8 unstable releases (3 breaking)

0.18.1 Nov 13, 2023
0.18.0 Nov 2, 2023
0.17.2 Sep 20, 2023
0.17.0-dev1 Aug 31, 2023
0.0.0 Apr 23, 2023

#1063 in Network programming

Download history 9/week @ 2024-09-22 8/week @ 2024-09-29

98 downloads per month
Used in 2 crates

Apache-2.0

240KB
6K SLoC

Vineyard Rust SDK

[!NOTE] Rust nightly is required. The vineyard Rust SDK is still under development. The API may change in the future.

crates.io Downloads Docs.rs

Connecting to Vineyard

  • Resolve the UNIX-domain socket from the environment variable VINEYARD_IPC_SOCKET:

    use vineyard::client::*;
    
    let mut client = vineyard::default().unwrap();
    
  • Or, using explicit parameter:

    use vineyard::client::*;
    
    let mut client = vineyard::connect("/var/run/vineyard.sock").unwrap();
    

Interact with Vineyard

  • Creating blob:

    let mut blob_writer = client.create_blob(N)?;
    
  • Get object:

    let mut meta_writer = client.get::<DataFrame>(object_id)?;
    

Inter-op with Python: numpy.ndarray

  • Python:

    import numpy as np
    import vineyard
    
    client = vineyard.connect()
    
    np_array = np.random.rand(10, 20).astype(np.int32)
    object_id = int(client.put(np_array))
    
  • Rust:

    let mut client = IPCClient::default()?;
    let tensor = client.get::<Int32Tensor>(object_id)?;
    assert_that!(tensor.shape().to_vec()).is_equal_to(vec![10, 20]);
    

Inter-op with Python: pandas.DataFrame

  • Python

    import pandas as pd
    import vineyard
    
    client = vineyard.connect()
    
    df = pd.DataFrame({'a': ["1", "2", "3", "4"], 'b': ["5", "6", "7", "8"]})
    object_id = int(client.put(df))
    
  • Rust

    let mut client = IPCClient::default()?;
    let dataframe = client.get::<DataFrame>(object_id)?;
    assert_that!(dataframe.num_columns()).is_equal_to(2);
    assert_that!(dataframe.names().to_vec()).is_equal_to(vec!["a".into(), "b".into()]);
    for index in 0..dataframe.num_columns() {
        let column = dataframe.column(index);
        assert_that!(column.len()).is_equal_to(4);
    }
    

Inter-op with Python: pyarrow.RecordBatch

  • Python

    import pandas as pd
    import pyarrow as pa
    import vineyard
    
    client = vineyard.connect()
    
    arrays = [
        pa.array([1, 2, 3, 4]),
        pa.array(["foo", "bar", "baz", "qux"]),
        pa.array([3.0, 5.0, 7.0, 9.0]),
    ]
    batch = pa.RecordBatch.from_arrays(arrays, ["f0", "f1", "f2"])
    object_id = int(client.put(batch))
    
  • Rust

    let batch = client.get::<RecordBatch>(object_id)?;
    assert_that!(batch.num_columns()).is_equal_to(3);
    assert_that!(batch.num_rows()).is_equal_to(4);
    let schema = batch.schema();
    let names = ["f0", "f1", "f2"];
    let recordbatch = batch.as_ref().as_ref();
    

Inter-op with Python: pyarrow.Table

  • Python

    batches = [batch] * 5
    table = pa.Table.from_batches(batches)
    object_id = int(client.put(table))
    
  • Rust

    let mut client = IPCClient::default()?;
    let table = client.get::<Table>(object_id)?;
    assert_that!(table.num_batches()).is_equal_to(5);
    for batch in table.batches().iter() {
        // ...
    }
    

Inter-op with Python: polars.DataFrame

  • Python

    import polars
    
    dataframe = polars.DataFrame(table)
    object_id = int(client.put(dataframe))
    
  • Rust

    use vineyard_polars::ds::dataframe::DataFrame;
    
    let mut client = IPCClient::default()?;
    let dataframe = client.get::<DataFrame>(object_id)?;
    let dataframe = dataframe.as_ref().as_ref();
    assert_that!(dataframe.width()).is_equal_to(3);
    for column in dataframe.get_columns() {
        // ...
    }
    

Inter-op with Python: polars.DataFrame

  • Python

    batches = [batch] * 5
    table = pa.Table.from_batches(batches)
    object_id = int(client.put(table))
    
  • Rust

    use vineyard_datafusion::ds::dataframe::DataFrame;
    
    let mut client = IPCClient::default()?;
    let dataframe = client.get::<DataFrame>(object_id)?;
    
    let ctx = SessionContext::new();
    let table = ctx.read_table(dataframe.table_provider()).unwrap();
    
    assert_that!(block_on(table.count()).unwrap()).is_equal_to(1000);
    

Dependencies

~11–17MB
~239K SLoC