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Bert

Bert

Overview

bert is a fully-featured CLI benchmarking tool that can handle anything from the simplest ad-hoc A/B command benchmarks to multi-command scenarios with custom environment variables, working directories and more. bert can report results in several formats and forms. Reports from different runs can be marked with labels and accumulated into the same report file for later analysis. This can come handy when you want to compare different environment factors like wired network and WiFi, different disks, different software versions etc.

Key Features

Benchmark any number of commands
Perceived time measurements alongside user and system CPU time measurements
Run quick ad-hoc benchmarks or use config files to unlock all the features
Rerun the exact same benchmark on different machines or environments using config files
Accumulate results for different runs and compare them later
Set the number of times every scenario is executed
Choose between alternate executions and sequential execution of the same command
Fail-fast to exit immediately when a benchmark error is reported
Save results in txt, json, csv, csv/raw, md and md/raw formats
Control your benchmark environment
- Set optional working directory per scenario and/or command
- Set optional custom environment variables per scenario
- Set optional global setup/teardown commands per scenario
- Set optional before/after commands for each run
Constant progress indication

Installation

Install From a Homebrew Tap

# Tap the formula repository (https://docs.brew.sh/Taps)
brew tap sha1n/tap

# Install bert
brew install bert

# Update bert
brew upgrade bert

Download A Pre-Built Release

Download the appropriate binary and put it in your PATH.

# macOS single line example (assuming that '$HOME/.local/bin' is in your PATH):
bash -c 'VERSION="2.3.12" && TMP=$(mktemp -d) && cd $TMP && curl -sSL "https://github.com/sha1n/bert/releases/download/v${VERSION}/bert_${VERSION}_Darwin_x86_64.tar.gz" | tar -xz && mv bert ~/.local/bin && rm -rf $TMP'


# breaking it down

# specify which version you want
VERSION="2.3.12"
# create a temp directory
TMP=$(mktemp -d)
# change directory 
cd "$TMP"
# download and extract the archive in the temp dir
curl -sSL "https://github.com/sha1n/bert/releases/download/v${VERSION}/bert_${VERSION}_Darwin_x86_64.tar.gz" | tar -xz
# move the binary file to a dir in your PATH
mv bert ~/.local/bin

# optionally install completion scripts from ./completions

# delete the temp directory
rm -rf $TMP

# Once installed, you can update using the update command (this doesn't work when installing using Homebrew)
bert update

Build From Sources

If you are a Go developer or have the tools required to build Go programs, you should be able to do so by following these commands.

# macOS Example (assuming that '$HOME/.local/bin' is in your PATH):
git clone git@github.com:sha1n/bert.git
cd bert
make 
cp ./bin/bert-darwin-amd64 ~/.local/bin/bert

Usage

Quick Ad-Hoc Benchmarks

Use this form if you want to quickly measure the execution time of a command or a set of commands.

# One command 
bert 'command -opt' --executions 100

# Multiple commands
bert 'command -optA' 'command -optB' 'anotherCommand' --executions 100

# Multiple commands with alternate execution
bert 'command -optA' 'command -optB' 'anotherCommand' --executions 100 --alternate

Using a Configuration File

In order to gain full control over benchmark configuration bert uses a configuration file. The configuration file can be either in YAML or JSON format. bert treats files with the .json extension as JSON, otherwise it assumes YAML. You may create a configuration file manually or use the config command to interactively generate your configuration.

Why use a config file?

unlock advanced features such as alternate execution, custom environment variables, working directories and setup commands per scenario
easily share elaborate benchmark configurations, store them in VCS and reuse them on different environments and over time

More about configuration here.

bert --config benchmark-config.yml

# Equivalent shorthand version of the above
bert -c benchmark-config.yml

Directory Local Configuration (.bertconfig)

When a file named .bertconfig exists in bert’s current directory and no other configuration method is specified, bert assumes that file is a benchmark configuration file and attempts to load specs from it.

Reports

Report Formats

There are three supported report formats, two of them support raw mode as follows. The formats are txt, json, csv, csv/raw, md and md/raw.

txt is the default report format. It contains stats per scenario and a header section that describes the main characteristics of the benchmark. txt format is primarily designed to be used in a terminal.
json contains the same stats as txt does, minus the header section and is formatted as a JSON document. JSON is a very popular data representation format amongst programming languages and web applications particularly. This format is designed to help integrate bert reported data with other programs.
csv contains the same stats in CSV format. It is especially useful when you want to accumulate stats from multiple benchmarks in a standard convenient format. In which case you can combine the csv format with -o and possibly --header=false if you want to accumulate data from separate runs in one file.
csv/raw is streaming raw trace events as CSV records and is useful if you want to load that data into a spreadsheet or other tools for further analysis.
md and md/raw and similar to csv and csv/raw respectively, but write in Markdown table format.

