UFS is an eBPF unfair scheduler that improves time-sensitive task throughput by up to 2X and halves tail latency in mixed PostgreSQL workloads by limiting background tasks to idle capacity and using hints to prevent priority inversion.
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RelaNN associates tuples with learnable embeddings and lifts relational queries to jointly process data and embeddings, enabling declarative implementation of graph neural networks inside database systems.
citing papers explorer
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Unfair by design: eBPF-based scheduling of mixed database workloads
UFS is an eBPF unfair scheduler that improves time-sensitive task throughput by up to 2X and halves tail latency in mixed PostgreSQL workloads by limiting background tasks to idle capacity and using hints to prevent priority inversion.
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Incorporating Deep Learning Design in Database Queries
RelaNN associates tuples with learnable embeddings and lifts relational queries to jointly process data and embeddings, enabling declarative implementation of graph neural networks inside database systems.