graph

Overview

The `graph` file is a Python utility script designed to **aggregate**, **tabulate**, and **visualize** benchmark results from JSON serialization and deserialization performance tests. It processes JSON-formatted benchmark output files generated by prior runs, organizes metrics by benchmark group and JSON library, and produces:

• Markdown-formatted tables summarizing median latencies, throughput (operations per second), and relative performance.

• Bar plots comparing libraries' relative throughput for serialization and deserialization tasks, saved as image files.

This file primarily supports the **benchmark reporting and visualization** phase within the benchmarking suite for JSON libraries like `orjson` and the standard Python `json` module. It transforms raw benchmark data into readable reports and comparative graphs, aiding performance analysis and decision-making.

Detailed Description of Functions

aggregate()

def aggregate():

Purpose

Reads benchmark JSON result files from the `.benchmarks` directory, extracts relevant metrics, and aggregates data by benchmark group and library.

Behavior

• Locates the first subdirectory inside .benchmarks/ which contains the benchmark JSON files.

• For each benchmark result file:

  • Loads JSON data using orjson.

  • Iterates over individual benchmark entries.

  • Extracts and converts latency data from seconds to milliseconds.

  • Collects median latency, operations per second (ops), and correctness flags.

• Organizes results into a nested dictionary:
  res[group][library] = { data, median, ops, correct }

Returns

• res: A defaultdict(dict) mapping benchmark group names to dictionaries keyed by JSON library names, each containing benchmark statistics.

Usage Example

results = aggregate()
# results might look like:
# {
#   "github deserialization": {
#       "orjson": {"median": 1.2, "ops": 8000, "correct": True, ...},
#       "json": {"median": 10.5, "ops": 900, "correct": True, ...},
#   },
#   ...
# }

tab(obj)

def tab(obj):

Purpose

Generates formatted tables and comparative bar plots from aggregated benchmark data.

Parameters

• obj: A nested dictionary structured as returned by aggregate(). For each benchmark group and library, it contains latency, throughput, and correctness data.

Behavior

• Initializes a string buffer to accumulate Markdown-formatted output.

• Defines headers for tabular output.

• Configures seaborn and matplotlib plotting styles.

• For each benchmark group (sorted in reverse order):

  • Writes a Markdown section header with the group name.

  • Builds a table of results for the libraries orjson and json:

    • Outputs median latency (ms), operations per second, correctness flag.

    • Calculates relative latency compared to the orjson baseline.

  • Adds entries to a list used for plotting.

  • Formats latency values with appropriate decimal precision.

• For each operation type (serialization and deserialization):

  • Filters relevant data.

  • Computes relative throughput normalized to the json standard library.

  • Uses seaborn to generate bar plots with error bars representing standard deviation.

  • Customizes plot axes, labels, titles, and legend.

  • Saves plots as PNG images under doc/serialization and doc/deserialization.

• Prints the accumulated Markdown tables to standard output.

Returns

• None (prints tables and saves plots as side effects).

Usage Example

results = aggregate()
tab(results)
# Outputs tables to stdout and saves plots to disk.

Important Implementation Details and Algorithms

• Data Conversion: Latency data from benchmarks comes in seconds. The code converts these to milliseconds (val * 1000) for human-friendly reporting.

• Relative Performance Calculation:

  • Latency relative to orjson's latency is computed as latency / orjson_baseline.

  • Throughput relative to Python's json library is computed as ops / json_baseline.

• Plotting:

  • Bar plots visualize throughput relative to standard json for each document group and library.

  • Y-axis ticks are adjusted to show integer multiples with a minimum at 1x and a maximum rounded up for clarity.

  • A dashed horizontal line at y=1 marks the baseline performance of the standard json library.

• Data Cleaning:

  • Corrects a known typo in a group name from "witter.json" to "twitter.json".

• Styling:

  • Uses seaborn's darkgrid style and transparent figure face color.

  • Bar plots include error bars representing standard deviation (errorbar="sd").

Interaction with Other System Components

• Input Source:

  • Reads raw benchmark JSON output files generated by benchmark runs stored in the .benchmarks/ directory.

  • These files are produced by serialization and deserialization benchmark scripts that measure performance of different JSON libraries on various test fixtures.

• Libraries Used:

  • orjson for fast JSON loading.

  • pandas and seaborn for data manipulation and plotting.

  • matplotlib for saving plots.

  • tabulate for formatting tables in Markdown.

• Output:

  • Markdown tables are printed to standard output, suitable for direct inclusion in documentation or reports.

  • Bar plot images are saved to the doc/ directory for visualization in reports or websites.

• Role in Benchmarking Suite:

  • Serves as the post-processing visualization and reporting tool for the benchmarking framework.

  • Complements the benchmark execution scripts by providing human-readable summaries and visual insights into relative performance.

Visual Diagram: Class/Function Structure

Since this file is a utility script with two main functions and no classes, a flowchart illustrating the main function relationships and workflow is most appropriate.

flowchart TD
    A[Start] --> B[aggregate()]
    B --> C[Parse benchmark JSON files]
    C --> D[Build nested dict: group -> library -> stats]
    D --> E[Return aggregated data]

    E --> F[tab(obj)]
    F --> G[Format Markdown tables]
    F --> H[Calculate relative performance]
    F --> I[Generate bar plots for serialization and deserialization]
    G & I --> J[Output tables and save plots]

    J --> K[End]

Summary

The `graph` file is a **benchmark result aggregator and reporter** that:

• Parses benchmark JSON results.

• Organizes latency and throughput metrics by benchmark group and JSON library.

• Prints formatted Markdown tables with performance summaries.

• Generates comparative bar plots showing throughput relative to the standard Python json library.

• Saves plots for inclusion in documentation.

This file is crucial for delivering actionable insights from raw benchmark data in the form of human-readable reports and visual comparisons, supporting the overall goal of evaluating and showcasing JSON serialization/deserialization library performance.