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Posted to commits@arrow.apache.org by gi...@apache.org on 2023/06/26 12:45:08 UTC
[arrow-site] branch asf-site updated: Updating built site (build e2b078bdd599b211e4adf60e9a2ffe855a103014)
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The following commit(s) were added to refs/heads/asf-site by this push:
new 585eabc761d Updating built site (build e2b078bdd599b211e4adf60e9a2ffe855a103014)
585eabc761d is described below
commit 585eabc761d0dc07a4b439ffa65eb0c960e63e91
Author: Laurent Quérel <l....@f5.com>
AuthorDate: Mon Jun 26 12:45:01 2023 +0000
Updating built site (build e2b078bdd599b211e4adf60e9a2ffe855a103014)
---
.../index.html | 458 +++++++++++++++++++++
blog/index.html | 15 +
docs/c_glib/index.html | 4 +-
feed.xml | 392 ++++++++----------
.../adaptive-schema-architecture.svg | 1 +
.../compressed-message-size.png | Bin 0 -> 125781 bytes
.../memory-usage-25k-traces.png | Bin 0 -> 94830 bytes
img/journey-apache-arrow/metric-dp-data-model.png | Bin 0 -> 173162 bytes
.../recursive-def-otel-attributes.svg | 1 +
release/0.1.0.html | 4 +-
release/0.10.0.html | 4 +-
release/0.11.0.html | 4 +-
release/0.11.1.html | 4 +-
release/0.12.0.html | 4 +-
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release/0.14.0.html | 4 +-
release/0.14.1.html | 4 +-
release/0.15.0.html | 4 +-
release/0.15.1.html | 4 +-
release/0.16.0.html | 4 +-
release/0.17.0.html | 4 +-
release/0.17.1.html | 4 +-
release/0.2.0.html | 4 +-
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release/0.4.1.html | 4 +-
release/0.5.0.html | 4 +-
release/0.6.0.html | 4 +-
release/0.7.0.html | 4 +-
release/0.7.1.html | 4 +-
release/0.8.0.html | 4 +-
release/0.9.0.html | 4 +-
release/1.0.0.html | 4 +-
release/1.0.1.html | 4 +-
release/10.0.0.html | 4 +-
release/10.0.1.html | 4 +-
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release/12.0.1.html | 4 +-
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release/4.0.1.html | 4 +-
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release/6.0.1.html | 4 +-
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release/9.0.0.html | 4 +-
release/index.html | 4 +-
50 files changed, 741 insertions(+), 294 deletions(-)
diff --git a/blog/2023/06/15/our-journey-at-f5-with-apache-arrow-part-2/index.html b/blog/2023/06/15/our-journey-at-f5-with-apache-arrow-part-2/index.html
new file mode 100644
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--- /dev/null
+++ b/blog/2023/06/15/our-journey-at-f5-with-apache-arrow-part-2/index.html
@@ -0,0 +1,458 @@
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+<meta name="description" content="In the previous article, 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 [...]
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+<h1>
+ Our journey at F5 with Apache Arrow (part 2): Adaptive Schemas and Sorting to Optimize Arrow Usage
+</h1>
+<hr class="mt-4 mb-3">
+
+
+
+<p class="mb-4 pb-1">
+ <span class="badge badge-secondary">Published</span>
+ <span class="published mr-3">
+ 15 Jun 2023
+ </span>
+ <br />
+ <span class="badge badge-secondary">By</span>
+
+ Laurent Quérel
+
+
+
+</p>
+
+
+ <!--
+
+-->
+
+<p>In the previous <a href="https://arrow.apache.org/blog/2023/04/11/our-journey-at-f5-with-apache-arrow-part-1/">article</a>, 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 [...]
+
+<p>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 a [...]
+
+<h2 id="handling-dynamic-and-unknown-data-distributions">Handling dynamic and unknown data distributions</h2>
+
+<p>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 th [...]
+
+<p>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 throu [...]
+
+<p>To optimize such scenarios, we have adopted an intermediary approach that we have named <strong>dynamic Arrow schema</strong>, 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. I [...]
+
+<p>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.</p>
+
+<div class="language-go highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">var</span> <span class="p">(</span>
+ <span class="c">// Arrow schema for the OTLP Arrow Traces record (without attributes, links, and events).</span>
+ <span class="n">TracesSchema</span> <span class="o">=</span> <span class="n">arrow</span><span class="o">.</span><span class="n">NewSchema</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="c">// Nullabe:true means the field is optional, in this case of 16 bit unsigned integers </span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span>< [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Resource</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">StructOf</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span [...]
+ <span class="c">// --- Use dictionary with 8 bit integers initially ----</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SchemaUrl</span><span class="p">,</span><span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span><span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</s [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedAttributesCount</span><span class="p">,</span><span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span><span class="n">Nullable</span><span class="o">:</span> <span cla [...]
+ <span class="p">}</span><span class="o">...</span><span class="p">),</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Scope</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">StructOf</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">acommon</ [...]
+ <span class="c">// --- Use dictionary with 8 bit integers initially ----</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">acommon</s [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Version</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">acommon [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedAttributesCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span [...]
+ <span class="p">}</span><span class="o">...</span><span class="p">),</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SchemaUrl</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</s [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StartTimeUnixNano</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">FixedWidthTypes</span><span class="o">.</span><span class="n">Timestamp_ns</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DurationTimeUnixNano</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">FixedWidthTypes</span><span class="o">.</span><span class="n">Duration_ms</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">TraceId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">16</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SpanId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">8</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">TraceState</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</ [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ParentSpanId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">8</span><span class="p" [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</span>< [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">KIND</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Int32</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</span [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedAttributesCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span cla [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedEventsCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class=" [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedLinksCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="n [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Status</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">StructOf</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StatusCode</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Int32</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">sche [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StatusMessage</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">sch [...]
+ <span class="p">}</span><span class="o">...</span><span class="p">),</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">},</span> <span class="no">nil</span><span class="p">)</span>
+ <span class="p">)</span>
+</code></pre></div></div>
+
+<p>In this example, Arrow field-level metadata are employed to designate when a field is optional (Nullable: true) or to specify the minimal dictionary encoding applicable to a particular field (Metadata Dictionary8/16/…). Now let’s imagine a scenario utilizing this schema in a straightforward scenario, wherein only a handful of fields are actually in use, and the cardinality of most dictionary-encoded fields is low (i.e., below 2^8). Ideally, we’d want a system capable of dynamically co [...]
+
+<div class="language-go highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">var</span> <span class="p">(</span>
+ <span class="c">// Simplified schema definition generated by the Arrow Record encoder based on </span>
+ <span class="c">// the data observed.</span>
+ <span class="n">TracesSchema</span> <span class="o">=</span> <span class="n">arrow</span><span class="o">.</span><span class="n">NewSchema</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><sp [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StartTimeUnixNano</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">FixedWidthTypes</span><span class="o">.</span><span class="n">Timestamp_ns</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">TraceId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">16</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SpanId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">8</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">DictionaryType</span> <span class="p">{</span>
+ <span class="n">IndexType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint8</span><span class="p">,</span>
+ <span class="n">ValueType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">KIND</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">DictionaryType</span> <span class="p">{</span>
+ <span class="n">IndexType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint8</span><span class="p">,</span>
+ <span class="n">ValueType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Int32</span><span class="p">,</span>
+ <span class="p">},</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">},</span> <span class="no">nil</span><span class="p">)</span>
+<span class="p">)</span>
+</code></pre></div></div>
+
+<p>Additionally, we desire a system capable of automatically adapting the aforementioned schema if it encounters new fields or existing fields with a cardinality exceeding the size of the current dictionary definition in future batches. In extreme scenarios, if the cardinality of a specific field surpasses a certain threshold, we would prefer the system to automatically revert to the non-dictionary representation (mechanism of dictionary overflow). That is precisely what we will elaborat [...]
