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EP151: 24 Good Resources to Learn Software Architecture in 2025
EP151: 24 Good Resources to Learn Software Architecture in 2025
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This week’s system design refresher:
24 Good Resources to Learn Software Architecture in 2025
SOAP vs REST vs GraphQL vs RPC
SQS vs SNS vs EventBridge vs Kinesis
Top 5 common ways to improve API performance
xAI’s Grok-3 One-Pager
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24 Good Resources to Learn Software Architecture in 2025
The resources can be divided into different types such as:
Software Design Books
Some books that can help are DDIA, System Design Volume 1 & 2, Clean Architecture, Domain-Driven Design, and Software Architecture: the Hard PartsTech Blogs and Newsletters
Read technical blogs by companies like Netflix, Uber, Meta, and Airbnb. Also, the ByteByteGo newsletter provides insights into software design every week.YouTube Channels and Architectural Resources
YouTube channels like MIT Distributed Systems, Goto Conferences, and ByteByteGo can help with software architecture and system design. Azure Architecture Center and AWS Architecture Blog are other important resources.WhitePapers
For deeper insights, read whitepapers like Facebook Memcache Scaling, Cassandra, Amazon DynamoDB, Kafka, and Google File System.Software Career Books
A Software Architect also needs to develop holistic skills. Books about software career aspects such as Pragmatic Programmer, The Software Architect Elevator, The Software Engineer's Guidebook, and Philosophy of Software Design can help.
Over to you: Which other resources will you add to the list?
SOAP vs REST vs GraphQL vs RPC
The diagram below illustrates the API timeline and API styles comparison.
Over time, different API architectural styles are released. Each of them has its own patterns of standardizing data exchange.
You can check out the use cases of each style in the diagram.SQS vs SNS vs EventBridge vs Kinesis
AWS SQS: It is a fully managed message queuing service that helps decouple communication between distributed components.
AWS SNS: A fully managed publish/subscribe messaging service using topics to send messages to multiple subscribers or endpoints (such as email, Lambda, SQS, and so on).
AWS EventBridge: It is a serverless event bus that facilitates the routing of events between AWS services, and applications based on specific routing rules and filters.
AWS Kinesis Data Streams: A real-time data streaming service that captures, processes, and analyzes data in near real-time.
How can you choose which service to use?
Here are some basic tips and examples:
For massive amounts of events like a clickstream from an application use Kinesis Data Stream
For events processed by a single consumer like an image processing job, SQS is a better choice.
For sending messages and notifications, SNS is a great choice.
For system events such as a new user registration that multiple services must receive and react to, use EventBridge.
Over to you: Have you used SQS, SNS, EventBridge, or Kinesis?
Top 5 common ways to improve API performance
Result Pagination:
This method is used to optimize large result sets by streaming them back to the client, enhancing service responsiveness and user experience.
Asynchronous Logging:
This approach involves sending logs to a lock-free buffer and returning immediately, rather than dealing with the disk on every call. Logs are periodically flushed to the disk, significantly reducing I/O overhead.
Data Caching:
Frequently accessed data can be stored in a cache to speed up retrieval. Clients check the cache before querying the database, with data storage solutions like Redis offering faster access due to in-memory storage.
Payload Compression:
To reduce data transmission time, requests and responses can be compressed (e.g., using gzip), making the upload and download processes quicker.
Connection Pooling:
This technique involves using a pool of open connections to manage database interaction, which reduces the overhead associated with opening and closing connections each time data needs to be loaded. The pool manages the lifecycle of connections for efficient resource use.
Over to you: What other ways do you use to improve API performance?xAI’s Grok-3 One-Pager
Grok 3 was unveiled on February 17. According to Elon Musk, it is “the smartest AI on Earth.”
But what makes it so powerful?
Grok 3 has been trained on the Colossus supercomputer with 100K Nvidia H100 GPUs. According to one stat, it has been trained for 200 million GPU hours. The key capabilities of Grok 3 are coding, science research, problem-solving, creating marketing campaigns, personalized assistance, and API integration.
Some special features of Grok 3 are:DeepSearch: Allows Grok to search the web and X posts for real-time data and information.
Think Mode: Decompose complex queries into steps just like human problem-solving.
Big Brain Mode: A heavy-duty mode for intensive tasks like simulations, data analysis, or reasoning.
