def _es_terms2(es, mvel, query):
"""
WE ASSUME THERE ARE JUST TWO EDGES, AND EACH HAS A SIMPLE value
"""
# REQUEST VALUES IN FIRST DIMENSION
q1 = query.copy()
q1.edges = query.edges[0:1:]
values1 = es_terms(es, mvel, q1).edges[0].domain.partitions.value
select = listwrap(query.select)
FromES = build_es_query(query)
for s in select:
for i, v in enumerate(values1):
FromES.facets[s.name + "," + str(i)] = {
"terms": {
"field": query.edges[1].value,
"size": coalesce(query.limit, 200000)
},
"facet_filter": simplify_esfilter({"and": [
query.where,
{"term": {query.edges[0].value: v}}
]})
}
data = es_post(es, FromES, query.limit)
# UNION ALL TERMS FROM SECOND DIMENSION
values2 = set()
for k, f in data.facets.items():
values2.update(f.terms.term)
values2 = jx.sort(values2)
term2index = {v: i for i, v in enumerate(values2)}
query.edges[1].domain.partitions = FlatList([{"name": v, "value": v} for v in values2])
# MAKE CUBE
output = {}
dims = [len(values1), len(values2)]
for s in select:
output[s.name] = Matrix(*dims)
# FILL CUBE
# EXPECTING ONLY SELECT CLAUSE FACETS
for facetName, facet in data.facets.items():
coord = facetName.split(",")
s = [s for s in select if s.name == coord[0]][0]
i1 = int(coord[1])
for term in facet.terms:
i2 = term2index[term.term]
output[s.name][(i1, i2)] = term[aggregates[s.aggregate]]
cube = Cube(query.select, query.edges, output)
cube.query = query
return cube
def _es_terms2(es, mvel, query):
"""
WE ASSUME THERE ARE JUST TWO EDGES, AND EACH HAS A SIMPLE value
"""
# REQUEST VALUES IN FIRST DIMENSION
q1 = query.copy()
q1.edges = query.edges[0:1:]
values1 = es_terms(es, mvel, q1).edges[0].domain.partitions.value
select = listwrap(query.select)
FromES = build_es_query(query)
for s in select:
for i, v in enumerate(values1):
FromES.facets[s.name + "," + str(i)] = {
"terms": {
"field": query.edges[1].value,
"size": coalesce(query.limit, 200000)
},
"facet_filter": simplify_esfilter({"and": [
query.where,
{"term": {query.edges[0].value: v}}
]})
}
data = es_post(es, FromES, query.limit)
# UNION ALL TERMS FROM SECOND DIMENSION
values2 = set()
for k, f in data.facets.items():
values2.update(f.terms.term)
values2 = jx.sort(values2)
term2index = {v: i for i, v in enumerate(values2)}
query.edges[1].domain.partitions = FlatList([{"name": v, "value": v} for v in values2])
# MAKE CUBE
output = {}
dims = [len(values1), len(values2)]
for s in select:
output[s.name] = Matrix(*dims)
# FILL CUBE
# EXPECTING ONLY SELECT CLAUSE FACETS
for facetName, facet in data.facets.items():
coord = facetName.split(",")
s = [s for s in select if s.name == coord[0]][0]
i1 = int(coord[1])
for term in facet.terms:
i2 = term2index[term.term]
output[s.name][(i1, i2)] = term[aggregates[s.aggregate]]
cube = Cube(query.select, query.edges, output)
cube.query = query
return cube
def es_countop(es, mvel, query):
"""
RETURN SINGLE COUNT
"""
select = listwrap(query.select)
FromES = build_es_query(query)
for s in select:
if is_variable_name(s.value):
FromES.facets[s.name] = {
"terms": {
"field": s.value,
"size": query.limit,
},
"facet_filter": {
"exists": {
"field": s.value
}
}
}
else:
# COMPLICATED value IS PROBABLY A SCRIPT, USE IT
FromES.facets[s.name] = {
"terms": {
"script_field":
es09.expressions.compile_expression(s.value, query),
"size":
200000
}
}
data = es_post(es, FromES, query.limit)
matricies = {}
for s in select:
