Your SSD lies but that's ok | Postgres fsync

The Backend Engineering Show with Hussein Nasser

#Hussein Nasser #Engineering #Business #Industries #Open Source #Programming #Tech #Software Development

28:39

For centuries, members of the B’doul Bedouin tribe lived in the caves around the ancient city of Petra, Jordan. Then, in the 1980s, the government forced the tribe to move in the name of preserving the geological site for tourists. But if the residents are forced to leave, and if their heritage has been permanently changed, then what exactly is being preserved? SHOW NOTES: Meet The Man Living in The Lost City Carved in Stone Jordan: Petra's tourism authority cracks down on Bedouin cave dwellers The tribes paying the brutal price of conservation “There is no future for Umm Sayhoun” Jordan’s Young Bedouins Are Documenting Their Traditions on TikTok Check out Sami's company Jordan Inspiration Tours Learn about your ad choices: dovetail.prx.org/ad-choices…

for 2 år siden 30:04

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fsync is a linux system call that flushes all pages and metadata for a given file to the disk. It is indeed an expensive operation but required for durability especially for database systems. Regular writes that make it to the disk controller are often placed in the SSD local cache to accumulate more writes before getting flushed to the NAND cells. However when the disk controller receives this flush command it is required to immediately persist all of the data to the NAND cells. Some SSDs however don't do that because they don't trust the host and no-op the fsync. In this video I explain this in details and go through details on how postgres provide so many options to fine tune fsync 0:00 Intro 1:00 A Write doesn’t write 2:00 File System Page Cache 6:00 Fsync 7:30 SSD Cache 9:20 SSD ignores the flush 9:30 15 Year old Firefox fsync bug 12:30 What happens if SSD loses power 15:00 What options does Postgres exposes? 15:30 open_sync (O_SYNC) 16:15 open_datasync (O_DSYNC) 17:10 O_DIRECT 19:00 fsync 20:50 fdatasync 21:13 fsync = off 23:30 Don’t make your API simple 26:00 Database on metal?

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Your SSD lies but that's ok | Postgres fsync

The Backend Engineering Show with Hussein Nasser

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published for 2 år siden

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MP3•Episode hjem

527 episoder

#Hussein Nasser #Engineering #Business #Industries #Open Source #Programming #Tech #Software Development

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1
Socket management and Kernel Data structures 31:26

for 1 day siden31:26

31:26

Fundamentals of Operating Systems Course This video is an overview of how the operating system kernel does socket management and the different data structures it utilizes to achieve that. timestamps 0:00 Intro 1:38 Socket vs Connections 7:50 SYN and Accept Queue 18:56 Socket Sharding 23:14 Receive and Send buffers 27:00 Summary…

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The genius of long polling 28:57

for 17 weeks siden28:57

28:57

Polling is the ability to interrogate a backend to see if a piece of information is ready. It can introduce a chatty system and as a result long polling was born. In this video I explain the beauty of this design pattern and how we can push it to its limit. 0:00 Intro 0:45 Polling 2:30 Problem with Polling 3:50 Long Polling 8:18 Timeouts 10:00 Long Polling Benefits 12:00 Make requests into Long Polling 17:36 Request Resumption 21:40 Summary…

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Six stages of a good software engineer 39:27

