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https://issues.apache.org/jira/browse/ARROW-1861?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16348924#comment-16348924
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ASF GitHub Bot commented on ARROW-1861:
---------------------------------------
pitrou commented on a change in pull request #1543: ARROW-1861: [Python] Rework
benchmark suite [skip ci]
URL: https://github.com/apache/arrow/pull/1543#discussion_r165424423
##########
File path: python/benchmarks/convert_builtins.py
##########
@@ -0,0 +1,291 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from functools import partial
+import itertools
+
+import numpy as np
+import pyarrow as pa
+
+from . import common
+
+
+DEFAULT_NONE_PROB = 0.3
+
+
+class BuiltinsGenerator(object):
+
+ def __init__(self, seed=42):
+ self.rnd = np.random.RandomState(seed)
+
+ def sprinkle_nones(self, lst, prob):
+ """
+ Sprinkle None entries in list *lst* with likelihood *prob*.
+ """
+ for i, p in enumerate(self.rnd.random_sample(size=len(lst))):
+ if p < prob:
+ lst[i] = None
+
+ def generate_int_list(self, n, none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of Python ints with *none_prob* probability of
+ an entry being None.
+ """
+ data = list(range(n))
+ self.sprinkle_nones(data, none_prob)
+ return data
+
+ def generate_float_list(self, n, none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of Python floats with *none_prob* probability of
+ an entry being None.
+ """
+ # Make sure we get Python floats, not np.float64
+ data = list(map(float, self.rnd.uniform(0.0, 1.0, n)))
+ assert len(data) == n
+ self.sprinkle_nones(data, none_prob)
+ return data
+
+ def generate_bool_list(self, n, none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of Python bools with *none_prob* probability of
+ an entry being None.
+ """
+ # Make sure we get Python bools, not np.bool_
+ data = [bool(x >= 0.5) for x in self.rnd.uniform(0.0, 1.0, n)]
+ assert len(data) == n
+ self.sprinkle_nones(data, none_prob)
+ return data
+
+ def _generate_varying_sequences(self, random_factory, n, min_size,
max_size, none_prob):
+ """
+ Generate a list of *n* sequences of varying size between *min_size*
+ and *max_size*, with *none_prob* probability of an entry being None.
+ The base material for each sequence is obtained by calling
+ `random_factory(<some size>)`
+ """
+ base_size = 10000
+ base = random_factory(base_size + max_size)
+ data = []
+ for i in range(n):
+ off = self.rnd.randint(base_size)
+ if min_size == max_size:
+ size = min_size
+ else:
+ size = self.rnd.randint(min_size, max_size + 1)
+ data.append(base[off:off + size])
+ self.sprinkle_nones(data, none_prob)
+ assert len(data) == n
+ return data
+
+ def generate_fixed_binary_list(self, n, size, none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of bytestrings with a fixed *size*.
+ """
+ return self._generate_varying_sequences(common.get_random_bytes, n,
+ size, size, none_prob)
+
+
+ def generate_varying_binary_list(self, n, min_size, max_size,
+ none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of bytestrings with a random size between
+ *min_size* and *max_size*.
+ """
+ return self._generate_varying_sequences(common.get_random_bytes, n,
+ min_size, max_size, none_prob)
+
+
+ def generate_ascii_string_list(self, n, min_size, max_size,
+ none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of ASCII strings with a random size between
+ *min_size* and *max_size*.
+ """
+ return self._generate_varying_sequences(common.get_random_ascii, n,
+ min_size, max_size, none_prob)
+
+
+ def generate_unicode_string_list(self, n, min_size, max_size,
+ none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of unicode strings with a random size between
+ *min_size* and *max_size*.
+ """
+ return self._generate_varying_sequences(common.get_random_unicode, n,
+ min_size, max_size, none_prob)
+
+
+ def generate_int_list_list(self, n, min_size, max_size,
+ none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of lists of Python ints with a random size between
+ *min_size* and *max_size*.
