System Design: Why is single-threaded Redis so fast?

Summary

Redis, a beloved in-memory database, is renowned for its speed and stability. Its in-memory design offers high throughput and low latency, with the trade-off of memory size limitations. Redis' single-threaded architecture simplifies code, enhancing stability, and utilizes I/O multiplexing for efficient handling of numerous connections. Despite not leveraging all CPU cores, Redis excels with its efficient data structures like linked lists, skip lists, and hash tables, maintaining its position as a top choice for performance and reliability in the market.

Takeaways

• 🔥 Redis is renowned for its speed, stability, and ease of use, making it one of the most popular databases among developers according to Stack Overflow's surveys.
• 💾 Redis's primary speed advantage comes from being an in-memory database, which allows for significantly faster memory access compared to disk I/O.
• 📚 The trade-off of in-memory storage is that the dataset size is limited by the available memory, which is a fundamental design decision of Redis.
• 🔩 The simplicity of in-memory data structures contributes to Redis's robust stability, as they are easier to implement than on-disk data structures.
• 🌟 Redis's performance is somewhat counterintuitively enhanced by its single-threaded architecture, which avoids the complexity and potential bugs associated with multi-threading.
• 🔒 Multi-threaded applications often require locks or synchronization mechanisms that can complicate reasoning and affect stability.
• 🧩 Redis uses I/O multiplexing to handle numerous incoming and outgoing requests efficiently with a single thread, without getting blocked on individual request completions.
• 🌐 I/O multiplexing is traditionally implemented with system calls like select or poll, but Linux's epoll provides a more performant solution for handling a large number of connections.
• 🔑 A limitation of Redis's single-threaded design is that it doesn't utilize multiple CPU cores, leading to scenarios where multiple Redis instances are run on a single server to maximize CPU usage.
• 🛠️ As an in-memory database, Redis can employ efficient low-level data structures like linked lists, skip lists, and hash tables without the overhead of disk persistence.
• 🚀 There are ongoing efforts to create Redis-compatible servers that can further enhance single-server performance, leveraging Redis's inherent ease of use and stability.

Q & A

Why is Redis considered to be fast?

Redis is fast primarily because it is an in-memory database, which allows for significantly faster memory access compared to disk I/O. This results in high read and write throughput and low latency.

What is the trade-off of Redis being an in-memory database?

The trade-off of Redis being an in-memory database is that the dataset size is limited by the available memory, as it cannot be larger than the system's RAM.

How does Redis maintain its stability despite being an in-memory database?

Redis maintains its stability by implementing in-memory data structures that are simpler to code and manage compared to on-disk counterparts, contributing to its robustness.

Why is Redis designed to be primarily single-threaded?

Redis is primarily single-threaded to avoid the complexity and potential bugs associated with multi-threaded applications that require locks or synchronization mechanisms, thus prioritizing stability.

How does a single-threaded Redis handle multiple incoming requests efficiently?

Redis uses I/O multiplexing, which allows a single thread to wait on many socket connections simultaneously, efficiently handling multiple requests without getting blocked.

What is the impact of Redis's single-threaded design on CPU core utilization?

The single-threaded design of Redis means it does not leverage all available CPU cores in modern hardware. For CPU-intensive workloads, multiple Redis instances may run on a single server to utilize more cores.

What role does I/O multiplexing play in Redis's performance?

I/O multiplexing allows Redis to manage a large number of connections efficiently with a single thread, using system calls like epoll on Linux, which supports thousands of connections in constant time.

What are some of the low-level data structures that Redis leverages due to its in-memory nature?

Redis leverages efficient low-level data structures such as linked lists, skip lists, and hash tables, which are optimized for in-memory operations without the need for disk persistence considerations.

Are there any attempts to improve the performance of Redis beyond its current capabilities?

Yes, there are attempts to implement new Redis-compatible servers to extract more performance from a single server, enhancing Redis's ease of use, stability, and performance.

How does Redis balance performance and stability in the market?