+
+<p>An overview of the different components and events used to implement this approach is depicted in figure 1.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/adaptive-schema-architecture.svg" width="100%" class="img-responsive" alt="Fig 1: Adaptive Arrow schema architecture overview." />
+ <figcaption>Fig 1: Adaptive Arrow schema architecture overview.</figcaption>
+</figure>
+
+<p>The overall Adaptive Arrow schema component takes a data stream segmented into batches and produces one or multiple streams of Arrow Records (one schema per stream). Each of these records is defined with an Arrow schema, which is based both on the annotated Arrow schema and the shape of fields observed in the incoming data.</p>
+
+<p>More specifically, the process of the Adaptive Arrow schema component consists of four main phases</p>
+
+<p><strong>Initialization phase</strong></p>
+
+<p>During the initialization phase, the Arrow Record Encoder reads the annotated Arrow schema (i.e. the reference schema) and generates a collection of transformations. When these transformations are applied to the reference schema, they yield the first minimal Arrow schema that adheres to the constraints depicted by these annotations. In this initial iteration, all optional fields are eliminated, and all dictionary-encoded fields are configured to utilize the smallest encoding as define [...]
+
+<p><strong>Feeding phase</strong></p>
+
+<p>Following the initialization is the feeding phase. Here, the Arrow Record Encoder scans the batch and attempts to store all the fields in an Arrow Record Builder, which is defined by the schema created in the prior step. If a field exists in the data but is not included in the schema, the encoder will trigger a <code class="language-plaintext highlighter-rouge">missing field</code> event. This process continues until the current batch is completely processed. An additional internal ch [...]
+
+<p><strong>Corrective phase</strong></p>
+
+<p>If at least one event has been generated, a corrective phase will be initiated to fix the schema. This optional stage considers all the events generated in the previous stage and adjusts the transformation tree accordingly to align with the observed data. A <code class="language-plaintext highlighter-rouge">missing field</code> event will remove a NoField transformation for the corresponding field. A <code class="language-plaintext highlighter-rouge">dictionary overflow</code> event w [...]
+
+<p><strong>Routing phase</strong></p>
+
+<p>Once a Record Builder has been properly fed, an Arrow Record is created, and the system transitions into the routing phase. The router component calculates a schema signature of the record and utilizes this signature to route the record to an existing Arrow stream compatible with the signature, or it initiates a new stream if there is no match.</p>
+
+<p>This four-phase process should gradually adapt and stabilize the schema to a structure and definition that is optimized for a specific data stream. Unused fields will never unnecessarily consume memory. Dictionary-encoded fields will be defined with the most optimal index size based on the observed data cardinality, and fields with a cardinality exceeding a certain threshold (defined by configuration) will automatically revert to their non-dictionary-encoded versions.</p>
+
+<p>To effectively execute this approach, you must ensure that there is a sufficient level of flexibility on the receiver side. It’s crucial that your downstream pipeline remains functional even when some fields are missing in the schema or when various dictionary index configurations are employed. While this may not always be feasible without implementing additional transformations upon reception, it proves worthwhile in certain scenarios.</p>
+
+<p>The following results highlight the significant memory usage reduction achieved through the application of various optimization techniques. These results were gathered using a schema akin to the one previously presented. The considerable memory efficiency underscores the effectiveness of this approach.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/memory-usage-25k-traces.png" width="100%" class="img-responsive" alt="Fig 2: Comparative analysis of memory usage for different schema optimizations." />
+ <figcaption>Fig 2: Comparative analysis of memory usage for different schema optimizations.</figcaption>
+</figure>
+
+<p>The concept of a transformation tree enables a generalized approach to perform various types of schema optimizations based on the knowledge acquired from the data. This architecture is highly flexible; the current implementation allows for the removal of unused fields, the application of the most specific dictionary encoding, and the optimization of union type variants. In the future, there is potential for introducing additional optimizations that can be expressed as transformations [...]
+
+<h2 id="handling-recursive-schema-definition">Handling recursive schema definition</h2>
+
+<p>Apache Arrow does not support recursive schema definitions, implying that data structures with variable depth cannot be directly represented. Figure 3 exemplifies such a recursive definition where the value of an attribute can either be a simple data type, a list of values, or a map of values. The depth of this definition cannot be predetermined.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/recursive-def-otel-attributes.svg" width="100%" class="img-responsive" alt="Fig 3: Recursive definition of OTel attributes." />
+ <figcaption>Fig 3: Recursive definition of OTel attributes.</figcaption>
+</figure>
+
+<p>Several strategies can be employed to circumvent this limitation. Technically, the dynamic schema concept we’ve presented could be expanded to dynamically update the schema to include any missing level of recursion. However, for this use case, this method is complex and has the notable downside of not offering any assurance on the maximum size of the schema. This lack of constraint can pose security issues; hence, this approach isn’t elaborated upon.</p>
+
+<p>The second approach consists of breaking the recursion by employing a serialization format that supports the definition of a recursive schema. The result of this serialization can then be integrated into the Arrow record as a binary type column, effectively halting the recursion at a specific level. To fully leverage the advantages of columnar representation, it is crucial to apply this ad-hoc serialization as deeply within the data structure as feasible. In the context of OpenTelemet [...]
+
+<p>A variety of serialization formats, such as protobuf or CBOR, can be employed to encode recursive data. Without particular treatment, these binary columns may not be easily queryable by the existing Arrow query engines. Therefore, it’s crucial to thoughtfully ascertain when and where to apply such a technique. While I’m not aware of any attempts to address this limitation within the Arrow system, it doesn’t seem insurmountable and would constitute a valuable extension. This would help [...]
+
+<h2 id="importance-of-sorting">Importance of sorting</h2>
+
+<p>In our preceding <a href="https://arrow.apache.org/blog/2023/04/11/our-journey-at-f5-with-apache-arrow-part-1/">article</a>, we explored a variety of strategies to represent hierarchical data models, including nested structures based on struct/list/map/union, denormalization and flattening representations, as well as a multi-record approach. Each method presents its unique advantages and disadvantages. However, in this last section, we’ll delve deeper into the multi-record approach, f [...]
+
+<p>In the OTel Arrow protocol, we leverage the multi-record approach to represent metrics, logs, and traces. The following entity-relationship diagram offers a simplified version of various record schemas and illustrates their relationships, specifically those used to represent gauges and sums. A comprehensive description of the Arrow data model employed in OpenTelemetry can be accessed <a href="https://github.com/f5/otel-arrow-adapter/blob/main/docs/data_model.md">here</a>.</p>
+
+<p>These Arrow records, also referred to as tables, form a hierarchy with <code class="language-plaintext highlighter-rouge">METRICS</code> acting as the primary entry point. Each table can be independently sorted according to one or more columns. This sorting strategy facilitates the grouping of duplicated data, thereby improving the compression ratio.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/metric-dp-data-model.png" width="100%" class="img-responsive" alt="Fig 4: A simplified entity-relationship diagram representing OTel sum & gauge metrics." />
+ <figcaption>Fig 4: A simplified entity-relationship diagram representing OTel sum & gauge metrics.</figcaption>
+</figure>
+
+<p>The relationship between the primary <code class="language-plaintext highlighter-rouge">METRICS</code> table and the secondary <code class="language-plaintext highlighter-rouge">RESOURCE_ATTRS</code>, <code class="language-plaintext highlighter-rouge">SCOPE_ATTRS</code>, and <code class="language-plaintext highlighter-rouge">NUMBER_DATA_POINTS</code> tables is established through a unique <code class="language-plaintext highlighter-rouge">id</code> in the main table and a <code class= [...]
