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+
+<h1>
+ Announcing arrow-avro in Arrow Rust
+</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">
+ 23 Oct 2025
+ </span>
+ <br>
+ <span class="badge badge-secondary">By</span>
+
+ <a class="mr-3" href="https://github.com/jecsand838"; target="_blank"
rel="noopener">Connor Sanders (jecsand838) </a>
+
+
+
+</p>
+
+
+ <!--
+
+-->
+<p><a href="https://crates.io/crates/arrow-avro"; target="_blank"
rel="noopener"><code>arrow-avro</code></a>, a newly rewritten Rust crate that
reads and writes <a href="https://avro.apache.org/"; target="_blank"
rel="noopener">Apache Avro</a> data directly as Arrow
<code>RecordBatch</code>es, is now available. It supports <a
href="https://avro.apache.org/docs/1.11.1/specification/#object-container-files";
target="_blank" rel="noopener">Avro Object Container Files</a> (OCF), <a
href="https: [...]
+<h2>Motivation</h2>
+<p>Apache Avro’s row‑oriented design is effective for encoding one record at a
time, while Apache Arrow’s columnar layout is optimized for vectorized
analytics. A major challenge lies in converting between these formats without
reintroducing row‑wise overhead. Decoding Avro a row at a time and then
building Arrow arrays incurs extra allocations and cache‑unfriendly access (the
very costs Arrow is designed to avoid). In the real world, this overhead
commonly shows up in analytical hot pat [...]
+<h3>Why not use the existing <code>apache-avro</code> crate?</h3>
+<p>Rust already has a mature, general‑purpose Avro crate, <a
href="https://crates.io/crates/apache-avro"; target="_blank"
rel="noopener">apache-avro</a>. It reads and writes Avro records as Avro value
types and provides Object Container File readers and writers. What it does not
do is decode directly into Arrow arrays, so any Arrow integration must
materialize rows and then build columns.</p>
+<p>What’s needed is a complementary approach that decodes column‑by‑column
straight into Arrow builders and emits <code>RecordBatch</code>es. This would
enable projection pushdown while keeping execution vectorized end to end. For
projects such as <a href="https://datafusion.apache.org/"; target="_blank"
rel="noopener">Apache DataFusion</a>, access to a mature, upstream Arrow‑native
reader and writer would help simplify the code path and reduce duplication.</p>
+<p>Modern pipelines heighten this need because <a
href="https://www.confluent.io/blog/avro-kafka-data/"; target="_blank"
rel="noopener">Avro is also used on the wire</a>, not just in files. Kafka
ecosystems commonly use Confluent’s Schema Registry framing, and many services
adopt the Avro Single‑Object Encoding format. An approach that enables decoding
straight into Arrow batches (rather than through per‑row values) would let
downstream compute remain vectorized at streaming rates.</p>
+<h3>Why this matters</h3>
+<p>Apache Avro is a first‑class format across stream processors and cloud
services:</p>
+<ul>
+<li>Confluent Schema Registry supports <a
href="https://docs.confluent.io/platform/current/schema-registry/fundamentals/serdes-develop/serdes-avro.html";
target="_blank" rel="noopener">Avro across multiple languages and
tooling</a>.</li>
+<li>Apache Flink exposes an <a
href="https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/connectors/table/formats/avro-confluent/";
target="_blank" rel="noopener"><code>avro-confluent</code> format for
Kafka</a>.</li>
+<li>AWS Lambda <a
href="https://aws.amazon.com/about-aws/whats-new/2025/06/aws-lambda-native-support-avro-protobuf-kafka-events/";
target="_blank" rel="noopener">(June 2025) added native handling for
Avro‑formatted Kafka events</a> with Glue and Confluent Schema Registry
integrations.</li>
+<li>Azure Event Hubs provides a <a
href="https://learn.microsoft.com/en-us/azure/event-hubs/schema-registry-overview";
target="_blank" rel="noopener">Schema Registry with Avro support</a> for
Kafka‑compatible clients.</li>
+</ul>
+<p>In short: Arrow users encounter Avro both on disk (OCF) and on the wire
(SOE). An Arrow‑first, vectorized reader/writer for OCF, SOE, and Confluent
framing removes a pervasive bottleneck and keeps pipelines columnar
end‑to‑end.</p>
+<h2>Introducing <code>arrow-avro</code>
+</h2>
+<p><a href="https://github.com/apache/arrow-rs/tree/main/arrow-avro";
target="_blank" rel="noopener"><code>arrow-avro</code></a> is a
high-performance Rust crate that converts between Avro and Arrow with a
column‑first, batch‑oriented design. On the read side, it decodes Avro Object
Container Files (OCF), Single‑Object Encoding (SOE), and the Confluent Schema
Registry wire format directly into Arrow <code>RecordBatch</code>es. Meanwhile,
the write path provides formats for encoding to OCF [...]
+<p>The crate exposes two primary read APIs: a high-level <code>Reader</code>
for OCF inputs and a low-level <code>Decoder</code> for streaming SOE frames.
For SOE and Confluent/Apicurio frames, a <code>SchemaStore</code> is provided
that resolves fingerprints or schema IDs to full Avro writer schemas, enabling
schema evolution while keeping the decode path vectorized.</p>
+<p>On the write side, <code>AvroWriter</code> produces OCF (including
container‑level compression), while <code>AvroStreamWriter</code> produces
framed Avro messages for Single‑Object or Confluent/Apicurio encodings, as
configured via the <code>WriterBuilder::with_fingerprint_strategy(...)</code>
knob.</p>
+<p>Configuration is intentionally minimal but practical. For instance, the
<code>ReaderBuilder</code> exposes knobs covering both batch file ingestion and
streaming systems without forcing format‑specific code paths.</p>
+<h3>How this mirrors Parquet in Arrow‑rs</h3>
+<p>If you have used Parquet with Arrow‑rs, you already know the pattern. The
<code>parquet</code> crate exposes a <a
href="https://docs.rs/parquet/latest/parquet/arrow/index.html"; target="_blank"
rel="noopener">parquet::arrow module</a> that reads and writes Arrow
<code>RecordBatch</code>es directly. Most users reach for
<code>ParquetRecordBatchReaderBuilder</code> when reading and
<code>ArrowWriter</code> when writing. You choose columns up front, set a batch
size, and the reader gives [...]
+<p><code>arrow‑avro</code> brings that same bridge to Avro. You get a single
<code>ReaderBuilder</code> that can produce a <code>Reader</code> for OCF, or a
streaming <code>Decoder</code> for on‑the‑wire frames. Both return Arrow
<code>RecordBatch</code>es, which means engines can keep projection and
filtering close to the reader and avoid building rows only to reassemble them
back into columns later. For evolving streams, a small <code>SchemaStore</code>
resolves fingerprints or ids bef [...]
+<p>The reason this pattern matters is straightforward. Arrow’s columnar format
is designed for vectorized work and good cache locality. When a format reader
produces Arrow batches directly, copies and branchy per‑row work are minimized,
keeping downstream operators fast. That is the same story that made
<code>parquet::arrow</code> popular in Rust, and it is what
<code>arrow‑avro</code> now enables for Avro.</p>
+<h2>Architecture & Technical Overview</h2>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 20px 15px;">
+<img src="/img/introducing-arrow-avro/arrow-avro-architecture.svg"
width="100%" alt="High-level `arrow-avro` architecture" style="background:#fff">
+</div>
+<p>At a high level, <a
href="https://arrow.apache.org/rust/arrow_avro/index.html";>arrow-avro</a>
splits cleanly into read and write paths built around Arrow
<code>RecordBatch</code>es. The read side turns Avro (OCF files or framed byte
streams) into batched Arrow arrays, while the write side takes Arrow batches
and produces OCF files or streaming frames. When using an
<code>AvroStreamWriter</code>, the framing (SOE or Confluent) is part of the
stream output based on the configured finger [...]
+<p>On the <a
href="https://arrow.apache.org/rust/arrow_avro/reader/index.html";>read</a>
path, everything starts with the <a
href="https://arrow.apache.org/rust/arrow_avro/reader/struct.ReaderBuilder.html";>ReaderBuilder</a>.
A single builder can create a <a
href="https://arrow.apache.org/rust/arrow_avro/reader/struct.Reader.html";>Reader</a>
for Object Container Files (OCF) or a streaming <a
href="https://arrow.apache.org/rust/arrow_avro/reader/struct.Decoder.html";>Decoder</a>
for SOE/Conf [...]
+<p>When reading an OCF, the <code>Reader</code> parses a header and then
iterates over blocks of encoded data. The header contains a metadata map with
the embedded Avro schema and optional compression (i.e., <code>deflate</code>,
<code>snappy</code>, <code>zstd</code>, <code>bzip2</code>, <code>xz</code>),
plus a 16‑byte sync marker used to delimit blocks. Each subsequent OCF block
then carries a row count and the encoded payload. The parsed OCF header and
block structures are also encod [...]
+<p>On the <a
href="https://arrow.apache.org/rust/arrow_avro/writer/index.html";>write</a>
path, the <a
href="https://arrow.apache.org/rust/arrow_avro/writer/struct.WriterBuilder.html";>WriterBuilder</a>
produces either an <a
href="https://arrow.apache.org/rust/arrow_avro/writer/type.AvroWriter.html";>AvroWriter</a>
(OCF) or an <a
href="https://arrow.apache.org/rust/arrow_avro/writer/type.AvroStreamWriter.html";>AvroStreamWriter</a>
(SOE/Message). The <code>with_compression(...)</code> knob i [...]
