ianmcook commented on code in PR #609:
URL: https://github.com/apache/arrow-site/pull/609#discussion_r1984069802
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_posts/2025-03-04-fast-streaming-inserts-in-duckdb-with-adbc.md:
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+---
+layout: post
+title: "Fast Streaming Inserts in DuckDB with ADBC"
+description: "ADBC enables high throughput insertion into DuckDB"
+date: "2025-03-04 00:00:00"
+author: loicalleyne
+categories: [application]
+image:
+ path: /img/adbc-duckdb/adbc-duckdb.png
+ height: 560
+ width: 1200
+---
+
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+
+# Fast Streaming Inserts in DuckDB with ADBC
+
+<img src="{{ site.baseurl }}/img/adbc-duckdb/adbc-duckdb.png" width="100%"
class="img-responsive" alt="" aria-hidden="true">
+# TL;DR
+
+DuckDB is rapidly becoming an essential part of data practitioners' toolbox,
finding use cases in data engineering, machine learning, and local analytics.
In many cases DuckDB has been used to query and process data that has already
been saved to storage (file-based or external database) by another process.
Arrow Database Connectivity APIs enable high-throughput data processing using
DuckDB as the engine.
+
+# How it started
+
+The company I work for is the leading digital out-of-home marketing platform,
including a programmatic ad tech stack. For several years, my technical
operations team was making use of logs emitted by the real-time programmatic
auction system in the [Apache Avro](http://avro.apache.org/) format. Over time
we've built an entire operations and analytics back end using this data. Avro
files are row-based which is less than ideal for analytics at scale, in fact
it's downright painful. So much so that I developed and contributed an Avro
reader feature to the [Apache Arrow Go](https://github.com/apache/arrow-go)
library to be able to convert Avro files to parquet. This data pipeline is now
humming along transforming hundreds of GB/day from Avro to Parquet.
+
+Since "any problem in computer science can be solved with another layer of
indirection", the original system has grown layers (like an onion) and started
to emit other logs, this time in [Apache Parquet](https://parquet.apache.org/)
format...
+<figure style="text-align: center;">
+ <img src="{{ site.baseurl }}/img/adbc-duckdb/muchrejoicing.gif" width="80%"
class="img-responsive" alt="Figure 1: And there was much rejoicing">
+ <figcaption>Figure 1: A pseudo-medieval tapestry displaying intrepid data
practitioners rejoicing due to a columnar data storage format.</figcaption>
+</figure>
+As we learned in Shrek, onions are like ogres: they're green, they have layers
and they make you cry, so this rejoicing was rather short-lived, as the
mechanism chosen to emit the parquet files was rather inefficient:
+
+* the new onion-layer (ahem...system component) sends Protobuf encoded
messages to Kafka topics
+* a Kafka Connect cluster with the S3 sink connector consumes topics and saves
the parquet files to object storage
+
+Due to the firehose of data the cluster size over time grew to \> 25 nodes and
was producing thousands of small parquet files (13 MB or smaller) an hour. This
led to ever-increasing query latency, in some cases breaking our tools due to
query timeouts (aka [the Small Files
Problem](https://www.dremio.com/blog/compaction-in-apache-iceberg-fine-tuning-your-iceberg-tables-data-files/#h-the-small-files-problem)).
Not to mention that running aggregations on the raw data in our data warehouse
wasn't fast or cheap.
+
+# DuckDB to the rescue... I think
+
+I'd used DuckDB to process and analyse Parquet data so I knew it could do that
very quickly. Then I came across this post on LinkedIn ([Real-Time Analytics
using Kafka and
DuckDB](https://www.linkedin.com/posts/shubham-dhal-349626ba_real-time-analytics-with-kafka-and-duckdb-activity-7258424841538555904-xfU6)),
where someone has built a system for near-realtime analytics in Go using
DuckDB.
+
+The slides listed DuckDB's limitations:
+<img src="{{ site.baseurl }}/img/adbc-duckdb/duckdb.png" width="100%"
class="img-responsive" alt="DuckDB limitations: Single Pod, *Data should fit in
memory, *Low Query Concurrency, *Low Ingest Rate - *Solvable with some efforts"
aria-hidden="true">
+The poster's solution batches data at the application layer managing to scale
up ingestion 100x to \~20k inserts/second, noting that they thought that using
the DuckDB Appender API could possibly increase this 10x. So, potentially
\~200k inserts/second. Yayyyyy...
+
+<figure style="text-align: center;">
+ <img src="{{ site.baseurl }}/img/adbc-duckdb/Yay.gif" width="40%"
class="img-responsive" alt="Figure 2: Yay">
+</figure>
+
+Then I noticed the data schema in the slides was flat and had only 4 fields
(vs.