+
+<p>To achieve this, the ordering process for the different tables adheres to the hierarchy, starting from the main table down to the leaf. The main table is sorted (by one or multiple columns), and then an incremental id is assigned to each row. This numerical id is stored using delta-encoding, which is implemented on top of Arrow.</p>
+
+<p>The secondary tables directly connected to the main table are sorted using the same principle, but the <code class="language-plaintext highlighter-rouge">parent_id</code> column is consistently utilized as the last column in the sort statement. Including the <code class="language-plaintext highlighter-rouge">parent_id</code> column in the sort statement enables the use of a variation of delta encoding. The efficiency of this approach is summarized in the chart below.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/compressed-message-size.png" width="100%" class="img-responsive" alt="Fig 5: Comparative analysis of compression ratios - OTLP Protocol vs. Two variations of the OTel Arrow Protocol with multivariate metrics stream. (lower percentage is better)" />
+ <figcaption>Fig 5: Comparative analysis of compression ratios - OTLP Protocol vs. Two variations of the OTel Arrow Protocol with multivariate metrics stream. (lower percentage is better)</figcaption>
+</figure>
+
+<p>The second column presents the average size of the OTLP batch both pre- and post-ZSTD compression for batches of varying sizes. This column serves as a reference point for the ensuing two columns. The third column displays results for the OTel Arrow protocol without any sorting applied, while the final column showcases results for the OTel Arrow protocol with sorting enabled.</p>
+
+<p>Before compression, the average batch sizes for the two OTel Arrow configurations are predictably similar. However, post-compression, the benefits of sorting each individual table on the compression ratio become immediately apparent. Without sorting, the OTel Arrow protocol exhibits a compression ratio that’s 1.40 to 1.67 times better than the reference. When sorting is enabled, the OTel Arrow protocol outperforms the reference by a factor ranging from 4.94 to 7.21 times!</p>
+
+<p>The gains in terms of compression obviously depend on your data and the redundancy of information present in your data batches. According to our observations, the choice of a good sort generally improves the compression ratio by a factor of 1.5 to 8.</p>
+
+<p>Decomposing a complex schema into multiple simpler schemas to enhance sorting capabilities, coupled with a targeted approach to efficiently encode the identifiers representing the relationships, emerges as an effective strategy for enhancing overall data compression. This method also eliminates complex Arrow data types, such as lists, maps, and unions. Consequently, it not only improves but also simplifies data query-ability. This simplification proves beneficial for existing query en [...]
+
+<h2 id="conclusion-and-next-steps">Conclusion and next steps</h2>
+
+<p>This article concludes our two-part series on Apache Arrow, wherein we have explored various strategies to maximize the utility of Apache Arrow within specific contexts. The adaptive schema architecture presented in the second part of this series paves the way for future optimization possibilities. We look forward to seeing what the community can add based on this contribution.</p>
+
+<p>Apache Arrow is an exceptional project, continually enhanced by a thriving ecosystem. However, throughout our exploration, we have noticed certain gaps or points of friction that, if addressed, could significantly enrich the overall experience.</p>
+<ul>
+ <li>Designing an efficient Arrow schema can, in some cases, prove to be a challenging task. Having the <strong>ability to collect statistics</strong> at the record level could facilitate this design phase (data distribution per field, dictionary stats, Arrow array sizes before/after compression, and so on). These statistics would also assist in identifying the most effective columns on which to base the record sorting.</li>
+ <li><strong>Native support for recursive schemas</strong> would also increase adoption by simplifying the use of Arrow in complex scenarios. While I’m not aware of any attempts to address this limitation within the Arrow system, it doesn’t seem insurmountable and would constitute a valuable extension. This would help reduce the complexity of integrating Arrow with other systems that rely on such recursive definitions.</li>
+ <li><strong>Harmonizing the support for data types as well as IPC stream capabilities</strong> would also be a major benefit. Predominant client libraries support nested and hierarchical schemas, but their use is limited due to a lack of full support across the rest of the ecosystem. For example, list and/or union types are not well supported by query engines or Parquet bridges. Also, the advanced dictionary support within IPC streams is not consistent across different implementations [...]
+ <li><strong>Optimizing the support of complex schemas</strong> in terms of memory consumption and compression rate could be improved by natively integrating the concept of the dynamic schema presented in this article.</li>
+ <li><strong>Detecting dictionary overflows</strong> (index level) is not something that is easy to test on the fly. The API could be improved to indicate this overflow as soon as an insertion occurs.</li>
+</ul>
+
+<p>Our effort to utilize Apache Arrow in conjunction with OpenTelemetry has produced encouraging results. While this has necessitated considerable investment in terms of development, exploration, and benchmarking, we hope that these articles will aid in accelerating your journey with Apache Arrow. Looking ahead, we envision an end-to-end integration with Apache Arrow and plan to significantly extend our use of the Arrow ecosystem. This extension involves providing a bridge with Parquet a [...]
+
+ </main>
+ </div>
+
+ <hr/>
+<footer class="footer">
+ <div class="row">
+ <div class="col-md-9">
+ <p>Apache Arrow, Arrow, Apache, the Apache feather logo, and the Apache Arrow project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p>
+ <p>© 2016-2023 The Apache Software Foundation</p>
+ </div>
+ <div class="col-md-3">
+ <a class="d-sm-none d-md-inline pr-2" href="https://www.apache.org/events/current-event.html">
+ <img src="https://www.apache.org/events/current-event-234x60.png"/>
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diff --git a/blog/index.html b/blog/index.html
index 7becfec9319..a255f050b59 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -281,6 +281,21 @@
+ <p>
+ <h3>
+ <a href="/blog/2023/06/15/our-journey-at-f5-with-apache-arrow-part-2/">Our journey at F5 with Apache Arrow (part 2): Adaptive Schemas and Sorting to Optimize Arrow Usage</a>
+ </h3>
+
+ <p>
+ <span class="blog-list-date">
+ 15 June 2023
+ </span>
+ </p>
+ In the previous article, 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...
+ </p>
+
+
+
<p>
<h3>
<a href="/blog/2023/06/13/12.0.1-release/">Apache Arrow 12.0.1 Release</a>
diff --git a/docs/c_glib/index.html b/docs/c_glib/index.html
index ece5c5ad1d6..04a1e3b5989 100644
--- a/docs/c_glib/index.html
+++ b/docs/c_glib/index.html
@@ -20,13 +20,13 @@
<meta property="og:site_name" content="Apache Arrow" />
<meta property="og:image" content="https://arrow.apache.org/img/arrow-logo_horizontal_black-txt_white-bg.png" />
<meta property="og:type" content="article" />
-<meta property="article:published_time" content="2023-06-25T06:12:40-04:00" />
+<meta property="article:published_time" content="2023-06-26T08:40:23-04:00" />
<meta name="twitter:card" content="summary_large_image" />
<meta property="twitter:image" content="https://arrow.apache.org/img/arrow-logo_horizontal_black-txt_white-bg.png" />
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+{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2023-06-26T08:40:23-04:00","datePublished":"2023-06-26T08:40:23-04:00","description":"Apache Arrow GLib (C) Apache Arrow GLib is a wrapper library for Apache Arrow C++. Apache Arrow GLib provides C API. Apache Arrow GLib supports GObject Introspection. It means that you can create language bindings at runtime or compile time automatically. API reference manuals Apache Arrow GLib Apache Parquet GLib Gandiva GLib Plasma [...]
