easybench 0.1.22

A simple and easy-to-use Python benchmarking library

EasyBench

Docs

A simple and easy-to-use Python benchmarking library.

Features

• Three flexible benchmarking styles (decorator, class-based, and command-line)
• Measure both execution time and estimated memory usage (see limitations)
• Visualization of benchmark results as boxplots for analyzing distribution and outliers
• Parametrized benchmarks to compare the same function with different input sizes
• A pytest-like fixture system for easy test data setup
• Complete lifecycle hooks (setup/teardown) for fine-grained benchmark control
• Switch between normal function execution and benchmarked execution on demand
• Customizable benchmark configuration with sorting and formatting options
• Command-line tool to run multiple benchmarks at once
• Multiple output formats (text tables, CSV, JSON, pandas.DataFrame)
• Extensible reporting system for custom output destinations

Installation

pip install easybench

Optional Dependencies

EasyBench supports optional dependencies for additional features:

# Install with visualization and analysis support
pip install easybench[all]

The all option includes:

• matplotlib: For visualization and plotting benchmark results
• seaborn: For enhanced statistical visualizations
• pandas: For outputting benchmark results as DataFrames
• tqdm: For progress tracking during benchmark execution

Quick Start

There are 3 ways to benchmark with easybench:

1. @bench decorator

   from easybench import bench
   
   # Add @bench with function parameters
   @bench(item=123, big_list=lambda: list(range(1_000_000)))
   def add_item(item, big_list):
       big_list.append(item)
   

   • When you need fresh data for each trial, use a function or lambda to generate new data on demand.
     (like lambda: list(range(1_000_000)) in the above)

2. EasyBench class

   from easybench import EasyBench, BenchConfig
   
   class BenchListOperation(EasyBench):
       # Benchmark configuration
       bench_config = BenchConfig(
           trials=10,
           memory=True,
           sort_by="avg"
       )
   
       # Setup for each trial
       def setup_trial(self):
           self.big_list = list(range(1_000_000))
   
       # Benchmark methods (must start with bench_)
       def bench_append(self):
           self.big_list.append(123)
   
       # You can define multiple benchmark methods
       def bench_pop(self):
           self.big_list.pop()
   
   if __name__ == "__main__":
       # Run benchmark
       BenchListOperation().bench()
   

3. easybench command

   1. Create a benchmarks directory

   2. Put bench_*.py scripts in the directory:

      from easybench import fixture
      
      # Fixture for each trial
      @fixture(scope="trial")
      def big_list():
          return list(range(1_000_000))
      
      # Benchmark functions (must start with bench_)
      def bench_append(big_list):
          big_list.append(123)
      
      # You can define multiple benchmark functions
      def bench_pop(big_list):
          big_list.pop()
      

   3. Run easybench command

      easybench --trials 10 --memory --sort-by avg
      

Example of benchmark results:

• Single benchmark

  Benchmark Results (5 trials):
  
  Function   Avg Time (s) Min Time (s) Max Time (s)
  ----------------------------------------------
  add_item   0.002393     0.000939     0.007362   
  

• Multiple benchmarks

  

• Boxplot Visualization

  

• Lineplot Visualization

  

Usage

Decorator-based Benchmarks (@bench decorator)

Basic usage

The @bench decorator provides the simplest way to benchmark a function:

from easybench import bench

# Add @bench with function parameters
@bench(item=123, big_list=list(range(1_000_000)))
def add_item(item, big_list):
    big_list.append(item)

Fresh data

In the example above, big_list is created once and the same list is used for all trials.
When you need fresh data for each trial, use a function or lambda to generate new data on demand:

from easybench import bench

# Create a new list for each trial
@bench(item=-1, big_list=lambda: list(range(1_000_000)))
def append(item, big_list):
    big_list.append(item)

Function parameters (@bench.fn_params)

Sometimes you may want to use functions as parameters.
In such cases, use the @bench.fn_params decorator:

def pop_first(some_list):
    """Remove the first element from the list"""
    some_list.pop(0)

@bench(big_list=list(range(1_000_000)))
@bench.fn_params(func=pop_first)
def apply_function(big_list, func):
    func(big_list)

Configuration (@bench.config)

To customize benchmark settings, use the @bench.config decorator:

@bench(big_list=list(range(10_000_000)))
@bench.config(trials=10, memory=True)
def pop_last(big_list):
    big_list.pop()

• Place the @bench.config decorator before (below) other bench decorators.

