This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:57:15
The video focuses on the design considerations and technical architecture for developing an online coding competition platform, similar to Codeforces, LeetCode, HackerRank, and CodeChef. Key requirements include handling high volumes of submissions, evaluating code against unit tests, and finalizing rankings at the end of competitions. The discussion covers various aspects such as network and computational needs, suggesting the use of AWS EC2 instances and pre-signed URLs for efficient data handling. Asynchronous processing techniques and distributed setups across global regions are recommended for optimizing performance and reducing server load.
The data flow involves transferring problem metadata via a CDN link and using S3 for file uploads with a message broker system for asynchronous submission processing. Results are pushed back to users using websockets. The system also incorporates a robust storage structure, separating metadata from content using an Object Store and employing MapReduce jobs for final leaderboard processing and ELO ranking updates.
Technical tools and strategies discussed include using Cassandra for metadata storage due to its compatibility with MapReduce and eventual consistency benefits, Redis for caching, and DynamoDB for efficient data retrieval. Emphasis is placed on handling global user rankings and addressing potential issues related to data partitioning and indexing, particularly around avoiding hot partitions.
The video also delves into the application of load balancers to manage node failures and ensure efficient system performance, highlighting their importance in real-world applications. Throughout, the speaker shares personal insights and anecdotes from their experiences, and mentions additional resources for further learning, including recorded sessions and books by Alex Xu and Martin Kleppmann.
00:00:00
In this part of the video, the host introduces the topic of designing a coding competition platform, also known as an online judge system. Examples such as Codeforces, LeetCode, HackerRank, and CodeChef are mentioned. The design will focus on letting users submit problems for evaluation and providing rankings post-contest rather than live updates. They outline requirements which include processing submissions to determine success or failure against unit tests and final ranking at the competition’s end. Statistical details are discussed, including handling approximately 20,000 contestants, with each making an average of six submissions during a 90-minute contest. The submission processing time is estimated at five seconds, and the average submission size is about 2,000 characters (or 2 kilobytes). The host calculates the throughput as around 22 submissions per second, resulting in a bandwidth requirement of 44 kilobytes per second to handle these submissions efficiently.
00:05:00
In this segment of the video, the speaker discusses the network and computational requirements for handling submissions in a system. They start by explaining the conversion from kilobytes to kilobits to assess bandwidth needs, concluding that a one gigabit internet connection is more than sufficient for their data transfer rates. They suggest using pre-signed URLs as a good practice during data transfers.
Next, they calculate the number of concurrent submissions (110) and explore the necessary computing resources using AWS EC2 instances. They analyze the capabilities of an M5.2xlarge instance with hyper-threading, estimating it can handle around 10 threads or 8 virtual CPUs. This leads to a requirement of approximately 11 machines for processing all submissions concurrently. The speaker plans to optimize this setup further potentially using asynchronous processing techniques.
Finally, they move on to diagramming the system, starting with a contestant’s browser interacting with the backend server to fetch problem data from a problem database. The segment concludes with the transition to detailed diagram explanations.
00:10:00
In this part of the video, the speaker discusses a data flow for handling problem metadata and submission processes. The process begins with the server making a request that leads to a CDN link, where a problem prompt is hosted and loaded into the browser. For submissions, the speaker suggests using pre-signed URLs with S3 for file uploads. The submission involves sending an acknowledgment to the backend server and uses a message broker for asynchronous processing, similar to pub/sub models in chat applications. Task runners evaluate the submissions and utilize websockets to push results back to the browser, ensuring efficient handling of processing times. The speaker also touches on the expected latencies within data centers.
00:15:00
In this part of the video, the speaker discusses optimizing server performance by making certain processes asynchronous to reduce the time spent on the server. By offloading tasks to a broker and using server-side push, the total processing time can be reduced from five seconds to 200 milliseconds, leading to a significant reduction in server load (25 times less). This efficiency gain implies that fewer machines are needed, possibly reducing the number from 11 to just one, and allowing the use of smaller instances (like m5.large instead of m5.2xlarge). The speaker suggests a distributed setup with machines placed in various global regions for redundancy, showing a preference for smaller instances like m5.large or even m4.medium. The discussion also covers calculating the required number of cores, emphasizing the necessity of continued evaluation based on varying processing needs.
00:20:00
In this part of the video, the speaker discusses the structure and storage mechanisms for an online coding contest system. They explain the importance of separating metadata storage from actual content storage, suggesting an Object Store for storing problem prompts and unit tests. Submissions are evaluated, and results are stored in a separate results metadata store before being sent back to the user’s browser.