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diff --git a/feed.xml b/feed.xml
index 877acff9af4..79d1f2a7afd 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.2.0">Jekyll</generator><link href="https://arrow.apache.org/feed.xml" rel="self" type="application/atom+xml" /><link href="https://arrow.apache.org/" rel="alternate" type="text/html" /><updated>2023-06-25T06:12:40-04:00</updated><id>https://arrow.apache.org/feed.xml</id><title type="html">Apache Arrow</title><subtitle>Apache Arrow is a cross-language developm [...]
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.2.0">Jekyll</generator><link href="https://arrow.apache.org/feed.xml" rel="self" type="application/atom+xml" /><link href="https://arrow.apache.org/" rel="alternate" type="text/html" /><updated>2023-06-26T08:40:23-04:00</updated><id>https://arrow.apache.org/feed.xml</id><title type="html">Apache Arrow</title><subtitle>Apache Arrow is a cross-language developm [...]
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@@ -416,7 +416,186 @@ been made: we refer you to the <a href="https://github.com/apache/arrow-adbc/blo
<p>We welcome questions and contributions from all interested. Issues
can be filed on <a href="https://github.com/apache/arrow-adbc/issues">GitHub</a>, and questions can be directed to GitHub
-or the <a href="/community/">Arrow mailing lists</a>.</p>]]></content><author><name>pmc</name></author><category term="release" /><summary type="html"><![CDATA[The Apache Arrow team is pleased to announce the 0.5.0 release of the Apache Arrow ADBC libraries. This covers includes 37 resolved issues from 12 distinct contributors. This is a release of the libraries, which are at version 0.5.0. The API specification is versioned separately and is at version 1.0.0. The release notes below are [...]
+or the <a href="/community/">Arrow mailing lists</a>.</p>]]></content><author><name>pmc</name></author><category term="release" /><summary type="html"><![CDATA[The Apache Arrow team is pleased to announce the 0.5.0 release of the Apache Arrow ADBC libraries. This covers includes 37 resolved issues from 12 distinct contributors. This is a release of the libraries, which are at version 0.5.0. The API specification is versioned separately and is at version 1.0.0. The release notes below are [...]
+
+-->
+
+<p>In the previous <a href="https://arrow.apache.org/blog/2023/04/11/our-journey-at-f5-with-apache-arrow-part-1/">article</a>, 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 [...]
+
+<p>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 a [...]
+
+<h2 id="handling-dynamic-and-unknown-data-distributions">Handling dynamic and unknown data distributions</h2>
+
+<p>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 th [...]
+
+<p>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 throu [...]
+
+<p>To optimize such scenarios, we have adopted an intermediary approach that we have named <strong>dynamic Arrow schema</strong>, 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. I [...]
+
+<p>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.</p>
+
+<div class="language-go highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">var</span> <span class="p">(</span>
+ <span class="c">// Arrow schema for the OTLP Arrow Traces record (without attributes, links, and events).</span>
+ <span class="n">TracesSchema</span> <span class="o">=</span> <span class="n">arrow</span><span class="o">.</span><span class="n">NewSchema</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="c">// Nullabe:true means the field is optional, in this case of 16 bit unsigned integers </span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span>< [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Resource</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">StructOf</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span [...]
+ <span class="c">// --- Use dictionary with 8 bit integers initially ----</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SchemaUrl</span><span class="p">,</span><span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span><span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</s [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedAttributesCount</span><span class="p">,</span><span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span><span class="n">Nullable</span><span class="o">:</span> <span cla [...]
+ <span class="p">}</span><span class="o">...</span><span class="p">),</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Scope</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">StructOf</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">acommon</ [...]
+ <span class="c">// --- Use dictionary with 8 bit integers initially ----</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">acommon</s [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Version</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">acommon [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedAttributesCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span [...]
+ <span class="p">}</span><span class="o">...</span><span class="p">),</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SchemaUrl</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</s [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StartTimeUnixNano</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">FixedWidthTypes</span><span class="o">.</span><span class="n">Timestamp_ns</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DurationTimeUnixNano</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">FixedWidthTypes</span><span class="o">.</span><span class="n">Duration_ms</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">TraceId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">16</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SpanId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">8</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">TraceState</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</ [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ParentSpanId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">8</span><span class="p" [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</span>< [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">KIND</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Int32</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">schema</span [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedAttributesCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span cla [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedEventsCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class=" [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">DroppedLinksCount</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint32</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="n [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Status</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">StructOf</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StatusCode</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Int32</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">sche [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StatusMessage</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">Metadata</span><span class="o">:</span> <span class="n">sch [...]
+ <span class="p">}</span><span class="o">...</span><span class="p">),</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">},</span> <span class="no">nil</span><span class="p">)</span>
+ <span class="p">)</span>
+</code></pre></div></div>
+
+<p>In this example, Arrow field-level metadata are employed to designate when a field is optional (Nullable: true) or to specify the minimal dictionary encoding applicable to a particular field (Metadata Dictionary8/16/…). Now let’s imagine a scenario utilizing this schema in a straightforward scenario, wherein only a handful of fields are actually in use, and the cardinality of most dictionary-encoded fields is low (i.e., below 2^8). Ideally, we’d want a system capable of dynamically co [...]
+
+<div class="language-go highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">var</span> <span class="p">(</span>
+ <span class="c">// Simplified schema definition generated by the Arrow Record encoder based on </span>
+ <span class="c">// the data observed.</span>
+ <span class="n">TracesSchema</span> <span class="o">=</span> <span class="n">arrow</span><span class="o">.</span><span class="n">NewSchema</span><span class="p">([]</span><span class="n">arrow</span><span class="o">.</span><span class="n">Field</span><span class="p">{</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">ID</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint16</span><span class="p">,</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><sp [...]
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">StartTimeUnixNano</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">FixedWidthTypes</span><span class="o">.</span><span class="n">Timestamp_ns</span><span class="p">},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">TraceId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">16</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">SpanId</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">FixedSizeBinaryType</span><span class="p">{</span><span class="n">ByteWidth</span><span class="o">:</span> <span class="m">8</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">DictionaryType</span> <span class="p">{</span>
+ <span class="n">IndexType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint8</span><span class="p">,</span>
+ <span class="n">ValueType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">BinaryTypes</span><span class="o">.</span><span class="n">String</span><span class="p">}},</span>
+ <span class="p">{</span><span class="n">Name</span><span class="o">:</span> <span class="n">constants</span><span class="o">.</span><span class="n">KIND</span><span class="p">,</span> <span class="n">Type</span><span class="o">:</span> <span class="o">&</span><span class="n">arrow</span><span class="o">.</span><span class="n">DictionaryType</span> <span class="p">{</span>
+ <span class="n">IndexType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Uint8</span><span class="p">,</span>
+ <span class="n">ValueType</span><span class="o">:</span> <span class="n">arrow</span><span class="o">.</span><span class="n">PrimitiveTypes</span><span class="o">.</span><span class="n">Int32</span><span class="p">,</span>
+ <span class="p">},</span> <span class="n">Nullable</span><span class="o">:</span> <span class="no">true</span><span class="p">},</span>
+ <span class="p">},</span> <span class="no">nil</span><span class="p">)</span>
+<span class="p">)</span>
+</code></pre></div></div>
+
+<p>Additionally, we desire a system capable of automatically adapting the aforementioned schema if it encounters new fields or existing fields with a cardinality exceeding the size of the current dictionary definition in future batches. In extreme scenarios, if the cardinality of a specific field surpasses a certain threshold, we would prefer the system to automatically revert to the non-dictionary representation (mechanism of dictionary overflow). That is precisely what we will elaborat [...]
