ianmcook commented on code in PR #348:
URL: https://github.com/apache/arrow-site/pull/348#discussion_r1180827378
##########
_posts/2023-04-24-adopting-apache-arrow-at-cloudquery.md:
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@@ -0,0 +1,65 @@
+---
+layout: post
+title: "Adopting Apache Arrow at CloudQuery"
+date: "2023-04-26 00:00:00"
+author: Yevgeny Pats
+categories: [application]
+---
+<!--
+{% 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 post is a collaboration with CloudQuery and cross-posted on the
CloudQuery
[blog](https://cloudquery.io/blog/adopting-apache-arrow-at-cloudquery).
+
+[CloudQuery](https://github.com/cloudquery/cloudquery) is an open source high
performance ELT framework written in Go. We
[previously](https://www.cloudquery.io/blog/building-cloudquery) discussed some
of the architecture and design decisions that we took to build a performant ELT
framework. A type system is a key component for creating a performant and
scalable ELT framework where sources and destinations are decoupled. In this
blog post we will go through why we decided to adopt Apache Arrow as our type
system and replace our in-house implementation.
+
+# What is a Type System?
+
+Let’s quickly
[recap](https://www.cloudquery.io/blog/building-cloudquery#type-system) what a
type system is and why an ELT framework needs one. At a very high level, an ELT
framework extracts data from some source and moves it to some destination with
a specific schema.
+
+```text
+API ---> [Source Plugin] -----> [Destination Plugin]
+ -----> [Destination Plugin]
+ gRPC
+```
+
+
+Sources and destinations are decoupled and communicate via gRPC. This is
crucial to allowing the addition of new destinations and updating old
destinations without requiring updates to source plugin code (which otherwise
would introduce an unmaintainable architecture).
+
+This is where a type system comes in. Source plugins extract information from
APIs in the most performant way possible, defining a schema and then
transforming the result from the API (JSON or any other format) to a
well-defined type system. The destination plugin can then easily create the
schema for its database and transform the incoming data to the destination
types. So to recap, the source plugin sends mainly two things to a destination:
1) schema 2) data that fits the defined schema.
+
+# Why Arrow?
+
+Before Arrow, we used our own type system that supported more than 14 types.
This served us well, but we started to hit limitations in various use-cases.
For example, in database to database replication, we needed to support many
more types, including nested types. Also, performance-wise, lots of the time
spent in an ELT process is around converting data from one format to another,
so we wanted to take a step back and see if we can avoid this [famous
XKCD](https://xkcd.com/927/) (by building yet another format):
+
+<figure style="text-align: center;">
+ <img src="https://imgs.xkcd.com/comics/standards.png"; width="100%"
class="img-responsive" alt="Yet another standard XKCD">
+</figure>
+
+
+This is where Arrow comes in. Apache Arrow defines a language-independent
columnar format for flat and hierarchical data, and brings the following
advantages:
+
+1. Cross-language with extensive libraries for different languages - The
[format](https://arrow.apache.org/docs/format/Columnar.html) is defined in such
way that you can parse it in any language and already has extensive support in
C/C++, C#, Go, Java, JavaScript, Julia, Matlab, Python, R, Ruby and Rust (at
the time of writing). For CloudQuery this is important as it makes it much
easier to develop source or destination plugins in different languages.
+2. Performance: Arrow adoption is rising especially in columnar based
databases ([DuckDB](https://duckdb.org/2021/12/03/duck-arrow.html),
[ClickHouse](https://clickhouse.com/docs/en/integrations/data-formats/arrow-avro-orc),
[BigQuery](https://cloud.google.com/bigquery/docs/samples/bigquerystorage-arrow-quickstart))
and file formats
([Parquet](https://arrow.apache.org/docs/python/parquet.html)) which makes it
easier to write CloudQuery destination or source plugins for databases that
already support arrow as well as much more efficient as we remove the need for
additional serialization and transformation step. Moreover, just the
performance of sending arrow format from source plugin to destination is
already more performant and memory efficient, given its “zero-copy” nature and
not needing serialization/deserialization.
Review Comment:
```suggestion
2. Performance: Arrow adoption is rising especially in columnar based
databases ([DuckDB](https://duckdb.org/2021/12/03/duck-arrow.html),
[ClickHouse](https://clickhouse.com/docs/en/integrations/data-formats/arrow-avro-orc),
[BigQuery](https://cloud.google.com/bigquery/docs/samples/bigquerystorage-arrow-quickstart))
and file formats
([Parquet](https://arrow.apache.org/docs/python/parquet.html)) which makes it
easier to write CloudQuery destination or source plugins for databases that
already support Arrow as well as much more efficient as we remove the need for
additional serialization and transformation step. Moreover, just the
performance of sending Arrow format from source plugin to destination is
already more performant and memory efficient, given its “zero-copy” nature and
not needing serialization/deserialization.
```
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