def _range_composer(self, edge, domain, es_query, to_float, schema):
# USE RANGES
_min = coalesce(domain.min, MIN(domain.partitions.min))
_max = coalesce(domain.max, MAX(domain.partitions.max))
output = Aggs()
if edge.allowNulls:
output.add(
FilterAggs(
"_missing",
NotOp(
AndOp([
edge.value.exists(),
GteOp([edge.value, Literal(to_float(_min))]),
LtOp([edge.value, Literal(to_float(_max))])
]).partial_eval()), self).add(es_query))
if is_op(edge.value, Variable):
calc = {"field": first(schema.leaves(edge.value.var)).es_column}
else:
calc = {"script": text_type(Painless[edge.value].to_es_script(schema))}
calc['ranges'] = [{
"from": to_float(p.min),
"to": to_float(p.max)
} for p in domain.partitions]
return output.add(RangeAggs("_match", calc, self).add(es_query))
def append_query(self, query_path, es_query):
if is_op(self.edge.value, FirstOp) and is_op(self.edge.value.term, Variable):
self.edge.value = self.edge.value.term # ES USES THE FIRST TERM FOR {"terms": } AGGREGATION
if not is_op(self.edge.value, Variable):
terms = TermsAggs(
"_match",
{
"script": {"lang": "painless", "inline": self.script.expr},
"size": self.domain.limit,
"order": self.es_order
},
self
)
else:
terms = TermsAggs(
"_match", {
"field": first(self.schema.leaves(self.edge.value.var)).es_column,
"size": self.domain.limit,
"order": self.es_order
},
self
)
output = Aggs()
output.add(FilterAggs("_filter", self.exists, None).add(terms.add(es_query)))
output.add(FilterAggs("_missing", self.missing, self).add(es_query))
return output
def append_query(self, query_path, es_query):
domain = self.domain
domain_key = domain.key
value = Painless[self.edge.value]
cnv = pull_functions[value.type]
include = tuple(cnv(p[domain_key]) for p in domain.partitions)
exists = Painless[AndOp([
InOp([value, Literal(include)])
])].partial_eval()
limit = coalesce(self.limit, len(domain.partitions))
if is_op(value, Variable):
es_field = first(self.query.frum.schema.leaves(value.var)).es_column # ALREADY CHECKED THERE IS ONLY ONE
match = TermsAggs(
"_match",
{
"field": es_field,
"size": limit,
"order": {"_term": self.sorted} if self.sorted else None
},
self
)
else:
match = TermsAggs(
"_match",
{
"script": text_type(value.to_es_script(self.schema)),
"size": limit
},
self
)
output = Aggs().add(FilterAggs("_filter", exists, None).add(match.add(es_query)))
if self.edge.allowNulls:
# FIND NULLS AT EACH NESTED LEVEL
for p in self.schema.query_path:
if p == query_path:
# MISSING AT THE QUERY DEPTH
output.add(
NestedAggs(p).add(FilterAggs("_missing0", NotOp(exists), self).add(es_query))
)
else:
# PARENT HAS NO CHILDREN, SO MISSING
column = first(self.schema.values(query_path, (OBJECT, EXISTS)))
output.add(
NestedAggs(column.nested_path[0]).add(
FilterAggs(
"_missing1",
NotOp(ExistsOp(Variable(column.es_column.replace(NESTED_TYPE, EXISTS_TYPE)))),
self
).add(es_query)
)
)
return output
def append_query(self, query_path, es_query):
edge = self.edge
range = edge.range
domain = edge.domain
aggs = Aggs()
for i, p in enumerate(domain.partitions):
filter_ = AndOp([
LteOp([range.min, Literal(self.to_float(p.min))]),
