def get_single_metric_info(projects: Sequence[Project],
metric_name: str) -> MetricMetaWithTagKeys:
assert projects
metric_id = indexer.resolve(metric_name)
if metric_id is None:
raise InvalidParams
for metric_type in ("counter", "set", "distribution"):
# TODO: What if metric_id exists for multiple types / units?
entity_key = METRIC_TYPE_TO_ENTITY[metric_type]
data = run_metrics_query(
entity_key=entity_key,
select=[Column("metric_id"),
Column("tags.key")],
where=[Condition(Column("metric_id"), Op.EQ, metric_id)],
groupby=[Column("metric_id"),
Column("tags.key")],
referrer="snuba.metrics.meta.get_single_metric",
projects=projects,
org_id=projects[0].organization_id,
)
if data:
tag_ids = {tag_id for row in data for tag_id in row["tags.key"]}
return {
"name":
metric_name,
"type":
metric_type,
"operations":
AVAILABLE_OPERATIONS[entity_key.value],
"tags":
sorted(
({
"key": reverse_resolve(tag_id)
} for tag_id in tag_ids),
key=itemgetter("key"),
),
"unit":
None,
}
raise InvalidParams(f"Raw metric {metric_name} does not exit")
def get_metrics(projects: Sequence[Project]) -> Sequence[MetricMeta]:
assert projects
metrics_meta = []
metric_ids_in_entities = {}
for metric_type in ("counter", "set", "distribution"):
metric_ids_in_entities.setdefault(metric_type, set())
for row in _get_metrics_for_entity(
entity_key=METRIC_TYPE_TO_ENTITY[metric_type],
projects=projects,
org_id=projects[0].organization_id,
):
metrics_meta.append(
MetricMeta(
name=reverse_resolve(row["metric_id"]),
type=metric_type,
operations=AVAILABLE_OPERATIONS[METRIC_TYPE_TO_ENTITY[metric_type].value],
unit=None, # snuba does not know the unit
)
)
metric_ids_in_entities[metric_type].add(row["metric_id"])
# In the previous loop, we find all available metric ids per entity with respect to the
# projects filter, and so to figure out which derived metrics are supported for these
# projects, we need to iterate over the list of derived metrics and generate the ids of
# their constituent metrics. A derived metric should be added to the response list if its
# metric ids are a subset of the metric ids in one of the entities i.e. Its an instance of
# SingularEntityDerivedMetric.
# ToDo(ahmed): When CompositeEntityDerivedMetrics are introduced we need to do these checks
# not on the instance of the CompositeEntityDerivedMetric but rather on its
# SingularEntityDerivedMetric constituents
found_derived_metrics = get_available_derived_metrics(metric_ids_in_entities)
for derived_metric_name in found_derived_metrics:
derived_metric_obj = DERIVED_METRICS[derived_metric_name]
metrics_meta.append(
MetricMeta(
name=derived_metric_obj.metric_name,
type=derived_metric_obj.result_type,
operations=derived_metric_obj.generate_available_operations(),
unit=derived_metric_obj.unit,
)
)
return sorted(metrics_meta, key=itemgetter("name"))
def translate_sessions_tag_keys_and_values(
data: List[Dict[str, Any]],
org_id: int,
alias: Optional[str] = None) -> Tuple[int, int]:
value_col_name = alias if alias else "value"
try:
translated_data: Dict[str, Any] = {}
session_status = resolve_tag_key("session.status")
for row in data:
tag_value = reverse_resolve(row[session_status])
translated_data[tag_value] = row[value_col_name]
total_session_count = translated_data.get("init", 0)
crash_count = translated_data.get("crashed", 0)
except MetricIndexNotFound:
metrics.incr(
"incidents.entity_subscription.metric_index_not_found")
total_session_count = crash_count = 0
return total_session_count, crash_count
def run_sessions_query(
org_id: int,
query: QueryDefinition,
span_op: str,
) -> SessionsQueryResult:
"""Convert a QueryDefinition to multiple snuba queries and reformat the results"""
# This is necessary so that we do not mutate the query object shared between different
# backend runs
query_clone = deepcopy(query)
data, metric_to_output_field = _fetch_data(org_id, query_clone)
data_points = _flatten_data(org_id, data)
intervals = list(get_intervals(query_clone))
timestamp_index = {
timestamp.isoformat(): index
for index, timestamp in enumerate(intervals)
}
def default_for(field: SessionsQueryFunction) -> SessionsQueryValue:
return 0 if field in ("sum(session)", "count_unique(user)") else None
GroupKey = Tuple[Tuple[GroupByFieldName, Union[str, int]], ...]
