riffq

riffq is a toolkit in python (built in Rust) for building PostgreSQL wire-compatible databases.
It allows you to serve data from Python over the PostgreSQL protocol — turning your Python-based logic or in-memory data into a queryable, network-exposed system. We also have a catalog emulation system in rust with datafusion.

Documentation: https://ybrs.github.io/riffq/

What It Does

• Implements the PostgreSQL wire protocol in Rust for performance and concurrency
• Sends raw SQL queries (Simple or Extended protocol) to Python for interpretation
• Implements postgres catalog compatibility layer, see pg_catalog_rs

Since you are in python, you can

• Allows you to connect to remote data sources (e.g., analytics DB, CRM) and expose them as a unified PostgreSQL database
• Enables serving Pandas DataFrames over the network as virtual SQL tables
• Can delegate SQL execution to DuckDB, Polars, or any other Python engine
• Acts as a programmable federated query engine or custom data service

Example Use Cases

• Serve a Pandas DataFrame as a PostgreSQL table to BI tools
• Build a custom federated engine from multiple APIs or databases
• Implement your own data lake query frontend
• Expose dynamic ML feature stores for training or real-time inference
• Provide fine-grained, code-controlled access to internal metrics or logs

Example

import logging
import duckdb
import pyarrow as pa
import riffq
logging.basicConfig(level=logging.DEBUG)

class Connection(riffq.BaseConnection):
    def handle_auth(self, user, password, host, database=None, callback=callable):
        # simple username/password check
        callback(user == "user" and password == "secret")

    def handle_connect(self, ip, port, server_name=None, metadata=None, callback=callable):
        # allow every incoming connection; metadata holds the handshake
        # (user, database, application_name, ...) for routing decisions
        callback(True)

    def handle_disconnect(self, ip, port, callback=callable):
        # invoked when client disconnects
        callback(True)

    def _handle_query(self, sql, callback, **kwargs):
        cur = duckdb_con.cursor()
        try:
            if sql.strip().lower() == "select err":
                # custom error returned to client
                callback(("ERROR", "42846", "bad type"), is_error=True)
                return
            reader = cur.execute(sql).fetch_record_batch()
            self.send_reader(reader, callback)
        except Exception as exc:
            logging.exception("error on executing query")
            batch = self.arrow_batch(
                [pa.array(["ERROR"]), pa.array([str(exc)])],
                ["error", "message"],
            )
            self.send_reader(batch, callback)

    def handle_query(self, sql, callback=callable, **kwargs):
        self.executor.submit(self._handle_query, sql, callback, **kwargs)

def main():
    global duckdb_con
    duckdb_con = duckdb.connect()
    duckdb_con.execute(
        """
        CREATE VIEW klines AS 
        SELECT * 
        FROM 'data/klines.parquet'
        """
    )
    server = riffq.RiffqServer("127.0.0.1:5433", connection_cls=Connection)
    server.set_tls("certs/server.crt", "certs/server.key")
    server.start(tls=True)

if __name__ == "__main__":
    main()

The Rust side calls this Python handler when a SQL query comes in via the PostgreSQL protocol.

Architecture

• Rust layer handles:
  • PostgreSQL protocol (via pgwire)
  • Connection management
  • Query routing
  • Metadata compatibility (pg_catalog emulation)
• Python layer handles:
  • SQL execution (via any engine: DuckDB, Polars, etc.)
  • Data transformation
  • Custom logic and dynamic schema definitions

Zero Copy

We try to achieve zero-copy by using arrow/pycapsule. So data from duckdb comes to python as a pycapsule pointer, which goes to thread in python which goes to the callback in rust still as a pycapsule pointer. We then stream to network with postgresql using pgwire.

https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html

Getting Started

Install the package

pip install riffq --pre

Extend BaseConnection class

You can extend the base class for

• handle_query
• handle_auth - To check for username/password
• handle_connect - To check ip/port restriction; receives the handshake as metadata
• handle_disconnect - WIP

Connection metadata and session settings

handle_connect receives a metadata dict with the handshake as the client sent it (user, database, application_name, client_encoding, ...), verbatim. The same snapshot is available later per connection id: server.get_client_metadata(connection_id) raises KeyError for an id that never connected, was refused, or already disconnected.

server.get_client_settings(connection_id) answers the live session instead: the variables SET stores per connection (application_name, search_path, timezone, ...). A SET the client sent shows up here, never in the metadata snapshot. A live connection with no session (a server without catalog emulation builds none) raises RuntimeError.