GtOp([range.max, Literal(self.to_float(p.min))])
])
aggs.add(FilterAggs("_match" + text_type(i), filter_, self).add(es_query))
return aggs
def append_query(self, query_path, es_query):
if is_op(self.edge.value, FirstOp) and is_op(self.edge.value.term,
Variable):
self.edge.value = self.edge.value.term # ES USES THE FIRST TERM FOR {"terms": } AGGREGATION
output = Aggs()
if not is_op(self.edge.value, Variable):
terms = TermsAggs(
"_match", {
"script": {
"lang": "painless",
"inline": self.script.expr
},
"size": self.domain.limit,
"order": self.es_order
}, self)
output.add(
FilterAggs("_filter", self.exists,
None).add(terms.add(es_query)))
else:
terms = TermsAggs(
"_match", {
"field": first(self.schema.leaves(
self.edge.value.var)).es_column,
"size": self.domain.limit,
"order": self.es_order
}, self)
output.add(terms.add(es_query))
if self.edge.allowNulls:
output.add(
FilterAggs("_missing", self.missing, self).add(es_query))
return output
def append_query(self, query_path, es_query):
parts = self.edge.domain.partitions
filters = []
notty = []
for p in parts:
w = p.where
filters.append(AndOp([w] + notty))
notty.append(NotOp(w))
output = Aggs().add(FiltersAggs("_match", filters, self).add(es_query))
if self.edge.allowNulls: # TODO: Use Expression.missing().esfilter() TO GET OPTIMIZED FILTER
output.add(FilterAggs("_missing", AndOp(notty), self).add(es_query))
return output
def append_query(self, query_path, es_query):
edge = self.edge
range = edge.range
domain = edge.domain
aggs = Aggs()
for i, p in enumerate(domain.partitions):
filter_ = AndOp([
LteOp([range.min, Literal(self.to_float(p.min))]),
GtOp([range.max, Literal(self.to_float(p.min))])
])
aggs.add(FilterAggs("_match" + text(i), filter_, self).add(es_query))
return aggs
def append_query(self, query_path, es_query):
edge = self.edge
range = edge.range
domain = edge.domain
aggs = Aggs()
for i, p in enumerate(domain.partitions):
filter_ = AndOp("and", [
InequalityOp("lte", [range.min, Literal("literal", self.to_float(p.min))]),
InequalityOp("gt", [range.max, Literal("literal", self.to_float(p.min))])
])
aggs.add(FilterAggs("_match" + text_type(i), filter_, self).add(es_query))
return aggs
def append_query(self, query_path, es_query):
parts = self.edge.domain.partitions
filters = []
notty = []
for p in parts:
w = p.where
filters.append(AndOp([w] + notty))
notty.append(NotOp(w))
output = Aggs().add(FiltersAggs("_match", filters, self).add(es_query))
if self.edge.allowNulls: # TODO: Use Expression.missing().esfilter() TO GET OPTIMIZED FILTER
output.add(FilterAggs("_missing", AndOp(notty), self).add(es_query))
return output
def append_query(self, query_path, es_query):
es_field = first(self.query.frum.schema.leaves(self.var)).es_column
return Aggs().add(TermsAggs("_match", {
"script": expand_template(LIST_TO_PIPE, {"expr": 'doc[' + quote(es_field) + '].values'}),
"size": self.limit
}, self).add(es_query))
def extract_aggs(select, query_path, schema):
"""
RETURN ES AGGREGATIONS
"""
new_select = Data(
) # MAP FROM canonical_name (USED FOR NAMES IN QUERY) TO SELECT MAPPING
formula = []
for s in select:
if is_op(s.value, Variable_):