+<p>Schema handling is centralized in the <a
href="https://arrow.apache.org/rust/arrow_avro/schema/index.html";>schema</a>
module. <a
href="https://arrow.apache.org/rust/arrow_avro/schema/struct.AvroSchema.html";>AvroSchema</a>
wraps a valid Avro Schema JSON string, supports computing a
<code>Fingerprint</code>, and can be loaded into a <a
href="https://arrow.apache.org/rust/arrow_avro/schema/struct.SchemaStore.html";>SchemaStore</a>
as a writer schema. At runtime, the <code>Reader</code>/<c [...]
+<p>At the heart of <code>arrow-avro</code> is a type‑mapping
<code>Codec</code> that the library uses to construct both encoders and
decoders. The <code>Codec</code> captures, for every Avro field, how it maps to
Arrow and how it should be encoded or decoded. The <code>Reader</code> logic
builds a <code>Codec</code> per <em>(writer, reader)</em> schema pair, which
the decoder later uses to vectorize parsing of Avro values directly into the
correct Arrow builders. The <code>Writer</code> [...]
+<p>Finally, by keeping container and stream framing (OCF vs. SOE) separate
from encoding and decoding, the crate composes naturally with the rest of
Arrow‑rs: you read or write Arrow <code>RecordBatch</code>es, pick OCF or SOE
streams as needed, and wire up fingerprints only when you're on a streaming
path. This results in a compact API surface that covers both batch files and
high‑throughput streams without sacrificing columnar, vectorized execution.</p>
+<h2>Examples</h2>
+<h3>Decoding a Confluent-framed Kafka Stream</h3>
+<div class="language-rust highlighter-rouge"><div class="highlight"><pre
class="highlight"><code data-lang="rust"><span class="k">use</span> <span
class="nn">arrow_avro</span><span class="p">::</span><span
class="nn">reader</span><span class="p">::</span><span
class="n">ReaderBuilder</span><span class="p">;</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">schema</span><span class="p">::{</span>
+ <span class="n">SchemaStore</span><span class="p">,</span> <span
class="n">AvroSchema</span><span class="p">,</span> <span
class="n">Fingerprint</span><span class="p">,</span> <span
class="n">FingerprintAlgorithm</span><span class="p">,</span> <span
class="n">CONFLUENT_MAGIC</span>
+<span class="p">};</span>
+
+<span class="k">fn</span> <span class="nf">main</span><span
class="p">()</span> <span class="k">-></span> <span
class="nb">Result</span><span class="o"><</span><span class="p">(),</span>
<span class="nb">Box</span><span class="o"><</span><span
class="k">dyn</span> <span class="nn">std</span><span class="p">::</span><span
class="nn">error</span><span class="p">::</span><span
class="n">Error</span><span class="o">>></span> <span class="p">{</span>
+ <span class="c1">// Register writer schema under Confluent id=1.</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">store</span> <span class="o">=</span> <span
class="nn">SchemaStore</span><span class="p">::</span><span
class="nf">new_with_type</span><span class="p">(</span><span
class="nn">FingerprintAlgorithm</span><span class="p">::</span><span
class="n">Id</span><span class="p">);</span>
+ <span class="n">store</span><span class="nf">.set</span><span
class="p">(</span>
+ <span class="nn">Fingerprint</span><span class="p">::</span><span
class="nf">Id</span><span class="p">(</span><span class="mi">1</span><span
class="p">),</span>
+ <span class="nn">AvroSchema</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span
class="s">r#"{"type":"record","name":"T","fields":[{"name":"x","type":"long"}]}"#</span><span
class="nf">.into</span><span class="p">()),</span>
+ <span class="p">)</span><span class="o">?</span><span class="p">;</span>
+
+ <span class="c1">// Define reader schema to enable projection/schema
evolution.</span>
+ <span class="k">let</span> <span class="n">reader_schema</span> <span
class="o">=</span> <span class="nn">AvroSchema</span><span
class="p">::</span><span class="nf">new</span><span class="p">(</span><span
class="s">r#"{"type":"record","name":"T","fields":[{"name":"x","type":"long"}]}"#</span><span
class="nf">.into</span><span class="p">());</span>
+
+ <span class="c1">// Build Decoder using reader and writer schemas</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">decoder</span> <span class="o">=</span> <span
class="nn">ReaderBuilder</span><span class="p">::</span><span
class="nf">new</span><span class="p">()</span>
+ <span class="nf">.with_reader_schema</span><span
class="p">(</span><span class="n">reader_schema</span><span class="p">)</span>
+ <span class="nf">.with_writer_schema_store</span><span
class="p">(</span><span class="n">store</span><span class="p">)</span>
+ <span class="nf">.build_decoder</span><span class="p">()</span><span
class="o">?</span><span class="p">;</span>
+
+ <span class="c1">// Simulate one frame: magic 0x00 + 4‑byte big‑endian
schema ID + Avro body (x=1 encoded as zig‑zag/VLQ).</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">frame</span> <span class="o">=</span> <span
class="nn">Vec</span><span class="p">::</span><span class="nf">from</span><span
class="p">(</span><span class="n">CONFLUENT_MAGIC</span><span
class="p">);</span> <span class="n">frame</span><span
class="nf">.extend_from_slice</span><span class="p">(</span><span
class="o">&</span><span class="mi">1u32</span><span
class="nf">.to_be_bytes</span><span class="p">());</span [...]
+
+ <span class="c1">// Consume from decoder</span>
+ <span class="k">let</span> <span class="n">_consumed</span> <span
class="o">=</span> <span class="n">decoder</span><span
class="nf">.decode</span><span class="p">(</span><span
class="o">&</span><span class="n">frame</span><span class="p">)</span><span
class="o">?</span><span class="p">;</span>
+ <span class="k">while</span> <span class="k">let</span> <span
class="nf">Some</span><span class="p">(</span><span class="n">batch</span><span
class="p">)</span> <span class="o">=</span> <span class="n">decoder</span><span
class="nf">.flush</span><span class="p">()</span><span class="o">?</span> <span
class="p">{</span>
+ <span class="nd">println!</span><span class="p">(</span><span
class="s">"rows={}, cols={}"</span><span class="p">,</span> <span
class="n">batch</span><span class="nf">.num_rows</span><span
class="p">(),</span> <span class="n">batch</span><span
class="nf">.num_columns</span><span class="p">());</span>
+ <span class="p">}</span>
+ <span class="nf">Ok</span><span class="p">(())</span>
+<span class="p">}</span>
+</code></pre></div></div>
+<p>The <code>SchemaStore</code> maps the incoming schema ID to the correct
Avro writer schema so the decoder can perform projection/evolution against the
reader schema. Confluent's wire format prefixes each message with a magic byte
<code>0x00</code> followed by a big‑endian 4‑byte schema ID. After decoding
Avro messages, the <code>Decoder::flush()</code> method yields Arrow
<code>RecordBatch</code>es suitable for vectorized processing.</p>
+<p>A more advanced example can be found <a
href="https://github.com/apache/arrow-rs/blob/main/arrow-avro/examples/decode_kafka_stream.rs";
target="_blank" rel="noopener">here</a>.</p>
+<h3>Writing a Snappy Compressed Avro OCF file</h3>
+<div class="language-rust highlighter-rouge"><div class="highlight"><pre
class="highlight"><code data-lang="rust"><span class="k">use</span> <span
class="nn">arrow_array</span><span class="p">::{</span><span
class="n">Int64Array</span><span class="p">,</span> <span
class="n">RecordBatch</span><span class="p">};</span>
+<span class="k">use</span> <span class="nn">arrow_schema</span><span
class="p">::{</span><span class="n">Schema</span><span class="p">,</span> <span
class="n">Field</span><span class="p">,</span> <span
class="n">DataType</span><span class="p">};</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">writer</span><span
class="p">::{</span><span class="n">Writer</span><span class="p">,</span> <span
class="n">WriterBuilder</span><span class="p">};</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">writer</span><span class="p">::</span><span
class="nn">format</span><span class="p">::</span><span
class="n">AvroOcfFormat</span><span class="p">;</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">compression</span><span
class="p">::</span><span class="n">CompressionCodec</span><span
class="p">;</span>
+<span class="k">use</span> <span class="nn">std</span><span
class="p">::{</span><span class="nn">sync</span><span class="p">::</span><span
class="nb">Arc</span><span class="p">,</span> <span class="nn">fs</span><span
class="p">::</span><span class="n">File</span><span class="p">,</span> <span
class="nn">io</span><span class="p">::</span><span
class="n">BufWriter</span><span class="p">};</span>
+
+<span class="k">fn</span> <span class="nf">main</span><span
class="p">()</span> <span class="k">-></span> <span
class="nb">Result</span><span class="o"><</span><span class="p">(),</span>
<span class="nb">Box</span><span class="o"><</span><span
class="k">dyn</span> <span class="nn">std</span><span class="p">::</span><span
class="nn">error</span><span class="p">::</span><span
class="n">Error</span><span class="o">>></span> <span class="p">{</span>
+ <span class="k">let</span> <span class="n">schema</span> <span
class="o">=</span> <span class="nn">Schema</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="nd">vec!</span><span
class="p">[</span><span class="nn">Field</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="s">"id"</span><span
class="p">,</span> <span class="nn">DataType</span><span
class="p">::</span><span class="n">Int64</span><span cl [...]
