How can you debug code that interacts with multiple systems effectively?

Debugging is an art, you cannot become an expert by just reading some stuff online. To debug code that interacts with multiple systems we need the following 1) Log trace mechanism - For eg:- logging of X-RequestIDs that can trace through particular logic or functional flow between systems 2) Tools - Enterprise-grade products need to have integration with tools such as Splunk or NewRelic or other open source which makes logging consistent and readable 3) Last but not least, Unit/Functional Test Automation. More the coverage the better but developers have to ask themselves "Does my automation cover the smallest functional unit in the code ?"

Debugging code that interacts with multiple systems can be difficult, but there are several strategies and tools available to help you. Here are some pointers: Code reviews, use of debugging tools, simulation of network issues, version control, automated testing, and monitoring of external systems are all part of the job.

To effectively debug code interacting with multiple systems, isolate each system and test individually to identify where issues originate. Use comprehensive logging to track the flow and pinpoint problems. Employ debugging tools specific to each system for targeted analysis. Simulate interactions in a controlled environment to reproduce issues. Regularly update and review documentation to understand system interdependencies. Collaboration with teams responsible for each system is also vital for efficient problem-solving.

To effectively pinpoint issues such as these, it is important to delineate all interfaces among the various systems and embed assertions within the code. Consider doing this within a segment of code that is compiled and executed exclusively during debugging. These assertions serve as explicit declarations to the compiler or interpreter of the assumption your code makes about external systems. Assertions provide a potent yet frequently underestimated means of getting your compiler or interpreter's assistance in verifying the expected data from external sources. Employing this technique can save you laborious variable monitoring throughout debugging sessions, which might otherwise alter the code's behavior due to timeouts or other factors.

In addition, to all the comments below also contemplate using the code in the development and test environments as early as possible. This will help identify any deficiencies as early as possible and clearly identify the testing scenarios. The design phase should address this integration requirements early.

This is a dream scene for an SRE. Multiple systems = high touch integrations = > inconsistent, native record ID. To trace master records and transactions end to end via multiple apps, you need: 1. Use logging and code your app to log using a consistent trace ID across multiple applications. 2. A capable observability solution: instrument tracing and APM at the micro service layer; use transformation if required to bring the record from producing app to same ID 3. This above instrumentation will let you ‘trace’ the master entity across multiple systems in a solution 4. Build monitors / Dashboards for APM workflows to visualize the transactions across systems

The tracing collected should be analyzed as if all traces are part of a single timeline of events. This requires multi column tools that sync up timestamps of multiple logfiles.

Logging and tracing is the essential and most effective part of any system debugging. - You can have same trace id generated for multiple micro-services which interacts with each other. - If trace id is not available for some reason, you can build your own custom solution by passing a unique id between services via headers that can track down the flow of requests. - Try logging all the essential id’s and metrics which you can visualize easily with tools like Grafana and Prometheus. - ELK stack with Kibana is great way to debug using these metrics and id’s in logs.

By combining logging and tracing, you can create a detailed record of your program's execution, making it easier to identify issues, understand the sequence of events, and locate the source of errors during debugging.

Always be able to measure precisely at the ingress & egress point. (Your technical logging tools must match the same demarcation points as your legal contract). This is the only way to eliminate ambiguity when dealing with multiple stakeholders as to scope and responsibility.

Using breakpoints is a fundamental technique in debugging that allows you to pause the execution of your program at specific points, giving you the opportunity to inspect variables, evaluate expressions, and step through the code.

In the late 1980s I literally had to single step the machine code for some ada one line at a time to find an issue which ended up being an issue with the way Intel processors at the time switched between protected and real mode and the fact that the always ada code we were using was running in protected mode but the cpu was flipping back and forth into real mode because of some timer functionality that relied on the dos clock. Once we saw the registers get corrupted between two steps we quickly realized what the problem was.

Modularity. While not absolutely necessary for using breakpoints, it is good to maintain simplicity and granularity in your code. Design your code to execute specific tasks, and ensure it's well-organized into modules or components. This approach aids in isolating and identifying logical errors, especially when dealing with complex systems with multiple APIs.

Using mock and stubs are useful in the SandPit environment because they are able to identify the coding algorithm required. However, the significant difference between the two environments means you will have difficulties as you promote the code into other environments.

Mocking is a technique commonly used in software development and testing to replace certain components of a system with simulated objects or functions. While mocking is more commonly associated with testing, it can also be useful in debugging to isolate specific parts of the code and focus on understanding or fixing issues.

