:])
vars_ = UNION([v.var for select in listwrap(query.select) for v in select.value.vars()])
schema = self.sf.tables[primary_nested_path].schema
active_columns = {".": set()}
for v in vars_:
for c in schema.leaves(v):
nest = c.nested_path[0]
active_columns.setdefault(nest, set()).add(c)
# ANY VARS MENTIONED WITH NO COLUMNS?
for v in vars_:
if not any(startswith_field(cname, v) for cname in schema.keys()):
active_columns["."].add(Column(
names={".": v},
type="null",
es_column=".",
es_index=".",
nested_path=["."]
))
# EVERY COLUMN, AND THE INDEX IT TAKES UP
index_to_column = {} # MAP FROM INDEX TO COLUMN (OR SELECT CLAUSE)
index_to_uid = {} # FROM NESTED PATH TO THE INDEX OF UID
sql_selects = [] # EVERY SELECT CLAUSE (NOT TO BE USED ON ALL TABLES, OF COURSE)
nest_to_alias = {
nested_path: "__" + unichr(ord('a') + i) + "__"
for i, (nested_path, sub_table) in enumerate(self.sf.tables.items())
}
sorts = []
if query.sort:
for select in query.sort:
col = select.value.to_sql(schema)[0]
for t, sql in col.sql.items():
json_type = sql_type_to_json_type[t]
if json_type in STRUCT:
continue
column_number = len(sql_selects)
# SQL HAS ABS TABLE REFERENCE
column_alias = _make_column_name(column_number)
sql_selects.append(sql_alias(sql, column_alias))
if select.sort == -1:
sorts.append(column_alias + SQL_IS_NOT_NULL)
sorts.append(column_alias + " DESC")
else:
sorts.append(column_alias + SQL_IS_NULL)
sorts.append(column_alias)
primary_doc_details = Data()
# EVERY SELECT STATEMENT THAT WILL BE REQUIRED, NO MATTER THE DEPTH
# WE WILL CREATE THEM ACCORDING TO THE DEPTH REQUIRED
nested_path = []
for step, sub_table in self.sf.tables.items():
nested_path.insert(0, step)
nested_doc_details = {
"sub_table": sub_table,
"children": [],
"index_to_column": {},
"nested_path": nested_path
}
# INSERT INTO TREE
if not primary_doc_details:
primary_doc_details = nested_doc_details
else:
def place(parent_doc_details):
if startswith_field(step, parent_doc_details['nested_path'][0]):
for c in parent_doc_details['children']:
if place(c):
return True
parent_doc_details['children'].append(nested_doc_details)
place(primary_doc_details)
alias = nested_doc_details['alias'] = nest_to_alias[step]
# WE ALWAYS ADD THE UID
column_number = index_to_uid[step] = nested_doc_details['id_coord'] = len(sql_selects)
sql_select = join_column(alias, quoted_UID)
sql_selects.append(sql_alias(sql_select, _make_column_name(column_number)))
if step != ".":
# ID AND ORDER FOR CHILD TABLES
index_to_column[column_number] = ColumnMapping(
sql=sql_select,
type="number",
nested_path=nested_path,
column_alias=_make_column_name(column_number)
)
column_number = len(sql_selects)
sql_select = join_column(alias, quoted_ORDER)
sql_selects.append(sql_alias(sql_select, _make_column_name(column_number)))
index_to_column[column_number] = ColumnMapping(
sql=sql_select,
type="number",
nested_path=nested_path,
column_alias=_make_column_name(column_number)
)
# WE DO NOT NEED DATA FROM TABLES WE REQUEST NOTHING FROM
if step not in active_columns:
continue
# ADD SQL SELECT COLUMNS FOR EACH jx SELECT CLAUSE
si = 0
for select in listwrap(query.select):
try:
column_number = len(sql_selects)
select.pull = get_column(column_number)
db_columns = select.value.partial_eval().to_sql(schema)
for column in db_columns:
if isinstance(column.nested_path, list):
column.nested_path = column.nested_path[0] # IN THE EVENT THIS "column" IS MULTIVALUED
for t, unsorted_sql in column.sql.items():
json_type = sql_type_to_json_type[t]
if json_type in STRUCT:
continue
column_number = len(sql_selects)
column_alias = _make_column_name(column_number)
sql_selects.append(sql_alias(unsorted_sql, column_alias))
if startswith_field(primary_nested_path, step) and isinstance(select.value, LeavesOp):
# ONLY FLATTEN primary_nested_path AND PARENTS, NOT CHILDREN
index_to_column[column_number] = nested_doc_details['index_to_column'][column_number] = ColumnMapping(
push_name=literal_field(get_property_name(concat_field(select.name, column.name))),
push_child=".",
push_column_name=get_property_name(concat_field(select.name, column.name)),
push_column=si,
pull=get_column(column_number),
sql=unsorted_sql,
type=json_type,
column_alias=column_alias,
nested_path=nested_path
)
si += 1
else:
index_to_column[column_number] = nested_doc_details['index_to_column'][column_number] = ColumnMapping(
push_name=select.name,
push_child=column.name,
push_column_name=select.name,
push_column=si,
pull=get_column(column_number),
sql=unsorted_sql,
type=json_type,
column_alias=column_alias,
nested_path=nested_path
)
finally:
si += 1
where_clause = BooleanOp("boolean", query.where).partial_eval().to_sql(schema, boolean=True)[0].sql.b
unsorted_sql = self._make_sql_for_one_nest_in_set_op(
".",
sql_selects,
where_clause,
active_columns,
index_to_column
)
for n, _ in self.sf.tables.items():
sorts.append(quote_column(COLUMN + text_type(index_to_uid[n])))
ordered_sql = (
SQL_SELECT + "*" +
SQL_FROM + sql_iso(unsorted_sql) +
SQL_ORDERBY + sql_list(sorts) +
SQL_LIMIT + quote_value(query.limit)
)
self.db.create_new_functions() # creating new functions: regexp
result = self.db.query(ordered_sql)
def _accumulate_nested(rows, row, nested_doc_details, parent_doc_id, parent_id_coord):
"""
:param rows: REVERSED STACK OF ROWS (WITH push() AND pop())
:param row: CURRENT ROW BEING EXTRACTED
:param nested_doc_details: {
"nested_path": wrap_nested_path(nested_path),
"index_to_column": map from column number to column details
"children": all possible direct decedents' nested_doc_details
}
:param parent_doc_id: the id of the parent doc (for detecting when to step out of loop)
:param parent_id_coord: the column number for the parent id (so we ca extract from each row)
:return: the nested property (usually an array)
"""
previous_doc_id = None
doc = Data()
output = []
id_coord = nested_doc_details['id_coord']
while True:
doc_id = row[id_coord]
if doc_id == None or (parent_id_coord is not None and row[parent_id_coord] != parent_doc_id):
rows.append(row) # UNDO PREVIOUS POP (RECORD IS NOT A NESTED RECORD OF parent_doc)
return output
if doc_id != previous_doc_id:
previous_doc_id = doc_id
doc = Data()
curr_nested_path = nested_doc_details['nested_path'][0]
index_to_column = nested_doc_details['index_to_column'].items()
if index_to_column:
for i, c in index_to_column:
value = row[i]
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
# ASSIGN INNER PROPERTIES
relative_path = concat_field(c.push_name, c.push_child)
else: # FACT IS EXPECTED TO BE A SINGLE VALUE, NOT AN OBJECT
relative_path = c.push_child
if relative_path == ".":
if value == '':
doc = Null
else:
doc = value
elif value != None and value != '':
doc[relative_path] = value
for child_details in nested_doc_details['children']:
# EACH NESTED TABLE MUST BE ASSEMBLED INTO A LIST OF OBJECTS
child_id = row[child_details['id_coord']]
if child_id is not None:
nested_value = _accumulate_nested(rows, row, child_details, doc_id, id_coord)
if nested_value:
push_name = child_details['nested_path'][0]
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
# ASSIGN INNER PROPERTIES
relative_path = relative_field(push_name, curr_nested_path)
else: # FACT IS EXPECTED TO BE A SINGLE VALUE, NOT AN OBJECT
relative_path = "."
if relative_path == "." and doc is Null:
doc = nested_value
elif relative_path == ".":
doc = unwraplist(nested_value)
else:
doc[relative_path] = unwraplist(nested_value)
output.append(doc)
try:
row = rows.pop()
except IndexError:
return output
cols = tuple([i for i in index_to_column.values() if i.push_name != None])
rows = list(reversed(unwrap(result.data)))
if rows:
row = rows.pop()
data = _accumulate_nested(rows, row, primary_doc_details, None, None)
else:
data = result.data
if query.format == "cube":
for f, _ in self.sf.tables.items():
if frum.endswith(f) or (test_dots(cols) and isinstance(query.select, list)):
num_rows = len(result.data)
num_cols = MAX([c.push_column for c in cols]) + 1 if len(cols) else 0
map_index_to_name = {c.push_column: c.push_column_name for c in cols}
temp_data = [[None] * num_rows for _ in range(num_cols)]
for rownum, d in enumerate(result.data):
for c in cols:
if c.push_child == ".":
temp_data[c.push_column][rownum] = c.pull(d)
else:
column = temp_data[c.push_column][rownum]
if column is None:
column = temp_data[c.push_column][rownum] = {}
column[c.push_child] = c.pull(d)
output = Data(
meta={"format": "cube"},
data={n: temp_data[c] for c, n in map_index_to_name.items()},
edges=[{
"name": "rownum",
"domain": {
"type": "rownum",
"min": 0,
"max": num_rows,
"interval": 1
}
}]
)
return output
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
num_rows = len(data)
temp_data = {c.push_column_name: [None] * num_rows for c in cols}
for rownum, d in enumerate(data):
for c in cols:
temp_data[c.push_column_name][rownum] = d[c.push_name]
return Data(
meta={"format": "cube"},
data=temp_data,
edges=[{
"name": "rownum",
"domain": {
"type": "rownum",
"min": 0,
"max": num_rows,
"interval": 1
}
}]
)
else:
num_rows = len(data)
map_index_to_name = {c.push_column: c.push_column_name for c in cols}
temp_data = [data]
return Data(
meta={"format": "cube"},
data={n: temp_data[c] for c, n in map_index_to_name.items()},
edges=[{
"name": "rownum",
"domain": {
"type": "rownum",
"min": 0,
"max": num_rows,
"interval": 1
}
}]
)
elif query.format == "table":
for f, _ in self.sf.tables.items():
if frum.endswith(f):
num_column = MAX([c.push_column for c in cols]) + 1
header = [None] * num_column
for c in cols:
header[c.push_column] = c.push_column_name
output_data = []
for d in result.data:
row = [None] * num_column
for c in cols:
set_column(row, c.push_column, c.push_child, c.pull(d))
output_data.append(row)
return Data(
meta={"format": "table"},
header=header,
data=output_data
)
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
column_names = [None] * (max(c.push_column for c in cols) + 1)
for c in cols:
column_names[c.push_column] = c.push_column_name
temp_data = []
for rownum, d in enumerate(data):
row = [None] * len(column_names)
for c in cols:
row[c.push_column] = d[c.push_name]
temp_data.append(row)
return Data(
meta={"format": "table"},
header=column_names,
data=temp_data
)
else:
column_names = listwrap(query.select).name
return Data(
meta={"format": "table"},
header=column_names,
data=[[d] for d in data]
)
else:
for f, _ in self.sf.tables.items():
if frum.endswith(f) or (test_dots(cols) and isinstance(query.select, list)):
data = []
for d in result.data:
row = Data()
for c in cols:
if c.push_child == ".":
row[c.push_name] = c.pull(d)
elif c.num_push_columns:
tuple_value = row[c.push_name]
if not tuple_value:
tuple_value = row[c.push_name] = [None] * c.num_push_columns
tuple_value[c.push_child] = c.pull(d)
else:
row[c.push_name][c.push_child] = c.pull(d)
data.append(row)
return Data(
meta={"format": "list"},
data=data
)
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
temp_data = []
for rownum, d in enumerate(data):
row = {}
for c in cols:
row[c.push_column_name] = d[c.push_name]
temp_data.append(row)
return Data(
meta={"format": "list"},
data=temp_data
)
else:
return Data(
meta={"format": "list"},
data=data
)
elif depth == 1:
return _MAX(cube)
else:
return _MAX(_max(depth - 1, c) for c in cube)
def _min(depth, cube):
if depth == 0:
return cube
elif depth == 1:
return _MIN(cube)
else:
return _MIN(_min(depth - 1, c) for c in cube)
aggregates = Data(max=_max, maximum=_max, min=_min, minimum=_min)
def _iter(cube, depth):
if depth == 1:
return cube.__iter__()
else:
def iterator():
for c in cube:
for b in _iter(c, depth - 1):
yield b
return iterator()
def apply_diff(text, diff, reverse=False, verify=True):
"""
SOME EXAMPLES OF diff
#@@ -1 +1 @@
#-before china goes live, the content team will have to manually update the settings for the china-ready apps currently in marketplace.
