alamb commented on code in PR #340:
URL: https://github.com/apache/arrow-site/pull/340#discussion_r1157590428
##########
_posts/2023-04-02-a-journey-with-apache-arrow-part-1.md:
##########
@@ -0,0 +1,382 @@
+---
+layout: post
+title: "A journey with Apache Arrow (part 1)"
+date: "2023-04-02 00:00:00"
+author: Laurent QuƩrel
+categories: [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 %}
+-->
+
+Apache Arrow is a technology widely adopted in big data, analytics, and
machine learning applications. This article discusses our journey with Arrow,
specifically its application to telemetry, and the challenges we encountered
while optimizing the OpenTelemetry protocol to significantly reduce bandwidth
costs. The promising results we achieved inspired us to share our insights.
This article specifically focuses on transforming data from an XYZ format into
an efficient Arrow representation that optimizes both compression ratio,
transport, and data processing. Our benchmarks thus far have shown promising
results, with compression ratio improvements ranging from 1.5x to 6x, depending
on the data type (metrics, logs, traces) and distribution. The approaches
presented for addressing these challenges may be applicable to other Arrow
domains as well. This article serves as the first installment in a two-part
series.
+
Review Comment:
I don't know what an `XYZ` format is -- is that a placeholder for some
internal format? If so, perhaps it would be clearer to say "from an internal
proprietary format" than XYZ.
##########
_posts/2023-04-02-a-journey-with-apache-arrow-part-1.md:
##########
@@ -0,0 +1,382 @@
+---
+layout: post
+title: "A journey with Apache Arrow (part 1)"
Review Comment:
I think this is a great idea -- maybe something like "Storing tracing,
metrics and logs efficiently with OpenTelemtry and Apache Arrow" š¤
##########
_posts/2023-04-02-a-journey-with-apache-arrow-part-1.md:
##########
@@ -0,0 +1,382 @@
+---
+layout: post
+title: "A journey with Apache Arrow (part 1)"
+date: "2023-04-02 00:00:00"
+author: Laurent QuƩrel
+categories: [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 %}
+-->
+
+Apache Arrow is a technology widely adopted in big data, analytics, and
machine learning applications. This article discusses our journey with Arrow,
specifically its application to telemetry, and the challenges we encountered
while optimizing the OpenTelemetry protocol to significantly reduce bandwidth
costs. The promising results we achieved inspired us to share our insights.
This article specifically focuses on transforming data from an XYZ format into
an efficient Arrow representation that optimizes both compression ratio,
transport, and data processing. Our benchmarks thus far have shown promising
results, with compression ratio improvements ranging from 1.5x to 6x, depending
on the data type (metrics, logs, traces) and distribution. The approaches
presented for addressing these challenges may be applicable to other Arrow
domains as well. This article serves as the first installment in a two-part
series.
Review Comment:
I suggest changing the text to be specific about whose ("our journey")
journey this is. I think it would be ok to say "our journey at F5 Networks"
Perhaps we could add another intro paragraph explaining that this blog
describes F5's experience adopting Arrow (for example, the way the intro of
https://arrow.apache.org/blog/2022/11/01/arrow-flight-sql-jdbc/ sets the
background for a Dremio contribution)
##########
_posts/2023-04-02-a-journey-with-apache-arrow-part-1.md:
##########
@@ -0,0 +1,382 @@
+---
+layout: post
+title: "A journey with Apache Arrow (part 1)"
+date: "2023-04-02 00:00:00"
+author: Laurent QuƩrel
+categories: [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 %}
+-->
+
+Apache Arrow is a technology widely adopted in big data, analytics, and
machine learning applications. This article discusses our journey with Arrow,
specifically its application to telemetry, and the challenges we encountered
while optimizing the OpenTelemetry protocol to significantly reduce bandwidth
costs. The promising results we achieved inspired us to share our insights.