Here’s how Grok 3 possibly works:
Grok 3 supports multi-modal inputs. The user can ask questions as text, upload images, or talk to Grok. The inputs are tokenized to initiate processing.
Synthetic data training enhances the query interpretation.
This is fed to the core transformer model that analyzes the input.
The core transformer issues a search directive to DeepSearch to retrieve necessary information from the web or real-time X data.
Next, a self-correct process ensures accuracy through an internal feedback loop.
Depending on the user’s input, the Think Mode or Big Brain Mode refines the responses as required. It might again use DeepSearch to find more information.
The answers are delivered to the user in a multimodal manner.
Note that these details are based on piecing together information from xAI blogs about Grok and new feature announcements. More details may be revealed in the coming days.
Over to you: Have you used xAI’s Grok 3?
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Curated by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of the CEO Shortlist, a biweekly newsletter of our best ideas for the C-suite. This week, we’re sharing key findings from the latest edition of our annual report on global M&A. We appreciate the opportunity to connect and hope you find our perspectives novel and insightful. Please let us know what you think! You can reach us at Alex_Panas@Mckinsey.com and Axel_Karlsson@Mckinsey.com. Thank you, as ever.
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The future of people management
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Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:33 - 21 Feb 2025 -
My FHL 10 keynote inside! (listen now)
OK, I just got back from Vegas, and that was the most amazing FHL ever!
Like, I knew it was going to be memorable, but I had no idea just how incredible the whole week would be!
So many ‘a-ha’ breakthrough moments… so many amazing marketing tactics… and SOOOO many emotions!!
Ah, it was amazing. I can't stop thinking about it and yet I’m trying to relax (...and for someone like me, that can be tough! ;)
Now, if you missed FHL 10… the last dance… I have some ways for you to hear all the presentations - coming out soon, so keep an eye on your email inbox. ;)
But, before that hits your inbox…
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Actually…
This is more than a podcast episode.. It’s my intro speech and final Funnel Hacking LIVE 10 keynote!
LISTEN TO MY FHL 10 KEYNOTE PRESENTATION ON APPLE HERE →
After ten years, thousands of entrepreneurs, and countless life-changing moments, I had one last chance to stand on that stage and deliver the message I felt mattered most.
And trust me, I thought about this for months…
If I could leave you with just one thing, it’s this…LISTEN TO MY FHL 10 KEYNOTE PRESENTATION ON APPLE HERE →
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P.S. - Don’t forget, you’re just one funnel away…
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The hero’s journey every entrepreneur must take if you TRULY want to step into your purpose and change the world!
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Returning to the office? Focus more on practices and less on the policy
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Non-Functional Requirements: The Backbone of Great Software - Part 2
Non-Functional Requirements: The Backbone of Great Software - Part 2
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Non-functional requirements (NFRs) are as critical as functional requirements because they define a system's qualities and operational parameters.
Functional requirements specify what a software product should do (for example, “users must be able to log in”). However, non-functional requirements define how well it must accomplish these tasks under real-world conditions (for example, “the login process should respond within two seconds under peak load” or “all user credentials must be encrypted and stored securely”).
Together, functional and non-functional requirements create a foundation for building great software systems.
In Part 1 of this topic, we also looked at trade-offs in non-functional requirements and the architectural impact of these requirements. Some key learning points from Part 1 were as follows:
Non-functional requirements determine a system's performance in areas such as scalability, response time, performance, security, etc., ensuring it meets quality standards and user expectations beyond mere functionality.
Functional requirements focus on what the system should do, while non-functional requirements define how well it operates under real-world conditions.
Optimizing one NFR (for example, performance) can negatively impact others (for example, maintainability), requiring a balanced approach based on project goals and constraints.
Non-functional needs (like scalability and availability) heavily influence architecture choice. For example, choosing microservices for high availability or plugin-based designs for modifiability.
In this article (Part 2), we’ll go further and look at some of the most important NFRs that should be considered while building systems.
Key NFRs to Consider
Some key non-functional requirements that should be considered while designing an application are as follows:
Response Time/Latency...
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API governance: The missing piece in your stack?
API governance: The missing piece in your stack?
Hi Md Abul,
The LEAP 2.0: API governance conference is happening next week (27 Feb), and the full agenda is now live.LEAP 2.0 is a fully curated conference featuring top experts handpicked to share valuable, real-world insights.
