Vega

An in-memory distributed data processing engine written in C++17. Vega provides a Spark-like RDD API with lazy evaluation, but replaces Spark's centralized driver/scheduler with a decentralized actor model where workers coordinate through message passing.

PyVega is the Python client: it connects to a C++ session server over TCP, records transformations lazily, and executes them on an embedded actor runtime with Python UDF support.

Documentation

Guide	Description
Docs index	Start here
Getting started	Build, install, first program
Server, workers, and clients	vega_server, worker threads, cluster peers
Python RDD guide	PyVega API reference with examples
DataFrame-style operations	SQL-like patterns using RDDs
PySpark compatibility	Run PySpark RDD code and upstream tests

Quick start (Python)

# Build session server
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DVEGA_BUILD_SERVER=ON
cmake --build build --target vega_server -j$(nproc)

# Install client
python -m venv .venv && source .venv/bin/activate
pip install -e ".[test]"
export VEGA_SERVER="$PWD/build/vega_server"

from pyvega import SparkContext

with SparkContext(4) as sc:
    print(sc.range(0, 10).map(lambda x: x * 2).collect())
    # [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

Manual server: vega-server --bind 127.0.0.1:9500 then SparkContext(remote="127.0.0.1:9500"). See server and clients.

Note: PyVega provides RDD APIs, not Spark SQL DataFrames. See DataFrame-style operations for RDD equivalents.

Architecture

                      User Code (Python or C++)
                                |
                    SparkContext / Cluster
                                |
                          +-----------+
                          |  JobActor |      (builds DAG, distributes tasks)
                          +-----------+
                           /    |     \
                   +------+ +------+ +------+
                   |Worker| |Worker| |Worker|   (execute partitions in parallel)
                   +------+ +------+ +------+
                       \       |       /
                    +-------------------------+
                    |   Actor System          |   (thread pool, message routing)
                    +-------------------------+
                    |   Transport Layer       |   (Local / TCP + Gossip)
                    +-------------------------+

For PyVega, the actor system runs inside vega_server. The Python process is a thin RPC client; worker threads are not separate OS processes.

Key difference from Spark: there is no central scheduler in cluster mode. Peers discover each other via gossip and coordinate execution directly. In single-node / session-server mode, the JobActor still orchestrates stages but all workers are local threads.

Actor framework

Vega includes a lightweight actor framework inspired by rotor:

• Message-based communication — typed messages dispatched to registered handlers
• Thread pool execution — actors process messages in parallel across worker threads
• Sequential per-actor — each actor processes one message at a time
• Python GIL integration — worker threads acquire the GIL when executing Python UDFs

Build (C++)

cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc)

Targets include vega_server, vega_worker, examples, and C++ tests.

Python API (PyVega)

from pyvega import SparkContext

with SparkContext(4) as sc:
    rdd = sc.parallelize([1, 2, 3, 4, 5], num_partitions=2)

    result = (
        rdd.filter(lambda x: x > 2)
        .map(lambda x: x * 10)
        .collect()
    )
    # [30, 40, 50]

    # Word count
    lines = ["hello world hello", "hello vega"]
    counts = (
        sc.parallelize(lines)
        .flat_map(lambda line: line.split())
        .map(lambda word: (word, 1))
        .reduce_by_key(lambda a, b: a + b)
        .collect()
    )

Full API: Python RDD guide.

Running on a cluster (C++)

vega_worker runs equal peers (no special driver role). PyVega does not connect to cluster peers yet; use this for C++ multi-node examples.

# Terminal 1
./build/vega_worker --bind 127.0.0.1:9001 --id peer-1

# Terminal 2
./build/vega_worker --bind 127.0.0.1:9002 --id peer-2 --seeds 127.0.0.1:9001

# Terminal 3 — submitter (same binary, not a "driver")
./build/examples/cluster_word_count --bind 127.0.0.1:9003 --id peer-3 \
    --seeds 127.0.0.1:9001 --expect-peers 3

Single-node demo: ./build/examples/cluster_word_count --local

Flag	Description
--bind HOST:PORT	Listen address
--seeds H1:P1,H2:P2	Gossip seed peers
--id NAME	Node identifier
--expect-peers N	Wait for N peers before running (examples)

Details: Server, workers, and clients.

C++ API

#include <vega/vega.hpp>

int main() {
    vega::SparkContext ctx(4);
    auto rdd = ctx.parallelize(std::vector<int>{1, 2, 3, 4, 5}, 2);
    auto result = rdd
        .filter([](const int& x) { return x > 2; })
        .map([](const int& x) { return x * 10; })
        .collect();
}

Available operations

Transformation	Python	C++
map	rdd.map(func)	rdd.map(func)
filter	rdd.filter(func)	rdd.filter(pred)
flat_map	rdd.flat_map(func)	rdd.flatMap(func)
reduceByKey	rdd.reduce_by_key(func)	rdd.reduceByKey(func)
groupByKey	rdd.group_by_key()	rdd.groupByKey()

Action	Python	C++
collect	rdd.collect()	rdd.collect()
count	rdd.count()	rdd.count()
reduce	rdd.reduce(func)	rdd.reduce(func)
foreach	rdd.foreach(func)	rdd.foreach(func)

Project structure

vega/
  docs/                      User guides (start at docs/README.md)
  include/vega/              C++ header-only library
    core/                    RDD, SparkContext, shuffle
    actors/                  JobActor, WorkerActor, messages
    execution/               DAG builder, stages
    cluster/                 P2P cluster, gossip
    network/                 TCP framing, routing
  src/
    vega_server.cpp          TCP session server (PyVega backend)
    server_session.cpp       Session RPC + embedded Python UDFs
    vega_worker.cpp          Cluster peer daemon
  python/pyvega/             PyVega client package
  scripts/
    pyspark_compat/          PySpark RDD API shim
    run_pyspark_tests.py     Upstream Spark RDD tests vs PyVega
  third_party/spark/         Vendored Spark test sources
  examples/                  word_count, cluster_word_count, benchmarks
  tests/                     C++ unit tests + Spark RDD suite ports

Spark compatibility testing

# C++ Spark RDD logic ports
cmake --build build --target test_spark_rdd_suite
cd build && ctest -R SparkRDDSuite --output-on-failure

# Upstream PySpark RDD tests (50/54 pass; 4 require JVM)
VEGA_SERVER=build/vega_server \
  python scripts/run_pyspark_tests.py --filter pyspark.tests.test_rdd

See PySpark compatibility.

Execution flow

PyVega (session server)

1. Client builds an RDD chain lazily (map, filter, …)
2. First action sends fused stages over TCP
3. Server materializes lineage and submits a job to JobActor
4. WorkerActors compute partitions (native C++ or embedded Python)
5. Shuffle stages (reduceByKey, groupByKey) run as multi-stage DAGs
6. Results return to the Python client

Multi-node C++ cluster

1. Peers start and discover each other via gossip
2. Submitter distributes partition data and task assignments over TCP
3. Peers execute tasks and return serialized results

Contributing

See CONTRIBUTING.md for build/test instructions and PR guidelines, and our Code of Conduct. Report security issues privately per SECURITY.md.

License

Licensed under the MIT License.

Vega is not affiliated with the Apache Software Foundation. "Apache Spark" and "PySpark" are trademarks of the ASF; Vega offers a Spark-like API for familiarity and compatibility testing only. See NOTICE for third-party attributions (Apache Spark test sources, pybind11, GoogleTest, cloudpickle).