.. _saving-data: =========== Saving Data =========== Ray Data lets you save data in files or other Python objects. This guide shows you how to: * `Write data to files <#writing-data-to-files>`_ * `Convert Datasets to other Python libraries <#converting-datasets-to-other-python-libraries>`_ Writing data to files ===================== Ray Data writes to local disk and cloud storage. Writing data to local disk ~~~~~~~~~~~~~~~~~~~~~~~~~~ To save your :class:`~ray.data.dataset.Dataset` to local disk, call a method like :meth:`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. .. testcode:: 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, see the :ref:`Saving Data API `. Writing data to cloud storage ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To save your :class:`~ray.data.dataset.Dataset` to cloud storage, authenticate all nodes with your cloud service provider. Then, call a method like :meth:`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, see the :ref:`Saving Data API `. .. tab-set:: .. tab-item:: S3 To save data to Amazon S3, specify a URI with the ``s3://`` scheme. .. testcode:: :skipif: True 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 `_. .. tab-item:: GCS To save data to Google Cloud Storage, install the `Filesystem interface to Google Cloud Storage `_ .. code-block:: console pip install gcsfs Then, create a ``GCSFileSystem`` and specify a URI with the ``gcs://`` scheme. .. testcode:: :skipif: True 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 `_. .. tab-item:: ABS To save data to Azure Blob Storage, install the `Filesystem interface to Azure-Datalake Gen1 and Gen2 Storage `_ .. code-block:: console pip install adlfs Then, create a ``AzureBlobFileSystem`` and specify a URI with the ``az://`` scheme. .. testcode:: :skipif: True 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 :class:`~ray.data.dataset.Dataset` to NFS file systems, call a method like :meth:`Dataset.write_parquet ` and specify a mounted directory. .. testcode:: :skipif: True 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, see the :ref:`Saving Data API `. .. _changing-number-output-files: 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. .. testcode:: 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/")) .. testoutput:: :options: +MOCK ['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 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). .. testcode:: import ray import pandas as pd from ray.data import DataContext from ray.data.context import ShuffleStrategy 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=/year=/.... # 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") .. testoutput:: :options: +MOCK 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 :class:`~ray.data.dataset.Dataset` to a pandas DataFrame, call :meth:`Dataset.to_pandas() `. Your data must fit in memory on the head node. .. testcode:: import ray ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") df = ds.to_pandas() print(df) .. testoutput:: :options: +NORMALIZE_WHITESPACE 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 [150 rows x 5 columns] Converting Datasets to distributed DataFrames ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ray Data interoperates with distributed data processing frameworks like `Daft `_, :ref:`Dask `, :ref:`Spark `, :ref:`Modin `, and :ref:`Mars `. .. tab-set:: .. tab-item:: Daft To convert a :class:`~ray.data.dataset.Dataset` to a `Daft Dataframe `_, call :meth:`Dataset.to_daft() `. .. testcode:: import ray ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") df = ds.to_daft() print(df) .. testoutput:: :options: +MOCK ╭───────────────────┬──────────────────┬───────────────────┬──────────────────┬────────╮ │ 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) .. tab-item:: Dask To convert a :class:`~ray.data.dataset.Dataset` to a `Dask DataFrame `__, call :meth:`Dataset.to_dask() `. .. We skip the code snippet below because `to_dask` doesn't work with PyArrow 14 and later. For more information, see https://github.com/ray-project/ray/issues/54837 .. testcode:: :skipif: True import ray ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") df = ds.to_dask() .. tab-item:: Spark To convert a :class:`~ray.data.dataset.Dataset` to a `Spark DataFrame `__, call :meth:`Dataset.to_spark() `. .. testcode:: :skipif: True 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) .. testcode:: :skipif: True :hide: raydp.stop_spark() .. tab-item:: Modin To convert a :class:`~ray.data.dataset.Dataset` to a Modin DataFrame, call :meth:`Dataset.to_modin() `. .. testcode:: import ray ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") mdf = ds.to_modin() .. tab-item:: Mars To convert a :class:`~ray.data.dataset.Dataset` from a Mars DataFrame, call :meth:`Dataset.to_mars() `. .. testcode:: :skipif: True import ray ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") mdf = ds.to_mars()