Omega359 commented on code in PR #33:
URL: https://github.com/apache/datafusion-site/pull/33#discussion_r1846883505
##########
_posts/2024-11-18-datafusion-fastest-single-node-parquet-clickbench.md:
##########
@@ -0,0 +1,347 @@
+---
+layout: post
+title: "Apache DataFusion is now the fastest single node engine for querying
Apache Parquet files"
+date: "2024-11-18 00:00:00"
+author: Andrew Lamb, Staff Engineer at InfluxData
+categories: [core, performance]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+I am extremely excited to announce that [Apache DataFusion] [43.0.0] is the
+fastest engine for querying Apache Parquet files in [ClickBench]. It is faster
+than [DuckDB], [chDB] and [Clickhouse] using the same hardware. It also marks
+the first time a [Rust] based engine holds the top spot, which has previously
+been held by traditional C/C++ based engines.
+
+[Apache DataFusion]: https://datafusion.apache.org/
+[43.0.0]: https://crates.io/crates/datafusion
+[ClickBench]: https://benchmark.clickhouse.com/
+[DuckDB]: https://duckdb.org/
+[chDB]: https://clickhouse.com/chdb
+[Clickhouse]: https://clickhouse.com/
+[Rust]: https://www.rust-lang.org/
+
+
+<img
+src="{{ site.baseurl }}/img/2x_bgwhite_original.png"
+width="80%"
+class="img-responsive"
+alt="Apache DataFusion Logo"
+/>
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/perf.png"
+width="100%"
+class="img-responsive"
+alt="ClickBench performance for DataFusion 43.0.0"
+/>
+
+**Figure 1**: 2024-11-16 [ClickBench Results] for the ‘hot’[^1] run against
the
+partitioned 14 GB Parquet dataset (100 files, each ~140MB) on a `c6a.4xlarge`
(16
+CPU / 32 GB RAM) VM. Measurements are relative (`1.x`) to results using
+different hardware.
+
+[ClickBench Results]:
https://benchmark.clickhouse.com/#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LHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWVdfQ==
+
+
+Best in class performance on Parquet is now available to anyone. DataFusion’s
+open design lets you start quickly with a full featured Query Engine, including
+SQL, data formats, catalogs, and more, and then customize any behavior you
need.
+I predict the continued emergence of new classes of data systems now that
+creators can focus the bulk of their innovation on areas such as query
+languages, system integrations, and data formats rather than trying to play
+catchup with core engine performance.
+
+ClickBench also includes results for proprietary storage formats, which require
+costly load / export steps, making them useful in fewer use cases and thus much
+less important than open formats (though the idea of use case specific formats
+is interesting[^2]).
+
+This blog post highlights some of the techniques we used to achieve this
+performance, and celebrates the teamwork involved.
+
+# Rallying The Community around Performance
+
+In July, 2024 [Mehmet Ozan Kabak], CEO of [Synnada], [called on the community
to
+improve DataFusion’s performance]. This got many of us excited (who doesn’t
love
+a challenge!), and we have subsequently rallied to steadily improve the
+performance release on release as shown in Figure 2.
+
+[Mehmet Ozan Kabak]: https://www.linkedin.com/in/mehmet-ozan-kabak/)
+[Synnada]: https://www.synnada.ai/
+[called on the community to improve DataFusion’s performance]:
https://github.com/apache/datafusion/issues/11442#issuecomment-2226834443
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/perf-over-time.png"
+width="100%"
+class="img-responsive"
+alt="ClickBench performance results over time for DataFusion"
+/>
+
+**Figure 2**: ClickBench performance improved over 30% between DataFusion 34
+(released Dec 2023) and DataFusion 43 (released Nov 2024).
+
+Like all good optimization efforts, ours took sustained effort as DataFusion
ran
+out [single 2x performance improvements] several years ago. Working together
our
+community of engineers from around the world[^3] and all experience levels[^4]
+pulled it off (check out [this discussion] to get a sense). It may be a "[hobo
+sandwich]" [^5], but it is a tasty one!