#+before china goes live (end January developer release, June general audience release) , the content team will have to manually update the settings for the china-ready apps currently in marketplace.
@@ -0,0 +1,3 @@
+before china goes live, the content team will have to manually update the settings for the china-ready apps currently in marketplace.
+
+kward has the details.
@@ -1 +1 @@
-before china goes live (end January developer release, June general audience release), the content team will have to manually update the settings for the china-ready apps currently in marketplace.
+before china goes live , the content team will have to manually update the settings for the china-ready apps currently in marketplace.
@@ -3 +3 ,6 @@
-kward has the details.+kward has the details.
+
+Target Release Dates :
+https://mana.mozilla.org/wiki/display/PM/Firefox+OS+Wave+Launch+Cross+Functional+View
+
+Content Team Engagement & Tasks : https://appreview.etherpad.mozilla.org/40
"""
if not diff:
return text
output = text
hunks = [
(new_diff[start_hunk], new_diff[start_hunk + 1:end_hunk])
for new_diff in [[
d.lstrip()
for d in diff if d.lstrip() and d != "\\ No newline at end of file"
] + ["@@"]] # ANOTHER REPAIR
for start_hunk, end_hunk in pairwise(
i for i, l in enumerate(new_diff) if l.startswith('@@'))
]
for header, hunk_body in (reversed(hunks) if reverse else hunks):
matches = DIFF_PREFIX.match(header.strip())
if not matches:
if not _Log:
_late_import()
_Log.error("Can not handle \n---\n{{diff}}\n---\n", diff=diff)
removes = tuple(int(i.strip()) for i in matches.group(1).split(
",")) # EXPECTING start_line, length TO REMOVE
remove = Data(
start=removes[0],
length=1 if len(removes) == 1 else removes[1]) # ASSUME FIRST LINE
adds = tuple(int(i.strip()) for i in matches.group(2).split(
",")) # EXPECTING start_line, length TO ADD
add = Data(start=adds[0], length=1 if len(adds) == 1 else adds[1])
if add.length == 0 and add.start == 0:
add.start = remove.start
def repair_hunk(hunk_body):
# THE LAST DELETED LINE MAY MISS A "\n" MEANING THE FIRST
# ADDED LINE WILL BE APPENDED TO THE LAST DELETED LINE
# EXAMPLE: -kward has the details.+kward has the details.
# DETECT THIS PROBLEM FOR THIS HUNK AND FIX THE DIFF
if reverse:
last_lines = [
o for b, o in zip(reversed(hunk_body), reversed(output))
if b != "+" + o
]
if not last_lines:
return hunk_body
last_line = last_lines[0]
for problem_index, problem_line in enumerate(hunk_body):
if problem_line.startswith('-') and problem_line.endswith(
'+' + last_line):
split_point = len(problem_line) - (len(last_line) + 1)
break
elif problem_line.startswith('+' + last_line + "-"):
split_point = len(last_line) + 1
break
else:
return hunk_body
else:
if not output:
return hunk_body
last_line = output[-1]
for problem_index, problem_line in enumerate(hunk_body):
if problem_line.startswith('+') and problem_line.endswith(
'-' + last_line):
split_point = len(problem_line) - (len(last_line) + 1)
break
elif problem_line.startswith('-' + last_line + "+"):
split_point = len(last_line) + 1
break
else:
return hunk_body
new_hunk_body = (
hunk_body[:problem_index] +
[problem_line[:split_point], problem_line[split_point:]] +
hunk_body[problem_index + 1:])
return new_hunk_body
hunk_body = repair_hunk(hunk_body)
if reverse:
new_output = (output[:add.start - 1] +
[d[1:] for d in hunk_body if d and d[0] == '-'] +
output[add.start + add.length - 1:])
else:
new_output = (output[:add.start - 1] +
[d[1:] for d in hunk_body if d and d[0] == '+'] +
output[add.start + remove.length - 1:])
output = new_output
if verify:
original = apply_diff(output, diff, not reverse, False)
if set(text) != set(original): # bugzilla-etl diffs are a jumble
for t, o in zip_longest(text, original):
if t in ['reports: https://goo.gl/70o6w6\r']:
break # KNOWN INCONSISTENCIES
if t != o:
if not _Log:
_late_import()
_Log.error("logical verification check failed")
break
return output
def update(self, command):
"""
:param command: EXPECTING dict WITH {"set": s, "clear": c, "where": w} FORMAT
"""
command = wrap(command)
# REJECT DEEP UPDATES
touched_columns = command.set.keys() | set(listwrap(command['clear']))
for c in self.schema.columns:
if c.name in touched_columns and len(c.nested_path) > 1:
Log.error("Deep update not supported")
# ADD NEW COLUMNS
where = jx_expression(command.where)
_vars = where.vars()
_map = {
v: c.es_column
for v in _vars
for c in self.columns.get(v, Null)
if c.jx_type not in STRUCT
}
where_sql = where.map(_map).to_sql(self.schema)
new_columns = set(command.set.keys()) - set(self.columns.keys())
for new_column_name in new_columns:
nested_value = command.set[new_column_name]
ctype = get_jx_type(nested_value)
column = Column(
name=new_column_name,
jx_type=ctype,
es_index=self.name,
es_type=json_type_to_sqlite_type(ctype),
es_column=typed_column(new_column_name, ctype),
last_updated=Date.now()
)
self.add_column(column)
# UPDATE THE NESTED VALUES
for nested_column_name, nested_value in command.set.items():
if get_jx_type(nested_value) == "nested":
nested_table_name = concat_field(self.name, nested_column_name)
nested_table = nested_tables[nested_column_name]
self_primary_key = sql_list(quote_column(c.es_column) for u in self.uid for c in self.columns[u])
extra_key_name = UID + text(len(self.uid))
extra_key = [e for e in nested_table.columns[extra_key_name]][0]
sql_command = (
SQL_DELETE + SQL_FROM + quote_column(nested_table.name) +
SQL_WHERE + "EXISTS" +
sql_iso(
SQL_SELECT + SQL_ONE +
SQL_FROM + sql_alias(quote_column(nested_table.name), "n") +
SQL_INNER_JOIN + sql_iso(
SQL_SELECT + self_primary_key +
SQL_FROM + quote_column(abs_schema.fact) +
SQL_WHERE + where_sql
) +
" t ON " +
SQL_AND.join(
quote_column("t", c.es_column) + SQL_EQ + quote_column("n", c.es_column)
for u in self.uid
for c in self.columns[u]
)
)
)
self.db.execute(sql_command)
# INSERT NEW RECORDS
if not nested_value:
continue
doc_collection = {}
for d in listwrap(nested_value):
nested_table.flatten(d, Data(), doc_collection, path=nested_column_name)
prefix = SQL_INSERT + quote_column(nested_table.name) + sql_iso(sql_list(
[self_primary_key] +
[quote_column(extra_key)] +
[
quote_column(c.es_column)
for c in doc_collection.get(".", Null).active_columns
]
))
# BUILD THE PARENT TABLES
parent = (
SQL_SELECT + self_primary_key +
SQL_FROM + quote_column(abs_schema.fact) +
SQL_WHERE + jx_expression(command.where).to_sql(schema)
)
# BUILD THE RECORDS
children = SQL_UNION_ALL.join(
SQL_SELECT +
sql_alias(quote_value(i), extra_key.es_column) + SQL_COMMA +
sql_list(
sql_alias(quote_value(row[c.name]), quote_column(c.es_column))
for c in doc_collection.get(".", Null).active_columns
)
for i, row in enumerate(doc_collection.get(".", Null).rows)
)
sql_command = (
prefix +
SQL_SELECT +
sql_list(
[quote_column("p", c.es_column) for u in self.uid for c in self.columns[u]] +
[quote_column("c", extra_key)] +
[quote_column("c", c.es_column) for c in doc_collection.get(".", Null).active_columns]
) +
SQL_FROM + sql_iso(parent) + " p" +
SQL_INNER_JOIN + sql_iso(children) + " c" + SQL_ON + SQL_TRUE
)
self.db.execute(sql_command)
# THE CHILD COLUMNS COULD HAVE EXPANDED
# ADD COLUMNS TO SELF
for n, cs in nested_table.columns.items():
for c in cs:
column = Column(
name=c.name,
jx_type=c.jx_type,
es_type=c.es_type,
es_index=c.es_index,
es_column=c.es_column,
nested_path=[nested_column_name] + c.nested_path,
last_updated=Date.now()
)
if c.name not in self.columns:
self.columns[column.name] = {column}
elif c.jx_type not in [c.jx_type for c in self.columns[c.name]]:
self.columns[column.name].add(column)
command = (
SQL_UPDATE + quote_column(abs_schema.fact) + SQL_SET +
sql_list(
[
quote_column(c) + SQL_EQ + quote_value(get_if_type(v, c.jx_type))
for k, v in command.set.items()
if get_jx_type(v) != "nested"
for c in self.columns[k]
if c.jx_type != "nested" and len(c.nested_path) == 1
] +
[
quote_column(c) + SQL_EQ + SQL_NULL
for k in listwrap(command['clear'])
if k in self.columns
for c in self.columns[k]
if c.jx_type != "nested" and len(c.nested_path) == 1
]
) +
SQL_WHERE + where_sql
)
self.db.execute(command)
def __init__(self, header=None, data=None):
self.header = header
self.data = data
self.meta = Data()
def __init__(
self,
hg=None, # hg CONNECTION INFO
repo=None, # CONNECTION INFO FOR ES CACHE
use_cache=False, # True IF WE WILL USE THE ES FOR DOWNLOADING BRANCHES
kwargs=None,
):
if not _hg_branches:
_late_imports()
if not is_text(repo.index):
Log.error("Expecting 'index' parameter")
self.repo_locker = Lock()
self.moves_locker = Lock()
self.todo = mo_threads.Queue("todo for hg daemon",
max=DAEMON_QUEUE_SIZE)
self.settings = kwargs
self.hg = Data(
url=hg.url,
timeout=Duration(coalesce(hg.timeout, "30second")).seconds,
retry={
"times": 3,
"sleep": DAEMON_HG_INTERVAL
},
)
self.last_cache_miss = Date.now()
# VERIFY CONNECTIVITY
with Explanation("Test connect with hg"):
http.head(self.settings.hg.url)
set_default(repo, {
"type": "revision",
"schema": revision_schema,
})
kwargs.branches = set_default(
{
"index": repo.index + "-branches",
"type": "branch"
},
repo,
)
moves = set_default(
{"index": repo.index + "-moves"},
repo,
)