s.query_path = query_path
if s.aggregate == "count":
new_select["count_" + literal_field(s.value.var)] += [s]
else:
new_select[literal_field(s.value.var)] += [s]
elif s.aggregate:
op, split_select = split_expression_by_path(s.value,
schema,
lang=Painless)
for si_key, si_value in split_select.items():
if si_value:
if s.query_path:
Log.error(
"can not handle more than one depth per select")
s.query_path = si_key
formula.append(s)
acc = Aggs()
agg_field(acc, new_select, query_path, schema)
agg_formula(acc, formula, query_path, schema)
return acc
def append_query(self, query_path, es_query):
decoder = self
for i, v in enumerate(self.fields):
exists = v.exists().partial_eval()
nest = Aggs()
nest.add(TermsAggs("_match", {
"field": first(self.schema.leaves(v.var)).es_column,
"size": self.domain.limit
}, decoder).add(es_query))
nest.add(FilterAggs("_missing", NotOp(exists), decoder).add(es_query))
es_query = nest
decoder = None
if self.domain.where:
es_query = FilterAggs("_filter", self.domain.where, None).add(es_query)
return es_query
def append_query(self, query_path, es_query):
domain = self.domain
domain_key = domain.key
value = self.edge.value
cnv = pull_functions[value.type]
include = tuple(cnv(p[domain_key]) for p in domain.partitions)
schema = self.schema
exists = InOp([value, Literal(include)]).partial_eval()
limit = coalesce(self.limit, len(domain.partitions))
if is_op(value, Variable):
es_field = first(schema.leaves(value.var)).es_column # ALREADY CHECKED THERE IS ONLY ONE
match = TermsAggs(
"_match",
{
"field": es_field,
"size": limit,
"order": {"_term": self.sorted} if self.sorted else None
},
self
)
else:
match = TermsAggs(
"_match",
{
"script": text(Painless[value].to_es_script(schema)),
"size": limit
},
self
)
output = Aggs().add(FilterAggs("_filter", exists, None).add(match.add(es_query)))
if self.edge.allowNulls:
# IF ALL NESTED COLUMNS ARE NULL, DOES THE FILTER PASS?
# MISSING AT THE QUERY DEPTH
# columns = schema[value.var]
concat_inner = split_expression(NotOp(exists), self.query)
for i, term in enumerate(concat_inner.terms):
acc = es_query
for nest in term.nests:
if nest.where is not TRUE:
acc = NestedAggs(nest.path.var).add(FilterAggs("_missing" + text(i), nest.where, self).add(acc))
output.add(acc)
return output
def append_query(self, query_path, es_query):
domain = self.domain
domain_key = domain.key
value = Painless[self.edge.value]
cnv = pull_functions[value.type]
include = tuple(cnv(p[domain_key]) for p in domain.partitions)
schema = self.schema
exists = Painless[AndOp([InOp([value,
Literal(include)])])].partial_eval()
limit = coalesce(self.limit, len(domain.partitions))
if is_op(value, Variable):
es_field = first(schema.leaves(
value.var)).es_column # ALREADY CHECKED THERE IS ONLY ONE
match = TermsAggs(
"_match", {
"field": es_field,
"size": limit,
"order": {
"_term": self.sorted
} if self.sorted else None
}, self)
else:
match = TermsAggs("_match", {
"script": text(value.to_es_script(schema)),
"size": limit
}, self)
output = Aggs().add(
FilterAggs("_filter", exists, None).add(match.add(es_query)))
if self.edge.allowNulls:
