comphead commented on code in PR #61:
URL: https://github.com/apache/datafusion-site/pull/61#discussion_r2006168286
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content/blog/2025-03-21-parquet-pushdown.md:
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+---
+layout: post
+title: Efficient Filter Pushdown in Parquet
+date: 2025-03-21
+author: Xiangpeng Hao
+categories: [performance]
+---
+
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+
+_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.
Review Comment:
We means Apache Datafusion?
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