# denet: a streaming process monitor
**denet** /de.net/ _v._ 1. _Turkish_: to monitor, to supervise, to audit. 2. to track metrics of a running process.
Denet is a streaming process monitoring tool that provides detailed metrics on running processes, including CPU, memory, I/O, and thread usage. Built with Rust, with Python bindings.
[](https://badge.fury.io/py/denet)
[](https://crates.io/crates/denet)
[](https://codecov.io/gh/btraven00/denet)
[](https://github.com/astral-sh/ruff)
[](https://www.gnu.org/licenses/gpl-3.0)
## Features
- Lightweight, cross-platform process monitoring
- Adaptive sampling intervals that automatically adjust based on runtime
- Memory usage tracking (RSS, VMS)
- CPU usage monitoring with accurate multi-core support
- I/O bytes read/written tracking
- Thread count monitoring
- Recursive child process tracking
- Command-line interface with colorized output
- Multiple output formats (JSON, JSONL, CSV)
- In-memory sample collection for Python API
- Python decorator and context manager for easy profiling
- Analysis utilities for metrics aggregation, peak detection, and resource utilization
- Process metadata preserved in output files (pid, command, executable path)
## Requirements
- Python 3.6+ (Python 3.12 recommended for best performance)
- Rust (for development)
- [pixi](https://prefix.dev/docs/pixi/overview) (for development only)
## Installation
```bash
pip install denet # Python package
cargo install denet # Rust binary
```
## Usage
### Understanding CPU Utilization
# CPU usage is reported in a `top`-compatible format where 100% represents one fully utilized CPU core:
# - 100% = one core fully utilized
# - 400% = four cores fully utilized
# - Child processes are tracked separately and aggregated for total resource usage
# - Process trees are monitored by default, tracking all child processes spawned by the main process
This is consistent with standard tools like `top` and `htop`. For example, a process using 3 CPU cores at full capacity will show 300% CPU usage, regardless of how many cores your system has.
### Command-Line Interface
```bash
# Basic monitoring with colored output
denet run sleep 5
# Output as JSON (actually JSONL format with metadata on first line)
denet --json run sleep 5 > metrics.json
# Write output to a file
denet --out metrics.log run sleep 5
# Custom sampling interval (in milliseconds)
denet --interval 500 run sleep 5
# Specify max sampling interval for adaptive mode
denet --max-interval 2000 run sleep 5
# Monitor existing process by PID
denet attach 1234
# Monitor just for 10 seconds
denet --duration 10 attach 1234
# Quiet mode (suppress process output)
denet --quiet --json --out metrics.jsonl run python script.py
# Monitor a CPU-intensive workload (shows aggregated metrics for all children)
denet run python cpu_intensive_script.py
# Disable child process monitoring (only track the parent process)
denet --no-include-children run python multi_process_script.py
```
### Python API
#### Basic Usage
```python
import json
import denet
# Create a monitor for a process
monitor = denet.ProcessMonitor(
cmd=["python", "-c", "import time; time.sleep(10)"],
base_interval_ms=100, # Start sampling every 100ms
max_interval_ms=1000, # Sample at most every 1000ms
store_in_memory=True, # Keep samples in memory
output_file=None, # Optional file output
include_children=True # Monitor child processes (default True)
)
# Let the monitor run automatically until the process completes
# Samples are collected at the specified sampling rate in the background
monitor.run()
# Access all collected samples after process completion
samples = monitor.get_samples()
print(f"Collected {len(samples)} samples")
# Get summary statistics
summary_json = monitor.get_summary()
summary = json.loads(summary_json)
print(f"Average CPU usage: {summary['avg_cpu_usage']}%")
print(f"Peak memory: {summary['peak_mem_rss_kb']/1024:.2f} MB")
print(f"Total time: {summary['total_time_secs']:.2f} seconds")
print(f"Sample count: {summary['sample_count']}")
print(f"Max processes: {summary['max_processes']}")
