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]
elif s.aggregate:
formula.append(s)
for canonical_name, many in new_select.items():
for s in many:
columns = frum.schema.values(s.value.var)
if s.aggregate == "count":
canonical_names = []
for column in columns:
cn = literal_field(column.es_column + "_count")
if column.jx_type == EXISTS:
canonical_names.append(cn + ".doc_count")
es_query.aggs[cn].filter.range = {column.es_column: {"gt": 0}}
else:
canonical_names.append(cn+ ".value")
es_query.aggs[cn].value_count.field = column.es_column
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 = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.field = columns[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(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 = 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 = columns[0].es_column
es_query.aggs[key].percentiles.percents += [percent]
es_query.aggs[key].percentiles.tdigest.compression = 2
s.pull = jx_expression_to_function(key + ".values." + literal_field(text_type(percent)))
elif s.aggregate == "cardinality":
canonical_names = []
for column in columns:
cn = literal_field(column.es_column + "_cardinality")
canonical_names.append(cn)
es_query.aggs[cn].cardinality.field = column.es_column
if len(columns) == 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(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)
es_query.aggs[stats_name].extended_stats.field = columns[0].es_column
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + "_percentile")
es_query.aggs[median_name].percentiles.field = columns[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 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 = encode_property(column.es_column)
if column.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": column.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(columns) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
elif len(columns) <1:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = jx_expression_to_function({"null":{}})
else:
# PULL VALUE OUT OF THE stats AGGREGATE
es_query.aggs[literal_field(canonical_name)].extended_stats.field = columns[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"
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().max(' + expand_template(COMPARE_TUPLE, {"dir": dir, "op": op}) + ').get()',
}}
if schema.query_path[0] == ".":
es_query.aggs[canonical_name] = script
s.pull = jx_expression_to_function(literal_field(canonical_name) + ".value")
else:
es_query.aggs[canonical_name] = {
"nested": {"path": schema.query_path[0]},
"aggs": {"_nested": script}
}
s.pull = jx_expression_to_function(literal_field(canonical_name) + "._nested.value")
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_es_script(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_es_script(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_es_script(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_es_script(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_es_script(schema).script(schema)
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + " percentile")
es_query.aggs[median_name].percentiles.script = s.value.to_es_script(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_es_script(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_es_script(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[0]}},
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):
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]
elif s.aggregate:
formula.append(s)
for canonical_name, many in new_select.items():
for s in many:
columns = frum.schema.values(s.value.var)
if s.aggregate == "count":
canonical_names = []
for column in columns:
cn = literal_field(column.es_column + "_count")
if column.jx_type == EXISTS:
canonical_names.append(cn + ".doc_count")
es_query.aggs[cn].filter.range = {column.es_column: {"gt": 0}}
else:
canonical_names.append(cn+ ".value")
es_query.aggs[cn].value_count.field = column.es_column
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 = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.field = columns[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(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 = 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 = columns[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 column in columns:
cn = literal_field(column.es_column + "_cardinality")
canonical_names.append(cn)
es_query.aggs[cn].cardinality.field = column.es_column
if len(columns) == 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(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)
es_query.aggs[stats_name].extended_stats.field = columns[0].es_column
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + "_percentile")
es_query.aggs[median_name].percentiles.field = columns[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 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 = encode_property(column.es_column)
if column.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": column.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(columns) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
elif len(columns) <1:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = jx_expression_to_function({"null":{}})
