How databases scale writes: The power of the log ✍️🗒️

Summary

In this informative video, G KCS explores the optimization of database write operations using log-structured merge trees (LSM trees). The script explains how traditional B+ trees in databases can be inefficient for scaling, and introduces the concept of LSM trees that prioritize fast writes using linked lists while also maintaining quick read times through sorted string tables. The video delves into the mechanics of LSM trees, including compaction and the use of bloom filters to speed up search queries, offering a comprehensive look at balancing performance in database management.

Takeaways

• 🌐 The video discusses optimizing database operations by reducing unnecessary data exchanges and I/O calls to improve response times.
• 📚 Traditional databases use B+ trees for efficient insertion and search operations, which are both O(log n) in complexity.
• 🔧 To scale databases, the video suggests condensing multiple data operations into a single query to minimize acknowledgments and headers.
• 🔑 The trade-off for condensing data is the need for additional memory on the server to temporarily store the queries before flushing them to the database.
• ✂️ Write operations can be optimized using linked lists, which offer O(1) constant time complexity for appending data.
• 🔍 However, linked lists are inefficient for read operations, which require a sequential search, leading to O(n) time complexity.
• 📈 The Log-Structured Merge Tree (LSM Tree) is introduced as a hybrid data structure that combines the fast write benefits of a log with the fast read benefits of a sorted array.
• 🔄 LSM Trees work by periodically merging sorted arrays in the background, reducing the number of required read operations and improving efficiency.
• 📊 The video explains the concept of 'compaction' in LSM Trees, where smaller sorted arrays are merged into larger ones to optimize read performance.
• 🛠️ Bloom filters are suggested as a method to speed up search queries by reducing the number of chunks that need to be checked, albeit with a chance of false positives.
• 🔑 The script concludes with the importance of balancing fast writes with efficient reads in database optimization, highlighting the LSM Tree as a practical solution.

Q & A

What is the main topic of the video script?

The main topic of the video script is optimizing database operations by discussing the use of data structures like B+ trees, linked lists, and log-structured merge trees for efficient write and read operations.

Why are B+ trees preferred in traditional databases?

B+ trees are preferred in traditional databases because they provide good insertion and search times, both of which are of the order log n, making them efficient for handling SQL commands like insert and select.

What is the main idea behind condensing data queries into a single query?

The main idea behind condensing data queries into a single query is to reduce unnecessary data exchange, such as acknowledgments and headers, and to decrease I/O calls, which in turn can free up resources and reduce request-response times.

What is the time complexity of write operations in a linked list?

The time complexity of write operations in a linked list is O(1), which means it is constant time because appending data to the end of the list is a straightforward operation.

What is the disadvantage of using a log for database storage in terms of read operations?

The disadvantage of using a log for database storage is that read operations are slow, as they require a sequential search through the entire log, which is an O(n) operation where n is the number of records in the log.

What is a log-structured merge tree (LSM tree) and how does it help with database optimization?

A log-structured merge tree (LSM tree) is a data structure that combines the fast write capabilities of a linked list with the fast read capabilities of sorted arrays or trees. It optimizes databases by allowing fast writes through sequential log appends and fast reads through sorted string tables that are created and merged in the background.

What is the purpose of merging sorted arrays in the background of an LSM tree?

The purpose of merging sorted arrays in the background of an LSM tree is to improve read performance by reducing the number of chunks that need to be searched during a query, thus decreasing the overall time complexity for read operations.

What is compaction in the context of LSM trees?

Compaction in the context of LSM trees is the process of taking multiple sorted string tables and merging them into a single, larger sorted array to facilitate faster search queries.

What is a bloom filter and how can it be used to speed up read queries in a database?

A bloom filter is a probabilistic data structure that can be used to test whether an element is a member of a set. In the context of databases, it can speed up read queries by quickly determining the presence of a record in a chunk, reducing the need for full sequential searches and thus speeding up the overall read operation, albeit with a possibility of false positives.