+ <span class="k">let</span> <span class="n">batch</span> <span
class="o">=</span> <span class="nn">RecordBatch</span><span
class="p">::</span><span class="nf">try_new</span><span class="p">(</span>
+ <span class="nn">Arc</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="n">schema</span><span
class="nf">.clone</span><span class="p">()),</span>
+ <span class="nd">vec!</span><span class="p">[</span><span
class="nn">Arc</span><span class="p">::</span><span class="nf">new</span><span
class="p">(</span><span class="nn">Int64Array</span><span
class="p">::</span><span class="nf">from</span><span class="p">(</span><span
class="nd">vec!</span><span class="p">[</span><span class="mi">1</span><span
class="p">,</span><span class="mi">2</span><span class="p">,</span><span
class="mi">3</span><span class="p">]))],</span>
+ <span class="p">)</span><span class="o">?</span><span class="p">;</span>
+ <span class="k">let</span> <span class="n">file</span> <span
class="o">=</span> <span class="nn">File</span><span class="p">::</span><span
class="nf">create</span><span class="p">(</span><span
class="s">"target/example.avro"</span><span class="p">)</span><span
class="o">?</span><span class="p">;</span>
+
+ <span class="c1">// Choose OCF block compression (e.g., None, Deflate,
Snappy, Zstd)</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">writer</span><span class="p">:</span> <span
class="n">Writer</span><span class="o"><</span><span class="n">_</span><span
class="p">,</span> <span class="n">AvroOcfFormat</span><span
class="o">></span> <span class="o">=</span> <span
class="nn">WriterBuilder</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="n">schema</span><span
class="p">)</span>
+ <span class="nf">.with_compression</span><span class="p">(</span><span
class="nf">Some</span><span class="p">(</span><span
class="nn">CompressionCodec</span><span class="p">::</span><span
class="n">Snappy</span><span class="p">))</span>
+ <span class="nf">.build</span><span class="p">(</span><span
class="nn">BufWriter</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="n">file</span><span
class="p">))</span><span class="o">?</span><span class="p">;</span>
+ <span class="n">writer</span><span class="nf">.write</span><span
class="p">(</span><span class="o">&</span><span class="n">batch</span><span
class="p">)</span><span class="o">?</span><span class="p">;</span>
+ <span class="n">writer</span><span class="nf">.finish</span><span
class="p">()</span><span class="o">?</span><span class="p">;</span>
+ <span class="nf">Ok</span><span class="p">(())</span>
+<span class="p">}</span>
+</code></pre></div></div>
+<p>The example above configures an Avro OCF <code>Writer</code>. It constructs
a <code>Writer<_, AvroOcfFormat></code> using
<code>WriterBuilder::new(schema)</code> and wraps a <code>File</code> in a
<code>BufWriter</code> for efficient I/O. The call to
<code>.with_compression(Some(CompressionCodec::Snappy))</code> enables
block‑level snappy compression. Finally, <code>writer.write(&batch)?</code>
serializes the batch as an Avro encoded block, and
<code>writer.finish()?</code> [...]
+<h2>Alternatives & Benchmarks</h2>
+<p>There are fundamentally two different approaches for bringing Avro into
Arrow:</p>
+<ol>
+<li>Row‑centric approach, typical of general Avro libraries such as
<code>apache-avro</code>, deserializes one record at a time into native Rust
values (i.e., <code>Value</code> or Serde types) and then builds Arrow arrays
from those values.</li>
+<li>Vectorized approach, what <code>arrow-avro</code> provides, decodes
directly into Arrow builders/arrays and emits <code>RecordBatch</code>es,
avoiding most per‑row overhead.</li>
+</ol>
+<p>This section compares the performance of both approaches using these <a
href="https://github.com/jecsand838/arrow-rs/tree/blog-benches/arrow-avro/benches";
target="_blank" rel="noopener">Criterion benchmarks</a>.</p>
+<h3>Read performance (1M)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/read_violin_1m.svg" width="100%" alt="1M
Row Read Violin Plot" style="background:#fff">
+</div>
+<h3>Read performance (10K)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/read_violin_10k.svg" width="100%"
alt="10K Row Read Violin Plot" style="background:#fff">
+</div>
+<h3>Write performance (1M)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/write_violin_1m.svg" width="100%"
alt="1M Row Write Violin Plot" style="background:#fff">
+</div>
+<h3>Write performance (10K)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/write_violin_10k.svg" width="100%"
alt="10K Row Write Violin Plot" style="background:#fff">
+</div>
+<p>Across benchmarks, the violin plots show lower medians and tighter spreads
for <code>arrow-avro</code> on both read and write paths. The gap widens when
per‑row work dominates (i.e., 10K‑row scenarios). At 1M rows, the distributions
remain favorable to <code>arrow-avro</code>, reflecting better cache locality
and fewer copies once decoding goes straight to Arrow arrays. The general
behavior is consistent with <code>apache-avro</code>'s record‑by‑record
iteration and <code>arrow-avro</ [...]
+<p>The table below lists the cases we report in the figures:</p>
+<ul>
+<li>10K vs 1M rows for multiple data shapes.</li>
+<li>
+<strong>Read cases:</strong>
+<ul>
+<li>
+<code>f8</code>: <em>Full schema, 8K batch size.</em>
+Decode all four columns with batch_size = 8192.</li>
+<li>
+<code>f1</code>: <em>Full schema, 1K batch size.</em>
+Decode all four columns with batch_size = 1024.</li>
+<li>
+<code>p8</code>: <em>Projected <code>{id,name}</code>, 8K batch size
(pushdown).</em>
+Decode only <code>id</code> and <code>name</code> with batch_size = 8192`.
+<em>How projection is applied:</em>
+<ul>
+<li>
+<code>arrow-avro/p8</code>: projection via reader schema
(<code>ReaderBuilder::with_reader_schema(...)</code>) so decoding is
column‑pushed down in the Arrow‑first reader.</li>
+<li>
+<code>apache-avro/p8</code>: projection via Avro reader schema
(<code>AvroReader::with_schema(...)</code>) so the Avro library decodes only
the projected fields.</li>
+</ul>
+</li>
+<li>
+<code>np</code>: <em>Projected <code>{id,name}</code>, no pushdown, 8K batch
size.</em>
+Both readers decode the full record (all four columns), materialize all
arrays, then project down to <code>{id,name}</code> after decode. This models
systems that can't push projection into the file/codec reader.</li>
+</ul>
+</li>
+<li>
+<strong>Write cases:</strong>
+<ul>
+<li>
+<code>c</code> (cold): <em>Schema conversion each iteration.</em>
+</li>
+<li>
+<code>h</code> (hot): <em>Avro JSON "hot" path.</em>
+</li>
+</ul>
+</li>
+<li>The resulting Apache‑Avro vs Arrow‑Avro medians with the computed
speedup.</li>
+</ul>
+<h3>Benchmark Median Time Results (Apple Silicon Mac)</h3>
+<table>
+<thead>
+<tr>
+<th>Case</th>
+<th align="right">apache-avro median</th>
+<th align="right">arrow-avro median</th>
+<th align="right">speedup</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>R/f8/10K</td>
+<td align="right">2.60 ms</td>
+<td align="right">0.24 ms</td>
+<td align="right">10.83x</td>
+</tr>
+<tr>
+<td>R/p8/10K</td>
+<td align="right">7.91 ms</td>
+<td align="right">0.24 ms</td>
+<td align="right">32.95x</td>
+</tr>
+<tr>
+<td>R/f1/10K</td>
+<td align="right">2.65 ms</td>
+<td align="right">0.25 ms</td>
+<td align="right">10.60x</td>
+</tr>
+<tr>
+<td>R/np/10K</td>
+<td align="right">2.62 ms</td>
+<td align="right">0.25 ms</td>
+<td align="right">10.48x</td>
+</tr>
+<tr>
+<td>R/f8/1M</td>
+<td align="right">267.21 ms</td>
+<td align="right">27.91 ms</td>
+<td align="right">9.57x</td>
+</tr>
+<tr>
+<td>R/p8/1M</td>
+<td align="right">791.79 ms</td>
+<td align="right">26.28 ms</td>
+<td align="right">30.13x</td>
+</tr>
+<tr>
+<td>R/f1/1M</td>
+<td align="right">262.93 ms</td>
+<td align="right">28.25 ms</td>
+<td align="right">9.31x</td>
+</tr>
+<tr>
+<td>R/np/1M</td>
+<td align="right">268.79 ms</td>
+<td align="right">27.69 ms</td>
+<td align="right">9.71x</td>
+</tr>
+<tr>
+<td>W/c/10K</td>
+<td align="right">4.78 ms</td>
+<td align="right">0.27 ms</td>
+<td align="right">17.70x</td>
+</tr>
+<tr>
+<td>W/h/10K</td>
+<td align="right">0.82 ms</td>
+<td align="right">0.28 ms</td>
+<td align="right">2.93x</td>
+</tr>
+<tr>
+<td>W/c/1M</td>
+<td align="right">485.58 ms</td>
+<td align="right">36.97 ms</td>
+<td align="right">13.13x</td>
+</tr>
+<tr>
+<td>W/h/1M</td>
+<td align="right">83.58 ms</td>
+<td align="right">36.75 ms</td>
+<td align="right">2.27x</td>
+</tr>
+</tbody>
+</table>
+<h2>Closing</h2>
+<p><code>arrow-avro</code> brings a purpose‑built, vectorized bridge
connecting Arrow-rs and Avro that covers Object Container Files (OCF),
Single‑Object Encoding (SOE), and the Confluent/Apicurio Schema Registry wire
formats. This means you can now keep your ingestion paths columnar for both
batch files and streaming systems. The reader and writer APIs shown above are
now available for you to use with the v57.0.0 release of
<code>arrow-rs</code>.</p>
+<p>This work is part of the ongoing Arrow‑rs effort to implement first-class
Avro support in Rust. We'd love your feedback on real‑world use-cases,
workloads, and integrations. We also welcome contributions, whether that's
issues, benchmarks, or PRs. To follow along or help, open an <a
href="https://github.com/apache/arrow-rs/issues"; target="_blank"
rel="noopener">issue on GitHub</a> and/or track <a
href="https://github.com/apache/arrow-rs/issues/4886"; target="_blank"
rel="noopener">Add [...]