matricies[s.name] = Matrix(value=data.hits.facets[s.name].total)
cube = Cube(query.select, query.edges, matricies)
cube.frum = query
return cube
def es_aggop(es, mvel, query):
select = listwrap(query.select)
FromES = build_es_query(query)
isSimple = AND(aggregates[s.aggregate] == "count" for s in select)
if isSimple:
return es_countop(es, query) # SIMPLE, USE TERMS FACET INSTEAD
value2facet = dict() # ONLY ONE FACET NEEDED PER
name2facet = dict() # MAP name TO FACET WITH STATS
for s in select:
if s.value not in value2facet:
if isinstance(s.value, Variable):
unwrap(FromES.facets)[s.name] = {
"statistical": {
"field": s.value.var
},
"facet_filter": query.where.to_esfilter()
}
else:
unwrap(FromES.facets)[s.name] = {
"statistical": {
"script": jx_expression_to_function(s.value)
},
"facet_filter": query.where.to_es_filter()
}
value2facet[s.value] = s.name
name2facet[s.name] = value2facet[s.value]
data = es_post(es, FromES, query.limit)
matricies = {
s.name: Matrix(value=fix_es_stats(data.facets[literal_field(s.name)])[
aggregates[s.aggregate]])
for s in select
}
cube = Cube(query.select, [], matricies)
cube.frum = query
return cube
def es_terms_stats(esq, mvel, query):
select = listwrap(query.select)
facetEdges = [] # EDGES THAT WILL REQUIRE A FACET FOR EACH PART
termsEdges = FlatList()
specialEdge = None
special_index = -1
# A SPECIAL EDGE IS ONE THAT HAS AN UNDEFINED NUMBER OF PARTITIONS AT QUERY TIME
# FIND THE specialEdge, IF ONE
for f, tedge in enumerate(query.edges):
if tedge.domain.type in domains.KNOWN:
for p, part in enumerate(tedge.domain.partitions):
part.dataIndex = p
# FACETS ARE ONLY REQUIRED IF SQL JOIN ON DOMAIN IS REQUIRED (RANGE QUERY)
# OR IF WE ARE NOT SIMPLY COUNTING
# OR IF NO SCRIPTING IS ALLOWED (SOME OTHER CODE IS RESPONSIBLE FOR SETTING isFacet)
# OR IF WE JUST WANT TO FORCE IT :)
# OF COURSE THE default EDGE IS NOT EXPLICIT, SO MUST BE A TERM
facetEdges.append(tedge)
else:
if specialEdge:
Log.error(
"There is more than one open-ended edge: self can not be handled"
)
specialEdge = tedge
special_index = f
termsEdges.append(tedge)
if not specialEdge:
# WE SERIOUSLY WANT A SPECIAL EDGE, OTHERWISE WE WILL HAVE TOO MANY FACETS
# THE BIGGEST EDGE MAY BE COLLAPSED TO A TERM, MAYBE?
num_parts = 0
special_index = -1
for i, e in enumerate(facetEdges):
l = len(e.domain.partitions)
if ((e.value and is_variable_name(e.value))
or len(e.domain.dimension.fields) == 1) and l > num_parts:
num_parts = l
specialEdge = e
special_index = i
facetEdges.pop(special_index)
termsEdges.append(specialEdge)
total_facets = PRODUCT(len(f.domain.partitions)
for f in facetEdges) * len(select)
if total_facets > 100:
# WE GOT A PROBLEM, LETS COUNT THE SIZE OF REALITY:
counts = esq.query({
"from": query.frum,
"select": {
"aggregate": "count"
},
"edges": facetEdges,
"where": query.where,
"limit": query.limit
})
esFacets = []
def add_facet(value, parts, cube):
if value:
esFacets.append(parts)
counts["count"].forall(add_facet)
Log.note(
"{{theory_count}} theoretical combinations, {{real_count}} actual combos found",
real_count=len(esFacets),
theory_count=total_facets)
if not esFacets:
# MAKE EMPTY CUBE
matricies = {}
dims = [
len(e.domain.partitions) + (1 if e.allowNulls else 0)
for e in query.edges
]
for s in select:
matricies[s.name] = Matrix(*dims)
cube = Cube(query.select, query.edges, matricies)
cube.frum = query
return cube
else:
