← Databricks Interview Insights

Databricks·Backend Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jul 2026

Summary

Databricks backend round, pretty algorithmic in nature. The main problem was a sliding window thing and then they pivoted to a system design follow-up that I was not expecting at all.

Questions Asked (2)

Q1

Given two strings, find the starting index of the first substring in the first string that is an anagram of the second string. Return -1 if none exists.

Algorithms & Data Structures
Author's notes

Knew immediately it was a sliding window with frequency counts.

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AI HintsAI Generated

Suggested Approach

Use a sliding window of length equal to the second string and compare character frequency counts with the target. Maintain a running count of matches to achieve O(n) time by updating counts incrementally as the window slides.

Pro tip: Clarify edge cases upfront (e.g., empty strings, window larger than first string) and mention that the solution can be extended to find all anagram indices if needed, showing awareness of variations.

1. Clarify and Validate Inputs

Confirm assumptions: strings may contain any characters, case sensitivity, and what to return if the second string is longer than the first. Handle empty strings appropriately.

2. Choose Frequency Counting Approach

Decide to use a fixed-size array (e.g., 26 for lowercase letters) or hash map for character counts. This allows O(1) updates per character.

3. Initialize Sliding Window

Build frequency count for the second string and for the first window of the first string. Compute initial matches between the two frequency arrays.

4. Slide Window and Update Counts

Move the window one step at a time: remove the left character, add the right character, and update the match count accordingly. Check if match count equals the number of distinct characters.

5. Return Result and Discuss Complexity

If a match is found, return the starting index; otherwise return -1. State time complexity O(n) and space complexity O(1) (or O(k) for hash map).

Key Points to Mention

  • Sliding window technique to avoid recomputing frequencies from scratch.
  • Using a fixed-size array (e.g., 26) for O(1) space when character set is limited.
  • Maintaining a match count to track how many characters have correct frequencies.
  • Time complexity O(n) and space complexity O(1) or O(k) where k is alphabet size.
  • Handling edge cases: empty strings, window size larger than first string, no anagram present.
  • Potential follow-up: return all starting indices or handle Unicode characters.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

How would your approach change if the input text was extremely large, streamed in chunks, or spread across multiple machines?

System DesignTechnical Trade-offs
Author's notes

This is where I stumbled.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the constraints: data size, latency requirements, and available resources. Then, systematically discuss how each constraint (large size, streaming, distributed) forces changes in algorithm choice, data structures, and system architecture, emphasizing trade-offs between memory, latency, and complexity.

Pro tip: Mention that you would first check if the problem can be solved with a streaming or approximate algorithm (e.g., sketches) before reaching for distributed systems, as simplicity often wins. Also, highlight the importance of backpressure and fault tolerance in streaming scenarios.

1. Clarify Requirements and Constraints

Ask about data volume, velocity, latency SLAs, and whether exact or approximate results are acceptable. This determines the feasible approaches.

2. Adapt Algorithm and Data Structures

For large data, consider external sorting, streaming algorithms, or probabilistic data structures (e.g., Bloom filters, HyperLogLog). For streaming, use windowing and incremental computation.

3. Design for Distribution and Scalability

If data is spread across machines, discuss partitioning, shuffling, and coordination (e.g., MapReduce, Spark). Address data skew and fault tolerance.

4. Address System-Level Concerns

Cover backpressure, checkpointing, exactly-once semantics, and resource management. Mention trade-offs between throughput and latency.

5. Summarize Trade-offs and Recommendations

Conclude with a clear recommendation based on constraints, acknowledging trade-offs between simplicity, cost, and performance.

Key Points to Mention

  • Streaming algorithms and approximate data structures (e.g., count-min sketch, HyperLogLog) for memory efficiency.
  • Partitioning and shuffling strategies in distributed systems (e.g., hash partitioning, range partitioning).
  • Fault tolerance and exactly-once processing in stream processing frameworks (e.g., Flink, Spark Streaming).
  • Backpressure handling and flow control to prevent overwhelming downstream systems.
  • Trade-offs between latency, throughput, and resource consumption (e.g., batch vs. micro-batch vs. true streaming).
  • Use of external storage or disk-based algorithms (e.g., external merge sort) when data exceeds memory.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.