Cache Systems Every Developer Should Know

Summary

This script explores the ubiquity of caching in enhancing system performance and reducing response times across computing layers. It delves into hardware caches like L1, L2, and L3, the TLB, and OS-level caches like page cache and inode cache. The script also examines application-level caching, including web browsers, CDNs, load balancers, message brokers, and distributed caches like Redis. It highlights how caching is integral to optimizing performance in various systems, from front-end web interactions to back-end databases and search engines.

Takeaways

• 🔧 Caching is a fundamental technique in computing used to enhance system performance and reduce response times across various applications and systems.
• 💻 In computer hardware, L1, L2, and L3 caches are essential for storing frequently accessed data and instructions, with L1 being the fastest and smallest, and L3 being the largest and slowest, often shared among CPU cores.
• 🔄 The Translation Lookaside Buffer (TLB) is a hardware cache that stores virtual-to-physical address translations, speeding up memory access by reducing the translation time.
• 🖥️ At the OS level, caching mechanisms like the page cache and inode cache are used to store disk blocks and file system metadata to expedite disk access and file system operations.
• 🌐 Web browsers can cache HTTP responses, providing faster data retrieval by reusing cached data when the same content is requested again.
• 📚 Content Delivery Networks (CDNs) improve content delivery by caching static content on edge servers, reducing the need to fetch content from the origin server repeatedly.
• 🔄 Load balancers can cache responses to reduce the load on back-end servers, serving cached content to users requesting the same resources, thereby improving response times.
• 📬 Messaging infrastructures like Kafka can cache large volumes of messages on disk, allowing consumers to retrieve messages at their own pace based on retention policies.
• 🔑 Distributed caches such as Redis offer high read/write performance for key-value pairs in memory, outperforming traditional databases in certain scenarios.
• 🔍 Full-text search engines like Elasticsearch index data for quick and efficient document and log searches, providing fast access to specific data.
• 🗃️ Within databases, multiple caching levels exist, including write-ahead logs (WAL), buffer pools for caching query results, and materialized views for precomputed query results, all contributing to optimized performance.

Q & A

What is the primary purpose of caching in computing?

Caching is used to enhance system performance and reduce response time by storing frequently accessed data and instructions in a faster access layer.

Why is caching important in a system architecture?

Caching plays a crucial role in improving the efficiency of various applications and systems by reducing the need to access slower storage or memory repeatedly.

What are the different types of hardware caches found in a computer?

The common hardware caches include L1, L2, and L3 caches, with L1 being the smallest and fastest, typically integrated into the CPU, and L2 and L3 being larger and slower, often shared between CPU cores.

How does the L1 cache differ from L2 and L3 caches in terms of size and speed?

L1 cache is the smallest and fastest, integrated into the CPU for quick access. L2 is larger but slower and can be on the CPU die or a separate chip. L3 is the largest and slowest, often shared by multiple CPU cores.

What is the function of the Translation Lookaside Buffer (TLB) in a computer system?

The TLB stores recently used virtual-to-physical address translations, allowing the CPU to quickly translate virtual memory addresses to physical memory addresses, thus reducing access time to data.

How does the operating system utilize caching to improve performance?

The operating system uses caches like the page cache and inode cache to store recently used disk blocks and speed up file system operations, respectively, by reducing the number of disk accesses required.

What role does caching play in web browsers when accessing data over HTTP?

Web browsers cache HTTP responses according to the expiration policy in the HTTP header, allowing for faster retrieval of data by returning it from the cache when requested again.

How do Content Delivery Networks (CDNs) use caching to improve content delivery?

CDNs cache content on their edge servers after fetching it from the origin server for the first time. Subsequent requests for the same content can be served directly from the cache, speeding up delivery and reducing load on the origin server.

What is the benefit of caching resources in load balancers?

Load balancers can cache responses to serve them directly to future users requesting the same content, which improves response times and reduces the load on back-end servers.

How does caching in messaging infrastructure, like Kafka, differ from traditional in-memory caching?

In messaging infrastructure, caching can involve storing a large number of messages on disk rather than in memory, allowing consumers to retrieve messages at their own pace and based on a retention policy.

What are some examples of advanced caching techniques used in databases?

Advanced caching techniques in databases include using a buffer pool to cache query results, materialized views to precompute query results for faster performance, and write-ahead logs (WAL) to ensure data integrity before indexing.

