diff --git a/rock/admin/metrics/gc_view_instrument_match.py b/rock/admin/metrics/gc_view_instrument_match.py
new file mode 100644
index 00000000..200faa04
--- /dev/null
+++ b/rock/admin/metrics/gc_view_instrument_match.py
@@ -0,0 +1,110 @@
+from time import time_ns
+from typing import Dict, List, Optional, Sequence
+
+from opentelemetry.sdk.metrics._internal._view_instrument_match import (
+    _ViewInstrumentMatch as _OrigViewInstrumentMatch,
+)
+from opentelemetry.sdk.metrics._internal.export import AggregationTemporality
+from opentelemetry.sdk.metrics._internal.measurement import Measurement
+from opentelemetry.sdk.metrics._internal.point import DataPointT
+
+
+class _GcViewInstrumentMatch(_OrigViewInstrumentMatch):
+    """
+    An extension of _ViewInstrumentMatch that adds garbage collection for idle
+    metric series (based on attributes). This is useful for preventing memory
+    leaks when dealing with high-cardinality metrics.
+    """
+    # Idle metric series are cleaned up after 20 minutes. This can be adjusted.
+    _IDLE_TIMEOUT_NS = 20 * 60 * 1_000_000_000
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._last_used_ns: Dict[frozenset, int] = {}
+
+    def consume_measurement(
+            self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        """
+        Consumes a measurement, aggregates it, and tracks its usage for GC.
+        """
+        attributes = measurement.attributes or {}
+        measurement_for_aggregation = measurement
+
+        if self._view._attribute_keys is not None:
+            filtered_attributes = {
+                key: value
+                for key, value in attributes.items()
+                if key in self._view._attribute_keys
+            }
+
+            # If attributes were filtered, a new Measurement object must be used
+            # for aggregation. This ensures that if an exemplar is recorded, it
+            # will have the correct, filtered set of attributes.
+            if len(filtered_attributes) != len(attributes):
+                attributes = filtered_attributes
+                measurement_for_aggregation = Measurement(
+                    value=measurement.value,
+                    instrument=measurement.instrument,
+                    attributes=attributes,
+                    time_unix_nano=measurement.time_unix_nano,
+                    context=measurement.context,
+                )
+
+        aggr_key = frozenset(attributes.items())
+        now_ns = time_ns()
+
+        if aggr_key not in self._attributes_aggregation:
+            # Let the parent class handle the thread-safe creation of the aggregation.
+            super().consume_measurement(
+                measurement_for_aggregation,
+                should_sample_exemplar,
+            )
+        else:
+            self._attributes_aggregation[aggr_key].aggregate(
+                measurement_for_aggregation, should_sample_exemplar
+            )
+        self._last_used_ns[aggr_key] = now_ns
+
+    def collect(
+            self,
+            collection_aggregation_temporality: AggregationTemporality,
+            collection_start_nanos: int,
+    ) -> Optional[Sequence[DataPointT]]:
+        """
+        Collects all data points for the metric, and garbage collects idle series.
+        """
+        data_points: List[DataPointT] = []
+        now_ns = time_ns()
+        to_delete: List[frozenset] = []
+
+        with self._lock:
+            # First, collect data points and identify idle series
+            for aggr_key, aggregation in self._attributes_aggregation.items():
+                dp = aggregation.collect(
+                    collection_aggregation_temporality,
+                    collection_start_nanos,
+                )
+                if dp is not None:
+                    data_points.append(dp)
+
+                last_used_ns = self._last_used_ns.get(aggr_key, 0)
+                if last_used_ns and (now_ns - last_used_ns > self._IDLE_TIMEOUT_NS):
+                    to_delete.append(aggr_key)
+
+            # Then, remove the idle series
+            for aggr_key in to_delete:
+                self._attributes_aggregation.pop(aggr_key, None)
+                self._last_used_ns.pop(aggr_key, None)
+
+        return data_points or None
+
+
+def patch_view_instrument_match() -> None:
+    # Call this once at application startup, before initializing any metric
+    # readers or providers, to replace the SDK's internal class.
+    import opentelemetry.sdk.metrics._internal._view_instrument_match as vim_mod
+    vim_mod._ViewInstrumentMatch = _GcViewInstrumentMatch
+
+    from opentelemetry.sdk.metrics._internal import metric_reader_storage as mrs
+    mrs._ViewInstrumentMatch = _GcViewInstrumentMatch