Main configuration options:

• trials: Number of trials (default: 5)
• memory: Also measure memory usage
  • False: Disable memory measurement (default)
  • True: Show memory in kilobytes
  • "B", "KB", "MB", "GB": Show memory in bytes, kilobytes, megabytes, or gigabytes
• time: Specify time measurement unit
  • "s": Display time in seconds (default)
  • "ms": Display time in milliseconds
  • "μs" or "us": Display time in microseconds
  • "ns": Display time in nanoseconds
  • "m": Display time in minutes
• For other options, see "Configuration Options" below

Multiple parameter sets (BenchParams)

When you want to benchmark a function with multiple parameter sets, you can pass a list of parameter sets created with BenchParams to the @bench decorator:

from easybench import bench, BenchParams

# Define parameter sets
small = BenchParams(
    name="Small",                                 # Parameter set name
    params={"lst": lambda: list(range(10_000))},  # Parameters for @bench
)
large = BenchParams(
    name="Large",
    params={"lst": lambda: list(range(1_000_000))}
)

# Benchmark with multiple parameter sets
@bench([small, large])
def pop_first(lst):
    return lst.pop(0)

On-demand benchmarking

If you want to run the benchmark only when needed, use the .bench() method:

@bench
def append_item(item, big_list):
    big_list.append(item)
    return len(big_list)

# Run as a normal function (without benchmarking)
result = append_item(3, list(range(1_000_000)))

# Run with benchmarking
result = append_item.bench(3, list(range(1_000_000)))
print(result)  # 10000001

• By default, the benchmark runs for 1 trial.
• To run multiple trials, specify the bench_trials parameter:

  result = append_item.bench(3, list(range(1_000_000)), bench_trials=10)
  

• When running multiple trials, the .bench() method returns the value from the first trial.

  result = append_item.bench(3, [1,2,3], bench_trials=10)
  print(result)  # 4
  

Class-based Benchmarks (EasyBench class)

For comparing multiple benchmarks or more complex setups, the class-based approach is useful:

from easybench import EasyBench, BenchConfig

class BenchListOperation(EasyBench):

    # Benchmark configuration
    bench_config = BenchConfig(
        trials=10,     # Number of trials
        memory=True,   # Measure memory usage
        sort_by="avg"  # Sort by average time
    )

    # Runs before each trial
    def setup_trial(self):
        self.big_list = list(range(10_000_000))

    # Benchmark methods (must start with bench_)
    def bench_append(self):
        self.big_list.append(-1)

    def bench_insert_start(self):
        self.big_list.insert(0, -1)

if __name__ == "__main__":
    BenchListOperation().bench()

How to use the class-based approach:

1. Create a class that inherits from EasyBench
2. Configure benchmark settings with the bench_config class variable
3. Prepare for each trial in the setup_trial method
4. Methods starting with bench_ will be benchmarked
5. Call the bench() method to execute the benchmarks
   • bench() displays the results on screen and returns a dictionary of measured value

Lifecycle Methods

In class-based benchmarks, you can use the following lifecycle methods:

class BenchExample(EasyBench):
    def setup_class(self):
        # Run once before all benchmarks in the class
        pass
        
    def teardown_class(self):
        # Run once after all benchmarks in the class
        pass
        
    def setup_function(self):
        # Run before each benchmark function
        pass
        
    def teardown_function(self):
        # Run after each benchmark function
        pass
        
    def setup_trial(self):
        # Run before each trial
        pass
        
    def teardown_trial(self):
        # Run after each trial
        pass

Parametrized Benchmarks (parametrize decorator)

