hhhizzz commented on code in PR #740:
URL: https://github.com/apache/arrow-site/pull/740#discussion_r2598012782
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_posts/2025-12-07-parquet-late-materialization-deep-dive.md:
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@@ -0,0 +1,310 @@
+---
+layout: post
+title: "A Practical Dive Into Late Materialization in arrow-rs Parquet Reads"
+description: "How arrow-rs pipelines predicates and projections to minimize
work during Parquet scans"
+date: "2025-12-07 00:00:00"
+author: hhhizzz and alamb
+categories: [application]
+translations:
+ - language: 简体中文
+ post_id: 2025-12-07-parquet-late-materialization-deep-dive-zh
+---
+<!--
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+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
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+-->
+
+This article dives into the decisions and pitfalls of implementing Late
Materialization in the [Apache Parquet] reader from [`arrow-rs`] (the reader
powering [Apache DataFusion] among other projects). We'll see how a seemingly
humble file reader requires complex logic to evaluate predicates—effectively
becoming a **tiny query engine** in its own right.
+
+[Apache Parquet]: https://parquet.apache.org/
+[Apache DataFusion]: https://datafusion.apache.org/
+[`arrow-rs`]: https://github.com/apache/arrow-rs
+
+## 1. Why Late Materialization?
+
+Columnar reads are a constant battle between **I/O bandwidth** and **CPU
decode costs**. While skipping data is generally good, the act of skipping
itself carries a computational cost. The goal of the Parquet reader in
`arrow-rs` is **pipeline-style late materialization**: evaluate predicates
first, then access projected columns. For predicates that filter many rows,
materializing after evaluation minimizes reads and decode work.
+
+The approach closely mirrors the **LM-pipelined** strategy from
[Materialization Strategies in a Column-Oriented
DBMS](https://www.cs.umd.edu/~abadi/papers/abadiicde2007.pdf) by Abadi et al.:
interleaving predicates and data column access instead of reading all columns
at once and trying to **stitch them back together** into rows.
+
+<figure style="text-align: center;">
+ <img src="{{ site.baseurl }}/img/late-materialization/fig1.png"
alt="LM-pipelined late materialization pipeline" width="100%"
class="img-responsive">
+</figure>
+
+To evaluate a query like `SELECT B, C FROM table WHERE A > 10 AND B < 5` using
late materialization, the reader follows these steps:
+
+1. Read column `A` and evaluate `A > 10` to build a `RowSelection` (a sparse
mask) representing the initial set of surviving rows.
+2. Use that `RowSelection` to read surviving values of column `B` and
evaluate `B < 5` and update the `RowSelection` to make it even sparser.
+3. Use the refined `RowSelection` to read column `C` (a projection column),
decoding only the final surviving rows.
+
+The rest of this post zooms in on how the code makes this path work.
+
+---
+
+## 2. Late Materialization in the Rust Parquet Reader
+
+### 2.1 LM-pipelined
+
+"LM-pipelined" might sound like something from a textbook. In `arrow-rs`, it
simply refers to a pipeline that runs sequentially: "read predicate column →
generate row selection → read data column". This contrasts with a **parallel**
strategy, where all predicate columns are read simultaneously. While
parallelism can maximize multi-core CPU usage, the pipelined approach is often
superior in columnar storage because each filtering step drastically reduces
the amount of data subsequent steps need to read and parse.
+
+The code is structured into a few core roles:
+
+-
**[ReadPlan](https://github.com/apache/arrow-rs/blob/bab30ae3d61509aa8c73db33010844d440226af2/parquet/src/arrow/arrow_reader/read_plan.rs#L302)
/
[ReadPlanBuilder](https://github.com/apache/arrow-rs/blob/bab30ae3d61509aa8c73db33010844d440226af2/parquet/src/arrow/arrow_reader/read_plan.rs#L34)**:
Encodes "which columns to read and with what row subset" into a plan. It does
not pre-read all predicate columns. It reads one, tightens the selection, and
then moves on.
+-
**[RowSelection](https://github.com/apache/arrow-rs/blob/bab30ae3d61509aa8c73db33010844d440226af2/parquet/src/arrow/arrow_reader/selection.rs#L139)**:
Describes "skip/select N rows" using either [Run-length encoding] (RLE)
(called a [`RowSelector`]) or a bitmask. This is the core mechanism that
carries sparsity through the pipeline.
Review Comment:
Good suggestion — the `bitmask` is actually an Arrow `BooleanArray`, and
I’ve added the link.
---
Thanks! Most parts of the diagrams were generated by **Gemini 3 Pro** using
HTML5. It handled most of the work such as the layout and color scheme, and I
only tweaked the arrows, numbering and some description. AI tools are
impressively capable now, though they still make small mistakes that can’t
always be corrected automatically, so I sometimes have to step in with my
limited frontend skills.╮(╯▽╰)╭
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