Deep Dive: Slack’s Multi-Tenancy Architecture

Slack is a multi-tenant collaboration platform that serves millions of businesses worldwide. It needs to store vast amounts of chat messages, files, and user data while ensuring strict isolation between different companies (workspaces).

🔹 Who is Involved?

• Provider: Slack (Built on AWS, PostgreSQL, and Vitess for scaling).
• Clients (Tenants): Companies like IBM, Airbnb, Stripe, and thousands of other businesses.

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1️⃣ Slack’s Multi-Tenancy Model: Shared Database, Shared Schema

🔍 How Slack Stores Data

Slack uses a single database for multiple workspaces but ensures data isolation using tenant identifiers (workspace_id).

Example: Messages Table (PostgreSQL)

CREATE TABLE messages (
    id SERIAL PRIMARY KEY,
    workspace_id INT NOT NULL,  -- Tenant Identifier
    channel_id INT NOT NULL,
    user_id INT NOT NULL,
    message TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT NOW()
);

When a user loads messages, Slack ensures they only see their workspace’s data:

SELECT * FROM messages WHERE workspace_id = 123;  -- Fetch messages for Workspace 123

🔹 Key Technologies Used

✅ PostgreSQL & Vitess – Scales databases across multiple regions.

✅ Row-Level Security (RLS) – Ensures users can only access their workspace’s data.

✅ Indexing on workspace_id – Improves performance when retrieving tenant-specific data.

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2️⃣ Scaling Slack’s Multi-Tenancy with Vitess

The Challenge:

• Millions of messages sent every second.
• Some workspaces (like IBM) generate far more data than small teams.
• Need a way to scale without performance bottlenecks.

Solution: Vitess for Database Sharding

Slack uses Vitess, an open-source database clustering system, to:

• Shard PostgreSQL databases by workspace_id (spreading data across multiple servers).
• Dynamically move tenants to different database clusters when needed.

Example: How Sharding Works

• Workspaces 1–1000 → Stored in db_shard_1
• Workspaces 1001–2000 → Stored in db_shard_2

SELECT * FROM db_shard_1.messages WHERE workspace_id = 500;

✅ This allows Slack to handle large workspaces like IBM without slowing down smaller teams.

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3️⃣ How Slack Ensures Data Isolation & Security

🔹 Row-Level Security (RLS)

Slack uses PostgreSQL’s RLS to enforce per-tenant data access:

CREATE POLICY workspace_isolation
ON messages
FOR SELECT
USING (workspace_id = current_setting('app.current_workspace')::int);

Before running queries, Slack sets the current workspace:

SET app.current_workspace = 123;
SELECT * FROM messages;  -- Only fetches messages for Workspace 123

🔹 Encryption & Compliance

• Data at rest: Encrypted using AWS KMS.
• Data in transit: Encrypted via TLS 1.2+.
• SOC 2, GDPR, HIPAA compliance for enterprise customers.

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4️⃣ Why Slack Uses Shared Schema Instead of Separate Databases

Approach	Isolation	Performance	Cost	Complexity
Shared Schema (Slack's Model)	Medium	High	Low	Low
Separate Databases per Tenant	High	Medium	High	High

Slack’s Reasoning:

• ✅ Shared schema allows efficient queries across multiple tenants.
• ✅ Indexing on workspace_id ensures high-speed retrieval.
• ✅ Vitess handles sharding for large-scale tenants.

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🔹 Final Takeaways

✔️ Slack uses a shared database with row-level multi-tenancy.

✔️ PostgreSQL with Vitess helps scale tenant workloads efficiently.

✔️ Row-Level Security (RLS) ensures data isolation per workspace.

✔️ Sharding prevents large tenants from slowing down the system.

🚀 This approach enables Slack to serve millions of businesses without performance issues.

Would you like me to cover another SaaS multi-tenancy example like Salesforce or Shopify? 😊