Getting Started#

Installing vineyard#

Vineyard is distributed as a Python package and can be effortlessly installed using pip:

$ pip3 install vineyard

Launching vineyard server#

$ python3 -m vineyard

A vineyard daemon server will be launched with default settings. By default, /var/run/vineyard.sock will be used by vineyardd to listen for incoming IPC connections.

To stop the running vineyardd instance, simply press Ctrl-C in the terminal.


If you encounter errors like cannot launch vineyardd on '/var/run/vineyard.sock': Permission denied,, it means you don’t have the permission to create a UNIX-domain socket at /var/run/vineyard.sock. You can either:

  • Run vineyard as root, using sudo:

    $ sudo -E python3 -m vineyard
  • Or, change the socket path to a writable location with the --socket command line option:

    $ python3 -m vineyard --socket /tmp/vineyard.sock

Connecting to vineyard#

Once launched, you can call vineyard.connect with the socket name to initiate a vineyard client from Python:

>>> import vineyard
>>> client = vineyard.connect('/var/run/vineyard.sock')

Storing and Retrieving Python Objects#

Vineyard is designed as an in-memory object store and offers two high-level APIs put and get for creating and accessing shared objects, enabling seamless interoperability with the Python ecosystem. The former returns a vineyard.ObjectID upon success, which can be used to retrieve shared objects from vineyard using the latter.

In the following example, we use client.put() to build a vineyard object from the numpy ndarray arr, which returns the object_id - a unique identifier in vineyard representing the object. Given the object_id, we can obtain a shared-memory object from vineyard with the client.get() method.

>>> import numpy as np
>>> object_id = client.put(np.random.rand(2, 4))
>>> object_id
>>> shared_array = client.get(object_id)
>>> shared_array
ndarray([[0.39736989, 0.38047846, 0.01948815, 0.38332264],
         [0.61671189, 0.48903213, 0.03875045, 0.5873005 ]])


shared_array does not allocate extra memory in the Python process; instead, it shares memory with the vineyard server via mmap in a zero-copy process.

The sharable objects can be complex and nested. Like numpy ndarray, the pandas dataframe df can be seamlessly stored in vineyard and retrieved with the .put() and .get() methods as follows:

>>> import pandas as pd
>>> df = pd.DataFrame({'u': [0, 0, 1, 2, 2, 3],
>>>                    'v': [1, 2, 3, 3, 4, 4],
>>>                    'weight': [1.5, 3.2, 4.7, 0.3, 0.8, 2.5]})
>>> object_id = client.put(df)
>>> shared_dataframe = client.get(object_id)
>>> shared_dataframe
   u  v  weight
0  0  1     1.5
1  0  2     3.2
2  1  3     4.7
3  2  3     0.3
4  2  4     0.8
5  3  4     2.5

Under the hood, vineyard implements a builder/resolver mechanism to represent arbitrary data structures as vineyard objects and resolve them back to native values in the corresponding programming languages and computing systems. See also I/O Drivers for more information.

Sharing objects between tasks#

Vineyard is designed for sharing intermediate data between tasks. The following example demonstrates how a dataframe can be passed between two processes using vineyard, namely the producer and consumer in the example below:

import multiprocessing as mp
import vineyard

import numpy as np
import pandas as pd

socket = '/var/run/vineyard.sock'

def produce(name):
   client = vineyard.connect(socket)
   client.put(pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD')),
              persist=True, name=name)

def consume(name):
   client = vineyard.connect(socket)

if __name__ == '__main__':
   name = 'dataset'

   producer = mp.Process(target=produce, args=(name,))
   consumer = mp.Process(target=consume, args=(name,))


Running the code above, you should see the following output:

A   -4.529080
B   -2.969152
C   -7.067356
D    4.003676
dtype: float64

Next steps#

Beyond the core functionality of sharing objects between tasks, vineyard also provides:

  • Distributed objects and stream abstraction over immutable chunks;

  • An IDL (Code Generation for Boilerplate) that helps integrate vineyard with other systems at minimal cost;

  • A mechanism of pluggable drivers for various tasks that serve as the glue between the core compute engine and the external world, e.g., data sources, data sinks;

  • Integration with Kubernetes for sharing between tasks in workflows deployed on cloud-native infrastructures.

Overview of vineyard.

Learn more about vineyard’s key concepts from the following user guides:

Vineyard Objects

Explore the design of the object model in vineyard.


Discover how vineyard integrates with other computing systems.

I/O Drivers

Understand the design and implementation of pluggable routines for I/O, repartition, migration, and more.

Vineyard is a natural fit for cloud-native computing, where it can be deployed and managed by the vineyard operator, providing data-aware scheduling for data analytical workflows to achieve efficient data sharing on Kubernetes. More details about vineyard on Kubernetes can be found here:

Deploy vineyard on Kubernetes and accelerate your big-data workflows.