Event sourcing in HLD - Architecture Diagram

System Overview - Event sourcing

Event sourcing is a system design pattern where all changes to application state are stored as a sequence of events. Instead of saving only the current state, the system records every change as an event, allowing full history and easy state reconstruction.

Key requirements include reliable event storage, event replay for rebuilding state, and handling commands that generate events.

Architecture Diagram

User
  |
  v
Command Handler
  |
  v
Event Store <--> Read Model Database
  |
  v
Event Processor
  |
  v
Notification Service

Legend:
- Arrows show data flow
- Double arrow between Event Store and Read Model Database indicates event replay and state projection

Components

User

actor

Initiates commands that change system state

Command Handler

service

Validates commands and generates events

Event Store

database

Stores all events in order as the source of truth

Read Model Database

database

Stores current state projections built from events for fast queries

Event Processor

service

Processes events from the store to update read models and trigger side effects

Notification Service

service

Sends notifications or triggers external actions based on events

Request Flow - 6 Hops

User → Command Handler

Command Handler → Event Store

Event Store → Event Processor

Event Processor → Read Model Database

Event Processor → Notification Service

User → Read Model Database

Failure Scenario

Component Fails:Event Store

Impact:New events cannot be stored, so state changes are lost and system cannot rebuild state from events

Mitigation:Use replication and backups for event store; degrade to read-only mode using existing read models until event store recovers

Architecture Quiz - 3 Questions

Which component stores the complete history of all changes in the system?

A Event Store

B Read Model Database

C Command Handler

D Notification Service

Design Principle

Event sourcing separates the write model (event store) from the read model (projections), enabling full history tracking, easy state reconstruction, and scalable read performance by building specialized views.

Practice

1. What is the main idea behind event sourcing in system design?

easy

A. Store all changes as a sequence of events to reconstruct state

B. Store only the latest snapshot of data for quick access

C. Use events only for logging errors in the system

D. Send events to users as notifications without storing them

Solution

1. Step 1: Understand event sourcing concept

   Event sourcing means saving every change as an event, not just the final data.

2. Step 2: Identify how state is managed

   The current state is rebuilt by applying all stored events in order, not by snapshots alone.

3. Final Answer:

   Store all changes as a sequence of events to reconstruct state -> Option A

4. Quick Check:

   Event sourcing = store events to rebuild state [OK]

Hint: Event sourcing saves changes as events, not just snapshots [OK]

Common Mistakes:

• Confusing event sourcing with snapshot-only storage
• Thinking events are only for error logs
• Believing events are just notifications

2. Which of the following is the correct way to represent an event in an event sourcing system?

easy

A. { "eventType": "UserCreated", "timestamp": "2024-06-01T12:00:00Z", "data": { "userId": 123 } }

B. [ "UserCreated", 123, "2024-06-01" ]

C. "UserCreated: userId=123 at 2024-06-01"

D. CREATE USER 123 AT 2024-06-01

Solution

1. Step 1: Identify proper event structure

   Events should be structured data with type, timestamp, and data fields for clarity and processing.

2. Step 2: Compare options

   { "eventType": "UserCreated", "timestamp": "2024-06-01T12:00:00Z", "data": { "userId": 123 } } uses a clear JSON object with eventType, timestamp, and data, which is standard practice.

3. Final Answer:

   { "eventType": "UserCreated", "timestamp": "2024-06-01T12:00:00Z", "data": { "userId": 123 } } -> Option A

4. Quick Check:

   Event = structured JSON with type and data [OK]

Hint: Events are structured objects with type, timestamp, and data [OK]

Common Mistakes:

• Using unstructured strings for events
• Confusing event data with SQL commands
• Using arrays without keys for event details

3. Given these events in order:
[{"eventType":"AddItem","data":{"itemId":1}}, {"eventType":"AddItem","data":{"itemId":2}}, {"eventType":"RemoveItem","data":{"itemId":1}}]
What is the final state of the item list?

medium

A. [1, 2]

B. [2]

C. [1]

D. []

Solution

1. Step 1: Apply events in order to the item list

   Start with empty list. Add item 1 -> [1]. Add item 2 -> [1, 2]. Remove item 1 -> [2].

2. Step 2: Determine final list content

   After all events, only item 2 remains in the list.

3. Final Answer:

   [2] -> Option B

4. Quick Check:

   Apply events sequentially = final list [2] [OK]

Hint: Apply events one by one to get final state [OK]

Common Mistakes:

• Ignoring remove event
• Applying events out of order
• Assuming all added items remain

4. You notice that replaying all events to rebuild state is very slow. What is a common solution to improve performance in event sourcing?

medium

A. Store only the latest event for each entity

B. Delete old events after 1 day to reduce size

C. Use snapshots to save intermediate states periodically

D. Switch to storing only current state, no events

Solution

1. Step 1: Identify performance issue cause

   Replaying all events from the start can be slow as event count grows.

2. Step 2: Choose common optimization

   Snapshots save the full state at points in time, so replay starts from snapshot, reducing replay time.

3. Final Answer:

   Use snapshots to save intermediate states periodically -> Option C

4. Quick Check:

   Snapshots speed up event replay [OK]

Hint: Use snapshots to avoid replaying all events every time [OK]

Common Mistakes:

• Deleting events breaks history and audit
• Keeping only latest event loses full history
• Abandoning events loses event sourcing benefits

5. You design an event sourcing system for a bank. Which approach best ensures data consistency and auditability when multiple transactions happen concurrently?

hard

A. Process events in random order to improve throughput

B. Allow events to overwrite each other without checks for speed

C. Store only final balances without event history to simplify design

D. Use optimistic concurrency control with event versioning and conflict detection

Solution

1. Step 1: Understand concurrency challenges in event sourcing

   Concurrent transactions can cause conflicts if events overwrite each other or are applied out of order.

2. Step 2: Choose method to maintain consistency and audit

   Optimistic concurrency control uses event version numbers to detect conflicts and prevent overwrites, preserving history and correctness.

3. Final Answer:

   Use optimistic concurrency control with event versioning and conflict detection -> Option D

4. Quick Check:

   Optimistic concurrency = safe concurrent event handling [OK]

Hint: Use versioning to detect conflicts in concurrent events [OK]

Common Mistakes:

• Ignoring conflicts causes data corruption
• Dropping event history loses audit trail
• Processing events unordered breaks state correctness