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Arpit Bhayani Blogs
001 Ai Topological Sort

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TLDR: Sliding Window Rate Limiting - Design and Implementation

Date: 2020-04-05 Source: https://arpitbhayani.me/blogs/sliding-window-ratelimiter

Overview

A rate limiter restricts the intended or unintended excessive usage of a system by regulating the number of requests made to/from it by discarding the surplus ones.

Key Points

  • A rate limiter restricts the intended or unintended excessive usage of a system by regulating the number of requests made to/from it by discarding the surplus ones.
  • Visualizing sliding window: Every time we get a request, we make a decision to either serve it or not; hence we check the number_of_requests made in last time_window_sec seconds.
  • The pseudocode: The core of the algorithm could be summarized in the following Python pseudocode.
  • Components of the Rate limiter: The Rate limiter has the following components
  • Deciding the datastores: Picking the right data store for the use case is extremely important.
001-ai-topological-sort.md
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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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010 Venting At Work
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011 Half Life
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012 Multi Paxos
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013 Mysql Replication Internals
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014 Bloom Filters
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015 Clock Sync Nightmare
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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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022 Heartbeats In Distributed Systems
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023 Cassandra Writes
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024 Redis Replication
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025 Arrogant People At Work
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026 Cdn Content Replication
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027 Cant Fix Everything Day One
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028 Emotions At Work
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029 Grpc Http2
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030 Meetings With No Agenda Are A Waste Of Time
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031 Growth Is Not About Doing Everything
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032 Career Longevity Vs Job Hopping
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033 Stay Relevant At Higher Salary Levels
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034 Why Consensus
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035 Database Deadlocks
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036 Cpu Cache Locality
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037 Eventual Consistency
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038 Dns Udp Tcp
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039 Masters
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040 Empathy Makes Great Engineers Unstoppable
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041 Good Mentors Build People
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042 Always Have Back Burner Projects
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043 Before You Push Back Know What Youre Standing On
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044 Be The One They Can Count On
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045 How Much People Bet On You
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046 How To Get Leadership To Say Yes To Your Project
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047 Dont Let Your Best Ideas Die In Silence
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048 Be Someone Others Want To Work With
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049 Dont Fall For Xy Problem Ask Right Questions
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050 Biggest Lie Startups Tell Engineers
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051 Promotions Are Proactive Not Reactive
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052 Not Enough To Be Right Learn To Be Heard
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053 No One Ships Alone
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054 Not Every Mistake Needs A Correction
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055 Build Influence At Work
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056 Your Soft Skills Arent Soft At All
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057 Experience Before Forming Opinion
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058 Curiosity And High Bias For Action
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059 Worklog
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060 Mistakes And Growth
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061 Own It Instead Of Sweeping It Aside
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062 Dont Wait Step Up
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063 Temporary Fix Is Permanent
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064 Interview Bias And What Sets You Apart
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065 Saying This Isnt My Problem Is A Problem
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066 Okr
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067 Miscommunication
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068 When In Doubt Code It Out
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069 Follow Up Without Annoying People
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070 Lead Projects That Land
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071 Abstract Thinking Skill Next Decade
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072 We Engineers Suck At Task Estimation
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073 Shiny Object Syndrome In Tech
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074 3p
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075 Leverage The Equilibrium
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076 On Demand Container Loading In Aws Lambda
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077 Sql Has Problems We Can Fix Them Pipe Syntax In Sql
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078 Nanolog A Nanosecond Scale Logging System
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079 Best Resource Is Mythical
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080 Wtf The Who To Follow Service At Twitter
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081 Know A Lot
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082 Out Of Syllabus
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083 Negotiate The Offer
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084 Never Bad Mouth Your Ex Exployer
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085 Culture Fit
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086 Quantification In Resume
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087 Hiring Is Unfair
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088 Questions For Interviewers
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089 Collaboration Communication
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090 Out Of Vicious Interview Cycle
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091 Pitch Projects Not Ideas
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092 Read Design Docs
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093 Read Rca Docs
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094 Start Generalist
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095 Do Not Rely On Summaries
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096 Structure Your Design Interviews
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097 Title Inflation
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098 Find Your Own Project
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099 Six Pointers To Crack Coding And Design Interviews
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100 Keep Yourself Unblocked
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101 Genetic Knapsack
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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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120 Architectures In Distributed Systems
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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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134 1d Terrain
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135 Jaccard Minhash
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136 Ts Smoothing
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137 Lfu
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138 Morris Counter
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139 Slowsort
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140 Bitcask
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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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150 Fractional Cascading
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151 Copy On Write
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152 Midpoint Insertion Caching Strategy
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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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  • Data models and data structures: Now we take a look at data models and data structures we would use to build this generic rate limiter.

    • Takeaway

    While deploying it to production we could use a memory store like Redis whose features, like Key expiration, transaction, locks, sorted, would come in handy.