If you're building, scaling, or securing APIs, this free online event offers deep dives, live workshops, and case studies from Zeiss, Dynatrace, Grafana Labs, NorthWestern Mutual, Coforge, Solace, and many more.
Here’s some sessions you can’t miss:
🎤 Tyk AI studio workshop – Get hands-on with AI-powered API tools
🎤 Scaling APIM with GitOps – Multi-tenancy, Argo CD & Tyk Operator in action (Zeiss)
🎤 API observability – The missing link in your governance strategy (Dynatrace, Grafana Labs, Nivenly Foundation)
🎤 API governance & sustainability – Practical strategies for implementers🎤 Our series of lightning talks covering OpenAPI, JSON Schema, OpenID, AI, and sustainable API practices - quick and impactful!
Each session is designed to give you actionable insights that can be immediately applied to solve real-world challenges.
Plus, gain exclusive access to the API governance fundamentals certification to showcase your expertise in API management, security, and compliance.
Event details:
📅 Date: February 27, 2025
🕘 Time: 9 AM - 3 PM ET | 2 PM - 8 PM GMTSee you there!
The LEAP 2.0 team
Tyk, Huckletree 199 Bishopsgate, Broadgate, London, City of London EC2M 3TY, United Kingdom, +44 (0)20 3409 1911
by "Budhaditya Bhattacharya" <budha@tyk.io> - 06:00 - 20 Feb 2025 -
New Relic Certification is live!
New Relic
We are excited to announce that New Relic Certifications are now live for a range of professional role-based observability certifications.
Whether you're just starting your observability journey, or are a seasoned professional, we encourage you to take the certification exams available, use it as an opportunity to gain a certification, and become part of the New Relic Certified community. If successful in passing the exam you will be awarded the professional credential, and will be able to share it with your network and peers.
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by "New Relic University" <certification@newrelic.com> - 05:07 - 20 Feb 2025 -
What are the benefits of AI guardrails?
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Safer use of AI. Just as barriers on a road can protect cars from veering off course, guardrails for AI systems can help ensure that a company’s AI tools—especially large language models—are operating safely and responsibly, McKinsey Senior Partners Ida Kristensen and Lareina Yee and coauthors explain. Since AI systems are vulnerable to attacks from malicious actors, guardrails can protect an organization and its customers from the misuse of AI. By monitoring AI-created content—continuously, in some cases—guardrails can also reduce the risk that erroneous content will be released externally.
•
Creating a competitive edge. AI guardrails don’t just enable companies to meet compliance and ethical requirements. They can also help create a competitive advantage: for example, by helping organizations build trust with consumers and avoid costly legal issues. One financial-services company developed an AI chatbot with guardrails that it used to filter out sensitive personal data, which allowed the company to provide safe outputs to its customers and also comply with regulatory standards. Read more about the five types of AI guardrails and how they work to benefit the business.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:48 - 20 Feb 2025 -
You still have time to register
Hi Abul, our invitation still stands.
We're excited to invite you from Odoo to our upcoming event, New Relic Now+.
This free virtual event will unveil the most transformative platform update yet, designed to enhance business impact and developer velocity.
Don’t miss out on this opportunity to gain valuable insights that could greatly benefit your organization.
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by "Thomas Sampassa-Ilunga" <tilunga@newrelic.com> - 12:25 - 19 Feb 2025 -
How leaders are promoting resilience at work
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Overcoming resistance to change. In an environment where disruptions—including the growing use of AI, geopolitical and economic shifts, and evolving employee expectations—are the norm, leaders should foster resilience and adaptability in themselves and in their employees. Yet this is easier said than done, McKinsey Senior Partners Aaron De Smet and Dana Maor and coauthors reveal. McKinsey research shows that when the path forward at work (or in life) is unclear, people tend to do what’s familiar or predictable, regardless of whether that approach is appropriate for the situation at hand.
•
The value of resilience. When a company’s people are resilient and adaptable, it can have profound effects on innovation, growth, and organizational health. Those who are both resilient and adaptable demonstrate better overall health and are more engaged at work than their peers, according to the McKinsey Health Institute. What can leaders do to foster these traits within their own workforces? See the four actions leaders can take to help their people weather the inevitable storms that lie ahead.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:33 - 19 Feb 2025 -
The Perfect Faceoff: NHL Teams vs. ERP Systems
The Perfect Faceoff: NHL Teams vs. ERP Systems
Hi MD Abul,
Hockey season is heating up, and just like the NHL, the ERP world is full of powerhouses, underdogs, and rising stars.