for 22 weeks siden39:27

39:27

You get better as a software engineer when you go through these stages. 0:00 Intro 1:15 Understand a technology 7:07 Articulate how it works 15:30 Understand its’ limitations 19:48 Try to build something better 27:45 Realize what you built also has limitations 32:48 Appreciate the original tech as is Understand a technology We use technologies all the time without knowing how it works. And it is ok not knowing how things work if interests isn’t there. But when there is interest to understand how something works, pursue it. It feels good when you understand how something works because you work better with it, you swim with the tide instead of against it. When I learned how TCP/IP work.. you would appreciate every connection request, how you read requests. You will ask questions, what is my code doing here? When exactly I’m creating connections? When am I reading from the connection? Is it safe to share connections? Articulate how it works This one is not easy, you might think you understand something until you try to explain how it works. If you find yourself using jargon you probably don’t understand and you just try to impress others. Have you seen people who want to talk about something to show they understand it? It’s the opposite. Try to truly articlate how it works, you will really understand it , back to 1. I thought I understand how backend reads requests until I tried to speak to it. Understand the technology limitations Once 1,2 are done you will truly understand the tech, now you are confidant, you are excited about the tech and you will truly see when you can use the tech to its full potential and also know the weak points of the tech where it breaks, this happens a lot with TCP/IP. We know tcps limitations. Try to build something better This one is optional and can be skipped, but attempting to design or building something better then the tech because you know the limitations will truly reveal how you became better. But the challenge here is the ego, you might understand the limitations but you problem is thinking that what you will build is flawless. This step must be proceed with caution. Realize what you build also has limitation Dust settles.. this step hurts, and you may take a while to realize it, but whatever you build will have flaws… and when you realize this it is when you get better as an engineer. Appreciate the tech as is This is when you are back full circle you are back to the first stage, look at the technology and understand it but don’t judge it.. just know the limitations and its strength and flow with it. Stop fighting and instead build around a tech, does that mean you shouldn’t build anything new, of course not. Go build, but don’t stress around making something better to defeat existing tech. But actually build it for building it.…

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This new Linux patch can speed up Reading Requests 18:12

for 23 weeks siden18:12

18:12

Fundamentals of Operating Systems Course https://oscourse.win Very clever! We often call read/rcv system call to read requests from a connection, this copies data from kernel receive buffer to user space which has a cost. This new patch changes this to allow zero copy with notification. “Reading' data out of a socket instead becomes a “notification” mechanism, where the kernel tells userspace where the data is.” This kernel patch enables zero copy from the receive queue. https://lore.kernel.org/io-uring/ZwW7_cRr_UpbEC-X@LQ3V64L9R2/T/ 0:00 Intro 1:30 patch summary 7:00 Normal Connection Read (Kernel Copy) 12:40 Zero copy Read 15:30 Performance…

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Cloudflare's 150ms global cache purge | Deep Dive 1:02:21

for 24 weeks siden1:02:21

1:02:21

Cloudflare built a global cache purge system that runs under 150 ms. This is how they did it. Using RockDB to maintain local CDN cache, and a peer-to-peer data center distributed system and clever engineering, they went from 1.5 second purge, down to 150 ms. However, this isn’t full picture, because that 150 ms is just actually the P50. In this video I explore Clouldflare CDN work, how the old core-based centralized quicksilver, lazy purge work compared to the new coreless, decentralized active purge. In it I explore the pros and cons of both systems and give you my thoughts of this system. 0:00 Intro 4:25 From Core Base Lazy Purge to Coreless Active 12:50 CDN Basics 16:00 TTL Freshness 17:50 Purge 20:00 Core-Based Purge 24:00 Flexible Purges 26:36 Lazy Purge 30:00 Old Purge System Limitations 36:00 Coreless / Active Purge 39:00 LSM vs BTree 45:30 LSM Performance issues 48:00 How Active Purge Works 50:30 My thoughts about the new system 58:30 Summary Cloudflare blog https://blog.cloudflare.com/instant-purge/ Mentioned Videos Cloudflare blog https://blog.cloudflare.com/instant-purge/ Percentile Tail Latency Explained (95%, 99%) Monitor Backend performance with this metric https://www.youtube.com/watch?v=3JdQOExKtUY How Discord Stores Trillions of Messages | Deep Dive https://www.youtube.com/watch?v=xynXjChKkJc Fundamentals of Operating Systems Course https://os.husseinnasser.com Backend Troubleshooting Course https://performance.husseinnasser.com…

