Update benchmarks.json file too

python/.asv/results/benchmarks.json

@@ -1244,6 +1244,94 @@
         "version": "ed1d1ccb6458095a627788bfa2b53afa310ca8c8118a6405c91204724c865d6c",
         "warmup_time": -1
     },
+    "resample.Resample.peakmem_resample": {
+        "code": "class Resample:\n    def peakmem_resample(self, num_rows, downsampling_factor, col_type, aggregation):\n        if col_type == \"datetime\" and aggregation == \"sum\" or col_type == \"str\" and aggregation in [\"sum\", \"mean\", \"min\", \"max\"]:\n            raise SkipNotImplemented(f\"{aggregation} not supported on columns of type {col_type}\")\n        else:\n            self.lib.read(col_type, date_range=self.date_range, query_builder=self.query_builder)\n\n    def setup(self, num_rows, downsampling_factor, col_type, aggregation):\n        self.ac = Arctic(self.CONNECTION_STRING)\n        self.lib = self.ac[self.LIB_NAME]\n        self.date_range = (pd.Timestamp(0), pd.Timestamp(num_rows, unit=\"us\"))\n        self.query_builder = QueryBuilder().resample(f\"{downsampling_factor}us\").agg({\"col\": aggregation})\n\n    def setup_cache(self):\n        ac = Arctic(self.CONNECTION_STRING)\n        ac.delete_library(self.LIB_NAME)\n        lib = ac.create_library(self.LIB_NAME)\n        rng = np.random.default_rng()\n        col_types = self.params[2]\n        rows = max(self.params[0])\n        for col_type in col_types:\n            if col_type == \"str\":\n                num_unique_strings = 100\n                unique_strings = random_strings_of_length(num_unique_strings, 10, True)\n            sym = col_type\n            num_segments = rows // self.ROWS_PER_SEGMENT\n            for idx in range(num_segments):\n                index = pd.date_range(pd.Timestamp(idx * self.ROWS_PER_SEGMENT, unit=\"us\"), freq=\"us\", periods=self.ROWS_PER_SEGMENT)\n                if col_type == \"int\":\n                    col_data = rng.integers(0, 100_000, self.ROWS_PER_SEGMENT)\n                elif col_type == \"bool\":\n                    col_data = rng.integers(0, 2, self.ROWS_PER_SEGMENT)\n                    col_data = col_data.astype(bool)\n                elif col_type == \"float\":\n                    col_data = 100_000 * rng.random(self.ROWS_PER_SEGMENT)\n                elif col_type == \"datetime\":\n                    col_data = rng.integers(0, 100_000, self.ROWS_PER_SEGMENT)\n                    col_data = col_data.astype(\"datetime64[s]\")\n                elif col_type == \"str\":\n                    col_data = np.random.choice(unique_strings, self.ROWS_PER_SEGMENT)\n                df = pd.DataFrame({\"col\": col_data}, index=index)\n                lib.append(sym, df)",
+        "name": "resample.Resample.peakmem_resample",
+        "param_names": [
+            "num_rows",
+            "downsampling_factor",
+            "col_type",
+            "aggregation"
+        ],
+        "params": [
+            [
+                "1000000",
+                "10000000"
+            ],
+            [
+                "10",
+                "100",
+                "100000"
+            ],
+            [
+                "'bool'",
+                "'int'",
+                "'float'",
+                "'datetime'",
+                "'str'"
+            ],
+            [
+                "'sum'",
+                "'mean'",
+                "'min'",
+                "'max'",
+                "'first'",
+                "'last'",
+                "'count'"
+            ]
+        ],
+        "setup_cache_key": "resample:38",
+        "type": "peakmemory",
+        "unit": "bytes",
+        "version": "e64300ebb5bd625e1a0f3774aadd035e5738b41295ec2a8ce082d2e9add9b580"
+    },
+    "resample.Resample.time_resample": {
+        "code": "class Resample:\n    def time_resample(self, num_rows, downsampling_factor, col_type, aggregation):\n        if col_type == \"datetime\" and aggregation == \"sum\" or col_type == \"str\" and aggregation in [\"sum\", \"mean\", \"min\", \"max\"]:\n            raise SkipNotImplemented(f\"{aggregation} not supported on columns of type {col_type}\")\n        else:\n            self.lib.read(col_type, date_range=self.date_range, query_builder=self.query_builder)\n\n    def setup(self, num_rows, downsampling_factor, col_type, aggregation):\n        self.ac = Arctic(self.CONNECTION_STRING)\n        self.lib = self.ac[self.LIB_NAME]\n        self.date_range = (pd.Timestamp(0), pd.Timestamp(num_rows, unit=\"us\"))\n        self.query_builder = QueryBuilder().resample(f\"{downsampling_factor}us\").agg({\"col\": aggregation})\n\n    