Tonbo

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Tonbo is an embedded database for serverless and edge runtimes. Your data is stored as Parquet on S3, coordination happens through a manifest, and compute stays fully stateless.

Why Tonbo?

Serverless compute is stateless, but your data isn't. Tonbo bridges this gap:

• Async-first: The entire storage and query engine is fully async, built for serverless and edge environments.
• No server to manage: Data lives on S3, coordination via manifest, compute is stateless
• Arrow-native: Define rich data type, declarative schemas, query with zero-copy RecordBatch
• Runs anywhere: Tokio, WASM, edge runtimes, or as a storage engine for building your own data infrastructure.
• Open formats: Standard Parquet files readable by any tool

When to use Tonbo?

• Build serverless or edge applications that need a durable state layer without running a database.
• Store append-heavy or event-like data directly in S3 and query it with low overhead.
• Embed a lightweight MVCC + Parquet storage engine inside your own data infrastructure.
• Run workloads in WASM or Cloudflare Workers that require structured persistence.

Quick Start

use tonbo::{db::{AwsCreds, ObjectSpec, S3Spec}, prelude::*};

#[derive(Record)]
struct User {
    #[metadata(k = "tonbo.key", v = "true")]
    id: String,
    name: String,
    score: Option<i64>,
}

// Open on S3
let s3 = S3Spec::new("my-bucket", "data/users", AwsCreds::from_env()?);
let db = DbBuilder::from_schema(User::schema())?
    .object_store(ObjectSpec::s3(s3))?.open().await?;

// Insert
let users = vec![User { id: "u1".into(), name: "Alice".into(), score: Some(100) }];
let mut builders = User::new_builders(users.len());
builders.append_rows(users);
db.ingest(builders.finish().into_record_batch()).await?;

// Query
let filter = Predicate::gt(ColumnRef::new("score"), ScalarValue::from(80_i64));
let results = db.scan().filter(filter).collect().await?;

For local development, use .on_disk("/tmp/users")? instead. See examples/ for more.

Installation

cargo add tonbo@0.4.0-a0 tokio

Or add to Cargo.toml:

[dependencies]
tonbo = "0.4.0-a0"
tokio = { version = "1", features = ["rt-multi-thread", "macros"] }

Examples

Run with cargo run --example <name>:

• 01_basic: Define schema, insert, and query in 30 lines
• 02_transaction: MVCC transactions with upsert, delete, and read-your-writes
• 02b_snapshot: Consistent point-in-time reads while writes continue
• 03_filter: Predicates: eq, gt, in, is_null, and, or, not
• 04_s3: Store Parquet files on S3/R2/MinIO with zero server config
• 05_scan_options: Projection pushdown reads only the columns you need
• 06_composite_key: Multi-column keys for time-series and partitioned data
• 07_streaming: Process millions of rows without loading into memory
• 08_nested_types: Deep struct nesting + Lists stored as Arrow StructArray
• 09_time_travel: Query historical snapshots via MVCC timestamps

Architecture

Tonbo implements a merge-tree optimized for object storage: writes go to WAL → MemTable → Parquet SSTables, with MVCC for snapshot isolation and a manifest for coordination via compare-and-swap:

• Stateless compute: A worker only needs to read and update the manifest; no long-lived coordinator is required.

• Object storage CAS: The manifest is committed using compare-and-swap on S3, so any function can safely participate in commits.

• Immutable data: Data files are write-once Parquet SSTables, which matches the strengths of S3 and other object stores.

See docs/overview.md for the full design.

Documentation

• User Guide: tonbo.io/docs: tutorials and concepts
• API Reference: docs.rs/tonbo: full Rust API documentation
• RFCs: docs/rfcs/: design documents for contributors

Project status

Tonbo is currently in alpha. APIs may change, and we're actively iterating based on feedback. We recommend starting with development and non-critical workloads before moving to production.

Features

Storage

• Parquet files on object storage (S3, R2) or local filesystem
• Manifest-driven coordination (CAS commits, no server needed)
• (in-progress) Remote compaction (offload to serverless functions)
• (in-progress) Branching (git-like fork and merge for datasets)
• Time-window compaction strategy

Schema & Query

• Arrow-native schemas (#[derive(Record)] or dynamic Schema)
• Projection pushdown (read only needed columns)
• Zero-copy reads via Arrow RecordBatch
• (in-progress) Filter pushdown (predicates evaluated at storage layer)

Backends

• Local filesystem
• S3 / S3-compatible (R2, MinIO)
• (in-progress) OPFS (browser storage)

Runtime

• Async-first (Tokio)
• Edge runtimes (Deno, Cloudflare Workers)
• WebAssembly
• (in-progress) io_uring
• (in-progress) Python async bindings
• JavaScript/TypeScript async bindings

Integrations

• DataFusion TableProvider
• Postgres Foreign Data Wrapper

License

Apache License 2.0. See LICENSE for details.