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Modules

01The HLD interview5
The round
02Core concepts12
Scale and performance
- Scalability
- Latency vs throughput vs bandwidth
Reliability
Spreading data across machines
- Consistent hashing
- CAP theorem and PACELC
Design principles
- Ten principles senior engineers design by
- Cloud design patterns cheat sheet
03Networking and APIs20
How a request travels
API styles
Protecting the API
04Data and storage51
Choosing a database
Schema design
Transactions and concurrency
Query performance
Scaling the data layer
Data structures at scale
Deep dives
Search engines series
05Caching fundamentals6
Caching
06Messaging and streams9
Queues and topics
Data in motion
07Distributed coordination7
Agreeing and finding each other
Transactions and ordering
- Distributed transactions: two-phase commit vs sagas
- Clocks, ordering and conflict resolution
08Running in production14
Resilience and recovery
Observability
- Observability: logs, metrics and traces
Scaling the fleet
- Autoscaling done right
Shipping safely
09Why is it slow? Performance anti-patterns10
On the data path
In the service
Under shared load
- Noisy neighbour
- Retry storm
10Security5
Identity and access
Protecting data
- Encryption and key management
- Multi-tenancy and isolation
11Multi-tenant SaaS7
Tenancy
Serving tenants
12Architecture styles8
Architecture styles
Designing services
13System design trade-offs7
Trade-offs
14Key technologies12
Data stores
Plumbing
AI building blocks
- Embeddings and semantic search
- LLM inference serving
15Common patterns8
Patterns
16Infrastructure8
Design problems
17Search & discovery6
Design problems
18Storage & media6
Design problems
19Social & feeds8
Design problems
20Real-time & collaboration7
Design problems
21Location & maps6
Design problems
22E-commerce & booking7
Design problems
23Payments & fintech5
Design problems
24Data, analytics & monitoring7
Design problems
25Developer platforms5
Design problems
26AI & LLM systems6
Design problems

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