+
+<p>An overview of the different components and events used to implement this approach is depicted in figure 1.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/adaptive-schema-architecture.svg" width="100%" class="img-responsive" alt="Fig 1: Adaptive Arrow schema architecture overview." />
+ <figcaption>Fig 1: Adaptive Arrow schema architecture overview.</figcaption>
+</figure>
+
+<p>The overall Adaptive Arrow schema component takes a data stream segmented into batches and produces one or multiple streams of Arrow Records (one schema per stream). Each of these records is defined with an Arrow schema, which is based both on the annotated Arrow schema and the shape of fields observed in the incoming data.</p>
+
+<p>More specifically, the process of the Adaptive Arrow schema component consists of four main phases</p>
+
+<p><strong>Initialization phase</strong></p>
+
+<p>During the initialization phase, the Arrow Record Encoder reads the annotated Arrow schema (i.e. the reference schema) and generates a collection of transformations. When these transformations are applied to the reference schema, they yield the first minimal Arrow schema that adheres to the constraints depicted by these annotations. In this initial iteration, all optional fields are eliminated, and all dictionary-encoded fields are configured to utilize the smallest encoding as define [...]
+
+<p><strong>Feeding phase</strong></p>
+
+<p>Following the initialization is the feeding phase. Here, the Arrow Record Encoder scans the batch and attempts to store all the fields in an Arrow Record Builder, which is defined by the schema created in the prior step. If a field exists in the data but is not included in the schema, the encoder will trigger a <code class="language-plaintext highlighter-rouge">missing field</code> event. This process continues until the current batch is completely processed. An additional internal ch [...]
+
+<p><strong>Corrective phase</strong></p>
+
+<p>If at least one event has been generated, a corrective phase will be initiated to fix the schema. This optional stage considers all the events generated in the previous stage and adjusts the transformation tree accordingly to align with the observed data. A <code class="language-plaintext highlighter-rouge">missing field</code> event will remove a NoField transformation for the corresponding field. A <code class="language-plaintext highlighter-rouge">dictionary overflow</code> event w [...]
+
+<p><strong>Routing phase</strong></p>
+
+<p>Once a Record Builder has been properly fed, an Arrow Record is created, and the system transitions into the routing phase. The router component calculates a schema signature of the record and utilizes this signature to route the record to an existing Arrow stream compatible with the signature, or it initiates a new stream if there is no match.</p>
+
+<p>This four-phase process should gradually adapt and stabilize the schema to a structure and definition that is optimized for a specific data stream. Unused fields will never unnecessarily consume memory. Dictionary-encoded fields will be defined with the most optimal index size based on the observed data cardinality, and fields with a cardinality exceeding a certain threshold (defined by configuration) will automatically revert to their non-dictionary-encoded versions.</p>
+
+<p>To effectively execute this approach, you must ensure that there is a sufficient level of flexibility on the receiver side. It’s crucial that your downstream pipeline remains functional even when some fields are missing in the schema or when various dictionary index configurations are employed. While this may not always be feasible without implementing additional transformations upon reception, it proves worthwhile in certain scenarios.</p>
+
+<p>The following results highlight the significant memory usage reduction achieved through the application of various optimization techniques. These results were gathered using a schema akin to the one previously presented. The considerable memory efficiency underscores the effectiveness of this approach.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/memory-usage-25k-traces.png" width="100%" class="img-responsive" alt="Fig 2: Comparative analysis of memory usage for different schema optimizations." />
+ <figcaption>Fig 2: Comparative analysis of memory usage for different schema optimizations.</figcaption>
+</figure>
+
+<p>The concept of a transformation tree enables a generalized approach to perform various types of schema optimizations based on the knowledge acquired from the data. This architecture is highly flexible; the current implementation allows for the removal of unused fields, the application of the most specific dictionary encoding, and the optimization of union type variants. In the future, there is potential for introducing additional optimizations that can be expressed as transformations [...]
+
+<h2 id="handling-recursive-schema-definition">Handling recursive schema definition</h2>
+
+<p>Apache Arrow does not support recursive schema definitions, implying that data structures with variable depth cannot be directly represented. Figure 3 exemplifies such a recursive definition where the value of an attribute can either be a simple data type, a list of values, or a map of values. The depth of this definition cannot be predetermined.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/recursive-def-otel-attributes.svg" width="100%" class="img-responsive" alt="Fig 3: Recursive definition of OTel attributes." />
+ <figcaption>Fig 3: Recursive definition of OTel attributes.</figcaption>
+</figure>
+
+<p>Several strategies can be employed to circumvent this limitation. Technically, the dynamic schema concept we’ve presented could be expanded to dynamically update the schema to include any missing level of recursion. However, for this use case, this method is complex and has the notable downside of not offering any assurance on the maximum size of the schema. This lack of constraint can pose security issues; hence, this approach isn’t elaborated upon.</p>
+
+<p>The second approach consists of breaking the recursion by employing a serialization format that supports the definition of a recursive schema. The result of this serialization can then be integrated into the Arrow record as a binary type column, effectively halting the recursion at a specific level. To fully leverage the advantages of columnar representation, it is crucial to apply this ad-hoc serialization as deeply within the data structure as feasible. In the context of OpenTelemet [...]
+
+<p>A variety of serialization formats, such as protobuf or CBOR, can be employed to encode recursive data. Without particular treatment, these binary columns may not be easily queryable by the existing Arrow query engines. Therefore, it’s crucial to thoughtfully ascertain when and where to apply such a technique. While I’m not aware of any attempts to address this limitation within the Arrow system, it doesn’t seem insurmountable and would constitute a valuable extension. This would help [...]
+
+<h2 id="importance-of-sorting">Importance of sorting</h2>
+
+<p>In our preceding <a href="https://arrow.apache.org/blog/2023/04/11/our-journey-at-f5-with-apache-arrow-part-1/">article</a>, we explored a variety of strategies to represent hierarchical data models, including nested structures based on struct/list/map/union, denormalization and flattening representations, as well as a multi-record approach. Each method presents its unique advantages and disadvantages. However, in this last section, we’ll delve deeper into the multi-record approach, f [...]
+
+<p>In the OTel Arrow protocol, we leverage the multi-record approach to represent metrics, logs, and traces. The following entity-relationship diagram offers a simplified version of various record schemas and illustrates their relationships, specifically those used to represent gauges and sums. A comprehensive description of the Arrow data model employed in OpenTelemetry can be accessed <a href="https://github.com/f5/otel-arrow-adapter/blob/main/docs/data_model.md">here</a>.</p>
+
+<p>These Arrow records, also referred to as tables, form a hierarchy with <code class="language-plaintext highlighter-rouge">METRICS</code> acting as the primary entry point. Each table can be independently sorted according to one or more columns. This sorting strategy facilitates the grouping of duplicated data, thereby improving the compression ratio.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/metric-dp-data-model.png" width="100%" class="img-responsive" alt="Fig 4: A simplified entity-relationship diagram representing OTel sum & gauge metrics." />
+ <figcaption>Fig 4: A simplified entity-relationship diagram representing OTel sum & gauge metrics.</figcaption>
+</figure>
+
+<p>The relationship between the primary <code class="language-plaintext highlighter-rouge">METRICS</code> table and the secondary <code class="language-plaintext highlighter-rouge">RESOURCE_ATTRS</code>, <code class="language-plaintext highlighter-rouge">SCOPE_ATTRS</code>, and <code class="language-plaintext highlighter-rouge">NUMBER_DATA_POINTS</code> tables is established through a unique <code class="language-plaintext highlighter-rouge">id</code> in the main table and a <code class= [...]
