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.

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