Developers often deploy database-specific network proxies whereby applications connect transparently to the proxy instead of directly connecting to the database management system (DBMS). This indirection improves system performance through connection pooling, load balancing, and other DBMS-specific optimizations. Instead of simply forwarding packets, these proxies implement DBMS protocol logic (i.e., at the application layer) to achieve this behavior. Consequently, existing proxies are user-space applications that process requests as they arrive on network sockets and forward them to the appropriate destinations. This approach incurs inefficiencies as the kernel repeatedly copies buffers between user-space and kernel-space, and the associated system calls add CPU overhead. This paper presents user-bypass, a technique to eliminate these overheads by leveraging modern operating system features that support custom code execution. User-bypass pushes application logic into kernel-space via Linux's eBPF infrastructure. To demonstrate its benefits, we implemented Tigger, a PostgreSQL-compatible DBMS proxy using user-bypass to eliminate the overheads of traditional proxy design. We compare Tigger's performance against other state-of-the-art proxies widely used in real-world deployments. Our experiments show that Tigger outperforms other proxies --- in one scenario achieving both the lowest transaction latencies (up to 29% reduction) and lowest CPU utilization (up to 42% reduction). The results show that user-bypass implementations like Tigger are well-suited to DBMS proxies' unique requirements.

A self-driving database management system (DBMS) aims to configure, deploy, and optimize almost all aspects of itself automatically without human intervention or guidance. Achieving this high level of automation relies on machine learning (ML) models that predict how a DBMS will behave in different scenarios. This behavior encompasses all DBMS runtime operations, including query execution and maintenance tasks. These ML-based behavior models for a self-driving DBMS require low-level training data about a DBMS's internals. Such training data includes (1) features that describe the workload, environment, and DBMS configuration, and (2) both DBMS- and hardware-level metrics. But it is difficult to collect training data from a DBMS while it is running because it can introduce performance and measurement degradations that hinder the ML models' ability to predict the DBMS's behavior correctly. We present the TScout (TS) framework for collecting training data from self-driving DBMSs. Our framework is an internal approach where developers annotate a DBMS's source code with hooks to monitor the system's behavior. TS then extracts these hooks and generates a kernel-level program (via Linux's BPF) that efficiently captures metrics from multiple sources (e.g., CPU performance counters, memory allocators). TS combines these metrics with internal DBMS state observations, generating training data for behavior models. We integrated TS in a PostgreSQL-compatible DBMS and measured its ability to collect training data for both OLTP and OLAP workloads. Our results show that TS generates training data for a deployed DBMS to train more accurate models than previous methods with only a 7% performance reduction.