[Data] Add approximate quantile to aggregator by owenowenisme · Pull Request #57598 · ray-project/ray

owenowenisme · 2025-10-09T13:18:53Z

Why are these changes needed?

Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL.

Reason:
• Enables efficient support for the summary API.
• More scalable than exact Quantile on large datasets.

Note:
• DataSketches is not added as a Ray dependency; if missing, users are prompted to install it.

Here's a simple test to show the efficiency difference between ApproximateQuantile and Quantile

import ray
import ray.data
import time

ray.init(num_cpus=16)
from ray.data.aggregate import ApproximateQuantile, Quantile

ds = ray.data.range(10**8)
start_time = time.time()

print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5])))
print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds")

ds = ray.data.range(10**8)
start_time = time.time()

print(ds.aggregate(Quantile(on="id", q=0.5)))
print(f"Time taken Quantile: {time.time() - start_time} seconds")

In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains.

When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is (49,999,999.5-49,979,428.0)/49,999,999.5= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median 13.11x faster.

{'approx_quantile(id)': [49979428.0]}
Time taken ApproximateQuantile: 12.457247257232666 seconds
{'quantile(id)': 49999999.5}
Time taken Quantile: 163.32705521583557 seconds

Related issue number

Checks

I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
I've run pre-commit jobs to lint the changes in this PR. (pre-commit setup)
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 :(

Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com>

Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com>

goutamvenkat-anyscale · 2025-10-10T17:39:52Z

python/ray/data/aggregate.py

+        """
+        self._require_datasketches()
+        self._quantiles = quantiles
+        self._k = k


instead of k, let's use capacity_per_level

capacity_per_level does not feel accurate to me, I think maybe we don't need to hide the detail of k, since user will need to see the doc from datasketches anyway.

I added link to k params description to guide users to the doc for more info.

The problem there is it's not obvious to a user what k represents.

They have to look up the algorithm to build intuition. Curious why do you say capacity_per_level is inaccurate?

It's just that I think the concept of "accuracy" should be in param name.
And from user's view capacity might make them confused.
How about accuracy_factor?

quantile_precision?

python/ray/data/aggregate.py

Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com>

alexeykudinkin · 2025-10-15T17:51:33Z

python/ray/data/aggregate.py

+        )
+
+    def zero(self, quantile_precision: int):
+        sketch_cls = self._require_datasketches()


This should only be needed in the ctor

Ditto everywhere

Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com>

## Why are these changes needed? Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL. Reason: • Enables efficient support for the summary API. • More scalable than exact Quantile on large datasets. Note: • DataSketches is not added as a Ray dependency; if missing, users are prompted to install it. --- Here's a simple test to show the efficiency difference between `ApproximateQuantile` and `Quantile` ```py import ray import ray.data import time ray.init(num_cpus=16) from ray.data.aggregate import ApproximateQuantile, Quantile ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5]))) print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds") ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(Quantile(on="id", q=0.5))) print(f"Time taken Quantile: {time.time() - start_time} seconds") ``` In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains. When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is `(49,999,999.5-49,979,428.0)/49,999,999.5`= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median **13.11x** faster. ``` {'approx_quantile(id)': [49979428.0]} Time taken ApproximateQuantile: 12.457247257232666 seconds {'quantile(id)': 49999999.5} Time taken Quantile: 163.32705521583557 seconds ```  ## Related issue number  ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] 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 :( --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Co-authored-by: Balaji Veeramani <balaji@anyscale.com>

## Why are these changes needed? Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL. Reason: • Enables efficient support for the summary API. • More scalable than exact Quantile on large datasets. Note: • DataSketches is not added as a Ray dependency; if missing, users are prompted to install it. --- Here's a simple test to show the efficiency difference between `ApproximateQuantile` and `Quantile` ```py import ray import ray.data import time ray.init(num_cpus=16) from ray.data.aggregate import ApproximateQuantile, Quantile ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5]))) print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds") ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(Quantile(on="id", q=0.5))) print(f"Time taken Quantile: {time.time() - start_time} seconds") ``` In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains. When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is `(49,999,999.5-49,979,428.0)/49,999,999.5`= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median **13.11x** faster. ``` {'approx_quantile(id)': [49979428.0]} Time taken ApproximateQuantile: 12.457247257232666 seconds {'quantile(id)': 49999999.5} Time taken Quantile: 163.32705521583557 seconds ```  ## Related issue number  ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] 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 :( --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Co-authored-by: Balaji Veeramani <balaji@anyscale.com> Signed-off-by: xgui <xgui@anyscale.com>

## Why are these changes needed? Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL. Reason: • Enables efficient support for the summary API. • More scalable than exact Quantile on large datasets. Note: • DataSketches is not added as a Ray dependency; if missing, users are prompted to install it. --- Here's a simple test to show the efficiency difference between `ApproximateQuantile` and `Quantile` ```py import ray import ray.data import time ray.init(num_cpus=16) from ray.data.aggregate import ApproximateQuantile, Quantile ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5]))) print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds") ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(Quantile(on="id", q=0.5))) print(f"Time taken Quantile: {time.time() - start_time} seconds") ``` In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains. When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is `(49,999,999.5-49,979,428.0)/49,999,999.5`= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median **13.11x** faster. ``` {'approx_quantile(id)': [49979428.0]} Time taken ApproximateQuantile: 12.457247257232666 seconds {'quantile(id)': 49999999.5} Time taken Quantile: 163.32705521583557 seconds ```  ## Related issue number  ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] 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 :( --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Co-authored-by: Balaji Veeramani <balaji@anyscale.com> Signed-off-by: elliot-barn <elliot.barnwell@anyscale.com>

