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Palo Alto Networks·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

System design round at Palo Alto Networks for a software engineer role. The whole thing revolved around one big streaming problem and they went deep on every layer of it.

Questions Asked (1)

Q1

You have an unbounded stream (list1) and a second list (list2) that may be too large to fit in memory. Design a system that outputs, in near real time, elements from the stream that are NOT present in list2. Cover ingestion, processing, storage, membership checks, Bloom filters, normalization, updates to list2, back-pressure, fault tolerance, scalability, and cost trade-offs.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This was basically the entire interview.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: stream rate, list2 size, update frequency, and latency/accuracy trade-offs. Then propose a pipeline: ingest stream, normalize elements, check membership using a Bloom filter (backed by a key-value store for false positives), and output non-members. Discuss how to handle updates to list2, back-pressure, fault tolerance, scalability, and cost trade-offs.

Pro tip: Emphasize that Bloom filters give false positives, not false negatives, so you can use them to quickly filter out most elements, then confirm with a secondary store to avoid outputting false positives. This shows you understand the probabilistic nature and how to mitigate it.

1. Clarify Requirements and Constraints

Ask about stream rate, list2 size, update frequency, latency requirements, and acceptable false positive rate. This determines the choice of data structures and architecture.

2. Design Ingestion and Normalization

Use a distributed message queue (e.g., Kafka) to ingest the stream, ensuring scalability and fault tolerance. Normalize elements (e.g., lowercase, trim) to ensure consistent membership checks.

3. Implement Membership Check with Bloom Filter

Build a Bloom filter from list2, sized based on expected number of elements and desired false positive rate. For each stream element, check the Bloom filter; if it says 'not present', output it; if 'possibly present', verify against a key-value store (e.g., RocksDB) to eliminate false positives.

4. Handle Updates to list2 and Back-pressure

For updates, use a versioned Bloom filter or rebuild periodically. Implement back-pressure by monitoring queue lag and scaling consumers or dropping elements if necessary.

5. Address Fault Tolerance, Scalability, and Cost

Replicate the Bloom filter and key-value store for fault tolerance. Partition the stream and list2 for scalability. Discuss cost trade-offs: Bloom filter memory vs. false positive rate, and storage vs. accuracy.

Key Points to Mention

  • Bloom filter parameters: size (m), number of hash functions (k), and false positive probability (p) formula: p ≈ (1 - e^(-kn/m))^k.
  • Use of a key-value store (e.g., RocksDB, Cassandra) to confirm Bloom filter positives and avoid false positives.
  • Normalization: case folding, Unicode normalization, and handling of different data types.
  • Handling updates to list2: versioning, periodic rebuild, or using a counting Bloom filter for deletions.
  • Back-pressure mechanisms: queue monitoring, auto-scaling, and load shedding.
  • Fault tolerance: replication, checkpointing, and exactly-once processing semantics.
  • Scalability: partitioning by key, distributed Bloom filters, and sharding the key-value store.
  • Cost trade-offs: memory vs. accuracy, latency vs. throughput, and cloud vs. on-premises.

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