chore: import upstream snapshot with attribution
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wehub-resource-sync
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# ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
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# ┃ ██████ ██████ ██████ █ █ █ █ █ █▄ ▀███ █ ┃
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# ┃ ▄▄▄▄▄█ █▄▄▄▄▄ ▄▄▄▄▄█ ▀▀▀▀▀█▀▀▀▀▀ █ ▀▀▀▀▀█ ████████▌▐███ ███▄ ▀█ █ ▀▀▀▀▀ ┃
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# ┃ █▀▀▀▀▀ █▀▀▀▀▀ █▀██▀▀ ▄▄▄▄▄ █ ▄▄▄▄▄█ ▄▄▄▄▄█ ████████▌▐███ █████▄ █ ▄▄▄▄▄ ┃
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# ┃ █ ██████ █ ▀█▄ █ ██████ █ ███▌▐███ ███████▄ █ ┃
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# ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┫
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# ┃ Copyright (c) 2017, the Perspective Authors. ┃
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# ┃ ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌ ┃
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# ┃ This file is part of the Perspective library, distributed under the terms ┃
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# ┃ of the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). ┃
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# ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
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import os
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from functools import partial
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from bench import Benchmark, Suite, Runner
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from perspective import Table
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import perspective
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import logging
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SUPERSTORE_ARROW = os.path.join(
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os.path.dirname(__file__),
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"..",
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"..",
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"..",
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"..",
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"node_modules",
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"superstore-arrow",
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"superstore.arrow",
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)
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with open(SUPERSTORE_ARROW, "rb") as f:
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SUPERSTORE_ARROW_DATA = f.read()
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VERSIONS = ["master", "0.4.1", "0.4.0rc6"]
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def make_meta(group, name):
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return {"group": group, "name": name}
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def empty_callback(port_id):
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pass
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class PerspectiveBenchmark(Suite):
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AGG_OPTIONS = [
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[{"column": "Sales", "op": "sum"}],
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[{"column": "State", "op": "dominant"}],
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[{"column": "Order Date", "op": "dominant"}],
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]
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split_by_OPTIONS = [[], ["Sub-Category"], ["Category", "Sub-Category"]]
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group_by_OPTIONS = [[], ["State"], ["State", "City"]]
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def __init__(self):
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"""Create a benchmark suite for the `perspective-python` runtime."""
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tbl = Table(SUPERSTORE_ARROW_DATA)
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for x in range(19):
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tbl.update(SUPERSTORE_ARROW_DATA)
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self._schema = tbl.schema()
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self._df_schema = tbl.schema()
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# mutate schema to have some integer columns, so as to force numpy
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# float-to-int demotion
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self._df_schema["Sales"] = int
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self._df_schema["Profit"] = int
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self._df_schema["Quantity"] = int
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self._view = tbl.view()
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self._table = tbl
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def _get_update_data(self, n=30):
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"""Retrieve n rows from self.records to be used as update data."""
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return SUPERSTORE_ARROW_DATA
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def register_benchmarks(self):
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"""Register all the benchmark methods - each method creates a number of
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lambdas, and then calls `setattr` on the Suite itself so that the
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`Runner` can find the tests at runtime."""
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self.benchmark_table_arrow()
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self.benchmark_table_csv()
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self.benchmark_view_zero()
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self.benchmark_view_one()
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self.benchmark_view_two()
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self.benchmark_view_zero_updates()
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self.benchmark_view_one_updates()
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self.benchmark_view_two_updates()
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self.benchmark_to_format_zero()
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self.benchmark_to_format_one()
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self.benchmark_to_format_two()
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def benchmark_table_arrow(self):
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"""Benchmark table from arrow separately as it requires opening the
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Arrow file from the filesystem."""
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test_meta = make_meta("table", "arrow")
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func = Benchmark(lambda: Table(SUPERSTORE_ARROW_DATA), meta=test_meta)
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setattr(self, "table_arrow", func)
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def benchmark_table_csv(self):
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"""Benchmark table from csv separately as it requires opening the
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Arrow file from the filesystem."""
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csv = self._view.to_csv()
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test_meta = make_meta("table", "csv")
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func = Benchmark(lambda: Table(csv), meta=test_meta)
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setattr(self, "table_csv", func)
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def benchmark_view_zero(self):
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"""Benchmark view creation with zero pivots."""
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func = Benchmark(lambda: self._table.view(), meta=make_meta("view", "zero"))
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setattr(self, "view_zero", func)
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def benchmark_view_zero_updates(self):
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"""Benchmark how long it takes for each update to resolve fully, using
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the on update callback that forces resolution of updates across
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10 views."""
