Dan Murphy

𝗛𝗼𝘄 𝗮 𝗦𝗶𝗻𝗴𝗹𝗲 𝗟𝗶𝗻𝗲 𝗢𝗳 𝗖𝗼𝗱𝗲 𝗕𝗿𝗼𝘂𝗴𝗵𝘁 𝗗𝗼𝘄𝗻 𝗔 𝗕𝗶𝗹𝗹𝗶𝗼𝗻 𝗗𝗼𝗹𝗹𝗮𝗿 𝗥𝗼𝗰𝗸𝗲𝘁 On June 4, 1996, the European Space Agency (ESA) launched the Ariane 5 rocket for the first time, marking a significant moment in the history of space exploration. This mission was not without difficulty, though, as 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗹𝗶𝗻𝗲 𝗼𝗳 𝗰𝗼𝗱𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝗲𝗱 𝗶𝗻 𝗮 𝗰𝗮𝘁𝗮𝘀𝘁𝗿𝗼𝗽𝗵𝗶𝗰 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 and the loss of the entire cargo, which was worth almost half a billion euros. Two communication satellites were intended to be transported into geostationary transfer orbit by the Ariane 5. Up until the rocket deviated off track and burst 37 seconds into the flight, the launch was going smoothly. 𝗔 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗳𝗹𝗮𝘄 𝗶𝗻 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲 𝘀𝘆𝘀𝘁𝗲𝗺, which was in charge of regulating the rocket's course, was found to be the root of the disaster. The Ariane 5 launch is acknowledged as one of the most expensive software failures in history. The destruction of the scientific satellites delayed scientific research into the workings of the Earth's magnetosphere for almost 4 years. 𝗪𝗵𝗮𝘁 𝘄𝗮𝘀 𝘁𝗵𝗲 𝗰𝗮𝘂𝘀𝗲? A dead part of code from the last Ariane 4 mission, which began almost ten years prior, contained a simple and fixable programming error. The rocket used a method known as the 𝗵𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗯𝗶𝗮𝘀, sometimes known as the BH value, to identify whether it was pointing up or down. This was represented using a 64-bit floating-point variable, which the guidance system used to convert to 16 16-bit signed integers. A 64-bit variable can represent billions of values, while a 16-bit can represent only 65,535 values. We know that integers are represented with the first bit used to store signs, and 16-bit integers can then be from -32,768 to 32,767. Yet, floating-point numbers are made to track a broader range of numbers, using the same numbers of bits between -1.8e+308 and -2.2e-308. If you try to store one into a 16-bit integer, it will be much out of the bound of a signed integer. So, what happened is a well-known 𝗶𝗻𝘁𝗲𝗴𝗲𝗿 𝗼𝘃𝗲𝗿𝗳𝗹𝗼𝘄. One more contributing factor to the disaster was user requirements that Ariane 5 use a much steeper trajectory than previous ones, which resulted in a more incredible vertical velocity. What are some takeaways from the disaster? 𝗮 𝗰𝗼𝗽𝘆-𝗽𝗮𝘀𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗶𝘁 (Cargo-cult) is a significant issue. Another is the omission of proper exception handling, neglecting changed user requirements, and the absence of appropriate tests. #technology #softwareengineering #programming #techworldwithmilan #softwaredesign

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 The recent article by Shopify Engineering explained the top 10 most useful tips and tricks for building resilient payment systems. Here is the list: 𝟭. 𝗟𝗼𝘄𝗲𝗿 𝗬𝗼𝘂𝗿 𝗧𝗶𝗺𝗲𝗼𝘂𝘁𝘀: They suggests investigating and setting low timeouts everywhere possible. For instance, Ruby's built-in Net::HTTP client has a default timeout of 60 seconds to open a connection, write data, and read a response. This is too long for online applications where a user is waiting. The author suggests an open timeout of one second with a write and read or query timeout of five seconds as a starting point. 𝟮. 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿𝘀: Circuit breakers, like Shopify's Semian, protect services by raising an exception once a service is detected as being down. This saves resources by not waiting for another timeout. Semian protects Net::HTTP, MySQL, Redis, and gRPC services with a circuit breaker in Ruby. 𝟯. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆: The author discusses Little's Law, which states that the average number of customers in a system equals their average arrival rate, multiplied by their average time. Understanding this relationship between queue size, throughput, and latency can help design systems that can handle load efficiently. 𝟰. 𝗔𝗱𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗹𝗲𝗿𝘁𝗶𝗻𝗴: Google's site reliability engineering (SRE) book lists four golden signals a user-facing system should be monitored for latency, traffic, errors, and saturation. Monitoring these metrics can help identify when a system is at risk of going down due to overload. 𝟱. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗟𝗼𝗴𝗴𝗶𝗻𝗴: They recommends using structured logging in a machine-readable format, like key=value pairs or JSON, which allows log aggregation systems to parse and index the data. In distributed systems, passing along a correlation identifier to understand what happened inside a single web request or background job is useful. 𝟲. 𝗨𝘀𝗲 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝗰𝘆 𝗞𝗲𝘆𝘀: To ensure payment or refund happens exactly once, they recommends using Idempotency keys, which track attempts and provide only a single request is sent to financial partners. This can be achieved by sending a unique idempotency key for each shot. 𝟳. 𝗕𝗲 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗪𝗶𝘁𝗵 𝗥𝗲𝗰𝗼𝗻𝗰𝗶𝗹𝗶𝗮𝘁𝗶𝗼𝗻: Reconciliation ensures that records are consistent with those of financial partners. Any discrepancies are recorded and automatically remediated where possible. This process helps maintain accurate records for tax purposes and generate accurate reports for merchants. 𝟴. 𝗜𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗟𝗼𝗮𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Regular load testing helps test systems' limits and protection mechanisms by simulating large-volume flash sales. Shopify uses scriptable load balancers to throttle the number of checkouts happening at any time. 𝟵. 𝗚𝗲𝘁 𝗼𝗻 𝗧𝗼𝗽 𝗼𝗳 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Shopify uses a Slack bot to manage incidents, with roles for coordinating the incident, public communication, and restoring stability. This process starts when the on-call service owners get paged, either by an automatic alert based on monitoring or by hand, if someone notices a problem. 𝟭𝟬. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗥𝗲𝘁𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲𝘀: Retrospective meetings are held within a week after an incident to understand what happened, correct incorrect assumptions, and prevent the same thing from happening again. This is a crucial step in learning from failures and improving system resilience. Check the full text in the comments. #technology #softwareengineering #programming #techworldwithmilan #distributedsystems