# GENERATE ALL COMBOS
esFacets = getAllEdges(facetEdges)
calcTerm = compileEdges2Term(mvel, termsEdges, FlatList())
term2parts = calcTerm.term2parts
if len(esFacets) * len(select) > 1000:
Log.error(
"not implemented yet"
) # WE HAVE SOME SERIOUS PERMUTATIONS, WE MUST ISSUE MULTIPLE QUERIES
pass
FromES = build_es_query(query)
for s in select:
for parts in esFacets:
condition = FlatList()
constants = FlatList()
name = [literal_field(s.name)]
for f, fedge in enumerate(facetEdges):
name.append(str(parts[f].dataIndex))
condition.append(buildCondition(mvel, fedge, parts[f]))
constants.append({
"name": fedge.domain.name,
"value": parts[f]
})
condition.append(query.where)
name = ",".join(name)
FromES.facets[name] = {
"terms_stats": {
"key_field":
calcTerm.field,
"value_field":
s.value if is_variable_name(s.value) else None,
"value_script":
mvel.compile_expression(s.value)
if not is_variable_name(s.value) else None,
"size":
coalesce(query.limit, 200000)
}
}
if condition:
FromES.facets[name].facet_filter = simplify_esfilter(
{"and": condition})
data = es_post(esq.es, FromES, query.limit)
if specialEdge.domain.type not in domains.KNOWN:
# WE BUILD THE PARTS BASED ON THE RESULTS WE RECEIVED
partitions = FlatList()
map = {}
for facetName, parts in data.facets.items():
for stats in parts.terms:
if not map[stats]:
part = {"value": stats, "name": stats}
partitions.append(part)
map[stats] = part
partitions.sort(specialEdge.domain.compare)
for p, part in enumerate(partitions):
part.dataIndex = p
specialEdge.domain.map = map
specialEdge.domain.partitions = partitions
# MAKE CUBE
matricies = {}
dims = [
len(e.domain.partitions) + (1 if e.allowNulls else 0)
for e in query.edges
]
for s in select:
matricies[s.name] = Matrix(*dims)
name2agg = {s.name: aggregates[s.aggregate] for s in select}
# FILL CUBE
for edgeName, parts in data.facets.items():
temp = edgeName.split(",")
pre_coord = tuple(int(c) for c in temp[1:])
sname = temp[0]
for stats in parts.terms:
if specialEdge:
special = term2parts(stats.term)[0]
coord = pre_coord[:special_index] + (
special.dataIndex, ) + pre_coord[special_index:]
else:
coord = pre_coord
matricies[sname][coord] = stats[name2agg[sname]]
cube = Cube(query.select, query.edges, matricies)
cube.frum = query
return cube
def es_terms(es, mvel, query):
"""
RETURN LIST OF ALL EDGE QUERIES
EVERY FACET IS NAMED <select.name>, <c1>, ... <cN> WHERE <ci> ARE THE ELEMENT COORDINATES
WE TRY TO PACK DIMENSIONS INTO THE TERMS TO MINIMIZE THE CROSS-PRODUCT EXPLOSION
"""
if len(query.edges) == 2:
return _es_terms2(es, mvel, query)
select = listwrap(query.select)
FromES = build_es_query(query)
packed_term = compileEdges2Term(mvel, query.edges, wrap([]))
for s in select:
FromES.facets[s.name] = {
"terms": {
"field": packed_term.field,
"script_field": packed_term.expression,
"size": coalesce(query.limit, 200000)
},
"facet_filter": simplify_esfilter(query.where)
}
term2Parts = packed_term.term2parts
data = es_post(es, FromES, query.limit)
# GETTING ALL PARTS WILL EXPAND THE EDGES' DOMAINS
# BUT HOW TO UNPACK IT FROM THE term FASTER IS UNKNOWN
for k, f in data.facets.items():
for t in f.terms:
term2Parts(t.term)
# NUMBER ALL EDGES FOR jx INDEXING
for f, e in enumerate(query.edges):
e.index = f
if e.domain.type in ["uid", "default"]:
# e.domain.partitions = jx.sort(e.domain.partitions, "value")
for p, part in enumerate(e.domain.partitions):
part.dataIndex = p
