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Arpit Bhayani Blogs
001 Ai Topological Sort

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TLDR: How Python Handles Integers Under the Hood

Date: 2020-05-17 Source: https://arpitbhayani.me/blogs/python-caches-integers

Overview

Dive into Python's integer implementation! Explore how singletons and reference counting optimize performance for small integers. An integer in Python is not a traditional 2, 4, or 8-byte implementation but rather it is implemented as an array of digits in base 2^30 which enables Python to support super long integers.

Key Points

  • An integer in Python is not a traditional 2, 4, or 8-byte implementation but rather it is implemented as an array of digits in base 2^30 which enables Python to support super long integers.
  • Reference Counts: Reference count holds the number of different places there are that have a reference to the object.
  • Since there is no explicit limit on the size, working with integers in Python is extremely convenient as we can carry out operations on very long numbers without worrying about integer overflows.
  • This convenience comes at a cost of allocation being expensive and trivial operations like addition, multiplication, division being inefficient.
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002 Temporal Primer
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003 Rag Production
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004 Structure Of Llm Chat
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005 How Llms Work
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006 Monolith Is Distributed System
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007 Defensive Databases
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008 Bm25
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009 Join Algorithms
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011 Half Life
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013 Mysql Replication Internals
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014 Bloom Filters
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016 Kafka Partitions
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017 Product Quantization
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018 Qkv Matrices
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019 Deleted Production
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020 How Llm Inference Works
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021 Blocking Queues
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024 Redis Replication
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037 Eventual Consistency
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039 Masters
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050 Biggest Lie Startups Tell Engineers
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057 Experience Before Forming Opinion
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074 3p
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075 Leverage The Equilibrium
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091 Pitch Projects Not Ideas
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100 Keep Yourself Unblocked
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102 Pseudorandom Number Generation Lfsr
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103 How Indexes Work On Partitioned And Sharded Data
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104 Some Data Partitioning Strategies For Distributed Data Stores
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105 Data Partitioning
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106 Leaderless Replication
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107 Conflict Resolution
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108 Conflict Detection
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109 Multi Master Replication
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110 Monotonic Reads
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111 Read Your Write Consistency
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112 Handling Outages Master Replica
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113 Replication Formats
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114 Replication Strategies
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115 Master Replica Replication
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116 Durability
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117 Isolation
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118 Atomicity
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119 Consistency
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121 Mistaken Beliefs Of Distributed Systems
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122 Fork Bomb
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123 Chained Operators Python
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124 Taxonomy On Sql
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125 The Weird Walrus
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126 Fully Persistent Arrays
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127 Persistent Data Structures Introduction
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128 Constant Folding Python
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129 String Interning Python
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130 Recursion Visualizer Python
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131 Flajolet Martin
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132 2q Cache
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133 Israeli Queues
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141 Phi Accrual
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142 10x Engineer
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143 Decipher Repeated Key Xor
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144 Decipher Single Xor
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145 Python Iterable Integers
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146 Inheritance C
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147 Rum
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148 Consistent Hashing
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149 Python Caches Integers
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151 Copy On Write
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153 Fsm Python
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154 Bayesian Average
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155 Sliding Window Ratelimiter
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156 Idf
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157 Better Programmer
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158 Python Prompts
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159 Rule 30 Cellular Automata
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160 Function Overloading
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161 Isolation Forest
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162 Image Steganography
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163 Long Integers Python
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164 I Changed My Python
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165 Benchmark And Compare Pagination Approach In Mongodb
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166 Mongodb Cursor Skip Is Slow
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167 Fast And Efficient Pagination In Mongodb
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168 Making Http Requests Using Netcat
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  • Each integer in python is implemented as a C structure illustrated below.
  • It is observed that smaller integers in the range -5 to 256, are used very frequently as compared to other longer integers and hence to gain performance benefit Python preallocates this range of integers during initialization and makes them singleton and hence every time a smaller integer value is referenced instead of allocating a new integer it passes the reference of the corresponding singleton.

    • Takeaway

    Reference count holds the number of different places there are that have a reference to the object.