+<h3>Acknowledgments</h3>
+<p>Special thanks to:</p>
+<ul>
+<li>
+<a href="https://github.com/tustvold"; target="_blank"
rel="noopener">tustvold</a> for laying an incredible zero-copy foundation.</li>
+<li>
+<a href="https://github.com/nathaniel-d-ef"; target="_blank"
rel="noopener">nathaniel-d-ef</a> and <a href="https://github.com/elastiflow";
target="_blank" rel="noopener">ElastiFlow</a> for their numerous and invaluable
project-wide contributions.</li>
+<li>
+<a href="https://github.com/veronica-m-ef"; target="_blank"
rel="noopener">veronica-m-ef</a> for making Impala‑related contributions to the
<code>Reader</code>.</li>
+<li>
+<a href="https://github.com/Supermetal-Inc"; target="_blank"
rel="noopener">Supermetal</a> for contributions related to Apicurio Registry
and Run-End Encoding type support.</li>
+<li>
+<a href="https://github.com/kumarlokesh"; target="_blank"
rel="noopener">kumarlokesh</a> for contributing <code>Utf8View</code>
support.</li>
+<li>
+<a href="https://github.com/alamb"; target="_blank" rel="noopener">alamb</a>,
<a href="https://github.com/scovich"; target="_blank"
rel="noopener">scovich</a>, <a href="https://github.com/mbrobbel";
target="_blank" rel="noopener">mbrobbel</a>, and <a
href="https://github.com/klion26"; target="_blank" rel="noopener">klion26</a>
for their thoughtful reviews, detailed feedback, and support throughout the
development of <code>arrow-avro</code>.</li>
+</ul>
+<p>If you have any questions about this blog post, please feel free to contact
the author, <a href="mailto:[email protected]";>Connor Sanders</a>.</p>
+
+ </main>
+ </div>
+
+ <hr>
+<footer class="footer">
+ <div class="row">
+ <div class="col-md-9">
+ <p>Apache Arrow, Arrow, Apache, the Apache 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>© 2016-2025 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"; target="_blank"
rel="noopener">
+ <img src="https://www.apache.org/events/current-event-234x60.png";>
+ </a>
+ </div>
+ </div>
+</footer>
+
+ </div>
+</body>
+</html>
diff --git a/blog/index.html b/blog/index.html
index 153a7eb0326..a9b19cb5481 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -273,6 +273,26 @@ faster than previous versions thanks to a ne...
+ <p>
+ </p>
+<h3>
+ <a href="/blog/2025/10/23/introducing-arrow-avro/">Announcing arrow-avro
in Arrow Rust</a>
+ </h3>
+
+ <p>
+ <span class="blog-list-date">
+ 23 October 2025
+ </span>
+ </p>
+
+arrow-avro, a newly rewritten Rust crate that reads and writes Apache Avro
data directly as Arrow RecordBatches, is now available. It supports Avro Object
Container Files (OCF), Single‑Object Encoding (SOE), the Confluent Schema
Registry wire format, and the Apicurio Registry wire format, with
projection/evolution, tunable batch sizing, and opt...
+
+ <a href="/blog/2025/10/23/introducing-arrow-avro/">Read More →</a>
+
+
+
+
+
<p>
</p>
<h3>
diff --git a/feed.xml b/feed.xml
index 080eb8b8f86..ce0eac04ada 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,4 +1,272 @@
-<?xml version="1.0" encoding="utf-8"?><feed
xmlns="http://www.w3.org/2005/Atom"; ><generator uri="https://jekyllrb.com/";
version="4.4.1">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>2025-10-23T12:22:18-04:00</updated><id>https://arrow.apache.org/feed.xml</id><title
type="html">Apache Arrow</title><subtitle>Apache Arrow is the universal
columnar fo [...]
+<?xml version="1.0" encoding="utf-8"?><feed
xmlns="http://www.w3.org/2005/Atom"; ><generator uri="https://jekyllrb.com/";
version="4.4.1">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>2025-10-23T14:27:54-04:00</updated><id>https://arrow.apache.org/feed.xml</id><title
type="html">Apache Arrow</title><subtitle>Apache Arrow is the universal
columnar fo [...]
+
+-->
+<p><a href="https://crates.io/crates/arrow-avro";><code>arrow-avro</code></a>,
a newly rewritten Rust crate that reads and writes <a
href="https://avro.apache.org/";>Apache Avro</a> data directly as Arrow
<code>RecordBatch</code>es, is now available. It supports <a
href="https://avro.apache.org/docs/1.11.1/specification/#object-container-files";>Avro
Object Container Files</a> (OCF), <a
href="https://avro.apache.org/docs/1.11.1/specification/#single-object-encoding";>Single‑Object
Encoding</ [...]
+<h2>Motivation</h2>
+<p>Apache Avro’s row‑oriented design is effective for encoding one record at a
time, while Apache Arrow’s columnar layout is optimized for vectorized
analytics. A major challenge lies in converting between these formats without
reintroducing row‑wise overhead. Decoding Avro a row at a time and then
building Arrow arrays incurs extra allocations and cache‑unfriendly access (the
very costs Arrow is designed to avoid). In the real world, this overhead
commonly shows up in analytical hot pat [...]
+<h3>Why not use the existing <code>apache-avro</code> crate?</h3>
+<p>Rust already has a mature, general‑purpose Avro crate, <a
href="https://crates.io/crates/apache-avro";>apache-avro</a>. It reads and
writes Avro records as Avro value types and provides Object Container File
readers and writers. What it does not do is decode directly into Arrow arrays,
so any Arrow integration must materialize rows and then build columns.</p>
+<p>What’s needed is a complementary approach that decodes column‑by‑column
straight into Arrow builders and emits <code>RecordBatch</code>es. This would
enable projection pushdown while keeping execution vectorized end to end. For
projects such as <a href="https://datafusion.apache.org/";>Apache
DataFusion</a>, access to a mature, upstream Arrow‑native reader and writer
would help simplify the code path and reduce duplication.</p>
+<p>Modern pipelines heighten this need because <a
href="https://www.confluent.io/blog/avro-kafka-data/";>Avro is also used on the
wire</a>, not just in files. Kafka ecosystems commonly use Confluent’s Schema
Registry framing, and many services adopt the Avro Single‑Object Encoding
format. An approach that enables decoding straight into Arrow batches (rather
than through per‑row values) would let downstream compute remain vectorized at
streaming rates.</p>
+<h3>Why this matters</h3>
+<p>Apache Avro is a first‑class format across stream processors and cloud
services:</p>
+<ul>
+<li>Confluent Schema Registry supports <a
href="https://docs.confluent.io/platform/current/schema-registry/fundamentals/serdes-develop/serdes-avro.html";>Avro
across multiple languages and tooling</a>.</li>
+<li>Apache Flink exposes an <a
href="https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/connectors/table/formats/avro-confluent/";><code>avro-confluent</code>
format for Kafka</a>.</li>
+<li>AWS Lambda <a
href="https://aws.amazon.com/about-aws/whats-new/2025/06/aws-lambda-native-support-avro-protobuf-kafka-events/";>(June
2025) added native handling for Avro‑formatted Kafka events</a> with Glue and
Confluent Schema Registry integrations.</li>
+<li>Azure Event Hubs provides a <a
href="https://learn.microsoft.com/en-us/azure/event-hubs/schema-registry-overview";>Schema
Registry with Avro support</a> for Kafka‑compatible clients.</li>
+</ul>
+<p>In short: Arrow users encounter Avro both on disk (OCF) and on the wire
(SOE). An Arrow‑first, vectorized reader/writer for OCF, SOE, and Confluent
framing removes a pervasive bottleneck and keeps pipelines columnar
end‑to‑end.</p>
+<h2>Introducing <code>arrow-avro</code></h2>
+<p><a
href="https://github.com/apache/arrow-rs/tree/main/arrow-avro";><code>arrow-avro</code></a>
is a high-performance Rust crate that converts between Avro and Arrow with a
column‑first, batch‑oriented design. On the read side, it decodes Avro Object
Container Files (OCF), Single‑Object Encoding (SOE), and the Confluent Schema
Registry wire format directly into Arrow <code>RecordBatch</code>es. Meanwhile,
the write path provides formats for encoding to OCF and SOE as well.</p>
+<p>The crate exposes two primary read APIs: a high-level <code>Reader</code>
for OCF inputs and a low-level <code>Decoder</code> for streaming SOE frames.
