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The following commit(s) were added to refs/heads/main by this push:
new 2a239bfcdee [Website]: Blog post about arrow-avro Rust crate (#712)
2a239bfcdee is described below
commit 2a239bfcdeed9e2e10c8ce4907e85bfd86ed30a3
Author: Connor Sanders <[email protected]>
AuthorDate: Thu Oct 23 13:27:13 2025 -0500
[Website]: Blog post about arrow-avro Rust crate (#712)
Part of the work to add first class Avro support to arrow-rs is to tell
people about it:
- Closes: https://github.com/apache/arrow-rs/issues/8428
- Part of https://github.com/apache/arrow-rs/issues/4886
@alamb Here's my first pass at the blog post. Sorry about it taking it a
bit longer than anticipated. Let me know what you think and I'm 100%
down to collaborate on this. 😃
---------
Co-authored-by: Andrew Lamb <[email protected]>
---
_data/contributors.yml | 3 +
_posts/2025-10-23-introducing-arrow-avro.md | 268 +++++++++++++++++++++
.../arrow-avro-architecture.svg | 1 +
img/introducing-arrow-avro/read_violin_10k.svg | 79 ++++++
img/introducing-arrow-avro/read_violin_1m.svg | 79 ++++++
img/introducing-arrow-avro/write_violin_10k.svg | 65 +++++
img/introducing-arrow-avro/write_violin_1m.svg | 71 ++++++
7 files changed, 566 insertions(+)
diff --git a/_data/contributors.yml b/_data/contributors.yml
index 9591d2bc959..8cefa315122 100644
--- a/_data/contributors.yml
+++ b/_data/contributors.yml
@@ -70,6 +70,9 @@
- name: Rossi Sun
apacheId: zanmato
githubId: zanmato1984
+- name: Connor Sanders
+ apacheId: jecsand838 # Not a real apacheId
+ githubId: jecsand838
- name: Andrew Lamb
apacheId: alamb
githubId: alamb
diff --git a/_posts/2025-10-23-introducing-arrow-avro.md
b/_posts/2025-10-23-introducing-arrow-avro.md
new file mode 100644
index 00000000000..da6d0e65285
--- /dev/null
+++ b/_posts/2025-10-23-introducing-arrow-avro.md
@@ -0,0 +1,268 @@
+---
+layout: post
+title: "Announcing arrow-avro in Arrow Rust"
+description: "A new native Rust vectorized reader/writer for Avro to Arrow,
with OCF, Single‑Object, and Confluent wire format support."
+date: "2025-10-23 00:00:00"
+author: jecsand838
+categories: [application]
+---
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+[`arrow-avro`], a newly rewritten Rust crate that reads and writes [Apache
Avro](https://avro.apache.org/) data directly as Arrow `RecordBatch`es, is now
available. It supports [Avro Object Container
Files](https://avro.apache.org/docs/1.11.1/specification/#object-container-files)
(OCF), [Single‑Object
Encoding](https://avro.apache.org/docs/1.11.1/specification/#single-object-encoding)
(SOE), the [Confluent Schema Registry wire
format](https://docs.confluent.io/platform/current/schema-re [...]
+
+[`arrow-avro`]: https://crates.io/crates/arrow-avro
+
+## Motivation
+
+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 paths. [...]
+
+### Why not use the existing `apache-avro` crate?
+
+Rust already has a mature, general‑purpose Avro crate,
[apache-avro](https://crates.io/crates/apache-avro). 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.
+
+What’s needed is a complementary approach that decodes column‑by‑column
straight into Arrow builders and emits `RecordBatch`es. This would enable
projection pushdown while keeping execution vectorized end to end. For projects
such as [Apache DataFusion](https://datafusion.apache.org/), access to a
mature, upstream Arrow‑native reader and writer would help simplify the code
path and reduce duplication.
+
+Modern pipelines heighten this need because [Avro is also used on the
wire](https://www.confluent.io/blog/avro-kafka-data/), 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.
