Distributed Write

Warning

Lance provides out-of-the-box Ray and Spark integrations.

This page is intended for users who wish to perform distributed operations in a custom manner, i.e. using slurm or Kubernetes without the Lance integration.

Overview

The Lance format is designed to support parallel writing across multiple distributed workers. A distributed write operation can be performed by two phases:

1. Parallel Writes: Generate new lance.LanceFragment in parallel across multiple workers.
2. Commit: Collect all the lance.FragmentMetadata and commit into a single dataset in a single lance.LanceOperation.

Write new data

Writing or appending new data is straightforward with lance.fragment.write_fragments.

import json
from lance.fragment import write_fragments

# Run on each worker
data_uri = "./dist_write"
schema = pa.schema([
    ("a", pa.int32()),
    ("b", pa.string()),
])

# Run on worker 1
data1 = {
    "a": [1, 2, 3],
    "b": ["x", "y", "z"],
}
fragments_1 = write_fragments(data1, data_uri, schema=schema)
print("Worker 1: ", fragments_1)

# Run on worker 2
data2 = {
    "a": [4, 5, 6],
    "b": ["u", "v", "w"],
}
fragments_2 = write_fragments(data2, data_uri, schema=schema)
print("Worker 2: ", fragments_2)

Output:

Worker 1:  [FragmentMetadata(id=0, files=...)]
Worker 2:  [FragmentMetadata(id=0, files=...)]

Now, use lance.fragment.FragmentMetadata.to_json to serialize the fragment metadata, and collect all serialized metadata on a single worker to execute the final commit operation.

import json
from lance import FragmentMetadata, LanceOperation

# Serialize Fragments into JSON data
fragments_json1 = [json.dumps(fragment.to_json()) for fragment in fragments_1]
fragments_json2 = [json.dumps(fragment.to_json()) for fragment in fragments_2]

# On one worker, collect all fragments
all_fragments = [FragmentMetadata.from_json(f) for f in \
    fragments_json1 + fragments_json2]

# Commit the fragments into a single dataset
# Use LanceOperation.Overwrite to overwrite the dataset or create new dataset.
op = lance.LanceOperation.Overwrite(schema, all_fragments)
read_version = 0 # Because it is empty at the time.
lance.LanceDataset.commit(
    data_uri,
    op,
    read_version=read_version,
)

# We can read the dataset using the Lance API:
dataset = lance.dataset(data_uri)
assert len(dataset.get_fragments()) == 2
assert dataset.version == 1
print(dataset.to_table().to_pandas())

Output:

     a  b
0  1  x
1  2  y
2  3  z
3  4  u
4  5  v
5  6  w

Append data

Appending additional data follows a similar process. Use lance.LanceOperation.Append to commit the new fragments, ensuring that the read_version is set to the current dataset's version.

import lance

ds = lance.dataset(data_uri)
read_version = ds.version # record the read version

op = lance.LanceOperation.Append(schema, all_fragments)
lance.LanceDataset.commit(
    data_uri,
    op,
    read_version=read_version,
)

Add New Columns

Lance Format excels at operations such as adding columns. Thanks to its two-dimensional layout (see this blog post), adding new columns is highly efficient since it avoids copying the existing data files. Instead, the process simply creates new data files and links them to the existing dataset using metadata-only operations.

import lance
from pyarrow import RecordBatch
import pyarrow.compute as pc

dataset = lance.dataset("./add_columns_example")
assert len(dataset.get_fragments()) == 2
assert dataset.to_table().combine_chunks() == pa.Table.from_pydict({
    "name": ["alice", "bob", "charlie", "craig", "dave", "eve"],
    "age": [25, 33, 44, 55, 66, 77],
}, schema=schema)


def name_len(names: RecordBatch) -> RecordBatch:
    return RecordBatch.from_arrays(
        [pc.utf8_length(names["name"])],
        ["name_len"],
    )

# On Worker 1
frag1 = dataset.get_fragments()[0]
new_fragment1, new_schema = frag1.merge_columns(name_len, ["name"])

# On Worker 2
frag2 = dataset.get_fragments()[1]
new_fragment2, _ = frag2.merge_columns(name_len, ["name"])

# On Worker 3 - Commit
all_fragments = [new_fragment1, new_fragment2]
op = lance.LanceOperation.Merge(all_fragments, schema=new_schema)
lance.LanceDataset.commit(
    "./add_columns_example",
    op,
    read_version=dataset.version,
)

# Verify dataset
dataset = lance.dataset("./add_columns_example")
print(dataset.to_table().to_pandas())

Output:

      name  age  name_len
0    alice   25         5
1      bob   33         3
2  charlie   44         7
3    craig   55         5
4     dave   66         4
5      eve   77         3