Redis provides a balance between performance and stability by offering high throughput, low latency, and robustness, making it a popular choice for various applications according to the Stack Overflow developer survey.

What resources are available for those interested in learning more about system design?

For those interested in system design, there are books and a weekly newsletter available, which viewers are encouraged to subscribe to for more insights.

Outlines

00:00

🔥 Redis Speed and Design Decisions

Redis is renowned for its speed and stability, largely due to its in-memory architecture. This design choice allows for high read and write throughput with low latency, despite limiting the dataset size to memory capacity. The simplicity of in-memory data structures contributes to Redis' robustness. Contrary to common belief, Redis' single-threaded nature is a performance asset, avoiding the complexities and potential instability of multi-threading. I/O multiplexing, particularly Linux's epoll, enables efficient handling of numerous connections without blocking. While this design doesn't utilize all CPU cores, it's a trade-off for the ease of understanding and maintaining the codebase. Redis also excels by employing efficient low-level data structures like linked lists, skip lists, and hash tables, which are optimized for in-memory operations. Despite the existence of alternative Redis-compatible servers, Redis maintains a superior balance of performance and stability, making it a top choice for system design.

Keywords

💡Redis

Redis is an open-source, in-memory data structure store, used as a database, cache, and message broker. It supports various data structures such as strings, hashes, lists, sets, and more. In the video, Redis is highlighted as a fast, popular, and beloved in-memory database, which is a central theme as the script explores why it is so efficient.

💡In-memory database

An in-memory database is a database management system that primarily stores data in RAM rather than on disk. This allows for faster data access and manipulation. The script emphasizes that Redis's speed is largely due to its in-memory nature, which provides high throughput and low latency compared to disk-based databases.

💡Memory access

Memory access refers to the process of reading from or writing to memory. The video script explains that memory access is significantly faster than disk I/O, which is a fundamental reason for Redis's performance. The script mentions that pure memory access is a key design decision that contributes to Redis's speed.

💡Trade-off

In the context of the script, a trade-off refers to the limitation that the dataset size in Redis cannot exceed the available memory, due to its in-memory nature. This trade-off is mentioned to explain the constraints of Redis's design in exchange for the benefits of speed and performance.

💡Single-threaded

Single-threaded refers to a program or process that runs in only one thread of execution at a time. The script points out that Redis is primarily single-threaded, which might seem counterintuitive for high performance but actually contributes to its simplicity and stability, avoiding the complexities of multi-threading.

💡I/O multiplexing

I/O multiplexing is a technique that allows a single thread to handle multiple I/O operations concurrently. The script explains that Redis uses I/O multiplexing to manage thousands of incoming and outgoing requests efficiently, which is crucial for its performance as a single-threaded application.

💡epoll

epoll is a Linux-specific I/O event notification mechanism that is used for I/O multiplexing. The script mentions epoll as a performant variant that supports a large number of connections in constant time, which is important for Redis to handle many connections without performance degradation.

💡CPU cores

CPU cores refer to the individual processing units within a CPU. The script discusses that while Redis's single-threaded design does not leverage multiple CPU cores, for workloads that require it, multiple Redis instances can run on a single server to utilize more cores, thus enhancing performance.

💡Data structures

Data structures are specialized formats for organizing, storing, and manipulating data. The script highlights that Redis can utilize efficient low-level data structures like linked lists, skip lists, and hash tables because it is an in-memory database and does not need to worry about disk persistence, which contributes to its speed.

💡Stability

Stability in the context of the script refers to the reliability and consistency of Redis's performance. The single-threaded design and in-memory data structures contribute to Redis's stability, as mentioned in the script, making it 'rock solid' and easy to reason about.

💡System design

System design is the process of defining the architecture, components, and modules of a system. The script briefly mentions system design when encouraging viewers to learn more about it through books and newsletters, indicating that understanding system design principles is important for appreciating Redis's architecture and performance.

Highlights

Redis is renowned for its speed, stability, and ease of use, making it one of the most beloved databases according to Stack Overflow's developer survey.

