rustream 0.1.2

Fast Postgres → Parquet sync tool

rustream

Fast Postgres to Parquet sync tool. Reads tables from Postgres, writes Parquet files to local disk or S3. Supports incremental sync via updated_at watermark tracking.

Installation

From PyPI

pipx install rustream
# or
pip install rustream

From source

git clone https://github.com/kraftaa/rustream.git
cd rustream
cargo build --release
# binary is at target/release/rustream

With maturin (local dev)

pip install maturin
maturin develop --release
# now `rustream` is on your PATH

Usage

# Copy and edit the example config
cp config.example.yaml config.yaml

# Preview what will be synced (no files written)
rustream sync --config config.yaml --dry-run

# Run sync
rustream sync --config config.yaml

Enable debug logging with RUST_LOG:

RUST_LOG=rustream=debug rustream sync --config config.yaml

Configuration

Specific tables (recommended)

postgres:
  host: localhost
  database: mydb
  user: postgres
  password: secret

output:
  type: local
  path: ./output

tables:
  - name: users
    incremental_column: updated_at
    columns:          # optional: pick specific columns
      - id
      - email
      - created_at
      - updated_at

  - name: orders
    incremental_column: updated_at

  - name: products    # no incremental_column = full sync every run

All tables (auto-discover)

Omit tables to sync every table in the schema. Use exclude to skip some:

postgres:
  host: localhost
  database: mydb
  user: postgres

output:
  type: local
  path: ./output

# schema: public    # default
exclude:
  - schema_migrations
  - ar_internal_metadata

S3 output

output:
  type: s3
  bucket: my-data-lake
  prefix: raw/postgres
  region: us-east-1

AWS credentials come from environment variables, ~/.aws/credentials, or IAM role.

Config reference

Field	Description
postgres.host	Postgres host
postgres.port	Postgres port (default: 5432)
postgres.database	Database name
postgres.user	Database user
postgres.password	Database password (optional)
output.type	local or s3
output.path	Local directory for Parquet files (when type=local)
output.bucket	S3 bucket (when type=s3)
output.prefix	S3 key prefix (when type=s3)
output.region	AWS region (when type=s3, optional)
batch_size	Rows per Parquet file (default: 10000)
state_dir	Directory for SQLite watermark state (default: .rustream_state)
schema	Schema to discover tables from (default: public)
exclude	List of table names to skip when using auto-discovery
tables[].name	Table name
tables[].schema	Schema name (default: public)
tables[].columns	Columns to sync (default: all)
tables[].incremental_column	Column for watermark-based incremental sync
tables[].partition_by	Partition output files: date, month, or year

How it works

1. Connects to Postgres and introspects each table's schema via information_schema
2. Maps Postgres column types to Arrow types automatically
3. Reads rows in batches, converting to Arrow RecordBatches
4. Writes each batch as a Snappy-compressed Parquet file
5. Tracks the high watermark (max value of incremental_column) in local SQLite
6. On next run, only reads rows where incremental_column > last_watermark

Tables without incremental_column do a full sync every run.

Supported Postgres types

Postgres	Arrow
boolean	Boolean
smallint	Int16
integer, serial	Int32
bigint, bigserial	Int64
real	Float32
double precision	Float64
numeric / decimal	Utf8 (preserves precision)
text, varchar, char	Utf8
bytea	Binary
date	Date32
timestamp	Timestamp(Microsecond)
timestamptz	Timestamp(Microsecond, UTC)
uuid	Utf8
json, jsonb	Utf8
arrays	Utf8 (JSON serialized)

Publishing

The project uses maturin to package the Rust binary as a Python wheel (same approach as ruff, uv, etc). The CI workflow in .github/workflows/release.yml builds wheels for Linux, macOS, and Windows, then publishes to PyPI on tagged releases.

To publish manually:

# Build wheels for current platform
maturin build --release

# Upload to PyPI (needs PYPI_API_TOKEN)
maturin publish

License

MIT