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Posted to commits@arrow.apache.org by GitBox <gi...@apache.org> on 2022/10/05 20:01:06 UTC
[GitHub] [arrow-site] iravid commented on a diff in pull request #246: ARROW-17909: [Website] Arrow and Parquet Part 2: Nested and Hierarchal Data using Structs and Lists
iravid commented on code in PR #246:
URL: https://github.com/apache/arrow-site/pull/246#discussion_r985506018
##########
_posts/2022-10-01-arrow-parquet-encoding-part-2.md:
##########
@@ -0,0 +1,341 @@
+---
+layout: post
+title: Arrow and Parquet Part 2: Nested and Hierarchal Data using Structs and 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 second, in 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 `Struct` and `List` types.
+
+
+[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.
+
+
+## Struct / Group Columns
+
+Both Parquet and Arrow have the concept of a struct column, this is a column that contains one or more other columns.
+
+For example consider the following three JSON documents
+
+```json
+{ <-- First record
+ "a": 1, <-- the top level fields are a, b, c, and d
+ "b": {
+ "b1": 1, <-- b1 and b2 are "nested" fields of "b"
+ "b2": 3 <-- b2 is always provided (not null)
+ },
+ "d": {
+ "d1": 1 <-- d1 is a "nested" field of "d"
+ }
+}
+```
+```json
+{ <-- Second record
+ "a": 2,
+ "b": {
+ "b2": 4 <-- note "b1" is NULL in this record
+ },
+ "c": { <-- note "c" was NULL in the first record
+ "c1": 6 but when "c" is provided, c1 is also always provided
+ },
+ "d": {
+ "d1": 2,
+ "d2": 1
+ }
+}
+```
+```json
+{ <-- Third record
+ "b": {
+ "b1": 5,
+ "b2": 6
+ },
+ "c": {
+ "c1": 7
+ }
+}
+```
+Documents of this format could be stored in this arrow schema
+
+```text
+Field(name: "a", nullable: true, datatype: Int32)
+Field(name: "b", nullable: false, datatype: Struct[
+ Field(name: "b1", nullable: true, datatype: Int32),
+ Field(name: "b2", nullable: false, datatype: Int32)
+])
+Field(name: "c"), nullable: true, datatype: Struct[
+ Field(name: "c1", nullable: false, datatype: Int32)
+])
+Field(name: "d"), nullable: true, datatype: Struct[
+ Field(name: "d1", nullable: false, datatype: Int32)
+ Field(name: "d2", nullable: true, datatype: Int32)
+])
+```
+
+
+Arrow represents each `StructArray` hierarchically using a parent child relationship, with separate validity masks on each of the individual nullable arrays
+
+```text
+ ┌───────────────────┐ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐
+ │ │ ┌─────────────────┐ ┌────────────┐
+ │ ┌─────┐ ┌─────┐ │ │ │┌─────┐ ┌─────┐│ │ ┌─────┐ │ │
+ │ │ 1 │ │ 1 │ │ ││ 1 │ │ 1 ││ │ │ 3 │ │
+ │ ├─────┤ ├─────┤ │ │ │├─────┤ ├─────┤│ │ ├─────┤ │ │
+ │ │ 1 │ │ 2 │ │ ││ 0 │ │ ?? ││ │ │ 4 │ │
+ │ ├─────┤ ├─────┤ │ │ │├─────┤ ├─────┤│ │ ├─────┤ │ │
+ │ │ 0 │ │ ?? │ │ ││ 1 │ │ 5 ││ │ │ 6 │ │
+ │ └─────┘ └─────┘ │ │ │└─────┘ └─────┘│ │ └─────┘ │ │
+ │ Validity Values │ │Validity Values│ │ Values │
+ │ │ │ │ │ │ │ │
+ │ "a" │ │"b.b1" │ │ "b.b2" │
+ │ PrimitiveArray │ │ │PrimitiveArray │ │ Primitive │ │
+ └───────────────────┘ │ │ │ Array │
+ │ └─────────────────┘ └────────────┘ │
+ "b"
+ │ StructArray │
+ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+
+┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+ ┌───────────┐ ┌──────────┐┌──────────────────┐ │
+│ ┌─────┐ │ ┌─────┐ │ │ │ ┌─────┐ │┌─────┐ ││ ┌─────┐ ┌─────┐│
+ │ 0 │ │ │ ?? │ │ │ 1 │ ││ 1 │ ││ │ 0 │ │ ?? ││ │
+│ ├─────┤ │ ├─────┤ │ │ │ ├─────┤ │├─────┤ ││ ├─────┤ ├─────┤│
+ │ 1 │ │ │ 6 │ │ │ 1 │ ││ 2 │ ││ │ 1 │ │ 1 ││ │
+│ ├─────┤ │ ├─────┤ │ │ │ ├─────┤ │├─────┤ ││ ├─────┤ ├─────┤│
+ │ 1 │ │ │ 7 │ │ │ 0 │ ││ ?? │ ││ │ ?? │ │ ?? ││ │
+│ └─────┘ │ └─────┘ │ │ │ └─────┘ │└─────┘ ││ └─────┘ └─────┘│
+ Validity │ Values │ Validity │ Values ││ Validity Values│ │
+│ │ │ │ │ │ ││ │
+ │ "c.c1" │ │"d.d1" ││ "d.d2" │ │
+│ │ Primitive │ │ │ │Primitive ││ PrimitiveArray │
+ │ Array │ │Array ││ │ │
+│ └───────────┘ │ │ └──────────┘└──────────────────┘
+ "c" "d" │
+│ StructArray │ │ StructArray
+ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
+ ```
+
+### Definition Levels
+Unlike Arrow, Parquet does not encode validity in a structured fashion, instead only storing definition levels for each of the primitive columns, i.e. those that aren’t groups. The definition level of a given element, is the depth in the schema at which it is fully defined.