+ """
+ return self._generate_varying_sequences(
+ partial(self.generate_int_list, none_prob=none_prob),
+ n, min_size, max_size, none_prob)
+
+
+ def generate_dict_list(self, n, none_prob=DEFAULT_NONE_PROB):
+ """
+ Generate a list of dicts with a random size between *min_size* and
+ *max_size*.
+ Each dict has the form `{'u': int value, 'v': float value, 'w': bool
value}`
+ """
+ ints = self.generate_int_list(n, none_prob=none_prob)
+ floats = self.generate_float_list(n, none_prob=none_prob)
+ bools = self.generate_bool_list(n, none_prob=none_prob)
+ dicts = []
+ # Keep half the Nones, omit the other half
+ keep_nones = itertools.cycle([True, False])
+ for u, v, w in zip(ints, floats, bools):
+ d = {}
+ if u is not None or next(keep_nones):
+ d['u'] = u
+ if v is not None or next(keep_nones):
+ d['v'] = v
+ if w is not None or next(keep_nones):
+ d['w'] = w
+ dicts.append(d)
+ self.sprinkle_nones(dicts, none_prob)
+ assert len(dicts) == n
+ return dicts
+
+ def get_type_and_builtins(self, n, type_name):
+ """
+ Return a `(arrow type, list)` tuple where the arrow type
+ corresponds to the given logical *type_name*, and the list
+ is a list of *n* random-generated Python objects compatible
+ with the arrow type.
+ """
+ size = None
+
+ if type_name in ('bool', 'ascii', 'unicode', 'int64 list', 'struct'):
+ kind = type_name
+ elif type_name.startswith(('int', 'uint')):
+ kind = 'int'
+ elif type_name.startswith('float'):
+ kind = 'float'
+ elif type_name == 'binary':
+ kind = 'varying binary'
+ elif type_name.startswith('binary'):
+ kind = 'fixed binary'
+ size = int(type_name[6:])
+ assert size > 0
+ else:
+ raise ValueError("unrecognized type %r" % (type_name,))
+
+ if kind in ('int', 'float'):
+ ty = getattr(pa, type_name)()
+ elif kind == 'bool':
+ ty = pa.bool_()
+ elif kind == 'fixed binary':
+ ty = pa.binary(size)
+ elif kind == 'varying binary':
+ ty = pa.binary()
+ elif kind in ('ascii', 'unicode'):
+ ty = pa.string()
+ elif kind == 'int64 list':
+ ty = pa.list_(pa.int64())
+ elif kind == 'struct':
+ ty = pa.struct([pa.field('u', pa.int64()),
+ pa.field('v', pa.float64()),
+ pa.field('w', pa.bool_())])
+
+ factories = {
+ 'int': self.generate_int_list,
+ 'float': self.generate_float_list,
+ 'bool': self.generate_bool_list,
+ 'fixed binary': partial(self.generate_fixed_binary_list,
+ size=size),
+ 'varying binary': partial(self.generate_varying_binary_list,
+ min_size=3, max_size=40),
+ 'ascii': partial(self.generate_ascii_string_list,
+ min_size=3, max_size=40),
+ 'unicode': partial(self.generate_unicode_string_list,
+ min_size=3, max_size=40),
+ 'int64 list': partial(self.generate_int_list_list,
+ min_size=0, max_size=20),
+ 'struct': self.generate_dict_list,
+ }
Review comment:
Okk.
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> [Python] Fix up ASV setup, add developer instructions for writing new
> benchmarks and running benchmark suite locally
> --------------------------------------------------------------------------------------------------------------------
>
> Key: ARROW-1861
> URL: https://issues.apache.org/jira/browse/ARROW-1861
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Wes McKinney
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
> Fix For: 0.9.0
>
>
> We need to start writing more microbenchmarks as we go to prevent
> unintentional performance regressions (this has been a constant thorn in my
> side for years:
> http://wesmckinney.com/blog/introducing-vbench-new-code-performance-analysis-and-monitoring-tool/).
>
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