+
+### Why this matters
+
+Apache Avro is a first‑class format across stream processors and cloud
services:
+- Confluent Schema Registry supports [Avro across multiple languages and
tooling](https://docs.confluent.io/platform/current/schema-registry/fundamentals/serdes-develop/serdes-avro.html).
+- Apache Flink exposes an [`avro-confluent` format for
Kafka](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/connectors/table/formats/avro-confluent/).
+- AWS Lambda [(June 2025) added native handling for Avro‑formatted Kafka
events](https://aws.amazon.com/about-aws/whats-new/2025/06/aws-lambda-native-support-avro-protobuf-kafka-events/)
with Glue and Confluent Schema Registry integrations.
+- Azure Event Hubs provides a [Schema Registry with Avro
support](https://learn.microsoft.com/en-us/azure/event-hubs/schema-registry-overview)
for Kafka‑compatible clients.
+
+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.
+
+## Introducing `arrow-avro`
+
+[`arrow-avro`](https://github.com/apache/arrow-rs/tree/main/arrow-avro) 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 `RecordBatch`es. Meanwhile, the write
path provides formats for encoding to OCF and SOE as well.
+
+The crate exposes two primary read APIs: a high-level `Reader` for OCF inputs
and a low-level `Decoder` for streaming SOE frames. For SOE and
Confluent/Apicurio frames, a `SchemaStore` is provided that resolves
fingerprints or schema IDs to full Avro writer schemas, enabling schema
evolution while keeping the decode path vectorized.
+
+On the write side, `AvroWriter` produces OCF (including container‑level
compression), while `AvroStreamWriter` produces framed Avro messages for
Single‑Object or Confluent/Apicurio encodings, as configured via the
`WriterBuilder::with_fingerprint_strategy(...)` knob.
+
+Configuration is intentionally minimal but practical. For instance, the
`ReaderBuilder` exposes knobs covering both batch file ingestion and streaming
systems without forcing format‑specific code paths.
+
+### How this mirrors Parquet in Arrow‑rs
+
+If you have used Parquet with Arrow‑rs, you already know the pattern. The
`parquet` crate exposes a [parquet::arrow
module](https://docs.rs/parquet/latest/parquet/arrow/index.html) that reads and
writes Arrow `RecordBatch`es directly. Most users reach for
`ParquetRecordBatchReaderBuilder` when reading and `ArrowWriter` when writing.
You choose columns up front, set a batch size, and the reader gives you Arrow
batches that flow straight into vectorized operators. This is the widely adopte
[...]
+
+`arrow‑avro` brings that same bridge to Avro. You get a single `ReaderBuilder`
that can produce a `Reader` for OCF, or a streaming `Decoder` for on‑the‑wire
frames. Both return Arrow `RecordBatch`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
`SchemaStore` resolves fingerprints or ids before decoding, so the batches that
come out are already shaped for vec [...]
+
+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
`parquet::arrow` popular in Rust, and it is what `arrow‑avro` now enables for
Avro.
+
+
+## Architecture & Technical Overview
+
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 20px 15px;">
+<img src="{{ site.baseurl
}}/img/introducing-arrow-avro/arrow-avro-architecture.svg"
+ width="100%"
+ alt="High-level `arrow-avro` architecture"
+ style="background:#fff">
+</div>
+
+At a high level,
[arrow-avro](https://arrow.apache.org/rust/arrow_avro/index.html) splits
cleanly into read and write paths built around Arrow `RecordBatch`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 `AvroStreamWriter`, the framing (SOE or Confluent) is
part of the stream output based on the configured fingerprint strategy; thus no
separate fra [...]
+
+On the [read](https://arrow.apache.org/rust/arrow_avro/reader/index.html)
path, everything starts with the
[ReaderBuilder](https://arrow.apache.org/rust/arrow_avro/reader/struct.ReaderBuilder.html).
