Apache Hudi pipelines face petabyte-scale delays

At petabyte scale, Kafka offset lag shows how many messages a consumer hasn’t processed—but not how old that data is. For Apache Hudi data lake pipelines, these measurements can differ significantly, causing missed freshness SLAs even when monitoring tools report healthy throughput.

Hudi Delta Streamer, the ingestion engine used in many large-scale pipelines, tracks its own checkpoints separately from Kafka’s consumer group offsets. These checkpoints are stored in S3 with table data, not in Kafka’s offset storage. Standard tools like Burrow only see Kafka’s offsets, so they miss when Hudi falls behind.

Twilio’s data lake processes over five trillion records monthly across messaging, email, and voice products. The gap became clear only when downstream analytics teams reported stale data hours after Kafka offsets indicated everything was current. The problem wasn’t throughput—it was visibility.

The solution calculates a time-based lag metric by reading the latest Hudi commit file in S3. It extracts the checkpoint offset and measures the timestamp difference between that offset and the current time. Changes to producers, consumers, or existing pipeline infrastructure are not required.

The process begins by fetching the most recent Hudi commit from the active timeline. If that commit lacks checkpoint metadata—a common issue when a parallel legacy pipeline made the last commit—it reviews earlier commits until it finds one. Once located, it seeks to that offset in each Kafka partition, reads the next message, and determines how long that message has waited.

This method works because it uses artifacts the system already produces: the offset Hudi commits to S3 and the timestamps Kafka attaches to messages.

Under the hood, the reporter uses the Apache Hudi SDK’s HoodieTableMetaClient to read the .hoodie/ timeline directory from S3. The SDK relies on Hadoop’s S3A filesystem, configured via the Spark session the reporter operates in—though no Spark data processing happens. The reporter simply uses Spark’s runtime to access S3.

This approach handles Twilio’s peak load of 12.9 million messages per second, as observed on Cyber Monday 2025. By making time-based lag a primary metric, pipeline owners can set custom freshness SLAs per pipeline and receive alerts when data exceeds their thresholds. Offset monitoring and time-lag monitoring now run together, offering a full view of pipeline health that neither metric alone could provide.

The method doesn’t require new instrumentation or pipeline changes. The necessary data—Hudi’s checkpoints and Kafka’s message timestamps—already exists. The reporter only connects them.

For businesses where data freshness affects decisions, fraud detection, or machine learning models, this visibility is essential. The change from counting records to measuring time in queue reflects a broader trend toward metrics that match how data is used, not just how it’s moved.

The implementation is simple. The reporter retrieves the latest Hudi commit, parses the checkpoint metadata, seeks to the corresponding Kafka offsets, and computes the lag. The code, available in the source, organizes these steps into a few methods: one to read the Hudi timeline, another to seek and poll Kafka, and a final step to calculate the time difference.

Teams running Hudi at scale can enforce SLAs without complicating the ingestion process. The metric becomes part of the data contract, giving pipeline owners a clear signal when issues arise—and confirmation when systems work as expected.

This approach aligns with recent updates that boost speed and efficiency in data processing pipelines.