+
+<p>To achieve this, the ordering process for the different tables adheres to the hierarchy, starting from the main table down to the leaf. The main table is sorted (by one or multiple columns), and then an incremental id is assigned to each row. This numerical id is stored using delta-encoding, which is implemented on top of Arrow.</p>
+
+<p>The secondary tables directly connected to the main table are sorted using the same principle, but the <code class="language-plaintext highlighter-rouge">parent_id</code> column is consistently utilized as the last column in the sort statement. Including the <code class="language-plaintext highlighter-rouge">parent_id</code> column in the sort statement enables the use of a variation of delta encoding. The efficiency of this approach is summarized in the chart below.</p>
+
+<figure style="text-align: center;">
+ <img src="/img/journey-apache-arrow/compressed-message-size.png" width="100%" class="img-responsive" alt="Fig 5: Comparative analysis of compression ratios - OTLP Protocol vs. Two variations of the OTel Arrow Protocol with multivariate metrics stream. (lower percentage is better)" />
+ <figcaption>Fig 5: Comparative analysis of compression ratios - OTLP Protocol vs. Two variations of the OTel Arrow Protocol with multivariate metrics stream. (lower percentage is better)</figcaption>
+</figure>
+
+<p>The second column presents the average size of the OTLP batch both pre- and post-ZSTD compression for batches of varying sizes. This column serves as a reference point for the ensuing two columns. The third column displays results for the OTel Arrow protocol without any sorting applied, while the final column showcases results for the OTel Arrow protocol with sorting enabled.</p>
+
+<p>Before compression, the average batch sizes for the two OTel Arrow configurations are predictably similar. However, post-compression, the benefits of sorting each individual table on the compression ratio become immediately apparent. Without sorting, the OTel Arrow protocol exhibits a compression ratio that’s 1.40 to 1.67 times better than the reference. When sorting is enabled, the OTel Arrow protocol outperforms the reference by a factor ranging from 4.94 to 7.21 times!</p>
+
+<p>The gains in terms of compression obviously depend on your data and the redundancy of information present in your data batches. According to our observations, the choice of a good sort generally improves the compression ratio by a factor of 1.5 to 8.</p>
+
+<p>Decomposing a complex schema into multiple simpler schemas to enhance sorting capabilities, coupled with a targeted approach to efficiently encode the identifiers representing the relationships, emerges as an effective strategy for enhancing overall data compression. This method also eliminates complex Arrow data types, such as lists, maps, and unions. Consequently, it not only improves but also simplifies data query-ability. This simplification proves beneficial for existing query en [...]
+
+<h2 id="conclusion-and-next-steps">Conclusion and next steps</h2>
+
+<p>This article concludes our two-part series on Apache Arrow, wherein we have explored various strategies to maximize the utility of Apache Arrow within specific contexts. The adaptive schema architecture presented in the second part of this series paves the way for future optimization possibilities. We look forward to seeing what the community can add based on this contribution.</p>
+
+<p>Apache Arrow is an exceptional project, continually enhanced by a thriving ecosystem. However, throughout our exploration, we have noticed certain gaps or points of friction that, if addressed, could significantly enrich the overall experience.</p>
+<ul>
+ <li>Designing an efficient Arrow schema can, in some cases, prove to be a challenging task. Having the <strong>ability to collect statistics</strong> at the record level could facilitate this design phase (data distribution per field, dictionary stats, Arrow array sizes before/after compression, and so on). These statistics would also assist in identifying the most effective columns on which to base the record sorting.</li>
+ <li><strong>Native support for recursive schemas</strong> would also increase adoption by simplifying the use of Arrow in complex scenarios. While I’m not aware of any attempts to address this limitation within the Arrow system, it doesn’t seem insurmountable and would constitute a valuable extension. This would help reduce the complexity of integrating Arrow with other systems that rely on such recursive definitions.</li>
+ <li><strong>Harmonizing the support for data types as well as IPC stream capabilities</strong> would also be a major benefit. Predominant client libraries support nested and hierarchical schemas, but their use is limited due to a lack of full support across the rest of the ecosystem. For example, list and/or union types are not well supported by query engines or Parquet bridges. Also, the advanced dictionary support within IPC streams is not consistent across different implementations [...]
+ <li><strong>Optimizing the support of complex schemas</strong> in terms of memory consumption and compression rate could be improved by natively integrating the concept of the dynamic schema presented in this article.</li>
+ <li><strong>Detecting dictionary overflows</strong> (index level) is not something that is easy to test on the fly. The API could be improved to indicate this overflow as soon as an insertion occurs.</li>
+</ul>
+
+<p>Our effort to utilize Apache Arrow in conjunction with OpenTelemetry has produced encouraging results. While this has necessitated considerable investment in terms of development, exploration, and benchmarking, we hope that these articles will aid in accelerating your journey with Apache Arrow. Looking ahead, we envision an end-to-end integration with Apache Arrow and plan to significantly extend our use of the Arrow ecosystem. This extension involves providing a bridge with Parquet a [...]
-->
@@ -1265,211 +1444,4 @@ For details, see the <a href="https://lists.apache.org/thread/247z3t06mf132nocng
<p>We welcome questions and contributions from all interested. Issues
can be filed on <a href="https://github.com/apache/arrow-adbc/issues">GitHub</a>, and questions can be directed to GitHub
-or the <a href="/community/">Arrow mailing lists</a>.</p>]]></content><author><name>pmc</name></author><category term="release" /><summary type="html"><![CDATA[The Apache Arrow team is pleased to announce the 0.3.0 release of the Apache Arrow ADBC libraries. This covers includes 24 resolved issues from 7 distinct contributors. This is a release of the libraries, which are at version 0.3.0. The API specification is versioned separately and is at version 1.0.0. The release notes below are [...]
-
--->
-
-<p>The Apache Arrow team is pleased to announce the 0.1.0 release of
-Apache Arrow nanoarrow. This initial release covers 31 resolved issues from
-6 contributors.</p>
-
-<h2 id="release-highlights">Release Highlights</h2>
-
-<p>This initial release includes the following:</p>
-
-<ul>
- <li>A <a href="#using-nanoarrow-in-c">C library</a> bundled as two files (nanoarrow.c
-and nanoarrow.h).</li>
- <li>An <a href="#using-nanoarrow-in-c-r-and-python">R package</a> providing bindings for users
-of the R programming language.</li>
-</ul>
-
-<p>See the
-<a href="https://github.com/apache/arrow-nanoarrow/blob/apache-arrow-nanoarrow-0.1.0/CHANGELOG.md">Changelog</a>
-for a detailed list of contributions leading up to the initial release.</p>
-
-<h2 id="why-nanoarrow">Why nanoarrow?</h2>
-
-<p>The adoption of the
-<a href="https://arrow.apache.org/docs/format/CDataInterface.html">Arrow C Data Interface</a>
-and the <a href="https://arrow.apache.org/docs/format/CStreamInterface.html">Arrow C Stream Interface</a>
-since their
-<a href="https://arrow.apache.org/blog/2020/05/03/introducing-arrow-c-data-interface/">introduction</a>
-have been impressive and enthusiastic: not only have Arrow language bindings
-adopted the standard to pass data among themselves, a growing number of
-high-profile libraries like
-<a href="https://gdal.org/development/rfc/rfc86_column_oriented_api.html">GDAL</a> and
-<a href="https://duckdb.org/2021/12/03/duck-arrow.html">DuckDB</a> use the standard to
-improve performance and provide an ABI-stable interface to tabular input and output.</p>
-
-<p>GDAL and DuckDB are fortunate to have hard-working and forward-thinking maintainers
-that were motivated to provide support for the Arrow C Data and Stream interfaces
-even though the code to do so required an intimate knowledge of both the interface
-and the columnar specification on which it is based.</p>
-
-<p>The vision of <a href="https://github.com/apache/arrow-nanoarrow">nanoarrow</a>
-is that it should be trivial for a library or application to implement an Arrow-based
-interface: if a library consumes or produces tabular data, Arrow should be the
-first place developers look. Developers shouldn’t have to be familiar with the
-details of the columnar specification—nor should they have to take on any
-build-time dependencies—to get started.</p>
-
-<p>The <a href="https://arrow.apache.org/docs/format/ADBC.html">Arrow Database Connectivity (ADBC)</a>
-specification is a good example of such a project, and provided a strong
-motivator for the development of nanoarrow: at the heart of ADBC is the
-idea of a core “driver manager” and database-specific drivers that are distributed
-as independent C/C++/Python/R/Java/Go projects. At least in R and Python,
-embedding an existing Arrow implementation (e.g., Arrow C++) is challenging
-in the context of multiple packages intended to be loaded into the same process.
