Airport I/O Benchmarks

Performance benchmarks for Airport extension operations including scalar functions, table creation, and selection with 40M-100M row datasets.

This document presents performance benchmarks for core operations using the Airport extension for DuckDB. The benchmarks evaluate scalar functions, table creation, and selection operations with both BIGINT and 32-byte string data types.

Results are measured in execution time (duration_sec), CPU usage (cpu_sec), system CPU usage (cpu_system), and throughput (rows per second).

All tests were conducted using datasets of 40M to 100M rows, simulating high-throughput analytical workloads typical in production environments.

Summary Table

Operation	Rows	Mean Duration (s)	Rows/sec
Scalar Function Echo BIGINT	100M	2.06	48.56M
Scalar Function Add BIGINT	100M	3.42	29.27M
Scalar Function Echo 32-byte string	40M	1.07	37.36M
Create remote table from 32-byte string	40M	3.53	11.33M
Select from 32-byte string table	40M	1.10	36.21M
Create remote table from BIGINT	100M	12.22	8.18M
Select from remote BIGINT table	100M	2.39	41.78M

Note: Rows rounded to 2 decimal places for clarity.

Scalar Function Benchmarks

Echo BIGINT

Duration (mean): 2.06s
Rows/sec: 48.56M
This represents a nearly zero-cost server side scalar operation, a BIGINT is sent to the server and the value is echoed back.

Add BIGINT

Duration (mean): 3.42s
Rows/sec: 29.27M
Slightly more expensive than echo, as it performs arithmetic computation, two BIGINT values are sent to the server, and a BIGINT is returned.

Echo 32-byte String

Duration (mean): 1.07s
Rows/sec: 37.36M
High throughput despite operating on 32-byte strings, showing efficient memory handling.

Remote Table Creation

Create Remote Table from 32-byte String

Duration: 3.53s
Rows/sec: 11.33M
Performance reflects single threaded serialization and data transport overhead.

Create Remote Table from BIGINT

Duration: 12.22s
Rows/sec: 8.18M
Performance reflects single threaded serialization and data transport overhead.

Select Queries

Select from 32-byte String Table

Duration (mean): 1.10s
Rows/sec: 36.21M
Very fast reads from the remote table, indicating efficient decoding.

Select from BIGINT Table

Duration (mean): 2.39s
Rows/sec: 41.78M
Excellent performance, even at scale (100M rows), shows the power of Arrow Flight transport.

Observations

Scalar functions scale exceptionally well, with Echo BIGINT and Echo 32-byte String both exceeding 35M rows/sec.
Table creation is the most resource-intensive operation, particularly for large datasets, due to single-threaded insert limitations.
Select queries from remote tables deliver excellent performance, often exceeding 40M rows/sec.
System CPU usage remains low across all operations, indicating efficient kernel-level I/O handling.

Conclusion

These benchmarks show that the Airport extension for DuckDB delivers high throughput and low-latency data access via Apache Arrow Flight, particularly for read-heavy workloads.

It is especially suitable for:

Real-time analytics
High-volume batch reads
Remote function calls with low overhead

Further work may explore concurrency scaling and performance under streaming ingestion.

--- title: "Airport I/O Benchmarks" description: "Performance benchmarks for Airport extension operations including scalar functions, table creation, and selection with 40M-100M row datasets." format: html: toc: true code-fold: true theme: cosmo code-tools: true --- This document presents performance benchmarks for core operations using the **Airport** extension for DuckDB. The benchmarks evaluate scalar functions, table creation, and selection operations with both `BIGINT` and 32-byte string data types. Results are measured in execution time (`duration_sec`), CPU usage (`cpu_sec`), system CPU usage (`cpu_system`), and throughput (rows per second). All tests were conducted using datasets of 40M to 100M rows, simulating high-throughput analytical workloads typical in production environments. # Summary Table | Operation | Rows | Mean Duration (s) | Rows/sec | |------------------------------------------|-----------:|------------------:|-----------------:| | Scalar Function Echo BIGINT | 100M | 2.06 | 48.56M | | Scalar Function Add BIGINT | 100M | 3.42 | 29.27M | | Scalar Function Echo 32-byte string | 40M | 1.07 | 37.36M | | Create remote table from 32-byte string | 40M | 3.53 | 11.33M | | Select from 32-byte string table | 40M | 1.10 | 36.21M | | Create remote table from BIGINT | 100M | 12.22 | 8.18M | | Select from remote BIGINT table | 100M | 2.39 | 41.78M | _Note: Rows rounded to 2 decimal places for clarity._ # Scalar Function Benchmarks ## Echo BIGINT - **Duration (mean):** 2.06s - **Rows/sec:** 48.56M - This represents a nearly zero-cost server side scalar operation, a `BIGINT` is sent to the server and the value is echoed back. ## Add BIGINT - **Duration (mean):** 3.42s - **Rows/sec:** 29.27M - Slightly more expensive than echo, as it performs arithmetic computation, two `BIGINT` values are sent to the server, and a `BIGINT` is returned. ## Echo 32-byte String - **Duration (mean):** 1.07s - **Rows/sec:** 37.36M - High throughput despite operating on 32-byte strings, showing efficient memory handling. # Remote Table Creation ## Create Remote Table from 32-byte String - **Duration:** 3.53s - **Rows/sec:** 11.33M - Performance reflects single threaded serialization and data transport overhead. ## Create Remote Table from BIGINT - **Duration:** 12.22s - **Rows/sec:** 8.18M - Performance reflects single threaded serialization and data transport overhead. # Select Queries ## Select from 32-byte String Table - **Duration (mean):** 1.10s - **Rows/sec:** 36.21M - Very fast reads from the remote table, indicating efficient decoding. ## Select from BIGINT Table - **Duration (mean):** 2.39s - **Rows/sec:** 41.78M - Excellent performance, even at scale (100M rows), shows the power of Arrow Flight transport. # Observations - **Scalar functions** scale exceptionally well, with `Echo BIGINT` and `Echo 32-byte String` both exceeding 35M rows/sec. - **Table creation** is the most resource-intensive operation, particularly for large datasets, due to single-threaded insert limitations. - **Select queries** from remote tables deliver excellent performance, often exceeding 40M rows/sec. - **System CPU usage** remains low across all operations, indicating efficient kernel-level I/O handling. # Conclusion These benchmarks show that the **Airport** extension for DuckDB delivers high throughput and low-latency data access via Apache Arrow Flight, particularly for read-heavy workloads. It is especially suitable for: - Real-time analytics - High-volume batch reads - Remote function calls with low overhead Further work may explore concurrency scaling and performance under streaming ingestion.