From 516901fe2f90cfd21c69ca027117d614fbfcc763 Mon Sep 17 00:00:00 2001 From: Patrick Hoefler <61934744+phofl@users.noreply.github.com> Date: Wed, 11 Oct 2023 17:36:42 +0100 Subject: [PATCH 1/2] Add dask-expr benchmarking post --- _posts/2023-10-05-dask-expr-tpch-dask.md | 135 ++++++++++++++++++ .../tpch-comparison-dask-confidence.png | Bin 0 -> 123198 bytes .../dask_expr/tpch-comparison-dask-memory.png | Bin 0 -> 249105 bytes images/dask_expr/tpch-comparison-dask.png | Bin 0 -> 254849 bytes 4 files changed, 135 insertions(+) create mode 100644 _posts/2023-10-05-dask-expr-tpch-dask.md create mode 100644 images/dask_expr/tpch-comparison-dask-confidence.png create mode 100644 images/dask_expr/tpch-comparison-dask-memory.png create mode 100644 images/dask_expr/tpch-comparison-dask.png diff --git a/_posts/2023-10-05-dask-expr-tpch-dask.md b/_posts/2023-10-05-dask-expr-tpch-dask.md new file mode 100644 index 0000000..632cff0 --- /dev/null +++ b/_posts/2023-10-05-dask-expr-tpch-dask.md @@ -0,0 +1,135 @@ +--- +layout: post +date: 2023-10-05 +tags: [dask, query optimizer, performance, benchmarks] +author: JPatrick Hoefler +theme: twitter +canonical_url: https://blog.coiled.io/blog/dask-expr-tpch-dask.html + +--- + +# TPC-H Benchmarks for Query Optimization with Dask Expressions + + + + +[Dask-expr](https://github.com/dask-contrib/dask-expr) is an ongoing effort to add a +[logical query optimization layer](https://medium.com/coiled-hq/high-level-query-optimization-in-dask-995640564ed7) to Dask DataFrames. +We now have the first benchmark results to share that were run against the current DataFrame +implementation. + +Dask-expr is up to 3 times faster and more memory efficient in its current state than the status quo. +This is a very promising result for our future development efforts. + +# Results + +We are comparing Dask 2023.09.02 with the main branch of Dask-expr. Both implementations +will use the [P2P shuffling algorithm](https://medium.com/coiled-hq/shuffling-large-data-at-constant-memory-in-dask-bb683e92d70b). The results were produced on 100GB of data, e.g. scale +100 for the [TPC-H](https://www.tpc.org/tpch/) benchmarks. The cluster had 20 workers with 16GB each. + + +Runtime on a per-query basis of Dask DataFrame and Dask-expr + +We can see that Dask-expr performs better on every single query, up to a 3-times improvement +compared to the status quo. + +Dask-expr reorders the query that was given by the user to only execute things that +are strictly necessary. It will, for example, drop unnecessary columns and thus reduce +the amount of data that is required. We can see that the biggest improvements are for +the queries that contain merge operations. These queries write a lot of data to disk +and send it over the network. Reducing the size of the data that is transferred has +a big impact on the performance. + +The reordering results in a reduced memory footprint as well. This is especially helpful +for users that struggle with memory pressure on their clusters. + + +Memory usage on a per query basis of Dask DataFrame and Dask-expr + +We did another run on 1TB of data, which showed similar results but with a few notable differences: + +- Getting Dask-expr to compute the results successfully was very easy. 20 workers very sufficient to + compute the results, which is the same number of workers as we used for the 100GB benchmarks. +- The original version needed a bigger cluster with 50 workers and with 32GB memory each. +- Some queries didn't complete the original version at all, because there were memory spikes on + single clusters. Dask-expr was able to complete all queries successfully. + +Summarizing, getting these queries to complete was significantly easier with Dask-expr. + +These results show us that we are on the right course and motivates +us to improve the performance of Dask-expr further. There is still a lot of untapped potential. + +# About the benchmarks + +The TPC-H benchmarks are a set of queries that are commonly used to compare performance of +different databases. Thus, they are very ``merge`` and ``groupby`` heavy. Historically, the current +implementation didn't perform very well on these types of queries. The +[new P2P shuffling algorithm](https://medium.com/coiled-hq/shuffling-large-data-at-constant-memory-in-dask-bb683e92d70b) that was introduced earlier this year and now Dask-expr improves +the performance of Dask a lot. + +These queries only capture a small part of the [Dask DataFrame API](https://docs.dask.org/en/stable/dataframe.html) but