Add dask-expr benchmarking post

_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
+
+<a href="/images/dask_expr/tpch-comparison-dask-confidence.png">
+<img src="/images/dask_expr/tpch-comparison-dask-confidence.png"
+     width="70%"
+     alt=""></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 
+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.
+
+<a href="/images/dask_expr/tpch-comparison-dask.png">
+<img src="/images/dask_expr/tpch-comparison-dask.png"
+     width="70%"
+     alt="Runtime on a per-query basis of Dask DataFrame and Dask-expr"></a>
+
+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.
+
+<a href="/images/dask_expr/tpch-comparison-dask-memory.png">
+<img src="/images/dask_expr/tpch-comparison-dask-memory.png"
+     width="70%"
+     alt="Memory usage on a per query basis of Dask DataFrame and Dask-expr"></a>
+
+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.