Horizontally scaling a system to handle more load is vital but comes with challenges. There are three core strategies to scale out: distributing, caching, and asynchronous processing. Each approach also involves tradeoffs to consider.
Distributing
Distributing spreads the workload across multiple hardware resources or systems:
- Common tactics include load balancing and sharding data across nodes
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Benefits
- Handles more users and transactions without single point of failure
- If one node fails, others take over for reliability
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Drawbacks
- Increases complexity around networking, deployment, and data consistency
- Sharding introduces specific data consistency challenges
Caching
Caching stores frequently accessed data in fast access storage:
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Benefits
- Reduces load on backend systems
- Lowers response times by avoiding repeat queries
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Drawbacks
- Potential for increased stale data
- Requires managing scaling of cache resources
Asynchronous Processing
Async decouples non-critical tasks using queues and events:
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Benefits
- Improves user experience by keeping app responsive
- Enables better resource use by spreading loads
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Drawbacks
- Increases complexity around failure handling and consistency
- Eventual delays can still degrade consistency
In distributed systems, there are inevitable messy tradeoffs to make.
Read more on my blog.
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