foundationdb/contrib/ddsketch_calc.py
Kevin Hoxha 058276493f
ddsketch_utility: Add utility scripts for ddsketch (#7602)
* Adds ddsketch_calc.py which implements a class for DDSketch related calculations
* Adds ddsketch_conversion.py for quickly computing a bucket index to a value or vice-versa
* Adds ddsketch_compare.py to compute how similar two ddsketch distributions are
* Adds export_graph.py to graph the ddsketch distribution outputted from mako

The arguments for ddsketch_conversion.py are:
-b, --bucket: the bucket index that we need to calculate the value from (optional)
-v, --value: the value that we need to calculate the bucket index from (optional)
-e, --error_guarantee: the error guarantee for ddsketch (optional, default is 0.005)

The arguments for ddsketch_compare.py are:
--file1: Path to first ddsketch json
--file2: Path to second ddsketch json
--txn1: The transaction type for the first file
--txn2: The transaction type for the second file
--op: The operation name (ex: GRV, GET ...)

The arguments for export_graph.py:
--file: path to ddsketch distribution
--txn, -t: Transaction type from file
--title: title for graph (optional, otherwise "Title" is used)
--savefig: Path to save the image plot (optional)
--op: Which operation to plot
2022-07-25 10:29:33 -07:00

73 lines
2.4 KiB
Python

#!/usr/bin/env python3
#
# ddsketch_calc.py
#
# This source file is part of the FoundationDB open source project
#
# Copyright 2013-2022 Apple Inc. and the FoundationDB project authors
#
# Licensed 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.
#
import numpy as np
import math as m
# Implements a DDSketch class as desrcibed in:
# https://arxiv.org/pdf/1908.10693.pdf
# This class has methods that use cubic interpolation to quickly compute log
# and inverse log. The coefficients A,B,C as well as correctingFactor are
# all constants used for interpolating.
# The implementation for interpolation was originally seen here in:
# https://github.com/DataDog/sketches-java/
# in the file CubicallyInterpolatedMapping.java
class DDSketch(object):
A = 6.0 / 35.0
B = -3.0 / 5.0
C = 10.0 / 7.0
EPS = 1e-18
correctingFactor = 1.00988652862227438516
offset = 0
multiplier = 0
gamma = 0
def __init__(self, errorGuarantee):
self.gamma = (1 + errorGuarantee) / (1 - errorGuarantee)
self.multiplier = (self.correctingFactor * m.log(2)) / m.log(self.gamma)
self.offset = self.getIndex(1.0 / self.EPS)
def fastlog(self, value):
s = np.frexp(value)
e = s[1]
s = s[0]
s = s * 2 - 1
return ((self.A * s + self.B) * s + self.C) * s + e - 1
def reverseLog(self, index):
exponent = m.floor(index)
d0 = self.B * self.B - 3 * self.A * self.C
d1 = 2 * self.B * self.B * self.B - 9 * self.A * self.B * self.C - 27 * self.A * self.A * (index - exponent)
p = np.cbrt((d1 - np.sqrt(d1 * d1 - 4 * d0 * d0 * d0)) / 2)
significandPlusOne = - (self.B + p + d0 / p) / (3 * self.A) + 1
return np.ldexp(significandPlusOne / 2, exponent + 1)
def getIndex(self, sample):
return m.ceil(self.fastlog(sample) * self.multiplier) + self.offset
def getValue(self, idx):
return self.reverseLog((idx - self.offset) / self.multiplier) * 2.0 / (1 + self.gamma)