Maryk: Cross-Platform Data Modeling and Storage

Maryk is a Kotlin Multiplatform framework for defining, validating, serializing, and storing data models consistently across multiple platforms, including iOS, macOS, Linux, Windows, Android, JVM, and JavaScript. With a fully version-aware data store and flexible querying, Maryk makes it easy to maintain complex data structures while ensuring backward compatibility and efficient data handling.

Note: While Maryk’s data modeling and serialization are cross-platform, its persistant storage layer currently only runs on the JVM.

Key Features

• Unified Data Modeling: Define your data models once and use them everywhere, ensuring a single source of truth across platforms.

• Flexible Property Types and Inheritance: Create models with a variety of property types, and reuse model structures to build complex data hierarchies.

• Built-in Validation: Enforce data quality with validations such as required fields, uniqueness, min/max constraints, and regex checks.

• Cross-Platform Serialization: Seamlessly serialize and deserialize data as JSON, YAML, or Protocol Buffers, facilitating easy communication between clients and services.

• Model Serialization & Compatibility: Serialize your schemas themselves and run compatibility checks across different clients, ensuring smooth upgrades and migrations.

• Version-Aware Storage and Queries: Store data in NoSQL data stores (in-memory/RocksDB/HBase) and leverage versioning to request historical states, compare past values, and minimize bandwidth by fetching only changed fields.

• Data Aggregations & Insights: Perform aggregations (count, sum, average, min/max, grouped by time intervals or enums) for richer analytics and decision-making.

Getting Started

1. Add Maryk Core Dependency:
   In your build.gradle.kts:

implementation("io.maryk:maryk-core:<version>")

2. Define Your Data Models:
   Create a Kotlin data model:

object Person : RootDataModel<Person>() {
   val firstName by string(index = 1u)
   val lastName by string(index = 2u)
   val dateOfBirth by date(index = 3u)
}

3. Create and Validate Instances:

val johnSmith = Person.run { create(
   firstName with "John",
   lastName with "Smith",
   dateOfBirth with LocalDate(2017, 12, 5),
) }

// Validate the object
Person.validate(johnSmith)

4. Serialize Your Data Objects:

// Serialize to JSON
val json = Person.writeJson(johnSmith)

// Deserialize from JSON
val personFromJson = Person.readJson(json)

5. Choose a Data Store:

• In-memory store (non-persistent, suitable for testing)
• RocksDB-based store (persistent, efficient for local storage)
• HBase-based store (persistent and scalable store)

Documentation

For detailed information, check out:

• Core – Data models, queries, parsers, readers.
• Library – Shared utilities for things like Strings and ByteArrays.
• JSON & YAML – Streaming parsers and writers.
• Generator – Code generation from YAML and JSON models.
• Test Library – Testing utilities and helpers.
• DataFrame Integration – DataFrame helper functions for Maryk objects.
• Stores:
  • Shared – Shared logic for building stores.
  • Memory – In-memory store (non-persistent).
  • RocksDB – Persistent, high-performance store.
  • HBase – Persistent, scalabable high-performance store.
  • Tests – Common tests to ensure store reliability.

Contributing

We welcome contributions through feature requests, issue reports, and pull requests.

Your involvement helps Maryk grow and improve!