+
+[single 2x performance improvements]:
https://www.influxdata.com/blog/aggregating-millions-groups-fast-apache-arrow-datafusion
+[this discussion]: https://github.com/apache/datafusion/issues/12821
+[hobo sandwich]: https://db.cs.cmu.edu/seminar2024/
+
+Of course, most of these techniques have been implemented and described before,
+but until now they were only available in proprietary systems such as
+[Vertica](https://www.vertica.com/), [DataBricks
+Photon](https://www.databricks.com/product/photon), or
+[Snowflake](https://www.snowflake.com/en/) or in tightly integrated open source
+systems such as [DuckDB](https://duckdb.org/) or
+[ClickHouse](https://clickhouse.com/) which were not designed to extend.
Review Comment:
```suggestion
[ClickHouse](https://clickhouse.com/) which were not designed to be extended.
```
##########
_posts/2024-11-18-datafusion-fastest-single-node-parquet-clickbench.md:
##########
@@ -0,0 +1,347 @@
+---
+layout: post
+title: "Apache DataFusion is now the fastest single node engine for querying
Apache Parquet files"
+date: "2024-11-18 00:00:00"
+author: Andrew Lamb, Staff Engineer at InfluxData
+categories: [core, performance]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+I am extremely excited to announce that [Apache DataFusion] [43.0.0] is the
+fastest engine for querying Apache Parquet files in [ClickBench]. It is faster
+than [DuckDB], [chDB] and [Clickhouse] using the same hardware. It also marks
+the first time a [Rust] based engine holds the top spot, which has previously
+been held by traditional C/C++ based engines.
+
+[Apache DataFusion]: https://datafusion.apache.org/
+[43.0.0]: https://crates.io/crates/datafusion
+[ClickBench]: https://benchmark.clickhouse.com/
+[DuckDB]: https://duckdb.org/
+[chDB]: https://clickhouse.com/chdb
+[Clickhouse]: https://clickhouse.com/
+[Rust]: https://www.rust-lang.org/
+
+
+<img
+src="{{ site.baseurl }}/img/2x_bgwhite_original.png"
+width="80%"
+class="img-responsive"
+alt="Apache DataFusion Logo"
+/>
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/perf.png"
+width="100%"
+class="img-responsive"
+alt="ClickBench performance for DataFusion 43.0.0"
+/>
+
+**Figure 1**: 2024-11-16 [ClickBench Results] for the ‘hot’[^1] run against
the
+partitioned 14 GB Parquet dataset (100 files, each ~140MB) on a `c6a.4xlarge`
(16
+CPU / 32 GB RAM) VM. Measurements are relative (`1.x`) to results using
+different hardware.
+
+[ClickBench Results]:
https://benchmark.clickhouse.com/#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LHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWUsdHJ1ZSx0cnVlLHRydWVdfQ==
+
+
+Best in class performance on Parquet is now available to anyone. DataFusion’s
+open design lets you start quickly with a full featured Query Engine, including
+SQL, data formats, catalogs, and more, and then customize any behavior you
need.
+I predict the continued emergence of new classes of data systems now that
+creators can focus the bulk of their innovation on areas such as query
+languages, system integrations, and data formats rather than trying to play
+catchup with core engine performance.
+
+ClickBench also includes results for proprietary storage formats, which require
+costly load / export steps, making them useful in fewer use cases and thus much
+less important than open formats (though the idea of use case specific formats
+is interesting[^2]).
+
+This blog post highlights some of the techniques we used to achieve this
+performance, and celebrates the teamwork involved.
+
+# Rallying The Community around Performance
+
+In July, 2024 [Mehmet Ozan Kabak], CEO of [Synnada], [called on the community
to
+improve DataFusion’s performance]. This got many of us excited (who doesn’t
love
+a challenge!), and we have subsequently rallied to steadily improve the
+performance release on release as shown in Figure 2.
+
+[Mehmet Ozan Kabak]: https://www.linkedin.com/in/mehmet-ozan-kabak/)
+[Synnada]: https://www.synnada.ai/
+[called on the community to improve DataFusion’s performance]:
https://github.com/apache/datafusion/issues/11442#issuecomment-2226834443
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/perf-over-time.png"
+width="100%"
+class="img-responsive"
+alt="ClickBench performance results over time for DataFusion"
+/>
+
+**Figure 2**: ClickBench performance improved over 30% between DataFusion 34
+(released Dec 2023) and DataFusion 43 (released Nov 2024).
+
+Like all good optimization efforts, ours took sustained effort as DataFusion
ran
+out [single 2x performance improvements] several years ago. Working together
our
+community of engineers from around the world[^3] and all experience levels[^4]
+pulled it off (check out [this discussion] to get a sense). It may be a "[hobo
+sandwich]" [^5], but it is a tasty one!
