- The “Two Towers” Core: Retrieval uses dual neural networks to match user history with content features in milliseconds.
- Database Sharding: A custom PostgreSQL strategy maps billions of photos to logical shards, solving the “infinite scale” problem.
- Viral Engineering: New “Trial Reels” sandboxes allow content to test engagement on non-followers before viral distribution.
- The Kill Switch: “Memcache Leases” prevent database implosions during celebrity posts or viral spikes.
Most marketers think the algorithm is magic. It isn’t.
It is a brutal, mathematical orchestration of distributed systems. While you see a seamless feed of Reels, the backend is fighting to survive billions of requests per second.
We aren’t discussing “posting tips” today. We are dissecting the engineering decisions that decide if your content lives or dies.
Why does this matter?
Because if you understand the machine, you stop guessing and start engineering your growth.
The Core: From Monolith to Microservices
Instagram launched as a massive Python monolith. It was simple. It was fast. But it doesn’t scale to 2.5 billion users.
Today, the platform is a swarm of microservices. This allows engineers to update the “Reels Ranking” service without breaking the “Direct Message” service.
But here is the catch:
They didn’t abandon their roots. They still run one of the world’s largest deployments of Django, but it’s heavily modified. They use a technique called “Lazy Loading” to ensure servers only load the code they strictly need.
Database Strategy: The Sharding Secret
How do you store 50 billion photos in a relational database without it exploding?
You cheat. You use Sharding.
| Component | Tech Stack | The Job |
|---|---|---|
| User Data | PostgreSQL | Stores likes, profiles, and media metadata. Sharded by User ID. |
| Activity Feed | Apache Cassandra | Handles massive write speeds for logs and activity streams globally. |
| Graph Data | TAO (Custom) | Meta’s internal graph store that maps “User A follows User B”. |
The system maps thousands of “logical shards” to a few thousand physical servers. If you post a photo, your data lives on the specific shard assigned to your User ID.
This ensures linear scalability. To add more users, they just add more servers. Simple, yet brilliant.
But storing data is easy. Ranking it is where the real engineering happens.
The Algorithm 2025: “Two Towers” & MTML
The “algorithm” is actually a suite of over 1,000 machine learning models. But for the Feed and Explore, it boils down to two critical phases.
1. Retrieval (The Funnel)
When you open Instagram, the system cannot score billions of posts instantly. That would take days.
Instead, it uses the Two Towers Neural Network.
- Tower A (User): Analyzes your history, interests, and current context.
- Tower B (Item): Analyzes the content’s video frames, audio, and hashtags.
These two towers “meet” in a vector space to identify a candidate set of ~500 posts that are mathematically similar to you. This happens in milliseconds.
2. Ranking (The MTML Score)
Now, the heavy lifting begins.
A Multi-Task Multi-Label (MTML) model takes those 500 candidates and predicts probabilities for specific actions.
In 2025, the weights have shifted drastically:
If the AI predicts you are 80% likely to Send a Reel to a friend, that video jumps to position #1. If you are only likely to “Like” it, it drops.
But wait, there’s a new layer added this year.
The “Trial Reels” Sandbox
In 2025, Instagram introduced a hidden testing layer called “Trial Reels”.
Before your content hits your followers, it is often routed to a small “cold” audience of non-followers. The system measures retention beyond 3 seconds.
If the retention is low, the content is killed. If it’s high, it is injected into the main Two Towers retrieval pool. This is why your first 3 seconds are technically “life or death” for the post.
Hidden Architectural Features
These are the engineering feats that keep the app from crashing during the Super Bowl.
Snowflake ID Generation
Generating unique IDs across thousands of servers is a nightmare. If two servers create ID #100 at the same time, the database crashes.
Instagram uses Snowflake IDs (inspired by Twitter). Each 64-bit ID contains:
- 41 Bits: Timestamp (Millisecond precision).
- 13 Bits: Logical Shard ID.
- 10 Bits: Auto-increment sequence.
This means every ID is unique and sortable by time. It allows the database to index billions of rows without breaking a sweat.
The Memcache Lease (The Safety Valve)
Imagine Justin Bieber posts a photo. Millions of people request it instantly.
This creates a “Thundering Herd” problem. If the cache is empty, millions of requests hit the database simultaneously. The database melts.
Instagram solves this with a “Lease” mechanism. When the first request finds a stale cache, it gets a “lease” to go fetch the data. All other millions of requests are told to wait or use old data.
One request updates the cache. The rest are served safely.
2025 Video Pipeline: AV1 & Adaptive Bitrate
Video is now the primary asset class. To handle this, Instagram has moved to the AV1 Codec.
When you upload a Reel, it isn’t just saved. It is transcoded into 5+ different quality versions. Using Adaptive Bitrate Streaming (HLS), the app detects your network speed in real-time.
If you are on 5G, you get 1080p. If you walk into an elevator, it seamlessly switches to 480p without buffering. This switch happens client-side, ensuring the dopamine loop never breaks.
The Verdict
Instagram isn’t magic; it is math.
The platform is a masterclass in eventual consistency, sharding, and predictive modeling.
For the engineer or growth hacker, the lesson is clear: You aren’t optimizing for a human editor. You are optimizing for a neural network that rewards private shares (Sends) and retention.
Feed the machine the data it wants, and the architecture will do the rest.
