Optimizing Inter-data-center Large-Scale Database Parallel Replication with Workload-Driven Partitioning

Geographically distributed data centers are deployed for non-stop business operations by many enterprises. In case of disastrous events, ongoing workloads must be failed over from the current data center to another active one within just a few seconds to achieve continuous service availability. Software-based parallel database replication techniques are designed to meet very high throughput with near-real-time latency. Understanding workload characteristics is one of the key factors for improving replication performance. In this paper, we propose a workload-driven method to optimize database replication latency and minimize transaction splits with a minimum of parallel replication consistency groups. Our two-phased approach includes (1) a log-based mechanism for workload pattern discovery; (2) a history-based algorithm on pattern analysis, database partitioning and partition adjustment. The experimental results from a real banking batch workload and a benchmark OLTP workload demonstrate the effectiveness of the solution even for partitioning 1000 s of database tables in very large workloads. Finally, the algorithm to automate the cyclic flow of workload profile capturing and partitioning readjustment is developed and verified.