Last time we optimized file formats for faster analytics. But even with Parquet, scanning entire tables for specific data is still slow and expensive.
Imagine searching for one customer's orders in a billion-row table - that's a lot of unnecessary scanning!
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If you could check 10 people per second, roughly how long would it take to find one in a crowd of a million?
This is where partitioning saves the day! Partitioning divides your large table into smaller, organized chunks based on column values like date, region, or category.
Think of it like organizing files in folders - you go directly to the right folder instead of searching everything.
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What's one benefit of organizing files into folders instead of keeping them all together?
Date partitioning is the most common strategy. A sales table might be partitioned by month, so queries for January data only scan January's partition.
This eliminates 11/12 of the data from your scan - making queries up to 12x faster!
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With monthly partitions, what fraction of data can you skip when querying last month's sales?
Choosing the right partition key is crucial. It should align with your most common query patterns. If you frequently filter by region, partition by region.
Bad partitioning can actually hurt performance by creating too many tiny partitions or uneven data distribution.
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Name one factor you'd consider when choosing a partition key?
Here's a practical example: an e-commerce company partitions their orders table by year and month. Queries for recent orders skip years of historical data.
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