Recently accompanied a trainee through Databricks The SDE Virtual Onsite is quite representative of the SDE Virtual Onsite, so I've organized the process for your reference. The overall atmosphere is more relaxed than expected, the format is BQ + coding, without the oppressive feeling of "being interrogated". The interviewer was very patient and asked questions along with the candidate's thoughts, so I was able to cope with it as long as I stayed calm.
Part BQ: Helping trainees frame their responses
The first BQ is:
"Describe one of the most complex or challenging data projects you have worked on. What difficulties did you encounter? How was it resolved?"
I immediately prompted him with a voice prompt to answer according to the STAR framework:
S (Situational): This was a project involving real-time log processing, and the team's goal was to clean and visualize terabytes of data in seconds.
T (mandate)He is responsible for the design of the data processing pipeline and the monitoring module for abnormal data.
A (Action): I reminded him to emphasize two points: first, how to solve performance bottlenecks with distributed frameworks; and second, how to drive the solution to the ground when the team disagrees.
R (results): Finally, we end with the hard result of "data latency reduced by 60%, and the system has been running stably for half a year without any major incidents".
The interviewer nodded his head repeatedly after listening to the interview, obviously satisfied with this kind of organized and practical results of the answer. He passed the whole BQ section with flying colors.
Coding part: the field to accompany the problem solving
The next step into coding is the main event. The title demands it:
Use Python's time library to get information about the current seconds, maintain a 300-second sliding window, and calculate the frequency of visits.
When the question first came out, the student's first reaction was a bit panicky, thinking that it was not easy to start with this kind of "strong sense of system design" questions. I immediately gave him tips: "Don't worry, there are only three test points in this kind of questions: data structure selection, window maintenance, boundary conditions."
Together, we'll break down our thoughts into steps:
1. Recording visits per second
I suggested he use deque Acts as a queue, checking the end of the queue every time there is a request:
If the same second already exists, add +1 to the count.
Otherwise, create a new [timestamp, count] Records.
2. Removal of obsolete data
The central point is to maintain the 300 second window. I reminded him to add a loop to clear the elements at the head of the queue that are older than 300 seconds with each access. A lot of people will miss this, leading to bigger and bigger data and memory explosions.
3. Calculation of average visit rates
Finally, it's straightforward: add up all the counts in the queue and divide by 300.
When he wrote this, I made a point of reminding him, "Don't forget to consider the boundary cases of empty queues and time fallbacks." If you ignore these two points, you will probably hang up under the interviewer's test case.
After writing the code, the interviewer ran a few sets of data, resulting in a one-time AC, and the trainee was relieved on the spot.
Follow-up Dig Deeper
But the interview didn't end there, the interviewer threw out two follow-ups right after:
How to optimize memory if the time span is large but the requests are very sparse?
I gave him a quick tip: instead of storing every second, you can just record the timestamp and cumulative total, and use the difference to extrapolate and avoid stuffing the queue with a bunch of empty seconds.
How do I count the global access frequency if I'm going to deploy on multiple machines?
I reminded him to lean on distributed system design, and the answer could be: use Kafka/message queues to collect request logs, and then use an aggregator (e.g., Flink or Spark Streaming) at the upper level to unify the computation.
The trainee heard these two prompts and immediately organized a full response, which the interviewer was clearly pleased with and nodded his head and said "Good point."
Databricks SDE VO FAQ
Q1: Is the Databricks VO topic difficult?
A1: In fact, the difficulty is medium, the test is more ideas and details, like sliding window this common question type as long as you brush through in advance can be stable. The real gap is the follow-up part, to see if you have the awareness of scalability.
Q2: How to prepare for BQ?
A2: Databricks' BQs won't be too tricky, the focus is on being well-organized, and it's best to use the STAR framework to talk about the experience, emphasizing more on your contributions and results in the team.
Q3: What should I do if I am nervous at the scene and can't answer?
A3: Many students will be stuck for the first time, in fact, there is a next to remind the key points, you can immediately pull back the ideas. So usually must do more simulation, to avoid the real interview blank.
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