You can run the same benchmark method with different parameter sets using the parametrize decorator:

from easybench import BenchParams, EasyBench, parametrize

class BenchListOperations(EasyBench):
    # Define parameter sets with BenchParams
    small_params = BenchParams(
        name="Small List",
        params={"size": 10_000}
    )
    
    large_params = BenchParams(
        name="Large List",
        params={"size": 1_000_000}
    )
    
    # Apply parametrize decorator with a list of parameter sets
    @parametrize([small_params, large_params])
    def bench_create_list(self, size):
        return list(range(size))

if __name__ == "__main__":
    BenchListOperations().bench()

This will run the benchmark with each parameter set and include the parameter set name in the results:

Benchmark Results (5 trials):

Function                         Avg Time (s) Min Time (s) Max Time (s)
--------------------------------------------------------------------
bench_create_list (Small List)   0.000442     0.000309     0.000855    
bench_create_list (Large List)   0.092680     0.062617     0.129535    

Fixtures (fixture decorator)

To provide common test data, you can use pytest-style fixtures:

from easybench import EasyBench, fixture

# Define a fixture
@fixture(scope="trial")
def big_list():
    return list(range(10_000_000))

class BenchListOperation(EasyBench):
    # Receive the fixture as an argument
    def bench_append(self, big_list):
        big_list.append(-1)

    def bench_insert_start(self, big_list):
        big_list.insert(0, -1)

if __name__ == "__main__":
    BenchListOperation().bench()

The scope parameter of the fixture decorator specifies the lifetime of the fixture:

• "trial": Created for each trial (default)
• "function": Created once per benchmark function
• "class": Created once per benchmark class

Configuration Options

The following settings are available in the BenchConfig class:

from easybench import BenchConfig, EasyBench, customize

class MyBenchmark(EasyBench):
    bench_config = BenchConfig(
        trials=5,            # Number of trials
        warmups=2,           # Number of warmup trials before actual measurement
        sort_by="avg",       # Sort criterion
        reverse=False,       # Sort order (False=ascending, True=descending)
        memory="MB",         # Enable memory measurement and show in megabytes
        color=True,          # Use color output in results
        show_output=False,   # Display function return values
        loops_per_trial=1,   # Number of function executions per trial (see explanation below)
        reporters=[],        # Custom reporters (see explanation below)
        progress=True,       # Enable progress tracking with tqdm
    )
    
    # You can also customize settings for individual methods
    @customize(loops_per_trial=1000)
    def bench_fast_operation(self):
        # This method uses 1000 loops per trial
        pass

Sorting options (sort_by):

• "def": Definition order (default)
• "avg": Average execution time
• "min": Minimum execution time
• "max": Maximum execution time
• "avg_memory": Average memory usage (when memory=True)
• "max_memory": Maximum memory usage (when memory=True)

Memory measurement options (memory):

• False: Disable memory measurement (default)
• True: Enable memory measurement and display in kilobytes
• "B": Display memory usage in bytes
• "KB": Display memory usage in kilobytes
• "MB": Display memory usage in megabytes
• "GB": Display memory usage in gigabytes

Time measurement options (time):

• "s": Display time in seconds (default)
• "ms": Display time in milliseconds
• "μs" or "us": Display time in microseconds
• "ns": Display time in nanoseconds
• "m": Display time in minutes

Progress tracking options (progress):

• False: Disable progress tracking (default)
• True: Enable progress tracking using tqdm
• Custom function: Use a custom progress tracking function that follows the tqdm interface

Improving Measurement Accuracy with warmups

When benchmarking, the initial runs might be affected by various factors like code compilation, cache warmup, or other system effects. To get more stable and accurate measurements, you can use the warmups parameter to specify how many trial runs should be performed before the actual measurement begins:

@bench
@bench.config(trials=5, warmups=3, time="ms")
def my_function():
    # This function will be run 3 times as warmup (results discarded)
    # before the 5 actual trials that are measured
    # ...