## Why are these changes needed? Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL. Reason: • Enables efficient support for the summary API. • More scalable than exact Quantile on large datasets. Note: • DataSketches is not added as a Ray dependency; if missing, users are prompted to install it. --- Here's a simple test to show the efficiency difference between `ApproximateQuantile` and `Quantile` ```py import ray import ray.data import time ray.init(num_cpus=16) from ray.data.aggregate import ApproximateQuantile, Quantile ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5]))) print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds") ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(Quantile(on="id", q=0.5))) print(f"Time taken Quantile: {time.time() - start_time} seconds") ``` In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains. When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is `(49,999,999.5-49,979,428.0)/49,999,999.5`= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median **13.11x** faster. ``` {'approx_quantile(id)': [49979428.0]} Time taken ApproximateQuantile: 12.457247257232666 seconds {'quantile(id)': 49999999.5} Time taken Quantile: 163.32705521583557 seconds ```  ## Related issue number  ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] 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 :( --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Co-authored-by: Balaji Veeramani <balaji@anyscale.com>

## Why are these changes needed? Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL. Reason: • Enables efficient support for the summary API. • More scalable than exact Quantile on large datasets. Note: • DataSketches is not added as a Ray dependency; if missing, users are prompted to install it. --- Here's a simple test to show the efficiency difference between `ApproximateQuantile` and `Quantile` ```py import ray import ray.data import time ray.init(num_cpus=16) from ray.data.aggregate import ApproximateQuantile, Quantile ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5]))) print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds") ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(Quantile(on="id", q=0.5))) print(f"Time taken Quantile: {time.time() - start_time} seconds") ``` In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains. When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is `(49,999,999.5-49,979,428.0)/49,999,999.5`= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median **13.11x** faster. ``` {'approx_quantile(id)': [49979428.0]} Time taken ApproximateQuantile: 12.457247257232666 seconds {'quantile(id)': 49999999.5} Time taken Quantile: 163.32705521583557 seconds ```  ## Related issue number  ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] 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 :( --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Co-authored-by: Balaji Veeramani <balaji@anyscale.com> Signed-off-by: Aydin Abiar <aydin@anyscale.com>

## Why are these changes needed? Add ApproximateQuantile aggregator to Ray Data using DataSketches KLL. Reason: • Enables efficient support for the summary API. • More scalable than exact Quantile on large datasets. Note: • DataSketches is not added as a Ray dependency; if missing, users are prompted to install it. --- Here's a simple test to show the efficiency difference between `ApproximateQuantile` and `Quantile` ```py import ray import ray.data import time ray.init(num_cpus=16) from ray.data.aggregate import ApproximateQuantile, Quantile ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(ApproximateQuantile(on="id", quantiles=[0.5]))) print(f"Time taken ApproximateQuantile: {time.time() - start_time} seconds") ds = ray.data.range(10**8) start_time = time.time() print(ds.aggregate(Quantile(on="id", q=0.5))) print(f"Time taken Quantile: {time.time() - start_time} seconds") ``` In this run with 1e8 rows, the approximate median returned 49,979,428.0 in ~12.46s, while the exact Quantile returned 49,999,999.5 in ~163.33s. The difference reflects the sketch’s accuracy trade-off for significant speed and scalability gains. When k=800 (the default), we are guaranteed to have the error rate < 0.45% , in this test our error rate is `(49,999,999.5-49,979,428.0)/49,999,999.5`= 0.00041143 = 0.041143% which is < 0.45% , but we get the approximate median **13.11x** faster. ``` {'approx_quantile(id)': [49979428.0]} Time taken ApproximateQuantile: 12.457247257232666 seconds {'quantile(id)': 49999999.5} Time taken Quantile: 163.32705521583557 seconds ```  ## Related issue number  ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] 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 :( --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Co-authored-by: Balaji Veeramani <balaji@anyscale.com> Signed-off-by: Future-Outlier <eric901201@gmail.com>

owenowenisme force-pushed the data/add-approximate-quantile-to-aggregrator branch from e0584b6 to 45381b1 Compare October 9, 2025 13:20

owenowenisme added the go add ONLY when ready to merge, run all tests label Oct 9, 2025

use test deps for datasketches

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owenowenisme force-pushed the data/add-approximate-quantile-to-aggregrator branch from 45381b1 to 024f199 Compare October 9, 2025 23:55

owenowenisme added 4 commits October 10, 2025 00:39

add import error

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update requirements_compiled.txt

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add stability annotation

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add to rst

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update KLL link

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[Data] Add approximate quantile to aggregator#57598

[Data] Add approximate quantile to aggregator#57598
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