# IF ALL NESTED COLUMNS ARE NULL, DOES THE FILTER PASS?
# MISSING AT THE QUERY DEPTH
op, split = split_expression_by_path(NotOp(exists), schema)
for i, p in enumerate(reversed(sorted(split.keys()))):
e = split.get(p)
if e:
not_match = NestedAggs(p).add(
FilterAggs("_missing" + text(i), e,
self).add(es_query))
output.add(not_match)
return output
def append_query(self, query_path, es_query):
domain = self.domain
domain_key = domain.key
value = self.edge.value
cnv = pull_functions[value.type]
include = tuple(cnv(p[domain_key]) for p in domain.partitions)
exists = AndOp("and", [
InOp("in", [value, Literal("literal", include)])
]).partial_eval()
limit = coalesce(self.limit, len(domain.partitions))
if isinstance(value, Variable):
es_field = first(self.query.frum.schema.leaves(value.var)).es_column # ALREADY CHECKED THERE IS ONLY ONE
match = TermsAggs(
"_match",
{
"field": es_field,
"size": limit,
"order": {"_term": self.sorted} if self.sorted else None
},
self
)
else:
match = TermsAggs(
"_match",
{
"script": {
"lang": "painless",
"inline": value.to_es_script(self.schema).script(self.schema)
},
"size": limit
},
self
)
output = Aggs().add(FilterAggs("_filter", exists, None).add(match.add(es_query)))
if self.edge.allowNulls:
# FIND NULLS AT EACH NESTED LEVEL
for p in self.schema.query_path:
if p == query_path:
# MISSING AT THE QUERY DEPTH
output.add(
NestedAggs(p).add(FilterAggs("_missing0", NotOp(None, exists), self).add(es_query))
)
else:
# PARENT HAS NO CHILDREN, SO MISSING
column = first(self.schema.values(query_path, (OBJECT, EXISTS)))
output.add(
NestedAggs(column.nested_path[0]).add(
FilterAggs(
"_missing1",
NotOp(None, ExistsOp(None, Variable(column.es_column.replace(NESTED_TYPE, EXISTS_TYPE)))),
self
).add(es_query)
)
)
return output
def append_query(self, query_path, es_query):
decoder = self
for i, v in enumerate(self.fields):
nest = Aggs().add(
TermsAggs("_match", {
"field": v,
"size": self.domain.limit
}, decoder).add(es_query)).add(
FilterAggs("_missing", MissingOp(Variable(v)),
decoder).add(es_query))
es_query = nest
decoder = None
return es_query
def _range_composer(self, edge, domain, es_query, to_float, schema):
# USE RANGES
_min = coalesce(domain.min, MIN(domain.partitions.min))
_max = coalesce(domain.max, MAX(domain.partitions.max))
output = Aggs()
if edge.allowNulls:
output.add(FilterAggs(
"_missing",
NotOp("not", AndOp("and", [
edge.value.exists(),
InequalityOp("gte", [edge.value, Literal(None, to_float(_min))]),
InequalityOp("lt", [edge.value, Literal(None, to_float(_max))])
]).partial_eval()),
self
).add(es_query))
if isinstance(edge.value, Variable):
calc = {"field": first(schema.leaves(edge.value.var)).es_column}
else:
calc = {"script": edge.value.to_es_script(schema).script(schema)}
calc['ranges'] = [{"from": to_float(p.min), "to": to_float(p.max)} for p in domain.partitions]
return output.add(RangeAggs("_match", calc, self).add(es_query))
def _range_composer(self, edge, domain, es_query, to_float, schema):
# USE RANGES
_min = coalesce(domain.min, MIN(domain.partitions.min))
_max = coalesce(domain.max, MAX(domain.partitions.max))
output = Aggs()
if edge.allowNulls:
output.add(FilterAggs(
"_missing",
NotOp(AndOp([
edge.value.exists(),
GteOp([edge.value, Literal(to_float(_min))]),
LtOp([edge.value, Literal(to_float(_max))])
]).partial_eval()),
self
).add(es_query))
if is_op(edge.value, Variable):
calc = {"field": first(schema.leaves(edge.value.var)).es_column}
else:
calc = {"script": text_type(Painless[edge.value].to_es_script(schema))}
calc['ranges'] = [{"from": to_float(p.min), "to": to_float(p.max)} for p in domain.partitions]
return output.add(RangeAggs("_match", calc, self).add(es_query))
def append_query(self, query_path, es_query):
decoder = self
