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):
if isinstance(self.edge.value, FirstOp) and isinstance(self.edge.value.term, Variable):
self.edge.value = self.edge.value.term # ES USES THE FIRST TERM FOR {"terms": } AGGREGATION
if not isinstance(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 leaves(self, prefix):
head = self.namespace.get(prefix, None)
if not head:
return Null
full_name = first(head).name
return set(
c for k, cs in self.namespace.items()
if startswith_field(k, full_name) and k != GUID or k == full_name
for c in cs if c.jx_type not in [OBJECT, EXISTS])
def get_decoders_by_path(query):
"""
RETURN MAP FROM QUERY PATH TO LIST OF DECODER ARRAYS
:param query:
:return:
"""
schema = query.frum.schema
output = Data()
if query.edges:
if query.sort and query.format != "cube":
# REORDER EDGES/GROUPBY TO MATCH THE SORT
query.edges = sort_edges(query, "edges")
elif query.groupby:
if query.sort and query.format != "cube":
query.groupby = sort_edges(query, "groupby")
for edge in wrap(coalesce(query.edges, query.groupby, [])):
limit = coalesce(edge.domain.limit, query.limit, DEFAULT_LIMIT)
if edge.value != None and not isinstance(edge.value, NullOp):
edge = edge.copy()
vars_ = edge.value.vars()
for v in vars_:
if not schema.leaves(v.var):
Log.error("{{var}} does not exist in schema", var=v)
elif edge.range:
vars_ = edge.range.min.vars() | edge.range.max.vars()
for v in vars_:
if not schema[v.var]:
Log.error("{{var}} does not exist in schema", var=v)
elif edge.domain.dimension:
vars_ = edge.domain.dimension.fields
edge.domain.dimension = edge.domain.dimension.copy()
edge.domain.dimension.fields = [schema[v].es_column for v in vars_]
elif all(edge.domain.partitions.where):
vars_ = set()
for p in edge.domain.partitions:
vars_ |= p.where.vars()
vars_ |= edge.value.vars()
depths = set(c.nested_path[0] for v in vars_
for c in schema.leaves(v.var))
if not depths:
Log.error("Do not know of column {{column}}",
column=unwraplist(
[v for v in vars_ if schema[v] == None]))
if len(depths) > 1:
Log.error("expression {{expr|quote}} spans tables, can not handle",
expr=edge.value)
decoder = AggsDecoder(edge, query, limit)
output[literal_field(first(depths))] += [decoder]
return output
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("not", 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 _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 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'})
}, self).add(es_query))
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 isinstance(e.value, text_type):
Log.error("Expecting Variable or Expression, not plain string")
if isinstance(e.value, LeavesOp):
return object.__new__(ObjectDecoder)
elif isinstance(e.value, TupleOp):
# THIS domain IS FROM A dimension THAT IS A SIMPLE LIST OF fields
# JUST PULL THE FIELDS
if not all(isinstance(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 isinstance(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.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 and len(col.partitions) < 10:
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 isinstance(fields, Mapping):
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 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 isinstance(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)
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(
ComplexAggs(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":
if len(columns) > 1:
Log.error(
"Do not know how to count 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":
if len(columns) > 1:
Log.error(
"Do not know how to count columns with more than one type (script probably)"
)
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = 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)
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":
if len(columns) > 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)
complex = ComplexAggs(s).add(
ExprAggs(canonical_name, {
"extended_stats": {
"field": first(columns).es_column
}
}, None))
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + "_percentile")
complex.add(
ExprAggs(
canonical_name + "_percentile", {
"percentiles": {
"field": first(columns).es_column,
"percents": [50]
}
}, None))
acc.add(complex)
s.pull = get_pull_stats(stats_name, median_name)
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).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 isinstance(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 = TupleOp("tuple", [NULL] * len(
s.value.terms)).partial_eval().to_es_script(schema).expr
selfy = s.value.partial_eval().to_es_script(schema).expr
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":
s.value.partial_eval().to_es_script(schema).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":
s.value.to_es_script(schema).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 = Math.round(s.percentile * 100, decimal=6)
nest.add(
ExprAggs(
key, {
"percentiles": {
"script":
s.value.to_es_script(schema).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":
s.value.to_es_script(schema).script(schema)
}
}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "stats":
# REGULAR STATS
stats_name = canonical_name
nest.add(
ComplexAggs(s).add(
ExprAggs(
stats_name, {
"extended_stats": {
"script":
s.value.to_es_script(schema).script(schema)
}
}, None)))
# GET MEDIAN TOO!
median_name = canonical_name + " percentile"
nest.add(
ExprAggs(
median_name, {
"percentiles": {
"script":
s.value.to_es_script(schema).script(schema),
"percents": [50]
}
}, s))
s.pull = get_pull_stats(None, stats_name, median_name)
elif s.aggregate == "union":
# USE TERMS AGGREGATE TO SIMULATE union
nest.add(
TermsAggs(canonical_name, {
"script_field":
s.value.to_es_script(schema).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":
s.value.to_es_script(schema).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)
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("and", 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)