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table = Table(self._schema)
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views = [table.view() for i in range(25)]
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for v in views:
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v.on_update(empty_callback)
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update_data = self._get_update_data(1000)
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def resolve_update():
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table.update(update_data)
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table.size()
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func = Benchmark(resolve_update, meta=make_meta("update", "zero"))
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setattr(self, "update_zero", func)
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def benchmark_view_one(self):
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"""Benchmark view creation with different pivots."""
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for pivot in PerspectiveBenchmark.group_by_OPTIONS:
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if len(pivot) == 0:
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continue
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test_meta = make_meta("view", "one_{0}_pivot".format(len(pivot)))
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view_constructor = partial(self._table.view, group_by=pivot)
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func = Benchmark(lambda: view_constructor(), meta=test_meta)
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setattr(self, "view_{0}".format(test_meta["name"]), func)
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def benchmark_view_one_updates(self):
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"""Benchmark how long it takes for each update to resolve fully, using
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the on update callback that forces resolution of updates across
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25 views."""
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table = Table(self._schema)
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views = [table.view(group_by=["State", "City"]) for i in range(25)]
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for v in views:
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v.on_update(empty_callback)
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update_data = self._get_update_data(1000)
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def resolve_update():
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table.update(update_data)
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table.size()
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func = Benchmark(resolve_update, meta=make_meta("update", "one"))
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setattr(self, "update_one", func)
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def benchmark_view_two(self):
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"""Benchmark view creation with row and Split By."""
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for i in range(len(PerspectiveBenchmark.group_by_OPTIONS)):
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RP = PerspectiveBenchmark.group_by_OPTIONS[i]
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CP = PerspectiveBenchmark.split_by_OPTIONS[i]
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if len(RP) == 0 and len(CP) == 0:
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continue
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test_meta = make_meta("view", "two_{0}x{1}_pivot".format(len(RP), len(CP)))
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view_constructor = partial(self._table.view, group_by=RP, split_by=CP)
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func = Benchmark(lambda: view_constructor(), meta=test_meta)
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setattr(self, "view_{0}".format(test_meta["name"]), func)
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def benchmark_view_two_updates(self):
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"""Benchmark how long it takes for each update to resolve fully, using
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the on update callback that forces resolution of updates across
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25 views."""
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table = Table(self._schema)
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views = [table.view(group_by=["State", "City"], split_by=["Category", "Sub-Category"]) for i in range(25)]
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for v in views:
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v.on_update(empty_callback)
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update_data = self._get_update_data(1000)
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def resolve_update():
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table.update(update_data)
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table.size()
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func = Benchmark(resolve_update, meta=make_meta("update", "two"))
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setattr(self, "update_two", func)
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def benchmark_to_format_zero(self):
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"""Benchmark each `to_format` method."""
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for name in (
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"arrow",
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"csv",
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"columns",
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"records",
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):
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method = "to_{0}".format(name)
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test_meta = make_meta("to_format", method)
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func = Benchmark(getattr(self._view, method), meta=test_meta)
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setattr(self, "to_format_{0}".format(name), func)
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def benchmark_to_format_one(self):
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"""Benchmark each `to_format` method for one-sided contexts."""
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for name in (
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"arrow",
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"csv",
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"columns",
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"records",
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):
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for pivot in PerspectiveBenchmark.group_by_OPTIONS:
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if len(pivot) == 0:
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continue
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test_meta = make_meta("to_format", "to_{0}_r{1}".format(name, len(pivot)))
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view = self._table.view(group_by=pivot)
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method = "to_{0}".format(name)
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func = Benchmark(getattr(view, method), meta=test_meta)
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setattr(self, "to_format_{0}".format(test_meta["name"]), func)
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def benchmark_to_format_two(self):
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"""Benchmark each `to_format` method for two-sided contexts."""
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for name in (
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"arrow",
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"csv",
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"columns",
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"records",
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):
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for i in range(len(PerspectiveBenchmark.group_by_OPTIONS)):
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RP = PerspectiveBenchmark.group_by_OPTIONS[i]
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CP = PerspectiveBenchmark.split_by_OPTIONS[i]
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if len(RP) == 0 and len(CP) == 0:
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continue
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test_meta = make_meta("to_format", "to_{0}_r{1}_c{2}".format(name, len(RP), len(CP)))
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view = self._table.view(group_by=RP, split_by=CP)
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method = "to_{0}".format(name)
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func = Benchmark(getattr(view, method), meta=test_meta)
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setattr(self, "to_format_{0}".format(test_meta["name"]), func)
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if __name__ == "__main__":
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VERSION = os.environ.get("PSP_VERSION", "Unknown Version")
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# Initialize a suite and runner, then call `.run()`
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suite = PerspectiveBenchmark()
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runner = Runner(suite)
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logging.info("Benchmarking perspective-python=={}".format(VERSION))
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logging.info("Detected {} {}".format(perspective.__version__, perspective.__file__))
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runner.run(VERSION)
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runner.write_results()
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