alamb commented on code in PR #740:
URL: https://github.com/apache/arrow-site/pull/740#discussion_r2592232067
##########
_posts/2025-12-03-parquet-late-materialization-deep-dive.md:
##########
@@ -0,0 +1,259 @@
+---
+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: "2025-12-03 00:00:00"
+author: hhhizzz
+categories: [application]
+translations:
+ - language: 简体中文
+ post_id: 2025-12-03-parquet-late-materialization-deep-dive-zh
+---
+<!--
+{% 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 %}
+-->
+
+This article dives into the decisions and pitfalls of Late Materialization in
`arrow-rs` (the engine powering DataFusion). We'll see how a humble file reader
has evolved into something with the complex logic of a query engine—effectively
becoming a **tiny query engine** in its own right.
+
+## 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 in `arrow-rs` is **pipeline-style
late materialization**: evaluate predicates first, then access projected
columns, keeping the pipeline tight at the page level to ensure minimal reads
and minimal decode work.
+
+Borrowing Abadi's classification from his
[paper](https://www.cs.umd.edu/~abadi/papers/abadiicde2007.pdf), the target
architecture is **LM-pipelined**: 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;">
Review Comment:
Update: I now see it is a reworked example from the paper. I still think
https://github.com/hhhizzz/arrow-site/pull/1 is a better image
##########
_posts/2025-12-03-parquet-late-materialization-deep-dive.md:
##########
@@ -0,0 +1,259 @@
+---
+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: "2025-12-03 00:00:00"
+author: hhhizzz
+categories: [application]
+translations:
+ - language: 简体中文
+ post_id: 2025-12-03-parquet-late-materialization-deep-dive-zh
+---
+<!--
+{% 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 %}
+-->
+
+This article dives into the decisions and pitfalls of Late Materialization in
`arrow-rs` (the engine powering DataFusion). We'll see how a humble file reader
has evolved into something with the complex logic of a query engine—effectively
becoming a **tiny query engine** in its own right.
+
+## 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 in `arrow-rs` is **pipeline-style
late materialization**: evaluate predicates first, then access projected
columns, keeping the pipeline tight at the page level to ensure minimal reads
and minimal decode work.
+
+Borrowing Abadi's classification from his
[paper](https://www.cs.umd.edu/~abadi/papers/abadiicde2007.pdf), the target
architecture is **LM-pipelined**: 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;">
Review Comment:
Update: I now see it is a reworked example from the paper. I still think
https://github.com/hhhizzz/arrow-site/pull/1 is a better image for the intro
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