+
+[single 2x performance improvements]:
https://www.influxdata.com/blog/aggregating-millions-groups-fast-apache-arrow-datafusion
+[this discussion]: https://github.com/apache/datafusion/issues/12821
+[hobo sandwich]: https://db.cs.cmu.edu/seminar2024/
+
+Of course, most of these techniques have been implemented and described before,
+but until now they were only available in proprietary systems such as
+[Vertica](https://www.vertica.com/), [DataBricks
+Photon](https://www.databricks.com/product/photon), or
+[Snowflake](https://www.snowflake.com/en/) or in tightly integrated open source
+systems such as [DuckDB](https://duckdb.org/) or
+[ClickHouse](https://clickhouse.com/) which were not designed to extend.
+
+
+## StringView
+
+Performance improved for all queries when DataFusion switched to using Arrow
+`StringView`. Using `StringView` “just” saves some copies and avoids one memory
+access for certain comparisons. However, these copies and comparisons happen to
+occur in many of the hottest loops during query processing, so optimizing them
+resulted in measurable performance improvements.
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/string-view-take.png"
+width="80%"
+class="img-responsive"
+alt="Illustration of how take works with StringView"
+/>
+
+**Figure 3:** Figure from [Using StringView / German Style Strings to Make
+Queries Faster: Part 1] showing how `StringView` saves copying data in many
cases.
+
+Using StringView to make DataFusion faster for ClickBench required substantial
+careful, low level optimization work described in [Using StringView / German
+Style Strings to Make Queries Faster: Part 1] and [Part 2]. However, it *also*
+required extending the rest of DataFusion’s operations to support the new type.
+You can get a sense of the magnitude of the work required by looking at the
100+
+pull requests linked to the epic in arrow-rs
+([here](https://github.com/apache/arrow-rs/issues/5374)) and three major epics
+([here](https://github.com/apache/datafusion/issues/10918),
+[here](https://github.com/apache/datafusion/issues/11790) and
+[here](https://github.com/apache/datafusion/issues/11752)) in DataFusion.
+
+[Using StringView / German Style Strings to Make Queries Faster: Part 1]:
https://www.influxdata.com/blog/faster-queries-with-stringview-part-one-influxdb/
+[Part 2]:
https://www.influxdata.com/blog/faster-queries-with-stringview-part-two-influxdb/
+
+Here is a partial list of people involved in the project (I am sorry to those
whom I forgot)
+
+* **Arrow**: [Xiangpeng Hao](https://github.com/XiangpengHao) (InfluxData’s
amazing 2024 summer intern and UW Madison PhD), [Yijun
Zhao](https://github.com/ariesdevil) from DataBend Labs, and [Raphael
Taylor-Davies](https://github.com/tustvold) laid the foundation.
[RinChanNOW](https://github.com/RinChanNOWWW) from Tencent and [Andrew
Duffy](https://github.com/a10y) from SpiralDB helped push it along in the early
days, and [Liang-Chi Hsieh](https://github.com/viirya), [Daniël
Heres](https://github.com/Dandandan) reviewed and provided guidance.
+* **DataFusion**: [Xiangpeng Hao](https://github.com/XiangpengHao), again
charted the initial path and [Alex Huang](https://github.com/Weijun-H), [Dharan
Aditya](https://github.com/dharanad) [Lordworms](https://github.com/Lordworms),
[Jax Liu](https://github.com/goldmedal), [wiedld](https://github.com/wiedld),
[Tai Le Manh](https://github.com/tlm365), [yi
wang](https://github.com/my-vegetable-has-exploded),
[doupache](https://github.com/doupache), [Jay
Zhan](https://github.com/jayzhan211) , [Xin Li](https://github.com/xinlifoobar)
and [Kaifeng Zheng](https://github.com/Kev1n8) made it real.
+* **DataFusion String Function Migration**: [Trent
Hauck](https://github.com/tshauck) organized the effort and set the patterns,
[Jax Liu](https://github.com/goldmedal) made a clever testing framework, and
[Austin Liu](https://github.com/austin362667), [Dmitrii
Bu](https://github.com/demetribu), [Tai Le Manh](https://github.com/tlm365),
[Chojan Shang](https://github.com/PsiACE),
[WeblWabl](https://github.com/devanbenz),
[Lordworms](https://github.com/Lordworms),
[iamthinh](https://github.com/thinh2), [Bruce
Ritchie](https://github.com/Omega359), [Kaifeng
Zheng](https://github.com/Kev1n8), and [Xin Li](https://github.com/xinlifoobar)
bashed out the conversions.