are helpful to compare +performance. + +The full implementation of all queries is available [here](https://github.com/coiled/benchmarks/blob/main/tests/benchmarks/test_tpch.py). +A single example query is copied below, so you can get a sense of what they look like: + +```python +var1 = datetime.strptime("1995-03-15", "%Y-%m-%d") +var2 = "BUILDING" + +lineitem_ds = read_data("lineitem") +orders_ds = read_data("orders") +cutomer_ds = read_data("customer") + +flineitem = lineitem_ds[lineitem_ds.l_shipdate > var1] +forders = orders_ds[orders_ds.o_orderdate < var1] +fcustomer = cutomer_ds[cutomer_ds.c_mktsegment == var2] + +jn1 = fcustomer.merge(forders, left_on="c_custkey", right_on="o_custkey") +jn2 = jn1.merge(flineitem, left_on="o_orderkey", right_on="l_orderkey") +jn2["revenue"] = jn2.l_extendedprice * (1 - jn2.l_discount) +total = jn2.groupby(["l_orderkey", "o_orderdate", "o_shippriority"])[ + "revenue" +].sum() +total.reset_index().compute().sort_values(["revenue"], ascending=False).head(10)[ + ["l_orderkey", "revenue", "o_orderdate", "o_shippriority"] +] +``` + +# What's next + +We have some additional steps planned over the next couple of weeks. These include: + +- Benchmarking Dask expressions against Spark +- Scaling the benchmarks to 1TB, 10TB and 100TB. + +We are hoping that we can get additional insights into our implementation and are hoping to identify +bottlenecks. + +# Can I use Dask-expr? + +Though Dask-expr is still under active development, the project is in a state where users can +try it out. The API coverage is pretty good already. We are still adding new +optimizations, so performance will improve in the future. + +You can try it out with: + +```python +pip install dask-expr +``` + +We don't expect that there exists any hard-to-find-bugs. We did not +reimplement any of the algorithms from Dask DataFrame, so we don't expect hard to identify issues +or an unexpected loss of data. + +# Conclusion + +Dask Expressions provides a faster and more memory efficient implementation of Dask DataFrames. +The project is still under active development, but it already outperforms the current status quo +by a significant margin on the TPC-H benchmarks. diff --git a/images/dask_expr/tpch-comparison-dask-confidence.png b/images/dask_expr/tpch-comparison-dask-confidence.png new file mode 100644 index 0000000000000000000000000000000000000000..554c90bde98ed4057a9110c8371b2ce0d8054a1c GIT binary patch literal 123198 zcmeFZby!thw>FM+hr~ueQblS564E7&bV`SGH%PgW5(yD0Nh#?T=}t-MPU!~e`sRMl zIq!SU_dPtnzkk^;|9bImp8827mEF?p|~AcccThKYcHfFmOWbD3|8 zE`Oe`Uat20-SQr#a;){wc%dPDU#5t9il&Z6tr^c6fpPLJQc*TLIt7t-5RG#InVEM+ zfPjOMk)r9@$W47+EiS&LSG#`R@xaZkHmApx02D!*sLirYzz*7mhR|ePMe8VpAkJXV z&0_GVy#$ek9UFg1LM4f&lS?J(NvGFqZbx?Jkm-9$DvJ)_MsxK21?t}>-s4icq;VrU 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_posts/2023-10-05-dask-expr-tpch-dask.md | 35 ++++++++++++------------ 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/_posts/2023-10-05-dask-expr-tpch-dask.md b/_posts/2023-10-05-dask-expr-tpch-dask.md index 632cff0..2e5daeb 100644 --- a/_posts/2023-10-05-dask-expr-tpch-dask.md +++ b/_posts/2023-10-05-dask-expr-tpch-dask.md @@ -5,7 +5,6 @@ tags: [dask, query optimizer, performance, benchmarks] author: JPatrick Hoefler theme: twitter canonical_url: https://blog.coiled.io/blog/dask-expr-tpch-dask.html - --- # TPC-H Benchmarks for Query Optimization with Dask Expressions @@ -15,15 +14,15 @@ canonical_url: https://blog.coiled.io/blog/dask-expr-tpch-dask.html width="70%" alt=""> -[Dask-expr](https://github.com/dask-contrib/dask-expr) is an ongoing effort to add a +[Dask-expr](https://github.com/dask-contrib/dask-expr) is an ongoing effort to add a [logical query optimization layer](https://medium.com/coiled-hq/high-level-query-optimization-in-dask-995640564ed7) to Dask DataFrames. -We now have the first benchmark results to share that were run against the current DataFrame +We now have the first benchmark results to share that were run against the current DataFrame implementation. -Dask-expr is up to 3 times faster and more memory efficient in its current state than the status quo. +Dask-expr is up to 3 times faster and more memory efficient in its current state than the status quo. This is a very promising result for our future development efforts. -# Results +## Results We are comparing Dask 2023.09.02 with the main branch of Dask-expr. Both implementations will use the [P2P shuffling algorithm](https://medium.com/coiled-hq/shuffling-large-data-at-constant-memory-in-dask-bb683e92d70b). The