For SOE and Confluent/Apicurio frames, a <code>SchemaStore</code> is provided
that resolves fingerprints or schema IDs to full Avro writer schemas, enabling
schema evolution while keeping the decode path vectorized.</p>
+<p>On the write side, <code>AvroWriter</code> produces OCF (including
container‑level compression), while <code>AvroStreamWriter</code> produces
framed Avro messages for Single‑Object or Confluent/Apicurio encodings, as
configured via the <code>WriterBuilder::with_fingerprint_strategy(...)</code>
knob.</p>
+<p>Configuration is intentionally minimal but practical. For instance, the
<code>ReaderBuilder</code> exposes knobs covering both batch file ingestion and
streaming systems without forcing format‑specific code paths.</p>
+<h3>How this mirrors Parquet in Arrow‑rs</h3>
+<p>If you have used Parquet with Arrow‑rs, you already know the pattern. The
<code>parquet</code> crate exposes a <a
href="https://docs.rs/parquet/latest/parquet/arrow/index.html";>parquet::arrow
module</a> that reads and writes Arrow <code>RecordBatch</code>es directly.
Most users reach for <code>ParquetRecordBatchReaderBuilder</code> when reading
and <code>ArrowWriter</code> when writing. You choose columns up front, set a
batch size, and the reader gives you Arrow batches that flow str [...]
+<p><code>arrow‑avro</code> brings that same bridge to Avro. You get a single
<code>ReaderBuilder</code> that can produce a <code>Reader</code> for OCF, or a
streaming <code>Decoder</code> for on‑the‑wire frames. Both return Arrow
<code>RecordBatch</code>es, which means engines can keep projection and
filtering close to the reader and avoid building rows only to reassemble them
back into columns later. For evolving streams, a small <code>SchemaStore</code>
resolves fingerprints or ids bef [...]
+<p>The reason this pattern matters is straightforward. Arrow’s columnar format
is designed for vectorized work and good cache locality. When a format reader
produces Arrow batches directly, copies and branchy per‑row work are minimized,
keeping downstream operators fast. That is the same story that made
<code>parquet::arrow</code> popular in Rust, and it is what
<code>arrow‑avro</code> now enables for Avro.</p>
+<h2>Architecture & Technical Overview</h2>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 20px 15px;">
+<img src="/img/introducing-arrow-avro/arrow-avro-architecture.svg"
+ width="100%"
+ alt="High-level `arrow-avro` architecture"
+ style="background:#fff">
+</div>
+<p>At a high level, <a
href="https://arrow.apache.org/rust/arrow_avro/index.html";>arrow-avro</a>
splits cleanly into read and write paths built around Arrow
<code>RecordBatch</code>es. The read side turns Avro (OCF files or framed byte
streams) into batched Arrow arrays, while the write side takes Arrow batches
and produces OCF files or streaming frames. When using an
<code>AvroStreamWriter</code>, the framing (SOE or Confluent) is part of the
stream output based on the configured finger [...]
+<p>On the <a
href="https://arrow.apache.org/rust/arrow_avro/reader/index.html";>read</a>
path, everything starts with the <a
href="https://arrow.apache.org/rust/arrow_avro/reader/struct.ReaderBuilder.html";>ReaderBuilder</a>.
A single builder can create a <a
href="https://arrow.apache.org/rust/arrow_avro/reader/struct.Reader.html";>Reader</a>
for Object Container Files (OCF) or a streaming <a
href="https://arrow.apache.org/rust/arrow_avro/reader/struct.Decoder.html";>Decoder</a>
for SOE/Conf [...]
+<p>When reading an OCF, the <code>Reader</code> parses a header and then
iterates over blocks of encoded data. The header contains a metadata map with
the embedded Avro schema and optional compression (i.e., <code>deflate</code>,
<code>snappy</code>, <code>zstd</code>, <code>bzip2</code>, <code>xz</code>),
plus a 16‑byte sync marker used to delimit blocks. Each subsequent OCF block
then carries a row count and the encoded payload. The parsed OCF header and
block structures are also encod [...]
+<p>On the <a
href="https://arrow.apache.org/rust/arrow_avro/writer/index.html";>write</a>
path, the <a
href="https://arrow.apache.org/rust/arrow_avro/writer/struct.WriterBuilder.html";>WriterBuilder</a>
produces either an <a
href="https://arrow.apache.org/rust/arrow_avro/writer/type.AvroWriter.html";>AvroWriter</a>
(OCF) or an <a
href="https://arrow.apache.org/rust/arrow_avro/writer/type.AvroStreamWriter.html";>AvroStreamWriter</a>
(SOE/Message). The <code>with_compression(...)</code> knob i [...]
+<p>Schema handling is centralized in the <a
href="https://arrow.apache.org/rust/arrow_avro/schema/index.html";>schema</a>
module. <a
href="https://arrow.apache.org/rust/arrow_avro/schema/struct.AvroSchema.html";>AvroSchema</a>
wraps a valid Avro Schema JSON string, supports computing a
<code>Fingerprint</code>, and can be loaded into a <a
href="https://arrow.apache.org/rust/arrow_avro/schema/struct.SchemaStore.html";>SchemaStore</a>
as a writer schema. At runtime, the <code>Reader</code>/<c [...]
+<p>At the heart of <code>arrow-avro</code> is a type‑mapping
<code>Codec</code> that the library uses to construct both encoders and
decoders. The <code>Codec</code> captures, for every Avro field, how it maps to
Arrow and how it should be encoded or decoded. The <code>Reader</code> logic
builds a <code>Codec</code> per <em>(writer, reader)</em> schema pair, which
the decoder later uses to vectorize parsing of Avro values directly into the
correct Arrow builders. The <code>Writer</code> [...]
+<p>Finally, by keeping container and stream framing (OCF vs. SOE) separate
from encoding and decoding, the crate composes naturally with the rest of
Arrow‑rs: you read or write Arrow <code>RecordBatch</code>es, pick OCF or SOE
streams as needed, and wire up fingerprints only when you're on a streaming
path. This results in a compact API surface that covers both batch files and
high‑throughput streams without sacrificing columnar, vectorized execution.</p>
+<h2>Examples</h2>
+<h3>Decoding a Confluent-framed Kafka Stream</h3>
+<div class="language-rust highlighter-rouge"><div class="highlight"><pre
class="highlight"><code data-lang="rust"><span class="k">use</span> <span
class="nn">arrow_avro</span><span class="p">::</span><span
class="nn">reader</span><span class="p">::</span><span
class="n">ReaderBuilder</span><span class="p">;</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">schema</span><span class="p">::{</span>
+ <span class="n">SchemaStore</span><span class="p">,</span> <span
class="n">AvroSchema</span><span class="p">,</span> <span
class="n">Fingerprint</span><span class="p">,</span> <span
class="n">FingerprintAlgorithm</span><span class="p">,</span> <span
class="n">CONFLUENT_MAGIC</span>
+<span class="p">};</span>
+
+<span class="k">fn</span> <span class="nf">main</span><span
class="p">()</span> <span class="k">-></span> <span
class="nb">Result</span><span class="o"><</span><span class="p">(),</span>
<span class="nb">Box</span><span class="o"><</span><span
class="k">dyn</span> <span class="nn">std</span><span class="p">::</span><span
class="nn">error</span><span class="p">::</span><span
class="n">Error</span><span class="o">>></span> <span class="p">{</span>
+ <span class="c1">// Register writer schema under Confluent id=1.</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">store</span> <span class="o">=</span> <span
class="nn">SchemaStore</span><span class="p">::</span><span
class="nf">new_with_type</span><span class="p">(</span><span
class="nn">FingerprintAlgorithm</span><span class="p">::</span><span
class="n">Id</span><span class="p">);</span>
+ <span class="n">store</span><span class="nf">.set</span><span
class="p">(</span>
+ <span class="nn">Fingerprint</span><span class="p">::</span><span
class="nf">Id</span><span class="p">(</span><span class="mi">1</span><span
class="p">),</span>
+ <span class="nn">AvroSchema</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span
class="s">r#"{"type":"record","name":"T","fields":[{"name":"x","type":"long"}]}"#</span><span
class="nf">.into</span><span class="p">()),</span>
+ <span class="p">)</span><span class="o">?</span><span class="p">;</span>
+
+ <span class="c1">// Define reader schema to enable projection/schema
evolution.</span>
+ <span class="k">let</span> <span class="n">reader_schema</span> <span
class="o">=</span> <span class="nn">AvroSchema</span><span
class="p">::</span><span class="nf">new</span><span class="p">(</span><span
class="s">r#"{"type":"record","name":"T","fields":[{"name":"x","type":"long"}]}"#</span><span
class="nf">.into</span><span class="p">());</span>
+
+ <span class="c1">// Build Decoder using reader and writer schemas</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">decoder</span> <span class="o">=</span> <span
class="nn">ReaderBuilder</span><span class="p">::</span><span
class="nf">new</span><span class="p">()</span>
+ <span class="nf">.with_reader_schema</span><span
class="p">(</span><span class="n">reader_schema</span><span class="p">)</span>
+ <span class="nf">.with_writer_schema_store</span><span
class="p">(</span><span class="n">store</span><span class="p">)</span>
+ <span class="nf">.build_decoder</span><span class="p">()</span><span
class="o">?</span><span class="p">;</span>
+
+ <span class="c1">// Simulate one frame: magic 0x00 + 4‑byte big‑endian
schema ID + Avro body (x=1 encoded as zig‑zag/VLQ).</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">frame</span> <span class="o">=</span> <span
class="nn">Vec</span><span class="p">::</span><span class="nf">from</span><span
class="p">(</span><span class="n">CONFLUENT_MAGIC</span><span
class="p">);</span> <span class="n">frame</span><span
class="nf">.extend_from_slice</span><span class="p">(</span><span
class="o">&</span><span class="mi">1u32</span><span
class="nf">.to_be_bytes</span><span class="p">());</span [...]