So, what if ERP systems were NHL teams? Would SAP be the Tampa Bay Lightning? Is Microsoft Dynamics 365 the Pittsburgh Penguins? And who’s the Montreal Canadiens of ERP?
We broke it all down in our latest blog, matching the biggest ERP systems with the biggest teams in hockey. Whether you're an NHL fan or just love a creative take on digital transformation, you won’t want to miss this one!
Let us know—did we get the comparisons right, or do you have a better match?
Best regards,
Eric Kimberling
by "Eric Kimberling" <eric.kimberling@thirdstage-consulting.com> - 01:07 - 19 Feb 2025 -
A healthy workplace starts with healthy workers
Five Fifty
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Enhanced employee health can boost engagement and productivity and has the potential to create up to $11.7 trillion in global economic value, say McKinsey’s Barbara Jeffery, Brooke Weddle, Jacqueline Brassey, and Shail Thaker. To learn more about how companies can support their people to help them thrive, check out the latest edition of the Five Fifty.
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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 04:23 - 18 Feb 2025 -
How Instagram Scaled Its Infrastructure To Support a Billion Users
How Instagram Scaled Its Infrastructure To Support a Billion Users
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Insider tips for navigating caching strategies and tradeoffs
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Disclaimer: The details in this post have been derived from Instagram Engineering Blog and other sources. All credit for the technical details goes to the Instagram engineering team. The links to the original articles are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Instagram was launched in 2010 by Kevin Systrom and Mike Krieger as a simple photo-sharing app. The app allowed users to instantly upload, edit, and share photos, applying filters that gave images a professional touch. Unlike other social media platforms at the time, Instagram focused exclusively on visual storytelling, making it an instant hit among smartphone users.
The platform's simplicity and mobile-first approach led to rapid adoption. Within just two months, Instagram had gained 1 million users, a milestone that took Facebook nearly a year to achieve. The app grew exponentially, reaching 10 million users in one year and 30 million users by early 2012.
By April 2012, Instagram had reached 50 million users and was attracting serious investment interest. Facebook acquired Instagram for $1 billion, a shocking move at the time, as the company had just 13 employees. However, Mark Zuckerberg saw Instagram’s potential as a mobile-first social network and wanted to integrate it into Facebook’s ecosystem while allowing it to function independently.
At the time of the acquisition, Instagram was still operating on a relatively small-scale infrastructure hosted on Amazon Web Services (AWS). The app was experiencing major scalability challenges, such as server overloads, database scaling issues, and limited engineering resources.
After joining Facebook, Instagram migrated from AWS to Facebook’s data centers, preparing it for its next phase of hypergrowth. Under Facebook’s ownership, Instagram continued to scale at an unprecedented pace. By 2014, it had 300 million users, and by 2018, it had breached the billion-user mark.
To handle billions of daily interactions, Instagram had to adopt advanced scaling strategies, including distributed databases, caching, and multi-region infrastructure. In this article, we’ll look at how Instagram scaled its infrastructure to handle this massive scale.
Instagram’s Early Infrastructure Challenges
When Instagram first launched in 2010, it was a small company with limited engineering resources.
However, with time, Instagram grew into one of the most widely used social media platforms in the world. This rapid increase in user engagement placed an enormous strain on its infrastructure, requiring continuous optimization and scaling efforts.
Some key metrics highlighting Instagram’s scale (in 2017-2018) are as follows:
400 million daily active users that open Instagram multiple times a day, generating billions of interactions.
100 million media uploads per day. Every media file must be optimized for different devices, ensuring smooth playback while minimizing bandwidth usage.
4 billion likes per day where every like generates a database write operation, which must be efficiently managed to avoid overloading storage and query systems
The platform’s rapid growth exposed significant scalability challenges, forcing engineers to adopt temporary solutions to keep the system running. However, as user activity continued to increase exponentially, these short-term fixes became unsustainable, leading to the urgent need for automation and a scalable architecture.
Some challenges faced by Instagram in its early days are as follows:
Manual Server Scaling: In the early days, Instagram’s engineering team manually added servers before weekends to prevent system failures. Traffic spikes were common on Fridays and weekends when user engagement surged due to increased social activity. Engineers would monitor CPU loads and decide if additional servers needed to be provisioned to handle the expected load.