Additionally, rankings are processed at the end using a MapReduce job, which aggregates data and updates the leaderboard database. The speaker considers using Cassandra for event sourcing and a MapReduce job to handle ranking efficiently. Lastly, the speaker talks about maintaining ELO rankings, which adjust the user’s global rank over time, using user-specific databases for pulling and displaying ranking information on user pages.
00:25:00
In this segment, the speaker discusses the ELO ranking system, detailing how your expected and actual performance determines your final ranking. They explain the process of viewing user rankings on a service page. The discussion then shifts to database schemas, focusing on the submission results metadata. The process includes submitting code, running unit tests, and recording the results with timestamps. Key components mentioned are the user ID, submission UUID, S3 links for submissions, and whether the tests passed or failed. The speaker emphasizes the importance of time taken for unit tests and suggests recording whether submissions pass or fail directly in the schema to avoid ambiguity.
00:30:00
In this part of the video, the speaker discusses the process of building and maintaining a leaderboard database using MapReduce jobs, which involves several key steps. The initial step involves a reduce operation to find the earliest timestamp for the first successful result of each user for each problem. This includes identifying the least value timestamp rather than just summing or counting. Next, it involves counting only failed submissions that occurred before the first successful result, and possibly considering additional submissions made after achieving a passing result for fun or practice.
The speaker then explains sorting the users on the leaderboard based on their timestamps and failure penalties, which entails performing three MapReduce jobs sequentially. The leaderboard schema includes details like user ID, rank, and submission details such as successful submission links, failure counts, and first success timestamps.
Furthermore, the speaker clarifies that the leaderboard will display users’ ranks and submission details, noting that while some users have minimal or no failures, others might have multiple failed attempts, especially in lower ranking positions. The user information section contains user ID and contest ranks, detailing each contest number and user’s contest rank.
00:35:00
In this part of the video, the discussion centers around managing user rankings and metadata storage in a system. Key points include handling global rankings affected by inactive or stale accounts, storing and indexing problem metadata efficiently, and strategies for optimizing data retrieval. The idea of using an object store and possibly DynamoDB for metadata due to its read-heavy nature and ability to handle eventual consistency is discussed, along with the potential use of Redis for caching. The importance of indexing, particularly by user ID for leaderboard lookups and past submission retrieval, is emphasized to ensure efficient access without excessive complexity.
00:40:00
In this part of the video, the speaker discusses the design considerations for implementing a database schema using Cassandra. They decide on Cassandra because it integrates well with MapReduce jobs and provides eventual consistency. They plan to use an event sourcing approach for a leaderboard schema and consider potential issues like hot partitions and celebrity issues. For indexing, they discuss the importance of having contest IDs and rank being indexed, and consider using Redis as a caching layer to manage high traffic. The speaker also considers pagination and consistent hashing to distribute the data evenly across partitions and avoid scatter-gather operations.
00:45:00
In this part of the video, the speaker discusses the importance of partition keys and sort keys in managing data, particularly around avoiding hot partition issues by distributing celebrities across different partitions. The speaker reflects on previous estimations and expresses satisfaction with the current setup, hinting at the use of Redis for additional management if necessary. As the segment wraps up, questions are answered about related design problems, with recommendations to explore other YouTube videos and specific books by authors like Alex Xu and Martin Kleppmann for a deeper understanding of similar data handling issues. The speaker also mentions that the session will be recorded, uploaded, and shared via Discord for further review.
00:50:00
In this segment of the video, several key technical topics are discussed, primarily focusing on system design and message brokers. The speaker dives into the comparison between different messaging systems such as WhatsApp, Discord, and Redis Pub/Sub, while explaining the merits of using solutions like SQS and Kafka for persistent message handling and point-to-point communication. They also touch on handling retries and failures within these systems, emphasizing that brokers like SQS handle retries automatically, thereby preventing the client from managing retries, which could lead to latency issues. Additionally, there’s a discussion about scheduling conflicts due to the speaker’s participation in LeetCode competitions, resulting in a tentative shift of the regular start time to 9 PM. The speaker also mentions their intent to upload the session materials on YouTube and Discord. Questions from participants about node failures and system scalability are addressed, particularly highlighting the robust retry mechanisms afforded by brokers and the distinction between client-side and server-side processing.
00:55:00
In this part of the video, the speaker discusses the importance of including load balancers in system design, noting their role in handling node failures. They share a personal habit of forgetting to add load balancers to diagrams and recount experiences at Amazon where load balancers were often implicitly included without explicit mention. The speaker also highlights that, in Google system design interviews, it is crucial to clarify the type of load balancer used (e.g., L5 or L7, reverse proxy, or hardware load balancer). The segment concludes with the speaker addressing questions from the audience and mentioning that recorded videos from previous weeks are available on their YouTube channel.