Release 1.4.27

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.27" pytemporal==1.4.27

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.27.zip (includes flamegraphs)

Wheels Built

Linux:

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

macOS:

• arm64 (Apple Silicon M1/M2/M3) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.27):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.26...v1.4.27

Release 1.4.26

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.26" pytemporal==1.4.26

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.26.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.26):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.25...v1.4.26

Release 1.4.25

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.25" pytemporal==1.4.25

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.25.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.25):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.24...v1.4.25

Release 1.4.24

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.24" pytemporal==1.4.24

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.24.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.24):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.23...v1.4.24

Release 1.4.23

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.23" pytemporal==1.4.23

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.23.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.23):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.22...v1.4.23

Release 1.4.22

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.22" pytemporal==1.4.22

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.22.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.22):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.21...v1.4.22

Release 1.4.21

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.21" pytemporal==1.4.21

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.21.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.21):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.20...v1.4.21

Release 1.4.20

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.20" pytemporal==1.4.20

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.20.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.20):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.19...v1.4.20

Release 1.4.19

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.19" pytemporal==1.4.19

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.19.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.19):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.18...v1.4.19

Release 1.4.18

Installation

pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.4.18" pytemporal==1.4.18

Performance Documentation

📊 View Interactive Benchmark Reports

📦 Download Complete Benchmark Data: benchmarks-v1.4.18.zip (includes flamegraphs)

Linux Wheels Built

• x86_64 (Intel/AMD 64-bit) for Python 3.9, 3.10, 3.11, 3.12

Features

• High-performance bitemporal timeseries processing
• Microsecond precision timestamps for audit trails
• Conflation optimization for reduced storage
• Adaptive parallelisation for large datasets

Performance Benchmarks

• 📈 Benchmark Dashboard - Historical trends
• 📊 Criterion Reports - Detailed analysis

Typical performance (v1.4.18):

• Small Dataset (5 records): ~30-35 µs
• Medium Dataset (100 records): ~165-170 µs
• Large Dataset (500k records): ~900-950 ms

Full Changelog: v1.4.17...v1.4.18