What are the best mobile payment gateway routing data structure practices?

1 Data structures for routing

A data structure is a way of organizing and storing data in a computer. Different data structures have different advantages and disadvantages depending on the type and amount of data, the operations performed on the data, and the resources available. For mobile payment gateway routing, the data structure should be able to handle large volumes of transactions, support multiple processing options, and minimize latency and errors. Some of the common data structures used for routing are arrays, lists, queues, stacks, trees, graphs, and hash tables.

2 Arrays and lists

Arrays and lists are linear data structures that store data in a sequential order. Arrays have a fixed size and allocate memory at compile time, while lists have a variable size and allocate memory at run time. Arrays and lists are useful for storing and accessing transactions that have a predictable order and frequency, such as recurring payments, subscriptions, or batch processing. However, arrays and lists are not very efficient for inserting, deleting, or searching transactions that have a dynamic order or frequency, such as one-time payments, refunds, or chargebacks.

3 Queues and stacks

Queues and stacks are linear data structures that store data in a first-in-first-out (FIFO) or last-in-first-out (LIFO) order, respectively. Queues and stacks are useful for storing and processing transactions that have a priority or dependency, such as authorization requests, settlement requests, or retry attempts. Queues and stacks can help reduce latency and errors by ensuring that transactions are processed in the correct order and by avoiding congestion or deadlock. However, queues and stacks are not very efficient for accessing or searching transactions that are not at the front or the top of the data structure.

4 Trees and graphs

Trees and graphs are hierarchical data structures that store data in a parent-child or node-edge relationship. Trees and graphs are useful for storing and routing transactions that have multiple processing options, such as different payment methods, currencies, regions, or providers. Trees and graphs can help optimize performance and costs by finding the best processing option for each transaction based on various criteria and constraints. However, trees and graphs are not very easy to maintain or update, as they require complex algorithms and data structures to traverse, balance, or modify.

5 Hash tables

Hash tables are associative data structures that store data in key-value pairs. Hash tables are useful for storing and routing transactions that have unique identifiers, such as transaction IDs, customer IDs, or payment tokens. Hash tables can help speed up access and search by using a hash function to map keys to values in a constant time. However, hash tables are not very reliable or secure, as they are prone to collisions, where two keys map to the same value, or attacks, where malicious keys are used to exploit the hash function.