Cost Efficiency @ Scale in Big Data File Format

Cost Efficiency @ Scale in Big Data File Format

摘要

Our Apache Hadoop® based data platform ingests hundreds of petabytes of analytical data with minimum latency and stores it in a data lake built on top of the Hadoop Distributed File System (HDFS). We use Apache Hudi™ as our ingestion table format and Apache Parquet™ as the underlying file format. Our data platform leverages Apache Hive™, Apache Presto™, and Apache Spark™ for both interactive and long-running queries, serving the myriad needs of different teams at Uber.

Uber’s growth over the last few years exponentially increased both the volume of data and the associated access loads required to process it. As data volume grows, so do the associated storage and compute costs, resulting in growing hardware purchasing requirements, higher resource usage, and even causing out-of-memory (OOM) or high GC pause. The main goal of this blog is to address storage cost efficiency issues, but the side benefits also include CPU, IO, and network consumption usage.

We started several initiatives to reduce storage cost, including setting TTL (Time to Live) to old partitions, moving data from hot/warm to cold storage, and reducing data size in the file format level. In this blog, we will focus on reducing the data size in storage at the file format level, essentially at Parquet.