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MySQL is having a bumpy journey 28:34

for 27 weeks siden28:34

28:34

Fundamentals of Database Engineering udemy course https://databases.win MySQL has been having bumpy journey since 2018 with the release of the version 8.0. Critical crashes that made to the final product, significant performance regressions, and tons of stability and bugs issues. In this video I explore what happened to MySql, are these issues getting fixed? And what is the current state of MySQL at the end of 2024. 0:00 Intro 2:00 MySQL 8.0 vs 5.7 Performance 11:00 Critical Crash in 8.0.38, 8.4.1 and 9.0.0 15:40 Is 8.4 better than 8.0.36? 16:30 More Features = More Bugs 22:30 Summary and my thoughts resources https://x.com/MarkCallaghanDB/status/1786428909376164263 https://www.percona.com/blog/do-not-upgrade-to-any-version-of-mysql-after-8-0-37/ http://smalldatum.blogspot.com/2024/09/mysql-innodb-vs-sysbench-on-large-server.html https://www.percona.com/blog/mysql-8-0-vs-5-7-are-the-newer-versions-more-problematic/…

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How many kernel calls in NodeJS vs Bun vs Python vs native C 20:41

for 28 weeks siden20:41

20:41

Fundamentals of Operating Systems Course https://oscourse.win In this video I use strace a performance tool that measures how many system calls does a process makes. We compare a simple task of reading from a file, and we run the program in different runtimes, namely nodejs, buns , python and native C. We discuss the cost of kernel mode switches, system calls and pe 0:00 Intro 5:00 Code Explanation 6:30 Python 9:30 NodeJS 12:30 BunJS 13:12 C 16:00 Summary…

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When do you use threads? 31:08

for 29 weeks siden31:08

31:08

Fundamentals of Operating Systems Course https://os.husseinnasser.com When do you use threads? I would say in scenarios where the task is either 1) IO blocking task 2) CPU heavy 3) Large volume of small tasks In any of the cases above, it is favorable to offload the task to a thread. 1) IO blocking task When you read from or write to disk, depending on how you do it and the kernel interface you used, the write might be blocking. This means the process that executes the IO will not be allowed to execute any more code until the write/read completes. That is why you see most logging operations are done on a secondary thread (like libuv that Node uses) this way the thread is blocked but the main process/thread can resume its work. If you can do file reads/writes asynchronously with say io_uring then you technically don't need threading. Now notice how I said file IO because it is different than socket IO which is always done asynchronously with epoll/select etc. 2) CPU heavy The second use case is when the task requires lots of CPU time, which then starves/blocks the rest of the process from doing its normal job. So offloading that task to a thread so that it runs on a different core can allow the main process to continue running on its the original core. 3) Large volume of small tasks The third use case is when you have large amount of small tasks and single process can't deliver as much throughput. An example would be accepting connections, a single process can only accept connections so fast, to increase the throughput in case where you have massive amount of clients connecting, you would spin multiple threads to accept those connections and of course read and process requests. Perhaps you would also enable port reuse so that you avoid accept mutex locking. Keep in mind threads come with challenges and problems so when it is not required. 0:00 Intro 1:40 What are threads? 7:10 IO blocking Tasks 17:30 CPU Intensive Tasks 22:00 Large volume of small tasks…

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Frontend and Backends Timeouts 25:23

for 30 weeks siden25:23

25:23

I am fascinated by how timeouts affect backend and frontend programming. When a party is waiting on something you can place a timeout to break the wait. This is useful for freeing resources to more critical processes, detecting slow operations and even avoiding DOS attacks. Contrary to common beliefs, timeouts are not exclusive to request processing, they can be applied to other parts of the frontend-backend communications. Let us explore this briefly. 0:00 Intro 2:30 Connection Timeout 5:00 Request Read timeout 10:00 Wait Timeout 12:00 Usage Timeout 14:00 Response Timeout 16:00 Canceling a request 19:50 Proxies and timeouts…