class Group(TypedDict):
series: MutableMapping[SessionsQueryFunction, List[SessionsQueryValue]]
totals: MutableMapping[SessionsQueryFunction, SessionsQueryValue]
groups: MutableMapping[GroupKey, Group] = defaultdict(
lambda: {
"totals":
{field: default_for(field)
for field in query_clone.raw_fields},
"series": {
field: len(intervals) * [default_for(field)]
for field in query_clone.raw_fields
},
})
if len(data_points) == 0:
# We're only interested in `session.status` group-byes. The rest of the
# conditions require work (e.g. getting all environments) that we can't
# get without querying the DB, including group-byes consisting of
# multiple parameters (even if `session.status` is one of them).
if query_clone.raw_groupby == ["session.status"]:
for status in get_args(_SessionStatus):
gkey: GroupKey = (("session.status", status), )
groups[gkey]
else:
for key in data_points.keys():
try:
output_field = metric_to_output_field[key.metric_key,
key.column]
except KeyError:
continue # secondary metric, like session.error
by: MutableMapping[GroupByFieldName, Union[str, int]] = {}
if key.release is not None:
# Every session has a release, so this should not throw
by["release"] = reverse_resolve(key.release)
if key.environment is not None:
# To match behavior of the old sessions backend, session data
# without environment is grouped under the empty string.
by["environment"] = reverse_resolve_weak(key.environment) or ""
if key.project_id is not None:
by["project"] = key.project_id
for status_value in output_field.get_values(data_points, key):
if status_value.session_status is not None:
by["session.status"] = status_value.session_status # !
group_key: GroupKey = tuple(sorted(by.items()))
group: Group = groups[group_key]
value = status_value.value
if value is not None:
value = finite_or_none(value)
if key.bucketed_time is None:
group["totals"][output_field.get_name()] = value
else:
index = timestamp_index[key.bucketed_time]
group["series"][output_field.get_name()][index] = value
groups_as_list: List[SessionsQueryGroup] = [{
"by": dict(by),
"totals": group["totals"],
"series": group["series"],
} for by, group in groups.items()]
def format_datetime(dt: datetime) -> str:
return dt.isoformat().replace("+00:00", "Z")
return {
"start": format_datetime(query_clone.start),
"end": format_datetime(query_clone.end),
"query": query_clone.query,
"intervals": [format_datetime(dt) for dt in intervals],
"groups": groups_as_list,
}
def _fetch_tags_or_values_per_ids(
projects: Sequence[Project],
metric_names: Optional[Sequence[str]],
referrer: str,
column: str,
) -> Tuple[Union[Sequence[Tag], Sequence[TagValue]], Optional[str]]:
"""
Function that takes as input projects, metric_names, and a column, and based on the column
selection, either returns tags or tag values for the combination of projects and metric_names
selected or in the case of no metric_names passed, returns basically all the tags or the tag
values available for those projects. In addition, when exactly one metric name is passed in
metric_names, then the type (i.e. mapping to the entity) is also returned
"""
try:
metric_ids = _get_metrics_filter_ids(metric_names)
except MetricDoesNotExistInIndexer:
raise InvalidParams(
f"Some or all of the metric names in {metric_names} do not exist in the indexer"
)
else:
where = [Condition(Column("metric_id"), Op.IN, list(metric_ids))] if metric_ids else []
tag_or_value_ids_per_metric_id = defaultdict(list)
# This dictionary is required as a mapping from an entity to the ids available in it to
# validate that constituent metrics of a SingleEntityDerivedMetric actually span a single
# entity by validating that the ids of the constituent metrics all lie in the same entity
supported_metric_ids_in_entities = {}
for metric_type in ("counter", "set", "distribution"):
entity_key = METRIC_TYPE_TO_ENTITY[metric_type]
rows = run_metrics_query(
entity_key=entity_key,