This article specifically focuses on transforming data from an XYZ format into
an efficient Arrow representation that optimizes both compression ratio,
transport, and data processing. Our benchmarks thus far have shown promising
results, with compression ratio improvements ranging from 1.5x to 6x, depending
on the data type (metrics, logs, traces) and distribution. The approaches
presented for addressing these challenges may be applicable to other Arrow
domains as well. This article serves as the first installment in a two-part
series.
Review Comment:
I am not sure it is possible without making the introduction way too large,
but some other key things this article offers that might be valuable to
highlight are:
1. Discussion of how to map relatively complex data models to an Arrow schema
2. Discussion on the tradeoffs between different mappings and normalization
strategies
3. Discussion of streaming and batch oriented communication using Arrow and
Arrow Flight
##########
_posts/2023-04-02-a-journey-with-apache-arrow-part-1.md:
##########
@@ -0,0 +1,382 @@
+---
+layout: post
+title: "A journey with Apache Arrow (part 1)"
+date: "2023-04-02 00:00:00"
+author: Laurent QuƩrel
+categories: [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 %}
+-->
+
+Apache Arrow is a technology widely adopted in big data, analytics, and
machine learning applications. This article discusses our journey with Arrow,
specifically its application to telemetry, and the challenges we encountered
while optimizing the OpenTelemetry protocol to significantly reduce bandwidth
costs. The promising results we achieved inspired us to share our insights.
This article specifically focuses on transforming data from an XYZ format into
an efficient Arrow representation that optimizes both compression ratio,
transport, and data processing. Our benchmarks thus far have shown promising
results, with compression ratio improvements ranging from 1.5x to 6x, depending
on the data type (metrics, logs, traces) and distribution. The approaches
presented for addressing these challenges may be applicable to other Arrow
domains as well. This article serves as the first installment in a two-part
series.
+
+## What is Apache Arrow
+
+[Apache Arrow](https://arrow.apache.org/docs/index.html) is an open-source
project offering a standardized, language-agnostic in-memory format for
representing structured and semi-structured data. This enables data sharing and
zero-copy data access between systems, eliminating the need for serialization
and deserialization when exchanging datasets between varying CPU architectures
and programming languages. Furthermore, Arrow features an extensive set of
high-performance, parallel, and vectorized kernel functions designed for
efficiently processing massive amounts of columnar data. These features make
Arrow an appealing technology for big data processing, data transport,
analytics, and machine learning applications. The growing number of [products
and open-source projects](https://arrow.apache.org/powered_by/) that have
adopted Apache Arrow at their core or offer Arrow support reflects the
widespread recognition and appreciation of its benefits (refer to this
[article](https://www.d
remio.com/blog/apache-arrows-rapid-growth-over-the-years/) for an in-depth
overview of the Arrow ecosystem and adoption). Over 11,000 GitHub users support
this project, and 840+ are contributors who make this project an undeniable
success.
Review Comment:
```suggestion
[Apache Arrow](https://arrow.apache.org/docs/index.html) is an open-source
project offering a standardized, language-agnostic in-memory format for
representing structured and semi-structured data. This enables data sharing and
zero-copy data access between systems, eliminating the need for serialization
and deserialization when exchanging datasets between varying CPU architectures
and programming languages. Furthermore, Arrow libraries feature an extensive
set of high-performance, parallel, and vectorized kernel functions designed for
efficiently processing massive amounts of columnar data. These features make
Arrow an appealing technology for big data processing, data transport,
analytics, and machine learning applications. The growing number of [products
and open-source projects](https://arrow.apache.org/powered_by/) that have
adopted Apache Arrow at their core or offer Arrow support reflects the
widespread recognition and appreciation of its benefits (refer to this
[article](ht
tps://www.dremio.com/blog/apache-arrows-rapid-growth-over-the-years/) for an
in-depth overview of the Arrow ecosystem and adoption). Over 11,000 GitHub
users support this project, and 840+ are contributors who make this project an
undeniable success.
```
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