self.branches = _hg_branches.get_branches(kwargs=kwargs)
cluster = elasticsearch.Cluster(kwargs=repo)
self.repo = cluster.get_or_create_index(kwargs=repo)
self.moves = cluster.get_or_create_index(kwargs=moves)
def setup_es(please_stop):
with suppress_exception:
self.repo.add_alias()
with suppress_exception:
self.moves.add_alias()
with suppress_exception:
self.repo.set_refresh_interval(seconds=1)
with suppress_exception:
self.moves.set_refresh_interval(seconds=1)
Thread.run("setup_es", setup_es)
Thread.run("hg daemon", self._daemon)
def format_list(decoders, aggs, start, query, select):
new_edges = count_dim(aggs, decoders)
def data():
dims = tuple(
len(e.domain.partitions) + (0 if e.allowNulls is False else 1)
for e in new_edges)
is_sent = Matrix(dims=dims, zeros=0)
if query.sort and not query.groupby:
# TODO: USE THE format_table() TO PRODUCE THE NEEDED VALUES INSTEAD OF DUPLICATING LOGIC HERE
all_coord = is_sent._all_combos(
) # TRACK THE EXPECTED COMBINATIONS
for row, coord, agg in aggs_iterator(aggs, decoders):
missing_coord = all_coord.next()
while coord != missing_coord:
# INSERT THE MISSING COORDINATE INTO THE GENERATION
output = Data()
for i, d in enumerate(decoders):
output[query.edges[i].name] = d.get_value(
missing_coord[i])
for s in select:
if s.aggregate == "count":
output[s.name] = 0
yield output
missing_coord = all_coord.next()
output = Data()
for e, c, d in zip(query.edges, coord, decoders):
output[e.name] = d.get_value(c)
for s in select:
output[s.name] = s.pull(agg)
yield output
else:
is_sent = Matrix(dims=dims, zeros=0)
for row, coord, agg in aggs_iterator(aggs, decoders):
is_sent[coord] = 1
output = Data()
for e, c, d in zip(query.edges, coord, decoders):
output[e.name] = d.get_value(c)
for s in select:
output[s.name] = s.pull(agg)
yield output
# EMIT THE MISSING CELLS IN THE CUBE
if not query.groupby:
for c, v in is_sent:
if not v:
output = Data()
for i, d in enumerate(decoders):
output[query.edges[i].name] = d.get_value(c[i])
for s in select:
if s.aggregate == "count":
output[s.name] = 0
yield output
output = Data(meta={"format": "list"}, data=list(data()))
return output
Log.error("Can not deal with date like {{date|json}}", date=date)
def minimize_repo(repo):
"""
RETURN A MINIMAL VERSION OF THIS CHANGESET
"""
if repo == None:
return Null
output = wrap(_copy_but(repo, _exclude_from_repo))
output.changeset.description = strings.limit(output.changeset.description,
1000)
return output
_exclude_from_repo = Data()
for k in [
"changeset.files",
"changeset.diff",
"changeset.moves",
"etl",
"branch.last_used",
"branch.description",
"branch.etl",
"branch.parent_name",
"children",
"parents",
"phase",
"bookmarks",
"tags",
]:
def _get_from_hg(self,
revision,
locale=None,
get_diff=False,
get_moves=True):
# RATE LIMIT CALLS TO HG (CACHE MISSES)
next_cache_miss = self.last_cache_miss + (
Random.float(WAIT_AFTER_CACHE_MISS * 2) * SECOND)
self.last_cache_miss = Date.now()
if next_cache_miss > self.last_cache_miss:
Log.note(
"delaying next hg call for {{seconds|round(decimal=1)}} seconds",
seconds=next_cache_miss - self.last_cache_miss,
)
Till(till=next_cache_miss.unix).wait()
# CLEAN UP BRANCH NAME
found_revision = copy(revision)
if isinstance(found_revision.branch, (text, binary_type)):
lower_name = found_revision.branch.lower()
else:
lower_name = found_revision.branch.name.lower()
if not lower_name:
Log.error("Defective revision? {{rev|json}}",
rev=found_revision.branch)
b = found_revision.branch = self.branches[(lower_name, locale)]
if not b:
b = found_revision.branch = self.branches[(lower_name,
DEFAULT_LOCALE)]
if not b:
Log.warning(
"can not find branch ({{branch}}, {{locale}})",
branch=lower_name,
locale=locale,
)
return Null
# REFRESH BRANCHES, IF TOO OLD
if Date.now() - Date(b.etl.timestamp) > _hg_branches.OLD_BRANCH:
self.branches = _hg_branches.get_branches(kwargs=self.settings)
# FIND THE PUSH
push = self._get_push(found_revision.branch,
found_revision.changeset.id)
id12 = found_revision.changeset.id[0:12]
base_url = URL(found_revision.branch.url)
with Explanation("get revision from {{url}}",
url=base_url,
debug=DEBUG):
raw_rev2 = Null
automation_details = Null
try:
raw_rev1 = self._get_raw_json_info((base_url / "json-info") +
{"node": id12})
raw_rev2 = self._get_raw_json_rev(base_url / "json-rev" / id12)
automation_details = self._get_raw_json_rev(
base_url / "json-automationrelevance" / id12)
except Exception as e:
if "Hg denies it exists" in e:
raw_rev1 = Data(node=revision.changeset.id)
else:
raise e
raw_rev3_changeset = first(r for r in automation_details.changesets
if r.node[:12] == id12)
if last(automation_details.changesets) != raw_rev3_changeset:
Log.note("interesting")
output = self._normalize_revision(
set_default(raw_rev1, raw_rev2, raw_rev3_changeset),
found_revision,
push,
get_diff,
get_moves,
)
if output.push.date >= Date.now() - MAX_TODO_AGE:
self.todo.extend([
(output.branch, listwrap(output.parents), None),
(output.branch, listwrap(output.children), None),
(
output.branch,
listwrap(output.backsoutnodes),
output.push.date,
),
])
if not get_diff: # DIFF IS BIG, DO NOT KEEP IT IF NOT NEEDED
output.changeset.diff = None
if not get_moves:
output.changeset.moves = None
return output
def _output():
for g, v in itertools.groupby(data, get_key):
group = Data()
for k, gg in zip(keys, g):
group[k] = gg
yield (group, wrap(list(v)))
def selector(d):
output = Data()
for n, p in push_and_pull:
output[n] = unwraplist(p(wrap(d)))
return unwrap(output)
def get_selects(query):
schema = query.frum.schema
query_level = len(schema.query_path)
query_path = schema.query_path[0]
# SPLIT select INTO ES_SELECT AND RESULTSET SELECT
split_select = OrderedDict((p, ESSelectOp(p)) for p in schema.query_path)
def expand_split_select(c_nested_path):
es_select = split_select.get(c_nested_path)
if not es_select:
temp = [(k, v) for k, v in split_select.items()]
split_select.clear()
split_select.update({c_nested_path: ESSelectOp(c_nested_path)})
split_select.update(temp)
return split_select[c_nested_path]
new_select = FlatList()
post_expressions = {}
selects = list_to_data([unwrap(s.copy()) for s in listwrap(query.select)])
# WHAT PATH IS _source USED, IF ANY?
for select in selects:
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
if is_op(select.value, LeavesOp) and is_op(select.value.term,
Variable):
term = select.value.term
leaves = schema.leaves(term.var)
if any(c.jx_type == NESTED for c in leaves):
split_select["."].source_path = "."
elif is_op(select.value, Variable):
for selected_column in schema.values(select.value.var,
exclude_type=(OBJECT,
EXISTS)):
if selected_column.jx_type == NESTED:
expand_split_select(
selected_column.es_column
).source_path = selected_column.es_column
continue
leaves = schema.leaves(selected_column.es_column)
for c in leaves:
if c.jx_type == NESTED:
split_select[c.es_column].source_path = c.es_column
# IF WE GET THE SOURCE FOR PARENT, WE ASSUME WE GOT SOURCE FOR CHILD
source_path = None
source_level = 0
for level, es_select in enumerate(reversed(list(split_select.values()))):
if source_path:
es_select.source_path = source_path
elif es_select.source_path:
source_level = level + 1
source_path = es_select.source_path
def get_pull_source(c):
nested_path = c.nested_path
nested_level = len(nested_path)
pos = text(nested_level)
if nested_level <= query_level:
if not source_level or nested_level < source_level:
field = join_field([pos, "fields", c.es_column])
return jx_expression_to_function(field)
elif nested_level == source_level:
field = relative_field(c.es_column, nested_path[0])
def pull_source(row):
return untyped(row.get(pos, Null)._source[field])
return pull_source
else:
field = relative_field(c.es_column, nested_path[0])
def pull_property(row):
return untyped(row.get(pos, Null)[field])
return pull_property
else:
pos = text(query_level)
if not source_level or nested_level < source_level:
# PULL FIELDS AND THEN AGGREGATE THEM
value = jx_expression_to_function(
join_field(["fields", c.es_column]))
name = literal_field(nested_path[0])
index = jx_expression_to_function("_nested.offset")
def pull_nested_field(doc):
hits = doc.get(pos, Null).inner_hits[name].hits.hits
if not hits:
return []
temp = [(index(h), value(h)) for h in hits]
acc = [None] * len(temp)
for i, v in temp:
acc[i] = unwraplist(v)
return acc
return pull_nested_field
else:
# PULL SOURCES
value = jx_expression_to_function(
concat_field("_source",
relative_field(c.es_column, nested_path[0])))
name = literal_field(nested_path[0])
index = jx_expression_to_function(
join_field(["_nested"] * (len(c.nested_path) - 1) +
["offset"]))
def pull_nested_source(doc):
hits = doc.get(pos, Null).inner_hits[name].hits.hits
if not hits:
return []
temp = [(index(h), value(h)) for h in hits]
acc = [None] * len(temp)
for i, v in temp:
acc[i] = untyped(v)
return acc
return pull_nested_source
put_index = 0
for select in selects:
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
if is_op(select.value, LeavesOp) and is_op(select.value.term,
Variable):
term = select.value.term
leaves = schema.leaves(term.var)
for c in leaves:
c_nested_path = c.nested_path[0]
simple_name = relative_field(c.es_column,
query_path).lstrip(".")