for i, v in enumerate(self.fields):
exists = v.exists().partial_eval()
nest = Aggs()
nest.add(TermsAggs("_match", {
"field": first(self.schema.leaves(v.var)).es_column,
"size": self.domain.limit
}, decoder).add(es_query))
nest.add(FilterAggs("_missing", NotOp(exists), decoder).add(es_query))
es_query = nest
decoder = None
if self.domain.where:
es_query = FilterAggs("_filter", self.domain.where, None).add(es_query)
return es_query
def es_aggsop(es, frum, query):
query = query.copy() # WE WILL MARK UP THIS QUERY
schema = frum.schema
query_path = schema.query_path[0]
select = listwrap(query.select)
new_select = Data(
) # MAP FROM canonical_name (USED FOR NAMES IN QUERY) TO SELECT MAPPING
formula = []
for s in select:
if is_op(s.value, Variable_):
s.query_path = query_path
if s.aggregate == "count":
new_select["count_" + literal_field(s.value.var)] += [s]
else:
new_select[literal_field(s.value.var)] += [s]
elif s.aggregate:
split_select = split_expression_by_path(s.value,
schema,
lang=Painless)
for si_key, si_value in split_select.items():
if si_value:
if s.query_path:
Log.error(
"can not handle more than one depth per select")
s.query_path = si_key
formula.append(s)
acc = Aggs()
for _, many in new_select.items():
for s in many:
canonical_name = s.name
if s.aggregate in ("value_count", "count"):
columns = frum.schema.values(s.value.var,
exclude_type=(OBJECT, NESTED))
else:
columns = frum.schema.values(s.value.var)
if s.aggregate == "count":
canonical_names = []
for column in columns:
es_name = column.es_column + "_count"
if column.jx_type == EXISTS:
if column.nested_path[0] == query_path:
canonical_names.append("doc_count")
acc.add(
NestedAggs(column.nested_path[0]).add(
CountAggs(s)))
else:
canonical_names.append("value")
acc.add(
NestedAggs(column.nested_path[0]).add(
ExprAggs(es_name, {
"value_count": {
"field": column.es_column
}
}, s)))
if len(canonical_names) == 1:
s.pull = jx_expression_to_function(canonical_names[0])
else:
s.pull = jx_expression_to_function(
{"add": canonical_names})
elif s.aggregate == "median":
columns = [
c for c in columns if c.jx_type in (NUMBER, INTEGER)
]
if len(columns) != 1:
Log.error(
"Do not know how to perform median on columns with more than one type (script probably)"
)
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = canonical_name + " percentile"
acc.add(
ExprAggs(
key, {
"percentiles": {
"field": first(columns).es_column,
"percents": [50]
}
}, s))
s.pull = jx_expression_to_function("values.50\\.0")
elif s.aggregate == "percentile":
columns = [
c for c in columns if c.jx_type in (NUMBER, INTEGER)
]
if len(columns) != 1:
Log.error(
"Do not know how to perform percentile on columns with more than one type (script probably)"
)
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = canonical_name + " percentile"
if is_text(
s.percentile) or s.percetile < 0 or 1 < s.percentile:
Log.error(
"Expecting percentile to be a float from 0.0 to 1.0")
percent = mo_math.round(s.percentile * 100, decimal=6)
acc.add(
ExprAggs(
key, {
"percentiles": {
"field": first(columns).es_column,
"percents": [percent],
"tdigest": {
"compression": 2
}
}
}, s))
s.pull = jx_expression_to_function(
join_field(["values", text_type(percent)]))
elif s.aggregate == "cardinality":
for column in columns:
path = column.es_column + "_cardinality"
acc.add(
ExprAggs(path,
{"cardinality": {
"field": column.es_column
}}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "stats":
columns = [
c for c in columns if c.jx_type in (NUMBER, INTEGER)
]
if len(columns) != 1:
Log.error(
"Do not know how to perform stats on columns with more than one type (script probably)"
)
# REGULAR STATS
acc.add(
ExprAggs(canonical_name, {
"extended_stats": {
"field": first(columns).es_column
}
}, s))
s.pull = get_pull_stats()