+
+ <span class="c1">// Consume from decoder</span>
+ <span class="k">let</span> <span class="n">_consumed</span> <span
class="o">=</span> <span class="n">decoder</span><span
class="nf">.decode</span><span class="p">(</span><span
class="o">&</span><span class="n">frame</span><span class="p">)</span><span
class="o">?</span><span class="p">;</span>
+ <span class="k">while</span> <span class="k">let</span> <span
class="nf">Some</span><span class="p">(</span><span class="n">batch</span><span
class="p">)</span> <span class="o">=</span> <span class="n">decoder</span><span
class="nf">.flush</span><span class="p">()</span><span class="o">?</span> <span
class="p">{</span>
+ <span class="nd">println!</span><span class="p">(</span><span
class="s">"rows={}, cols={}"</span><span class="p">,</span> <span
class="n">batch</span><span class="nf">.num_rows</span><span
class="p">(),</span> <span class="n">batch</span><span
class="nf">.num_columns</span><span class="p">());</span>
+ <span class="p">}</span>
+ <span class="nf">Ok</span><span class="p">(())</span>
+<span class="p">}</span>
+</code></pre></div></div>
+<p>The <code>SchemaStore</code> maps the incoming schema ID to the correct
Avro writer schema so the decoder can perform projection/evolution against the
reader schema. Confluent's wire format prefixes each message with a magic byte
<code>0x00</code> followed by a big‑endian 4‑byte schema ID. After decoding
Avro messages, the <code>Decoder::flush()</code> method yields Arrow
<code>RecordBatch</code>es suitable for vectorized processing.</p>
+<p>A more advanced example can be found <a
href="https://github.com/apache/arrow-rs/blob/main/arrow-avro/examples/decode_kafka_stream.rs";>here</a>.</p>
+<h3>Writing a Snappy Compressed Avro OCF file</h3>
+<div class="language-rust highlighter-rouge"><div class="highlight"><pre
class="highlight"><code data-lang="rust"><span class="k">use</span> <span
class="nn">arrow_array</span><span class="p">::{</span><span
class="n">Int64Array</span><span class="p">,</span> <span
class="n">RecordBatch</span><span class="p">};</span>
+<span class="k">use</span> <span class="nn">arrow_schema</span><span
class="p">::{</span><span class="n">Schema</span><span class="p">,</span> <span
class="n">Field</span><span class="p">,</span> <span
class="n">DataType</span><span class="p">};</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">writer</span><span
class="p">::{</span><span class="n">Writer</span><span class="p">,</span> <span
class="n">WriterBuilder</span><span class="p">};</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">writer</span><span class="p">::</span><span
class="nn">format</span><span class="p">::</span><span
class="n">AvroOcfFormat</span><span class="p">;</span>
+<span class="k">use</span> <span class="nn">arrow_avro</span><span
class="p">::</span><span class="nn">compression</span><span
class="p">::</span><span class="n">CompressionCodec</span><span
class="p">;</span>
+<span class="k">use</span> <span class="nn">std</span><span
class="p">::{</span><span class="nn">sync</span><span class="p">::</span><span
class="nb">Arc</span><span class="p">,</span> <span class="nn">fs</span><span
class="p">::</span><span class="n">File</span><span class="p">,</span> <span
class="nn">io</span><span class="p">::</span><span
class="n">BufWriter</span><span class="p">};</span>
+
+<span class="k">fn</span> <span class="nf">main</span><span
class="p">()</span> <span class="k">-></span> <span
class="nb">Result</span><span class="o"><</span><span class="p">(),</span>
<span class="nb">Box</span><span class="o"><</span><span
class="k">dyn</span> <span class="nn">std</span><span class="p">::</span><span
class="nn">error</span><span class="p">::</span><span
class="n">Error</span><span class="o">>></span> <span class="p">{</span>
+ <span class="k">let</span> <span class="n">schema</span> <span
class="o">=</span> <span class="nn">Schema</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="nd">vec!</span><span
class="p">[</span><span class="nn">Field</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="s">"id"</span><span
class="p">,</span> <span class="nn">DataType</span><span
class="p">::</span><span class="n">Int64</span><span cl [...]
+ <span class="k">let</span> <span class="n">batch</span> <span
class="o">=</span> <span class="nn">RecordBatch</span><span
class="p">::</span><span class="nf">try_new</span><span class="p">(</span>
+ <span class="nn">Arc</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="n">schema</span><span
class="nf">.clone</span><span class="p">()),</span>
+ <span class="nd">vec!</span><span class="p">[</span><span
class="nn">Arc</span><span class="p">::</span><span class="nf">new</span><span
class="p">(</span><span class="nn">Int64Array</span><span
class="p">::</span><span class="nf">from</span><span class="p">(</span><span
class="nd">vec!</span><span class="p">[</span><span class="mi">1</span><span
class="p">,</span><span class="mi">2</span><span class="p">,</span><span
class="mi">3</span><span class="p">]))],</span>
+ <span class="p">)</span><span class="o">?</span><span class="p">;</span>
+ <span class="k">let</span> <span class="n">file</span> <span
class="o">=</span> <span class="nn">File</span><span class="p">::</span><span
class="nf">create</span><span class="p">(</span><span
class="s">"target/example.avro"</span><span class="p">)</span><span
class="o">?</span><span class="p">;</span>
+
+ <span class="c1">// Choose OCF block compression (e.g., None, Deflate,
Snappy, Zstd)</span>
+ <span class="k">let</span> <span class="k">mut</span> <span
class="n">writer</span><span class="p">:</span> <span
class="n">Writer</span><span class="o"><</span><span class="n">_</span><span
class="p">,</span> <span class="n">AvroOcfFormat</span><span
class="o">></span> <span class="o">=</span> <span
class="nn">WriterBuilder</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="n">schema</span><span
class="p">)</span>
+ <span class="nf">.with_compression</span><span class="p">(</span><span
class="nf">Some</span><span class="p">(</span><span
class="nn">CompressionCodec</span><span class="p">::</span><span
class="n">Snappy</span><span class="p">))</span>
+ <span class="nf">.build</span><span class="p">(</span><span
class="nn">BufWriter</span><span class="p">::</span><span
class="nf">new</span><span class="p">(</span><span class="n">file</span><span
class="p">))</span><span class="o">?</span><span class="p">;</span>
+ <span class="n">writer</span><span class="nf">.write</span><span
class="p">(</span><span class="o">&</span><span class="n">batch</span><span
class="p">)</span><span class="o">?</span><span class="p">;</span>
+ <span class="n">writer</span><span class="nf">.finish</span><span
class="p">()</span><span class="o">?</span><span class="p">;</span>
+ <span class="nf">Ok</span><span class="p">(())</span>
+<span class="p">}</span>
+</code></pre></div></div>
+<p>The example above configures an Avro OCF <code>Writer</code>. It constructs
a <code>Writer<_, AvroOcfFormat></code> using
<code>WriterBuilder::new(schema)</code> and wraps a <code>File</code> in a
<code>BufWriter</code> for efficient I/O. The call to
<code>.with_compression(Some(CompressionCodec::Snappy))</code> enables
block‑level snappy compression. Finally, <code>writer.write(&batch)?</code>
serializes the batch as an Avro encoded block, and
<code>writer.finish()?</code> [...]
