Tetcore Runtime Benchmarking Framework

This crate contains a set of utilities that can be used to benchmark and weigh FABRIC nobles that you develop for your Tetcore Runtime.

Overview

Tetcore's FABRIC framework allows you to develop custom logic for your blockchain that can be included in your runtime. This flexibility is key to help you design complex and interactive nobles, but without accurate weights assigned to dispatchables, your blockchain may become vulnerable to denial of service (DoS) attacks by malicious actors.

The Tetcore Runtime Benchmarking Framework is a tool you can use to mitigate DoS attacks against your blockchain network by benchmarking the computational resources required to execute different functions in the runtime, for example extrinsics, on_initialize, verify_unsigned, etc...

The general philosophy behind the benchmarking system is: If your node can know ahead of time how long it will take to execute an extrinsic, it can safely make decisions to include or exclude that extrinsic based on its available resources. By doing this, it can keep the block production and import process running smoothly.

To achieve this, we need to model how long it takes to run each function in the runtime by:

• Creating custom benchmarking logic that executes a specific code path of a function.
• Executing the benchmark in the Wasm execution environment, on a specific set of hardware, with a custom runtime configuration, etc...
• Executing the benchmark across controlled ranges of possible values that may affect the result of the benchmark (called "components").
• Executing the benchmark multiple times at each point in order to isolate and remove outliers.
• Using the results of the benchmark to create a linear model of the function across its components.

With this linear model, we are able to estimate ahead of time how long it takes to execute some logic, and thus make informed decisions without actually spending any significant resources at runtime.

Note that we assume that all extrinsics are assumed to be of linear complexity, which is why we are able to always fit them to a linear model. Quadratic or higher complexity functions are, in general, considered to be dangerous to the runtime as the weight of these functions may explode as the runtime state or input becomes too complex.

The benchmarking framework comes with the following tools:

• A set of macros (benchmarks!, add_benchmark!, etc...) to make it easy to write, test, and add runtime benchmarks.
• A set of linear regression analysis functions for processing benchmark data.
• A CLI extension to make it easy to execute benchmarks on your node.

The end-to-end benchmarking pipeline is disabled by default when compiling a node. If you want to run benchmarks, you need to enable it by compiling with a Rust feature flag runtime-benchmarks. More details about this below.

Weight

Tetcore represents computational resources using a generic unit of measurement called "Weight". It defines 10^12 Weight as 1 second of computation on the physical machine used for benchmarking. This means that the weight of a function may change based on the specific hardware used to benchmark the runtime functions.

By modeling the expected weight of each runtime function, the blockchain is able to calculate how many transactions or system level functions it will be able to execute within a certain period of time. Often, the limiting factor for a blockchain is the fixed block production time for the network.

Within FABRIC, each dispatchable function must have a #[weight] annotation with a function that can return the expected weight for the worst case scenario execution of that function given its inputs. This benchmarking framework will result in a file that automatically generates those formulas for you, which you can then use in your noble.

Writing Benchmarks

Writing a runtime benchmark is much like writing a unit test for your noble. It needs to be carefully crafted to execute a certain logical path in your code. In tests you want to check for various success and failure conditions, but with benchmarks you specifically look for the most computationally heavy path, a.k.a the "worst case scenario".

This means that if there are certain storage items or runtime state that may affect the complexity of the function, for example triggering more iterations in a for loop, to get an accurate result, you must set up your benchmark to trigger this.

It may be that there are multiple paths your function can go down, and it is not clear which one is the heaviest. In this case, you should just create a benchmark for each scenario! You may find that there are paths in your code where complexity may become unbounded depending on user input. This may be a hint that you should enforce sane boundaries for how a user can use your noble. For example: limiting the number of elements in a vector, limiting the number of iterations in a for loop, etc...

Examples of end-to-end benchmarks can be found in the nobles provided by Tetcore, and the specific details on how to use the benchmarks! macro can be found in its documentation.

Testing Benchmarks

You can test your benchmarks using the same test runtime that you created for your noble's unit tests. By creating your benchmarks in the benchmarks! macro, it automatically generates test functions for you:

fn test_benchmark_[benchmark_name]<T>::() -> Result<(), &'static str>

Simply add these functions to a unit test and ensure that the result of the function is Ok(()).

Note: If your test runtime and production runtime have different configurations, you may get different results when testing your benchmark and actually running it.

In general, benchmarks returning Ok(()) is all you need to check for since it signals the executed extrinsic has completed successfully. However, you can optionally include a verify block with your benchmark, which can additionally verify any final conditions, such as the final state of your runtime.

These additional verify blocks will not affect the results of your final benchmarking process.

To run the tests, you need to enable the runtime-benchmarks feature flag. This may also mean you need to move into your node's binary folder. For example, with the Tetcore repository, this is how you would test the Balances noble's benchmarks:

cargo test -p noble-balances --features runtime-benchmarks

NOTE: Tetcore uses a virtual workspace which does not allow you to compile with feature flags.

error: --features is not allowed in the root of a virtual workspace`

To solve this, navigate to the folder of the node (cd bin/node/cli) or noble (cd fabric/noble) and run the command there.

Adding Benchmarks

The benchmarks included with each noble are not automatically added to your node. To actually execute these benchmarks, you need to implement the fabric_benchmarking::Benchmark trait. You can see an example of how to do this in the included Tetcore node.

Assuming there are already some benchmarks set up on your node, you just need to add another instance of the add_benchmark! macro:

///  configuration for running benchmarks
///               |    name of your noble's crate (as imported)
///               v                   v
add_benchmark!(params, batches, noble_balances, Balances);
///                       ^                          ^
///    where all benchmark results are saved         |
///            the `struct` created for your noble by `construct_runtime!`

Once you have done this, you will need to compile your node binary with the runtime-benchmarks feature flag:

cd bin/node/cli
cargo build --release --features runtime-benchmarks

Running Benchmarks

Finally, once you have a node binary with benchmarks enabled, you need to execute your various benchmarks.

You can get a list of the available benchmarks by running:

./target/release/tetcore benchmark --chain dev --noble "*" --extrinsic "*" --repeat 0

Then you can run a benchmark like so:

./target/release/tetcore benchmark \
    --chain dev \                  # Configurable Chain Spec
    --execution=wasm \             # Always test with Wasm
    --wasm-execution=compiled \    # Always used `wasm-time`
    --noble noble_balances \     # Select the noble
    --extrinsic transfer \         # Select the extrinsic
    --steps 50 \                   # Number of samples across component ranges
    --repeat 20 \                  # Number of times we repeat a benchmark
    --output <path> \              # Output benchmark results into a folder or file

This will output a file noble_name.rs which implements the WeightInfo trait you should include in your noble. Each blockchain should generate their own benchmark file with their custom implementation of the WeightInfo trait. This means that you will be able to use these modular Tetcore nobles while still keeping your network safe for your specific configuration and requirements.

The benchmarking CLI uses a Handlebars template to format the final output file. You can optionally pass the flag --template pointing to a custom template that can be used instead. Within the template, you have access to all the data provided by the TemplateData struct in the benchmarking CLI writer. You can find the default template used here.

There are some custom Handlebars helpers included with our output generation:

• underscore: Add an underscore to every 3rd character from the right of a string. Primarily to be used for delimiting large numbers.
• join: Join an array of strings into a space-separated string for the template. Primarily to be used for joining all the arguments passed to the CLI.

To get a full list of available options when running benchmarks, run:

./target/release/tetcore benchmark --help

License: Apache-2.0