Jing En Cheam ジャン・ジンエン @CoderJing

@drainpipe_ @AdamRackis @Cal_Irvine TC39 hasn't ignored it, you assume wrongly. Graydon Hoare when he and I were working on ES4 attended 754r meetings. We didn't get Decimal into ES4 of course, but thanks to TC39 members (esp. BBG/@paprocki & @littledan) working on it, it's now at stage 1: github.com/tc39/proposal-….

🚨🚨 90% Of Code Is AI Generated - 650,000,000 Check Boxes In One Website 🚨🚨

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#leetcode That's my first February batch.

🔥 Fun fact: emojis can be made of MULTIPLE emojis javascript can iterate through every 🤦‍ in a 👨‍👩‍👧‍👦

Worm hole😂

Gary Grady @GaryJGrady

3 years ago

Advent of Code 2023 Day 11 #AdventOfCode

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Advent of Code 2023 Day 11 #AdventOfCode

Advent Of Code is so much fun! #AdventOfCode

Do you use Pino for logging, or are you thinking about it? In this blog, I looked at: ‣ The benefits of Pino ‣ Getting started ‣ Key features ‣ Pino's advantages over other logging libraries ‣ How to log with Pino & @Platformatic hubs.ly/Q027Bt4c0

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗿𝗼𝘀𝘀-𝗢𝗿𝗶𝗴𝗶𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 (𝗖𝗢𝗥𝗦)? Browsers use CORS, a method, to prevent websites from requesting data from different URLs. A request from a browser includes an origin header in the request message. The browser allows it if it gets to the server of the exact origin; if not, the browser blocks it. We can deal with CORS issues on the backend. Cross-origin requests require that the values for origin and 𝗔𝗰𝗰𝗲𝘀𝘀-𝗖𝗼𝗻𝘁𝗿𝗼𝗹-𝗔𝗹𝗹𝗼𝘄-𝗢𝗿𝗶𝗴𝗶𝗻 in the response headers match and it is set by the server. When you add an origin to the backend code, the CORS middleware only permits this URL to communicate with other origins and utilize it for cross-origin resource requests. There are two ways to fix CORS issues: 𝟭. 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗲 𝘁𝗵𝗲 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝘁𝗼 𝗔𝗹𝗹𝗼𝘄 𝗖𝗢𝗥𝗦 Server can let all domains with  𝗔𝗰𝗰𝗲𝘀𝘀-𝗖𝗼𝗻𝘁𝗿𝗼𝗹-𝗔𝗹𝗹𝗼𝘄-𝗢𝗿𝗶𝗴𝗶𝗻: *. This actually turns off same-origin policy, which is not recommended. Another optin would be only to allow particular domain, which is better option, e.g., 𝗔𝗰𝗰𝗲𝘀𝘀-𝗖𝗼𝗻𝘁𝗿𝗼𝗹-𝗔𝗹𝗹𝗼𝘄-𝗢𝗿𝗶𝗴𝗶𝗻: 𝗵𝘁𝘁𝗽𝘀://𝘀𝗼𝗺𝗲𝗱𝗼𝗺𝗮𝗶𝗻.𝗰𝗼𝗺. 𝟮. 𝗨𝘀𝗲 𝗮 𝗣𝗿𝗼𝘅𝘆 𝗦𝗲𝗿𝘃𝗲𝗿 We can use a proxy server to call external API. It acts as a middleware between client and the server. If the server doesn't return proper headers defined by CORS, we can add them in the proxy. Image credits: RapidAPI #softwareengineering #programming #api #apidesign #techworldwithmilan

Red flags in a React codebase 🚩 functions named handleClick,handleSubmit 🚩 preventDefault 🚩 useMemo 🚩 fetch inside useEffect 🚩 <div onClick/> 🚩 a "hooks" directory 🚩 css files 🚩 icon library in package.json

JWT Token Structure Simplified:

A Monolithic database is NOT bad. But sometimes it may not be enough. Uber realized this a few years back when they were experiencing a whopping 20% trip growth every month. It meant that soon their main trip storage database was going to run out of storage volume and IOPS. Unless they did something about it… Like most startups in the early days, Uber’s backend system was also monolithic. There were 3 main parts to this system: - Real-time services - Backend services - PostgreSQL DB This architecture was good enough for Uber’s initial operations. But it simply couldn’t keep up with its growth trajectory. Something had to be done… They decided to refactor the system to move the Trip data into a separate scalable data store. “Why only the Trip data?” - you may ask. Because it was taking up the largest percentage of the overall pie. It also contributed to the most IOPS. There were a few important requirements for the new Trip store: ✅ Should be horizontally scalable in storage capacity and IOPS ✅Should have high write availability. They were okay to trade off short-term read availability ✅ Should support secondary indexes so that trips can be looked up by various parameters ✅ No downtime for operations such as expanding storage, taking backups, and so on. Ultimately, the decision was made to go for a column-oriented, schemaless database. What does it mean? It meant organizing data as JSON blobs in a grid indexed by Trip-UUID. Since the DB was schemaless, new columns and fields could be added without any reconfiguration. This was in line with their philosophy of rapid application development. Unfortunately, Uber didn’t find a suitable NoSQL database with the above characteristics. Therefore, they decided to build their own sharded data store on top of MySQL. A few cool characteristics of this database were: - Append-only (no updates data model) - Buffered writes - Sharding - Sharded secondary indexes As a first step to migration, they targeted the below design: The lib/tripstore component exposed an API that could communicate with a schemaless-based implementation. This component had a switch so a query could be directed to either PostgreSQL or Schemaless. A few important phases of the project were: 1 - Changing all trip IDs to trip-UUIDs 2 - Building the Column layout for the new Trips data model 3 - Backfilling data from PostgreSQL to Schemaless 4 - Mirrored writes to PostgreSQL and Schemaless 5 - Rewriting all queries to work with Schemaless 6 - Lots of validation There were also many valuable lessons learned along this whole journey: - Always use UUIDs even when starting out. - Keep the data layer simple to debug and troubleshoot - The correct data model comes through trial and error - Don’t waste too much time on the final migration. It will always be a moving target. === That’s all for now! If you enjoyed this explanation of Uber’s migration from monolithic DB to Schemaless, don’t forget to: - Book a ride for the LIKE button - REPOST so that everyone can learn from Uber’s approach - BOOKMARK to revisit it later - Follow me for more posts like this.

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This brilliant technique for handling database queries literally saved Discord. It helped them store trillions of messages and fetch them without bringing their DB cluster to its knees. The technique is called Request Coalescing. And it’s too good to ignore. But what’s so special about it? If multiple users are requesting the same row at the same time, why not query the database only once? This is exactly what Request Coalescing helps us achieve. Here’s what happens under the hood: - The first user that makes a request causes a worker task to spin up in the data service - Subsequent requests for the same data will check for the existence of that task and subscribe to it - Once the initial worker task queries the database and gets the result, it will return the row to all subscribers at the same time. There are several pros to using Request Coalescing: - Efficient utilization of database resources - Ability to handle more concurrent requests without creating hot partitions - Reduce latency But there are some cons as well: - Implementation can be complex with regards to getting a fair distributed reader-write lock. Basically, multiple readers need to access the data simultaneously while preventing conflicting writes - Overall latency may go down, but certain requests will take more time Of course, this technique is NOT needed normally. But at a certain scale, it can actually save your business. === That’s all for now! If you enjoyed this post, don’t forget to: - Destroy the LIKE button - REPOST so that everyone can try Request Coalescing wherever applicable. - BOOKMARK for future reference - Follow me for more posts like this.