BUCKET Dataset Overview

• BUCKET Dataset is a term for bucketized data representations that partition requests, records, or keys into homogeneous groups to enable predictable memory behavior and efficient processing.
• It is applied across diverse domains such as LLM serving, secure computation, similarity search, and cloud storage, each tailoring bucket usage to optimize system throughput and resource management.
• Empirical studies report significant speedups, reduced overhead, and improved load balancing by dynamically adapting bucket boundaries to suit varying workloads and storage constraints.

Searching arXiv for papers relevant to “BUCKET Dataset” and bucket-based representations across domains. “BUCKET Dataset” is best understood as an Editor’s term for bucket-organized data representations rather than as a single standardized benchmark. Across the literature, “bucket” denotes a structural unit used to partition requests, records, vectors, keys, candidates, or ranked items into groups that can then be processed with bucket-level algorithms. In this sense, BUCKET Dataset refers to a family of representations: length-homogeneous request buckets in LLM serving, fixed-capacity buckets in oblivious sorting and perfect hashing, locality-sensitive hash buckets in high-dimensional similarity search, bucket orders for ranking data, and distance buckets for large-$k$ ANN result collection. A distinct use appears in astronomy, where ASTRONAUT is literally released as a public Amazon S3 bucket (Zheng et al., 23 Jul 2025, Asharov et al., 2020, Chen et al., 6 Apr 2026, Achab et al., 2018, Yin et al., 2 Apr 2026, Venterea et al., 22 Aug 2025).

1. Scope and canonical meanings

The literature does not present a single artifact universally named “BUCKET Dataset.” Instead, bucket-based representations recur in several technically different senses. In some papers, the bucket is a runtime partition of a live workload; in others, it is a fixed logical layout for secure computation, a discretization of a metric space, a compressed representation of ranking distributions, or a storage container in cloud infrastructure. This terminological multiplicity is not incidental: bucketization is typically introduced to trade fine-grained per-item processing for bounded local structure, predictable memory behavior, or cheaper candidate management (Zheng et al., 23 Jul 2025, Asharov et al., 2020, Chen et al., 6 Apr 2026, Achab et al., 2018, Venterea et al., 22 Aug 2025).

Domain	What is bucketed	Primary role
LLM serving	Requests by sequence-length range	Reduce padding and control memory
Oblivious sorting / MPHF	Keys or elements into fixed buckets	Localize placement and encode seeds
Similarity search / ANN	Vectors or candidates by code or distance	Probe, prune, and collect efficiently
Ranking data	Items into ordered subsets	Sparse approximation of permutation distributions
Cloud storage	Files in an object-store bucket	Public dataset distribution

A common misconception is that BUCKET Dataset must denote a conventional benchmark analogous to Alpaca or LongBench. That interpretation is explicitly contradicted in the BucketServe setting, where “bucket” is “not a dataset in the usual sense,” and in other works that likewise describe bucketized layouts or workload traces rather than a named benchmark corpus (Zheng et al., 23 Jul 2025, Asharov et al., 2020, Li et al., 25 Apr 2025).

2. Runtime bucketization in serving and distributed systems

In LLM serving, the bucket abstraction is a runtime organization of the request stream. BucketServe begins with a single bucket over the legal context range,

$B \gets \{[0, L_{\max})\},$

assigns each request by its input sequence length $S$, and then adaptively splits or merges buckets as the pending workload changes. The purpose is threefold: to reduce padding overhead, to make KV-cache usage predictable, and to enable memory-safe dynamic batch sizing. The safe memory budget is

$$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$

and the maximum safe batch size is defined by

$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$

Under mixed Alpaca–LongBench workloads, BucketServe reports up to $3.58\times$ throughput over UELLM, $1.93\times$ more request load than DistServe at $80\%$ SLO attainment, $1.975\times$ higher system load capacity than UELLM, average GPU utilization of $81.66\%$, and bucketing overhead below $B \gets \{[0, L_{\max})\},$0 of end-to-end execution time (Zheng et al., 23 Jul 2025).

This suggests that, in systems work, a BUCKET Dataset is often a dynamic workload model rather than a static corpus. The bucket boundaries $B \gets \{[0, L_{\max})\},$1, the request assignments $B \gets \{[0, L_{\max})\},$2, and the temporal evolution of bucket counts together form the effective dataset seen by the scheduler. Priority-aware scheduling then operates at bucket granularity, which changes the computational problem from scanning a flat request pool to selecting among a small number of homogeneous queues (Zheng et al., 23 Jul 2025).