Outlines

00:00

💾 Caching in Computing Systems

This paragraph delves into the significance of caching in enhancing system performance and reducing response times across various applications and systems. It begins by explaining the multi-layered caching strategies in a typical system architecture, tailored to specific application needs. The discussion then narrows down to the hardware level, detailing the roles of L1, L2, and L3 caches, as well as the Translation Lookaside Buffer (TLB) in speeding up data access. Moving to the operating system, it highlights the importance of page cache and file system caches like the inode cache. The paragraph further extends to the application front end, discussing how web browsers, Content Delivery Networks (CDNs), load balancers, and message brokers like Kafka leverage caching to optimize content delivery and system operations. It also touches upon distributed caches, full-text search engines, and database caching mechanisms, including the buffer pool, materialized views, and the transaction log.

05:03

🚀 The Impact of Caching on System Performance

The second paragraph emphasizes the critical role of caching in optimizing system performance and response times. It provides an overview of the various caching mechanisms at play, from the front end to the back end, and underscores their collective impact on improving efficiency. The paragraph concludes with a call to action, inviting viewers interested in system design to subscribe to a newsletter that covers large-scale system design topics and trends, trusted by 300,000 readers. The subscription link is provided for those who wish to delve deeper into the subject matter.

Keywords

💡Caching

Caching is a technique used in computing to store copies of data in a temporary storage area, allowing faster access to frequently used information. In the context of the video, caching is crucial for enhancing system performance and reducing response time across various layers of a system architecture, from hardware to software applications. Examples include L1, L2, and L3 caches in computer hardware, and web browser caching of HTTP responses.

💡System Performance

System performance refers to how well a computer system, application, or network does its job in terms of speed, efficiency, and reliability. The video emphasizes the role of caching in improving system performance by reducing the time it takes to access and process data, which is a central theme of the discussion on caching strategies.

💡Response Time

Response time is the duration it takes for a system to react to a request or input. The script discusses how caching at different levels can significantly reduce response time by providing quicker access to data, which is essential for maintaining a smooth user experience and efficient system operations.

💡Hardware Cache

Hardware cache, such as L1, L2, and L3 caches, are specialized memory locations within or near the CPU that store frequently accessed data. The video explains that these caches are essential for speeding up data retrieval, with L1 being the fastest and smallest, and L3 being the largest and shared among multiple CPU cores.

💡Translation Lookaside Buffer (TLB)

The TLB is a cache within the CPU that stores recent virtual-to-physical memory address translations. It allows the CPU to quickly access memory addresses without the need for time-consuming address translation, as mentioned in the script when discussing how the TLB reduces the time needed to access data from memory.

💡Operating System Cache

Operating system caches, such as the page cache and inode cache, are used to store frequently accessed data in memory to speed up system operations. The page cache, for instance, holds disk blocks in memory for quick access, as the script explains, reducing the need to read from the disk each time data is requested.

💡Content Delivery Network (CDN)

A CDN is a network of servers strategically distributed to deliver content, such as images and videos, to users more efficiently. The script describes how CDNs use caching to store copies of content on edge servers close to the user, thereby speeding up content delivery by eliminating the need to fetch content from the origin server repeatedly.

💡Load Balancer

A load balancer is a system that distributes network or application traffic across multiple servers to ensure no single server bears too much demand. The video mentions that some load balancers can cache resources to reduce the load on back-end servers and improve response times for users requesting the same content.

💡Message Broker

A message broker, such as Kafka mentioned in the script, is a software system that caches and manages the exchange of messages between different software components. It allows for the storage of a large number of messages on disk, enabling consumers to retrieve them at their own pace, which is vital for efficient messaging infrastructure.

💡Distributed Cache

Distributed caches, like Redis, are systems that store key-value pairs in memory across multiple nodes, providing high performance for read and write operations. The script explains that distributed caches offer better performance compared to traditional databases by allowing quick data retrieval and storage.

💡Full-text Search Engine

A full-text search engine, such as Elastic Search, is a software system that indexes data to enable efficient searching of documents and logs. The video script illustrates how these search engines use caching mechanisms to provide quick access to specific data, which is essential for performance in document search and log analysis.

💡Database Caching

Database caching involves storing frequently accessed data in memory to speed up query processing. The script discusses various levels of database caching, including the buffer pool for caching query results and materialized views for precomputed query results, which are essential for optimizing database performance.

Highlights

Caching is a common technique in modern computing to enhance system performance and reduce response time.

Caching plays a crucial role in improving the efficiency of various applications and systems from front end to back end.

A typical system architecture involves several layers of caching with multiple strategies and mechanisms.

L1, L2, and L3 caches are the most common hardware caches, with L1 being the smallest and fastest, integrated into the CPU.

L2 cache is larger and slower than L1, while L3 is the largest and slowest, often shared between CPU cores.

Translation Lookaside Buffer (TLB) is a hardware cache that stores virtual-to-physical address translations for quick access.