def setup_cache(self):\n        ac = Arctic(self.CONNECTION_STRING)\n        ac.delete_library(self.LIB_NAME)\n        lib = ac.create_library(self.LIB_NAME)\n        rng = np.random.default_rng()\n        col_types = self.params[2]\n        rows = max(self.params[0])\n        for col_type in col_types:\n            if col_type == \"str\":\n                num_unique_strings = 100\n                unique_strings = random_strings_of_length(num_unique_strings, 10, True)\n            sym = col_type\n            num_segments = rows // self.ROWS_PER_SEGMENT\n            for idx in range(num_segments):\n                index = pd.date_range(pd.Timestamp(idx * self.ROWS_PER_SEGMENT, unit=\"us\"), freq=\"us\", periods=self.ROWS_PER_SEGMENT)\n                if col_type == \"int\":\n                    col_data = rng.integers(0, 100_000, self.ROWS_PER_SEGMENT)\n                elif col_type == \"bool\":\n                    col_data = rng.integers(0, 2, self.ROWS_PER_SEGMENT)\n                    col_data = col_data.astype(bool)\n                elif col_type == \"float\":\n                    col_data = 100_000 * rng.random(self.ROWS_PER_SEGMENT)\n                elif col_type == \"datetime\":\n                    col_data = rng.integers(0, 100_000, self.ROWS_PER_SEGMENT)\n                    col_data = col_data.astype(\"datetime64[s]\")\n                elif col_type == \"str\":\n                    col_data = np.random.choice(unique_strings, self.ROWS_PER_SEGMENT)\n                df = pd.DataFrame({\"col\": col_data}, index=index)\n                lib.append(sym, df)",
+        "min_run_count": 2,
+        "name": "resample.Resample.time_resample",
+        "number": 5,
+        "param_names": [
+            "num_rows",
+            "downsampling_factor",
+            "col_type",
+            "aggregation"
+        ],
+        "params": [
+            [
+                "1000000",
+                "10000000"
+            ],
+            [
+                "10",
+                "100",
+                "100000"
+            ],
+            [
+                "'bool'",
+                "'int'",
+                "'float'",
+                "'datetime'",
+                "'str'"
+            ],
+            [
+                "'sum'",
+                "'mean'",
+                "'min'",
+                "'max'",
+                "'first'",
+                "'last'",
+                "'count'"
+            ]
+        ],
+        "repeat": 0,
+        "rounds": 2,
+        "sample_time": 0.01,
+        "setup_cache_key": "resample:38",
+        "type": "time",
+        "unit": "seconds",
+        "version": "2d10a27f3668632f382e90783829b4bb08cabb656c02754c00d5953ee42f3794",
+        "warmup_time": -1
+    },
     "version": 2,
     "version_chain.IterateVersionChain.time_list_undeleted_versions": {
         "code": "class IterateVersionChain:\n    def time_list_undeleted_versions(self, num_versions, caching, deleted):\n        self.lib.list_versions(symbol=self.symbol(num_versions, deleted))\n\n    def setup(self, num_versions, caching, deleted):\n        # Disable warnings for version not found\n        set_log_level(\"ERROR\")\n    \n        if caching==\"never\":\n            adb._ext.set_config_int(\"VersionMap.ReloadInterval\", 0)\n        if caching==\"forever\":\n            adb._ext.set_config_int(\"VersionMap.ReloadInterval\", sys.maxsize)\n        if caching==\"default\":\n            # Leave the default reload interval\n            pass\n    \n        self.ac = Arctic(IterateVersionChain.CONNECTION_STRING)\n        self.lib = self.ac[IterateVersionChain.LIB_NAME]\n    \n        if caching != \"never\":\n            # Pre-load the cache\n            self.load_all(self.symbol(num_versions, deleted))\n\n    def setup_cache(self):\n        self.ac = Arctic(IterateVersionChain.CONNECTION_STRING)\n        num_versions_list, caching_list, deleted_list = IterateVersionChain.params\n    \n        self.ac.delete_library(IterateVersionChain.LIB_NAME)\n        lib = self.ac.create_library(IterateVersionChain.LIB_NAME)\n    \n        small_df = generate_random_floats_dataframe(2, 2)\n    \n        for num_versions in num_versions_list:\n            for deleted in deleted_list:\n                symbol = self.symbol(num_versions, deleted)\n                for i in range(num_versions):\n                    lib.write(symbol, small_df)\n                    if (i == math.floor(deleted * num_versions)):\n                        lib.delete(symbol)\n    \n        del self.ac",