Lessons Learned Running Presto at Meta Scale
📰 High Scalability
Meta shares lessons learned from running Presto at scale over the past decade
Action Steps
- Identify potential bottlenecks in Presto deployment
- Implement scalable architecture to handle growing demands
- Monitor and optimize query performance
- Develop strategies for troubleshooting and debugging
Who Needs to Know This
Data engineers and architects on a team can benefit from understanding the challenges and solutions for scaling Presto, as it can inform their own decisions for large-scale data querying
Key Insight
💡 Scaling Presto requires careful planning, monitoring, and optimization to overcome unexpected challenges
Share This
🚀 Scaling Presto to Meta scale: lessons learned from 10 years of deployment
Key Takeaways
Meta shares lessons learned from running Presto at scale over the past decade
Full Article
Presto is a free, open source SQL query engine. We’ve been using it at Meta for the past ten years, and learned a lot while doing so. Running anything at scale - tools, processes, services - takes problem solving to overcome unexpected challenges. Here are four things we learned while scaling up Presto to Meta scale, and some advice if you’re interested in running your own queries at scale. Scaling Presto rapidly to meet growing demands: What challenges did we face? Deploying
DeepCamp AI