select=[Column("metric_id"), Column(column)],
where=where,
groupby=[Column("metric_id"), Column(column)],
referrer=referrer,
projects=projects,
org_id=projects[0].organization_id,
)
for row in rows:
metric_id = row["metric_id"]
if column.startswith("tags["):
value_id = row[column]
if value_id > 0:
tag_or_value_ids_per_metric_id[metric_id].append(value_id)
else:
tag_or_value_ids_per_metric_id[metric_id].extend(row[column])
supported_metric_ids_in_entities.setdefault(metric_type, []).append(row["metric_id"])
# If we get not results back from snuba, then raise an InvalidParams with an appropriate
# error message
if not tag_or_value_ids_per_metric_id:
if metric_names:
error_str = f"The following metrics {metric_names} do not exist in the dataset"
else:
error_str = "Dataset contains no metric data for your project selection"
raise InvalidParams(error_str)
tag_or_value_id_lists = tag_or_value_ids_per_metric_id.values()
if metric_names:
# If there are metric_ids that map to the metric_names provided as an arg that were not
# found in the dataset, then we raise an instance of InvalidParams exception
if metric_ids != set(tag_or_value_ids_per_metric_id.keys()):
# This can occur for metric names that don't have an equivalent in the dataset.
raise InvalidParams(
f"Not all the requested metrics or the constituent metrics in {metric_names} have "
f"data in the dataset"
)
# At this point, we are sure that every metric_name/metric_id that was requested is
# present in the dataset, and now we need to check that for all derived metrics requested
# (if any are requested) are setup correctly i.e. constituent of
# SingularEntityDerivedMetric actually span a single entity
_validate_requested_derived_metrics_in_input_metrics(
metric_names=metric_names,
supported_metric_ids_in_entities=supported_metric_ids_in_entities,
)
# Only return tags/tag values that occur in all metrics
tag_or_value_ids = set.intersection(*map(set, tag_or_value_id_lists))
else:
tag_or_value_ids = {tag_id for ids in tag_or_value_id_lists for tag_id in ids}
if column.startswith("tags["):
tag_id = column.split("tags[")[1].split("]")[0]
tags_or_values = [
{"key": reverse_resolve(int(tag_id)), "value": reverse_resolve(value_id)}
for value_id in tag_or_value_ids
]
tags_or_values.sort(key=lambda tag: (tag["key"], tag["value"]))
else:
tags_or_values = [{"key": reverse_resolve(tag_id)} for tag_id in tag_or_value_ids]
tags_or_values.sort(key=itemgetter("key"))
if metric_names and len(metric_names) == 1:
metric_type = list(supported_metric_ids_in_entities.keys())[0]
return tags_or_values, metric_type
return tags_or_values, None
def get_tag_values(
projects: Sequence[Project], tag_name: str,
metric_names: Optional[Sequence[str]]) -> Sequence[TagValue]:
"""Get all known values for a specific tag"""
assert projects
tag_id = indexer.resolve(tag_name)
if tag_id is None:
raise InvalidParams
try:
metric_ids = _get_metrics_filter_ids(metric_names)
except MetricDoesNotExistInIndexer:
return []
else:
where = [Condition(Column("metric_id"), Op.IN, list(metric_ids))
] if metric_ids else []
tag_values = defaultdict(list)
# This dictionary is required as a mapping from an entity to the ids available in it to
# validate that constituent metrics of a SingleEntityDerivedMetric actually span a single
# entity by validating that the ids of the constituent metrics all lie in the same entity
supported_metric_ids_in_entities = {}
column_name = f"tags[{tag_id}]"
for metric_type in ("counter", "set", "distribution"):
supported_metric_ids_in_entities.setdefault(metric_type, [])
entity_key = METRIC_TYPE_TO_ENTITY[metric_type]
rows = run_metrics_query(
entity_key=entity_key,
select=[Column("metric_id"),
Column(column_name)],
where=where,
groupby=[Column("metric_id"),
Column(column_name)],
referrer="snuba.metrics.meta.get_tag_values",
projects=projects,
org_id=projects[0].organization_id,