Unreliable Performance Due to Limited Infrastructure: Instagram initially operated on Amazon Web Services (AWS), using a small number of virtual machines. The infrastructure was not optimized for handling sudden surges in traffic, leading to slow response times and occasional outages. The backend services struggled to process requests efficiently, causing delays in content loading and interactions.
Database Overload: The increasing number of users posting photos, liking content, and following accounts placed a heavy burden on Instagram’s relational database. As data volume grew, the read and write operations on the database became slower, affecting the overall performance of the platform. Engineers had to manually optimize queries and shift loads across multiple database replicas, a time-consuming process.
Lack of Automated Monitoring: Since Instagram had a small engineering team, monitoring server performance was done manually, often requiring constant attention to avoid system failures. Engineers used basic dashboards and alerts to detect issues, but there was no real-time system in place to automatically respond to performance problems.
Lack of Load Balancing: Load balancing across multiple servers was not fully automated, leading to uneven traffic distribution and bottlenecks.
Engineering Bottlenecks: Since infrastructure management was largely manual, engineers had to spend significant time scaling and maintaining servers instead of focusing on product development. As the team was small, scaling efforts often delayed feature releases and made it difficult to introduce new functionalities.
The Three Dimensions of Scaling at Instagram
As Instagram’s user base grew exponentially, the company had to develop scalable infrastructure to handle billions of daily interactions.
Scaling is not just about adding more hardware. It also involves optimizing resources and ensuring that the engineering team can operate efficiently. Instagram approached scalability in three key dimensions:
1 - Scaling Out: Expanding Infrastructure with More Servers
Scaling out refers to adding more hardware (servers, storage, and computing power) to handle increased traffic.
Some new strategies were adopted:
Instagram migrated from AWS to Facebook’s data centers, allowing horizontal scaling by distributing the workload across multiple servers. They expanded to multiple geographically distributed data centers, ensuring high availability and redundancy.
Instead of relying on a few powerful machines, Instagram distributed workloads across multiple servers and data centers.
Load balancing systems were implemented to distribute user requests across different data centers.
Instead of a single monolithic database, Instagram adopted a distributed database system, using PostgreSQL and Cassandra for high availability.
Migrating to Facebook’s data centers allowed Instagram to take advantage of Facebook’s existing tools and infrastructure, such as:
TAO: A highly efficient distributed database system for social data.
Scuba: A real-time analytics and monitoring tool for debugging performance issues.
Tupperware: Facebook’s containerized deployment system for managing large-scale applications.
However, the migration was quite challenging. Instagram needed to migrate petabytes of media files, user data, and metadata from AWS to Facebook’s data centers. Instead of a single "big bang" migration, data was incrementally copied and synchronized across both systems to ensure consistency. Facebook’s Haystack storage system was used to optimize media storage and retrieval.
Instagram’s backend services were initially built to run on AWS’s Elastic Compute Cloud (EC2). To optimize performance, these services were re-engineered to run on Facebook’s Tupperware containerization system, allowing for better resource utilization and auto-scaling.
2 - Scaling Up: Optimizing Efficiency of Each Server
Scaling up means improving the performance of existing infrastructure rather than adding more hardware. This involved reducing CPU load, optimizing queries, and using caching mechanisms to maximize server efficiency.
In Instagram’s case, adding more servers was not a sustainable long-term solution due to cost and resource limitations. Each request from a user (loading a feed, liking a post, adding a comment) resulted in multiple database queries, slowing down response times. Inefficient memory usage and unoptimized Python code caused unnecessary resource consumption.
The Instagram engineering team implemented some solutions to deal with these issues:
Optimized database queries by reducing redundant operations and batching requests.
Used Memcached and Cassandra to cache frequently accessed data, reducing direct database queries.
Profiled Python code to identify inefficient functions and replaced them with C++ implementations for better performance.
Improved server memory management, enabling a more efficient use of RAM.
3 - Scaling the Engineering Team
Scaling the engineering team means ensuring that developers can continue to work efficiently as the company grows. This involves better deployment strategies, automation, and minimizing bottlenecks in the development process.
Instagram grew from a small startup to a company with hundreds of engineers, leading to challenges in code management and deployment. More engineers working on the same codebase increased merge conflicts, regressions, and debugging complexity.