How does the script suggest managing the trade-off between fast writes and fast reads in database optimization?

The script suggests using a hybrid approach where writes are fast due to the use of a linked list-like structure, while reads are optimized by merging sorted arrays in the background to form sorted string tables. Additionally, bloom filters can be used to further speed up read queries by reducing unnecessary searches.

Outlines

00:00

📚 Database Optimization with B+ Trees and Logs

In this paragraph, the speaker discusses the challenges of scaling databases and introduces the B+ tree data structure, which is crucial for efficient SQL command execution. The B+ tree is likened to a binary search tree but with multiple paths from each node, offering good insertion and search times. The speaker then explores the idea of reducing unnecessary data exchange and I/O calls to improve database performance. The concept of condensing data queries into a single operation is proposed to enhance efficiency, although this comes with the trade-off of requiring additional memory. The paragraph concludes with a teaser about the fastest data structure for write operations, hinting at the linked list's advantages.

05:03

🔄 Leveraging Linked Lists and Sorted Arrays for Optimal Performance

The speaker elaborates on the use of linked lists for fast write operations and the subsequent need for a data structure that supports quick read operations. The concept of a log-structured merge tree (LSM tree) is introduced as a hybrid approach, combining the benefits of fast writes from a linked list and efficient reads from sorted arrays. The mechanics of how data is appended to a log and then sorted before being persisted in the database are explained. The speaker also addresses the inefficiency of constantly sorting large datasets and introduces the idea of maintaining sorted chunks to optimize read operations. The paragraph ends with a discussion on the trade-offs between read and write speeds and the strategy for merging sorted arrays to improve performance.

10:05

🔍 Enhancing Database Read Efficiency with Merging and Bloom Filters

This paragraph delves into the process of merging sorted arrays to form larger chunks, which reduces the time complexity for search queries. The speaker explains how the database can manage chunks of varying sizes and the decision-making process for when to merge these chunks, drawing parallels to the merge sort algorithm. The concept of a bloom filter is introduced as a method to speed up search queries by reducing false positives, which in turn decreases read times. The speaker suggests that creating a bloom filter for each chunk can help avoid unnecessary reads and improve the overall efficiency of the database system.

15:06

🌐 Conclusion on LSM Trees and Future Optimization Techniques

In the final paragraph, the speaker wraps up the discussion on log-structured merge trees, summarizing the key points about using linked lists for fast writes and sorted arrays for efficient reads. The process of compaction, which merges multiple sorted string tables into a single array, is highlighted as a critical component of LSM trees. The speaker also hints at further optimization techniques, such as the use of bloom filters, to enhance search query performance. The paragraph concludes with an invitation for feedback and suggestions from the audience, emphasizing the ongoing nature of database optimization and the potential for further improvements.

Keywords

💡Database Optimization

Database optimization refers to the process of improving the performance and efficiency of a database system. In the context of the video, it is about finding ways to scale the database to handle a large volume of data and queries without sacrificing speed or reliability. The script discusses optimizing write operations and reducing unnecessary data exchanges like acknowledgments.

💡B+ Tree

A B+ tree is a type of data structure that allows for efficient insertion and search operations, which are both of order log n. The video explains that traditional databases use B+ trees because they provide multiple paths from the root to the leaves, making them ideal for managing large datasets with fast access times.

💡I/O Calls

I/O calls, or Input/Output calls, are operations that manage the transfer of data between the storage system and the central processing unit. The script mentions reducing I/O calls as a way to free up resources and decrease request-response times, which is crucial for database performance.

💡Condensing Data

Condensing data in the script refers to the process of combining multiple data queries into a single query to reduce the number of I/O operations. This approach aims to increase bandwidth efficiency and reduce the workload on the database by minimizing the data sent and the number of acknowledgments received.

💡Linked List

A linked list is a basic data structure consisting of nodes that are connected via pointers. The video discusses the use of linked lists for their efficiency in write operations, as appending data to the end of a list is a constant time operation, which is beneficial for databases that require fast writes.