A single builder can create a
[Reader](https://arrow.apache.org/rust/arrow_avro/reader/struct.Reader.html)
for Object Container Files (OCF) or a streaming
[Decoder](https://arrow.apache.org/rust/arrow_avro/reader/struct.Decoder.html)
for SOE/Confluent/Apicurio frames. The `Reader` pulls OCF b [...]
+
+When reading an OCF, the `Reader` 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., `deflate`, `snappy`, `zstd`,
`bzip2`, `xz`), 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 encoded with variable‑length
integers that use zig‑zag encoding for signed [...]
+
+On the [write](https://arrow.apache.org/rust/arrow_avro/writer/index.html)
path, the
[WriterBuilder](https://arrow.apache.org/rust/arrow_avro/writer/struct.WriterBuilder.html)
produces either an
[AvroWriter](https://arrow.apache.org/rust/arrow_avro/writer/type.AvroWriter.html)
(OCF) or an
[AvroStreamWriter](https://arrow.apache.org/rust/arrow_avro/writer/type.AvroStreamWriter.html)
(SOE/Message). The `with_compression(...)` knob is used for OCF block
compression while `with_fingerprint_s [...]
+
+Schema handling is centralized in the
[schema](https://arrow.apache.org/rust/arrow_avro/schema/index.html) module.
[AvroSchema](https://arrow.apache.org/rust/arrow_avro/schema/struct.AvroSchema.html)
wraps a valid Avro Schema JSON string, supports computing a `Fingerprint`, and
can be loaded into a
[SchemaStore](https://arrow.apache.org/rust/arrow_avro/schema/struct.SchemaStore.html)
as a writer schema. At runtime, the `Reader`/`Decoder` can use a `SchemaStore`
to resolve fingerprints be [...]
+
+At the heart of `arrow-avro` is a type‑mapping `Codec` that the library uses
to construct both encoders and decoders. The `Codec` captures, for every Avro
field, how it maps to Arrow and how it should be encoded or decoded. The
`Reader` logic builds a `Codec` per *(writer, reader)* schema pair, which the
decoder later uses to vectorize parsing of Avro values directly into the
correct Arrow builders. The `Writer` logic uses the same `Codec` mappings to
drive pre-computed record encoding 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 `RecordBatch`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.
+
+## Examples
+
+### Decoding a Confluent-framed Kafka Stream
+
+```rust
+use arrow_avro::reader::ReaderBuilder;
+use arrow_avro::schema::{
+ SchemaStore, AvroSchema, Fingerprint, FingerprintAlgorithm, CONFLUENT_MAGIC
+};
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+ // Register writer schema under Confluent id=1.
+ let mut store = SchemaStore::new_with_type(FingerprintAlgorithm::Id);
+ store.set(
+ Fingerprint::Id(1),
+
AvroSchema::new(r#"{"type":"record","name":"T","fields":[{"name":"x","type":"long"}]}"#.into()),
+ )?;
+
+ // Define reader schema to enable projection/schema evolution.
+ let reader_schema =
AvroSchema::new(r#"{"type":"record","name":"T","fields":[{"name":"x","type":"long"}]}"#.into());
+
+ // Build Decoder using reader and writer schemas
+ let mut decoder = ReaderBuilder::new()
+ .with_reader_schema(reader_schema)
+ .with_writer_schema_store(store)
+ .build_decoder()?;
+
+ // Simulate one frame: magic 0x00 + 4‑byte big‑endian schema ID + Avro
body (x=1 encoded as zig‑zag/VLQ).
+ let mut frame = Vec::from(CONFLUENT_MAGIC);
frame.extend_from_slice(&1u32.to_be_bytes()); frame.extend_from_slice(&[2]);
+
+ // Consume from decoder
+ let _consumed = decoder.decode(&frame)?;
+ while let Some(batch) = decoder.flush()? {
+ println!("rows={}, cols={}", batch.num_rows(), batch.num_columns());
+ }
+ Ok(())
+}
+```
+
+The `SchemaStore` 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 `0x00`
followed by a big‑endian 4‑byte schema ID. After decoding Avro messages, the
`Decoder::flush()` method yields Arrow `RecordBatch`es suitable for vectorized
processing.
