tldr - powered by Generative AI

• GitHub has a standard MySQL setup with 80 clusters and 2000 instances
• They have a read-heavy load with 330 terabytes of data across primaries and replicas
• Their scaling strategy includes setting up separate clusters for new features, breaking up existing clusters, and adding more replicas
• They ran into problems with scaling approaches and schema migration times
• They implemented v-test as a solution, which is a sharding model that fits their data model well
• v-test allows for seamless schema changes, automatic failure detection and repair, and query consolidation
• GitHub has successfully migrated 20 key spaces to v-test, reducing the number of hosts needed and improving read and write rates

tldr - powered by Generative AI

• Sparse histograms are a new type of histogram that allows for efficient monitoring of metrics with high resolution and low memory usage.
• The implementation of sparse histograms in Prometheus and Grafana allows for efficient scraping and visualization of metrics.
• The use of sparse histograms can be applied to various types of metrics, including latency and memory usage.
• The implementation of sparse histograms is open source and available for use in the client golang library and Prometheus server.