When network links were slow, cloud and distributed database systems could rely on generic kernel abstractions and treat network communication as a black box. With today's fast cloud networks, this approach breaks down: database performance becomes limited by the CPU overhead of the kernel TCP stack. Replacing TCP with user-space UDP can reduce this overhead, but it requires reimplementing essential guarantees, such as reliability and ordering. To solve this conundrum, database systems should no longer treat networking as a black box but co-design it with database operations. We propose the bi-channel paradigm for database systems, which separates communication into two channels: A high-performance data path for latency- and bandwidth-sensitive operations, and a reliable control path for coordination and recovery. We implement the paradigm by combining user-space UDP and kernel-based TCP, though other stack combinations are possible. This design exploits modern NIC capabilities while preserving TCP's reliability. We demonstrate the paradigm's efficiency and simplicity in two representative settings: a distributed shuffle saturating 200 Gbit/s with three CPU cores, and a replicated key-value store processing millions of messages per second.
Succinct tries are powerful string dictionaries because of their low memory footprint and fast query performance. However, existing succinct trie implementations face two key challenges to spatial locality: 1) they incur unnecessary cache misses during queries, especially during trie navigation operations, and 2) they waste significant space when the data contains many unary paths. We propose C^2, a set of two techniques: C_1 introduces a more cache-friendly layout for the \bv underlying succinct tries, and C_2 compresses redundant unary paths. We thoroughly redesign three state-of-the-art succinct tries: FST, CoCo-trie, and Marisa, producing C^2-FST, C^2-CoCo, and C^2-Marisa. Experiments on six diverse datasets show that the C_1 optimization improves query performance by 1.58x, 1.12x, and 1.42x, respectively, compared to the original FST, CoCo-trie, and Marisa. Furthermore, the C_2 optimization achieves a 1.3x smaller memory footprint on average. The succinct tries optimized with both aspects of C^2 achieve better space-time tradeoffs than their original versions and other state-of-the-art succinct tries, while using significantly less space than non-succinct tries like ART and C-ART.
Approximate nearest neighbor search under universal L_p metrics (ANNS-U-L_p) is an important and challenging research problem, as it requires answering queries under all possible p (0<p <= 2) values simultaneously without building an index for each possible p value. The state-of-the-art solution, called MLSH, is a Locality-Sensitive Hashing (LSH)-based ANNS method with barely acceptable query performance. In contrast, graph-based ANNS methods, which offer significantly improved query efficiency on the ANNS-L_p problem (with a fixed p-value), cannot be naively extended to the ANNS-U-L_p problem. In this paper, we propose U-HNSW, the first graph-based method for ANNS-U-L_p. Our scheme uses HNSW graph indexes built on two base metrics (L_1 and L_2) to generate promising nearest neighbors candidates, and then verifies these candidates with an early-termination strategy that substantially reduces the number of expensive L_p distance computations. Experimental results show that U-HNSW not only achieves up to 2670 times shorter query times than the original MLSH implementation running on a RAM disk (up to 15 times shorter than the idealized MLSH), but also outperforms the original HNSW on the ANNS-L_p problem (with a fixed p-value), except for a few special p values.
