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001 Ai Topological Sort

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Partitioning Strategies for Distributed Databases

Source: https://arpitbhayani.me/blogs/data-partitioning Date: 2022-01-24

Learn database partitioning! Scale reads/writes & improve availability by splitting your database. Explore horizontal/vertical strategies.

Partitioning plays a vital role in scaling a database beyond a certain scale of reads and writes. In this essay, we take a detailed look into Partitioning basics and understand how it can help us scale our Reads and Writes beyond a single machine.

What is Partitioning?

A database is partitioned when we split it, logically or physically, into mutually exclusive segments. Each partition of the database is a subset that can operate as a smaller independent database on its own. A database is always deterministically partitioned on a particular attribute like User ID, Time, Location, etc., allowing all records having the same attribute value to reside in the same partition. This will enable us to fire localized queries on a partitioned attribute.

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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029 Grpc Http2
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030 Meetings With No Agenda Are A Waste Of Time
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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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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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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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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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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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153 Fsm Python
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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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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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Partitioning

Say a database has grown to be 100GB big, and we choose to partition it on User ID into 4 partitions. To decide which record goes in which partition, we can use a Hash Function applied on the User ID to map one record to exactly one partition. Hence to trace where a record of a particular user resides, we pass it through the same Hash Function and find the owning partition.

We will talk about partitioning strategies in detail in future essays, so keep an eye.

Why do we partition?

We need to partition a database for several reasons, but load distribution and availability are the primary reasons. Let’s dive deeper into each and see how partitioning benefits us.

Load Distribution

A database is partitioned when it needs to handle more reads or writes than one over-scaled database. Our go-to strategy to handle more reads or more writes is to scale the database vertically. Given that vertical scaling has a limit due to hardware constraints, we have to go horizontal and distribute the load across multiple nodes.

Scaling Reads

By partitioning a database into multiple segments, we get a significant boost in the performance of localized queries. Say we have a database with 100M rows split into 4 partitions with roughly 25M rows each. Now, instead of one database supporting querying over 100M rows, we split the read load across 4 databases allowing us to quickly execute the query and serve the results to the users.

If the read query is localized by partitioned attribute, we need only one (of the four) partitions to execute the query and get the results, thus distributing the read load. For example, in a blogging platform, if our database is partitioned by User ID and we want to find the total number of posts made by a user, this query only needs to be executed on one small partition of data.

Scaling Reads with Partitioning

Suppose the read queries require us to fetch records from multiple partitions, given that each partition is independent. In that case, we can parallelize the execution and then merge the results before sending them out to the users. In either case, we get a massive performance boost in query execution.

Scaling Reads with Partitioning - Parallel Reads

Scaling Writes

In a traditional Master-Replica setup, there is one Master node that takes in all the write requests, and to scale reads, this Master has a few configured Replicas. To handle more Write operations in such a setup, one approach is to scale the Master node vertically by adding more CPU and RAM. The second approach is to scale it horizontally by adding multiple nodes acting as independent Multiple Master nodes.

Given that vertical scaling has a limit, scaling writes that adding multiple independent Master nodes becomes a go-to strategy beyond a certain scale, where Partitioning plays a key role. In a partitioned setup, since one record can is present on one partition, the total write operations are evenly distributed across all the Master nodes, allowing us to scale beyond a single machine.

Scaling writes with Partitioning

Improving Availability

By partitioning a database, we also get a massive improvement in data availability. Since our data is divided across multiple data nodes, even if one of the nodes abruptly crashes and becomes unrecoverable, we only lose a fraction of our data and not the whole of it.

We can further improve the availability of our data by replicating it across multiple secondary data nodes. Thus each partition resides on multiple data nodes, and in case of them crashes, we can fully recover the lost data from the secondary node, giving our fault tolerance a massive boost.

Partitioning for High Availability

Each record thus belongs to exactly one partition, but the replicated copy of the record can be stored on other data nodes for fault tolerance. These replicated copies are similar to Read Replicas that either synchronously or asynchronously follow the primary copy and keep itself updated.

Types of partitioning

Data can be partitioned in two ways - Horizontal and Vertical. In terms of relational databases, Horizontal Partitioning involves putting different rows into different partitions, and Vertical Partitioning involves putting different columns into separate partitions.

Horizontal partitioning is a very common practice in scaling relational and non-relational databases. It allows us to visit just one partition and get our query answered. It also enables us to split our query load across partitions by making one partition responsible for a particular row/record.

Vertical partitioning is seen in action in Data Warehouses, where we have to crunch a lot of numbers and fire complex aggregation queries. Vertical partitioning is particularly useful when we are not querying all the columns of a particular record and refer to querying a fewer set of columns in each query.