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

• The network-aware framework uses a combination of plugins and algorithms to optimize workload scheduling based on network topology and bandwidth resources.
• The framework includes an application group and network topology controller, load watcher component, and a scheduler with filtering and scoring functions.
• The framework was tested with the Redis cluster application and was able to improve throughput by 20% on average.
• The framework is not yet production-ready but is expected to be included in the Seek scheduling community in the next few months.
• Future plans include adding a plugin for monitoring bandwidth and dynamically adjusting workload scheduling based on real-time network congestion.
• An anecdote was provided demonstrating the performance improvement of the online boutique application with the network-aware framework compared to the default Kubernetes scheduler.

tldr - powered by Generative AI

• The rough store layer in TechAV uses rough consensus algorithm to make the system fault-tolerant.
• The star series in the rough store layer handles the work of multi-rough groups and uses roughed eyes as consensus aggression module.
• The duration time of a message append and a message append response is equal to network round trip time (RTT) and contains an unnecessary 0.5 IO duration.
• Applying unpersistent entries in advance can significantly reduce the tail latency of store duration.
• Privately applying process a single serious theory for each rough group can reduce the tail latency of reply duration.