#ironman #eu4 #ck3 #clausewitz

jomini

Low level, performance oriented parser for EU4, CK3, HOI4 save files and other formats

12 releases (5 breaking)

0.6.0 Sep 12, 2020
0.5.0 Sep 10, 2020
0.4.2 Sep 7, 2020
0.3.1 Sep 2, 2020
0.1.2 Aug 6, 2020

#22 in Parser tooling

Download history 50/week @ 2020-08-01 108/week @ 2020-08-08 195/week @ 2020-08-15 285/week @ 2020-08-22 319/week @ 2020-08-29 589/week @ 2020-09-05 277/week @ 2020-09-12

522 downloads per month
Used in 4 crates (3 directly)

MIT license

230KB
6K SLoC

ci Version

Jomini

A low level, performance oriented parser for EU4 save files and other PDS developed titles.

Jomini is the cornerstone of the Rakaly, an EU4 achievement leaderboard and save file analyzer. This library is also used in the Paradox Game Converters project to parse ironman EU4 and CK3 saves.

Features

  • ✔ Versatile: Handle both plaintext and binary encoded data
  • ✔ Fast: Parse data at 1 GB/s
  • ✔ Small: Compile with zero dependencies
  • ✔ Safe: Extensively fuzzed against potential malicious input
  • ✔ Ergonomic: Use serde-like macros to have parsing logic automatically implemented
  • ✔ Embeddable: Cross platform native apps, statically compiled services, or in the browser via WASM
  • ✔ Agnostic: Parse EU4, HOIV, Imperator, CK3, etc save files

Quick Start

Below is a demonstration on parsing plaintext data using jomini tools.

use jomini::{JominiDeserialize, TextDeserializer};

#[derive(JominiDeserialize, PartialEq, Debug)]
pub struct Model {
    human: bool,
    first: Option<u16>,
    #[jomini(alias = "forth")]
    fourth: u16,
    #[jomini(alias = "core", duplicated)]
    cores: Vec<String>,
    names: Vec<String>,
}

let data = br#"
    human = yes
    forth = 10
    core = "HAB"
    names = { "Johan" "Frederick" }
    core = FRA
"#;

let expected = Model {
    human: true,
    first: None,
    fourth: 10,
    cores: vec!["HAB".to_string(), "FRA".to_string()],
    names: vec!["Johan".to_string(), "Frederick".to_string()],
};

let actual: Model = TextDeserializer::from_windows1252_slice(data)?;
assert_eq!(actual, expected);

Binary Parsing

Parsing data encoded in the binary format is done in a similar fashion but with an extra step. Tokens can be encoded into 16 integers, and so one must provide a map from these integers to their textual representations

use jomini::{JominiDeserialize, BinaryDeserializer};
use std::collections::HashMap;

#[derive(JominiDeserialize, PartialEq, Debug)]
struct MyStruct {
    field1: String,
}

let data = [ 0x82, 0x2d, 0x01, 0x00, 0x0f, 0x00, 0x03, 0x00, 0x45, 0x4e, 0x47 ];

let mut map = HashMap::new();
map.insert(0x2d82, "field1");

let actual: MyStruct = BinaryDeserializer::from_eu4(&data[..], &map)?;
assert_eq!(actual, MyStruct { field1: "ENG".to_string() });

When done correctly, one can use the same structure to represent both the plaintext and binary data without any duplication.

One can configure the behavior when a token is unknown (ie: fail immediately or try to continue).

Caveats

Caller is responsible for:

  • Determining the correct format (text or binary) ahead of time
  • Stripping off any header that may be present (eg: EU4txt / EU4bin)
  • Providing the token resolver for the binary format
  • Providing the conversion to reconcile how, for example, a date may be encoded as an integer in the binary format, but as a string when in plaintext.

The plaintext parser is geared towards save file parsing and is not yet general enough to handle files that embed operators other than equals.

One Level Lower

If the automatic deserialization via JominiDeserialize is too high level, one can interact with the raw data directly via TextTape and BinaryTape.

use jomini::{TextTape, TextToken, Scalar};

let data = b"foo=bar";

assert_eq!(
    TextTape::from_slice(&data[..])?.tokens(),
    &[
        TextToken::Scalar(Scalar::new(b"foo")),
        TextToken::Scalar(Scalar::new(b"bar")),
    ]
);

If one will only use TextTape and BinaryTape then jomini can be compiled without default features, resulting in a build without dependencies.

Benchmarks

Benchmarks are ran with the following command:

cargo clean
cargo bench -- 'parse|deserialize'
find ./target -wholename "*/new/raw.csv" -print0 | xargs -0 xsv cat rows > assets/jomini-benchmarks.csv

And can be analyzed with the R script found in the assets directory.

Below is a graph generated from benchmarking on an arbitrary computer.

jomini-bench-throughput.png

Dependencies

~195KB