[GitHub] [arrow-site] lquerel commented on a diff in pull request #369: [Website] Our journey at F5 with Apache Arrow - Part 2 - POST

["lquerel (via GitHub)" <gi...@apache.org>]

lquerel commented on code in PR #369:
URL: https://github.com/apache/arrow-site/pull/369#discussion_r1234245616


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_posts/2023-06-15-our-journey-at-f5-with-apache-arrow-part-2.md:
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+---
+layout: post
+title: "Our journey at F5 with Apache Arrow (part 2)"
+date: "2023-06-15 00:00:00"
+author: Laurent Quérel
+categories: [application]
+---
+<!--
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+
+In the previous [article](https://arrow.apache.org/blog/2023/04/11/our-journey-at-f5-with-apache-arrow-part-1/), we discussed our use of Apache Arrow within the context of the OpenTelemetry project. We investigated various techniques to maximize the efficiency of Apache Arrow, aiming to find the optimal balance between data compression ratio and queryability. The compression results speak for themselves, boasting improvements ranging from 1.5x to 5x better than the original OTLP protocol. In this article, we will delve into three techniques that have enabled us to enhance both the compression ratio and memory usage of Apache Arrow buffers within the current version of the [OTel Arrow protocol](https://github.com/f5/otel-arrow-adapter).
+
+The first technique we'll discuss aims to optimize schemas in terms of memory usage. As you'll see, the gains can be substantial, potentially halving memory usage in certain cases. The second section will delve more deeply into the various approaches that can be used to handle recursive schema definitions. Lastly, we'll emphasize that the design of your schema(s), coupled with the sorts you can apply at the record level, play a pivotal role in maximizing the benefits of Apache Arrow and its columnar representation.
+
+## Handling dynamic and unknown data distributions
+
+In certain contexts, the comprehensive definition of an Arrow schema can end up being overly broad and complex in order to cover all possible cases that you intend to represent in columnar form. However, as is often the case with complex schemas, only a subset of this schema will actually be utilized for a specific deployment. Similarly, it's not always possible to determine the optimal dictionary encoding for one or more fields in advance. Employing a broad and very general schema that covers all cases is usually more memory-intensive. This is because, for most implementations, a column without value still continues to consume memory space. Likewise, a column with dictionary encoding that indexes a uint64 will occupy four times more memory than the same column with a dictionary encoding based on a uint8.
+
+To illustrate this more concretely, let's consider an OTel collector positioned at the output of a production environment, receiving a telemetry data stream produced by a large and dynamic set of servers. Invariably, the content of this telemetry stream will change in volume and nature over time. It's challenging to predict the optimal schema in such a scenario, and it's equally difficult to know in advance the distribution of a particular attribute of the telemetry data passing through this point.
+
+To optimize such scenarios, we have adopted an intermediary approach that we have named **dynamic Arrow schema**, aiming to gradually adapt the schema based on the observed data. The general principle is relatively simple. We start with a general schema defining the maximum envelope of what should be represented. Some fields of this schema will be declared optional, while other fields will be encoded with multiple possible options depending on the observed distribution. In theory, this principle can be applied to other types of transformations (e.g., recursive column creation) but we will let your imagination explore these other options. So if you encounter data streams where certain fields are not utilized, some union variants remain unused, and/or the value distribution of a field cannot be determined a priori, it may be worthwhile to invest time in implementing this model. This can lead to improved efficiency in terms of compression ratio, memory usage, and processing speed.
+
+The following Go Arrow schema definition provides an example of such a schema, instrumented with a collection of annotations. These annotations will be processed by an enhanced Record Builder, equipped with the ability to dynamically adapt the schema. The structure of this system is illustrated in Figure 1.
+
+```go
+var (
+  // Arrow schema for the OTLP Arrow Traces record (without attributes, links, and events).
+  TracesSchema = arrow.NewSchema([]arrow.Field{
+      {Name: constants.ID, Type: arrow.PrimitiveTypes.Uint16, Nullable: true},
+      {Name: constants.Resource, Type: arrow.StructOf([]arrow.Field{
+        {Name: constants.ID, Type: arrow.PrimitiveTypes.Uint16, Nullable: true},
+        {Name: constants.SchemaUrl,Type: arrow.BinaryTypes.String,Metadata: schema.Metadata(schema.Dictionary8), Nullable: true},
+        {Name: constants.DroppedAttributesCount,Type: arrow.PrimitiveTypes.Uint32,Nullable: true},
+      }...), Nullable: true},
+      {Name: constants.Scope, Type: arrow.StructOf([]arrow.Field{
+          {Name: constants.ID, Type: arrow.PrimitiveTypes.Uint16, Metadata: acommon.Metadata(acommon.DeltaEncoding), Nullable: true},
+          {Name: constants.Name, Type: arrow.BinaryTypes.String, Metadata: acommon.Metadata(acommon.Dictionary8), Nullable: true},

Review Comment:
   Agreed



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