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The Art of REST API Design: Idempotency, Pagination, and Security
The Art of REST API Design: Idempotency, Pagination, and Security
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APIs are the front doors to most systems.
They expose functionality, enable integrations, and define how teams, services, and users interact. But while it’s easy to get an API working, it’s much harder to design one that survives change, handles failure gracefully, and remains a joy to work with six months later.
Poorly designed APIs don’t just annoy consumers. They slow teams down, leak data, cause outages, and break integrations. One inconsistent response structure can turn into dozens of custom client parsers. One missing idempotency check can result in duplicate charges. One weak authorization path can cause a security breach.
Well-designed APIs, on the other hand, create leverage and help the team do more. Some defining features are as follows:
They act as contracts, not just access points.
They scale with usage.
They reduce surprises for the developers and other stakeholders.
And they are reliable, internally and externally.
Most of the pain in API systems doesn’t come from the initial development. It comes from evolving them: adding new fields without breaking old clients, handling retries without state duplication, and syncing data between systems without losing events.
A good API design is defensive and anticipates growth, chances of misuse, and failures. It understands that integration points are long-lived and every decision has an impact down the line.
In this article, we explore the core principles and techniques of good API Design that make APIs dependable, usable, and secure. While our focus will primarily be on REST APIs, we will also explore some concepts related to gRPC APIs to have a slightly more holistic view.
Principles of Good API Design...
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How AMEX Processes Millions of Daily Transactions With Millisecond Latency
How AMEX Processes Millions of Daily Transactions With Millisecond Latency
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Disclaimer: The details in this post have been derived from the presentation by the American Express engineering team on the Monster Scale Summit by ScyllaDB. All credit for the technical details goes to the American Express engineering team. Special thanks to ScyllaDB for organizing the event. The links to the original articles are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
American Express processes trillions of dollars worth of transactions every year, which means millions of daily transactions worldwide.
Considering how impatient users can become if a transaction takes too long, these transactions must support low-latency responses in milliseconds. To support this requirement, American Express rebuilt its payment system in 2018 to create a more modern, flexible, and reliable infrastructure.
But what was the problem with the old system?
The older system was a traditional on-premise payment network that relied on legacy infrastructure. It had several limitations that made it less suitable for the future. Also, it was difficult to change and scale based on demand. AMEX wanted to ensure that its payment network could handle increasing transaction volumes while maintaining high speed and security.
The key reasons for the move are as follows:
Cloud Readiness: The previous system was not fully designed for cloud environments. American Express aimed to build a system that could take advantage of cloud computing for better scalability and resilience.
Flexibility and Adaptability: The financial industry evolves rapidly, with new payment technologies and regulations emerging frequently. A redesigned system would allow for quicker updates and integrations with partners.
Security and Reliability: As a major payment provider, American Express needed a network that could process millions of transactions securely and without failure. The updated system was designed to be more resilient against disruptions.
Low Latency Processing: Payments must be processed in milliseconds. The older system faced challenges in meeting modern performance expectations. By upgrading the network, American Express ensured faster transaction approvals and smoother customer experiences.
Handling More Transactions – The volume of digital payments continues to grow. The new system was built to handle a significantly larger number of transactions per second, reducing the risk of slowdowns or failures.
In this article, we’ll look at the architectural and design decisions AMEX engineers made when building the new payment system.
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Global Transaction Router
The centerpiece of the new payment system is the Global Transaction Router (GTR). The AMEX payment system has to interact with different entities involved in handling payment transactions.
Acquirers (Merchant Banks): These are financial institutions or banks that process card transactions on behalf of a merchant. When a customer uses their AMEX card at a store or online, the acquirer is responsible for sending the transaction request to a payment network like AMEX.
Processors (Payment Service Providers): These are companies that manage the technical infrastructure required to handle payment transactions between merchants, acquiring banks, and card networks. Processors ensure that the payment data is securely transmitted and correctly formatted for authorization.
Issuers (Card-Issuing Banks): An issuer is a bank or financial institution that provides the payment card to the customer. The issuer is responsible for verifying whether the cardholder has sufficient funds of credit available to approve the transaction.