💡Log Structured Merge Tree (LSM Tree)

The Log Structured Merge Tree is a data structure that combines the fast write capabilities of a log with the fast read capabilities of a sorted array. The video explains that LSM trees are used to optimize write operations in databases by first writing data to a log and then merging it into sorted arrays for efficient searching.

💡Compaction

Compaction in the context of LSM trees is the process of merging multiple sorted string tables into a single, larger sorted array. This is done to improve read performance by reducing the number of areas that need to be searched during a query, as explained in the script.

💡Sorted String Table

A sorted string table is a sorted array of records within an LSM tree. The script mentions that these tables are created in the background to facilitate fast read operations, as they allow for binary search rather than sequential search.

💡Bloom Filter

A bloom filter is a probabilistic data structure used to test whether an element is a member of a set. The video suggests using bloom filters to speed up search queries in databases by quickly determining the presence or absence of a record, thus reducing unnecessary read operations.

💡False Positives

In the context of bloom filters, false positives occur when the filter incorrectly indicates that an element is present in the set. The script explains that while bloom filters can speed up queries, they may sometimes return a positive result even when the record is not actually in the database, which is a trade-off for the increased search speed.

Highlights

Optimizing database writes by reducing unnecessary data exchange and I/O calls for faster response times.

Introduction to the B+ tree data structure, which offers good insertion and search times due to its multiple paths per node.

The concept of condensing data into a single block for efficient bandwidth use and reduced I/O operations.

Trade-off between using additional memory for condensed data queries and the benefits of fewer I/O operations.

The LinkedList data structure's advantage in write operations due to its constant time complexity.

The Log-Structured Merge Tree (LSM Tree) as a hybrid approach combining the benefits of fast writes and sorted arrays for efficient reads.

The disadvantages of using a log for read operations, which require sequential search and are slow compared to tree-based structures.

The importance of maintaining fast writes and reducing I/O operations while addressing the slow read issue.

The use of sorted arrays to improve search times from O(n) to O(log n) after converting a linked list.

Mechanics of persisting data in sorted order to the database to facilitate efficient querying.

The process of merging sorted chunks in the background to maintain a balance between write and read performance.

The concept of compaction in LSM Trees, which merges sorted string tables to optimize read queries.

The management of memory and chunks to prevent slowing down write operations while optimizing read times.

The use of bloom filters to speed up search queries by reducing the number of chunks that need to be searched.

The strategy of merging sorted arrays in the background to form larger sorted arrays for faster searches.

The decision-making process for when to merge sorted arrays in LSM Trees, similar to merge sort.

The impact of varying block sizes on read operations and the use of bloom filters to speed up queries across blocks.

The introduction to log-structured merge trees and their role in optimizing both write and read operations in databases.

Transcripts

hey everyone this is G kcs today we'll

be talking about a way in which we can

optimize rights in our database so you

have your client which sends information

to your server the server has to write

to the database because it needs to

maintain some state how do you scale

this you can think of your database as a

data structure the speedup queries the

traditional database uses a data

structure called a B+ tree this is like

a binary search tree without the binary

inside it there's multiple paths that

each node can follow and the B+ tree is

preferred because it gives you good

insertion and search times both of them

are order log n so whenever you fire a

SQL command of an insert or a select

that maps to an insertion or a search

operation in the B+ tree and the

underlying data structure is manipulated

each of these operations requires an

acknowledgment from the database which

says yes I have successfully executed

the request the scale our database we

want to reduce the unnecessary exchange

of data which is acknowledgments and

headers and also we want to reduce the

i/o calls which will help us free up

some resources and reduce the request

response times if we can condense all of

this data into one block and send it in

one shot to the database and get one

acknowledgment that's a good idea

so that would be more efficient use of

your bandwidth because you don't have

any extra data that you are sending

around and also you're going to be doing

less work because there'll be one i/o

called one I or response condense data

queries into a single query that's our

first idea the server has to give up

some memory to condense this data query

these bunch of data queries so that

requires additional memory so this is

one of the drawbacks and the reason we

did this which is one of the advantages

is that we need to use lesser i/o

operations if you have a lot of write

operations what is the fastest data

structure which can help you write

operations quickly

[Music]

