Saving Data#
Ray Data lets you save data in files or other Python objects.
This guide shows you how to:
Writing data to files#
Ray Data writes to local disk and cloud storage.
Writing data to local disk#
To save your Dataset to local disk, call a method
like Dataset.write_parquet and specify a local
directory with the local:// scheme.
Warning
If your cluster contains multiple nodes and you don’t use local://, Ray Data
writes different partitions of data to different nodes.
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
ds.write_parquet("local:///tmp/iris/")
To write data to formats other than Parquet, read the Input/Output reference.
Writing data to cloud storage#
To save your Dataset to cloud storage, authenticate all nodes
with your cloud service provider. Then, call a method like
Dataset.write_parquet and specify a URI with
the appropriate scheme. URI can point to buckets or folders.
To write data to formats other than Parquet, read the Input/Output reference.
To save data to Amazon S3, specify a URI with the s3:// scheme.
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
ds.write_parquet("s3://my-bucket/my-folder")
Ray Data relies on PyArrow to authenticate with Amazon S3. For more on how to configure your credentials to be compatible with PyArrow, see their S3 Filesystem docs.
To save data to Google Cloud Storage, install the Filesystem interface to Google Cloud Storage
pip install gcsfs
Then, create a GCSFileSystem and specify a URI with the gcs:// scheme.
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
filesystem = gcsfs.GCSFileSystem(project="my-google-project")
ds.write_parquet("gcs://my-bucket/my-folder", filesystem=filesystem)
Ray Data relies on PyArrow for authentication with Google Cloud Storage. For more on how to configure your credentials to be compatible with PyArrow, see their GCS Filesystem docs.
To save data to Azure Blob Storage, install the Filesystem interface to Azure-Datalake Gen1 and Gen2 Storage
pip install adlfs
Then, create a AzureBlobFileSystem and specify a URI with the az:// scheme.
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
filesystem = adlfs.AzureBlobFileSystem(account_name="azureopendatastorage")
ds.write_parquet("az://my-bucket/my-folder", filesystem=filesystem)
Ray Data relies on PyArrow for authentication with Azure Blob Storage. For more on how to configure your credentials to be compatible with PyArrow, see their fsspec-compatible filesystems docs.
Writing data to NFS#
To save your Dataset to NFS file systems, call a method
like Dataset.write_parquet and specify a
mounted directory.
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
ds.write_parquet("/mnt/cluster_storage/iris")
To write data to formats other than Parquet, read the Input/Output reference.
Changing the number of output files#
When you call a write method, Ray Data writes your data to several files. To control the
number of output files, configure min_rows_per_file.
Note
min_rows_per_file is a hint, not a strict limit. Ray Data might write more or
fewer rows to each file. Under the hood, if the number of rows per block is
larger than the specified value, Ray Data writes
the number of rows per block to each file.
import os
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
ds.write_csv("/tmp/few_files/", min_rows_per_file=75)
print(os.listdir("/tmp/few_files/"))
['0_000001_000000.csv', '0_000000_000000.csv', '0_000002_000000.csv']
Writing into Partitioned Dataset#
When writing partitioned dataset (using Hive-style, folder-based partitioning) it’s recommended to repartition the dataset by the partition columns prior to writing into it.
This allows you to have the control over the file-sizes and their number. When the dataset is repartitioned by the partition columns every block should contain all of the rows corresponding to particular partition,
meaning that the number of files created should be controlled based on the configuration provided to,
for example, write_parquet method (such as min_rows_per_file, max_rows_per_file).
Since every block is written out independently, when writing the dataset without prior
repartitioning you could potentially get an N number of files per partition
(where N is the number of blocks in your dataset) with very limited ability to control the
number of files & their sizes (since every block could potentially carry the rows corresponding to any partition).
def print_directory_tree(start_path: str) -> None:
"""
Prints the directory tree structure starting from the given path.