else:
# PULL VALUE OUT OF THE stats AGGREGATE
es_query.aggs[literal_field(canonical_name)].extended_stats.field = columns[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"
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().max(' + expand_template(COMPARE_TUPLE, {"dir": dir, "op": op}) + ').get()',
}}
if schema.query_path[0] == ".":
es_query.aggs[canonical_name] = script
s.pull = jx_expression_to_function(literal_field(canonical_name) + ".value")
else:
es_query.aggs[canonical_name] = {
"nested": {"path": schema.query_path[0]},
"aggs": {"_nested": script}
}
s.pull = jx_expression_to_function(literal_field(canonical_name) + "._nested.value")
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_es_script(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_es_script(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_es_script(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_es_script(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_es_script(schema).script(schema)
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + " percentile")
es_query.aggs[median_name].percentiles.script = s.value.to_es_script(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_es_script(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_es_script(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[0]}},
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_deepop(es, query):
schema = query.frum.schema
query_path = schema.query_path[0]
# TODO: FIX THE GREAT SADNESS CAUSED BY EXECUTING post_expressions
# THE EXPRESSIONS SHOULD BE PUSHED TO THE CONTAINER: ES ALLOWS
# {"inner_hit":{"script_fields":[{"script":""}...]}}, BUT THEN YOU
# LOOSE "_source" BUT GAIN "fields", FORCING ALL FIELDS TO BE EXPLICIT
post_expressions = {}
es_query, es_filters = es_query_template(query_path)
# SPLIT WHERE CLAUSE BY DEPTH
wheres = split_expression_by_depth(query.where, schema)
for f, w in zip_longest(es_filters, wheres):
script = ES52[AndOp(w)].partial_eval().to_esfilter(schema)
set_default(f, script)
if not wheres[1]:
# INCLUDE DOCS WITH NO NESTED DOCS
more_filter = {
"bool": {
"filter": [AndOp(wheres[0]).partial_eval().to_esfilter(schema)],
"must_not": {
"nested": {
"path": query_path,
"query": MATCH_ALL
}
}
}
}
else:
more_filter = None
es_query.size = coalesce(query.limit, DEFAULT_LIMIT)
map_to_es_columns = schema.map_to_es()
query_for_es = query.map(map_to_es_columns)
es_query.sort = jx_sort_to_es_sort(query_for_es.sort, schema)
es_query.stored_fields = []
is_list = is_list_(query.select)
selects = wrap([unwrap(s.copy()) for s in listwrap(query.select)])
new_select = FlatList()
put_index = 0
for select in selects:
if is_op(select.value, LeavesOp) and is_op(select.value.term, Variable):
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
leaves = schema.leaves(select.value.term.var)
col_names = set()
for c in leaves:
if c.nested_path[0] == ".":
if c.jx_type == NESTED:
continue
es_query.stored_fields += [c.es_column]
c_name = untype_path(relative_field(c.name, query_path))
col_names.add(c_name)
new_select.append({
"name": concat_field(select.name, c_name),
"nested_path": c.nested_path[0],
"put": {"name": concat_field(select.name, literal_field(c_name)), "index": put_index, "child": "."},
"pull": get_pull_function(c)
})
put_index += 1
# REMOVE DOTS IN PREFIX IF NAME NOT AMBIGUOUS
for n in new_select:
if n.name.startswith("..") and n.name.lstrip(".") not in col_names:
n.put.name = n.name = n.name.lstrip(".")
col_names.add(n.name)
elif is_op(select.value, Variable):
net_columns = schema.leaves(select.value.var)
if not net_columns:
new_select.append({
"name": select.name,
"nested_path": ".",
"put": {"name": select.name, "index": put_index, "child": "."},
"pull": NULL
})
else:
for n in net_columns:
pull = get_pull_function(n)
if n.nested_path[0] == ".":
if n.jx_type == NESTED:
continue
es_query.stored_fields += [n.es_column]
# WE MUST FIGURE OUT WHICH NAMESSPACE s.value.var IS USING SO WE CAN EXTRACT THE child
for np in n.nested_path:
c_name = untype_path(relative_field(n.name, np))
if startswith_field(c_name, select.value.var):
# PREFER THE MOST-RELATIVE NAME
child = relative_field(c_name, select.value.var)
break
else:
continue
new_select.append({
"name": select.name,
"pull": pull,
"nested_path": n.nested_path[0],
"put": {
"name": select.name,
"index": put_index,
"child": child
}
})
put_index += 1
else:
expr = select.value
for v in expr.vars():
for c in schema[v.var]:
if c.nested_path[0] == ".":
es_query.stored_fields += [c.es_column]
# else:
# Log.error("deep field not expected")
pull_name = EXPRESSION_PREFIX + select.name
map_to_local = MapToLocal(schema)
pull = jx_expression_to_function(pull_name)
post_expressions[pull_name] = jx_expression_to_function(expr.map(map_to_local))
new_select.append({
"name": select.name if is_list else ".",
"pull": pull,
"value": expr.__data__(),
"put": {"name": select.name, "index": put_index, "child": "."}
})
put_index += 1
es_query.stored_fields = sorted(es_query.stored_fields)
# <COMPLICATED> ES needs two calls to get all documents
more = []
def get_more(please_stop):
more.append(es_post(
es,
Data(
query=more_filter,
stored_fields=es_query.stored_fields
