Approximate set membership data structures (ASMDSs) are ubiquitous in computing. They trade a tunable, often small, error rate (ϵ\documentclass[12pt]{minimal}
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\begin{document}$$\epsilon $$\end{document}) for large space savings. The canonical ASMDS is the Bloom filter, which supports lookups and insertions but not deletions in its simplest form. Cuckoo filters (CFs), a recently proposed class of ASMDSs, add deletion support and often use fewer bits per item for equal ϵ\documentclass[12pt]{minimal}
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\begin{document}$$\epsilon $$\end{document}. This work introduces the Morton filter (MF), a novel CF variant that introduces several key improvements to its progenitor. Like CFs, MFs support lookups, insertions, and deletions, and when using an optional batching interface raise their respective throughputs by up to 2.5×\documentclass[12pt]{minimal}
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\begin{document}$$\times $$\end{document}, 20.8×\documentclass[12pt]{minimal}
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\begin{document}$$\times $$\end{document}, and 1.3×\documentclass[12pt]{minimal}
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\begin{document}$$\times $$\end{document}. MFs achieve these improvements by (1) introducing a compressed block format that permits storing a logically sparse filter compactly in memory, (2) leveraging succinct embedded metadata to prune unnecessary memory accesses, and (3) more heavily biasing insertions to use a single hash function. With these optimizations, lookups, insertions, and deletions often only require accessing a single hardware cache line from the filter. MFs and CFs are then extended to support self-resizing, a feature of quotient filters (another ASMDS that uses fingerprints). MFs self-resize up to 13.9×\documentclass[12pt]{minimal}
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\begin{document}$$\times $$\end{document} faster than rank-and-select quotient filters (a state-of-the-art self-resizing filter). These improvements are not at a loss in space efficiency, as MFs typically use comparable to slightly less space than CFs for equal ϵ\documentclass[12pt]{minimal}
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\begin{document}$$\epsilon $$\end{document}.