the link list it's the most basic data

structure if you think of it all you

need to do is when whenever there's a

write in a linked list you just go to

the end and append something to it and

that's exactly what we are trying to

optimize your so the write operations in

a linked list are order one it's

constant time because all you need to do

is you have this linked list over here

and you go to the end and you just add a

node and it goes to the next node we

have a well-known data structure which

follows the philosophy of a linked list

which is called a log okay and you can

get the idea of whether log structured

merge tree is coming from we are going

to be using a log to assist data because

it's really fast when it comes to write

operations what is the disadvantage of a

log read operations are really slow in a

log if you need to find something you

just can't jump to one point you have to

actually search through sequentially the

entire log and if you can think about

your database as a log that's a huge

amount of data which you'll need to read

for every query that a person will be

sending so the advantage of this is that

you have fast writes but the clear

disadvantage is that you have slow reads

so we have two clear advantages with us

which we want to keep because fast

writes and lesser i/o operations is

something that sounds really good but

the drawbacks are something that we want

to cut down on so the additional memory

you can't really help much because if

you need to condense the queries and

keep them somewhere they have to be

somewhere so the additional memory is

fine it's not something we can optimize

on much all you can do is you can kind

of constrain the maximum number of

blocks that you'll be keeping in memory

before you flush into the database so

this thing can be managed what we really

don't want to do is we don't want to

slow down our read operations if you're

thinking about Facebook where there's a

lot of people who are posting things the

only worse thing compared to a slow post

is a slow reader operation where you

want to know what your friends are doing

and it takes you 30 seconds to actually

get that news feed onto your onto your

browser so slow leagues is something

that we definitely want to avoid and

what can we do about that well this is

some good

- the first thing is that slow reads on

what you know your your reading not on

the log you don't want to be reading on

this linked list because it gives you by

definition a sequential search which is

order n so that's pretty bad

can you improve on this no not as a

linguist you can't but if you convert

your data to a far more sensible data

structure which is something like a tree

or something like a sorted list a sorted

array that makes your searching really

fast yeah so if you have instead of the

linked list if you use something like a

sorted array then your search time

becomes order log n but this seems

counterintuitive because you already had

a B plus tree which was giving you log

in such time it was giving you log in

insertion time so what should we take a

B plus tree or a linked list a linked

list is great at writing but terrible at

reading but the reason we have moved on

to it is because B trees give us or a

write performance and that's why we have

the MAGIX addition of using a linked

list with a sorted array to get great

write speeds and great read speeds so if

we would have this data in a sorted

array it would take us login time which

is really fast this takes less time

compared to order n of course the

sequential search is really slow so can

we convert our linked list to a sorted

array we can we can we don't want to do

it in the memory because that defeats

the purpose the whole point of having a

linked list was so that you do fast

insertions if you want a sorted arrays

insertions are really slow so you won't

do it here you instead will do it

somewhere over here in the database okay

so whatever information you get from the

client you sort it and then you persist

it so that the read operations are super

fast let's talk about the mechanics of

how this is going to work firstly we

mentioned that we need some sort of a

log that's over here which is being

appended to by the server so every time

a new record comes in the latest record

over here is J

so point 31 you keep appending to this

log after a certain point which is a

threshold of the number of Records you

can keep in memory you persist this to

the database in one shot so what's

happening here in the DB is that this

data is going to be sorted before it is

persisted or rather it's going to be

persisted in a sorted fashion so you can

see that the numbers here are jumbled up

while here it's 12 17 19 23 31 47 it's a

nice sorted way what you can do here on

top of this is do a binary search which

allows you to query this data in a

efficient manner but what if you have

some more data coming in so if after

this append is complete you want to

append new stuff to your database so you

don't just take it and immediately push