How warmups works:

• Before actual measurements begin, the function is executed warmups times
• Each warmup is a complete trial execution including setup_trial/teardown_trial
• Results from warmup trials are discarded and not included in measurements
• After warmups are complete, regular trials begin with results being recorded

When to use:

• For functions that need JIT compilation to reach optimal performance
• When the system needs time to "warm up" caches or reach steady state
• When you notice that the first few runs consistently show different performance characteristics

Improving Timer Precision with loops_per_trial

In environments with poor timer resolution (e.g., certain virtual machines or systems where time.perf_counter() has limited precision), you may need to run a function multiple times to get meaningful timing results.

The loops_per_trial parameter allows you to specify how many times a function should be executed in a single timing measurement (trial):

# Measure the average time it takes to append the number 1 to a list
# that initially contains 100 elements, repeated 1000 times
# (Note: the same list instance is used throughout the 1000 append operations)
# Repeat this process 500 times to perform the benchmark
@bench(small_list=lambda: list(range(100)))
@bench.config(trials=500, loops_per_trial=1000, time="us")
def append_item(small_list):
    small_list.append(1)

How loops_per_trial works:

• The function is executed loops_per_trial times in a loop within a single timing measurement (trial)
• The total execution time is divided by loops_per_trial to get the average time per execution
• This provides more accurate measurements for very fast operations where individual timing would be affected by timer resolution limits

When to use:

• For very fast operations (microsecond or nanoseconds)
• In environments with poor timer precision
• When you notice high variability in timing results for simple operations

Memory Measurement Limitations

EasyBench uses Python's built-in tracemalloc module to measure memory usage.
This has some important limitations:

• tracemalloc only tracks memory allocations made through Python's memory manager
• Memory allocated by C extensions (like NumPy, Pandas, or other native libraries) often bypasses Python's memory manager and won't be accurately measured
• The reported memory usage reflects Python objects only, not the total process memory consumption For applications heavily using C extensions, consider using external profilers like memory_profiler or system monitoring tools for more accurate measurements.

Command Line Interface (easybench command)

To run multiple benchmarks at once, use the easybench command:

easybench [options] [path]

• By default, it runs files named bench_*.py in the benchmarks directory
• You can specify either a directory containing benchmark files or a specific benchmark file
• Benchmark scripts must follow these rules:
  • For class-based benchmarks, class names should start with Bench
  • For function-based benchmarks, function names should start with bench_

Command Options

easybench [--trials N] [--memory] [--sort-by METRIC] [--reverse] [--no-color] [--show-output] [path]

• --trials N: Number of trials (default: 5)
• --memory: Enable memory measurement
• --sort-by METRIC: Sort criterion (def/avg/min/max/avg_memory/max_memory)
• --reverse: Sort results in descending order
• --no-color: Disable colored output
• --show-output: Display function return values
• path: Directory containing benchmark files or a specific benchmark file (default: "benchmarks")

Function-based Benchmark Example

Example of a function-based benchmark to be run from the command line:

# Filename: benchmarks/bench_list_operations.py
from easybench import fixture

@fixture(scope="trial")
def big_list():
    return list(range(10_000_000))

def bench_append(big_list):
    """Append an element to the end of the list"""
    big_list.append(-1)

def bench_insert_start(big_list):
    """Insert an element at the beginning of the list"""
    big_list.insert(0, -1)

Save this file in the benchmarks folder and run the easybench command to benchmark both functions and compare the results:

easybench --trials 10 --memory

Advanced Usage

Custom Output (Formatter and Reporter)

EasyBench uses a mechanism called Reporter to output benchmark results. By default, ConsoleReporter is used.

ConsoleReporter is a reporter that outputs data to the console screen, and by default formats the data in tabular form (TableFormatter). In EasyBench, you can change both the Formatter (output format) and the Reporter (output method) to enable various forms of output.

Using Reporters

To use reporters, set them as a list in the reporters parameter of your benchmark configuration (BenchConfig or @bench.config). Since reporters is a list, you can specify multiple output methods simultaneously.