+
+For example consider the case of d.d2, which contains two nullable levels d and d2.
+
+A definition level of 0 would imply a null at the level of d:
+
+```json
+{
+}
+```
+
+A definition level of 1 would imply a null at the level of d.d2
+
+```json
+{
+ d: { .. }
+}
+```
+
+A definition level of 2 would imply a defined value for d.d2:
+
+```json
+{
+ d: { d2: .. }
+}
+```
+
+
+Goin back to the JSON documents above, this format could be stored in this parquet schema
Review Comment:
```suggestion
Goin back to the JSON documents above, this format could be stored in this Parquet schema
```
##########
_posts/2022-10-01-arrow-parquet-encoding-part-2.md:
##########
@@ -0,0 +1,341 @@
+---
+layout: post
+title: Arrow and Parquet Part 2: Nested and Hierarchal Data using Structs and 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 second, in 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 `Struct` and `List` types.
+
+
+[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.
+
+
+## Struct / Group Columns
+
+Both Parquet and Arrow have the concept of a struct column, this is a column that contains one or more other columns.
+
+For example consider the following three JSON documents
+
+```json
+{ <-- First record
+ "a": 1, <-- the top level fields are a, b, c, and d
+ "b": {
+ "b1": 1, <-- b1 and b2 are "nested" fields of "b"
+ "b2": 3 <-- b2 is always provided (not null)
+ },
+ "d": {
+ "d1": 1 <-- d1 is a "nested" field of "d"
+ }
+}
+```
+```json
+{ <-- Second record
+ "a": 2,
+ "b": {
+ "b2": 4 <-- note "b1" is NULL in this record
+ },
+ "c": { <-- note "c" was NULL in the first record
+ "c1": 6 but when "c" is provided, c1 is also always provided
+ },
+ "d": {
+ "d1": 2,
+ "d2": 1
+ }
+}
+```
+```json
+{ <-- Third record
+ "b": {
+ "b1": 5,
+ "b2": 6
+ },
+ "c": {
+ "c1": 7
+ }
+}
+```
+Documents of this format could be stored in this arrow schema
+
+```text
+Field(name: "a", nullable: true, datatype: Int32)
+Field(name: "b", nullable: false, datatype: Struct[
+ Field(name: "b1", nullable: true, datatype: Int32),
+ Field(name: "b2", nullable: false, datatype: Int32)
+])
+Field(name: "c"), nullable: true, datatype: Struct[
+ Field(name: "c1", nullable: false, datatype: Int32)
+])
+Field(name: "d"), nullable: true, datatype: Struct[
+ Field(name: "d1", nullable: false, datatype: Int32)
+ Field(name: "d2", nullable: true, datatype: Int32)
+])
+```
+
+
+Arrow represents each `StructArray` hierarchically using a parent child relationship, with separate validity masks on each of the individual nullable arrays
+
+```text
+ ┌───────────────────┐ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐
+ │ │ ┌─────────────────┐ ┌────────────┐
+ │ ┌─────┐ ┌─────┐ │ │ │┌─────┐ ┌─────┐│ │ ┌─────┐ │ │
+ │ │ 1 │ │ 1 │ │ ││ 1 │ │ 1 ││ │ │ 3 │ │
+ │ ├─────┤ ├─────┤ │ │ │├─────┤ ├─────┤│ │ ├─────┤ │ │
