Integrated Microservice Calculation Orchestration
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Solution Overview
Problem
Conventional integration techniques for calculations in microservice environments often result in siloed and isolated calculations, leading to inefficiencies, scalability issues, and resource overhead due to duplication of data and code, causing breaks and discrepancies between products.
Innovation Solution
A method and system for facilitating integrated computations in a microservice environment by determining a list of profiles based on a generated global context, loading data sets, aggregating trade data, retrieving position data, invoking computation devices, and notifying downstream applications, with data persistence and structured data set generation for efficient calculation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional integration techniques are used for calculations, then calculations can be performed in isolation, but this leads to siloed and isolated calculations causing inefficiencies and scalability issues
Solution Approach 1:
The system segments calculations into distinct computation devices that can operate independently but are coordinated through a unified architecture. Each computation device handles specific calculation profiles, allowing parallel processing while maintaining integration through the orchestration layer that manages data flow and coordination between segments.
Solution Approach 2:
The system creates a universal integration layer that handles multiple calculation types and data sources through a single coordinated architecture. The unified approach allows different computation devices to access shared data repositories and communicate through standardized interfaces, eliminating the need for separate integration mechanisms for each calculation type.
2Adaptability or versatility
If siloed calculations are used, then each calculation can be independent, but this results in duplication of data and code increasing resource overhead
Solution Approach 1:
The system merges data storage and code repositories into shared resources that are accessed by multiple computation devices. A centralized data repository stores reference data and market data that can be accessed by any computation device, eliminating duplication. Similarly, a shared code repository manages calculation logic that can be reused across different computation profiles.
Solution Approach 2:
The system introduces an intermediary orchestration layer that mediates between independent calculation components and shared resources. This intermediary manages data requests, coordinates computation devices, and handles communication protocols, allowing independent calculations to access shared data without direct coupling and thus avoiding duplication.
3Productivity
If siloed calculations are used, then each calculation operates independently, but this causes breaks and discrepancies between products
Solution Approach 1:
The system implements feedback mechanisms where computation devices report their results and data access patterns to the orchestration layer. This feedback loop enables the system to monitor data consistency across products, detect discrepancies, and coordinate corrections. The feedback mechanism also allows real-time monitoring of calculation progress and data availability.
Solution Approach 2:
The system performs preliminary actions by pre-loading reference data and market data into shared repositories before calculations begin. This ensures that all computation devices access the same baseline data, preventing discrepancies. The orchestration layer also performs preliminary coordination to ensure data availability before initiating calculations, reducing the risk of breaks between products.
Data Source
AI summary
A method for facilitating integrated computations in a microservice environment is disclosed. The method includes determining a list of profiles based on a generated global context, the list relating to an ordered representation of the profiles for calculating; loading data sets from various sources, each of the data sets including a context identifier; aggregating trade data from a trade capture listener to prepare for the calculating, the trade data including trade valuation data; retrieving position data for each of the profiles in the list, the position data including corresponding position valuation data; invoking computation devices for each of the profiles in the list based on a corresponding agreement type, the data sets, the trade data, and the position data; and notifying downstream applications based on the corresponding agreement type, the notification including an availability of a calculation result from the computation devices.