There are three ways to specify reporters:

1. As a string: Specify the reporter name as a string

   • "console": Standard tabular console output
   • "simple": Simple console output
   • "plot" or "boxplot": Visualization with boxplot
   • "lineplot": Visualization with lineplot
   • "*.csv" or "*.json": File output

2. With arguments: Specify in the format (reporter_name, parameter_dict)

3. As Reporter objects: Directly specify an instance of a Reporter class

• Usage example

  from easybench import BenchConfig
  from easybench.reporters import FileReporter
  
  # Multiple output formats with different specification methods
  config = BenchConfig(
      reporters=[
          "console",                                  # Specified as string
          ("simple", {"metric": "min"}),              # Specified with arguments
          ("plot", {"log_scale": False}),             # Plot with arguments
          "results.csv",                              # Specified as file path
          FileReporter("results.json"),               # Specified as object
      ]
  )
  
  # Simpler configurations
  bench_config = BenchConfig(reporters=["console"])       # Console output only
  bench_config = BenchConfig(reporters=["plot"])          # Boxplot only
  
  bench_config = BenchConfig(reporters=["output.csv"])    # CSV file output only
  

Creating Custom Reporters

For advanced use cases, you can create custom reporters:

from easybench.reporters import (
    Reporter, TableFormatter, JSONFormatter, CSVFormatter
)

# Custom reporter example - sends results to a web API
class WebAPIReporter(Reporter):
    def __init__(self, api_url, auth_token):
        super().__init__(JSONFormatter())  # Use JSON format
        self.api_url = api_url
        self.auth_token = auth_token
    
    def _send(self, formatted_output):
        # Send formatted results to an API endpoint
        import requests
        headers = {"Authorization": f"Bearer {self.auth_token}"}
        requests.post(self.api_url, headers=headers, json=formatted_output)

# Use with BenchConfig
bench_config = BenchConfig(
    reporters=[
        ConsoleReporter(),  # Still show in console
        WebAPIReporter("https://api.example.com/benchmarks", "my_token")
    ]
)

Boxplot Visualization (BoxPlotFormatter)

You can visualize benchmark results as boxplots, which is useful for analyzing distribution and outliers across multiple trials:

from easybench import BenchConfig, EasyBench
from easybench.visualization import BoxPlotFormatter, PlotReporter


class BenchList(EasyBench):
    bench_config = BenchConfig(
        trials=100,
        reporters=[
            PlotReporter(
                BoxPlotFormatter(
                    showfliers=True,           # Show outliers
                    log_scale=True,            # Use logarithmic scale
                    engine="seaborn",          # Use seaborn as plotting engine
                    orientation="horizontal",  # Horizontal or vertical orientation
                    width=0.5,                 # Box width (passed directly to seaborn's boxplot)
                    linewidth=0.5,             # Line width (passed directly to seaborn's boxplot)
                )
            )
        ],
    )

    def setup_trial(self):
        self.big_list = list(range(1_000_000))

    def bench_append(self):
        self.big_list.append(-1)

    def bench_insert_start(self):
        self.big_list.insert(0, -1)

    def bench_insert_middle(self):
        self.big_list.insert(len(self.big_list) // 2, -1)

    def bench_pop(self):
        self.big_list.pop()

    def bench_pop_zero(self):
        self.big_list.pop(0)


if __name__ == "__main__":
    import seaborn as sns

    # Optional seaborn style settings
    sns.set_theme(style="darkgrid", palette="Set2")
    BenchList().bench()

Main BoxPlotFormatter options

• showfliers: Whether to show outliers (default: True)
• log_scale: Whether to use logarithmic scale (default: False)
• data_limit: Specify axis data range (e.g., (0, 0.01))
• trim_outliers: Percentile for trimming outliers (0.0 to 0.5)
• winsorize_outliers: Percentile for winsorizing outliers (0.0 to 0.5)
• figsize: Figure size (default: (10, 6))
• engine: Plotting engine ("matplotlib" or "seaborn")
• orientation: Boxplot orientation ("vertical" or "horizontal")

PlotReporter options

• formatter: Plot formatter to use (e.g., BoxPlotFormatter)
• show: Whether to display the plot on screen (default: True)
• save_path: File path to save the plot
• dpi: Image resolution (default: 100)

To use boxplots, you need to install matplotlib:

pip install matplotlib

If you want to use the seaborn engine, also install seaborn:

pip install seaborn

License

MIT