name = concat_field(select.name, untype_path(simple_name))
put_name = concat_field(
select.name, literal_field(untype_path(simple_name)))
split_select[c_nested_path].fields.append(c.es_column)
new_select.append({
"name": name,
"value": Variable(c.es_column),
"put": {
"name": put_name,
"index": put_index,
"child": ".",
},
"pull": get_pull_source(c),
})
put_index += 1
elif is_op(select.value, Variable):
if select.value.var == ".":
# PULL ALL SOURCE
new_select.append({
"name":
select.name,
"value":
select.value,
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
"pull":
get_pull_source(
Data(es_column=query_path,
nested_path=schema.query_path)),
})
continue
for selected_column in schema.values(select.value.var,
exclude_type=(EXISTS,
OBJECT)):
if selected_column.jx_type == NESTED:
new_select.append({
"name":
select.name,
"value":
select.value,
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
"pull":
get_pull_source(
Data(
es_column=selected_column.es_column,
nested_path=(selected_column.es_column, ) +
selected_column.nested_path,
)),
})
continue
leaves = schema.leaves(
selected_column.es_column,
exclude_type=INTERNAL) # LEAVES OF OBJECT
if leaves:
for c in leaves:
if c.es_column == "_id":
new_select.append({
"name": select.name,
"value": Variable(c.es_column),
"put": {
"name": select.name,
"index": put_index,
"child": ".",
},
"pull": pull_id,
})
continue
c_nested_path = c.nested_path[0]
expand_split_select(c_nested_path).fields.append(
c.es_column)
child = untype_path(
relative_field(
c.es_column,
selected_column.es_column,
))
new_select.append({
"name": select.name,
"value": Variable(c.es_column),
"put": {
"name": select.name,
"index": put_index,
"child": child,
},
"pull": get_pull_source(c),
})
else:
new_select.append({
"name": select.name,
"value": NULL,
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
})
put_index += 1
else:
op, split_scripts = split_expression_by_path(select.value,
schema,
lang=Painless)
for p, script in split_scripts.items():
es_select = split_select[p]
es_select.scripts[select.name] = {
"script":
text(Painless[script].partial_eval().to_es_script(schema))
}
new_select.append({
"name":
select.name,
"pull":
jx_expression_to_function(
join_field([
text(p),
"fields",
select.name,
])),
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
})
put_index += 1
def inners(query_path, parent_pos):
"""
:param query_path:
:return: ITERATOR OVER TUPLES ROWS AS TUPLES, WHERE row[len(nested_path)] HAS INNER HITS
AND row[0] HAS post_expressions
"""
pos = text(int(parent_pos) + 1)
if not query_path:
def base_case(row):
extra = {}
for k, e in post_expressions.items():
extra[k] = e(row)
row["0"] = extra
yield row
return base_case
if pos == "1":
more = inners(query_path[:-1], "1")
def first_case(results):
for result in results:
for hit in result.hits.hits:
seed = {"0": None, pos: hit}
for row in more(seed):
yield row
return first_case
else:
more = inners(query_path[:-1], pos)
if source_path and source_path < query_path[-1]:
rel_path = relative_field(query_path[-1], source_path)
def source(acc):
for inner_row in acc[parent_pos][rel_path]:
acc[pos] = inner_row
for tt in more(acc):
yield tt
return source
else:
path = literal_field(query_path[-1])
def recurse(acc):
hits = acc[parent_pos].inner_hits[path].hits.hits
if hits:
for inner_row in hits:
acc[pos] = inner_row
for tt in more(acc):
yield tt
else:
for tt in more(acc):
yield tt
return recurse
return new_select, split_select, inners(schema.query_path, "0")
def flatten_many(self, docs, path="."):
"""
:param docs: THE JSON DOCUMENTS
:param path: FULL PATH TO THIS (INNER/NESTED) DOCUMENT
:return: TUPLE (success, command, doc_collection) WHERE
success: BOOLEAN INDICATING PROPER PARSING
command: SCHEMA CHANGES REQUIRED TO BE SUCCESSFUL NEXT TIME
doc_collection: MAP FROM NESTED PATH TO INSERTION PARAMETERS:
{"active_columns": list, "rows": list of objects}
"""
# TODO: COMMAND TO ADD COLUMNS
# TODO: COMMAND TO NEST EXISTING COLUMNS
# COLLECT AS MANY doc THAT DO NOT REQUIRE SCHEMA CHANGE
_insertion = Data(
active_columns=Queue(),
rows=[]
)
doc_collection = {".": _insertion}
# KEEP TRACK OF WHAT TABLE WILL BE MADE (SHORTLY)
required_changes = []
facts = self.container.get_or_create_facts(self.name)
snowflake = facts.snowflake
def _flatten(data, uid, parent_id, order, full_path, nested_path, row=None, guid=None):
"""
:param data: the data we are pulling apart
:param uid: the uid we are giving this doc
:param parent_id: the parent id of this (sub)doc
:param order: the number of siblings before this one
:param full_path: path to this (sub)doc
:param nested_path: list of paths, deepest first
:param row: we will be filling this
:return:
"""
table = concat_field(self.name, nested_path[0])
insertion = doc_collection[nested_path[0]]
if not row:
row = {GUID: guid, UID: uid, PARENT: parent_id, ORDER: order}
insertion.rows.append(row)
if is_data(data):
items = [(concat_field(full_path, k), v ) for k, v in wrap(data).leaves()]
else:
# PRIMITIVE VALUES
items = [(full_path, data)]
for cname, v in items:
jx_type = get_jx_type(v)
if jx_type is None:
continue
insertion = doc_collection[nested_path[0]]
if jx_type == NESTED:
c = first(
cc
for cc in insertion.active_columns + snowflake.columns
if cc.jx_type in STRUCT and untyped_column(cc.name)[0] == cname
)
else:
c = first(
cc
for cc in insertion.active_columns + snowflake.columns
if cc.jx_type == jx_type and cc.name == cname
)
if isinstance(c, list):
Log.error("confused")
if not c:
# WHAT IS THE NESTING LEVEL FOR THIS PATH?
deeper_nested_path = "."
for path in snowflake.query_paths:
if startswith_field(cname, path[0]) and len(deeper_nested_path) < len(path):
deeper_nested_path = path
c = Column(
name=cname,
jx_type=jx_type,
es_type=json_type_to_sqlite_type.get(jx_type, jx_type),
es_column=typed_column(cname, json_type_to_sql_type.get(jx_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now()
)
if jx_type == NESTED:
snowflake.query_paths.append(c.es_column)
required_changes.append({'nest': c})
else:
insertion.active_columns.add(c)
required_changes.append({"add": c})
elif c.jx_type == NESTED and jx_type == OBJECT:
# ALWAYS PROMOTE OBJECTS TO NESTED
jx_type = NESTED
v = [v]
elif len(c.nested_path) < len(nested_path):
from_doc = doc_collection.get(c.nested_path[0], None)
column = c.es_column
from_doc.active_columns.remove(c)
snowflake._remove_column(c)
required_changes.append({"nest": c})
deep_c = Column(
name=cname,
jx_type=jx_type,
es_type=json_type_to_sqlite_type.get(jx_type, jx_type),
es_column=typed_column(cname, json_type_to_sql_type.get(jx_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now()
)
snowflake._add_column(deep_c)
snowflake._drop_column(c)
from_doc.active_columns.remove(c)
for r in from_doc.rows:
r1 = unwrap(r)
if column in r1:
row1 = {UID: self.container.next_uid(), PARENT: r1["__id__"], ORDER: 0, column: r1[column]}
insertion.rows.append(row1)
elif len(c.nested_path) > len(nested_path):
insertion = doc_collection[c.nested_path[0]]
row = {UID: self.container.next_uid(), PARENT: uid, ORDER: order}
insertion.rows.append(row)
# BE SURE TO NEST VALUES, IF NEEDED
if jx_type == NESTED:
deeper_nested_path = [cname] + nested_path
if not doc_collection.get(cname):
doc_collection[cname] = Data(
active_columns=Queue(),
rows=[]
)
for i, r in enumerate(v):
child_uid = self.container.next_uid()
_flatten(r, child_uid, uid, i, cname, deeper_nested_path)
elif jx_type == OBJECT:
_flatten(v, uid, parent_id, order, cname, nested_path, row=row)
elif c.jx_type:
row[c.es_column] = v
for doc in docs:
_flatten(doc, self.container.next_uid(), 0, 0, full_path=path, nested_path=["."], guid=generateGuid())
if required_changes:
snowflake.change_schema(required_changes)
required_changes = []
return doc_collection
def flatten_many(self, docs, path="."):
"""
:param docs: THE JSON DOCUMENT
:param path: FULL PATH TO THIS (INNER/NESTED) DOCUMENT
:return: TUPLE (success, command, doc_collection) WHERE
success: BOOLEAN INDICATING PROPER PARSING
command: SCHEMA CHANGES REQUIRED TO BE SUCCESSFUL NEXT TIME
doc_collection: MAP FROM NESTED PATH TO INSERTION PARAMETERS:
{"active_columns": list, "rows": list of objects}
"""
# TODO: COMMAND TO ADD COLUMNS
# TODO: COMMAND TO NEST EXISTING COLUMNS
# COLLECT AS MANY doc THAT DO NOT REQUIRE SCHEMA CHANGE
_insertion = Data(active_columns=set(), rows=[])
doc_collection = {".": _insertion}
# KEEP TRACK OF WHAT TABLE WILL BE MADE (SHORTLY)
required_changes = []
snowflake = self.facts.snowflake
abs_schema = BasicSnowflake(snowflake.query_paths, snowflake.columns)
def _flatten(data,
uid,
parent_id,
order,
full_path,
nested_path,
row=None,
guid=None):
"""
:param data: the data we are pulling apart
:param uid: the uid we are giving this doc
:param parent_id: the parent id of this (sub)doc
:param order: the number of siblings before this one
:param full_path: path to this (sub)doc
:param nested_path: list of paths, deepest first
:param row: we will be filling this
:return:
"""
table = concat_field(self.name, nested_path[0])
insertion = doc_collection[nested_path[0]]
if not row:
row = {GUID: guid, UID: uid, PARENT: parent_id, ORDER: order}
insertion.rows.append(row)
if not isinstance(data, Mapping):
data = {".": data}
for k, v in data.items():
insertion = doc_collection[nested_path[0]]
cname = concat_field(full_path, literal_field(k))
value_type = get_type(v)
if value_type is None:
continue
if value_type in STRUCT:
c = unwraplist([
cc for cc in abs_schema.column[cname]
if cc.jx_type in STRUCT
])
else:
c = unwraplist([
cc for cc in abs_schema.column[cname]
if cc.jx_type == value_type
])
if not c:
# WHAT IS THE NESTING LEVEL FOR THIS PATH?
deeper_nested_path = "."
for path in abs_schema.query_paths:
if startswith_field(
cname,
path) and len(deeper_nested_path) < len(path):
deeper_nested_path = path
c = Column(name=cname,
jx_type=value_type,
es_type=json_type_to_sqlite_type.get(
value_type, value_type),
es_column=typed_column(
cname,
json_type_to_sql_type.get(value_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now())
abs_schema.columns.append(c)
if value_type == "nested":
abs_schema.query_paths.append(c.es_column)
required_changes.append({'nest': (c, nested_path)})
else:
required_changes.append({"add": c})
# INSIDE IF BLOCK BECAUSE WE DO NOT WANT IT TO ADD WHAT WE columns.get() ALREADY
insertion.active_columns.add(c)
elif c.jx_type == "nested" and value_type == "object":
value_type = "nested"
v = [v]
elif len(c.nested_path) < len(nested_path):
from_doc = doc_collection.get(c.nested_path[0], None)
column = c.es_column
from_doc.active_columns.remove(c)
abs_schema._remove_column(c)
required_changes.append({"nest": (c, nested_path)})
deep_c = Column(name=cname,
jx_type=value_type,
es_column=typed_column(
cname,
json_type_to_sql_type(value_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now())
abs_schema._add_column(deep_c)
insertion.active_columns.add(deep_c)
for r in from_doc.rows:
r1 = unwrap(r)
if column in r1:
row1 = {
UID: self.next_uid(),
PARENT: r1["__id__"],
ORDER: 0,
column: r1[column]
}
insertion.rows.append(row1)
elif len(c.nested_path) > len(nested_path):
insertion = doc_collection[c.nested_path[0]]
row = {UID: self.next_uid(), PARENT: uid, ORDER: order}
insertion.rows.append(row)
# BE SURE TO NEST VALUES, IF NEEDED
if value_type == "nested":
row[c.es_column] = "."
deeper_nested_path = [cname] + nested_path
insertion = doc_collection.get(cname, None)
if not insertion:
insertion = doc_collection[cname] = Data(
active_columns=set(), rows=[])
for i, r in enumerate(v):
child_uid = self.next_uid()
_flatten(r, child_uid, uid, i, cname,
deeper_nested_path)
elif value_type == "object":
row[c.es_column] = "."