results were produced on 100GB of data, e.g. scale @@ -34,7 +33,7 @@ will use the [P2P shuffling algorithm](https://medium.com/coiled-hq/shuffling-la width="70%" alt="Runtime on a per-query basis of Dask DataFrame and Dask-expr"> -We can see that Dask-expr performs better on every single query, up to a 3-times improvement +We can see that Dask-expr performs better on every single query, up to a 3-times improvement compared to the status quo. Dask-expr reorders the query that was given by the user to only execute things that @@ -54,10 +53,10 @@ for users that struggle with memory pressure on their clusters. We did another run on 1TB of data, which showed similar results but with a few notable differences: -- Getting Dask-expr to compute the results successfully was very easy. 20 workers very sufficient to +- Getting Dask-expr to compute the results successfully was very easy. 20 workers very sufficient to compute the results, which is the same number of workers as we used for the 100GB benchmarks. - The original version needed a bigger cluster with 50 workers and with 32GB memory each. -- Some queries didn't complete the original version at all, because there were memory spikes on +- Some queries didn't complete the original version at all, because there were memory spikes on single clusters. Dask-expr was able to complete all queries successfully. Summarizing, getting these queries to complete was significantly easier with Dask-expr. @@ -65,18 +64,18 @@ Summarizing, getting these queries to complete was significantly easier with Das These results show us that we are on the right course and motivates us to improve the performance of Dask-expr further. There is still a lot of untapped potential. -# About the benchmarks +## About the benchmarks The TPC-H benchmarks are a set of queries that are commonly used to compare performance of -different databases. Thus, they are very ``merge`` and ``groupby`` heavy. Historically, the current -implementation didn't perform very well on these types of queries. The -[new P2P shuffling algorithm](https://medium.com/coiled-hq/shuffling-large-data-at-constant-memory-in-dask-bb683e92d70b) that was introduced earlier this year and now Dask-expr improves +different databases. Thus, they are very `merge` and `groupby` heavy. Historically, the current +implementation didn't perform very well on these types of queries. The +[new P2P shuffling algorithm](https://medium.com/coiled-hq/shuffling-large-data-at-constant-memory-in-dask-bb683e92d70b) that was introduced earlier this year and now Dask-expr improves the performance of Dask a lot. These queries only capture a small part of the [Dask DataFrame API](https://docs.dask.org/en/stable/dataframe.html) but are helpful to compare performance. -The full implementation of all queries is available [here](https://github.com/coiled/benchmarks/blob/main/tests/benchmarks/test_tpch.py). +The full implementation of all queries is available [here](https://github.com/coiled/benchmarks/blob/main/tests/benchmarks/test_tpch.py). A single example query is copied below, so you can get a sense of what they look like: ```python @@ -102,7 +101,7 @@ total.reset_index().compute().sort_values(["revenue"], ascending=False).head(10) ] ``` -# What's next +## What's next We have some additional steps planned over the next couple of weeks. These include: @@ -112,11 +111,11 @@ We have some additional steps planned over the next couple of weeks. These inclu We are hoping that we can get additional insights into our implementation and are hoping to identify bottlenecks. -# Can I use Dask-expr? +## Can I use Dask-expr? Though Dask-expr is still under active development, the project is in a state where users can try it out. The API coverage is pretty good already. We are still adding new -optimizations, so performance will improve in the future. +optimizations, so performance will improve in the future. You can try it out with: @@ -124,11 +123,11 @@ You can try it out with: pip install dask-expr ``` -We don't expect that there exists any hard-to-find-bugs. We did not +We don't expect that there exists any hard-to-find-bugs. We did not reimplement any of the algorithms from Dask DataFrame, so we don't expect hard to identify issues or an unexpected loss of data. -# Conclusion +## Conclusion Dask Expressions provides a faster and more memory efficient implementation of Dask DataFrames. The project is still under active development, but it already outperforms the current status quo