The GTR sits at the edge of the AMEX network, meaning it is the first system that interacts with these different entities. Here’s how the various entities work with the GTR:
A merchant (through an acquirer) submits a payment request when a customer uses their AMEX card.
The request reaches GTR, which determines the best route to process the transaction.
GTR forwards the request to the processor, which verifies transaction details and forwards it to the issuer.
The issuer checks if the cardholder has sufficient funds or credit and sends an approve/decline response back through GTR.
The merchant receives the final approval, and the payment is completed.
Settlement occurs later when the issuer transfers funds to the acquirer, who then pays the merchant.
Unique Challenges for the GTR
There were several unique challenges when it came to building the GTR:
Managing Long-Lived TCP Sessions: Unlike modern web services that use short-lived HTTP-based APIs, payment systems often rely on ISO 8583, an older financial messaging standard that operates over long-lived TCP connections. This meant that persistent connections must be maintained for extended periods.
Handling ISO 8583 Messages: ISO 8583 is a widely used but outdated messaging protocol in financial transactions.
Traffic Spikes and Load Surges: Financial transactions do not occur at a constant rate. The system had to handle sudden spikes such as during Black Friday and holiday shopping seasons.
Ensuring Ultra-Low Latency: Payment systems operate on a millisecond scale. Even a small delay in processing a transaction can result in declined payments due to timeouts and bad customer experience if a transaction takes too long to complete.
Design Decisions of the GTR
The AMEX engineering team made several technical decisions when designing the GTR to ensure it overcame the challenges. Some of the major decisions are as follows:
1 - Language Choice: Go (Golang)
The team selected Go (Golang) as the primary language for developing GTR.
But why Go?
One of the major reasons was Go’s concurrency model. Payment transactions involve thousands of concurrent connections, each requiring processing in real time. Go provides goroutines that can handle thousands of open connections efficiently without excessive CPU or memory overhead.
Second, Go compiles ahead of time (AOT), meaning applications start instantly without the need for runtime optimizations. This is important for low-latency transaction processing.
Go’s garbage collector is also optimized for low-latency applications, ensuring that memory cleanup does not interrupt transactional processing. Compared to languages like Java, where garbage collection can cause random pauses (GC spikes), Go’s GC is designed to minimize transaction delays.
2 - gRPC over HTTP/2
AMEX opted for gRPC over HTTP/2 to improve internal communication within the payment system.
gRPC uses Protocol Buffers (protobuf) instead of JSON, which results in smaller message sizes and faster serialization and deserialization. When routing payments, GTR converts ISO 8583 messages into gRPC messages using protobuf, making internal processing faster.
Moreover, gRPC over HTTP/2 allows multiplexing, meaning multiple requests can be sent and processed simultaneously on a single TCP connection. This is critical for handling thousands of real-time transactions without delays.
See the diagram below that shows HTTP/2 multiplexing in action.
3 - Asynchronous Logging for Performance Optimization
In traditional systems, logging is synchronous, meaning every time the application writes a log message, it pauses execution until the log is written to a file or database. This slows down transaction processing, especially when dealing with millions of log entries per second.
GTR uses asynchronous logging. Log messages are written to an in-memory buffer instead of being processed immediately. A separate goroutine reads from the buffer and writes logs in batches, reducing contention. This way transactions continue processing while logs are written in the background.
See the diagram below:
Also, AMEX built custom logging handlers using Go’s standard “slog” library. The library helps buffer logs in memory and structure them for easy debugging. AMEX also optimized logging by using log sampling, where only a subset of logs is recorded under heavy load conditions.
Optimization Strategies
To ensure that the Global Transaction Router could handle millions of transactions per second while maintaining low latency and high reliability, the American Express (AMEX) engineering team implemented several key optimization strategies.
Let’s look at the three major areas of optimization.
1 - Profiling and Benchmarking
Profiling is the process of analyzing a program while it is running to identify performance bottlenecks (for example, slow functions, high CPU usage, or memory inefficiencies).
On the other hand, benchmarking involves running a set of tests to measure how well the system performs under different conditions, such as high transaction volumes.
The team used Go’s built-in profiling tools (pprof) to analyze the system’s performance. They measured CPU usage, memory consumption, and execution time for different functions in the codebase. This helped them identify which functions were taking too long to execute and needed optimization.