+ │ │ 1 │ │ 2 │ │ ││ 0 │ │ ?? ││ │ │ 4 │ │
+ │ ├─────┤ ├─────┤ │ │ │├─────┤ ├─────┤│ │ ├─────┤ │ │
+ │ │ 0 │ │ ?? │ │ ││ 1 │ │ 5 ││ │ │ 6 │ │
+ │ └─────┘ └─────┘ │ │ │└─────┘ └─────┘│ │ └─────┘ │ │
+ │ Validity Values │ │Validity Values│ │ Values │
+ │ │ │ │ │ │ │ │
+ │ "a" │ │"b.b1" │ │ "b.b2" │
+ │ PrimitiveArray │ │ │PrimitiveArray │ │ Primitive │ │
+ └───────────────────┘ │ │ │ Array │
+ │ └─────────────────┘ └────────────┘ │
+ "b"
+ │ StructArray │
+ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+
+┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+ ┌───────────┐ ┌──────────┐┌──────────────────┐ │
+│ ┌─────┐ │ ┌─────┐ │ │ │ ┌─────┐ │┌─────┐ ││ ┌─────┐ ┌─────┐│
+ │ 0 │ │ │ ?? │ │ │ 1 │ ││ 1 │ ││ │ 0 │ │ ?? ││ │
+│ ├─────┤ │ ├─────┤ │ │ │ ├─────┤ │├─────┤ ││ ├─────┤ ├─────┤│
+ │ 1 │ │ │ 6 │ │ │ 1 │ ││ 2 │ ││ │ 1 │ │ 1 ││ │
+│ ├─────┤ │ ├─────┤ │ │ │ ├─────┤ │├─────┤ ││ ├─────┤ ├─────┤│
+ │ 1 │ │ │ 7 │ │ │ 0 │ ││ ?? │ ││ │ ?? │ │ ?? ││ │
+│ └─────┘ │ └─────┘ │ │ │ └─────┘ │└─────┘ ││ └─────┘ └─────┘│
+ Validity │ Values │ Validity │ Values ││ Validity Values│ │
+│ │ │ │ │ │ ││ │
+ │ "c.c1" │ │"d.d1" ││ "d.d2" │ │
+│ │ Primitive │ │ │ │Primitive ││ PrimitiveArray │
+ │ Array │ │Array ││ │ │
+│ └───────────┘ │ │ └──────────┘└──────────────────┘
+ "c" "d" │
+│ StructArray │ │ StructArray
+ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
+ ```
+
+### Definition Levels
+Unlike Arrow, Parquet does not encode validity in a structured fashion, instead only storing definition levels for each of the primitive columns, i.e. those that aren’t groups. The definition level of a given element, is the depth in the schema at which it is fully defined.
+
+For example consider the case of d.d2, which contains two nullable levels d and d2.
+
+A definition level of 0 would imply a null at the level of d:
+
+```json
+{
+}
+```
+
+A definition level of 1 would imply a null at the level of d.d2
+
+```json
+{
+ d: { .. }
+}
+```
+
+A definition level of 2 would imply a defined value for d.d2:
+
+```json
+{
+ d: { d2: .. }
+}
+```
+
+
+Goin back to the JSON documents above, this format could be stored in this parquet schema
Review Comment:
```suggestion
Going back to the JSON documents above, this format could be stored in this Parquet schema
```
##########
_posts/2022-10-01-arrow-parquet-encoding-part-2.md:
##########
@@ -0,0 +1,341 @@
+---
+layout: post
+title: Arrow and Parquet Part 2: Nested and Hierarchal Data using Structs and 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 second, in 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 `Struct` and `List` types.
+
+
+[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.
+
+
+## Struct / Group Columns
+
+Both Parquet and Arrow have the concept of a struct column, this is a column that contains one or more other columns.