Selecting Report Format:

# The following command will generate a report in CSV format and save it into a file 
# named 'benchmark-report.csv' in the current directory.
bert --config benchmark-config.yml --format csv --out-file benchmark-report.csv

# Here is an equivalent command that uses shorthand flag names.
bert -c benchmark-config.yml -f csv -o benchmark-report.csv

Accumulating Data

When an output file is specified, bert appends data to the specified report file. If you are using one of the tabular report formats and want to accumulate data from different runs into the same report, you can specify --headers=false starting from the second run, to indicate that you don’t want table headers.

Labelling Data

Sometimes what you really want to measure is the impact of environmental changes on your commands and not the command themselves. In such cases, it is sometimes easier to run the exact same benchmark configuration several times, with different machine configuration. For example, WiFi vs wired network, different disks, different software versions etc. In such situations, it is helpful to label your reports in a way that allows you to easily identify each run. bert provides the optional --label or -l flag just for that. When specified, the provided labels will be attached to the report results of all the commands in that run.

Example:

Timestamp	Scenario	Samples	Labels	Min	Max	Mean	Median	Percentile 90	StdDev	User Time	System Time	Errors
2021-06-20T21:01:24Z	curl	100	vpn,wifi	3.5ms	6.4ms	4.4ms	4.3ms	5.0ms	552.2µs	646.5µs	1.6ms	0%
2021-06-20T21:02:05Z	curl	100	vpn,wired	3.4ms	16.0ms	4.3ms	4.1ms	4.9ms	1.3ms	634.0µs	1.6ms	0%
2021-06-20T21:02:33Z	curl	100	wired	0.6ms	8.1ms	1.3ms	1.1ms	5.9ms	0.8ms	559.4µs	1.4ms	0%

Understanding User & System Time Measurements

The user and system values are the calculated mean of measured user and system CPU time. It is important to understand that each measurement is the sum of the CPU times measured on all CPU cores and therefore can measure higher than perceived time measurements (min, max, mean, median, p90). The following report shows the measurements of two go test commands, one executed with -p 1 which limits concurrency to 1 and the other with automatic parallelism. Notice how close the user and system metrics are and how they compare to the other metrics.

 BENCHMARK SUMMARY
     labels:
       date: Jun 15 2021
       time: 20:31:20
  scenarios: 2
 executions: 10
  alternate: true

---------------------------------------------------------------

   SCENARIO: [go test -p 1]
        min: 2.7s          mean: 2.8s        median: 2.8s
        max: 3.0s        stddev: 102.0ms        p90: 3.0s
       user: 2.3s        system: 1.2s        errors: 0%

---------------------------------------------------------------

   SCENARIO: [go test]
        min: 1.2s          mean: 1.3s        median: 1.2s
        max: 1.3s        stddev: 43.3ms         p90: 1.3s
       user: 2.8s        system: 1.6s        errors: 0%

---------------------------------------------------------------

Examples

Text Example

 BENCHMARK SUMMARY
     labels: example-label
       date: Jun 12 2021
       time: 18:07:11
  scenarios: 2
 executions: 10
  alternate: true

---------------------------------------------------------------

   SCENARIO: scenario A
        min: 1.0s          mean: 1.0s        median: 1.0s
        max: 1.0s        stddev: 1.5ms          p90: 1.0s
       user: 538.5µs     system: 1.2ms       errors: 0%

---------------------------------------------------------------

   SCENARIO: scenario B
        min: 3.4ms         mean: 3.7ms       median: 3.6ms
        max: 4.3ms       stddev: 243.9µs        p90: 3.8ms
       user: 539.9µs     system: 1.2ms       errors: 0%

---------------------------------------------------------------

JSON Example

{
	"records": [{
		"timestamp": "2021-06-16T20:13:07.946273Z",
		"name": "scenario A",
		"executions": 10,
		"labels": ["example-label"],
		"min": 1003598013,
		"max": 1008893354,
		"mean": 1006113519,
		"stddev": 1638733,
		"median": 1005970135,
		"p90": 1008442779,
		"user": 516700,
		"system": 1101100,
		"errorRate": 0
	}, {
		"timestamp": "2021-06-16T20:13:07.946273Z",
		"name": "scenario B",
		"executions": 10,
		"labels": ["example-label"],
		"min": 3244148,
		"max": 3907661,
		"mean": 3717243,
		"stddev": 190237,
		"median": 3795931,
		"p90": 3863124,
		"user": 544600,
		"system": 1188500,
		"errorRate": 0
	}]
}