Next, the team ran benchmark tests by simulating high-traffic scenarios to test how GTR performs under stress. They ran tests to measure parameters such as latency, throughput, and failure recovery.
2 - Using Reader-Writer Mutexes
A mutex (mutual exclusion lock) is a mechanism used to control access to shared resources in a concurrent system. If multiple Go routines (threads) try to read and write the same data at the same time, data corruption can occur. A mutex prevents this by allowing only one thread to access the resource at a time.
Instead of using a standard mutex, the AMEX team implemented a Reader-Writer Mutex. This is because a standard mutex locks a resource completely, meaning no other thread can access it until the lock is released. Such an approach is inefficient when multiple threads only need to read the data and creates unnecessary delays by blocking all access.
The Reader-Writer Mutex works differently. It allows multiple read operations to happen at the same time. Write operations still require exclusive access.
See the diagram below that shows the difference between a standard mutex and reader-writer mutex.
3 - Avoiding Excessive Use of Go Channels
Go channels are a way for Go routines to communicate with each other by passing data between them. They are often used to coordinate asynchronous tasks.
Initially, AMEX used Go channels to process transaction data, but they found that this approach was slower than direct socket communication.
Channels introduce extra overhead because:
Messages need to be queued and synchronized before they are processed.
Each transaction had to wait for a channel to be read before moving forward.
This added unnecessary complexity in high-speed financial transactions.
Instead of reading from a TCP socket, sending data through a Go channel, and then writing to a gRPC socket, the team removed the intermediate channel step. Transactions were processed directly from TCP to gRPC, eliminating unnecessary delays.
See the diagram below:
Operational Best Practices
Lastly, the AMEX engineering team also follows some operational best practices to ensure that the Global Transaction Router works efficiently under all conditions.
1 - Continuous Performance Testing For Every Release
Even a small change in the codebase could affect performance, scalability, or transaction speed. A minor update that seems harmless could introduce unexpected bottlenecks in high-traffic scenarios.
Therefore, every release undergoes performance testing, regardless of how small the update is. Benchmark tests are run to measure transaction processing speed, response time, and system resource usage. Load testing tools simulate real-world transaction volumes to ensure the system can handle peak loads.
2 - Chaos Testing
Payment networks must remain operational 24/7, even during sudden traffic surges, network outages, or system crashes.
To ensure this, AMEX performs regular chaos testing.
Chaos testing involves intentionally introducing failures or extreme conditions to observe how the system responds. It helps ensure that GTR can recover quickly from unexpected issues without service disruption.
3 - Iterative Development Process
Instead of making large, infrequent updates, the AMEX team continuously improves GTR through small, incremental enhancements.
Developers analyze profiling data to find areas where performance can be improved. Instead of deploying large, risky changes, small updates are made frequently.
This approach ensures that performance, security, and scalability are always improving.
Conclusion
The development of the Global Transaction Router at American Express showcases how a well-architected payment system can achieve high scalability, low latency, and fault tolerance.
By making strategic technical decisions such as using Go for concurrency, gRPC for efficient communication, and asynchronous logging to reduce bottlenecks, the engineering team ensured that GTR could handle millions of transactions per second without performance degradation.
Optimization strategies like profiling and benchmarking, reader-writer mutexes, and direct socket writes further improved transaction processing speed. This has enabled American Express to build a cutting-edge financial transaction system capable of meeting the ever-growing demands of the digital economy.
References:
Scaling Payments Routing at American Express - Monster Scale Summit 2025
Route It Like It’s Hot: Scaling Payments Routing at American Express
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Get a head start on AI-readiness with our latest webinar
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Why the gender pay gap persists
On McKinsey Perspectives
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Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
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Not a linear path. In the US, much of the gender wage gap is attributable to diverging patterns of work experience, according to a McKinsey Global Institute (MGI) analysis of approximately 86,000 people. Women, who may bear more responsibility for caregiving and household labor, accumulate less time doing paid work than men, explain McKinsey Senior Partners Kweilin Ellingrud and Sven Smit and their coauthors. What’s more, women typically take longer career breaks than men do. And while men and women switch roles at roughly the same rate, women are likelier to shift to lower-paying positions.
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