),
query.limit
))
if more_filter:
need_more = Thread.run("get more", target=get_more)
with Timer("call to ES") as call_timer:
data = es_post(es, es_query, query.limit)
# EACH A HIT IS RETURNED MULTIPLE TIMES FOR EACH INNER HIT, WITH INNER HIT INCLUDED
def inners():
for t in data.hits.hits:
for i in t.inner_hits[literal_field(query_path)].hits.hits:
t._inner = i._source
for k, e in post_expressions.items():
t[k] = e(t)
yield t
if more_filter:
Thread.join(need_more)
for t in more[0].hits.hits:
yield t
# </COMPLICATED>
try:
formatter, groupby_formatter, mime_type = format_dispatch[query.format]
output = formatter(inners(), new_select, query)
output.meta.timing.es = call_timer.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
Log.error("problem formatting", e)
def es_deepop(es, query):
schema = query.frum.schema
query_path = schema.query_path[0]
# TODO: FIX THE GREAT SADNESS CAUSED BY EXECUTING post_expressions
# THE EXPRESSIONS SHOULD BE PUSHED TO THE CONTAINER: ES ALLOWS
# {"inner_hit":{"script_fields":[{"script":""}...]}}, BUT THEN YOU
# LOOSE "_source" BUT GAIN "fields", FORCING ALL FIELDS TO BE EXPLICIT
post_expressions = {}
es_query, es_filters = es_query_template(query_path)
# SPLIT WHERE CLAUSE BY DEPTH
wheres = split_expression_by_depth(query.where, schema)
for i, f in enumerate(es_filters):
script = AndOp("and", wheres[i]).partial_eval().to_esfilter(schema)
set_default(f, script)
if not wheres[1]:
more_filter = {
"bool": {
"filter": [AndOp("and", wheres[0]).partial_eval().to_esfilter(schema)],
"must_not": {
"nested": {
"path": query_path,
"query": {
"match_all": {}
}
}
}
}
}
else:
more_filter = None
es_query.size = coalesce(query.limit, DEFAULT_LIMIT)
# es_query.sort = jx_sort_to_es_sort(query.sort)
map_to_es_columns = schema.map_to_es()
# {c.names["."]: c.es_column for c in schema.leaves(".")}
query_for_es = query.map(map_to_es_columns)
es_query.sort = jx_sort_to_es_sort(query_for_es.sort, schema)
es_query.stored_fields = []
is_list = isinstance(query.select, list)
new_select = FlatList()
i = 0
for s in listwrap(query.select):
if isinstance(s.value, LeavesOp) and isinstance(s.value.term, Variable):
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
leaves = schema.leaves(s.value.term.var)
col_names = set()
for c in leaves:
if c.nested_path[0] == ".":
if c.jx_type == NESTED:
continue
es_query.stored_fields += [c.es_column]
c_name = untype_path(c.names[query_path])
col_names.add(c_name)
new_select.append({
"name": concat_field(s.name, c_name),
"nested_path": c.nested_path[0],
"put": {"name": concat_field(s.name, literal_field(c_name)), "index": i, "child": "."},
"pull": get_pull_function(c)
})
i += 1
# REMOVE DOTS IN PREFIX IF NAME NOT AMBIGUOUS
for n in new_select:
if n.name.startswith("..") and n.name.lstrip(".") not in col_names:
n.put.name = n.name = n.name.lstrip(".")
col_names.add(n.name)
elif isinstance(s.value, Variable):
net_columns = schema.leaves(s.value.var)
if not net_columns:
new_select.append({
"name": s.name,
"nested_path": ".",
"put": {"name": s.name, "index": i, "child": "."},
"pull": NULL
})
else:
for n in net_columns:
pull = get_pull_function(n)
if n.nested_path[0] == ".":
if n.jx_type == NESTED:
continue
es_query.stored_fields += [n.es_column]
# WE MUST FIGURE OUT WHICH NAMESSPACE s.value.var IS USING SO WE CAN EXTRACT THE child
for np in n.nested_path:
c_name = untype_path(n.names[np])
if startswith_field(c_name, s.value.var):
child = relative_field(c_name, s.value.var)
break
else:
child = relative_field(untype_path(n.names[n.nested_path[0]]), s.value.var)
new_select.append({
"name": s.name,
"pull": pull,
"nested_path": n.nested_path[0],
"put": {
"name": s.name,
"index": i,
"child": child
}
})
i += 1
else:
expr = s.value
for v in expr.vars():
for c in schema[v.var]:
if c.nested_path[0] == ".":
es_query.stored_fields += [c.es_column]
# else:
# Log.error("deep field not expected")
pull_name = EXPRESSION_PREFIX + s.name
map_to_local = MapToLocal(schema)
pull = jx_expression_to_function(pull_name)
post_expressions[pull_name] = compile_expression(expr.map(map_to_local).to_python())
new_select.append({
"name": s.name if is_list else ".",
"pull": pull,
"value": expr.__data__(),
"put": {"name": s.name, "index": i, "child": "."}
})
i += 1
# <COMPLICATED> ES needs two calls to get all documents
more = []
def get_more(please_stop):
more.append(es_post(
es,
Data(
query=more_filter,
stored_fields=es_query.stored_fields
),
query.limit
))
if more_filter:
need_more = Thread.run("get more", target=get_more)
with Timer("call to ES") as call_timer:
data = es_post(es, es_query, query.limit)
# EACH A HIT IS RETURNED MULTIPLE TIMES FOR EACH INNER HIT, WITH INNER HIT INCLUDED
def inners():
for t in data.hits.hits:
for i in t.inner_hits[literal_field(query_path)].hits.hits:
t._inner = i._source
for k, e in post_expressions.items():
t[k] = e(t)
yield t
if more_filter:
Thread.join(need_more)
for t in more[0].hits.hits:
yield t
#</COMPLICATED>
try:
formatter, groupby_formatter, mime_type = format_dispatch[query.format]
output = formatter(inners(), new_select, query)
output.meta.timing.es = call_timer.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
Log.error("problem formatting", e)