To handle these problems, the Instagram engineering team embraced continuous deployment. Instead of periodic large updates, they shifted to a rolling release model, deploying code changes 40+ times per day. This ensured that small updates could be tested, monitored, and rolled back quickly if needed.
New features were deployed gradually to a small subset of users before being rolled out globally. Engineers could monitor performance metrics and detect bugs early, preventing widespread failures. They also implemented real-time monitoring tools to detect performance regressions.
Instagram’s Backend Architecture
As Instagram grew into a platform handling billions of interactions daily, it required a robust, scalable, and efficient backend architecture.
The system needed to process a huge number of user requests, including media uploads, likes, comments, and notifications while maintaining high availability and low latency. The main components of the backend architecture are as follows:
1 - Django - The Core Web Framework
Django, a high-level Python web framework, serves as the foundation of Instagram’s backend. It provides the core structure for handling HTTP requests, user authentication, database interactions, and API endpoints.
The main advantages of Django are as follows:
Rapid development: Pre-built components allow engineers to quickly iterate on features.
Scalability: Supports ORM (Object-Relational Mapping), enabling efficient database queries.
Security: Built-in protections against common vulnerabilities like SQL injection and cross-site scripting.
2 - RabbitMQ - Message Broker
RabbitMQ acts as a message broker, facilitating communication between different services. Instead of handling everything in a single request, RabbitMQ enables asynchronous task execution, improving performance and reliability.
Instagram uses RabbitMQ to decouple components and allow different services to interact without being directly dependent on each other. It handles spikes in traffic and manages message persistence even if one part of the system temporarily fails.
3 - Celery for Async Task Processing
Celery is a distributed task queue system that works alongside RabbitMQ to execute background tasks. It ensures that long-running processes do not slow down real-time user interactions.
Instagram uses Celery to handle asynchronous tasks such as sending emails or processing large media files. Celery also has fault tolerance where a task failure results in automatic retries. Celery workers can be distributed across multiple servers, handling high workloads efficiently.
How the Components Work Together?
Instagram’s backend components operate in a coordinated manner, ensuring efficiency. Let’s understand the process when a user likes a post:
User Action: The user taps the “Like” button on a post.
Django Processes the Request: The request is sent to Django’s web server. Django updates the like count in the PostgreSQL database.
Caching and Asynchronous Processing: If the post has been liked before, the like count is retrieved from Memcached instead of querying the database. Django sends a task to RabbitMQ to notify the post owner about the like.
RabbitMQ Queues the Notification Task: Instead of handling the notification immediately, RabbitMQ queues it for later processing.
Celery Worker Sends the Notification: A Celery worker retrieves the task from RabbitMQ, generates the notification, and sends it to the recipient.
Storage Services in Instagram’s Architecture
Storage services are responsible for persisting user data, media files, and metadata across multiple distributed databases.
Instagram employs a combination of structured and distributed storage solutions to manage different types of data efficiently.
PostgreSQL: Stores structured data such as user profiles, comments, relationships, and metadata. It follows a master-replica architecture to ensure high availability and scalability. PostgreSQL also supports ACID compliance.
Cassandra: Used for storing highly distributed data, such as user feeds, activity logs, and analytics data. Unlike PostgreSQL, Cassandra follows an eventual consistency model, meaning data updates may take time to propagate across different regions. It provides high write throughput, making it ideal for operations like logging and real-time analytics.
Memcached: Used to reduce database load by caching frequently accessed data. It stores temporary copies of user profiles, posts, and like counts to prevent repeated queries to PostgreSQL or Cassandra. This helps optimize API response times
Haystack: Stores images and videos efficiently, minimizing the number of file system operations required to fetch content. It improves media loading times by serving cached versions of files through CDNs.
See the diagram below for PostgreSQL scaling out with leader-follower replication.
Also, the diagram below shows Cassandra scaling.
Since Instagram’s storage services are distributed across multiple data centers and geographic regions, maintaining data consistency is a major challenge.
The key data consistency challenges are as follows:
Replication Latency: In a distributed system, database replicas take time to sync, leading to temporary inconsistencies. For example, a user likes a post, but another user may not immediately see the updated like count because data replication has not been completed.
Eventual Consistency in NoSQL Systems: Cassandra uses an eventual consistency model, meaning updates propagate asynchronously across different database nodes. While this improves scalability, it can cause temporary discrepancies where different users see different versions of the same data.