-As of this writing, ADBC includes nanoarrow-based SQLite and PostgreSQL drivers
-and a nanoarrow-based validation suite for drivers.</p>
-
-<h2 id="using-nanoarrow-in-c">Using nanoarrow in C</h2>
-
-<p>The nanoarrow C library is distributed as
-<a href="https://github.com/apache/arrow-nanoarrow/tree/main/dist">two files (nanoarrow.h and nanoarrow.c)</a>
-that can be copied and vendored into an existing code base. This results in
-a static library of about 50 KB and builds in less than a second. Some features
-that nanoarrow provides are:</p>
-
-<ul>
- <li><a href="https://apache.github.io/arrow-nanoarrow/dev/c.html#creating-schemas">Helpers to create types, schemas, and metadata</a></li>
- <li><a href="https://apache.github.io/arrow-nanoarrow/dev/c.html#owning-growable-buffers">Growable buffers</a>,
-including the option for custom allocators/deallocators.</li>
- <li><a href="https://apache.github.io/arrow-nanoarrow/dev/c.html#bitmap-utilities">Bitmap (i.e., bitpacked boolean) utilities</a></li>
- <li>An <a href="https://apache.github.io/arrow-nanoarrow/dev/c.html#creating-arrays">API for building arrays from buffers</a></li>
- <li>An <a href="https://apache.github.io/arrow-nanoarrow/dev/c.html#creating-arrays">API for building arrays element-wise</a></li>
- <li>An <a href="https://apache.github.io/arrow-nanoarrow/dev/c.html#reading-arrays">API to extract elements element-wise</a>
-from an existing array.</li>
-</ul>
-
-<p>For example, one can build an integer array element-wise:</p>
-
-<div class="language-c highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="cp">#include</span> <span class="cpf">"nanoarrow.h"</span><span class="cp">
-</span>
-<span class="kt">int</span> <span class="nf">make_simple_array</span><span class="p">(</span><span class="k">struct</span> <span class="n">ArrowArray</span><span class="o">*</span> <span class="n">array_out</span><span class="p">,</span> <span class="k">struct</span> <span class="n">ArrowSchema</span><span class="o">*</span> <span class="n">schema_out</span><span class="p">)</span> <span class="p">{</span>
- <span class="k">struct</span> <span class="n">ArrowError</span> <span class="n">error</span><span class="p">;</span>
- <span class="n">array_out</span><span class="o">-></span><span class="n">release</span> <span class="o">=</span> <span class="nb">NULL</span><span class="p">;</span>
- <span class="n">schema_out</span><span class="o">-></span><span class="n">release</span> <span class="o">=</span> <span class="nb">NULL</span><span class="p">;</span>
-
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayInitFromType</span><span class="p">(</span><span class="n">array_out</span><span class="p">,</span> <span class="n">NANOARROW_TYPE_INT32</span><span class="p">));</span>
-
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayStartAppending</span><span class="p">(</span><span class="n">array_out</span><span class="p">));</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayAppendInt</span><span class="p">(</span><span class="n">array_out</span><span class="p">,</span> <span class="mi">1</span><span class="p">));</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayAppendInt</span><span class="p">(</span><span class="n">array_out</span><span class="p">,</span> <span class="mi">2</span><span class="p">));</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayAppendInt</span><span class="p">(</span><span class="n">array_out</span><span class="p">,</span> <span class="mi">3</span><span class="p">));</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayFinishBuilding</span><span class="p">(</span><span class="n">array_out</span><span class="p">,</span> <span class="o">&</span><span class="n">error</span><span class="p">));</span>
-
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowSchemaInitFromType</span><span class="p">(</span><span class="n">schema_out</span><span class="p">,</span> <span class="n">NANOARROW_TYPE_INT32</span><span class="p">));</span>
-
- <span class="k">return</span> <span class="n">NANOARROW_OK</span><span class="p">;</span>
-<span class="p">}</span>
-</code></pre></div></div>
-
-<p>Similarly, one can extract elements from an array:</p>
-
-<div class="language-c highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="cp">#include</span> <span class="cpf"><stdio.h></span><span class="cp">
-#include</span> <span class="cpf">"nanoarrow.h"</span><span class="cp">
-</span>
-<span class="kt">int</span> <span class="nf">print_simple_array</span><span class="p">(</span><span class="k">struct</span> <span class="n">ArrowArray</span><span class="o">*</span> <span class="n">array</span><span class="p">,</span> <span class="k">struct</span> <span class="n">ArrowSchema</span><span class="o">*</span> <span class="n">schema</span><span class="p">)</span> <span class="p">{</span>
- <span class="k">struct</span> <span class="n">ArrowError</span> <span class="n">error</span><span class="p">;</span>
- <span class="k">struct</span> <span class="n">ArrowArrayView</span> <span class="n">array_view</span><span class="p">;</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayViewInitFromSchema</span><span class="p">(</span><span class="o">&</span><span class="n">array_view</span><span class="p">,</span> <span class="n">schema</span><span class="p">,</span> <span class="o">&</span><span class="n">error</span><span class="p">));</span>
-
- <span class="k">if</span> <span class="p">(</span><span class="n">array_view</span><span class="p">.</span><span class="n">storage_type</span> <span class="o">!=</span> <span class="n">NANOARROW_TYPE_INT32</span><span class="p">)</span> <span class="p">{</span>
- <span class="n">printf</span><span class="p">(</span><span class="s">"Array has storage that is not int32</span><span class="se">\n</span><span class="s">"</span><span class="p">);</span>
- <span class="p">}</span>
-
- <span class="kt">int</span> <span class="n">result</span> <span class="o">=</span> <span class="n">ArrowArrayViewSetArray</span><span class="p">(</span><span class="o">&</span><span class="n">array_view</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="o">&</span><span class="n">error</span><span class="p">);</span>
- <span class="k">if</span> <span class="p">(</span><span class="n">result</span> <span class="o">!=</span> <span class="n">NANOARROW_OK</span><span class="p">)</span> <span class="p">{</span>
- <span class="n">ArrowArrayViewReset</span><span class="p">(</span><span class="o">&</span><span class="n">array_view</span><span class="p">);</span>
- <span class="k">return</span> <span class="n">result</span><span class="p">;</span>
- <span class="p">}</span>
-
- <span class="k">for</span> <span class="p">(</span><span class="kt">int64_t</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">array</span><span class="o">-></span><span class="n">length</span><span class="p">;</span> <span class="n">i</span><span class="o">++</span><span class="p">)</span> <span class="p">{</span>
- <span class="n">printf</span><span class="p">(</span><span class="s">"%d</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span> <span class="p">(</span><span class="kt">int</span><span class="p">)</span><span class="n">ArrowArrayViewGetIntUnsafe</span><span class="p">(</span><span class="o">&</span><span class="n">array_view</span><span class="p">,</span> <span class="n">i</span><span class="p">));</span>
- <span class="p">}</span>
-
- <span class="n">ArrowArrayViewReset</span><span class="p">(</span><span class="o">&</span><span class="n">array_view</span><span class="p">);</span>
- <span class="k">return</span> <span class="n">NANOARROW_OK</span><span class="p">;</span>
-<span class="p">}</span>
-</code></pre></div></div>
-
-<h2 id="using-nanoarrow-in-c-r-and-python">Using nanoarrow in C++, R, and Python</h2>
-
