Instagram serves billions of photos, videos, messages, likes, and comments across hundreds of millions of users every day. Behind the scenes, it runs one of the largest and most sophisticated social data platforms in the world. But how does Instagram actually store, manage, and deliver all of that data so quickly and reliably?
In this post, we’ll explore the major systems, databases, and design principles Instagram uses to store data at massive scale.
At its core, Instagram is a social graph — users, posts, relationships, interactions, and activities. The system has to support:
user profiles
photos and videos
comments and likes
direct messages
stories and reels
follows and relationships
activity feeds and notifications
Each of these features generates enormous volumes of data that must be:
stored efficiently
retrieved quickly
replicated globally
kept reliable under heavy load
To achieve this, Instagram uses a combination of relational databases, key-value stores, object storage, and caching layers.
Instagram’s core structured data — such as users, posts, and relationships — is stored in relational databases, historically built on PostgreSQL and later scaled out using MySQL once the platform grew inside Meta’s infrastructure.
Relational databases are ideal for:
well-structured records
strong consistency guarantees
relationships between entities (users → posts → comments)
enforcing constraints and integrity
Example types of relational records include:
user accounts
post metadata (caption, author, timestamps)
comments and likes
follow relationships
permissions and settings
Because the dataset is far too large for a single database, Instagram distributes tables across multiple database shards to keep performance fast as data grows.
As user and post data exploded, Instagram could no longer store everything in one database. To scale horizontally, it uses database sharding.
Sharding means:
users are divided across multiple database clusters
each shard stores only a subset of the total data
applications route requests to the correct shard based on an ID key
Benefits include:
higher write throughput
less contention on any single node
more total storage capacity
independent scaling of hot workloads
Sharding works particularly well for social apps because most interactions are user-centric (e.g., viewing your feed, your posts, your followers).
Instagram does not store images and videos in its relational databases.
Instead, media files are saved in distributed object storage systems, similar to Amazon S3, backed by Meta’s internal infrastructure.
The database stores only:
file references
URLs or IDs
metadata (dimensions, type, upload time)
Object storage is ideal because it:
scales globally
handles very large files
supports redundancy and replication
delivers content efficiently via CDNs
From there, content is served through content delivery networks (CDNs) to minimize latency worldwide.
To keep feeds and profiles fast, Instagram relies heavily on caching layers, particularly Redis and Memcached.
Caching is used for:
frequently accessed profile data
timelines and feed queries
session and authentication data
counts (likes, followers, views)
This reduces database load and improves response times — critical for mobile performance.
Some workloads don’t fit well in relational systems, especially:
real-time engagement counts
messaging metadata
ephemeral content like Stories
event logs and activity streams
For these, Instagram uses NoSQL and key-value stores such as:
Cassandra
RocksDB-based systems
in-memory queues and stream stores
NoSQL databases excel when:
the data structure is flexible
write throughput is very high
strict joins aren’t required
horizontal scaling is essential
These systems help Instagram handle millions of writes per second across the globe.
When you open Instagram, your feed doesn’t always load posts on demand. Instead, Instagram precomputes feed content and stores it ahead of time.
This approach:
reduces computation at read time
limits expensive cross-user queries
improves scroll performance
Background workers continually:
detect new posts from accounts you follow
compute ranking and relevance
store feed entries in fast lookup stores
When you open the app — the feed is already prepared.
Data must remain available even if servers fail. Instagram ensures durability through:
multi-region replication
failover clusters
backup and restore systems
redundancy across storage tiers
This protects user data from:
hardware failures
network outages
catastrophic events
For a platform of this size, reliability is not optional — it is engineering priority number one.
Operational databases aren’t used for analytics. Instead, Instagram pipelines stream data into:
data warehouses
distributed processing systems
machine-learning pipelines
These power:
recommendation models
spam detection
content ranking
product insights
This separation keeps core databases fast while enabling powerful data science workflows.
Instagram stores data using a layered, large-scale architecture that combines:
relational databases for core social graph data
sharded clusters for horizontal scalability
object storage for photos and videos
caching systems for fast reads
NoSQL stores for high-volume workloads
precomputed feeds for responsiveness
replicated storage for durability
separate analytics pipelines for insights
Together, these systems allow Instagram to operate reliably at global, internet-scale — delivering a social experience instantly to hundreds of millions of users.