+<h2>Alternatives & Benchmarks</h2>
+<p>There are fundamentally two different approaches for bringing Avro into
Arrow:</p>
+<ol>
+<li>Row‑centric approach, typical of general Avro libraries such as
<code>apache-avro</code>, deserializes one record at a time into native Rust
values (i.e., <code>Value</code> or Serde types) and then builds Arrow arrays
from those values.</li>
+<li>Vectorized approach, what <code>arrow-avro</code> provides, decodes
directly into Arrow builders/arrays and emits <code>RecordBatch</code>es,
avoiding most per‑row overhead.</li>
+</ol>
+<p>This section compares the performance of both approaches using these <a
href="https://github.com/jecsand838/arrow-rs/tree/blog-benches/arrow-avro/benches";>Criterion
benchmarks</a>.</p>
+<h3>Read performance (1M)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/read_violin_1m.svg"
+ width="100%"
+ alt="1M Row Read Violin Plot"
+ style="background:#fff">
+</div>
+<h3>Read performance (10K)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/read_violin_10k.svg"
+ width="100%"
+ alt="10K Row Read Violin Plot"
+ style="background:#fff">
+</div>
+<h3>Write performance (1M)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/write_violin_1m.svg"
+ width="100%"
+ alt="1M Row Write Violin Plot"
+ style="background:#fff">
+</div>
+<h3>Write performance (10K)</h3>
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="/img/introducing-arrow-avro/write_violin_10k.svg"
+ width="100%"
+ alt="10K Row Write Violin Plot"
+ style="background:#fff">
+</div>
+<p>Across benchmarks, the violin plots show lower medians and tighter spreads
for <code>arrow-avro</code> on both read and write paths. The gap widens when
per‑row work dominates (i.e., 10K‑row scenarios). At 1M rows, the distributions
remain favorable to <code>arrow-avro</code>, reflecting better cache locality
and fewer copies once decoding goes straight to Arrow arrays. The general
behavior is consistent with <code>apache-avro</code>'s record‑by‑record
iteration and <code>arrow-avro</ [...]
+<p>The table below lists the cases we report in the figures:</p>
+<ul>
+<li>10K vs 1M rows for multiple data shapes.</li>
+<li><strong>Read cases:</strong>
+<ul>
+<li><code>f8</code>: <em>Full schema, 8K batch size.</em>
+Decode all four columns with batch_size = 8192.</li>
+<li><code>f1</code>: <em>Full schema, 1K batch size.</em>
+Decode all four columns with batch_size = 1024.</li>
+<li><code>p8</code>: <em>Projected <code>{id,name}</code>, 8K batch size
(pushdown).</em>
+Decode only <code>id</code> and <code>name</code> with batch_size = 8192`.
+<em>How projection is applied:</em>
+<ul>
+<li><code>arrow-avro/p8</code>: projection via reader schema
(<code>ReaderBuilder::with_reader_schema(...)</code>) so decoding is
column‑pushed down in the Arrow‑first reader.</li>
+<li><code>apache-avro/p8</code>: projection via Avro reader schema
(<code>AvroReader::with_schema(...)</code>) so the Avro library decodes only
the projected fields.</li>
+</ul>
+</li>
+<li><code>np</code>: <em>Projected <code>{id,name}</code>, no pushdown, 8K
batch size.</em>
+Both readers decode the full record (all four columns), materialize all
arrays, then project down to <code>{id,name}</code> after decode. This models
systems that can't push projection into the file/codec reader.</li>
+</ul>
+</li>
+<li><strong>Write cases:</strong>
+<ul>
+<li><code>c</code> (cold): <em>Schema conversion each iteration.</em></li>
+<li><code>h</code> (hot): <em>Avro JSON "hot" path.</em></li>
+</ul>
+</li>
+<li>The resulting Apache‑Avro vs Arrow‑Avro medians with the computed
speedup.</li>
+</ul>
+<h3>Benchmark Median Time Results (Apple Silicon Mac)</h3>
+<table>
+<thead>
+<tr>
+<th>Case</th>
+<th align="right">apache-avro median</th>
+<th align="right">arrow-avro median</th>
+<th align="right">speedup</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>R/f8/10K</td>
+<td align="right">2.60 ms</td>
+<td align="right">0.24 ms</td>
+<td align="right">10.83x</td>
+</tr>
+<tr>
+<td>R/p8/10K</td>
+<td align="right">7.91 ms</td>
+<td align="right">0.24 ms</td>
+<td align="right">32.95x</td>
+</tr>
+<tr>
+<td>R/f1/10K</td>
+<td align="right">2.65 ms</td>
+<td align="right">0.25 ms</td>
+<td align="right">10.60x</td>
+</tr>
+<tr>
+<td>R/np/10K</td>
+<td align="right">2.62 ms</td>
+<td align="right">0.25 ms</td>
+<td align="right">10.48x</td>
+</tr>
+<tr>
+<td>R/f8/1M</td>
+<td align="right">267.21 ms</td>
+<td align="right">27.91 ms</td>
+<td align="right">9.57x</td>
+</tr>
+<tr>
+<td>R/p8/1M</td>
+<td align="right">791.79 ms</td>
+<td align="right">26.28 ms</td>
+<td align="right">30.13x</td>
+</tr>
+<tr>
+<td>R/f1/1M</td>
+<td align="right">262.93 ms</td>
+<td align="right">28.25 ms</td>
+<td align="right">9.31x</td>
+</tr>
+<tr>
+<td>R/np/1M</td>
+<td align="right">268.79 ms</td>
+<td align="right">27.69 ms</td>
+<td align="right">9.71x</td>
+</tr>
+<tr>
+<td>W/c/10K</td>
+<td align="right">4.78 ms</td>
+<td align="right">0.27 ms</td>
+<td align="right">17.70x</td>
+</tr>
+<tr>
+<td>W/h/10K</td>
+<td align="right">0.82 ms</td>
+<td align="right">0.28 ms</td>
+<td align="right">2.93x</td>
+</tr>
+<tr>
+<td>W/c/1M</td>
+<td align="right">485.58 ms</td>
+<td align="right">36.97 ms</td>
+<td align="right">13.13x</td>
+</tr>
+<tr>
+<td>W/h/1M</td>
+<td align="right">83.58 ms</td>
+<td align="right">36.75 ms</td>
+<td align="right">2.27x</td>
+</tr>
+</tbody>
+</table>
+<h2>Closing</h2>
+<p><code>arrow-avro</code> brings a purpose‑built, vectorized bridge
connecting Arrow-rs and Avro that covers Object Container Files (OCF),
Single‑Object Encoding (SOE), and the Confluent/Apicurio Schema Registry wire
formats. This means you can now keep your ingestion paths columnar for both
batch files and streaming systems. The reader and writer APIs shown above are
now available for you to use with the v57.0.0 release of
<code>arrow-rs</code>.</p>
+<p>This work is part of the ongoing Arrow‑rs effort to implement first-class
Avro support in Rust. We'd love your feedback on real‑world use-cases,
workloads, and integrations. We also welcome contributions, whether that's
issues, benchmarks, or PRs. To follow along or help, open an <a
href="https://github.com/apache/arrow-rs/issues";>issue on GitHub</a> and/or
track <a href="https://github.com/apache/arrow-rs/issues/4886";>Add Avro
Support</a> in <code>apache/arrow-rs</code>.</p>
+<h3>Acknowledgments</h3>
+<p>Special thanks to:</p>
+<ul>
+<li><a href="https://github.com/tustvold";>tustvold</a> for laying an
incredible zero-copy foundation.</li>
+<li><a href="https://github.com/nathaniel-d-ef";>nathaniel-d-ef</a> and <a
href="https://github.com/elastiflow";>ElastiFlow</a> for their numerous and
invaluable project-wide contributions.</li>
+<li><a href="https://github.com/veronica-m-ef";>veronica-m-ef</a> for making
Impala‑related contributions to the <code>Reader</code>.</li>
+<li><a href="https://github.com/Supermetal-Inc";>Supermetal</a> for
contributions related to Apicurio Registry and Run-End Encoding type
support.</li>
+<li><a href="https://github.com/kumarlokesh";>kumarlokesh</a> for contributing
<code>Utf8View</code> support.</li>
+<li><a href="https://github.com/alamb";>alamb</a>, <a
href="https://github.com/scovich";>scovich</a>, <a
href="https://github.com/mbrobbel";>mbrobbel</a>, and <a
href="https://github.com/klion26";>klion26</a> for their thoughtful reviews,
detailed feedback, and support throughout the development of
<code>arrow-avro</code>.</li>
+</ul>
+<p>If you have any questions about this blog post, please feel free to contact
the author, <a href="mailto:[email protected]";>Connor
Sanders</a>.</p>]]></content><author><name>jecsand838</name></author><category
term="application" /><summary type="html"><![CDATA[A new native Rust vectorized
reader/writer for Avro to Arrow, with OCF, Single‑Object, and Confluent wire
format support.]]></summary><media:thumbnail
xmlns:media="http://search.yahoo.com/mrss/"; url="https://arrow.apache.org/img/
[...]
-->
<p><em>Editor’s Note: While <a href="https://arrow.apache.org/";>Apache
Arrow</a> and <a href="https://parquet.apache.org/";>Apache Parquet</a> are
separate projects,
@@ -1075,67 +1343,4 @@ This is a minor release since the last release <a
href="https://github.com/apach
<li>GH-463: Improve TZ support for JDBC driver by @aiguofer in <a
href="https://github.com/apache/arrow-java/pull/464";>#464</a></li>
<li>GH-729: [JDBC] Fix BinaryConsumer consuming null value by @hnwyllmm in <a
href="https://github.com/apache/arrow-java/pull/730";>#730</a></li>
</ul>
-<p><strong>Full Changelog</strong>: <a
href="https://github.com/apache/arrow-java/commits/v18.3.0";>changelog</a></p>]]></content><author><name>pmc</name></author><category
term="release" /><summary type="html"><![CDATA[The Apache Arrow team is
pleased to announce the v18.3.0 release of Apache Arrow Java. This is a minor
release since the last release v18.2.0. Changelog New Features and Enhancements
MINOR: ZstdCompressionCodec should use decompressedSize to get error name by
@libenchao in [...]