class Connection(riffq.BaseConnection):
    def handle_auth(self, user, password, host, database=None, callback=callable):
        # simple username/password check
        callback(user == "user" and password == "secret")

    def handle_connect(self, ip, port, server_name=None, metadata=None, callback=callable):
        # allow every incoming connection; metadata holds the handshake
        # (user, database, application_name, ...) for routing decisions
        callback(True)

    def handle_disconnect(self, ip, port, callback=callable):
        # invoked when client disconnects
        callback(True)

    def _handle_query(self, sql, callback, **kwargs):
        cur = duckdb_con.cursor()
        try:
            if sql.strip().lower() == "select err":
                # custom error returned to client
                callback(("ERROR", "42846", "bad type"), is_error=True)
                return
            reader = cur.execute(sql).fetch_record_batch()
            self.send_reader(reader, callback)
        except Exception as exc:
            logging.exception("error on executing query")
            batch = self.arrow_batch(
                [pa.array(["ERROR"]), pa.array([str(exc)])],
                ["error", "message"],
            )
            self.send_reader(batch, callback)

    def handle_query(self, sql, callback=callable, **kwargs):
        self.executor.submit(self._handle_query, sql, callback, **kwargs)

Start the server

    global duckdb_con
    duckdb_con = duckdb.connect()
    duckdb_con.execute(
        """
        CREATE VIEW klines AS 
        SELECT * 
        FROM 'data/klines.parquet'
        """
    )
    server = riffq.RiffqServer("127.0.0.1:5433", connection_cls=Connection)
    server.set_tls("certs/server.crt", "certs/server.key")
    server.start()

You can check server implementations on test_concurrency/ and example/ directory

Then connect using any PostgreSQL client:

psql -h localhost -p 5433

Enabling TLS

Generate a temporary certificate and key:

openssl req -newkey rsa:2048 -nodes -keyout server.key -x509 -days 1 -out server.crt -subj "/CN=localhost"

Enabling Catalog Emulation

Postgresql clients sends queries to pg_catalog schema to find out databases, schemas, tables, columns.

We have this pg_catalog_rs for this purpose.

You can register your own database and your tables.

For example

    server = riffq.RiffqServer(f"127.0.0.1:{port}", connection_cls=Connection)
    server.set_tls("certs/server.crt", "certs/server.key")

    server._server.register_database("duckdb")

    tbls = duckdb_con.execute(
        "SELECT table_schema, table_name FROM information_schema.tables "
        "WHERE table_schema NOT IN ('pg_catalog','information_schema')"
    ).fetchall()

    for schema_name, table_name in tbls:
        server._server.register_schema("duckdb", schema_name)
        cols_info = duckdb_con.execute(
            "SELECT column_name, data_type, is_nullable FROM information_schema.columns "
            "WHERE table_schema=? AND table_name=?",
            (schema_name, table_name),
        ).fetchall()
        columns = []
        for col_name, data_type, is_nullable in cols_info:
            columns.append(
                {
                    col_name: {
                        "type": map_type(data_type),
                        "nullable": is_nullable.upper() == "YES",
                    }
                }
            )
        server._server.register_table("duckdb", schema_name, table_name, columns)

    server.start(catalog_emulation=True)

Lazy (callback-driven) catalog

The register_* calls above snapshot the catalog at startup. For a live source, install a lazy catalog instead: supply one source object and Riffq pulls catalog metadata from it on every pg_catalog scan, so tables created after startup show up automatically — nothing is cached.

class MyCatalog:
    def databases(self, callback):
        callback([{"oid": 16384, "name": "appdb"}])
    def schemas(self, database, callback):
        callback([{"oid": 16385, "name": "public"}])
    def relations(self, database, schema, callback):
        callback([{"oid": 20001, "reltype_oid": 30001, "name": "users",
                   "kind": "table", "has_index": False}])
    def columns(self, database, schema, relation, callback):
        callback([{"name": "id", "type_oid": 23, "nullable": False}])  # 23 = int4

server.set_lazy_catalog(MyCatalog())   # replaces the eager register_* calls
server.start(catalog_emulation=True)

You own the OIDs (stable + unique); type_oid is a pg_type OID. See docs/catalog.md for the full contract and example/lazy_catalog.py for a runnable example; Teleduck uses this path against a live DuckDB connection.

Status

• ✅ Wire protocol support (simple + extended)
• ✅ Query dispatching to Python
• ✅ Thread based non-blocking query execution (long queries don't block)
• ✅ DuckDB, Pandas, Polars compatibility
• ✅ Limited SQL parsing on Rust side (forwarded to Python)
• ✅ Optional TLS encryption
• ✅ Integration with optional catalog emulation layer
• 🟡 More examples
• 🟡 Better logging, monitoring, observability

Installation

We currently have a pre release on pypi. You can install it with --pre tag.

pip install riffq --pre

Running Locally

Install the development requirements and run the test suite:

git clone git@github.com:ybrs/riffq.git
cd riffq
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

maturin build --profile=fast -i python3
pip install target/wheels/*.whl
# or maturin developer
make all-tests

The tests require the Rust extension to build successfully; any build failure will cause the suite to fail.

License

MIT or Apache 2.0 — your choice.

Contributing

Contributions are welcome! Especially for:

• For the emulation layer, I am currently testing with

  • Intellij Datagrid
  • Dbeaver
  • psql cli
  • Vscode Postgresql Extension

  So testing with other clients, especially BI tools are very welcomed.

• Better Python DX

• Example apps (data lake, feature store, etc.)