_flatten(v,
uid,
parent_id,
order,
cname,
nested_path,
row=row)
elif c.jx_type:
row[c.es_column] = v
for doc in docs:
_flatten(doc,
self.next_uid(),
0,
0,
full_path=path,
nested_path=["."],
guid=self.next_guid())
if required_changes:
snowflake.change_schema(required_changes)
required_changes = []
return doc_collection
def _flatten(data, uid, parent_id, order, full_path, nested_path, row=None, guid=None):
"""
:param data: the data we are pulling apart
:param uid: the uid we are giving this doc
:param parent_id: the parent id of this (sub)doc
:param order: the number of siblings before this one
:param full_path: path to this (sub)doc
:param nested_path: list of paths, deepest first
:param row: we will be filling this
:return:
"""
table = concat_field(self.name, nested_path[0])
insertion = doc_collection[nested_path[0]]
if not row:
row = {GUID: guid, UID: uid, PARENT: parent_id, ORDER: order}
insertion.rows.append(row)
if is_data(data):
items = [(concat_field(full_path, k), v ) for k, v in wrap(data).leaves()]
else:
# PRIMITIVE VALUES
items = [(full_path, data)]
for cname, v in items:
jx_type = get_jx_type(v)
if jx_type is None:
continue
insertion = doc_collection[nested_path[0]]
if jx_type == NESTED:
c = first(
cc
for cc in insertion.active_columns + snowflake.columns
if cc.jx_type in STRUCT and untyped_column(cc.name)[0] == cname
)
else:
c = first(
cc
for cc in insertion.active_columns + snowflake.columns
if cc.jx_type == jx_type and cc.name == cname
)
if isinstance(c, list):
Log.error("confused")
if not c:
# WHAT IS THE NESTING LEVEL FOR THIS PATH?
deeper_nested_path = "."
for path in snowflake.query_paths:
if startswith_field(cname, path[0]) and len(deeper_nested_path) < len(path):
deeper_nested_path = path
c = Column(
name=cname,
jx_type=jx_type,
es_type=json_type_to_sqlite_type.get(jx_type, jx_type),
es_column=typed_column(cname, json_type_to_sql_type.get(jx_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now()
)
if jx_type == NESTED:
snowflake.query_paths.append(c.es_column)
required_changes.append({'nest': c})
else:
insertion.active_columns.add(c)
required_changes.append({"add": c})
elif c.jx_type == NESTED and jx_type == OBJECT:
# ALWAYS PROMOTE OBJECTS TO NESTED
jx_type = NESTED
v = [v]
elif len(c.nested_path) < len(nested_path):
from_doc = doc_collection.get(c.nested_path[0], None)
column = c.es_column
from_doc.active_columns.remove(c)
snowflake._remove_column(c)
required_changes.append({"nest": c})
deep_c = Column(
name=cname,
jx_type=jx_type,
es_type=json_type_to_sqlite_type.get(jx_type, jx_type),
es_column=typed_column(cname, json_type_to_sql_type.get(jx_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now()
)
snowflake._add_column(deep_c)
snowflake._drop_column(c)
from_doc.active_columns.remove(c)
for r in from_doc.rows:
r1 = unwrap(r)
if column in r1:
row1 = {UID: self.container.next_uid(), PARENT: r1["__id__"], ORDER: 0, column: r1[column]}
insertion.rows.append(row1)
elif len(c.nested_path) > len(nested_path):
insertion = doc_collection[c.nested_path[0]]
row = {UID: self.container.next_uid(), PARENT: uid, ORDER: order}
insertion.rows.append(row)
# BE SURE TO NEST VALUES, IF NEEDED
if jx_type == NESTED:
deeper_nested_path = [cname] + nested_path
if not doc_collection.get(cname):
doc_collection[cname] = Data(
active_columns=Queue(),
rows=[]
)
for i, r in enumerate(v):
child_uid = self.container.next_uid()
_flatten(r, child_uid, uid, i, cname, deeper_nested_path)
elif jx_type == OBJECT:
_flatten(v, uid, parent_id, order, cname, nested_path, row=row)
elif c.jx_type:
row[c.es_column] = v
def _flatten(data,
uid,
parent_id,
order,
full_path,
nested_path,
row=None,
guid=None):
"""
:param data: the data we are pulling apart
:param uid: the uid we are giving this doc
:param parent_id: the parent id of this (sub)doc
:param order: the number of siblings before this one
:param full_path: path to this (sub)doc
:param nested_path: list of paths, deepest first
:param row: we will be filling this
:return:
"""
table = concat_field(self.name, nested_path[0])
insertion = doc_collection[nested_path[0]]
if not row:
row = {GUID: guid, UID: uid, PARENT: parent_id, ORDER: order}
insertion.rows.append(row)
if not isinstance(data, Mapping):
data = {".": data}
for k, v in data.items():
insertion = doc_collection[nested_path[0]]
cname = concat_field(full_path, literal_field(k))
value_type = get_type(v)
if value_type is None:
continue
if value_type in STRUCT:
c = unwraplist([
cc for cc in abs_schema.column[cname]
if cc.jx_type in STRUCT
])
else:
c = unwraplist([
cc for cc in abs_schema.column[cname]
if cc.jx_type == value_type
])
if not c:
# WHAT IS THE NESTING LEVEL FOR THIS PATH?
deeper_nested_path = "."
for path in abs_schema.query_paths:
if startswith_field(
cname,
path) and len(deeper_nested_path) < len(path):
deeper_nested_path = path
c = Column(name=cname,
jx_type=value_type,
es_type=json_type_to_sqlite_type.get(
value_type, value_type),
es_column=typed_column(
cname,
json_type_to_sql_type.get(value_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now())
abs_schema.columns.append(c)
if value_type == "nested":
abs_schema.query_paths.append(c.es_column)
required_changes.append({'nest': (c, nested_path)})
else:
required_changes.append({"add": c})
# INSIDE IF BLOCK BECAUSE WE DO NOT WANT IT TO ADD WHAT WE columns.get() ALREADY
insertion.active_columns.add(c)
elif c.jx_type == "nested" and value_type == "object":
value_type = "nested"
v = [v]
elif len(c.nested_path) < len(nested_path):
from_doc = doc_collection.get(c.nested_path[0], None)
column = c.es_column
from_doc.active_columns.remove(c)
abs_schema._remove_column(c)
required_changes.append({"nest": (c, nested_path)})
deep_c = Column(name=cname,
jx_type=value_type,
es_column=typed_column(
cname,
json_type_to_sql_type(value_type)),
es_index=table,
nested_path=nested_path,
last_updated=Date.now())
abs_schema._add_column(deep_c)
insertion.active_columns.add(deep_c)
for r in from_doc.rows:
r1 = unwrap(r)
if column in r1:
row1 = {
UID: self.next_uid(),
PARENT: r1["__id__"],
ORDER: 0,
column: r1[column]
}
insertion.rows.append(row1)
elif len(c.nested_path) > len(nested_path):
insertion = doc_collection[c.nested_path[0]]
row = {UID: self.next_uid(), PARENT: uid, ORDER: order}
insertion.rows.append(row)
# BE SURE TO NEST VALUES, IF NEEDED
if value_type == "nested":
row[c.es_column] = "."
deeper_nested_path = [cname] + nested_path
insertion = doc_collection.get(cname, None)
if not insertion:
insertion = doc_collection[cname] = Data(
active_columns=set(), rows=[])
for i, r in enumerate(v):
child_uid = self.next_uid()
_flatten(r, child_uid, uid, i, cname,
deeper_nested_path)
elif value_type == "object":
row[c.es_column] = "."
_flatten(v,
uid,
parent_id,
order,
cname,
nested_path,
row=row)
elif c.jx_type:
row[c.es_column] = v
def __new__(cls, e=None, query=None, *args, **kwargs):
e.allowNulls = coalesce(e.allowNulls, True)
if e.value and e.domain.type == "default":
# if query.groupby:
# return object.__new__(DefaultDecoder, e)
if is_text(e.value):
Log.error("Expecting Variable or Expression, not plain string")
if is_op(e.value, LeavesOp):
return object.__new__(ObjectDecoder)
elif is_op(e.value, TupleOp):
# THIS domain IS FROM A dimension THAT IS A SIMPLE LIST OF fields
# JUST PULL THE FIELDS
if not all(is_op(t, Variable) for t in e.value.terms):
Log.error("Can only handle variables in tuples")
e.domain = Data(dimension={"fields": e.value.terms})
return object.__new__(DimFieldListDecoder)
elif is_op(e.value, Variable):
schema = query.frum.schema
cols = schema.leaves(e.value.var)
if not cols:
return object.__new__(DefaultDecoder)
if len(cols) != 1:
return object.__new__(ObjectDecoder)
col = first(cols)
limit = coalesce(e.domain.limit, query.limit, DEFAULT_LIMIT)
if col.cardinality == None:
DEBUG and Log.warning(
"metadata for column {{name|quote}} (id={{id}}) is not ready",
name=concat_field(col.es_index, col.es_column),
id=id(col))
e.domain = set_default(DefaultDomain(limit=limit),
e.domain.__data__())
return object.__new__(DefaultDecoder)
elif col.multi <= 1 and col.partitions == None:
e.domain = set_default(DefaultDomain(limit=limit),
e.domain.__data__())
return object.__new__(DefaultDecoder)
else:
DEBUG and Log.note("id={{id}} has parts!!!", id=id(col))
if col.multi > 1:
return object.__new__(MultivalueDecoder)
partitions = col.partitions[:limit:]
if e.domain.sort == -1:
partitions = list(reversed(sorted(partitions)))
else:
partitions = sorted(partitions)
e.domain = SimpleSetDomain(partitions=partitions,
limit=limit)
else:
return object.__new__(DefaultDecoder)
if e.value and e.domain.type in PARTITION:
return object.__new__(SetDecoder)
if isinstance(e.domain.dimension, Dimension):
e.domain = e.domain.dimension.getDomain()
return object.__new__(SetDecoder)
if e.value and e.domain.type == "time":
return object.__new__(TimeDecoder)
if e.range:
return object.__new__(GeneralRangeDecoder)
if e.value and e.domain.type == "duration":
return object.__new__(DurationDecoder)
elif e.value and e.domain.type == "range":
return object.__new__(RangeDecoder)
elif not e.value and e.domain.dimension.fields:
# THIS domain IS FROM A dimension THAT IS A SIMPLE LIST OF fields
# JUST PULL THE FIELDS
fields = e.domain.dimension.fields
if is_data(fields):
Log.error("No longer allowed: All objects are expressions")
else:
return object.__new__(DimFieldListDecoder)
elif not e.value and all(e.domain.partitions.where):
return object.__new__(GeneralSetDecoder)
else:
Log.error("domain type of {{type}} is not supported yet",
type=e.domain.type)
def _run(self):
with CProfiler():
self.id = thread.get_ident()
with ALL_LOCK:
ALL[self.id] = self
try:
if self.target is not None:
a, k, self.args, self.kwargs = self.args, self.kwargs, None, None
response = self.target(*a, **k)
with self.synch_lock:
self.end_of_thread = Data(response=response)
else:
with self.synch_lock:
self.end_of_thread = Null
except Exception as e:
e = Except.wrap(e)
with self.synch_lock:
self.end_of_thread = Data(exception=e)
if self not in self.parent.children:
# THREAD FAILURES ARE A PROBLEM ONLY IF NO ONE WILL BE JOINING WITH IT
try:
Log.fatal("Problem in thread {{name|quote}}", name=self.name, cause=e)
except Exception:
sys.stderr.write(b"ERROR in thread: " + str(self.name) + b" " + str(e) + b"\n")
finally:
try:
children = copy(self.children)
for c in children:
try:
if DEBUG:
sys.stdout.write(b"Stopping thread " + str(c.name) + b"\n")
c.stop()
except Exception as e:
Log.warning("Problem stopping thread {{thread}}", thread=c.name, cause=e)
for c in children:
try:
if DEBUG:
sys.stdout.write(b"Joining on thread " + str(c.name) + b"\n")
c.join()
except Exception as e:
Log.warning("Problem joining thread {{thread}}", thread=c.name, cause=e)
finally:
if DEBUG:
sys.stdout.write(b"Joined on thread " + str(c.name) + b"\n")
self.stopped.go()
if DEBUG:
Log.note("thread {{name|quote}} stopping", name=self.name)
del self.target, self.args, self.kwargs
with ALL_LOCK:
del ALL[self.id]
except Exception as e:
if DEBUG:
Log.warning("problem with thread {{name|quote}}", cause=e, name=self.name)
finally:
self.stopped.go()
if DEBUG:
Log.note("thread {{name|quote}} is done", name=self.name)
# License, v. 2.0. If a copy of the MPL was not distributed with this file, # You can obtain one at http://mozilla.org/MPL/2.0/. # # Author: Kyle Lahnakoski ([email protected]) # from __future__ import unicode_literals from collections import Mapping from jx_base import container from jx_python import containers from mo_dots import Data from mo_dots import wrap, set_default, split_field from mo_logs import Log config = Data() # config.default IS EXPECTED TO BE SET BEFORE CALLS ARE MADE _ListContainer = None _meta = None def _delayed_imports(): global _ListContainer global _meta from jx_python import meta as _meta from jx_python.containers.list_usingPythonList import ListContainer as _ListContainer _ = _ListContainer _ = _meta
def __init__(self, dim, parent, jx):
dim = wrap(dim)
self.name = dim.name
self.parent = coalesce(parent)
self.full_name = join_field(split_field(self.parent.full_name)+[self.name])
self.edges = None # FOR NOW
dot.set_default(self, dim)
self.where = dim.where
self.type = coalesce(dim.type, "set")
self.limit = coalesce(dim.limit, DEFAULT_QUERY_LIMIT)
self.index = coalesce(dim.index, coalesce(parent, Null).index, jx.settings.index)
if not self.index:
Log.error("Expecting an index name")
# ALLOW ACCESS TO SUB-PART BY NAME (IF ONLY THERE IS NO NAME COLLISION)
self.edges = Data()
for e in listwrap(dim.edges):
new_e = Dimension(e, self, jx)
self.edges[new_e.full_name] = new_e
self.partitions = wrap(coalesce(dim.partitions, []))
parse_partition(self)
fields = coalesce(dim.field, dim.fields)
if not fields:
return # NO FIELDS TO SEARCH
elif isinstance(fields, Mapping):
self.fields = wrap(fields)
edges = wrap([{"name": k, "value": v, "allowNulls": False} for k, v in self.fields.items()])
else:
self.fields = listwrap(fields)
edges = wrap([{"name": f, "value": f, "index": i, "allowNulls": False} for i, f in enumerate(self.fields)])
if dim.partitions:
return # ALREADY HAVE PARTS
if self.type not in KNOWN - ALGEBRAIC:
return # PARTS OR TOO FUZZY (OR TOO NUMEROUS) TO FETCH
jx.get_columns()
with Timer("Get parts of {{name}}", {"name": self.name}):
parts = jx.query({
"from": self.index,
"select": {"name": "count", "aggregate": "count"},
"edges": edges,
"where": self.where,
"limit": self.limit
})
Log.note("{{name}} has {{num}} parts", name= self.name, num= len(parts))
d = parts.edges[0].domain
if dim.path:
if len(edges) > 1:
Log.error("Not supported yet")
# EACH TERM RETURNED IS A PATH INTO A PARTITION TREE
temp = Data(partitions=[])
for i, count in enumerate(parts):
a = dim.path(d.getEnd(d.partitions[i]))
if not isinstance(a, list):
Log.error("The path function on " + dim.name + " must return an ARRAY of parts")
addParts(
temp,
dim.path(d.getEnd(d.partitions[i])),
count,
0
)
self.value = coalesce(dim.value, "name")
self.partitions = temp.partitions
elif isinstance(fields, Mapping):
self.value = "name" # USE THE "name" ATTRIBUTE OF PARTS
partitions = FlatList()
for g, p in parts.groupby(edges):
if p:
partitions.append({
"value": g,
"where": {"and": [
{"term": {e.value: g[e.name]}}
for e in edges
]},
"count": int(p)
})
self.partitions = partitions
elif len(edges) == 1:
self.value = "name" # USE THE "name" ATTRIBUTE OF PARTS
# SIMPLE LIST OF PARTS RETURNED, BE SURE TO INTERRELATE THEM
self.partitions = wrap([
{
"name": str(d.partitions[i].name), # CONVERT TO STRING
"value": d.getEnd(d.partitions[i]),
"where": {"term": {edges[0].value: d.partitions[i].value}},
"count": count
}
for i, count in enumerate(parts)
])
self.order = {p.value: i for i, p in enumerate(self.partitions)}
elif len(edges) == 2:
self.value = "name" # USE THE "name" ATTRIBUTE OF PARTS
d2 = parts.edges[1].domain
# SIMPLE LIST OF PARTS RETURNED, BE SURE TO INTERRELATE THEM
array = parts.data.values()[0].cube # DIG DEEP INTO RESULT (ASSUME SINGLE VALUE CUBE, WITH NULL AT END)
def edges2value(*values):
if isinstance(fields, Mapping):
output = Data()
for e, v in zip(edges, values):
output[e.name] = v
return output
else:
return tuple(values)
self.partitions = wrap([
{
"name": str(d.partitions[i].name), # CONVERT TO STRING
"value": d.getEnd(d.partitions[i]),
"where": {"term": {edges[0].value: d.partitions[i].value}},
"count": SUM(subcube),
"partitions": [
{
"name": str(d2.partitions[j].name), # CONVERT TO STRING
"value": edges2value(d.getEnd(d.partitions[i]), d2.getEnd(d2.partitions[j])),
"where": {"and": [
{"term": {edges[0].value: d.partitions[i].value}},
{"term": {edges[1].value: d2.partitions[j].value}}
]},
"count": count2
}
for j, count2 in enumerate(subcube)
if count2 > 0 # ONLY INCLUDE PROPERTIES THAT EXIST
]
}
for i, subcube in enumerate(array)
])
else:
Log.error("Not supported")
parse_partition(self) # RELATE THE PARTS TO THE PARENTS
def _get_single_branch_from_hg(settings, description, dir):
if dir == "users":
return []
response = http.get(settings.url + "/" + dir)
doc = BeautifulSoup(response.all_content, "html.parser")
output = []
try:
all_branches = doc("table")[0]
except Exception:
return []
for i, b in enumerate(all_branches("tr")):
if i == 0:
continue # IGNORE HEADER
columns = b("td")
try:
path = columns[0].a.get('href')
if path == "/":
continue
name, desc, last_used = [c.text.strip() for c in columns][0:3]
if last_used.startswith('at'):
last_used = last_used[2:]
detail = Data(
name=name.lower(),
locale=DEFAULT_LOCALE,
parent_name=description,
url=settings.url + path,
description=desc,
last_used=Date(last_used),
etl={"timestamp": Date.now()}
)
if detail.description == "unknown":
detail.description = None
# SOME BRANCHES HAVE NAME COLLISIONS, IGNORE LEAST POPULAR
if path in [
"/projects/dxr/", # moved to webtools
"/build/compare-locales/", # ?build team likes to clone?
"/build/puppet/", # ?build team likes to clone?
"/SeaMonkey/puppet/", # looses the popularity contest
"/releases/gaia-l10n/v1_2/en-US/", # use default branch
"/releases/gaia-l10n/v1_3/en-US/", # use default branch
"/releases/gaia-l10n/v1_4/en-US/", # use default branch
"/releases/gaia-l10n/v2_0/en-US/", # use default branch
"/releases/gaia-l10n/v2_1/en-US/", # use default branch
"/build/autoland/"
]:
continue
# MARKUP BRANCH IF LOCALE SPECIFIC
if path.startswith("/l10n-central"):
_path = path.strip("/").split("/")
detail.locale = _path[-1]
detail.name = "mozilla-central"
elif path.startswith("/releases/l10n/"):
_path = path.strip("/").split("/")
detail.locale = _path[-1]
detail.name = _path[-2].lower()
elif path.startswith("/releases/gaia-l10n/"):
_path = path.strip("/").split("/")
detail.locale = _path[-1]
detail.name = "gaia-" + _path[-2][1::]
elif path.startswith("/weave-l10n"):
_path = path.strip("/").split("/")
detail.locale = _path[-1]
detail.name = "weave"
if BRANCH_WHITELIST is not None:
found = False
for br in BRANCH_WHITELIST:
if br in str(detail.name):
found = True
break
if not found:
continue
Log.note("Branch {{name}} {{locale}}", name=detail.name, locale=detail.locale)
output.append(detail)
except Exception as e:
Log.warning("branch digestion problem", cause=e)
return output
def format_table(aggs, es_query, query, decoders, all_selects):
new_edges = wrap(count_dim(aggs, es_query, decoders))
dims = tuple(len(e.domain.partitions) + (0 if e.allowNulls is False else 1) for e in new_edges)
rank = len(dims)
header = tuple(new_edges.name + all_selects.name)
name2index = {s.name: i + rank for i, s in enumerate(all_selects)}
def data():
is_sent = Matrix(dims=dims)
give_me_zeros = query.sort and not query.groupby
if give_me_zeros:
# WE REQUIRE THE ZEROS FOR SORTING
all_coord = is_sent._all_combos() # TRACK THE EXPECTED COMBINATIONS
ordered_coord = next(all_coord)[::-1]
output = None
for row, coord, agg, ss in aggs_iterator(aggs, es_query, decoders):
if coord != ordered_coord:
# output HAS BEEN YIELDED, BUT SET THE DEFAULT VALUES
if output is not None:
for s in all_selects:
i = name2index[s.name]
if output[i] is None:
output[i] = s.default
# WE CAN GET THE SAME coord MANY TIMES, SO ONLY ADVANCE WHEN NOT
ordered_coord = next(all_coord)[::-1]
while coord != ordered_coord:
# HAPPENS WHEN THE coord IS AHEAD OF ordered_coord
record = [d.get_value(ordered_coord[i]) for i, d in enumerate(decoders)] + [s.default for s in all_selects]
yield record
ordered_coord = next(all_coord)[::-1]
# coord == missing_coord
output = [d.get_value(c) for c, d in zip(coord, decoders)] + [None for s in all_selects]
for select in ss:
v = select.pull(agg)
if v != None:
union(output, name2index[select.name], v, select.aggregate)
yield output
else:
last_coord = None # HANG ONTO THE output FOR A BIT WHILE WE FILL THE ELEMENTS
output = None
for row, coord, agg, ss in aggs_iterator(aggs, es_query, decoders):
if coord != last_coord:
if output:
# SET DEFAULTS
for i, s in enumerate(all_selects):
v = output[rank+i]
if v == None:
output[rank+i] = s.default
yield output
output = is_sent[coord]
if output == None:
output = is_sent[coord] = [d.get_value(c) for c, d in zip(coord, decoders)] + [None for _ in all_selects]
last_coord = coord
# THIS IS A TRICK! WE WILL UPDATE A ROW THAT WAS ALREADY YIELDED
for select in ss:
v = select.pull(agg)
if v != None:
union(output, name2index[select.name], v, select.aggregate)
if output:
# SET DEFAULTS ON LAST ROW
for i, s in enumerate(all_selects):
v = output[rank+i]
if v == None:
output[rank+i] = s.default
yield output
# EMIT THE MISSING CELLS IN THE CUBE
if not query.groupby:
for coord, output in is_sent:
if output == None:
record = [d.get_value(c) for c, d in zip(coord, decoders)] + [s.default for s in all_selects]
yield record
return Data(
meta={"format": "table"},
header=header,
data=list(data())
)
from mo_logs import Log
from mo_logs.exceptions import Except
from mo_logs.strings import utf82unicode, unicode2utf8
from mo_math import Math
from mo_threads import Lock
from mo_threads import Till
from mo_times.durations import Duration
from pyLibrary import convert
from pyLibrary.env.big_data import safe_size, ibytes2ilines, icompressed2ibytes
DEBUG = False
FILE_SIZE_LIMIT = 100 * 1024 * 1024
MIN_READ_SIZE = 8 * 1024
ZIP_REQUEST = False
default_headers = Data(
) # TODO: MAKE THIS VARIABLE A SPECIAL TYPE OF EXPECTED MODULE PARAMETER SO IT COMPLAINS IF NOT SET
default_timeout = 600
DEFAULTS = {
"allow_redirects": True,
"stream": True,