[OpenRTB](https://github.com/InteractiveAdvertisingBureau/openrtb2.x/blob/main/2.6.md#31---object-model-)
schema with deeply nested Lists and Structs); and then looked at our
monitoring dashboards whereupon I realized that at peak our system was emitting
\>250k events/second. \[cue sad trombone\]
+
+Undeterred (and not particularly enamored with the idea of setting
up/running/maintaining a Spark cluster), I suspected that Apache Arrow's
columnar memory representation might still make DuckDB viable since it has an
Arrow API; getting Parquet files would be as easy as running `COPY...TO (format
parquet)`.
+
+Using a pattern found in a Github issue, I wrote a POC using
[github.com/marcboeker/go-duckdb](http://github.com/marcboeker/go-duckdb) to
connect to a DB, retrieve an Arrow, create an Arrow Reader, register a view on
the reader, then run an INSERT statement from the view.
+
+This felt a bit like a rabbit pulling itself out of a hat, but no matter, it
managed between \~74k and \~110k rows/sec on my laptop.
+
+To make sure this was really the right solution, I also tried out DuckDB's
Appender API (at time of writing the official recommendation for fast inserts)
and managed... \~63k rows/sec on my laptop. OK, but... meh.
+
+# A new hope
+
+In a discussion on the Gopher Slack, Matthew Topol aka
[zeroshade](https://github.com/zeroshade) suggested using
[ADBC](http://arrow.apache.org/adbc) with its much simpler API. Who is Matt
Topol you ask? Just the guy who *literally* wrote the book on Apache Arrow,
that's who ([***In-Memory Analytics with Apache Arrow: Accelerate data
analytics for efficient processing of flat and hierarchical data structures 2nd
Edition***](https://www.packtpub.com/en-ca/product/in-memory-analytics-with-apache-arrow-9781835461228)).
It's an excellent resource and guide for working with Arrow.
+BTW, should you prefer an acronym to remember the name of the book, it's
***IMAAA:ADAFEPOFAHDS2E***.
+<img src="{{ site.baseurl }}/img/adbc-duckdb/imaaapfedaobfhsd2e.png"
width="100%" class="img-responsive" alt="Episode IX: In-Memory Analytics with
Apache Arrow: Perform fast and efficient data analytics on both flat and
hierarchical structured data 2nd Edition aka IMAAA:PFEDAOBFHSD2E by Matt Topol"
aria-hidden="true">
+But I digress. Matt is also a member of the Apache Arrow PMC, a major
contributor to Apache Iceberg \- Go and generally a nice, helpful guy.
+
+# ADBC
+
+Going back to the drawing board, I created
[Quacfka](https://github.com/loicalleyne/quacfka), a Go library built using
ADBC and split out my system into 3 worker pools, connected by channels:
+
+* Kafka clients consuming topic messages and writing the bytes to a message
channel
+* Processing routines using the
[Bufarrow](https://github.com/loicalleyne/bufarrow) library to deserialize
Protobuf data and append it to Arrow arrays, writing Arrow Records to a record
channel
+* DuckDB inserters binding the Arrow Records to ADBC statements and executing
insertions
+
+I first ran these in series to determine how fast each could run:
+
+ *2025/01/23 23:39:27 kafka read start with 8 readers*
+ *2025/01/23 23:39:41 read 15728642 kafka records in 14.385530 secs @
1093365.498477 messages/sec*
+ *2025/01/23 23:39:41 deserialize \[\]byte to proto, convert to arrow records
with 32 goroutines start*
+ *2025/01/23 23:40:04 deserialize to arrow done \- 15728642 records in
22.283532 secs @ 705841.509812 messages/sec*
+ *2025/01/23 23:40:04 ADBC IngestCreateAppend start with 32 connections*
+ *2025/01/23 23:40:25 duck ADBC insert 15728642 records in 21.145649535 secs
@ **743824.007783 rows/sec***
+
+<img src="{{ site.baseurl }}/img/adbc-duckdb/holdmybeer.png" width="100%"
class="img-responsive" alt="20k rows/sec? Hold my beer" aria-hidden="true">
+
+With this architecture decided, I then started running the workers
concurrently, instrumenting the system, profiling my code to identify
performance issues and tweaking the settings to maximize throughput. It seemed
to me that there was enough performance headroom to allow for in-flight
aggregations.
+
+One issue: Despite DuckDB's excellent [lightweight
compression](https://duckdb.org/2022/10/28/lightweight-compression.html)
inserts from this source were making the file size increase at a rate of
***\~8GB/minute***, putting inserts on hold to export the Parquet files and
release the storage would reduce the overall throughput to an unacceptable
level. I decided to implement a rotation of database files based on a file size
threshold.
Review Comment:
```suggestion
One issue: Despite DuckDB's excellent [lightweight
compression](https://duckdb.org/2022/10/28/lightweight-compression.html),
inserts from this source were making the file size increase at a rate of
***\~8GB/minute***. Putting inserts on hold to export the Parquet files and
release the storage would reduce the overall throughput to an unacceptable
level. I decided to implement a rotation of database files based on a file size
threshold.
```
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