A python script that demonstrates the response time of a cloud-based computation
Many finance analytics have two phases of calculation - The first phase is to read the raw positions from the database and construct a monthly/daily view. The second phase is to aggregate the monthly/daily view to a historical range spanning multiple years. How much parallelism you build into these two phases will determine the real-time response of your analytics - whether you have a system of O(N), O(log N) or O(1).
- Clone the project
- Modify the two latency variables to reflect relative cost of these two opreations.
- Run the script with Python 2.7