Mastering S3 Dynamic Partitioning In Redpanda Connect

Hey there, data enthusiasts! Ever found yourself scratching your head trying to get your data pipelines just right, especially when it comes to writing neatly organized files to S3? You're not alone, guys! Today, we're diving deep into a super common, yet often puzzling, scenario in Redpanda Connect: efficiently partitioning data in S3 using dynamic paths while also leveraging the power of archive: lines. This setup is incredibly valuable when you're moving mountains of data, say, from Kinesis to an S3 data lake, and you need those files structured by date, like year=YYYY/month=MM/day=DD/. But, as many of you might have experienced, simply setting a dynamic path in your output configuration and hoping archive: lines plays nice can lead to some unexpected headaches. We're talking about situations where batches of records with different timestamps end up bundled together in the wrong partition. This article isn't just about fixing a config; it's about understanding the mechanics behind Redpanda Connect's batching and how to wield group_by_value like a pro to achieve that perfect S3 organization. Get ready to transform your data pipelines from 'meh' to 'marvelous' by mastering this critical technique. We'll walk through a real-world example, dissect the problem, and then reveal the elegant solution that ensures your data lands exactly where it belongs, every single time.

The Challenge: Kinesis to S3 Partitioning with a Twist

Alright, let's set the stage, folks. Imagine you've got a stream of audit events flowing into Kinesis. Each event is a JSON record, and nestled inside each one is a crucial /audit/timestamp field. Your mission, should you choose to accept it, is to take these events, parse that timestamp, and then write them to S3 in a highly organized, date-based partition structure. Think year=2024/month=12/day=03/hour=14/ – super neat for downstream analytics, right? But here's where the plot thickens: you also want to batch these records into larger files using archive: lines to optimize performance and reduce S3 object overhead. Single-record JSON files are generally inefficient and costly. While archive: lines is fantastic for combining multiple records into a single file (each record becoming a new line), it often clashes with dynamic partitioning based on individual record metadata when not configured correctly. The core of our problem arises when Redpanda Connect processes a batch of records where some timestamps are 1733264297 (Dec 3, 2024) and others are 1733350697 (Dec 4, 2024). Without the right magic, all these records, despite having different day or hour values, might end up in a single file under the partition path derived from the first record in that batch. This completely defeats the purpose of your carefully crafted partitioning strategy, turning your pristine data lake into a bit of a swamp. This isn't just about making things work; it's about making them work efficiently and reliably at scale, ensuring data integrity and query performance down the line. We need a way to tell Redpanda Connect, "Hey, if these records belong to different paths, please, for the love of all that is data, put them in their own separate files within their correct partitions, even if they arrive in the same input batch!" This is the fundamental challenge we're tackling today, and trust me, the solution is as elegant as it is powerful.

Understanding the Current Redpanda Connect Configuration

Let's break down the configuration you've got, piece by piece, to truly understand its intent and where the hiccup occurs. It's an excellent starting point, and honestly, you're 90% of the way there. We'll pick apart each section to appreciate its role before we introduce the missing puzzle piece.

The Input: Kinesis Stream

Your journey begins with the input block, specifically configured for aws_kinesis. This is your data's entry point, acting as the listener for all those juicy audit events streaming in. You've correctly defined parameters like streams, region, and credentials, which are essential for Redpanda Connect to establish a secure and robust connection to your Kinesis stream. The start_from_oldest: false indicates that you're interested in new data rather than re-processing historical records, which is a common and sensible default for many real-time analytics scenarios. Kinesis is a fantastic choice for high-throughput, low-latency data ingestion, and Redpanda Connect's integration here is seamless. It ensures that every single event generated by your audit system is captured and prepared for its journey downstream. This input stage is critical because it dictates what data we'll be working with and sets the rhythm for the entire pipeline. Without a solid, reliable input, the rest of your pipeline, no matter how perfectly configured, simply won't have anything to process.

The Processor: Crafting Dynamic S3 Paths

Next up, we hit the pipeline section, where the processors work their magic. Here, you've got a mapping processor, and this is where you're dynamically generating your S3 partition paths. This is a brilliant move, guys, and absolutely the right way to approach dynamic pathing in Redpanda Connect. You're extracting the modifiedTimestamp from the audit field of your incoming JSON, converting it to a number (presumably a Unix timestamp), and then using ts_strftime to format it into the desired year=Y/month=M/day=D/hour=H structure. You're storing this dynamically created path in metadata as meta path. The meta timestamp = this.audit.modifiedTimestamp.number() line grabs that timestamp, and `meta path =