-
--->
-<p>The Apache Arrow team is pleased to announce the v18.3.0 release of Apache
Arrow Go.
-This minor release covers 21 commits from 8 distinct contributors.</p>
-<h2>Contributors</h2>
-<div class="language-console highlighter-rouge"><div class="highlight"><pre
class="highlight"><code data-lang="console"><span class="gp">$</span><span
class="w"> </span>git shortlog <span class="nt">-sn</span> v18.2.0..v18.3.0
-<span class="go"> 13 Matt Topol
- 2 Chris Pahl
- 1 Ashish Negi
- 1 David Li
- 1 Jeroen Demeyer
- 1 Mateusz Rzeszutek
- 1 Raúl Cumplido
- 1 Saurabh Singh
-</span></code></pre></div></div>
-<h2>Highlights</h2>
-<ul>
-<li>Fix alignment of atomic refcount handling for ARM <a
href="https://github.com/apache/arrow-go/pull/323";>#323</a></li>
-</ul>
-<h3>Arrow</h3>
-<ul>
-<li>Functions to convert RecordReader to Go iter.Seq and vice versa <a
href="https://github.com/apache/arrow-go/pull/314";>#314</a></li>
-<li>New "is_in" function for Arrow compute package</li>
-<li>Allow returning column remarks for FlightSQL CommandGetTables <a
href="https://github.com/apache/arrow-go/pull/361";>#361</a></li>
-</ul>
-<h3>Parquet</h3>
-<ul>
-<li>Added new <code>SeekToRow</code> function for pqarrow.RecordReader <a
href="https://github.com/apache/arrow-go/pull/321";>#321</a></li>
-<li>Bloom filters can now be read and written, then utilized for skipping <a
href="https://github.com/apache/arrow-go/pull/341";>#341</a> <a
href="https://github.com/apache/arrow-go/pull/336";>#336</a></li>
-<li>Fix a panic when <code>WriteDataPage</code> fails <a
href="https://github.com/apache/arrow-go/pull/366";>#366</a></li>
-</ul>
-<h2>Changelog</h2>
-<h3>What's Changed</h3>
-<ul>
-<li>feat(arrow/array): convert RecordReader and iterators by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/314";>#314</a></li>
-<li>refactor(arrow/array): replace some codegen with generics by @zeroshade in
<a href="https://github.com/apache/arrow-go/pull/315";>#315</a></li>
-<li>feat(parquet/pqarrow): Add SeekToRow for RecordReader by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/321";>#321</a></li>
-<li>fix: go's atomic operations require 64bit alignment in structs on ARM by
@sahib in <a href="https://github.com/apache/arrow-go/pull/323";>#323</a></li>
-<li>feat(arrow/compute): implement "is_in" function by @zeroshade in
<a href="https://github.com/apache/arrow-go/pull/319";>#319</a></li>
-<li>fix(parquet/pqarrow): fix propagation of FieldIds for nested fields by
@zeroshade in <a
href="https://github.com/apache/arrow-go/pull/324";>#324</a></li>
-<li>Fix: Handle null values in PlainFixedLenByteArrayEncoder gracefully by
@singh1203 in <a
href="https://github.com/apache/arrow-go/pull/320";>#320</a></li>
-<li>fix(parquet/pqarrow): fix definition levels with non-nullable lists by
@zeroshade in <a
href="https://github.com/apache/arrow-go/pull/325";>#325</a></li>
-<li>chore: fix macOS Go 1.24 CI by @lidavidm in <a
href="https://github.com/apache/arrow-go/pull/334";>#334</a></li>
-<li>feat(parquet/metadata): bloom filter implementation by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/336";>#336</a></li>
-<li>feat(parquet): Write/Read bloom filters from files by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/341";>#341</a></li>
-<li>fix: move from atomic.(Add|Load|Store) to atomic.Int64{} by @sahib in <a
href="https://github.com/apache/arrow-go/pull/326";>#326</a></li>
-<li>fix(parquet/file): restore goroutine safety for reader by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/343";>#343</a></li>
-<li>chore: Enable GitHub discussions on arrow-go repository by @raulcd in <a
href="https://github.com/apache/arrow-go/pull/353";>#353</a></li>
-<li>Compress: add MarshalText and UnmarshalText by @jdemeyer in <a
href="https://github.com/apache/arrow-go/pull/357";>#357</a></li>
-<li>fix(arrow/array): optional struct array with required field by @zeroshade
in <a href="https://github.com/apache/arrow-go/pull/359";>#359</a></li>
-<li>feat(parquet/schema): initial variant logical type by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/352";>#352</a></li>
-<li>chore(arrow): remove most lock copies by @zeroshade in <a
href="https://github.com/apache/arrow-go/pull/362";>#362</a></li>
-<li>Fix panic when WriteDataPage fails by @ashishnegi in <a
href="https://github.com/apache/arrow-go/pull/366";>#366</a></li>
-<li>GH-46087: [FlightSQL] Allow returning column remarks in FlightSQL's
CommandGetTables by @mateuszrzeszutek in <a
href="https://github.com/apache/arrow-go/pull/361";>#361</a></li>
-</ul>
-<h3>New Contributors</h3>
-<ul>
-<li>@sahib made their first contribution in <a
href="https://github.com/apache/arrow-go/pull/323";>#323</a></li>
-<li>@jdemeyer made their first contribution in <a
href="https://github.com/apache/arrow-go/pull/357";>#357</a></li>
-<li>@ashishnegi made their first contribution in <a
href="https://github.com/apache/arrow-go/pull/366";>#366</a></li>
-<li>@mateuszrzeszutek made their first contribution in <a
href="https://github.com/apache/arrow-go/pull/361";>#361</a></li>
-</ul>
-<p><strong>Full Changelog</strong>: <a
href="https://github.com/apache/arrow-go/compare/v18.2.0...v18.3.0";>https://github.com/apache/arrow-go/compare/v18.2.0...v18.3.0</a></p>]]></content><author><name>pmc</name></author><category
term="release" /><summary type="html"><![CDATA[The Apache Arrow team is
pleased to announce the v18.3.0 release of Apache Arrow Go. This minor release
covers 21 commits from 8 distinct contributors. Contributors $ git shortlog -sn
v18.2.0..v18.3.0 13 Matt Topol [...]
\ No newline at end of file
+<p><strong>Full Changelog</strong>: <a
href="https://github.com/apache/arrow-java/commits/v18.3.0";>changelog</a></p>]]></content><author><name>pmc</name></author><category
term="release" /><summary type="html"><![CDATA[The Apache Arrow team is
pleased to announce the v18.3.0 release of Apache Arrow Java. This is a minor
release since the last release v18.2.0. Changelog New Features and Enhancements
MINOR: ZstdCompressionCodec should use decompressedSize to get error name by
@libenchao in [...]
\ No newline at end of file
diff --git a/img/introducing-arrow-avro/arrow-avro-architecture.svg
b/img/introducing-arrow-avro/arrow-avro-architecture.svg
new file mode 100644
index 00000000000..f1f8feb1653
--- /dev/null
+++ b/img/introducing-arrow-avro/arrow-avro-architecture.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg";
xmlns:xlink="http://www.w3.org/1999/xlink"; xmlns:lucid="lucid" width="1462"
height="742"><g transform="translate(521 361)" lucid:page-tab-id="0_0"><path
d="M-220-354a6 6 0 0 1 6-6h848a6 6 0 0 1 6 6v728a6 6 0 0 1-6 6h-848a6 6 0 0
1-6-6z" stroke="#000" stroke-width="2" fill-opacity="0"/><path d="M110-314a6 6
0 0 1 6-6h188a6 6 0 0 1 6 6v208a6 6 0 0 1-6 6H116a6 6 0 0 1-6-6z" stroke="#000"
stroke-width="2" fill="#fff"/><use xlink:href="#a" transform="ma [...]
\ No newline at end of file
diff --git a/img/introducing-arrow-avro/read_violin_10k.svg
b/img/introducing-arrow-avro/read_violin_10k.svg
new file mode 100644
index 00000000000..6e63143ddf5
--- /dev/null
+++ b/img/introducing-arrow-avro/read_violin_10k.svg
@@ -0,0 +1,79 @@
+<?xml version="1.0" encoding="utf-8"?>
+<svg width="960" height="294" viewBox="0 0 960 294"
xmlns="http://www.w3.org/2000/svg";>
+ <text x="480" y="5" dy="0.76em" text-anchor="middle"
font-family="sans-serif" font-size="16.129032258064516" opacity="1"
fill="#000000" style="white-space: pre;">
+R/10K: Violin plot
+</text>
+ <text x="528" y="281" dy="-0.5ex" text-anchor="middle"
font-family="sans-serif" font-size="9.67741935483871" opacity="1"
fill="#000000" style="white-space: pre;">
+Average time (ms)
+</text>
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diff --git a/img/introducing-arrow-avro/write_violin_10k.svg
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