CSV Example

Timestamp,Scenario,Samples,Labels,Min,Max,Mean,Median,Percentile 90,StdDev,User Time,System Time,Errors
2021-06-20T21:07:03Z,scenario A,10,example-label,1003901921,1007724021,1005953076,1006408481,1007591335,1316896,444700,1182600,0%
2021-06-20T21:07:03Z,scenario B,10,example-label,3256932,6759926,3939343,3626742,3947804,963627,469500,1325200,0%

Markdown Example

| Timestamp            | Scenario   | Samples | Labels        | Min   | Max   | Mean  | Median | Percentile 90 | StdDev  | User Time | System Time | Errors |
| -------------------- | ---------- | ------- | ------------- | ----- | ----- | ----- | ------ | ------------- | ------- | --------- | ----------- | ------ |
| 2021-06-20T21:06:22Z | scenario A | 10      | example-label | 1.0s  | 1.0s  | 1.0s  | 1.0s   | 1.0s          | 1.8ms   | 459.5µs   | 1.2ms       | 0%     |
| 2021-06-20T21:06:22Z | scenario B | 10      | example-label | 3.1ms | 4.5ms | 3.4ms | 3.2ms  | 3.7ms         | 408.8µs | 439.0µs   | 1.1ms       | 0%     |

Raw CSV Example

Timestamp,Scenario,Labels,Duration,User Time,System Time,Error
2021-06-20T21:07:25Z,scenario A,example-label,1003657149,419000,1034000,false
2021-06-20T21:07:25Z,scenario B,example-label,3234965,407000,1018000,false
2021-06-20T21:07:26Z,scenario A,example-label,1007011109,462000,1066000,false
2021-06-20T21:07:26Z,scenario B,example-label,3627639,438000,1149000,false
2021-06-20T21:07:27Z,scenario A,example-label,1004486684,480000,1057000,false
2021-06-20T21:07:27Z,scenario B,example-label,3067241,407000,942000,false
2021-06-20T21:07:28Z,scenario A,example-label,1004747640,424000,952000,false
2021-06-20T21:07:28Z,scenario B,example-label,3108394,418000,973000,false
2021-06-20T21:07:29Z,scenario A,example-label,1003754833,452000,1072000,false
2021-06-20T21:07:29Z,scenario B,example-label,3273611,403000,1047000,false
2021-06-20T21:07:30Z,scenario A,example-label,1007069722,535000,1130000,false
2021-06-20T21:07:30Z,scenario B,example-label,3110162,431000,1081000,false
2021-06-20T21:07:31Z,scenario A,example-label,1003373155,421000,959000,false
2021-06-20T21:07:31Z,scenario B,example-label,3299774,394000,987000,false
2021-06-20T21:07:32Z,scenario A,example-label,1005966685,401000,971000,false
2021-06-20T21:07:32Z,scenario B,example-label,3246400,391000,1046000,false
2021-06-20T21:07:33Z,scenario A,example-label,1004069658,462000,1074000,false
2021-06-20T21:07:33Z,scenario B,example-label,3275813,418000,1003000,false
2021-06-20T21:07:34Z,scenario A,example-label,1006268385,447000,1001000,false
2021-06-20T21:07:34Z,scenario B,example-label,3444278,401000,1054000,false

Output Control

By default bert logs informative messages to standard err and report data to standard out (if no output file is specified). However, there are several ways you can control what is logged and in what level of details.

--pipe-stdout and --pipe-stderr - pipe the standard out and err of executed benchmark commands respectively, to standard err.
--silent or -s - sets the logging level to the lowest level possible, which includes only fatal errors. That is a softer version of 2>/dev/null and should be preferred in general.
--debug or -d - sets the logging level to the highest possible level, for troubleshooting.

Other Features

--alternate - when combined with multiple commands, bert uses alternate scenario execution instead of executing scenarios one after another.
--fail-fast - tells bert to exit immediately when a benchmark error is reported. This is handy for reproducing illusive errors using brute-force.

Shell Completion Scripts

bert comes with completion scripts for zsh, bash, fish and PowerShell. When installed with brew completions scripts are automatically installed to the appropriate location, otherwise the scripts can be found in the tar-ball version of the released binaries.

Alternatives

Before developing bert I looked into the following tools. Both target similar use-cases, but with different focus.