How DuckDB optimizes its performance

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Let's focus on the heavy-duty side of DuckDB—how it manages to process hundreds of millions of rows on a standard laptop without crashing. This is achieved through a combination of Vectorized Execution, Morsel-Driven Parallelism, and a highly sophisticated Query Optimizer.

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1. Vectorized Execution Theory

Traditional databases (like PostgreSQL) use a "Tuple-at-a-Time" (Volcano) model. For every row, the database must ask: "Is this an integer? Is it null? Should I add it?" This causes massive CPU overhead for large datasets.

DuckDB's Vectorized Approach:

Instead of one row, DuckDB processes data in Vectors (typically 2,048 values at once).

• SIMD Optimization: Modern CPUs can perform a single operation on multiple data points simultaneously (Single Instruction, Multiple Data). Vectorization allows DuckDB to "speak" directly to this hardware feature.

• Cache Friendliness: Because 2,048 values of a single column fit neatly into the CPU's L1/L2 cache, the processor doesn't have to keep jumping back to the slower RAM.

Example: Vectorized vs. Row-based

Imagine a query: SELECT price * 1.1 FROM sales.

• Row-based: "Get row 1, multiply price by 1.1. Get row 2, multiply price by 1.1..." (Repeat 1 million times).

• Vectorized: "Get the first 2,048 prices. Multiply the entire block by 1.1 in one CPU cycle." (Repeat ~488 times).

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2. Morsel-Driven Parallelism

DuckDB automatically uses every core on your machine. It breaks the data into small, manageable chunks called "Morsels" (usually 100k+ rows).

• Dynamic Load Balancing: Instead of pre-assigning 1GB of data to "Core 1," DuckDB puts all morsels in a queue. If Core 1 finishes its work early, it grabs the next morsel from the queue. This prevents one "slow" core from delaying the entire query.

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3. The Query Optimizer (The "Brain")

Before a single row is read, DuckDB’s optimizer rewrites your SQL to be faster.

Key Optimization Rules:

• Filter Pushdown: If you have SELECT * FROM table WHERE city = 'Astana', DuckDB "pushes" that filter down to the storage layer. It only reads the rows where city is Astana, rather than reading the whole table into memory and then filtering.

• Projection Pushdown: If your table has 100 columns but you only SELECT name, DuckDB will physically only read that one column from the disk.

• Join Reordering: If you join three tables, DuckDB calculates which join will result in the smallest intermediate dataset and does that one first to save memory.

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Practical Example: EXPLAIN ANALYZE

As a Data Engineer, your most important tool in Chapter 10 is EXPLAIN ANALYZE. This runs the query and shows you exactly where the "bottlenecks" are.

The Command:

EXPLAIN ANALYZE 
SELECT count(*), avg(fare_amount) 
FROM 'nyc_taxi/*.parquet' 
WHERE trip_distance > 10;

What to look for in the output:

1. Estimated vs. Actual Cardinality: If DuckDB estimated 100 rows but found 1 million, your statistics might be out of date (run ANALYZE).

2. Operator Timing: See if the PARQUET_SCAN (reading from disk) or the HASH_GROUP_BY (calculating the average) is taking the most time.

3. Spilling to Disk: If you see "Spilled to disk" in the logs, it means your data was larger than your RAM. DuckDB handled it, but adding more RAM or filtering earlier would make it faster.

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Performance Best Practices for DEs

• Columnar Formats: Always use Parquet. Querying a 20GB CSV is significantly slower than querying a 2GB Parquet file because DuckDB has to parse every single character in the CSV.

• Zone Maps: DuckDB stores the min and max values for every "morsel" in a Parquet file. If you filter by date and your file is sorted by date, DuckDB will skip 90% of the file entirely.

• Memory Management: Use SET memory_limit = 'XGB'; to ensure DuckDB doesn't fight with other apps on your laptop (like Chrome or Docker).

Summary

Feature	Benefit
Vectorization	High CPU efficiency and SIMD support.
Columnar Storage	Minimizes I/O by only reading needed columns.
Pushdowns	Minimizes memory usage by filtering data at the source.
Explain Analyze	The "X-ray" for debugging slow queries.

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