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Postgres is combining IO in version 17 27:39

for 31 weeks siden27:39

27:39

Learn more about database and OS internals, check out my courses Fundamentals of database engineering https://databases.win Fundamentals of operating systems https://oscourse.win This new PostgreSQL 17 feature is game changer. You see, postgres like most databases work with fixed size pages. Pretty much everything is in this format, indexes, table data, etc. Those pages are 8K in size, each page will have the rows, or index tuples and a fixed header. The pages are just bytes in files and they are read and cached in the buffer pool. To read page 0, for example, you would call read on offset 0 for 8192 bytes, To read page 1 that is another read system call from offset 8193 for 8192, page 7 is offset 57,345 for 8192 and so on. If table is 100 pages stored a file, to do a full table scan, we would be making 100 system calls, each system call had an overhead (I talk about all of that in my OS course). The enhancement in Postgres 17 is to combine I/Os you can specify how much IO to combine, so technically while possible you can scan that entire table in one system call doesn’t mean its always a good idea of course and Ill talk about that. This also seems to included a vectorized I/O, with preadv system call which takes an array of offsets and lengths for random reads. The challenge will become how to not read too much, say I’m doing a seq scan to find something, I read page 0 and found it and quit I don’t need to read any more pages. With this feature I might read 10 pages in one I/O and pull all its content, put in shared buffers only to find my result in the first page (essentially wasting disk bandwidth, memory etc) It is going to be interesting to balance this out.…

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Windows vs Linux Kernel 37:23

for 31 weeks siden37:23

37:23

Fundamentals of Operating Systems Course https://os.husseinnasser.com Why Windows Kernel connects slower than Linux I explore the behavior of TCP/IP stack in Windows kernel when it receives a RST from the backend server especially when the host is available but the port we are trying to connect to is not. This behavior is exacerbated by having both IPv6 and IPv4 and if the happy eye ball protocol is in place where IPv6 is favorable. 0:00 Intro 0:30 Fundamentals TCP/IP 3:00 Unreachable Port Behavior 6:00 Client Kernel Behavior (Linux vs Windows) 11:40 Slow TCP Connect on Windows 15:00 localhost, IPv6 and IPv4 20:00 Happy Eyeballs 28:00 Registry keys to change the behavior 31:00 Port Unreachable vs Host Unreachable https://daniel.haxx.se/blog/2024/08/14/slow-tcp-connect-on-windows/…

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Running out of TCP ephemeral source ports 20:06

for 32 weeks siden20:06

20:06

In this episode of the backend engineering show I describe an interesting bug I ran into where the web server ran out of ephemeral ports causing the system to halt. 0:00 Intro 0:30 System architecture 2:20 The behavior of the bug 4:00 Backend Troubleshooting 7:00 The cause 15:30 Ephemeral ports on loopback…

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io uring gets even faster 16:35

for 46 weeks siden16:35

16:35

Fundamentals of Operating Systems Course https://os.husseinnasser.com Linux I/O expert and subsystem maintainer Jens Axboe has submitted all of the IO_uring feature updates ahead of the imminent Linux 6.10 merge window. In this video I explore this with a focus on what zerocopy. 0:00 Intro 0:30 IO_uring gets faster 2:00 What is io_uring 7:00 How Normal Copying Work 12:00 How Zero Copy Works 13:50 ZeroCopy and TLS https://www.phoronix.com/news/Linux-6.10-IO_uring https://lore.kernel.org/io-uring/fef75ea0-11b4-4815-8c66-7b19555b279d@kernel.dk/?s=09…

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They made Python faster with this compiler option 29:04

for 48 weeks siden29:04

29:04

Fundamentals of Operating Systems Course https://oscourse.win Looks like fedora is compiling cpython with the -o3 flag, which does aggressive function inlining among other optimizations. This seems to improve python benchmarks performance by at most 1.16x at a cost of an extra 3MB in binary size (text segment). Although it does seem to slow down some benchmarks as well though not significantly. O1 - local register allocation, subexpression elimination O2 - Function inlining only small functions O3 - Agressive inlining, SMID 0:00 Intro 1:00 Fedora Linux gets Fast Python 5:40 What is Compiling? 9:00 Compiling with No Optimization 12:10 Compiling with -O1 15:30 Compiling with -O2 20:00 Compiling with -O3 23:20 Showing Numbers Backend Troubleshooting Course https://performance.husseinnasser.com…

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How Apache Kafka got faster by switching ext4 to XFS 33:52

for 49 weeks siden33:52