# GET MEDIAN TOO!
select_median = s.copy()
select_median.pull = jx_expression_to_function(
{"select": [{
"name": "median",
"value": "values.50\\.0"
}]})
acc.add(
ExprAggs(
canonical_name + "_percentile", {
"percentiles": {
"field": first(columns).es_column,
"percents": [50]
}
}, select_median))
elif s.aggregate == "union":
for column in columns:
script = {
"scripted_metric": {
'init_script':
'params._agg.terms = new HashSet()',
'map_script':
'for (v in doc[' + quote(column.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 = column.es_column
acc.add(
NestedAggs(column.nested_path[0]).add(
ExprAggs(stats_name, script, s)))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "count_values":
# RETURN MAP FROM VALUE TO THE NUMBER OF TIMES FOUND IN THE DOCUMENTS
# NOT A NESTED DOC, RATHER A MULTIVALUE FIELD
for column in columns:
script = {
"scripted_metric": {
'params': {
"_agg": {}
},
'init_script':
'params._agg.terms = new HashMap()',
'map_script':
'for (v in doc[' + quote(column.es_column) +
'].values) params._agg.terms.put(v, Optional.ofNullable(params._agg.terms.get(v)).orElse(0)+1);',
'combine_script':
'return params._agg.terms',
'reduce_script':
'''
HashMap output = new HashMap();
for (agg in params._aggs) {
if (agg!=null){
for (e in agg.entrySet()) {
String key = String.valueOf(e.getKey());
output.put(key, e.getValue() + Optional.ofNullable(output.get(key)).orElse(0));
}
}
}
return output;
'''
}
}
stats_name = encode_property(column.es_column)
acc.add(
NestedAggs(column.nested_path[0]).add(
ExprAggs(stats_name, script, s)))
s.pull = jx_expression_to_function("value")
else:
if not columns:
s.pull = jx_expression_to_function(NULL)
else:
for c in columns:
acc.add(
NestedAggs(c.nested_path[0]).add(
ExprAggs(
canonical_name,
{"extended_stats": {
"field": c.es_column
}}, s)))
s.pull = jx_expression_to_function(aggregates[s.aggregate])
for i, s in enumerate(formula):
s_path = [
k for k, v in split_expression_by_path(
s.value, schema=schema, lang=Painless).items() if v
]
if len(s_path) == 0:
# FOR CONSTANTS
nest = NestedAggs(query_path)
acc.add(nest)
elif len(s_path) == 1:
nest = NestedAggs(first(s_path))
acc.add(nest)
else:
Log.error("do not know how to handle")
canonical_name = s.name
if is_op(s.value, TupleOp):
if s.aggregate == "count":
# TUPLES ALWAYS EXIST, SO COUNTING THEM IS EASY
s.pull = jx_expression_to_function("doc_count")
elif s.aggregate in ('max', 'maximum', 'min', 'minimum'):
if s.aggregate in ('max', 'maximum'):
dir = 1
op = "max"
else:
dir = -1
op = 'min'
nully = Painless[TupleOp(
[NULL] *
len(s.value.terms))].partial_eval().to_es_script(schema)
selfy = text_type(
Painless[s.value].partial_eval().to_es_script(schema))
script = {
"scripted_metric": {
'init_script':
'params._agg.best = ' + nully + ';',
'map_script':
'params._agg.best = ' + expand_template(
MAX_OF_TUPLE, {
"expr1": "params._agg.best",
"expr2": selfy,
"dir": dir,
"op": op
}) + ";",
'combine_script':
'return params._agg.best',
'reduce_script':
'return params._aggs.stream().' + op + '(' +
expand_template(COMPARE_TUPLE, {
"dir": dir,
"op": op
}) + ').get()',
}
}
nest.add(
NestedAggs(query_path).add(
ExprAggs(canonical_name, script, s)))
s.pull = jx_expression_to_function("value")
else:
Log.error("{{agg}} is not a supported aggregate over a tuple",
agg=s.aggregate)
elif s.aggregate == "count":
nest.add(
ExprAggs(
canonical_name, {
"value_count": {
"script":
text_type(Painless[
