comphead commented on code in PR #61:
URL: https://github.com/apache/datafusion-site/pull/61#discussion_r2006162703
##########
content/blog/2025-03-21-parquet-pushdown.md:
##########
@@ -0,0 +1,259 @@
+---
+layout: post
+title: Efficient Filter Pushdown in Parquet
+date: 2025-03-21
+author: Xiangpeng Hao
+categories: [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 %}
+-->
+
+_Editor's Note: This blog was first published on [Xiangpeng Hao's blog].
Thanks to [InfluxData] for sponsoring this work as part of his PhD funding._
+
+[Xiangpeng Hao's blog]: https://blog.xiangpeng.systems/posts/parquet-pushdown/
+[InfluxData]: https://www.influxdata.com/
+<hr/>
+
+
+In the [previous post], we discussed how [Apache DataFusion] prunes [Apache
Parquet] files to skip irrelevant **files/row_groups** (sometimes also
[pages](https://parquet.apache.org/docs/file-format/pageindex/)).
+
+This post discusses how Parquet readers skip irrelevant **rows** while
scanning data.
+
+[previous post]: https://datafusion.apache.org/blog/2025/03/20/parquet-pruning
+[Apache DataFusion]: https://datafusion.apache.org/
+[Apache Parquet]: https://parquet.apache.org/
+
+## Why filter pushdown in Parquet?
+
+Below is a query that reads sensor data with filters on `date_time` and
`location`:
+
+```sql
+SELECT val, location
+FROM sensor_data
+WHERE date_time > '2025-03-12' AND location = 'office';
+```
+
+<img src="/blog/images/parquet-pushdown/pushdown-vs-no-pushdown.jpg"
alt="Parquet pruning skips irrelevant files/row_groups, while filter pushdown
skips irrelevant rows. Without filter pushdown, all rows from location, val,
and date_time columns are decoded before `location='office'` is evaluated.
Filter pushdown is especially useful when the filter is selective, i.e.,
removes many rows." width="80%" class="img-responsive">
+
+
+In our setup, sensor data is aggregated by date — each day has its own Parquet
file.
+DataFusion prunes the unneeded Parquet files, i.e., `2025-03-10/11.parquet`.
+
+Once the files to read are located, the [*current default
implementation*](https://github.com/apache/datafusion/issues/3463) reads all
the projected columns (`sensor_id`, `val`, and `location`) into Arrow
RecordBatches, then applies the filters over `location` to get the final set of
rows.
+
+A better approach is **filter pushdown**, which evaluates filter conditions
first and only decodes data that passes these conditions.
+In practice, this works by first processing only the filter columns (like
`location`), building a boolean mask of rows that satisfy our conditions, then
using this mask to selectively decode only the relevant rows from other columns
(`sensor_id`, `val`).
+This eliminates the waste of decoding rows that will be filtered out.
+
+While simple in theory, practical implementations often make performance worse.
+
+## Why slower?
+
+At a high level, the Parquet reader first builds a filter mask -- essentially
a boolean array indicating which rows meet the filter criteria -- and then uses
this mask to selectively decode only the needed rows from the remaining columns
in the projection.
+
+Let's dig into details of [how filter pushdown is
implemented](https://github.com/apache/arrow-rs/blob/d5339f31a60a4bd8a4256e7120fe32603249d88e/parquet/src/arrow/async_reader/mod.rs#L618-L712)
in the current Rust implementation of Parquet readers.
+
+<img src="/blog/images/parquet-pushdown/baseline-impl.jpg" alt="Implementation
of filter pushdown in Rust Parquet readers -- the first phase builds the filter
mask, the second phase applies the filter mask to the other columns"
width="80%" class="img-responsive">
+
+The filter pushdown has two phases:
+
+1. Build the filter mask (steps 1-3)
+
+2. Apply the filter mask to the other columns (steps 4-7)
+
+Within each phase, it takes three steps from Parquet to Arrow:
+
+1. Decompress the Parquet pages using generic decompression algorithms like
LZ4, Zstd, etc. (steps 1, 4, 6)
+
+2. Decode the page content into Arrow format (steps 2, 5, 7)
+
+3. Evaluate the filter over Arrow data (step 3)
+
+In the figure above, we can see that `location` is **decompressed and decoded
twice**, first when building the filter mask (steps 1, 2), and second when
building the output (steps 4, 5).