+
+For example consider the following three JSON documents
+
+```json
+{ <-- First record
+ "a": 1, <-- the top level fields are a, b, c, and d
+ "b": {
+ "b1": 1, <-- b1 and b2 are "nested" fields of "b"
+ "b2": 3 <-- b2 is always provided (not null)
+ },
+ "d": {
+ "d1": 1 <-- d1 is a "nested" field of "d"
+ }
+}
+```
+```json
+{ <-- Second record
+ "a": 2,
+ "b": {
+ "b2": 4 <-- note "b1" is NULL in this record
+ },
+ "c": { <-- note "c" was NULL in the first record
+ "c1": 6 but when "c" is provided, c1 is also always provided
+ },
+ "d": {
+ "d1": 2,
+ "d2": 1
+ }
+}
+```
+```json
+{ <-- Third record
+ "b": {
+ "b1": 5,
+ "b2": 6
+ },
+ "c": {
+ "c1": 7
+ }
+}
+```
+Documents of this format could be stored in this arrow schema
+
+```text
+Field(name: "a", nullable: true, datatype: Int32)
+Field(name: "b", nullable: false, datatype: Struct[
+ Field(name: "b1", nullable: true, datatype: Int32),
+ Field(name: "b2", nullable: false, datatype: Int32)
+])
+Field(name: "c"), nullable: true, datatype: Struct[
+ Field(name: "c1", nullable: false, datatype: Int32)
+])
+Field(name: "d"), nullable: true, datatype: Struct[
+ Field(name: "d1", nullable: false, datatype: Int32)
+ Field(name: "d2", nullable: true, datatype: Int32)
+])
+```
+
+
+Arrow represents each `StructArray` hierarchically using a parent child relationship, with separate validity masks on each of the individual nullable arrays
+
+```text
+ ┌───────────────────┐ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐
+ │ │ ┌─────────────────┐ ┌────────────┐
+ │ ┌─────┐ ┌─────┐ │ │ │┌─────┐ ┌─────┐│ │ ┌─────┐ │ │
+ │ │ 1 │ │ 1 │ │ ││ 1 │ │ 1 ││ │ │ 3 │ │
+ │ ├─────┤ ├─────┤ │ │ │├─────┤ ├─────┤│ │ ├─────┤ │ │
+ │ │ 1 │ │ 2 │ │ ││ 0 │ │ ?? ││ │ │ 4 │ │
+ │ ├─────┤ ├─────┤ │ │ │├─────┤ ├─────┤│ │ ├─────┤ │ │
+ │ │ 0 │ │ ?? │ │ ││ 1 │ │ 5 ││ │ │ 6 │ │
+ │ └─────┘ └─────┘ │ │ │└─────┘ └─────┘│ │ └─────┘ │ │
+ │ Validity Values │ │Validity Values│ │ Values │
+ │ │ │ │ │ │ │ │
+ │ "a" │ │"b.b1" │ │ "b.b2" │
+ │ PrimitiveArray │ │ │PrimitiveArray │ │ Primitive │ │
+ └───────────────────┘ │ │ │ Array │
+ │ └─────────────────┘ └────────────┘ │
+ "b"
+ │ StructArray │
+ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+
+┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+ ┌───────────┐ ┌──────────┐┌──────────────────┐ │
+│ ┌─────┐ │ ┌─────┐ │ │ │ ┌─────┐ │┌─────┐ ││ ┌─────┐ ┌─────┐│
+ │ 0 │ │ │ ?? │ │ │ 1 │ ││ 1 │ ││ │ 0 │ │ ?? ││ │
+│ ├─────┤ │ ├─────┤ │ │ │ ├─────┤ │├─────┤ ││ ├─────┤ ├─────┤│
+ │ 1 │ │ │ 6 │ │ │ 1 │ ││ 2 │ ││ │ 1 │ │ 1 ││ │
+│ ├─────┤ │ ├─────┤ │ │ │ ├─────┤ │├─────┤ ││ ├─────┤ ├─────┤│
+ │ 1 │ │ │ 7 │ │ │ 0 │ ││ ?? │ ││ │ ?? │ │ ?? ││ │
+│ └─────┘ │ └─────┘ │ │ │ └─────┘ │└─────┘ ││ └─────┘ └─────┘│
+ Validity │ Values │ Validity │ Values ││ Validity Values│ │
+│ │ │ │ │ │ ││ │
+ │ "c.c1" │ │"d.d1" ││ "d.d2" │ │
+│ │ Primitive │ │ │ │Primitive ││ PrimitiveArray │
+ │ Array │ │Array ││ │ │
+│ └───────────┘ │ │ └──────────┘└──────────────────┘
+ "c" "d" │
+│ StructArray │ │ StructArray
+ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
+ ```
+
+### Definition Levels
+Unlike Arrow, Parquet does not encode validity in a structured fashion, instead only storing definition levels for each of the primitive columns, i.e. those that aren’t groups. The definition level of a given element, is the depth in the schema at which it is fully defined.
+
+For example consider the case of d.d2, which contains two nullable levels d and d2.
+
+A definition level of 0 would imply a null at the level of d:
+
+```json
+{
+}
+```
+
+A definition level of 1 would imply a null at the level of d.d2
+
+```json
+{
+ d: { .. }
+}
+```
+
+A definition level of 2 would imply a defined value for d.d2:
+
+```json
+{
+ d: { d2: .. }
+}
+```
+
+
+Goin back to the JSON documents above, this format could be stored in this parquet schema
Review Comment:
```suggestion
Going back to the JSON documents above, this format could be stored in this Parquet schema:
```
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