s.value].partial_eval().to_es_script(schema))
}
}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "median":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
nest.add(
ExprAggs(
key, {
"percentiles": {
"script":
text_type(Painless[s.value].to_es_script(schema)),
"percents": [50]
}
}, s))
s.pull = jx_expression_to_function(join_field(["50.0"]))
elif s.aggregate == "percentile":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
percent = mo_math.round(s.percentile * 100, decimal=6)
nest.add(
ExprAggs(
key, {
"percentiles": {
"script":
text_type(Painless[s.value].to_es_script(schema)),
"percents": [percent]
}
}, s))
s.pull = jx_expression_to_function(
join_field(["values", text_type(percent)]))
elif s.aggregate == "cardinality":
# ES USES DIFFERENT METHOD FOR CARDINALITY
key = canonical_name + " cardinality"
nest.add(
ExprAggs(
key, {
"cardinality": {
"script":
text_type(Painless[s.value].to_es_script(schema))
}
}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "stats":
# REGULAR STATS
nest.add(
ExprAggs(
canonical_name, {
"extended_stats": {
"script":
text_type(Painless[s.value].to_es_script(schema))
}
}, s))
s.pull = get_pull_stats()
# GET MEDIAN TOO!
select_median = s.copy()
select_median.pull = jx_expression_to_function(
{"select": [{
"name": "median",
"value": "values.50\\.0"
}]})
nest.add(
ExprAggs(
canonical_name + "_percentile", {
"percentiles": {
"script":
text_type(Painless[s.value].to_es_script(schema)),
"percents": [50]
}
}, select_median))
s.pull = get_pull_stats()
elif s.aggregate == "union":
# USE TERMS AGGREGATE TO SIMULATE union
nest.add(
TermsAggs(
canonical_name, {
"script_field":
text_type(Painless[s.value].to_es_script(schema))
}, s))
s.pull = jx_expression_to_function("key")
else:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = jx_expression_to_function(aggregates[s.aggregate])
nest.add(
ExprAggs(
canonical_name, {
"extended_stats": {
"script":
text_type(
NumberOp(s.value).partial_eval().to_es_script(
schema))
}
}, s))
acc = NestedAggs(query_path).add(acc)
split_decoders = get_decoders_by_path(query)
split_wheres = split_expression_by_path(query.where,
schema=frum.schema,
lang=ES52)
start = 0
decoders = [None] * (len(query.edges) + len(query.groupby))
paths = list(reversed(sorted(split_wheres.keys() | split_decoders.keys())))
for path in paths:
literal_path = literal_field(path)
decoder = split_decoders[literal_path]
where = split_wheres[literal_path]
for d in decoder:
decoders[d.edge.dim] = d
acc = d.append_query(path, acc)
start += d.num_columns
if where:
acc = FilterAggs("_filter", AndOp(where), None).add(acc)
acc = NestedAggs(path).add(acc)
acc = NestedAggs('.').add(acc)
acc = simplify(acc)
es_query = wrap(acc.to_es(schema))
es_query.size = 0
with Timer("ES query time", silent=not DEBUG) as es_duration:
result = es_post(es, es_query, query.limit)
try:
format_time = Timer("formatting", silent=not DEBUG)
with format_time:
# result.aggregations.doc_count = coalesce(result.aggregations.doc_count, result.hits.total) # IT APPEARS THE OLD doc_count IS GONE
aggs = unwrap(result.aggregations)
formatter, groupby_formatter, aggop_formatter, mime_type = format_dispatch[
query.format]
if query.edges:
output = formatter(aggs, acc, query, decoders, select)
elif query.groupby:
output = groupby_formatter(aggs, acc, query, decoders, select)
else:
output = aggop_formatter(aggs, acc, query, decoders, 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)