it to the database if we do the same

thing as we did over here now that we

have these records and you want to

persist them the database all you need

to do is you need to send this

information to the DB when you want to

persist this in the DB the DB can do two

things one is it can take these six

records and merge it with these six

records so it'll create a sorted file of

size twelve but that's not maybe the

smartest thing to do because if you

think of it this is going to happen all

the time after 10,000 records in the DB

you send in six more records what it

needs to do is it needs to sort n

thousand and six records for the

additional six records that you sent

here so the increment of six records is

actually forcing the entire database to

be sorted you know flushing is nothing

compared to sorting huge bunch of

numbers this is time complexity and log

in so how do we optimize this one of the

things you can do is instead of merging

arrays all the time you can keep them in

sorted chunks so your database is going

to be represented by chunks this is a

chunk of size six this chunk after being

sorted will be of size six now whenever

there's a read query in your database

all you need to do is you need to search

through this chunk do a binary search on

this chunk if you don't find it then you

do a binary search on this chunk and

there's just two chunks that you have to

go through this is better than slowing

down your write operations tremendously

so

you're right operations are reasonably

fast even now but your read operations

are slightly slower so if you have n

insertions the number of chunks you'll

have is divided by six if you draw the

graph for this you know it's after all a

linear graph so this is again order n n

by six is the graph literally so your

read times are extremely slow even now

imagine in something like Facebook you

have billions of records in the database

1 billion divided by 6 is still very

very slow so how do you optimize this

well you can use some sort of a hybrid

approach which is taking some records

and merging it with the other records as

long as the short time is not too high

so when you have six ecords in six

Eckerd's over here you merge them

together to get one sorted array and

this uses the standard mode sort

technique so I take the first two

elements over here because they're

sorted I know that they're the smallest

in their respective areas so twelve and

one of the smallest elements in these

two respective areas which one is

smaller one so I take one and Sam and I

pump it out over here now my point is

still at twelve over here while my

pointer has changed to 26 over here 2l

is smaller than 36 I get Tim pointer

changes to this point John which is at

position 17 so 17 is smaller than 36 I

take John and this is the basic merging

process when you're taking two sorted

arrays and you want to merge them into a

single sorted array what you need to do

is you need to move with the two

pointers and keep taking the smaller

element so the next one will be iris

which is 19 and then push the pointer

forward so it had moved here these are

now gone so there's 23 again it is

bigger than 36 so gada will come and

they'll be Jane and then finally after

Jane when the pointers are 47 with Mac

47 is greater than 36 so you will pull

out Larry and then so on and so forth so

you get this nice sorted array of size

12 so now what's happening is that every

time you are getting a chunk you are

deciding on whether or not you should

merge this chunk with the existing

chunks in the background what we are

going to do is we are going to take

chunks and we are going to merge them

together to make

just sorted Aries so that when there is

a search query this large sorted array

can help us reduce the overall time

complexity in the background what we are

going to do is we are going to take

chunks and we are going to merge them

together to make larger sorted areas so

that when there is a search query this

large sorted array can help us reduce

the overall time complexity to clarify

things if you get three chunks in total

so that means you have 18 records in

total which are broken into three chunks

then the time required to search in one

chunk is log to the base 2 of 6 so that

is approximately equal to 3 and if you

have to search in all the chunks for a

particular record let's say I'm

searching for record number 8 I saw sure

I don't find it I searched sure I don't

find it answer and I find it in the end

so the total number of operations I'll

need to do here are log of 6 to the base

2 3 times so that is in total around 9

operations while instead if in the

background I was able to merge two

sorted arrays and change that into a

sorted array of size 12 then the total

number of operations I have to do in

this for these two chunks is just 4

operations and the number of operations

I have to do here are 3 so 3 plus 4

gives you 7 operations so there's a

clear saving here but obviously you need

to sort I mean you need to merge two

sorted arrays and then persist it so

that has some overhead let's see what is