Quick guidelines for debugging with mocks and objects: 1. Define Purpose: Clarify the purpose of mocks and objects. 2. Isolate Components: Use mocks to isolate the component being tested. 3. Verify Interactions: Ensure expected interactions with mock verification. 4. Stubbing: Use objects to stub methods for controlled responses. 5. Keep Tests Simple: Avoid overly complex setups for readability. 6. Clear Naming: Use clear names for mocks and `atun` objects. 7. Refactor as Needed: Simplify code if mocks become complex. 8. Use Real Objects When Necessary: Choose real objects when beneficial. 9. Check State Changes: Verify expected state changes with mocks. 10. Documentation: Document the purpose and usage of mocks in test cases.

Unit and integration tests are essential tools that can help developers ensure that their code works as expected and meets the requirements and specifications. They can also help developers avoid and fix errors and bugs, which can save time and resources. Furthermore, they can help developers improve their code by making it more readable, maintainable, and scalable.

Unit testing is a crucial practice in software development that involves testing individual units or components of a system to ensure they work as intended. While unit tests are traditionally associated with preventing and identifying issues early in the development process, they can also be useful during debugging to isolate problems and validate fixes.

Unit and integration tests are automated tests and need to be the part of every software. Unit test check the correctness and functionality of your code at different levels. These tests can validate and verify the output and performance of each system, as well as ensure the quality and reliability of your code. Additionally, they can detect and prevent the regressions defects and bugs, allowing you to refactor and improve your code. Unit test need to be mandatory in all the software development.

Unit tests are designed to test individual components or pieces of code in isolation. They are crucial for ensuring that each function or module performs as expected. By testing these smaller units independently, you can quickly identify and fix errors within specific parts of your code. Integration tests, on the other hand, focus on the interactions between different pieces of your code and external systems. They ensure that the integration points between different modules, services, or third-party APIs work together as intended. Both unit and integration tests help in validating the output of your code. They ensure that the system returns the correct responses under various conditions.

Using correlation IDs is a highly effective method for debugging code that interacts with multiple systems. It is a unique identifier attached to a transaction or series of requests across different systems. Generated at the transaction's entry point (like the user-facing API) and passed along with each request to various services, typically via HTTP headers or messaging metadata. Correlation IDs can help with - Isolation of Issues - Unified Log Analysis - Identifying Performance Bottlenecks - Error Tracking - Enhanced Testing and Monitoring Correlation IDs provide clarity in the flow of transactions across services, enabling easier isolation of faults, unified log analysis, performance optimization, and accurate error diagnosis.

In my experience granularity is one of the considerations that supports system debugging. One thing I found in reference to systems that interact with multiple systems (APIs), it is important to adhere to the fundamental principles of coding, which is to program one task at a time. Processing multiple tasks at once is independent of what each task is intended to do. Source code that is kept simple and programmed to execute a specific task helps in debugging. No matter how large the system is, if the source code is properly packaged and divided into modules, debugging the code will be much more feasible. mainly when it comes to identifying logical errors and/or access permissions between APIs.

Debugging tools often allow you to inspect the current state of your application, including variables, memory, and system resources. This visibility is crucial for understanding how data flows through your code and how different states affect your application’s behaviour. Many debugging frameworks provide the ability to execute code interactively. This means you can run code line by line, observe its behaviour, and modify it on the fly to test how changes affect the outcome. Modern debugging tools can be integrated into your development environment and can automate various debugging tasks. This includes automatic error reporting, performance profiling, and integration with logging and tracing systems.

Create custom dashboards that aggregate logs and metrics from different systems into a single view. This unified perspective can significantly speed up the identification of the root cause in interconnected systems. Additionally, Utilize AI-powered tools for anomaly detection in system logs and metrics. These tools can automatically flag unusual patterns or errors that might otherwise go unnoticed in large volumes of data.

Another critical aspect to address is the strategic planning of your logging and tracing approach from the outset. It is crucial to implement a system that allows you to independently toggle tracing levels on or off within each module. This enables you to avoid the overwhelming influx of messages, sparing you from the tedious task of sifting through thousands of entries during each review.

The reason why you should integrate with systems versus not should be considered early through the process. The more you integrate with systems the more the complexity you introduce and therefore risk. The core question should be 'do we need to integrate?' and if so, what is the reason

- Use logging and tracing with correlation IDs to follow the flow across boundaries. - Isolate components and test integrations independently first. - Mock dependencies and validate assumptions about external systems. - Analyze logs/metrics of all involved services holistically. - Add testability code like kill switches to ease testing. - Reproduce issues locally leveraging production telemetry. - Monitor health of all systems during testing for bottlenecks. - Use service virtualization to simulate hard to replicate edge cases. - For front-end, inspect network traffic and disable caching temporarily. The key is observability into the entire flow and methodically isolating components via testing.

First and foremost of course are two things 1) well designed architecture with proper layering proper hierarchy proper abstractions etc and 2) design for stability in the first place