Redis operates primarily in-memory, which significantly outperforms disk I/O in terms of access speed, contributing to its high throughput and low latency.

The in-memory design of Redis limits the dataset size to the available memory but simplifies code implementation and enhances stability.

Redis's single-threaded architecture might seem counterintuitive for high performance, but it avoids the complexities and potential bugs associated with multi-threading.

Single-threaded design in Redis leads to a straightforward code path that is easy to understand and maintain, enhancing its stability.

I/O multiplexing allows Redis to handle numerous connections with a single thread, overcoming the limitations of traditional select or poll system calls.

Epoll on Linux is highlighted as an efficient I/O multiplexing technique that supports a vast number of connections with constant time complexity.

Redis's single-threaded nature does not fully utilize modern hardware's multi-core CPUs, sometimes necessitating multiple Redis instances on a single server.

Redis's in-memory nature allows it to use efficient low-level data structures like linked lists, skip lists, and hash tables without disk persistence concerns.

Efforts are being made to create Redis-compatible servers that can maximize performance from a single server setup.

Redis is praised for offering the best tradeoff between performance and stability in the market due to its robust architecture.

Redis's design decisions made over a decade ago have proven to be timeless, maintaining its relevance and efficiency in modern computing.

The transcript suggests that Redis's simplicity and performance make it a top choice for system designers looking for a reliable in-memory database solution.

For those interested in system design, the transcript recommends books and a weekly newsletter as valuable resources for further learning.

The speaker encourages subscription to their resources for anyone who found the information valuable, promising more insights in future sessions.

Transcripts

Why is Redis so fast? What fundamental design  decisions did the developers make more than a  

decade ago that stood to test of time.  Let's take a look. Redis is a very popular  

in-memory database. It's rock solid, easy to  use, and fast. These attributes explain why  

it is one of the most loved databases according  to the Stack Overflow's annual developer survey.  

The first reason Redis is fast is because  it is an in-memory database. Memory access is  

several orders of magnitude faster than random  disk I/O. Pure memory access provides high read  

and write throughput and low latency. The trade-off  is that the dataset cannot be larger than memory.  

Code-wise, in-memory data structures are also much  easier to implement than the on-disk counterparts.  

This keeps the code simple, and it  contributes to Redis' rock solid stability.  

Another reason Redis is fast is a bit unintuitive.  It is primarily single threaded. Why would a single  

threaded design lead to high performance? Wouldn't  it be faster if it uses threads to leverage all the  

CPU cores? Multi-threaded applications require  locks or other synchronization mechanisms. They  

are notoriously hard to reason about. In many  applications, the added complexity is bug prone  

and sacrifices stability, making it  difficult to justify the performance gain.  

In the case of Redis, the single  threaded code path is easy to understand.  

How does a single threaded codebase  handle many thousands of incoming requests  

and outgoing responses at the same time? Won't  the thread get blocked waiting for the completion  

of each request individually? Now, this is  where I/O multiplexing comes into the picture.  

With I/O multiplexing, the operating system  allows a single thread to wait on many socket  

connections simultaneously. Traditionally, this  is done with the select or poll system calls.  

These system calls are not very performant  when there are many thousands of connections.  

On linux, epoll is a performant variant of I/O  multiplexing that supports many many thousands  

of connections in constant time. A drawback of  this single threaded design is that it does not  

leverage all the CPU cores available in modern  hardware. For some workloads, it is not uncommon  

to have several Redis instances running on  a single server to utilize more CPU cores.  

We alluded to the third reason why Redis is  fast. Since Redis is an in-memory database, 

it could leverage several efficient low-level  data structures without worrying about how to  

persist them to disk efficiently - linked list  skip list and hash table are some examples.  

It is true that there are attempts at implementing  new Redis compatible servers to squeeze more  

performance out of a single server. With Redis  ease of use, rock solid stability, and performance,  

it is in our view that Redis still provides the  best performance and stability tradeoff in the  

market. If you'd like to learn more about system  design, check out our books and weekly newsletter.  

Please subscribe if you learned something new. Thank you so much, and we'll see you next time.