DuckDB Outpaces SQLite and Pandas in Benchmark of 1M-Row Data Tasks
Imagine you’ve got a CSV with a million sales rows and need to crunch some numbers. Traditionally you’d either dump everything into Pandas - handy but it eats RAM fast - or swing over to SQLite, which is tiny on resources but can feel sluggish. Lately DuckDB has been popping up, promising database-style speed while feeling just like a Python library.
We gave all three a spin on a real-world workload: one million records, tasks like summing total sales, grouping by product category, and filtering by region. The tests weren’t meant to crown an all-purpose champion; they were just a way to see which engine feels snappier for everyday analysis.
The numbers tell a story of trade-offs. In some cases the gap is huge - think sports car versus delivery van on a short run. Knowing these quirks can save analysts and engineers a lot of waiting time. When data keeps growing, picking the right tool feels less like a luxury and more like a necessity.
// The Benchmark Queries We tested each engine on the same four everyday analytical tasks: - Total transaction value: summing a numeric column - Group by domain: aggregating transaction counts per category - Filter by location: filtering rows by a condition before aggregation - Group by domain & location: multi-field aggregation with averages # Benchmark Results // Query 1: Total Transaction Value Here we measure how Pandas, DuckDB, and SQLite perform when summing the Value column across the dataset. // Pandas Performance We calculate the total transaction value using .sum() on the Value column. pandas_results = [] def pandas_q1(): return df['Value'].sum() mem_before = memory_usage(-1)[0] start = time.time() pandas_q1() end = time.time() mem_after = memory_usage(-1)[0] pandas_results.append({ "engine": "Pandas", "query": "Total transaction value", "time": round(end - start, 4), "memory": round(mem_after - mem_before, 4) }) pandas_results Here is the output.
// DuckDB Performance We calculate the total transaction value using a full-column aggregation. duckdb_results = [] def duckdb_q1(): return duckdb.query("SELECT SUM(value) FROM bank_data").to_df() mem_before = memory_usage(-1)[0] start = time.time() duckdb_q1() end = time.time() mem_after = memory_usage(-1)[0] duckdb_results.append({ "engine": "DuckDB", "query": "Total transaction value", "time": round(end - start, 4), "memory": round(mem_after - mem_before, 4) }) duckdb_results Here is the output.
These benchmarks show that DuckDB really shines when the job is heavy on grouping and filtering - the kind of thing analysts do every day. SQLite still holds up well for transactional work, and Pandas remains flexible for ad-hoc manipulation, but DuckDB’s column-oriented storage and vectorized engine tend to pull ahead on pure analytical queries. It seems the decision shouldn’t hinge on which tool is more popular, but on what the workload actually looks like.
For teams that juggle medium-sized tables and need faster analytics, DuckDB offers a solid mix of SQL expressiveness and speed you usually only see in bigger data warehouses. In practice, companies that aren’t dealing with petabyte-scale data often find the trade-off between simplicity and performance tricky. DuckDB’s lightweight footprint means you can embed it in existing Python pipelines without a separate server, which may shave minutes off each nightly batch.
Of course, if you’re running a high-throughput OLTP system, SQLite probably remains the safer bet. As data volumes keep climbing, the speed gains we measured could turn into noticeable cost savings and faster turnaround on reports.
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Common Questions Answered
How does DuckDB's performance compare to SQLite and Pandas in the benchmark of 1M-row data tasks?
DuckDB significantly outpaced both SQLite and Pandas in the benchmark tests involving analytical workloads. Its performance advantages were most pronounced in tasks that involved grouping and filtering operations, which are common for data analysts.
What are the key features that give DuckDB an edge in analytical processing according to the benchmark results?
DuckDB's columnar storage and vectorized execution engine are identified as the primary features contributing to its superior performance. These architectural choices make it particularly efficient for analytical workloads compared to SQLite's transactional design and Pandas' memory-intensive flexibility.
Which specific analytical tasks were tested in the benchmark comparing DuckDB, SQLite, and Pandas?
The benchmark tested four everyday analytical tasks: summing a numeric column for total transaction value, aggregating transaction counts per category using group by domain, filtering rows by a condition before aggregation, and performing multi-field aggregation with averages. These tasks represent common data analysis operations.
Why might SQLite and Pandas still be viable choices despite DuckDB's performance advantages?
SQLite remains a robust choice for transactional operations due to its reliability and lightweight nature, while Pandas offers unparalleled flexibility for complex data manipulation tasks. DuckDB's strengths are specifically in analytical processing, making the tool choice dependent on the specific workload requirements.