At the OS level, page cache and file system caches like inode cache speed up disk block and file/directory access.

Web browsers cache HTTP responses to enable faster data retrieval based on expiration policies.

Content Delivery Networks (CDNs) improve static content delivery by caching content on edge servers.

Load balancers can cache resources to reduce load on back-end servers and improve response times.

Message brokers like Kafka can cache massive amounts of messages on disk for consumer retrieval.

Distributed caches such as Redis provide high read/write performance by storing key-value pairs in memory.

Full-text search engines like Elastic Search index data for quick and efficient document and log search.

Databases have multiple levels of caching, including write-ahead logs (WAL), buffer pools, and materialized views.

The transaction log and replication log in databases record transactions and track replication state in clusters.

Caching data is essential for optimizing system performance and reducing response time across various applications and systems.

The system design newsletter covers topics and trends in large-scale system design, trusted by 300,000 readers.

Transcripts

Caching is a common technique  in modern computing to enhance  

system performance and reduce response time.

From the front end to the back end,  

caching plays a crucial role in improving the  efficiency of various applications and systems.

A typical system architecture  involves several layers of caching.

At each layer, there are multiple  strategies and mechanisms for caching  

data, depending on the requirements and  constraints of the specific application.

Before diving into a typical system architecture,  

let’s zoom in and look at how prevalent  caching is within each computer itself.

Let’s first look at computer hardware.

The most common hardware cache  are L1, L2, and L3 caches.

L1 cache is the smallest and fastest cache,  typically integrated into the CPU itself.

It stores frequently accessed data  and instructions, allowing the CPU to  

quickly access them without having  to fetch them from slower memory.

L2 cache is larger but slower than L1 cache,  

and is typically located on the  CPU die or on a separate chip.

L3 cache is even larger and slower than L2 cache,  and is often shared between multiple CPU cores.

Another common hardware cache is the  translation lookaside buffer (TLB).

It stores recently used  virtual-to-physical address translations.

It is used by the CPU to quickly  translate virtual memory addresses  

to physical memory addresses, reducing the  time needed to access data from memory.

At the operating system level, there are  page cache and other file system caches.

Page cache is managed by the operating  system and resides in main memory.

It is used to store recently  used disk blocks in memory.

When a program requests data from the disk,  

the operating system can quickly retrieve the  data from memory instead of reading it from disk.

There are other caches managed by the  operating system, such as the inode cache.

These caches are used to speed up  file system operations by reducing  

the number of disk accesses required  to access files and directories.

Now let’s zoom out and look at how caching is  used in a typical application system architecture.

On the application front end,  

web browsers can cache HTTP responses  to enable faster retrieval of data.

When we request data over HTTP for the first time,  

and it is returned with an  expiration policy in the HTTP header;

we request the same data again, and the browser  returns the data from its cache if available.

Content Delivery Networks (CDNs) are widely  used to improve the delivery of static content,  

such as images, videos, and other web assets.  

One of the ways that CDNs speeds up  content delivery is through caching.

When a user requests content from a CDN,  

the CDN network looks for the  requested content in its cache.

If the content is not already in the cache,  

the CDN fetches it from the origin  server and caches it on its edge servers.

When another user requests the same content,  the CDN can deliver the content directly  

from its cache, eliminating the need to  fetch it from the origin server again.

Some load balancers can cache resources  to reduce the load on back-end servers.

When a user requests content from  a server behind a load balancer,  

the load balancer can cache the response and  serve it directly to future users who request  

the same content. This can improve response  times and reduce the load on back-end servers.

Caching does not always have to be in  memory. In the messaging infrastructure,  

message brokers such as Kafka can cache  a massive amount of messages on disk.

This allows consumers to retrieve  the messages at their own pace. The  

messages can be cached for a long period  of time based on the retention policy.

Distributed caches such as Redis  can store key-value pairs in memory,  

providing high read/write performance  compared to traditional databases

Full-text search engines like Elastic  Search can index data for document  

search and log search, providing quick  and efficient access to specific data.

Even within the database, there are  multiple levels of caching available.

Data is typically written to a write-ahead  log (WAL) before being indexed in a B-tree.

The buffer pool is a memory area  used to cache query results,  

while materialized views can precompute  query results for faster performance.

The transaction log records all  transactions and updates to the database,

while the replication log tracks the  replication state in a database cluster.

Overall, caching data is an essential  technique for optimizing system performance  

and reducing response time. From  the front end to the back end,  

there are many layers of caching to improve the  efficiency of various applications and systems.

If you like our videos, you may like our system  design newsletter as well. It covers topics and  

trends in large-scale system design. Trusted by  300,000 readers. Subscribe at blog.bytebytego.com