e.domain.NULL.dataIndex = len(e.domain.partitions)
# MAKE CUBE
output = {}
dims = [
len(e.domain.partitions) + (1 if e.allowNulls else 0)
for e in query.edges
]
for s in select:
output[s.name] = Matrix(*dims)
# FILL CUBE
# EXPECTING ONLY SELECT CLAUSE FACETS
for facetName, facet in data.facets.items():
for term in facet.terms:
term_coord = term2Parts(term.term).dataIndex
for s in select:
try:
output[s.name][term_coord] = term[aggregates[s.aggregate]]
except Exception as e:
# USUALLY CAUSED BY output[s.name] NOT BEING BIG ENOUGH TO HANDLE NULL COUNTS
pass
cube = Cube(query.select, query.edges, output)
cube.query = query
return cube
def es_terms(es, mvel, query):
"""
RETURN LIST OF ALL EDGE QUERIES
EVERY FACET IS NAMED <select.name>, <c1>, ... <cN> WHERE <ci> ARE THE ELEMENT COORDINATES
WE TRY TO PACK DIMENSIONS INTO THE TERMS TO MINIMIZE THE CROSS-PRODUCT EXPLOSION
"""
if len(query.edges) == 2:
return _es_terms2(es, mvel, query)
select = listwrap(query.select)
FromES = build_es_query(query)
packed_term = compileEdges2Term(mvel, query.edges, wrap([]))
for s in select:
FromES.facets[s.name] = {
"terms": {
"field": packed_term.field,
"script_field": packed_term.expression,
"size": coalesce(query.limit, 200000)
},
"facet_filter": simplify_esfilter(query.where)
}
term2Parts = packed_term.term2parts
data = es_post(es, FromES, query.limit)
# GETTING ALL PARTS WILL EXPAND THE EDGES' DOMAINS
# BUT HOW TO UNPACK IT FROM THE term FASTER IS UNKNOWN
for k, f in data.facets.items():
for t in f.terms:
term2Parts(t.term)
# NUMBER ALL EDGES FOR jx INDEXING
for f, e in enumerate(query.edges):
e.index = f
if e.domain.type in ["uid", "default"]:
# e.domain.partitions = jx.sort(e.domain.partitions, "value")
for p, part in enumerate(e.domain.partitions):
part.dataIndex = p
e.domain.NULL.dataIndex = len(e.domain.partitions)
# MAKE CUBE
output = {}
dims = [len(e.domain.partitions) + (1 if e.allowNulls else 0) for e in query.edges]
for s in select:
output[s.name] = Matrix(*dims)
# FILL CUBE
# EXPECTING ONLY SELECT CLAUSE FACETS
for facetName, facet in data.facets.items():
for term in facet.terms:
term_coord = term2Parts(term.term).dataIndex
for s in select:
try:
output[s.name][term_coord] = term[aggregates[s.aggregate]]
except Exception as e:
# USUALLY CAUSED BY output[s.name] NOT BEING BIG ENOUGH TO HANDLE NULL COUNTS
pass
cube = Cube(query.select, query.edges, output)
cube.query = query
return cube
def es_terms_stats(esq, mvel, query):
select = listwrap(query.select)
facetEdges = [] # EDGES THAT WILL REQUIRE A FACET FOR EACH PART
termsEdges = FlatList()
specialEdge = None
special_index = -1
# A SPECIAL EDGE IS ONE THAT HAS AN UNDEFINED NUMBER OF PARTITIONS AT QUERY TIME
# FIND THE specialEdge, IF ONE
for f, tedge in enumerate(query.edges):
if tedge.domain.type in domains.KNOWN:
for p, part in enumerate(tedge.domain.partitions):
part.dataIndex = p
# FACETS ARE ONLY REQUIRED IF SQL JOIN ON DOMAIN IS REQUIRED (RANGE QUERY)
# OR IF WE ARE NOT SIMPLY COUNTING
# OR IF NO SCRIPTING IS ALLOWED (SOME OTHER CODE IS RESPONSIBLE FOR SETTING isFacet)
# OR IF WE JUST WANT TO FORCE IT :)
# OF COURSE THE default EDGE IS NOT EXPLICIT, SO MUST BE A TERM
facetEdges.append(tedge)
else:
if specialEdge:
Log.error("There is more than one open-ended edge: self can not be handled")
specialEdge = tedge
special_index = f
termsEdges.append(tedge)
if not specialEdge:
# WE SERIOUSLY WANT A SPECIAL EDGE, OTHERWISE WE WILL HAVE TOO MANY FACETS
# THE BIGGEST EDGE MAY BE COLLAPSED TO A TERM, MAYBE?
num_parts = 0
special_index = -1
for i, e in enumerate(facetEdges):
l = len(e.domain.partitions)
if ((e.value and is_variable_name(e.value)) or len(e.domain.dimension.fields) == 1) and l > num_parts:
num_parts = l
specialEdge = e
special_index = i
facetEdges.pop(special_index)
termsEdges.append(specialEdge)
total_facets = PRODUCT(len(f.domain.partitions) for f in facetEdges)*len(select)
if total_facets > 100:
# WE GOT A PROBLEM, LETS COUNT THE SIZE OF REALITY:
counts = esq.query({
"from": query.frum,
"select": {"aggregate": "count"},
"edges": facetEdges,
"where": query.where,
"limit": query.limit
})
esFacets = []
def add_facet(value, parts, cube):
if value:
esFacets.append(parts)
counts["count"].forall(add_facet)
Log.note("{{theory_count}} theoretical combinations, {{real_count}} actual combos found", real_count= len(esFacets), theory_count=total_facets)
if not esFacets:
# MAKE EMPTY CUBE
matricies = {}
dims = [len(e.domain.partitions) + (1 if e.allowNulls else 0) for e in query.edges]
for s in select:
matricies[s.name] = Matrix(*dims)
cube = Cube(query.select, query.edges, matricies)
cube.frum = query
return cube
else:
# GENERATE ALL COMBOS
esFacets = getAllEdges(facetEdges)
calcTerm = compileEdges2Term(mvel, termsEdges, FlatList())
term2parts = calcTerm.term2parts
if len(esFacets) * len(select) > 1000:
Log.error("not implemented yet") # WE HAVE SOME SERIOUS PERMUTATIONS, WE MUST ISSUE MULTIPLE QUERIES
pass
FromES = build_es_query(query)
for s in select:
for parts in esFacets:
condition = FlatList()
constants = FlatList()
name = [literal_field(s.name)]
for f, fedge in enumerate(facetEdges):
name.append(str(parts[f].dataIndex))
condition.append(buildCondition(mvel, fedge, parts[f]))
constants.append({"name": fedge.domain.name, "value": parts[f]})
condition.append(query.where)
name = ",".join(name)
FromES.facets[name] = {
"terms_stats": {
"key_field": calcTerm.field,
"value_field": s.value if is_variable_name(s.value) else None,
"value_script": mvel.compile_expression(s.value) if not is_variable_name(s.value) else None,
"size": coalesce(query.limit, 200000)
}
}
if condition:
FromES.facets[name].facet_filter = simplify_esfilter({"and": condition})
data = es_post(esq.es, FromES, query.limit)
if specialEdge.domain.type not in domains.KNOWN:
# WE BUILD THE PARTS BASED ON THE RESULTS WE RECEIVED
partitions = FlatList()
map = {}
for facetName, parts in data.facets.items():
for stats in parts.terms:
if not map[stats]:
part = {"value": stats, "name": stats}
partitions.append(part)
map[stats] = part
partitions.sort(specialEdge.domain.compare)
for p, part in enumerate(partitions):
part.dataIndex = p
specialEdge.domain.map = map
specialEdge.domain.partitions = partitions
# MAKE CUBE
matricies = {}
dims = [len(e.domain.partitions) + (1 if e.allowNulls else 0) for e in query.edges]
for s in select:
matricies[s.name] = Matrix(*dims)
name2agg = {s.name: aggregates[s.aggregate] for s in select}
# FILL CUBE
for edgeName, parts in data.facets.items():
temp = edgeName.split(",")
pre_coord = tuple(int(c) for c in temp[1:])
sname = temp[0]
for stats in parts.terms:
if specialEdge:
special = term2parts(stats.term)[0]
coord = pre_coord[:special_index]+(special.dataIndex, )+pre_coord[special_index:]
else:
coord = pre_coord
matricies[sname][coord] = stats[name2agg[sname]]
cube = Cube(query.select, query.edges, matricies)
cube.frum = query
return cube