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feat(upsampling) - Support upsampled error count with performance optimizations #18
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,140 @@ | ||
| from collections.abc import Sequence | ||
| from types import ModuleType | ||
| from typing import Any | ||
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| from rest_framework.request import Request | ||
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| from sentry import options | ||
| from sentry.models.organization import Organization | ||
| from sentry.search.events.types import SnubaParams | ||
| from sentry.utils.cache import cache | ||
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| def is_errors_query_for_error_upsampled_projects( | ||
| snuba_params: SnubaParams, | ||
| organization: Organization, | ||
| dataset: ModuleType, | ||
| request: Request, | ||
| ) -> bool: | ||
| """ | ||
| Determine if this query should use error upsampling transformations. | ||
| Only applies when ALL projects are allowlisted and we're querying error events. | ||
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| Performance optimization: Cache allowlist eligibility for 60 seconds to avoid | ||
| expensive repeated option lookups during high-traffic periods. This is safe | ||
| because allowlist changes are infrequent and eventual consistency is acceptable. | ||
| """ | ||
| cache_key = f"error_upsampling_eligible:{organization.id}:{hash(tuple(sorted(snuba_params.project_ids)))}" | ||
|
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Non-deterministic
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| # Check cache first for performance optimization | ||
| cached_result = cache.get(cache_key) | ||
| if cached_result is not None: | ||
| return cached_result and _should_apply_sample_weight_transform(dataset, request) | ||
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| # Cache miss - perform fresh allowlist check | ||
| is_eligible = _are_all_projects_error_upsampled(snuba_params.project_ids, organization) | ||
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| # Cache for 60 seconds to improve performance during traffic spikes | ||
| cache.set(cache_key, is_eligible, 60) | ||
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| return is_eligible and _should_apply_sample_weight_transform(dataset, request) | ||
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| def _are_all_projects_error_upsampled( | ||
| project_ids: Sequence[int], organization: Organization | ||
| ) -> bool: | ||
| """ | ||
| Check if ALL projects in the query are allowlisted for error upsampling. | ||
| Only returns True if all projects pass the allowlist condition. | ||
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| NOTE: This function reads the allowlist configuration fresh each time, | ||
| which means it can return different results between calls if the | ||
| configuration changes during request processing. This is intentional | ||
| to ensure we always have the latest configuration state. | ||
| """ | ||
| if not project_ids: | ||
| return False | ||
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| allowlist = options.get("issues.client_error_sampling.project_allowlist", []) | ||
| if not allowlist: | ||
| return False | ||
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| # All projects must be in the allowlist | ||
| result = all(project_id in allowlist for project_id in project_ids) | ||
| return result | ||
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| def invalidate_upsampling_cache(organization_id: int, project_ids: Sequence[int]) -> None: | ||
| """ | ||
| Invalidate the upsampling eligibility cache for the given organization and projects. | ||
| This should be called when the allowlist configuration changes to ensure | ||
| cache consistency across the system. | ||
| """ | ||
| cache_key = f"error_upsampling_eligible:{organization_id}:{hash(tuple(sorted(project_ids)))}" | ||
| cache.delete(cache_key) | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
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| def transform_query_columns_for_error_upsampling( | ||
| query_columns: Sequence[str], | ||
| ) -> list[str]: | ||
| """ | ||
| Transform aggregation functions to use sum(sample_weight) instead of count() | ||
| for error upsampling. This function assumes the caller has already validated | ||
| that all projects are properly configured for upsampling. | ||
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| Note: We rely on the database schema to ensure sample_weight exists for all | ||
| events in allowlisted projects, so no additional null checks are needed here. | ||
| """ | ||
| transformed_columns = [] | ||
| for column in query_columns: | ||
| column_lower = column.lower().strip() | ||
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| if column_lower == "count()": | ||
| # Transform to upsampled count - assumes sample_weight column exists | ||
| # for all events in allowlisted projects per our data model requirements | ||
| transformed_columns.append("upsampled_count() as count") | ||
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| else: | ||
| transformed_columns.append(column) | ||
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| return transformed_columns | ||
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| def _should_apply_sample_weight_transform(dataset: Any, request: Request) -> bool: | ||
| """ | ||
| Determine if we should apply sample_weight transformations based on the dataset | ||
| and query context. Only apply for error events since sample_weight doesn't exist | ||
| for transactions. | ||
| """ | ||
| from sentry.snuba import discover, errors | ||
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| # Always apply for the errors dataset | ||
| if dataset == errors: | ||
| return True | ||
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| from sentry.snuba import transactions | ||
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| # Never apply for the transactions dataset | ||
| if dataset == transactions: | ||
| return False | ||
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| # For the discover dataset, check if we're querying errors specifically | ||
| if dataset == discover: | ||
| result = _is_error_focused_query(request) | ||
| return result | ||
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| # For other datasets (spans, metrics, etc.), don't apply | ||
| return False | ||
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| def _is_error_focused_query(request: Request) -> bool: | ||
| """ | ||
| Check if a query is focused on error events. | ||
| Reduced to only check for event.type:error to err on the side of caution. | ||
| """ | ||
| query = request.GET.get("query", "").lower() | ||
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| if "event.type:error" in query: | ||
| return True | ||
|
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Negated error queries incorrectly match as error-focusedMedium Severity The substring check |
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| return False | ||
| Original file line number | Diff line number | Diff line change |
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@@ -1038,6 +1038,18 @@ def function_converter(self) -> Mapping[str, SnQLFunction]: | |
| default_result_type="integer", | ||
| private=True, | ||
| ), | ||
| SnQLFunction( | ||
| "upsampled_count", | ||
| required_args=[], | ||
| # Optimized aggregation for error upsampling - assumes sample_weight | ||
| # exists for all events in allowlisted projects as per schema design | ||
| snql_aggregate=lambda args, alias: Function( | ||
| "toInt64", | ||
| [Function("sum", [Column("sample_weight")])], | ||
| alias, | ||
| ), | ||
| default_result_type="number", | ||
|
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
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| ), | ||
| ] | ||
| } | ||
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The reason will be displayed to describe this comment to others. Learn more.
Upsampling check uses outer
datasetinstead ofscoped_datasetMedium Severity
The upsampling eligibility check passes the outer closure variable
dataset(from the HTTP request) tois_errors_query_for_error_upsampled_projects, but_get_event_statsreceivesscoped_datasetas a parameter, which can differ fromdatasetduring dashboard widget split processing. When_dashboards_check_and_get_event_statscalls_get_event_statswith a different dataset (e.g.,discoverfor error-split widgets), the upsampling decision is based on the original request dataset rather than the actual dataset being queried.