# Save samples to different formats
monitor.save_samples("metrics.jsonl") # Default JSONL
monitor.save_samples("metrics.json", "json") # JSON array format
monitor.save_samples("metrics.csv", "csv") # CSV format
# JSONL files include a metadata line at the beginning with process info
# {"pid": 1234, "cmd": ["python"], "executable": "/usr/bin/python", "t0_ms": 1625184000000}
# Alternative approach: For more control, you can also monitor in a loop:
# while monitor.is_running():
# time.sleep(0.5)
# # Do other work while monitoring continues in background...
```
#### Function Decorator
```python
import denet
# Profile a function with the decorator
@denet.profile(
base_interval_ms=100,
max_interval_ms=1000,
output_file="profile_results.jsonl",
store_in_memory=True, # Store samples in memory (default)
include_children=True # Monitor child processes (default True)
)
def expensive_calculation():
# Long-running calculation
result = 0
for i in range(10_000_000):
result += i
return result
# Call the function and get both result and metrics
result, metrics = expensive_calculation()
print(f"Result: {result}, Collected {len(metrics)} samples")
# The decorator can also be used without parameters
@denet.profile
def simple_function():
return sum(range(1000000))
result, metrics = simple_function()
```
#### Context Manager
```python
import denet
import json
# Monitor a block of code
with denet.monitor(
base_interval_ms=100,
max_interval_ms=1000,
output_file=None, # Optional file output
store_in_memory=True, # Store samples in memory (default)
include_children=True # Monitor child processes (default True)
) as mon:
# Code to profile
for i in range(5):
# Do something CPU intensive
result = sum(i*i for i in range(1_000_000))
# Access collected metrics after the block
samples = mon.get_samples()
print(f"Collected {len(samples)} samples")
print(f"Peak CPU usage: {max(sample['cpu_usage'] for sample in samples)}%")
# Generate and print summary
summary_json = mon.get_summary()
summary = json.loads(summary_json)
print(f"Average CPU usage: {summary['avg_cpu_usage']}%")
print(f"Peak memory: {summary['peak_mem_rss_kb']/1024:.2f} MB")
# Save samples to a file (includes metadata line in JSONL format)
mon.save_samples("metrics.jsonl", "jsonl") # First line contains process metadata
```
## Adaptive Sampling
Denet uses an intelligent adaptive sampling strategy to balance detail and efficiency:
1. **First second**: Samples at the base interval rate (fast sampling for short processes)
2. **1-10 seconds**: Gradually increases from base to max interval
3. **After 10 seconds**: Uses the maximum interval rate
This approach ensures high-resolution data for short-lived processes while reducing overhead for long-running ones.
## Analysis Utilities
The Python API includes utilities for analyzing metrics:
```python
import denet
import json
# Load metrics from a file (automatically skips metadata line)
metrics = denet.load_metrics("metrics.jsonl")
# If you want to include the metadata in the results
metrics_with_metadata = denet.load_metrics("metrics.jsonl", include_metadata=True)
# Access the executable path from metadata
executable_path = metrics_with_metadata[0]["executable"] # First item is metadata when include_metadata=True
# Aggregate metrics to reduce data size
aggregated = denet.aggregate_metrics(metrics, window_size=5, method="mean")
# Find peaks in resource usage
cpu_peaks = denet.find_peaks(metrics, field='cpu_usage', threshold=50)
print(f"Found {len(cpu_peaks)} CPU usage peaks above 50%")
# Get comprehensive resource utilization statistics
stats = denet.resource_utilization(metrics)
print(f"Average CPU: {stats['avg_cpu']}%")
print(f"Total I/O: {stats['total_io_bytes']} bytes")
# Convert between formats
csv_data = denet.convert_format(metrics, to_format="csv")
with open("metrics.csv", "w") as f:
f.write(csv_data)
# Save metrics with custom options
denet.save_metrics(metrics, "data.jsonl", format="jsonl", include_metadata=True)
# Analyze process tree patterns
tree_analysis = denet.process_tree_analysis(metrics)
# Example: Analyze CPU usage from multi-process workload
# See scripts/analyze_cpu.py for detailed CPU analysis example
```
## Development
For detailed developer documentation, including project structure, development workflow, testing, and release process, see [Developer Documentation](docs/dev.md).
## License
GPL-3
## Acknowledgements
- [sysinfo](https://github.com/GuillaumeGomez/sysinfo) - Rust library for system information
- [PyO3](https://github.com/PyO3/pyo3) - Rust bindings for Python