A related but distinct systems setting appears in distributed deep learning with cloud storage. There, the “bucket dataset” is literal object-store residency: training samples remain in cloud storage buckets rather than on local disks. DELI shows that naive bucket access can make the training loop wait for data, but caching and pre-fetching reduce the time spent waiting by $B \gets \{[0, L_{\max})\},$3–$B \gets \{[0, L_{\max})\},$4 compared to loading directly from a storage bucket, while storing only a fraction of the data locally at a time (Krichevsky et al., 2021). Here the bucket is not a partitioning primitive over the data distribution; it is the remote storage substrate, and the main problem is I/O overlap rather than algorithmic grouping.

3. Buckets as algorithmic layout in secure computation, QRAM, and perfect hashing

In secure computation, the bucket is a fixed-capacity data layout. Bucket oblivious sort organizes an array of $B \gets \{[0, L_{\max})\},$5 encrypted elements into $B \gets \{[0, L_{\max})\},$6 buckets of size $B \gets \{[0, L_{\max})\},$7, with

$B \gets \{[0, L_{\max})\},$8

pads with dummy elements, and routes data through arrays $B \gets \{[0, L_{\max})\},$9 using repeated MergeSplit operations. The algorithm achieves $S$0 time, $S$1 client storage, and overflow probability

$S$2

In this context, a BUCKET Dataset is an array logically partitioned into fixed-size buckets and processed in a predetermined access pattern, which is precisely what yields obliviousness (Asharov et al., 2020).

Bucket brigade QRAM uses “buckets” in a different architectural sense: ancilla routing bits in a FANOUT–QUERY–FANIN circuit. In the parallel construction, the width is

$S$3

the T-count is

$S$4

and the depth is

$S$5

After address translation, the effective query depth is theoretical $S$6, while the price is $S$7 ancilla routing bits. The “BUCKET” here is therefore a routing-ancilla organization rather than a dataset partition, but it remains a bucket-based representation in the sense that state is encoded explicitly in bucket-indexed units (Paler et al., 2020).

Perfect hashing adds a third algorithmic meaning. PHOBIC keeps the bucket-placement paradigm of PTHash but derives an asymptotically optimal bucket-size distribution through the function

$S$8

Its second contribution, interleaved coding, stores seeds for the $S$9-th buckets from all partitions consecutively so that each bucket index can use a separately tuned compressor. PHOBIC reports $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$0 bits/key less space than PTHash for the same query time and construction throughput, and PHOBIC-GPU constructs a perfect hash function at $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$1 bits/key in $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$2 ns per key, queried in $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$3 ns on the CPU (Hermann et al., 2024). In this setting, BUCKET Dataset means a partitioned key set whose bucket statistics determine both construction work and compression entropy.

4. Bucketized similarity search and large-$$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$4 ANN

High-dimensional similarity search uses buckets to discretize geometry. In CE4HD, the dataset is

$$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$5

and Euclidean similarity range search asks for

$$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$6

The bucket structure is induced by E2LSH: $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$7 A query first probes its central bucket $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$8, then Hamming-neighbor buckets $$M_{\mathrm{safe}} = 0.9 \times M_{\mathrm{remain}},$$9, using progressive sampling and Chernoff-style confidence bounds to decide both how much to sample within a neighborhood and when to stop probing further neighborhoods. Accuracy is reported with

$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$0

This is a canonical example of a BUCKET Dataset as an indexed vector collection whose primary topology is the bucket graph induced by composite hash codes rather than the raw metric space (Chen et al., 6 Apr 2026).

Large-$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$1 ANN introduces yet another bucket layer: not on the dataset itself, but on the result collector. BBC partitions candidate distances into $$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$2 distance buckets

$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$3

and maintains a candidate superset rather than a continuously heap-maintained exact top-$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$4. Let $$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$5 be the smallest bucket index such that

$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$6

All buckets before $$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$7 are guaranteed members of the final answer; only the threshold bucket requires final selection. BBC combines this collector with two re-ranking algorithms: one for bounded quantization such as IVF+RaBitQ, which reduces the number of exactly re-ranked objects, and one for unbounded quantization such as IVF+PQ, which reduces cache misses through early re-ranking. On Wiki, C4, MSMARCO, and Deep100M, BBC accelerates existing quantization-based ANN methods by up to $$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$8 at recall@$$N_{\max} = \max \Biggl\{ N \in \mathbb{N} \;\Biggm|\; \sum_{i=1}^{N} S_i \leq \frac{M_{\mathrm{safe}}}{2 L H D B} \Biggr\}.$$9 for large-$3.58\times$0 queries (Yin et al., 2 Apr 2026).

