Just finished a game TikTok System Design VO, my overall feeling in this round is that the topic is not convoluted, but it is quite close to real business. What TikTok VO interviewers focus on is not how beautifully the architecture is drawn, but whether the solution can really run stably for a long time in a high-concurrency, massive data environment. The rhythm of the entire interview was discussion-oriented, and a lot of time was spent on details rather than one-way output.
This interview mainly consists of three parts: Behavioral Questions, system design plus coding, and multiple rounds of follow-up in-depth digging.
Behavioral Questions
TikTok’s BQ style is quite engineering. It is not a simple STAR routine, but uses real scenarios to determine whether candidates have experience in dealing with complex system problems.
The first question is about data consistency in distributed systems. The interviewer is more concerned about how you choose between consistency and latency in a system with high real-time requirements, and whether there is a clear monitoring and correction mechanism when data is temporarily inconsistent. In the context of TikTok's business, explainable and fixable transient inconsistencies are acceptable, but there must be clear boundaries.
The second issue is how to communicate when technical solutions and product requirements diverge. What the interviewer wants to see is not blind compromise, but whether you can express the technical risks and system costs in a way that the product and business can understand, and provide a variety of options to allow the product to make trade-offs between different goals.
The third issue revolves around monitoring and alerting. The key is not what tools you use, but whether you have chosen the right core indicators, whether the alarms are actionable, and whether there are emergency mechanisms such as automatic downgrade or current limiting after problems are discovered.
System Design and Coding Topics
The topic of system design plus coding is to design a real-time popular video detection system. Popularity is defined as a video playback volume that increases by more than 10 times within 5 minutes. This is a typical streaming computing scenario, and the inspection points focus on time window design, status management, and system scalability.
Design ideas and implementation plans
In the choice of stream processing framework, I used Flink as the core engine. The reason is that Flink’s support for event time is more mature, and its sliding window and state management capabilities are more suitable for complex real-time computing scenarios.
In terms of window design, a 5-minute window and 1-minute sliding configuration are adopted. This allows you to re-evaluate whether a video has become popular every minute and avoid detection delays caused by fixed window segmentation. At the same time, the sliding window can smooth changes in playback volume and reduce the risk of misjudgment caused by occasional peaks.
At the data storage level, Redis is introduced as a real-time cache to store the playback data of each video in different time windows. In terms of specific implementation, a time series is maintained for each video, the playback volume of multiple recent windows is recorded, and then the growth rate between the last 5 minutes and the previous 5 minutes is calculated in real time to determine whether the popular mark is triggered.
In this part, the interviewer focused on the division of responsibilities between Flink’s internal state and external storage, and how to avoid unlimited state growth in high concurrency scenarios.
Follow-up to dig deeper into the problem
The first follow-up is how to optimize the overall performance when the scale of video playback data is extremely large. The interviewer focuses on whether you can do reasonable partitioning and KeyBy design, whether you can consider state TTL, hot video offloading, and introduce approximate calculations in exchange for throughput when necessary.
The second follow-up revolves around the issue of boosting playback volume. This problem is quite close to TikTok’s actual business. Common ideas include deduplication based on users, devices or IPs, identifying abnormal behaviors, and using signals from the risk control system as auxiliary input to participate in popular determinations to avoid misjudgments caused by a single threshold.
The third follow-up is how the system scales to global scale data processing. The focus of the discussion includes multi-Region data collection, the combination of local pre-aggregation and central aggregation, the impact of cross-regional delay, and whether popular determination needs to be differentiated at the regional level.
Overall experience of the interview
The biggest feeling given by this TikTok system design VO is that the interviewer does not pursue a perfect architectural design, but constantly asks whether the solution can be implemented in a real business environment. There are no standard answers to many questions, but every choice needs to have a clear reason for the choice.
Many students are easily stuck in the system design process. It is not because they cannot draw architecture diagrams, but because they lack a deep enough understanding of stream processing, status management and monitoring systems. In this kind of interview, what really makes the difference is often the ability to control details and the way to deal with questioning.
If you are also preparing for a system design interview in TikTok or Byte Department, you must practice this type of real-time stream processing questions systematically in advance. Compared with templated answers, derivation and trade-offs based on real engineering experience are more likely to be recognized by the interviewer.
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