- Kaivalya Apte
How LinkedIn Scaled Its Profile Data Store While Reducing Costs
LinkedIn serves around 4.8 million member profiles per second, a testament to its massive scale. The journey from Oracle to their homegrown document store, Espresso, marked a significant shift, enabling horizontal scaling and cost-effective growth. But the challenges didn't stop there.
The Scaling Challenge
The platform's yearly doubling in scale and a read-heavy workload demanded a sustainable scaling solution. Enter Couchbase, a centralized storage tier cache, which remarkably achieved:
- A 99% hit rate
- 60% reduction in tail latencies
- 10% decrease in annual costs
Overcoming Legacy Challenges
- From Oracle to Memcached: Initial struggles with maintaining a Memcached infrastructure during cache expansions and node replacements.
- Transition to Espresso: Espresso's impressive scalability reduced reliance on additional caching but eventually hit an upper limit.
Making Caching Work
Strategies for effective caching included:
- Resiliency Against Couchbase Failures: Health monitors, operational retries, and tripling node replicas.
- Ensuring Data Availability: Keeping profile data cached across data centers with infinite TTL, periodically bootstrapping Couchbase.
- Strict SLOs: Maintaining minimal data divergence between the source and the cache.
The Reality of Scaling
Scaling isn't just about adding a cache or more nodes. It requires deep software engineering expertise, a keen understanding of systems, and the ability to navigate challenges and bottlenecks.
Further Reading: Dive into more details in the full blog post.
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