)
for row in rows:
value_id = row[column_name]
supported_metric_ids_in_entities[metric_type].append(
row["metric_id"])
if value_id > 0:
metric_id = row["metric_id"]
tag_values[metric_id].append(value_id)
# If we are trying to find the tag values for only one metric name, then no need to query
# other entities once we find data for that metric_name in one of the entities
if metric_names and len(metric_names) == 1 and rows:
break
value_id_lists = tag_values.values()
if metric_names is not None:
if metric_ids != set(tag_values.keys()):
return []
# At this point, we are sure that every metric_name/metric_id that was requested is
# present in the dataset, and now we need to check that all derived metrics requested are
# setup correctly
_validate_requested_derived_metrics(
metric_names=metric_names,
supported_metric_ids_in_entities=supported_metric_ids_in_entities,
)
# Only return tags that occur in all metrics
value_ids = set.intersection(*[set(ids) for ids in value_id_lists])
else:
value_ids = {value_id for ids in value_id_lists for value_id in ids}
tags = [{
"key": tag_name,
"value": reverse_resolve(value_id)
} for value_id in value_ids]
tags.sort(key=lambda tag: (tag["key"], tag["value"]))
return tags
def get_tags(projects: Sequence[Project],
metric_names: Optional[Sequence[str]]) -> Sequence[Tag]:
"""Get all metric tags for the given projects and metric_names"""
assert projects
try:
metric_ids = _get_metrics_filter_ids(metric_names)
except MetricDoesNotExistInIndexer:
return []
else:
where = [Condition(Column("metric_id"), Op.IN, list(metric_ids))
] if metric_ids else []
tag_ids_per_metric_id = defaultdict(list)
# This dictionary is required as a mapping from an entity to the ids available in it to
# validate that constituent metrics of a SingleEntityDerivedMetric actually span a single
# entity by validating that the ids of the constituent metrics all lie in the same entity
supported_metric_ids_in_entities = {}
for metric_type in ("counter", "set", "distribution"):
supported_metric_ids_in_entities.setdefault(metric_type, [])
entity_key = METRIC_TYPE_TO_ENTITY[metric_type]
rows = run_metrics_query(
entity_key=entity_key,
select=[Column("metric_id"),
Column("tags.key")],
where=where,
groupby=[Column("metric_id"),
Column("tags.key")],
referrer="snuba.metrics.meta.get_tags",
projects=projects,
org_id=projects[0].organization_id,
)
for row in rows:
tag_ids_per_metric_id[row["metric_id"]].extend(row["tags.key"])
supported_metric_ids_in_entities[metric_type].append(
row["metric_id"])
# If we are trying to find the tags for only one metric name, then no need to query other
# entities once we find data for that metric_name in one of the entity
if metric_names and len(metric_names) == 1 and rows:
break
# If we get not results back from snuba, then just return an empty set
if not tag_ids_per_metric_id:
return []
tag_id_lists = tag_ids_per_metric_id.values()
if metric_names:
# If there are metric_ids that were not found in the dataset, then just return an []
if metric_ids != set(tag_ids_per_metric_id.keys()):
# This can occur for metric names that don't have an equivalent in the dataset.
return []
# At this point, we are sure that every metric_name/metric_id that was requested is
# present in the dataset, and now we need to check that all derived metrics requested are
# setup correctly
_validate_requested_derived_metrics(
metric_names=metric_names,
supported_metric_ids_in_entities=supported_metric_ids_in_entities,
)
# Only return tags that occur in all metrics
tag_ids = set.intersection(*map(set, tag_id_lists))
else:
tag_ids = {tag_id for ids in tag_id_lists for tag_id in ids}
tags = [{"key": reverse_resolve(tag_id)} for tag_id in tag_ids]
tags.sort(key=itemgetter("key"))
return tags
def _parse_tag(self, tag_string: str) -> str:
tag_key = int(tag_string.replace("tags[", "").replace("]", ""))
return reverse_resolve(tag_key)
