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Querying 1 Thousand JSON Files From S3
Running DuckDB on AWS ECS Fargate outperformed EMR Serverless Spark for processing 1,000 JSON files by 3.4x while lowering compute costs.
What: The performance benchmark tested processing 1,000 JSON files stored in S3 using DuckDB deployed on ECS Fargate versus Apache Spark on EMR Serverless. DuckDB proved significantly faster and more cost-effective for this specific volume of data, challenging the default choice of using Spark for small-to-medium scale batch processing.
Why it matters: This highlights that for many medium-scale data tasks, the operational overhead and cluster startup costs of Spark/EMR are inefficient compared to lightweight, embedded analytical engines like DuckDB.
Deep dive
- DuckDB demonstrated a 3.4x performance improvement over Spark for querying 1,000 JSON files.
- ECS Fargate was used as the runtime environment for DuckDB to provide scalable compute.
- The comparison included EMR Serverless, showing that Spark's distributed nature can be a bottleneck for smaller datasets.
- Cost savings were realized by bypassing the cluster provisioning time and distributed shuffle overhead inherent in Spark.
- The analysis suggests that engineers should re-evaluate the threshold where moving to distributed processing (Spark) actually provides value.
Decoder
- DuckDB: An in-process SQL OLAP database management system designed for fast analytical queries.
- ECS Fargate: A serverless compute engine for containers that allows users to run applications without managing underlying EC2 instances.
- EMR Serverless: An AWS service that provides a serverless option for running big data frameworks like Apache Spark without managing clusters.
Original article
DuckDB on ECS Fargate processed 1,000 JSON files about 3.4× faster than Spark on EMR Serverless and at lower cost.
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