"verify": True,
"timeout": 600,
"zip": False,
"retry": {
"times": 1,
"sleep": 0,
"http": False
}
}
_warning_sent = False
request_count = 0
def es_setop(es, mvel, query):
FromES = es09.util.build_es_query(query)
select = listwrap(query.select)
isDeep = len(split_field(
query.frum.name)) > 1 # LOOKING INTO NESTED WILL REQUIRE A SCRIPT
isComplex = OR([
s.value == None and s.aggregate not in ("count", "none")
for s in select
]) # CONVERTING esfilter DEFINED PARTS WILL REQUIRE SCRIPT
if not isDeep and not isComplex:
if len(select) == 1 and isinstance(select[0].value, LeavesOp):
FromES = wrap({
"query": {
"bool": {
"query": {
"match_all": {}
},
"filter": query.where.to_esfilter()
}
},
"sort": query.sort,
"size": 0
})
elif all(isinstance(v, Variable) for v in select.value):
FromES = wrap({
"query": {
"bool": {
"query": {
"match_all": {}
},
"filter": query.where.to_esfilter()
}
},
"fields": select.value,
"sort": query.sort,
"size": coalesce(query.limit, 200000)
})
elif not isDeep:
simple_query = query.copy()
simple_query.where = TRUE # THE FACET FILTER IS FASTER
FromES.facets.mvel = {
"terms": {
"script_field": mvel.code(simple_query),
"size": coalesce(simple_query.limit, 200000)
},
"facet_filter": jx_expression(query.where).to_esfilter()
}
else:
FromES.facets.mvel = {
"terms": {
"script_field": mvel.code(query),
"size": coalesce(query.limit, 200000)
},
"facet_filter": jx_expression(query.where).to_esfilter()
}
data = es_post(es, FromES, query.limit)
if len(select) == 1 and isinstance(select[0].value, LeavesOp):
# SPECIAL CASE FOR SINGLE COUNT
cube = wrap(data).hits.hits._source
elif isinstance(select[0].value, Variable):
# SPECIAL CASE FOR SINGLE TERM
cube = wrap(data).hits.hits.fields
else:
data_list = unpack_terms(data.facets.mvel, select)
if not data_list:
cube = Cube(select, [], {s.name: Matrix.wrap([]) for s in select})
else:
output = zip(*data_list)
cube = Cube(
select, [],
{s.name: Matrix(list=output[i])
for i, s in enumerate(select)})
return Data(meta={"esquery": FromES}, data=cube)
def _accumulate_nested(rows, row, nested_doc_details, parent_doc_id, parent_id_coord):
"""
:param rows: REVERSED STACK OF ROWS (WITH push() AND pop())
:param row: CURRENT ROW BEING EXTRACTED
:param nested_doc_details: {
"nested_path": wrap_nested_path(nested_path),
"index_to_column": map from column number to column details
"children": all possible direct decedents' nested_doc_details
}
:param parent_doc_id: the id of the parent doc (for detecting when to step out of loop)
:param parent_id_coord: the column number for the parent id (so we ca extract from each row)
:return: the nested property (usually an array)
"""
previous_doc_id = None
doc = Data()
output = []
id_coord = nested_doc_details['id_coord']
while True:
doc_id = row[id_coord]
if doc_id == None or (parent_id_coord is not None and row[parent_id_coord] != parent_doc_id):
rows.append(row) # UNDO PREVIOUS POP (RECORD IS NOT A NESTED RECORD OF parent_doc)
return output
if doc_id != previous_doc_id:
previous_doc_id = doc_id
doc = Data()
curr_nested_path = nested_doc_details['nested_path'][0]
index_to_column = nested_doc_details['index_to_column'].items()
if index_to_column:
for i, c in index_to_column:
value = row[i]
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
# ASSIGN INNER PROPERTIES
relative_path = concat_field(c.push_name, c.push_child)
else: # FACT IS EXPECTED TO BE A SINGLE VALUE, NOT AN OBJECT
relative_path = c.push_child
if relative_path == ".":
if value == '':
doc = Null
else:
doc = value
elif value != None and value != '':
doc[relative_path] = value
for child_details in nested_doc_details['children']:
# EACH NESTED TABLE MUST BE ASSEMBLED INTO A LIST OF OBJECTS
child_id = row[child_details['id_coord']]
if child_id is not None:
nested_value = _accumulate_nested(rows, row, child_details, doc_id, id_coord)
if nested_value:
push_name = child_details['nested_path'][0]
if isinstance(query.select, list) or isinstance(query.select.value, LeavesOp):
# ASSIGN INNER PROPERTIES
relative_path = relative_field(push_name, curr_nested_path)
else: # FACT IS EXPECTED TO BE A SINGLE VALUE, NOT AN OBJECT
relative_path = "."
if relative_path == "." and doc is Null:
doc = nested_value
elif relative_path == ".":
doc = unwraplist(nested_value)
else:
doc[relative_path] = unwraplist(nested_value)
output.append(doc)
try:
row = rows.pop()
except IndexError:
return output
def es_aggsop(es, frum, query):
query = query.copy() # WE WILL MARK UP THIS QUERY
schema = frum.schema
select = listwrap(query.select)
es_query = Data()
new_select = Data() # MAP FROM canonical_name (USED FOR NAMES IN QUERY) TO SELECT MAPPING
formula = []
for s in select:
if s.aggregate == "count" and isinstance(s.value, Variable) and s.value.var == ".":
if schema.query_path == ".":
s.pull = jx_expression_to_function("doc_count")
else:
s.pull = jx_expression_to_function({"coalesce": ["_nested.doc_count", "doc_count", 0]})
elif isinstance(s.value, Variable):
if s.aggregate == "count":
new_select["count_"+literal_field(s.value.var)] += [s]
else:
new_select[literal_field(s.value.var)] += [s]
else:
formula.append(s)
for canonical_name, many in new_select.items():
for s in many:
es_cols = frum.schema.values(s.value.var)
if s.aggregate == "count":
canonical_names = []
for es_col in es_cols:
cn = literal_field(es_col.es_column + "_count")
if es_col.type == EXISTS:
canonical_names.append(cn + ".doc_count")
es_query.aggs[cn].filter.range = {es_col.es_column: {"gt": 0}}
else:
canonical_names.append(cn+ ".value")
es_query.aggs[cn].value_count.field = es_col.es_column
if len(es_cols) == 1:
s.pull = jx_expression_to_function(canonical_names[0])
else:
s.pull = jx_expression_to_function({"add": canonical_names})
elif s.aggregate == "median":
if len(es_cols) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.field = es_cols[0].es_column
es_query.aggs[key].percentiles.percents += [50]
s.pull = jx_expression_to_function(key + ".values.50\.0")
elif s.aggregate == "percentile":
if len(es_cols) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = literal_field(canonical_name + " percentile")
if isinstance(s.percentile, text_type) or s.percetile < 0 or 1 < s.percentile:
Log.error("Expecting percentile to be a float from 0.0 to 1.0")
percent = Math.round(s.percentile * 100, decimal=6)
es_query.aggs[key].percentiles.field = es_cols[0].es_column
es_query.aggs[key].percentiles.percents += [percent]
s.pull = jx_expression_to_function(key + ".values." + literal_field(text_type(percent)))
elif s.aggregate == "cardinality":
canonical_names = []
for es_col in es_cols:
cn = literal_field(es_col.es_column + "_cardinality")
canonical_names.append(cn)
es_query.aggs[cn].cardinality.field = es_col.es_column
if len(es_cols) == 1:
s.pull = jx_expression_to_function(canonical_names[0] + ".value")
else:
s.pull = jx_expression_to_function({"add": [cn + ".value" for cn in canonical_names], "default": 0})
elif s.aggregate == "stats":
if len(es_cols) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# REGULAR STATS
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].extended_stats.field = es_cols[0].es_column
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + "_percentile")
es_query.aggs[median_name].percentiles.field = es_cols[0].es_column
es_query.aggs[median_name].percentiles.percents += [50]
s.pull = get_pull_stats(stats_name, median_name)
elif s.aggregate == "union":
pulls = []
for es_col in es_cols:
script = {"scripted_metric": {
'init_script': 'params._agg.terms = new HashSet()',
'map_script': 'for (v in doc['+quote(es_col.es_column)+'].values) params._agg.terms.add(v)',
'combine_script': 'return params._agg.terms.toArray()',
'reduce_script': 'HashSet output = new HashSet(); for (a in params._aggs) { if (a!=null) for (v in a) {output.add(v)} } return output.toArray()',
}}
stats_name = encode_property(es_col.es_column)
if es_col.nested_path[0] == ".":
es_query.aggs[stats_name] = script
pulls.append(jx_expression_to_function(stats_name + ".value"))
else:
es_query.aggs[stats_name] = {
"nested": {"path": es_col.nested_path[0]},
"aggs": {"_nested": script}
}
pulls.append(jx_expression_to_function(stats_name + "._nested.value"))
if len(pulls) == 0:
s.pull = NULL
elif len(pulls) == 1:
s.pull = pulls[0]
else:
s.pull = lambda row: UNION(p(row) for p in pulls)
else:
if len(es_cols) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# PULL VALUE OUT OF THE stats AGGREGATE
es_query.aggs[literal_field(canonical_name)].extended_stats.field = es_cols[0].es_column
s.pull = jx_expression_to_function({"coalesce": [literal_field(canonical_name) + "." + aggregates[s.aggregate], s.default]})
for i, s in enumerate(formula):
canonical_name = literal_field(s.name)
if isinstance(s.value, TupleOp):
if s.aggregate == "count":
# TUPLES ALWAYS EXIST, SO COUNTING THEM IS EASY
s.pull = "doc_count"
else:
Log.error("{{agg}} is not a supported aggregate over a tuple", agg=s.aggregate)
elif s.aggregate == "count":
es_query.aggs[literal_field(canonical_name)].value_count.script = s.value.partial_eval().to_painless(schema).script(schema)
s.pull = jx_expression_to_function(literal_field(canonical_name) + ".value")
elif s.aggregate == "median":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.script = s.value.to_painless(schema).script(schema)
es_query.aggs[key].percentiles.percents += [50]
s.pull = jx_expression_to_function(key + ".values.50\.0")
elif s.aggregate == "percentile":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
percent = Math.round(s.percentile * 100, decimal=6)
es_query.aggs[key].percentiles.script = s.value.to_painless(schema).script(schema)
es_query.aggs[key].percentiles.percents += [percent]
s.pull = jx_expression_to_function(key + ".values." + literal_field(text_type(percent)))
elif s.aggregate == "cardinality":
# ES USES DIFFERENT METHOD FOR CARDINALITY
key = canonical_name + " cardinality"
es_query.aggs[key].cardinality.script = s.value.to_painless(schema).script(schema)
s.pull = jx_expression_to_function(key + ".value")
elif s.aggregate == "stats":
# REGULAR STATS
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].extended_stats.script = s.value.to_painless(schema).script(schema)
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + " percentile")
es_query.aggs[median_name].percentiles.script = s.value.to_painless(schema).script(schema)
es_query.aggs[median_name].percentiles.percents += [50]
s.pull = get_pull_stats(stats_name, median_name)
elif s.aggregate=="union":
# USE TERMS AGGREGATE TO SIMULATE union
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].terms.script_field = s.value.to_painless(schema).script(schema)
s.pull = jx_expression_to_function(stats_name + ".buckets.key")
else:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = jx_expression_to_function(canonical_name + "." + aggregates[s.aggregate])
es_query.aggs[canonical_name].extended_stats.script = s.value.to_painless(schema).script(schema)
decoders = get_decoders_by_depth(query)
start = 0
#<TERRIBLE SECTION> THIS IS WHERE WE WEAVE THE where CLAUSE WITH nested
split_where = split_expression_by_depth(query.where, schema=frum.schema)
if len(split_field(frum.name)) > 1:
if any(split_where[2::]):
Log.error("Where clause is too deep")
for d in decoders[1]:
es_query = d.append_query(es_query, start)
start += d.num_columns
if split_where[1]:
#TODO: INCLUDE FILTERS ON EDGES
filter_ = AndOp("and", split_where[1]).to_esfilter(schema)
es_query = Data(
aggs={"_filter": set_default({"filter": filter_}, es_query)}
)
es_query = wrap({
"aggs": {"_nested": set_default(
{
"nested": {
"path": schema.query_path
}
},
es_query
)}
})
else:
if any(split_where[1::]):
Log.error("Where clause is too deep")
if decoders:
for d in jx.reverse(decoders[0]):
es_query = d.append_query(es_query, start)
start += d.num_columns
if split_where[0]:
#TODO: INCLUDE FILTERS ON EDGES
filter = AndOp("and", split_where[0]).to_esfilter(schema)
es_query = Data(
aggs={"_filter": set_default({"filter": filter}, es_query)}
)
# </TERRIBLE SECTION>
if not es_query:
es_query = wrap({"query": {"match_all": {}}})
es_query.size = 0
with Timer("ES query time") as es_duration:
result = es_post(es, es_query, query.limit)
try:
format_time = Timer("formatting")
with format_time:
decoders = [d for ds in decoders for d in ds]
result.aggregations.doc_count = coalesce(result.aggregations.doc_count, result.hits.total) # IT APPEARS THE OLD doc_count IS GONE
formatter, groupby_formatter, aggop_formatter, mime_type = format_dispatch[query.format]
if query.edges:
output = formatter(decoders, result.aggregations, start, query, select)
elif query.groupby:
output = groupby_formatter(decoders, result.aggregations, start, query, select)
else:
output = aggop_formatter(decoders, result.aggregations, start, query, select)
output.meta.timing.formatting = format_time.duration
output.meta.timing.es_search = es_duration.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
if query.format not in format_dispatch:
Log.error("Format {{format|quote}} not supported yet", format=query.format, cause=e)
Log.error("Some problem", cause=e)
def es_aggsop(es, frum, query):
select = wrap([s.copy() for s in listwrap(query.select)])
# [0] is a cheat; each es_column should be a dict of columns keyed on type, like in sqlite
es_column_map = {v: frum.schema[v][0].es_column for v in query.vars()}
es_query = Data()
new_select = Data(
) #MAP FROM canonical_name (USED FOR NAMES IN QUERY) TO SELECT MAPPING
formula = []
for s in select:
if s.aggregate == "count" and isinstance(
s.value, Variable) and s.value.var == ".":
s.pull = "doc_count"
elif isinstance(s.value, Variable):
if s.value.var == ".":
if frum.typed:
# STATISITCAL AGGS IMPLY $value, WHILE OTHERS CAN BE ANYTHING
if s.aggregate in NON_STATISTICAL_AGGS:
#TODO: HANDLE BOTH $value AND $objects TO COUNT
Log.error("do not know how to handle")
else:
s.value.var = "$value"
new_select["$value"] += [s]
else:
if s.aggregate in NON_STATISTICAL_AGGS:
#TODO: WE SHOULD BE ABLE TO COUNT, BUT WE MUST *OR* ALL LEAF VALUES TO DO IT
Log.error("do not know how to handle")
else:
Log.error(
'Not expecting ES to have a value at "." which {{agg}} can be applied',
agg=s.aggregate)
elif s.aggregate == "count":
s.value = s.value.map(es_column_map)
new_select["count_" + literal_field(s.value.var)] += [s]
else:
s.value = s.value.map(es_column_map)
new_select[literal_field(s.value.var)] += [s]
else:
formula.append(s)
for canonical_name, many in new_select.items():
representative = many[0]
if representative.value.var == ".":
Log.error("do not know how to handle")
else:
field_name = representative.value.var
# canonical_name=literal_field(many[0].name)
for s in many:
if s.aggregate == "count":
es_query.aggs[literal_field(
canonical_name)].value_count.field = field_name
s.pull = literal_field(canonical_name) + ".value"
elif s.aggregate == "median":
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.field = field_name
es_query.aggs[key].percentiles.percents += [50]
s.pull = key + ".values.50\.0"
elif s.aggregate == "percentile":
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = literal_field(canonical_name + " percentile")
if isinstance(
s.percentile,
basestring) or s.percetile < 0 or 1 < s.percentile:
Log.error(
"Expecting percentile to be a float from 0.0 to 1.0")
percent = Math.round(s.percentile * 100, decimal=6)
es_query.aggs[key].percentiles.field = field_name
es_query.aggs[key].percentiles.percents += [percent]
s.pull = key + ".values." + literal_field(unicode(percent))
elif s.aggregate == "cardinality":
# ES USES DIFFERENT METHOD FOR CARDINALITY
key = literal_field(canonical_name + " cardinality")
es_query.aggs[key].cardinality.field = field_name
s.pull = key + ".value"
elif s.aggregate == "stats":
# REGULAR STATS
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].extended_stats.field = field_name
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + " percentile")
es_query.aggs[median_name].percentiles.field = field_name
es_query.aggs[median_name].percentiles.percents += [50]
s.pull = {
"count": stats_name + ".count",
"sum": stats_name + ".sum",
"min": stats_name + ".min",
"max": stats_name + ".max",
"avg": stats_name + ".avg",
"sos": stats_name + ".sum_of_squares",
"std": stats_name + ".std_deviation",
"var": stats_name + ".variance",
"median": median_name + ".values.50\.0"
}
elif s.aggregate == "union":
# USE TERMS AGGREGATE TO SIMULATE union
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].terms.field = field_name
es_query.aggs[stats_name].terms.size = Math.min(
s.limit, MAX_LIMIT)
s.pull = stats_name + ".buckets.key"
else:
# PULL VALUE OUT OF THE stats AGGREGATE
es_query.aggs[literal_field(
canonical_name)].extended_stats.field = field_name
s.pull = literal_field(canonical_name) + "." + aggregates1_4[
s.aggregate]
for i, s in enumerate(formula):
canonical_name = literal_field(s.name)
abs_value = s.value.map(es_column_map)
if isinstance(abs_value, TupleOp):
if s.aggregate == "count":
# TUPLES ALWAYS EXIST, SO COUNTING THEM IS EASY
s.pull = "doc_count"
else:
Log.error("{{agg}} is not a supported aggregate over a tuple",
agg=s.aggregate)
elif s.aggregate == "count":
es_query.aggs[literal_field(
canonical_name)].value_count.script = abs_value.to_ruby()
s.pull = literal_field(canonical_name) + ".value"
elif s.aggregate == "median":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.script = abs_value.to_ruby()
es_query.aggs[key].percentiles.percents += [50]
s.pull = key + ".values.50\.0"
elif s.aggregate == "percentile":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
percent = Math.round(s.percentile * 100, decimal=6)
es_query.aggs[key].percentiles.script = abs_value.to_ruby()
es_query.aggs[key].percentiles.percents += [percent]
s.pull = key + ".values." + literal_field(unicode(percent))
elif s.aggregate == "cardinality":
# ES USES DIFFERENT METHOD FOR CARDINALITY
key = canonical_name + " cardinality"
es_query.aggs[key].cardinality.script = abs_value.to_ruby()
s.pull = key + ".value"
elif s.aggregate == "stats":
# REGULAR STATS
stats_name = literal_field(canonical_name)
es_query.aggs[
stats_name].extended_stats.script = abs_value.to_ruby()
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + " percentile")
es_query.aggs[median_name].percentiles.script = abs_value.to_ruby()
es_query.aggs[median_name].percentiles.percents += [50]
s.pull = {
"count": stats_name + ".count",
"sum": stats_name + ".sum",
"min": stats_name + ".min",
"max": stats_name + ".max",
"avg": stats_name + ".avg",
"sos": stats_name + ".sum_of_squares",
"std": stats_name + ".std_deviation",
"var": stats_name + ".variance",
"median": median_name + ".values.50\.0"
}
elif s.aggregate == "union":
# USE TERMS AGGREGATE TO SIMULATE union
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].terms.script_field = abs_value.to_ruby()
s.pull = stats_name + ".buckets.key"
else:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = canonical_name + "." + aggregates1_4[s.aggregate]
es_query.aggs[
canonical_name].extended_stats.script = abs_value.to_ruby()
decoders = get_decoders_by_depth(query)
start = 0
vars_ = query.where.vars()
#<TERRIBLE SECTION> THIS IS WHERE WE WEAVE THE where CLAUSE WITH nested
split_where = split_expression_by_depth(query.where, schema=frum.schema)
if len(split_field(frum.name)) > 1:
if any(split_where[2::]):
Log.error("Where clause is too deep")
for d in decoders[1]:
es_query = d.append_query(es_query, start)
start += d.num_columns
if split_where[1]:
#TODO: INCLUDE FILTERS ON EDGES
filter_ = simplify_esfilter(
AndOp("and", split_where[1]).to_esfilter())
es_query = Data(
aggs={"_filter": set_default({"filter": filter_}, es_query)})
es_query = wrap({
"aggs": {
"_nested":
set_default({"nested": {
"path": frum.query_path
}}, es_query)
}
})
else:
if any(split_where[1::]):
Log.error("Where clause is too deep")
if decoders:
for d in jx.reverse(decoders[0]):
es_query = d.append_query(es_query, start)
start += d.num_columns
if split_where[0]:
#TODO: INCLUDE FILTERS ON EDGES
filter = simplify_esfilter(AndOp("and", split_where[0]).to_esfilter())
es_query = Data(
aggs={"_filter": set_default({"filter": filter}, es_query)})
# </TERRIBLE SECTION>
if not es_query:
es_query = wrap({"query": {"match_all": {}}})
es_query.size = 0
with Timer("ES query time") as es_duration:
result = es09.util.post(es, es_query, query.limit)
try:
format_time = Timer("formatting")
with format_time:
decoders = [d for ds in decoders for d in ds]
result.aggregations.doc_count = coalesce(
result.aggregations.doc_count,
result.hits.total) # IT APPEARS THE OLD doc_count IS GONE
formatter, groupby_formatter, aggop_formatter, mime_type = format_dispatch[
query.format]
if query.edges:
output = formatter(decoders, result.aggregations, start, query,
select)
elif query.groupby:
output = groupby_formatter(decoders, result.aggregations,
start, query, select)
else:
output = aggop_formatter(decoders, result.aggregations, start,
query, select)
output.meta.timing.formatting = format_time.duration
output.meta.timing.es_search = es_duration.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
if query.format not in format_dispatch:
Log.error("Format {{format|quote}} not supported yet",
format=query.format,
cause=e)
Log.error("Some problem", e)
def get_value_from_row(self, row):
output = Data()
for k, v in zip(self.put,
row[self.start:self.start + self.num_columns:]):
output[k] = v["key"]
return output
