drin commented on code in PR #247:
URL: https://github.com/apache/arrow-site/pull/247#discussion_r996732313
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_posts/2022-10-01-arrow-parquet-encoding-part-3.md:
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@@ -0,0 +1,169 @@
+---
+layout: post
+title: Arrow and Parquet Part 3: Arbitrary Nesting with Lists of Structs and
Structs of Lists
+date: "2022-10-01 00:00:00"
+author: tustvold, alamb
+categories: [parquet, arrow]
+---
+<!--
+{% 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 %}
+-->
+
+## Introduction
+
+This is the third of a three part series exploring how projects such as [Rust
Apache Arrow](https://github.com/apache/arrow-rs) support conversion between
[Apache Arrow](https://arrow.apache.org/) for in memory processing and [Apache
Parquet](https://parquet.apache.org/) for efficient storage. This post covers
how to combine the `Struct` and `List` types described in the previous posts
for arbitrary nesting.
+
+
+[Apache Arrow](https://arrow.apache.org/) is an open, language-independent
columnar memory format for flat and hierarchical data, organized for efficient
analytic operations. [Apache Parquet](https://parquet.apache.org/) is an open,
column-oriented data file format designed for very efficient data encoding and
retrieval.
+
+
+# Structs with Lists
+
+
+```json
+{ <-- First record
+ “a”: [1], <-- top-level field a containing list of integers
+ “b”: [ <-- top-level field b containing list of structures
+ { <-- list element of b containing two field b1 and b2
+ “b1”: 1 <-- b1 is always provided (not null)
+ },
+ {
+ “b1”: 1,
+ “b2”: [ <-- b2 contains list of integers
+ 3, 4 <-- list elements of b.b2 always provided (not null)
+ ]
+ }
+ ]
+}
+{
+ “b”: [ <-- b is always provided (not null)
+ {
+ “b1”: 2
+ },
+ ]
+}
+{
+ “a”: [null, null], <-- list elements of a are nullable
+ “b”: [null] <-- list elements of b are nullable
+}
+```
+
+Documents of this format could be stored in this arrow schema
+
+```text
+Field(name: “a”, nullable: true, datatype: List(
+ Field(name: “element”, nullable: true, datatype: Int32),
+)
+Field(name: “b”), nullable: false, datatype: List(
+ Field(name: “element”, nullable: true, datatype: Struct[
+ Field(name: “b2”, nullable: false, datatype: Int32),
+ Field(name: “c2”, nullable: true, datatype: List(
+ Field(name: “element”, nullable: false, datatype: Int32)
+ ))
+ ])
+))
+```
+
+Documents of this format could be stored in this parquet schema
+
+```text
+message schema {
+ optional group a (LIST) {
+ repeated group list {
+ optional int32 element;
+ }
+ }
+ required group b (LIST) {
+ repeated group list {
+ optional group element {
+ required int32 c1;
+ optional group c2 (LIST) {
+ repeated group list {
+ required int32 element;
+ }
+ }
+ }
+ }
+ }
+}
+```
+
+As explained previously, Arrow chooses to represent this in a hierarchical
fashion. To achieve this it stores a list of monotonically increasing integers
called offsets in the parent ListArray, and stores all the values that appear
in the lists in a single child array. Each consecutive pair of elements in this
offset array identifies a slice of the child array for that array index.
+
+```text
Review Comment:
I don't know if it's helpful, but I made these diagrams when I was trying to
build a mental image of `ArrayData` buffers (in C++):
[lucidchart
diagram](https://lucid.app/lucidchart/b509b478-3206-439d-b252-3ac1426506b0/edit?viewport_loc=-117%2C-225%2C3449%2C1777%2C0_0&invitationId=inv_68f57577-d909-41df-a620-6b63980c6cbe)
feel free to adapt it for any other visualization
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