"""
for root, dirs, files in os.walk(start_path):
level = root.replace(start_path, '').count(os.sep)
indent = ' ' * 4 * (level)
print(f'{indent}{os.path.basename(root)}/')
subindent = ' ' * 4 * (level + 1)
for f in files:
print(f'{subindent}{f}')
# Sample dataset that we’ll partition by ``city`` and ``year``.
df = pd.DataFrame(
{
"city": ["SF", "SF", "NYC", "NYC", "SF", "NYC", "SF", "NYC"],
"year": [2023, 2024, 2023, 2024, 2023, 2023, 2024, 2024],
"sales": [100, 120, 90, 115, 105, 95, 130, 110],
}
)
ds = ray.data.from_pandas(df)
DataContext.shuffle_strategy=ShuffleStrategy.HASH_SHUFFLE
# ── Partitioned write ──────────────────────────────────────────────────────
# 1. Repartition so all rows with the same (city, year) land in the same
# block – this minimises shuffling during the write.
# 2. Pass the same columns to ``partition_cols`` so Ray creates a
# Hive-style directory layout: city=<value>/year=<value>/....
# 3. Use ``min_rows_per_file`` / ``max_rows_per_file`` to control how many
# rows Ray puts in each Parquet file.
ds.repartition(keys=["city", "year"], num_blocks=4).write_parquet(
"/tmp/sales_partitioned",
partition_cols=["city", "year"],
min_rows_per_file=2, # At least 2 rows in each file …
max_rows_per_file=3, # … but never more than 3.
)
print_directory_tree("/tmp/sales_partitioned")
sales_partitioned/
city=NYC/
year=2024/
1_a2b8b82cd2904a368ec39f42ae3cf830_000000_000000-0.parquet
year=2023/
1_a2b8b82cd2904a368ec39f42ae3cf830_000001_000000-0.parquet
city=SF/
year=2024/
1_a2b8b82cd2904a368ec39f42ae3cf830_000000_000000-0.parquet
year=2023/
1_a2b8b82cd2904a368ec39f42ae3cf830_000001_000000-0.parquet
Converting Datasets to other Python libraries#
Converting Datasets to pandas#
To convert a Dataset to a pandas DataFrame, call
Dataset.to_pandas(). Your data must fit in memory
on the head node.
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
df = ds.to_pandas()
print(df)
sepal length (cm) sepal width (cm) ... petal width (cm) target
0 5.1 3.5 ... 0.2 0
1 4.9 3.0 ... 0.2 0
2 4.7 3.2 ... 0.2 0
3 4.6 3.1 ... 0.2 0
4 5.0 3.6 ... 0.2 0
.. ... ... ... ... ...
145 6.7 3.0 ... 2.3 2
146 6.3 2.5 ... 1.9 2
147 6.5 3.0 ... 2.0 2
148 6.2 3.4 ... 2.3 2
149 5.9 3.0 ... 1.8 2
<BLANKLINE>
[150 rows x 5 columns]
Converting Datasets to distributed DataFrames#
Ray Data interoperates with distributed data processing frameworks like Daft, Dask, Spark, Modin, and Mars.
To convert a Dataset to a Daft Dataframe, call
Dataset.to_daft().
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
df = ds.to_daft()
print(df)
╭───────────────────┬──────────────────┬───────────────────┬──────────────────┬────────╮
│ sepal length (cm) ┆ sepal width (cm) ┆ petal length (cm) ┆ petal width (cm) ┆ target │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ Float64 ┆ Float64 ┆ Float64 ┆ Float64 ┆ Int64 │
╞═══════════════════╪══════════════════╪═══════════════════╪══════════════════╪════════╡
│ 5.1 ┆ 3.5 ┆ 1.4 ┆ 0.2 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 4.9 ┆ 3 ┆ 1.4 ┆ 0.2 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 4.7 ┆ 3.2 ┆ 1.3 ┆ 0.2 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 4.6 ┆ 3.1 ┆ 1.5 ┆ 0.2 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5 ┆ 3.6 ┆ 1.4 ┆ 0.2 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5.4 ┆ 3.9 ┆ 1.7 ┆ 0.4 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 4.6 ┆ 3.4 ┆ 1.4 ┆ 0.3 ┆ 0 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 5 ┆ 3.4 ┆ 1.5 ┆ 0.2 ┆ 0 │
╰───────────────────┴──────────────────┴───────────────────┴──────────────────┴────────╯
(Showing first 8 of 150 rows)
To convert a Dataset to a
Dask DataFrame, call
Dataset.to_dask().
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
df = ds.to_dask()
To convert a Dataset to a Spark DataFrame,
call Dataset.to_spark().
import ray
import raydp
spark = raydp.init_spark(
app_name = "example",
num_executors = 1,
executor_cores = 4,
executor_memory = "512M"
)
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
df = ds.to_spark(spark)
To convert a Dataset to a Modin DataFrame, call
Dataset.to_modin().
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
mdf = ds.to_modin()
To convert a Dataset from a Mars DataFrame, call
Dataset.to_mars().
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
mdf = ds.to_mars()