The document discusses solving investment return calculations at scale using Apache Spark, focusing on the money weighted rate of return (MWRR) for numerous customers in real-time. It introduces the Newton-Raphson method for approximation and mentions various optimization techniques for computation within Spark's executors. Additionally, it lists different types of solvers applicable for the computations.

1. Equation solving at scale
using Apache Spark
- Praveen Rachabattuni, Sigmoid
@praveenr019

2. What are we solving?
• Calculate specialised rate of return of investment
data of different customers of a investment
company.
• Compute the returns of 1000s of customers on a
near real-time.

3. Money weighted rate of
return (MWRR)

4. MWRR Example
• V1 = 298,082
• V0 = 250,000
• 25,000 investment made on 258th day
• r = 0.00897 or 8.97%
4

5. Newton-Raphson Solver
• xn+1 = Better approximate
• xn = Initial approximate
• f(xn) = Function value at a value of x
• f’(xn) = Function differential value at a value of x

6. Demo

7. Spark
• Computation should all happen in executors
• Custom map operations should be well optimised

8. Apache Common Solvers
• BisectionSolver
• MullerSolver
• MullerSolver2
• NewtonRaphsonSolver
• SecantSolver

9. Queries ?

10. Thank you