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Consistent Hashing Explained Simply Let's break down consistent hashing in a way that's easy to get. It's a handy technique for distributing data evenly across servers. And if you're more of a visual learner, don't miss the attached video which brings this concept to life. Here's how it works:... We don't just hash our data keys; we also hash the server names using the same method. This puts everything on a scale, known as the hash space, which we think of as a big circular ring. So, imagine we've got a few servers. We hash them and place them on this ring. We do the same with our data, hashing it by key. To figure out where to store a piece of data, we travel clockwise on the ring from where the data lands until we bump into a server. Like, data key 0 might end up on server 0, and key 1 on server 1. Adding a new server? Only the data near the new server needs to shift. And if a server has to go? Only the data it was holding needs a new spot. But there's a little challenge: sometimes, data doesn't spread out evenly. The fix? Virtual nodes. They're like mini-replicas of a server placed around the ring. The more virtual nodes we have, the better the balance. But there's a catch: more virtual nodes mean more memory usage for tracking them, and it can make the whole system a bit more complex to manage. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages):show more

Sahn Lam

48,860 subscribers

36,787 görüntüleme • 2 yıl önce •via X (Twitter)

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Riccardo Causo profil fotoğrafı

Riccardo Causo2 yıl önce

the mother of all animations! 👍

Nirmalya Sengupta profil fotoğrafı

Nirmalya Sengupta2 yıl önce

The animation makes this even more retention-ready! Well done! 👍👍👍

Simform profil fotoğrafı

Simform2 yıl önce

I would add that consistent hashing is a fundamental concept in distributed systems and is used in many real-world applications. For instance, it’s used in load balancing, where requests are distributed across multiple servers, and in data sharding, where data is partitioned across multiple databases.

Manoj Khangaonkar profil fotoğrafı

Manoj Khangaonkar2 yıl önce

"Consistent" is a poor name choice for this technique.

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@levelsio

882,616 görüntüleme • 1 yıl önce

Once we started to work with large global retailers, we needed a better way to scale this process. Ideally, the staff at the store could do this themselves — rather than us flying our team across the world — and then we could lower the cost and timelines. So we built a self-serve version of our survey app, with a tutorial mode designed for beginners. Over time, we collected millions of data points, and so we were able to develop an algorithm which would auto-correct mistakes. In other words, if the surveyor accidentally placed their ground-truth location in the wrong place on the map, we could use our algorithms to detect it, and correct it. So now we have WiFi, and with and our efforts on producing a high quality survey, we have the best WiFi positioning available. With WiFi on its own, it’s achieving 3 meter accuracy. This is a great foundation to build on. WiFi + Motion data To refine this down to 1-meter accuracy, we realised that we could combine WiFi with the same technology behind self-driving cars and robotics: a motion system called SLAM (Simultaneous Localization and Mapping). SLAM uses the accelerometer, gyroscope and camera system to understand precise device motion. Imagine a car driving through a tunnel, using the motion since its last GPS ping to keep location accurate until it comes out the other side. On a phone, this technology is very reliable, and measures device motion with high precision. But SLAM is measuring motion within its own coordinate space, it’s not aligned with the real world. SLAM tracks the user’s relative motion, like “moved forward 2 meters, then turned left”, but does “forward” mean “north”, or some other direction? It’s not calibrated, so it could mean any location, any direction. We can’t rely on the compass to help us out with this, because phone compasses are notoriously incorrect — everyone knows the frustration of being sent the wrong way down a street. So our job was to align this motion data with the triangulation data we were receiving from WiFi. We designed an algorithm that could simulate every possibility, filter the unlikely scenarios, and hone in your location, using WiFi as an anchor. So WiFi gives us the initial blue dot, SLAM gives us motion, and as the user starts walking and we receive more data, our algorithms can refine location accuracy down to a consistent 1-meter accuracy. We’ve tested these algorithms in many locations, on hundreds of hours of ground-truth data:

Once we started to work with large global retailers, we needed a better way to scale this process. Ideally, the staff at the store could do this themselves — rather than us flying our team across the world — and then we could lower the cost and timelines. So we built a self-serve version of our survey app, with a tutorial mode designed for beginners. Over time, we collected millions of data points, and so we were able to develop an algorithm which would auto-correct mistakes. In other words, if the surveyor accidentally placed their ground-truth location in the wrong place on the map, we could use our algorithms to detect it, and correct it. So now we have WiFi, and with and our efforts on producing a high quality survey, we have the best WiFi positioning available. With WiFi on its own, it’s achieving 3 meter accuracy. This is a great foundation to build on. WiFi + Motion data To refine this down to 1-meter accuracy, we realised that we could combine WiFi with the same technology behind self-driving cars and robotics: a motion system called SLAM (Simultaneous Localization and Mapping). SLAM uses the accelerometer, gyroscope and camera system to understand precise device motion. Imagine a car driving through a tunnel, using the motion since its last GPS ping to keep location accurate until it comes out the other side. On a phone, this technology is very reliable, and measures device motion with high precision. But SLAM is measuring motion within its own coordinate space, it’s not aligned with the real world. SLAM tracks the user’s relative motion, like “moved forward 2 meters, then turned left”, but does “forward” mean “north”, or some other direction? It’s not calibrated, so it could mean any location, any direction. We can’t rely on the compass to help us out with this, because phone compasses are notoriously incorrect — everyone knows the frustration of being sent the wrong way down a street. So our job was to align this motion data with the triangulation data we were receiving from WiFi. We designed an algorithm that could simulate every possibility, filter the unlikely scenarios, and hone in your location, using WiFi as an anchor. So WiFi gives us the initial blue dot, SLAM gives us motion, and as the user starts walking and we receive more data, our algorithms can refine location accuracy down to a consistent 1-meter accuracy. We’ve tested these algorithms in many locations, on hundreds of hours of ground-truth data:

Andrew Hart

90,946 görüntüleme • 10 ay önce