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[Data] Add partitioning parameter to read_parquet #47553

Merged
merged 5 commits into from
Sep 16, 2024
Merged

[Data] Add partitioning parameter to read_parquet #47553

merged 5 commits into from
Sep 16, 2024

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@bveeramani bveeramani commented Sep 7, 2024 •
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Why are these changes needed?

To extract path partition information with read_parquet, you pass a PyArrow partitioning object to dataset_kwargs. For example:

schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))

This is problematic for two reasons:

  1. It tightly couples the interface with the implementation; partitioning only works if we use pyarrow.Dataset in a specific way in the implementation.
  2. It's inconsistent with all of the other file-based API. All other APIs use expose a top-level partitioning parameter (rather than dataset_kwargs) where you pass a Ray Data Partitioning object (rather than a PyArrow partitioning object).

Related issue number

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  • I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
  • I've run scripts/format.sh to lint the changes in this PR.
  • I've included any doc changes needed for https://docs.ray.io/en/master/.
    • I've added any new APIs to the API Reference. For example, if I added a
      method in Tune, I've added it in doc/source/tune/api/ under the
      corresponding .rst file.
  • I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
  • Testing Strategy
    • Unit tests
    • Release tests
    • This PR is not tested :(

@bveeramani
Add parameter
321f964 
Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
@bveeramani
Fix typo
b9b4e27 
Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
@bveeramani
Address review comments and fix test
4f15d25 
Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
@bveeramani
Appease lint
60ee52b 
Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
@bveeramani
Update docstring
d3585b0 
Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
@bveeramani bveeramani enabled auto-merge (squash) September 14, 2024 04:39
@github-actions github-actions bot added the go add ONLY when ready to merge, run all tests label Sep 14, 2024
@bveeramani bveeramani merged commit 1c80db5 into master Sep 16, 2024
7 checks passed
@bveeramani bveeramani deleted the parquet-partitioning-arg branch September 16, 2024 05:15
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
7f4cefc 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
2826c23 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
1105369 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
8c49818 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
e8c04f7 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
1acc942 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
77e4be4 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
804b4f3 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
ujjawal-khare pushed a commit to ujjawal-khare-27/ray that referenced this pull request Oct 15, 2024
@bveeramani @ujjawal-khare
[Data] Add partitioning parameter to read_parquet (ray-project#47553
8839ad4 
)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>
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@raulchen raulchen raulchen approved these changes

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