+
+A more advanced example can be found
[here](https://github.com/apache/arrow-rs/blob/main/arrow-avro/examples/decode_kafka_stream.rs).
+
+### Writing a Snappy Compressed Avro OCF file
+
+```rust
+use arrow_array::{Int64Array, RecordBatch};
+use arrow_schema::{Schema, Field, DataType};
+use arrow_avro::writer::{Writer, WriterBuilder};
+use arrow_avro::writer::format::AvroOcfFormat;
+use arrow_avro::compression::CompressionCodec;
+use std::{sync::Arc, fs::File, io::BufWriter};
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+ let schema = Schema::new(vec![Field::new("id", DataType::Int64, false)]);
+ let batch = RecordBatch::try_new(
+ Arc::new(schema.clone()),
+ vec![Arc::new(Int64Array::from(vec![1,2,3]))],
+ )?;
+ let file = File::create("target/example.avro")?;
+
+ // Choose OCF block compression (e.g., None, Deflate, Snappy, Zstd)
+ let mut writer: Writer<_, AvroOcfFormat> = WriterBuilder::new(schema)
+ .with_compression(Some(CompressionCodec::Snappy))
+ .build(BufWriter::new(file))?;
+ writer.write(&batch)?;
+ writer.finish()?;
+ Ok(())
+}
+```
+
+The example above configures an Avro OCF `Writer`. It constructs a `Writer<_,
AvroOcfFormat>` using `WriterBuilder::new(schema)` and wraps a `File` in a
`BufWriter` for efficient I/O. The call to
`.with_compression(Some(CompressionCodec::Snappy))` enables block‑level snappy
compression. Finally, `writer.write(&batch)?` serializes the batch as an Avro
encoded block, and `writer.finish()?` flushes and finalizes the outputted file.
+
+## Alternatives & Benchmarks
+
+There are fundamentally two different approaches for bringing Avro into Arrow:
+1. Row‑centric approach, typical of general Avro libraries such as
`apache-avro`, deserializes one record at a time into native Rust values (i.e.,
`Value` or Serde types) and then builds Arrow arrays from those values.
+2. Vectorized approach, what `arrow-avro` provides, decodes directly into
Arrow builders/arrays and emits `RecordBatch`es, avoiding most per‑row overhead.
+
+This section compares the performance of both approaches using these
[Criterion
benchmarks](https://github.com/jecsand838/arrow-rs/tree/blog-benches/arrow-avro/benches).
+
+### Read performance (1M)
+
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="{{ site.baseurl }}/img/introducing-arrow-avro/read_violin_1m.svg"
+ width="100%"
+ alt="1M Row Read Violin Plot"
+ style="background:#fff">
+</div>
+
+### Read performance (10K)
+
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="{{ site.baseurl }}/img/introducing-arrow-avro/read_violin_10k.svg"
+ width="100%"
+ alt="10K Row Read Violin Plot"
+ style="background:#fff">
+</div>
+
+### Write performance (1M)
+
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="{{ site.baseurl }}/img/introducing-arrow-avro/write_violin_1m.svg"
+ width="100%"
+ alt="1M Row Write Violin Plot"
+ style="background:#fff">
+</div>
+
+### Write performance (10K)
+
+<div style="display: flex; gap: 16px; justify-content: center; align-items:
flex-start; padding: 5px 5px;">
+<img src="{{ site.baseurl }}/img/introducing-arrow-avro/write_violin_10k.svg"
+ width="100%"
+ alt="10K Row Write Violin Plot"
+ style="background:#fff">
+</div>
+
+
+Across benchmarks, the violin plots show lower medians and tighter spreads for
`arrow-avro` 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 `arrow-avro`, reflecting better cache locality and fewer copies
once decoding goes straight to Arrow arrays. The general behavior is consistent
with `apache-avro`'s record‑by‑record iteration and `arrow-avro`'s
batch‑oriented design.
