Intro

Ever waited 10 minutes for a BigQuery job to finish, only to see a $5 charge for a 200GB scan? Partitioning is your secret weapon to avoid this. By organizing your data smarter, you’ll speed up queries and save money—daily. Let’s break it down.

What is Partitioning?

Partitioning splits large tables into smaller segments (partitions) based on a column’s value. Instead of scanning the entire table, BigQuery reads only relevant partitions.

Example:
A table with 3 years of sales data can be partitioned by date. Querying sales for Q3 2023? BigQuery scans only 3 months of data, not 3 years.

Types of Partitioning in BigQuery

  1. Time-based: Use DATE or TIMESTAMP columns (daily/hourly/monthly).

  2. Integer-range: Partition by integer columns (e.g., customer_id ranges).

  3. Ingestion-time: Auto-partition by data arrival time (less flexible).

How to Implement Partitioning

Step 1: Create a partitioned table:

 

CREATE TABLE my_dataset.sales_partitioned   PARTITION BY DATE(order_date)   AS   SELECT * FROM my_dataset.sales_raw;

 

Step 2: Query efficiently:

 

SELECT SUM(revenue)   FROM my_dataset.sales_partitioned   WHERE order_date BETWEEN '2023-07-01' AND '2023-09-30';  -- Scans only Q3 2023

 

Real-World Impact:
A client reduced monthly costs by 72% after partitioning their 50TB event logs by event_date.

Best Practices & Pitfalls

✅ Do:

  • Use time-based partitioning for timestamped data (logs, events).

  • Combine with clustering for nested optimizations.

  • Set partition expiration for transient data (e.g., temp logs).

❌ Don’t:

  • Over-partition (e.g., hourly partitioning for yearly reports).

  • Use high-cardinality columns (like UUIDs) – this backfires!

  • Forget to monitor partition skew (uneven data distribution).

Key Takeaways

  1. Partitioning cuts costs and time by limiting scanned data.

  2. Use DATE/TIMESTAMP columns for time-series data.

  3. Avoid partitioning on unique/random values.

Quick Checklist:

  • Identify frequent query filters (e.g., date, region).

  • Partition during table creation.

  • Test with EXPLAIN to verify partition pruning.

Wrap Up

Next time you build a BigQuery pipeline, ask: “Can I partition this?” Your wallet and users will thank you.

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