Cache Invalidation Issues: Memcached accelerates read operations, but if an update is made to the database, the cached data must be invalidated to reflect the latest changes. For example, if a user changes their profile picture, but the cached version is not updated immediately, they may see an outdated version of their profile.
Cross-Region Data Synchronization: Since Instagram operates multiple data centers, data must be synchronized across different geographic locations. Network failures or delays in replication can lead to inconsistencies between regions.
The Instagram engineering team uses several strategies to address these consistency challenges.
For example, PostgreSQL is used for critical transactions, such as financial transactions, user authentication, and content moderation, where strong consistency is required. On the other hand, Cassandra is used for highly scalable workloads, such as activity feeds, where slight delays in consistency are acceptable. Memcached entries are automatically invalidated when new data is written to PostgreSQL or Cassandra, preventing outdated information from being served.
See the diagram below that shows how cache invalidation is handled.
Memcache Lease Mechanism
As Instagram scaled to handle billions of daily interactions, it encountered a major challenge known as the thundering herd problem.
The issue occurs for frequently accessed data (like counts, user feeds, and comments), which is stored in Memcached for fast retrieval. When a cached value expires or is invalidated due to an update, multiple web servers request the same data from the database. If thousands or millions of users request the same piece of data at the same moment, the database gets overloaded with duplicate queries.
To mitigate this, Instagram implemented the Memcache Lease mechanism, which helps prevent multiple redundant database queries by controlling how cache misses are handled.
Here’s how it works:
A user requests an Instagram feed. For each post in the feed, there is associated data such as the like counts, comments, and media metadata.
The application first checks Memcached to retrieve this data.
If the requested data is not found in Memcached, instead of immediately querying the database, the server sends a lease request to Memcached. The first server to request the data is granted a lease token, meaning it is responsible for fetching fresh data from the database.
Other servers that request the same data do not query the database. Instead, they receive a stale value from Memcached or are told to wait. This ensures that only one server fetches the updated data from the database, while the rest hold back.
The server holding the lease token queries the database and fetches the fresh data. It also updates the Memcached entry.
The first server returns the fresh data to users while the rest wait until the cache is repopulated, avoiding a direct hit to the database.
Instagram’s Deployment Model
Instagram operates on a continuous deployment model, meaning new code is pushed to production multiple times per day. This approach allows Instagram to iterate quickly, fix issues promptly, and introduce new features without causing major disruptions.
Instagram’s deployment pipeline includes code review, automated testing, canary testing, and real-time monitoring to ensure that new changes do not introduce performance regressions.
Engineers submit changes through code review to ensure quality and maintainability. Every code change must pass unit tests, integration tests, and performance benchmarks before being merged into the main branch. Once code is merged, it is automatically tested in staging environments to identify functional or security issues.
Also, instead of deploying changes to all users at once, Instagram first deploys updates to a small subset of production servers (canary servers). Engineers monitor key performance metrics (CPU usage, memory consumption, error rates) to ensure the new code does not introduce regressions.
Lastly, Instagram also monitors and optimizes CPU usage.
It uses Linux-based profiling tools to track CPU usage at the function level. Engineers analyze which parts of the codebase consume the most CPU cycles and optimize them for better efficiency. They also use profiling tools and replace CPU-intensive Python functions with optimized C++ implementations for better performance.
Conclusion
Instagram’s ability to scale while maintaining high performance and reliability is proof of its well-designed infrastructure and deployment strategies.
By leveraging Django, RabbitMQ, Celery, PostgreSQL, and Cassandra, Instagram ensures seamless processing of billions of daily interactions It has separated computing and storage services to support efficient resource utilization and minimize database load. The implementation of techniques like Memcache leasing prevents the thundering herd problem, reducing redundant queries and improving system stability.
The platform’s continuous deployment model enables rapid innovation while minimizing risk through canary testing, feature gating, and automated rollbacks. Furthermore, real-time CPU tracking, performance regression testing, and automated monitoring allow engineers to detect and resolve inefficiencies before they impact users.
These strategies have positioned Instagram as one of the most resilient and scalable social media platforms, allowing it to support millions of concurrent users, high-traffic workloads, and rapid feature development without compromising performance or stability.
References:
Instagration Part 2: Scaling Infrastructure to Multiple Data Centers
What powers Instagram: Hundreds of Instances, Dozens of Technologies
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 18 Feb 2025