-<p>Recognizing that many projects for which nanoarrow may be useful will have
-access a higher-level runtime than C, there are experiments to provide
-these users with a minimal set of useful tools.</p>
-
-<p>For C++ projects, an experimental
-<a href="https://apache.github.io/arrow-nanoarrow/dev/cpp.html">“nanoarrow.hpp”</a>
-interface provides <code class="language-plaintext highlighter-rouge">unique_ptr</code>-like wrappers for nanoarrow C objects to
-reduce the verbosity of using the nanoarrow API. For example, the previous
-<code class="language-plaintext highlighter-rouge">print_simple_array()</code> implementation would collapse to:</p>
-
-<div class="language-cpp highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="cp">#include</span> <span class="cpf"><stdio.h></span><span class="cp">
-#include</span> <span class="cpf">"nanoarrow.hpp"</span><span class="cp">
-</span>
-<span class="kt">int</span> <span class="nf">print_simple_array2</span><span class="p">(</span><span class="k">struct</span> <span class="nc">ArrowArray</span><span class="o">*</span> <span class="n">array</span><span class="p">,</span> <span class="k">struct</span> <span class="nc">ArrowSchema</span><span class="o">*</span> <span class="n">schema</span><span class="p">)</span> <span class="p">{</span>
- <span class="k">struct</span> <span class="nc">ArrowError</span> <span class="n">error</span><span class="p">;</span>
- <span class="n">nanoarrow</span><span class="o">::</span><span class="n">UniqueArrayView</span> <span class="n">array_view</span><span class="p">;</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayViewInitFromSchema</span><span class="p">(</span><span class="n">array_view</span><span class="p">.</span><span class="n">get</span><span class="p">(),</span> <span class="n">schema</span><span class="p">,</span> <span class="o">&</span><span class="n">error</span><span class="p">));</span>
- <span class="n">NANOARROW_RETURN_NOT_OK</span><span class="p">(</span><span class="n">ArrowArrayViewSetArray</span><span class="p">(</span><span class="n">array_view</span><span class="p">.</span><span class="n">get</span><span class="p">(),</span> <span class="n">array</span><span class="p">,</span> <span class="o">&</span><span class="n">error</span><span class="p">));</span>
- <span class="k">for</span> <span class="p">(</span><span class="kt">int64_t</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">array</span><span class="o">-></span><span class="n">length</span><span class="p">;</span> <span class="n">i</span><span class="o">++</span><span class="p">)</span> <span class="p">{</span>
- <span class="n">printf</span><span class="p">(</span><span class="s">"%d</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span> <span class="p">(</span><span class="kt">int</span><span class="p">)</span><span class="n">ArrowArrayViewGetIntUnsafe</span><span class="p">(</span><span class="n">array_view</span><span class="p">.</span><span class="n">get</span><span class="p">(),</span> <span class="n">i</span><span class="p">));</span>
- <span class="p">}</span>
- <span class="k">return</span> <span class="n">NANOARROW_OK</span><span class="p">;</span>
-<span class="p">}</span>
-</code></pre></div></div>
-
-<p>For R packages, experimental
-<a href="https://apache.github.io/arrow-nanoarrow/dev/r/index.html">R bindings</a> provide
-a limited set of conversions between R vectors and Arrow arrays such that
-R bindings for a library with an Arrow-based interface do not need to provide
-this behaviour themselves. Additional features include printing and validating
-the content of the C structures at the heart of the C Data and C Stream
-interfaces to facilitate the development of bindings to Arrow-based libraries.</p>
-
-<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># install.packages("remotes")</span><span class="w">
-</span><span class="n">remotes</span><span class="o">::</span><span class="n">install_github</span><span class="p">(</span><span class="s2">"apache/arrow-nanoarrow/r"</span><span class="p">,</span><span class="w"> </span><span class="n">build</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">FALSE</span><span class="p">)</span><span class="w">
-</span><span class="n">library</span><span class="p">(</span><span class="n">nanoarrow</span><span class="p">)</span><span class="w">
-
-</span><span class="n">as_nanoarrow_array</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">5</span><span class="p">)</span><span class="w">
-</span><span class="c1">#> <nanoarrow_array int32[5]></span><span class="w">
-</span><span class="c1">#> $ length : int 5</span><span class="w">
-</span><span class="c1">#> $ null_count: int 0</span><span class="w">
-</span><span class="c1">#> $ offset : int 0</span><span class="w">
-</span><span class="c1">#> $ buffers :List of 2</span><span class="w">
-</span><span class="c1">#> ..$ :<nanoarrow_buffer_validity[0 b] at 0x0></span><span class="w">
-</span><span class="c1">#> ..$ :<nanoarrow_buffer_data_int32[20 b] at 0x135d13c28></span><span class="w">
-</span><span class="c1">#> $ dictionary: NULL</span><span class="w">
-</span><span class="c1">#> $ children : list()</span><span class="w">
-</span></code></pre></div></div>
-
-<p>A <a href="https://github.com/apache/arrow-nanoarrow/tree/main/python">Python package skeleton</a>
-exists in the nanoarrow repository and further functionality may be added once
-the C library interface has stabilized.</p>
-
-<h2 id="try-nanoarrow">Try nanoarrow</h2>
-
-<p>For any interested in giving nanoarrow a try, the easiest way to get started is to clone the
-<a href="https://github.com/apache/arrow-nanoarrow/tree/apache-arrow-nanoarrow-0.1.0">nanoarrow repository from GitHub</a>
-and build/modify the
-<a href="https://github.com/apache/arrow-nanoarrow/tree/apache-arrow-nanoarrow-0.1.0/examples/cmake-minimal">minimal CMake build example</a>.
-For applied usage, one can refer to the
-<a href="https://github.com/apache/arrow-adbc/tree/main/c/driver/sqlite">ADBC SQLite driver</a>
-and the <a href="https://github.com/apache/arrow-adbc/tree/main/c/driver/postgresql">ADBC PostgreSQL driver</a>.</p>
-
-<h2 id="contributors">Contributors</h2>
-
-<p>This initial release consists of contributions from 6 contributors in addition
-to the invaluable advice and support of the Apache Arrow developer mailing list.</p>
-
-<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>$ git shortlog -sn 8339114637919b661c1c8fae6764ceed532c935e..apache-arrow-nanoarrow-0.1.0 | grep -v "GitHub Actions"
- 100 Dewey Dunnington
- 7 David Li
- 2 Dirk Eddelbuettel
- 1 Dane Pitkin
- 1 Jonathan Keane
- 1 Joris Van den Bossche
-</code></pre></div></div>]]></content><author><name>pmc</name></author><category term="release" /><summary type="html"><![CDATA[The Apache Arrow team is pleased to announce the 0.1.0 release of Apache Arrow nanoarrow. This initial release covers 31 resolved issues from 6 contributors. Release Highlights This initial release includes the following: A C library bundled as two files (nanoarrow.c and nanoarrow.h). An R package providing bindings for users of the R programming language. See t [...]
\ No newline at end of file
+or the <a href="/community/">Arrow mailing lists</a>.</p>]]></content><author><name>pmc</name></author><category term="release" /><summary type="html"><![CDATA[The Apache Arrow team is pleased to announce the 0.3.0 release of the Apache Arrow ADBC libraries. This covers includes 24 resolved issues from 7 distinct contributors. This is a release of the libraries, which are at version 0.3.0. The API specification is versioned separately and is at version 1.0.0. The release notes below are [...]
\ No newline at end of file
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index 4d1462c4a11..b7bf2f0b55a 100644
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