jayzhan211 commented on code in PR #33:
URL: https://github.com/apache/datafusion-site/pull/33#discussion_r1847460386


##########
_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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
 
3MgKFBhcnF1ZXQpIjpmYWxzZSwiRGF0YWJlbmQiOmZhbHNlLCJEYXRhRnVzaW9uIChQYXJxdWV0LCBwYXJ0aXRpb25lZCkiOnRydWUsIkRhdGFGdXNpb24gKFBhcnF1ZXQsIHNpbmdsZSkiOmZhbHNlLCJBcGFjaGUgRG9yaXMiOmZhbHNlLCJEcnVpZCI6ZmFsc2UsIkR1Y2tEQiAoRGF0YUZyYW1lKSI6ZmFsc2UsIkR1Y2tEQiAoUGFycXVldCwgcGFydGl0aW9uZWQpIjp0cnVlLCJEdWNrREIiOmZhbHNlLCJFbGFzdGljc2VhcmNoIjpmYWxzZSwiRWxhc3RpY3NlYXJjaCAodHVuZWQpIjpmYWxzZSwiR2xhcmVEQiI6ZmFsc2UsIkdyZWVucGx1bSI6ZmFsc2UsIkhlYXZ5QUkiOmZhbHNlLCJIeWRyYSI6ZmFsc2UsIkluZm9icmlnaHQiOmZhbHNlLCJLaW5ldGljYSI6ZmFsc2UsIk1hcmlhREIgQ29sdW1uU3RvcmUiOmZhbHNlLCJNYXJpYURCIjpmYWxzZSwiTW9uZXREQiI6ZmFsc2UsIk1vbmdvREIiOmZhbHNlLCJNb3RoZXJEdWNrIjpmYWxzZSwiTXlTUUwgKE15SVNBTSkiOmZhbHNlLCJNeVNRTCI6ZmFsc2UsIk94bGEiOmZhbHNlLCJQYW5kYXMgKERhdGFGcmFtZSkiOmZhbHNlLCJQYXJhZGVEQiAoUGFycXVldCwgcGFydGl0aW9uZWQpIjp0cnVlLCJQYXJhZGVEQiAoUGFycXVldCwgc2luZ2xlKSI6ZmFsc2UsIlBpbm90IjpmYWxzZSwiUG9sYXJzIChEYXRhRnJhbWUpIjpmYWxzZSwiUG9zdGdyZVNRTCAodHVuZWQpIjpmYWxzZSwiUG9zdGdyZVNRTCI6ZmFsc2UsIlF1ZXN0REIgKHBhcnRpdGlvbmVkKSI6ZmFsc2UsIlF1ZX
 
N0REIiOmZhbHNlLCJSZWRzaGlmdCI6ZmFsc2UsIlNpbmdsZVN0b3JlIjpmYWxzZSwiU25vd2ZsYWtlIjpmYWxzZSwiU1FMaXRlIjpmYWxzZSwiU3RhclJvY2tzIjpmYWxzZSwiVGFibGVzcGFjZSI6ZmFsc2UsIlRlbWJvIE9MQVAgKGNvbHVtbmFyKSI6ZmFsc2UsIlRpbWVzY2FsZURCIChubyBjb2x1bW5zdG9yZSkiOmZhbHNlLCJUaW1lc2NhbGVEQiI6ZmFsc2UsIlRpbnliaXJkIChGcmVlIFRyaWFsKSI6ZmFsc2UsIlVtYnJhIjpmYWxzZX0sInR5cGUiOnsiQyI6dHJ1ZSwiY29sdW1uLW9yaWVudGVkIjp0cnVlLCJQb3N0Z3JlU1FMIGNvbXBhdGlibGUiOnRydWUsIm1hbmFnZWQiOnRydWUsImdjcCI6dHJ1ZSwic3RhdGVsZXNzIjp0cnVlLCJKYXZhIjp0cnVlLCJDKysiOnRydWUsIk15U1FMIGNvbXBhdGlibGUiOnRydWUsInJvdy1vcmllbnRlZCI6dHJ1ZSwiQ2xpY2tIb3VzZSBkZXJpdmF0aXZlIjp0cnVlLCJlbWJlZGRlZCI6dHJ1ZSwic2VydmVybGVzcyI6dHJ1ZSwiZGF0YWZyYW1lIjp0cnVlLCJhd3MiOnRydWUsImF6dXJlIjp0cnVlLCJhbmFseXRpY2FsIjp0cnVlLCJSdXN0Ijp0cnVlLCJzZWFyY2giOnRydWUsImRvY3VtZW50Ijp0cnVlLCJzb21ld2hhdCBQb3N0Z3JlU1FMIGNvbXBhdGlibGUiOnRydWUsInRpbWUtc2VyaWVzIjp0cnVlfSwibWFjaGluZSI6eyIxNiB2Q1BVIDEyOEdCIjp0cnVlLCI4IHZDUFUgNjRHQiI6dHJ1ZSwic2VydmVybGVzcyI6dHJ1ZSwiMTZhY3UiOnRydWUsImM2YS40eGxhcmdlLCA
 