+This happens for all columns that appear both in the filter and output.
+
+The table below shows the corresponding CPU time on the [ClickBench query
22](https://github.com/apache/datafusion/blob/main/benchmarks/queries/clickbench/queries.sql#L23):
+
+```
++------------+--------+-------------+--------+
+| Decompress | Decode | Apply filter| Others |
++------------+--------+-------------+--------+
+| 206 ms | 117 ms | 22 ms | 48 ms |
++------------+--------+-------------+--------+
+```
+
+Clearly, decompress/decode operations dominate the time spent. With filter
pushdown, we need to decompress/decode three times; but without filter
pushdown, we only need to do this twice.
+This explains why filter pushdown is slower.
+
+
+> **Note:** Highly selective filters may skip the entire page; but as long as
we read one row from the page, we need to decompress/decode the entire page.
+
+
+## Attempt: cache filter columns
+
+Intuitively, caching the filter columns and reusing them later could help.
+
+But caching consumes prohibitively high memory:
+
+1. We need to cache Arrow arrays, which are on average [4x larger than Parquet
data](https://github.com/XiangpengHao/liquid-cache/blob/main/dev/doc/liquid-cache-vldb.pdf).
+
+2. We need to cache the **entire column in memory**, because in Phase 1 we
build filters over the entire column, and only use it in Phase 2.
+
+3. The memory usage is proportional to the number of filter columns, which can
be unboundedly high.
+
+Worse, caching filter columns means we need to read partially from Parquet and
partially from cache, which is complex to implement and requires a radical
change to the current implementation.
+
+> **Feel the complexity:** consider building a cache that properly handles
nested columns, multiple filters, and filters with multiple columns.
+
+## Real solution
+
+We need a solution that:
+
+1. Is simple to implement, i.e., doesn't require thousands of lines of code.
+
+2. Incurs minimal memory overhead.
+
+This section describes my [<700 LOC PR (with lots of comments and
tests)](https://github.com/apache/arrow-rs/pull/6921#issuecomment-2718792433)
that **reduces total ClickBench time by 15%, with up to 2x lower latency for
some queries, no obvious regression on other queries, and caches at most 2
pages (~2MB) per column in memory**.
+
+
+<img src="/blog/images/parquet-pushdown/new-pipeline.jpg" alt="New decoding
pipeline, building filter mask and output columns are interleaved in a single
pass, allowing us to cache minimal pages for minimal amount of time"
width="80%" class="img-responsive">
+
+The new pipeline interleaves the previous two phases into a single pass, so
that:
+
+1. The page being decompressed is immediately used to build filter masks and
output columns.
+
+2. We cache the decompressed page for minimal time; after one pass (steps
1-6), the cache memory is released for the next pass.
+
+This allows the cache to only hold 1 page at a time, and to immediately
discard the previous page after it's used, significantly reducing the memory
requirement for caching.
+
+### What pages are cached?
+You may have noticed that only `location` is cached, not `val`, because `val`
is only used for output.
+More generally, only columns that appear both in the filter and output are
cached, and at most 1 page is cached for each such column.
+
+More examples:
+```sql
+SELECT val
+FROM sensor_data
+WHERE date_time > '2025-03-12' AND location = 'office';
+```
+
+In this case, we don't cache any columns, because `val` is not used for
filtering.
+
+```sql
+SELECT COUNT(*)
+FROM sensor_data
+WHERE date_time > '2025-03-12' AND location = 'office';
+```
+
+In this case, we also don't cache any columns, because the output projection
is empty after query plan optimization.
+
+### Then why cache 2 pages/column instead of 1?
Review Comment:
```suggestion
### Then why cache 2 pages per column instead of 1?
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org
For additional commands, e-mail: github-h...@datafusion.apache.org