the best approach

if you get another sorted chunk of size

six what I'll do is I'll take these two

chunks and merge them together and have

two sorted chunks of size 12 can I do

better yeah I can take those two sort

chunks of size 12 and merge them into a

single sort of chunk of size 24 so log

of 24 to the base 2 is equal to 5

approximately and instead if I had them

all 4 if I had all four chunks unmerged

then I would have 3 into 4 which is 12

operations so you can see that there's a

clear saving of 5 instead of 12 if you

sort arrays together so when do we

decide to sort arias it's very similar

to how the merge sort works if you have

two blocks of size 6 you convert them

into a block of size 12 if you have two

blocks of size 12 you convert them into

a block of size 24 and so on all the way

up till your maximum size which is going

to be n number of insertions that's the

maximum possible size you can have what

happens with this strategy is that you

have a large number of blocks of varying

size for example this one is of size six

this one is of size 12 and so on and so

forth you'll have some blocks of 24 also

which have been merged after some time

so your read operation is spread across

these blocks and to speed up your read

operation you can try some clever hacks

the idea you can apply here is something

called a bloom filter there's a video I

made on that you can check it out

the basic idea of a bloom filter is

quite simple if you have a book let's

say with a lot of words in it so you

want to find the word cat in that book

sociaty now one of the things you can do

is you can search the entire book for

the word cat or if the book provides you

a particular data structure which is

called a bloom filter yeah I had 26

positions in this bloom filter there's 1

for a 1 for B 1 for C D and so on and so

forth if there exists any word in the

book starting with the letter C I mark

this as true I mark this as 1 so the

position of C will be marked as 1 so if

the word cat exists in this book

definitely this point is going to give

marked as true

think about some edge cases what if I

have the word Karuna in this book will

this letter be marked yes but what if I

don't have the word cat and I just have

the word karana in this book will this

letter be mark yes so that's called a

false positive

so in bloom filters you can have false

positives you cannot have false

negatives if C if there is no word with

C there's no way that this letters going

to be marked that's the general idea of

a bloom filter and you can think about

you know your book being the database

and your search query being the bloom

filter query if you are looking for

whether this record exists in this chunk

all you need to do is you need to create

a bloom filter on top of this chunk

which tells you whether or Eckerd exists

in this in this list or not so this

speeds up your querying because you get

false positives the rate of false

positive can be managed by increasing

the number of bits yeah instead of

saying whether there's a letter with but

there's a word with C I can say are

there any words it's C a so I'm taking

combinations now so Co is going to be

marked but C is not going to be marked

so this is going to be a proper negative

and I can speed up queries that way I

can use that concept to create bloom

filters here and speed up my search

queries also anytime I merge two areas

I'm going to be requiring a separate

bloom filter for this one a good idea is

to create a bloom filter of larger size

because there's more information here

the chance of a false positive is higher

therefore I need to increase the

information in the bloom filter to

reduce the chance of a false positive

this way when you are reducing the false

positives you are indirectly reducing

the read times also you don't have any

useless reading that you are doing

because this is accurate there's a lot

more to log structured merge trees but

this is a reasonable introduction to the

concept what you want to do is you want

to speed up writes for that you use the

data structure which is a linked list

but you can't read well on linked lists

so you in the background merge sorted

arrays which can be binary searched to

get fast read operations so your rights

are fast because you're flushing rights

in in a sequential fashion but your

reads also fast because you are reading

from sorted tables which have been made

in the background that's the general

idea for lsdm some of the important

terms you can take from here are this

sorted set of Records is called a sorted

string table yeah the reason being that

it is a sorted string table the other

thing that we did off of taking these

two arrays and merging them together is

called compaction so compaction is

taking lots of sorted string tables and

using the merge process condensing them

into a single array so that we can do

fast search queries if you have any

doubts or suggestions on this you can

leave them in the comments below if you

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