The contrast between CE4HD and BBC is instructive. CE4HD bucketizes the dataset space and probes neighboring buckets to estimate cardinality; BBC bucketizes the distance-ranked candidate stream to reduce maintenance and re-ranking cost. Both are BUCKET representations, but they operate at different levels of the query pipeline (Chen et al., 6 Apr 2026, Yin et al., 2 Apr 2026).

5. Statistical, probabilistic, and combinatorial bucket models

In ranking statistics, buckets are ordered subsets of items that compress a distribution over permutations. For items $3.58\times$1 and a random permutation $3.58\times$2, a bucket partition

$3.58\times$3

induces a partial order in which all items in $3.58\times$4 are ranked above all items in $3.58\times$5 for $3.58\times$6, while within-bucket variability remains unconstrained. The distortion of a bucket order relative to the true ranking distribution $3.58\times$7 is

$3.58\times$8

where $3.58\times$9 is Kendall’s $1.93\times$0 distance and $1.93\times$1. This construction turns a full permutation distribution of dimension $1.93\times$2 into a bucket distribution of dimension

$1.93\times$3

The BUCKET Dataset here is a compressed statistical representation of ranking data, evaluated by an optimal-transport criterion rather than by retrieval or serving metrics (Achab et al., 2018).

Combinatorics supplies a structural analogue. Bucket increasing trees allow each node to contain up to $1.93\times$4 labels, with all internal nodes saturated. If $1.93\times$5 is the exponential generating function of a bucket increasing tree family, then

$1.93\times$6

with initial derivatives determined by the unsaturated-node weights $1.93\times$7. The paper develops stochastic growth processes for bucket recursive trees, $1.93\times$8-ary bucket increasing trees, and $1.93\times$9-plane oriented recursive bucket trees, and for bucket size two gives a bijection with increasing diamonds (Kuba et al., 2020). This suggests a BUCKET Dataset in the combinatorial sense is a multi-label, bucket-capacity-constrained growth object whose bucket loads and degree distributions are primary observables.

Probabilistic hashing highlights a cautionary aspect of bucketization. In the balls-into-buckets setting with $80\%$0 balls, $80\%$1 buckets, and only $80\%$2-independent hashing, there exists a $80\%$3-independent family such that the largest bucket size is

$80\%$4

with probability $80\%$5 when $80\%$6, and

$80\%$7

with probability $80\%$8 otherwise (Knudsen et al., 2015). For bucket-based datasets, this means that occupancy regularity cannot be inferred from low-order independence alone.

6. Literal storage buckets, bucket effects, and terminological caution

ASTRONAUT is the clearest case in which BUCKET refers to an actual public dataset delivered through a storage bucket. The ACT Collaboration released “A databaSe of millimeTeR ObservatioNs of Asteroids Using acT” as a public Amazon S3 bucket containing measurements for $80\%$9 asteroids observed by the Atacama Cosmology Telescope from $1.975\times$0 to $1.975\times$1, in bands centered near $1.975\times$2, $1.975\times$3, and $1.975\times$4 GHz. Each FITS table contains Time (Unix), Normalized Flux $1.975\times$5 (mJy), Flux Error (mJy), and Weight $1.975\times$6, with normalization

$1.975\times$7

In this context, BUCKET Dataset is literal cloud distribution infrastructure, not an algorithmic partitioning device (Venterea et al., 22 Aug 2025).

A further terminological complication appears in medication recommendation. “Combating the Bucket Effect: Multi-Knowledge Alignment for Medication Recommendation” introduces no dataset explicitly named BUCKET; instead, “bucket effect” denotes the fragmentation of multimodal drug knowledge as the number of modalities increases. MKMed addresses that regime by contrastively aligning five modalities into a shared structural space and reports improved recommendation accuracy on MIMIC-III and MIMIC-IV, but the paper is explicit that the relevant object is the bucket effect, not a BUCKET dataset artifact (Li et al., 25 Apr 2025).

Taken together, these usages delimit the term’s boundaries. BUCKET Dataset may denote a runtime partition, a fixed algorithmic layout, a statistical approximation, a result-collection buffer, or a literal object-store release. What unifies the term is not a shared benchmark, but a common design move: replacing unstructured per-item processing with bucket-level organization so that memory, scheduling, compression, pruning, or modeling can be controlled at the granularity of discrete groups rather than individual records.