+
+The table below lists the cases we report in the figures:
+* 10K vs 1M rows for multiple data shapes.
+* **Read cases:**
+ * `f8`: *Full schema, 8K batch size.*
+ Decode all four columns with batch_size = 8192.
+ * `f1`: *Full schema, 1K batch size.*
+ Decode all four columns with batch_size = 1024.
+ * `p8`: *Projected `{id,name}`, 8K batch size (pushdown).*
+ Decode only `id` and `name` with batch_size = 8192`.
+ *How projection is applied:*
+ * `arrow-avro/p8`: projection via reader schema
(`ReaderBuilder::with_reader_schema(...)`) so decoding is column‑pushed down in
the Arrow‑first reader.
+ * `apache-avro/p8`: projection via Avro reader schema
(`AvroReader::with_schema(...)`) so the Avro library decodes only the projected
fields.
+ * `np`: *Projected `{id,name}`, no pushdown, 8K batch size.*
+ Both readers decode the full record (all four columns), materialize all
arrays, then project down to `{id,name}` after decode. This models systems that
can't push projection into the file/codec reader.
+* **Write cases:**
+ * `c` (cold): *Schema conversion each iteration.*
+ * `h` (hot): *Avro JSON "hot" path.*
+* The resulting Apache‑Avro vs Arrow‑Avro medians with the computed speedup.
+
+### Benchmark Median Time Results (Apple Silicon Mac)
+
+| Case | apache-avro median | arrow-avro median | speedup |
+|----------|-------------------:|------------------:|--------:|
+| R/f8/10K | 2.60 ms | 0.24 ms | 10.83x |
+| R/p8/10K | 7.91 ms | 0.24 ms | 32.95x |
+| R/f1/10K | 2.65 ms | 0.25 ms | 10.60x |
+| R/np/10K | 2.62 ms | 0.25 ms | 10.48x |
+| R/f8/1M | 267.21 ms | 27.91 ms | 9.57x |
+| R/p8/1M | 791.79 ms | 26.28 ms | 30.13x |
+| R/f1/1M | 262.93 ms | 28.25 ms | 9.31x |
+| R/np/1M | 268.79 ms | 27.69 ms | 9.71x |
+| W/c/10K | 4.78 ms | 0.27 ms | 17.70x |
+| W/h/10K | 0.82 ms | 0.28 ms | 2.93x |
+| W/c/1M | 485.58 ms | 36.97 ms | 13.13x |
+| W/h/1M | 83.58 ms | 36.75 ms | 2.27x |
+
+## Closing
+
+`arrow-avro` 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 `arrow-rs`.
+
+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 [issue on
GitHub](https://github.com/apache/arrow-rs/issues) and/or track [Add Avro
Support](https://github.com/apache/arrow-rs/issues/4886) in `apache/arrow-rs`.
+
+### Acknowledgments
+
+Special thanks to:
+* [tustvold](https://github.com/tustvold) for laying an incredible zero-copy
foundation.
+* [nathaniel-d-ef](https://github.com/nathaniel-d-ef) and
[ElastiFlow](https://github.com/elastiflow) for their numerous and invaluable
project-wide contributions.
+* [veronica-m-ef](https://github.com/veronica-m-ef) for making Impala‑related
contributions to the `Reader`.
+* [Supermetal](https://github.com/Supermetal-Inc) for contributions related to
Apicurio Registry and Run-End Encoding type support.
+* [kumarlokesh](https://github.com/kumarlokesh) for contributing `Utf8View`
support.
+* [alamb](https://github.com/alamb), [scovich](https://github.com/scovich),
[mbrobbel](https://github.com/mbrobbel), and
[klion26](https://github.com/klion26) for their thoughtful reviews, detailed
feedback, and support throughout the development of `arrow-avro`.
+
+If you have any questions about this blog post, please feel free to contact
the author, [Connor Sanders](mailto:[email protected]).
\ 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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