+
+
+## Parquet
+
+Part DataFusion's speed in ClickBench is reading Parquet files (really)
quickly,
+which reflects invested effort in the Parquet reading system (see [Querying
+Parquet with Millisecond Latency]
Review Comment:
```suggestion
Parquet with Millisecond Latency] )
```
##########
_posts/2024-11-18-datafusion-fastest-single-node-parquet-clickbench.md:
##########
@@ -0,0 +1,347 @@
+---
+layout: post
+title: "Apache DataFusion is now the fastest single node engine for querying
Apache Parquet files"
+date: "2024-11-18 00:00:00"
+author: Andrew Lamb, Staff Engineer at InfluxData
+categories: [core, performance]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+I am extremely excited to announce that [Apache DataFusion] [43.0.0] is the
+fastest engine for querying Apache Parquet files in [ClickBench]. It is faster
+than [DuckDB], [chDB] and [Clickhouse] using the same hardware. It also marks
+the first time a [Rust] based engine holds the top spot, which has previously
+been held by traditional C/C++ based engines.
+
+[Apache DataFusion]: https://datafusion.apache.org/
+[43.0.0]: https://crates.io/crates/datafusion
+[ClickBench]: https://benchmark.clickhouse.com/
+[DuckDB]: https://duckdb.org/
+[chDB]: https://clickhouse.com/chdb
+[Clickhouse]: https://clickhouse.com/
+[Rust]: https://www.rust-lang.org/
+
+
+<img
+src="{{ site.baseurl }}/img/2x_bgwhite_original.png"
+width="80%"
+class="img-responsive"
+alt="Apache DataFusion Logo"
+/>
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/perf.png"
+width="100%"
+class="img-responsive"
+alt="ClickBench performance for DataFusion 43.0.0"
+/>
+
+**Figure 1**: 2024-11-16 [ClickBench Results] for the ‘hot’[^1] run against
the
+partitioned 14 GB Parquet dataset (100 files, each ~140MB) on a `c6a.4xlarge`
(16
+CPU / 32 GB RAM) VM. Measurements are relative (`1.x`) to results using
+different hardware.
+
+[ClickBench Results]:
https://benchmark.clickhouse.com/#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+
+
+Best in class performance on Parquet is now available to anyone. DataFusion’s
+open design lets you start quickly with a full featured Query Engine, including
+SQL, data formats, catalogs, and more, and then customize any behavior you
need.
+I predict the continued emergence of new classes of data systems now that
+creators can focus the bulk of their innovation on areas such as query
+languages, system integrations, and data formats rather than trying to play
+catchup with core engine performance.
+
+ClickBench also includes results for proprietary storage formats, which require
+costly load / export steps, making them useful in fewer use cases and thus much
+less important than open formats (though the idea of use case specific formats
+is interesting[^2]).
+
+This blog post highlights some of the techniques we used to achieve this
+performance, and celebrates the teamwork involved.
+
+# Rallying The Community around Performance
+
+In July, 2024 [Mehmet Ozan Kabak], CEO of [Synnada], [called on the community
to
+improve DataFusion’s performance]. This got many of us excited (who doesn’t
love
+a challenge!), and we have subsequently rallied to steadily improve the
+performance release on release as shown in Figure 2.
+
+[Mehmet Ozan Kabak]: https://www.linkedin.com/in/mehmet-ozan-kabak/)
+[Synnada]: https://www.synnada.ai/
+[called on the community to improve DataFusion’s performance]:
https://github.com/apache/datafusion/issues/11442#issuecomment-2226834443
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/perf-over-time.png"
+width="100%"
+class="img-responsive"
+alt="ClickBench performance results over time for DataFusion"
+/>
+
+**Figure 2**: ClickBench performance improved over 30% between DataFusion 34
+(released Dec 2023) and DataFusion 43 (released Nov 2024).
+
+Like all good optimization efforts, ours took sustained effort as DataFusion
ran
+out [single 2x performance improvements] several years ago. Working together
our
Review Comment:
```suggestion
out of [single 2x performance improvements] several years ago. Working
together our
```
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