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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 of [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 be extended.
+
+
+## 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 of the reason for 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] )
+
+The [DataFusion ParquetExec] (built on the [Rust Parquet Implementation]) is 
now the most
+sophisticated open source Parquet reader I know of. It has every optimization 
we
+can think of for reading Parquet, including projection pushdown, predicate
+pushdown (row group metadata, page index, and bloom filters), limit pushdown,
+parallel reading, interleaved I/O, and late materialized filtering (coming 
soon ™️
+by default). Some recent work from [June](https://github.com/itsjunetime)
+[recently unblocked a remaining hurdle] for enabling late materialized
+filtering, and conveniently [Xiangpeng Hao](https://github.com/XiangpengHao) is
+working on the [final piece] (no pressure😅)
+
+[recently unblocked a remaining hurdle]: 
https://github.com/apache/datafusion/pull/12135
+[final piece]: https://github.com/apache/arrow-datafusion/issues/3463
+
+[Querying Parquet with Millisecond Latency]: 
https://www.influxdata.com/blog/querying-parquet-millisecond-latency/
+[DataFusion ParquetExec]: 
https://docs.rs/datafusion/latest/datafusion/datasource/physical_plan/parquet/struct.ParquetExec.html
+[Rust Parquet Implementation]: https://crates.io/crates/parquet
+
+## Skipping Partial Aggregation When It Doesn't Help
+
+Many ClickBench queries are aggregations that summarize millions of rows, a
+common task for reporting and dashboarding. DataFusion uses state of the art
+[two phase aggregation] plans. Normally, two phase aggregation works well as 
the
+first phase consolidates many rows immediately after reading, while the data is
+still in cache. However, for certain “high cardinality” aggregate queries (that
+have large numbers of groups), [the two phase aggregation strategy used in
+DataFusion was inefficient](https://github.com/apache/datafusion/issues/6937),
+manifesting in relatively slower performance compared to other engines for
+ClickBench queries such as
+
+```sql
+SELECT "WatchID", "ClientIP", COUNT(*) AS c, ... 
+FROM hits 
+GROUP BY "WatchID", "ClientIP" /* <----- 13M Distinct Groups!!! */
+ORDER BY c DESC 
+LIMIT 10;
+```
+
+For such queries, the first first aggregation phase does not significantly
+reduce the number of rows, which wastes significant effort. [Eduard
+Karacharov](https://github.com/korowa) contributed a [dynamic strategy] to
+bypass the first phase when it is not working efficiently, shown in Figure 4.
+
+[two phase aggregation]: 
https://docs.rs/datafusion/latest/datafusion/physical_plan/trait.Accumulator.html#tymethod.state
+[dynamic strategy]: https://github.com/apache/datafusion/pull/11627
+
+<img
+src="{{ site.baseurl 
}}/img/clickbench-datafusion-43/skipping-partial-aggregation.png"
+width="100%"
+class="img-responsive"
+alt="Two phase aggregation diagram from DataFusion API docs annotated to show 
first phase not helping"
+/>
+
+**Figure 4**: Diagram from [DataFusion API docs] showing when the multi-phase
+grouping is not effective
+
+[DataFusion API docs]: 
https://docs.rs/datafusion/latest/datafusion/physical_plan/trait.Accumulator.html#tymethod.state
+
+## Optimized Multi-Column Grouping
+
+Another method for improving analytic database performance is specialized (aka
+highly optimized) versions of operations for different data types, which the
+system picks at runtime based on the query. Like other systems, DataFusion has
+specialized code for handling different types of group columns. For example,
+there is [special code] that handles `GROUP BY int_id`  and [different special
+code] that handles `GROUP BY string_id` .
+
+[special code]: 
https://github.com/apache/datafusion/blob/73507c307487708deb321e1ba4e0d302084ca27e/datafusion/physical-plan/src/aggregates/group_values/single_group_by/primitive.rs
+[different special code]: 
https://github.com/apache/datafusion/blob/73507c307487708deb321e1ba4e0d302084ca27e/datafusion/physical-plan/src/aggregates/group_values/single_group_by/bytes.rs
+
+When a query groups by multiple columns, it is tricker to apply this technique.
+For example `GROUP BY string_id, int_id` and `GROUP BY int_id, string_id` have
+different optimal structures, but it is not possible to include specialized
+versions for all possible combinations of group column types.
+
+DataFusion includes [a general Row based mechanism] that works for any
+combination of column types, but this general mechanism copies each value twice
+as shown in Figure 5. The cost of this copy [is especially high for variable
+length strings and binary data].
+
+[a general Row based mechanism]: 
https://github.com/apache/datafusion/blob/73507c307487708deb321e1ba4e0d302084ca27e/datafusion/physical-plan/src/aggregates/group_values/row.rs#L33-L39
+[is especially high for variable length strings and binary data]: 
https://github.com/apache/datafusion/issues/9403
+
+<img
+src="{{ site.baseurl }}/img/clickbench-datafusion-43/row-based-storage.png"
+width="100%"
+class="img-responsive"
+alt="Row based storage for multiple group columns"
+/>
+
+**Figure 5**: Prior to DataFusion 43.0.0, queries with multiple group columns
+used Row based group storage and copied each group value twice. This copy
+consumes a substantial amount of the query time for queries with many distinct
+groups, such as several of the queries in ClickBench.
+
+Many optimizations in Databases boil down to simply avoiding copies, and this
+was no exception. The trick was to figure out how to avoid copies without
+causing per-column comparison overhead to dominate or complexity to get out of
+hand. In a great example of diligent and disciplined engineering, [Jay
+Zhan](http://jayzhan211) tried [several], [different] approaches until arriving

Review Comment:
   https://github.com/jayzhan211



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