Goals and measurement:
1. Set performance goals
2. Set a performance 'budget' (e.g. 300ms for update etc)
3. Define performance testcases
Design:
4. Understand resources - disk, memory, network bandwidth, database connections, and local data protected by locks
5. Cut the deadwood - especially when reusing existing services and components, make sure your code doesn't do more (create more results, processes more data) than is needed.
6. Batch work - this will minimize roundtrips and enable efficiency gains from accessing resources only once.
7. Pool shared resources - such as database connections, TCP connections, or anything which has setup and teardown costs
8. Process independent tasks concurrently
9. Consider affinity - put resources near where they will be used; duplicate readonly resources (trade-off storage/memory for access time)
10. Use caching; design appropriate cache policy
11. Consider the efficiency of your algorithms; prototype algorithms early
12. Identify bottlenecks; there is always one more bottleneck
