Backport PR #33693 on branch 1.0.x (BUG: Fix memory issues in rolling.min/max) (#34027)

Author: simonjayhawkins

asv_bench/benchmarks/rolling.py

@@ -129,19 +129,18 @@ def time_quantile(self, constructor, window, dtype, percentile, interpolation):
         self.roll.quantile(percentile, interpolation=interpolation)
 
 
-class PeakMemFixed:
-    def setup(self):
-        N = 10
-        arr = 100 * np.random.random(N)
-        self.roll = pd.Series(arr).rolling(10)
-
-    def peakmem_fixed(self):
-        # GH 25926
-        # This is to detect memory leaks in rolling operations.
-        # To save time this is only ran on one method.
-        # 6000 iterations is enough for most types of leaks to be detected
-        for x in range(6000):
-            self.roll.max()
+class PeakMemFixedWindowMinMax:
+
+    params = ["min", "max"]
+
+    def setup(self, operation):
+        N = int(1e6)
+        arr = np.random.random(N)
+        self.roll = pd.Series(arr).rolling(2)
+
+    def peakmem_fixed(self, operation):
+        for x in range(5):
+            getattr(self.roll, operation)()
 
 
 from .pandas_vb_common import setup  # noqa: F401 isort:skip

doc/source/whatsnew/v1.0.4.rst

@@ -28,7 +28,7 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
-- 
+- Bug in :meth:`Rolling.min` and :meth:`Rolling.max`: Growing memory usage after multiple calls when using a fixed window (:issue:`30726`)
 - 
 
 Contributors

pandas/_libs/window/aggregations.pyx

@@ -965,8 +965,8 @@ cdef inline numeric calc_mm(int64_t minp, Py_ssize_t nobs,
     return result
 
 
-def roll_max_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
-                   ndarray[int64_t] end, int64_t minp, int64_t win):
+def roll_max_fixed(float64_t[:] values, int64_t[:] start,
+                   int64_t[:] end, int64_t minp, int64_t win):
     """
     Moving max of 1d array of any numeric type along axis=0 ignoring NaNs.
 
@@ -982,7 +982,7 @@ def roll_max_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
             make the interval closed on the right, left,
             both or neither endpoints
     """
-    return _roll_min_max_fixed(values, start, end, minp, win, is_max=1)
+    return _roll_min_max_fixed(values, minp, win, is_max=1)
 
 
 def roll_max_variable(ndarray[float64_t] values, ndarray[int64_t] start,
@@ -1005,8 +1005,8 @@ def roll_max_variable(ndarray[float64_t] values, ndarray[int64_t] start,
     return _roll_min_max_variable(values, start, end, minp, is_max=1)
 
 
-def roll_min_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
-                   ndarray[int64_t] end, int64_t minp, int64_t win):
+def roll_min_fixed(float64_t[:] values, int64_t[:] start,
+                   int64_t[:] end, int64_t minp, int64_t win):
     """
     Moving min of 1d array of any numeric type along axis=0 ignoring NaNs.
 
@@ -1019,7 +1019,7 @@ def roll_min_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
     index : ndarray, optional
        index for window computation
     """
-    return _roll_min_max_fixed(values, start, end, minp, win, is_max=0)
+    return _roll_min_max_fixed(values, minp, win, is_max=0)
 
 
 def roll_min_variable(ndarray[float64_t] values, ndarray[int64_t] start,
@@ -1121,9 +1121,7 @@ cdef _roll_min_max_variable(ndarray[numeric] values,
     return output
 
 
-cdef _roll_min_max_fixed(ndarray[numeric] values,
-                         ndarray[int64_t] starti,
-                         ndarray[int64_t] endi,
+cdef _roll_min_max_fixed(numeric[:] values,
                          int64_t minp,
                          int64_t win,
                          bint is_max):