Problem Solving: Modular Systems

Modern data systems are "systems of systems." We can combine discrete LEGO blocks—SQL, LSM Trees, H3, LSH—to address specific use cases.

SQL
Structured Logic
LSM
High-speed Writes
H3/LSH
Geo/Similarity
Hash
Scale & Partition

Goal 1: Artist-Facing UI for Royalty Stats

Problem: Artists want to analyze geographical trends and custom date ranges (e.g., influence after a song release). How do you support fast range queries on trillions of listens?

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  • Logic: Use a SQL engine for counts and zip-code mapping.

  • Indexing: Create a B+ Tree Index on Listens <song_id, date>.

  • The "Why": Date ranges are sequential. A B+ Tree allows you to jump to the start date and scan forward.

Goal 2: Fan Preference Clustering

Problem: You need to find clusters of similar users or songs for targeted marketing. How do you search through high-dimensional feature vectors?

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  • Concept: Embeddings + Locality Sensitive Hashing (LSH).

  • Design: Generate embeddings (lyrics, tempo, etc.) and use an LSH Index to bucket similar vectors together.

  • The "Why": Standard indexes (B+ Tree) fail in high dimensions. LSH maps "similar" items to the same hash bucket with high probability.

Goal 3: Local Fan Club Meetups

Problem: How do you identify hyper-local communities when zip codes are too coarse? You need to find zones with > 100 fans for "listen parties."

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  • Concept: Geo-Hashing (H3).

  • Design: Map user addresses to H3 cells. Run: GROUP BY H3(address) HAVING count(user) > 100.

  • The "Why": H3 provides a hierarchical grid system. Most databases support this via extensions like PostGIS.

Goal 4: "Live" Taylor Swift Drops

Problem: 50,000 users tune into a live song drop at once. A standard B+ Tree would suffer from random write overhead. How do you scale these updates?

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  • Concept: LSM Trees.

  • Design: Use an LSM Tree to manage the write surge. Keep partial counts in a MemTable and flush to SSTables.

  • The "Why": LSM Trees turn random writes into sequential flushes. It is the gold standard for high-bandwidth telemetry data.

Goal 5: Handling Viral Surges

Problem: How do you distribute table traffic across a cluster of machines when a song goes viral?

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  • Concept: Hash Partitioning.

  • Design: Partition the data across nodes using a Hash function on song_id or user_id.

  • The "Why": This balances the load on reads and writes, preventing "hot spots" and enabling massive parallelism.

Goal 6: Privacy-Preserving Recommendations

Problem: You want to expose a SQL API for third-party concert apps but must protect individual user privacy on aggregates.

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  • Concept: Differential Privacy.

  • Design: Integrate Differential Privacy noise into the SQL results (e.g., using BigQuery's built-in DP functions).

  • The "Why": DP mathematically guarantees that the presence of one user's data won't significantly change the aggregate result.

Goal 7: Messy External Data (ETL)

Problem: You ingest lyrics and album covers from messy third-party feeds. How do you ensure quality and allow for rollbacks?

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  • Concept: Versioned Tables + Reconciliation.

  • Design: Implement an ETL pipeline with cleaning/normalization steps. Store results in versioned tables.

  • The "Why": Messy data is inevitable. Versioning allows you to revert to a "last known good" state and reconciles feeds against original source logs.

Takeaway: We solve complex problems by combining discrete LEGO blocks: Clustering for batching, Hashing for searching/partitioning, and LSM for write scale.