Fast Information Streaming Handler (FisH)

• FisH is a unified framework for real-time streaming that integrates architectural, algorithmic, and system approaches to process heterogeneous data.
• It employs hashing-based sketches, epoch-driven load balancing, and SQL-enhanced pipelines to optimize memory usage and reduce latency.
• In applications like earthquake early warning, FisH leverages streaming neural networks for rapid, accurate multi-task inference.

A Fast Information Streaming Handler (FisH) refers to a set of architectural, algorithmic, and systems approaches explicitly designed for both high-throughput and low-latency processing of time-evolving, heterogeneous, and often real-time data streams. FisH emphasizes scalable stream manipulation, load balancing, efficient resource usage, and—in domain-specific cases such as seismology—end-to-end simultaneous inference tasks. The term “FisH” is used to denote unified solutions leveraging recent advances in multi-core communication primitives, hashing-based sketching, SQL-based declarative query frameworks, scalable key-grouping, assured fusion and code generation, and streaming neural networks for structured or scientific real-time data.

1. Architectural Paradigms and Communication Foundations

FisH architectures inherit and extend several foundational stream processing principles. On multi-core systems, frameworks such as FastFlow implement lock-free Single-Producer-Single-Consumer (SPSC) queues and compose higher-level Multiple-Producer-Multiple-Consumer (MPMC) channels, mediated by dedicated Emitter and Collector entities to circumvent memory fence and atomic operation overhead (0909.1187). These primitives underpin farm and pipeline skeletons, facilitating not only linear scalability but also order preservation and adaptive scheduling.

In distributed and hardware-accelerated deployments, FisH leverages hashing-based “sketches” to compress high-dimensional traffic streams with low memory. For instance, heavy-hitter detection utilizes reversible permutations and multi-dimensional hash histograms applied to permuted key spaces, allowing identification of anomalous patterns (e.g., DDoS) in high-speed networks via constant-memory, constant-update-time histograms suitable for FPGA implementation (Kallitsis et al., 2014).

When streaming data in High-Performance Computing (HPC) workflows, FisH exploits direct application-to-application communication via engines such as SST in ADIOS2, which transfer data using RDMA or MPI-based data planes instead of filesystem intermediaries. SST exposes a file-like API but internally manages data queues, asynchronous block retrieval, and per-step metadata coordination, thereby dramatically increasing effective throughput over file-based IO (Eisenhauer et al., 30 Sep 2024).

2. Task Grouping, Load Balancing, and Memory Efficiency

Efficient load balancing and state management are central to FisH, particularly for time-evolving key distributions. The grouping algorithm known as FISH introduces epoch-based recent hot key identification, utilizing intra-epoch frequency counting to retain only the most frequent keys per epoch and inter-epoch decaying to attenuate old activity using a decay factor $\alpha$ (Huang, 2018):

$c_v \leftarrow c_v \times \alpha$

Keys classified as “hot” are dynamically assigned to multiple candidate workers via logarithmic formulas:

$$d = W_{num} / 2^{\left\lfloor \log_2\left( \frac{f_{top}}{f_k} \right) \right\rfloor}$$

The approach maintains low memory overhead by restricting key storage to bounded-size lists and suppressing unnecessary state replication, achieving reductions in latency (up to 87.12% vs. W-Choices) and memory overhead (up to 99.96% vs. Shuffle Grouping).

In distributed SQL-based stream processing, FisH models extend SQL semantics with stream-specific syntax (e.g., STREAM keyword, HOP, and TUMBLE functions) to enable windowed aggregation and declarative event querying. Backend systems such as Apache Samza and Kafka provide partition-based parallelism, incremental checkpointing, and out-of-order event handling, all abstracted behind a declarative query pipeline (Pathirage et al., 2015).

3. Efficient Algorithms and Sketches for High-Speed Streams

FisH incorporates algorithmic modules for rapid anomaly detection and summarization:

• Simple Hashing Pursuit (SHP): Applies $q$ complete hash functions post-reversible permutation; updates histograms for each incoming packet and reconstructs heavy hitters via inverse mapping (Kallitsis et al., 2014).

$$\tilde{\omega}^* := \text{inverse}(\tilde{o}_1, \ldots, \tilde{o}_q)$$

• Max-Count Pursuit: Mitigates collision risk using hash-thinning into $m^\prime$ independent sub-streams.
• Max-Stable Hashing Pursuit (MSHP): Uses properties of 1-Fréchet distributions to accurately estimate set cardinalities, especially for port/host scanning detection.

$\xi =_{\mathcal{D}} nZ$

Because these algorithms require only a few hundred words of RAM and operate via bitwise operations, they are suitable for hardware deployment and distributed aggregation.

4. Stream Processing Libraries and Code Fusion

Declarative FisH implementations, such as the Strymonas library (Kiselyov et al., 2022), achieve “complete fusion” of stream combinators—map, filter, zip, flatmap—such that no intermediate data structures or closures are introduced. This results in hand-written state machine performance, with support for infinite and finite streams, stateful accumulation, compression, and windowing. Assured code generation for OCaml and C (sometimes via MetaOCaml) guarantees statically fused pipelines, enabling immediate vectorization and low-GC behavior. A typical fused computation for sum-of-filtered squares is:

int fn() { int acc = 0, count = N, num = start; while (count > 0) { int value = num * num; num++; if (value satisfies condition) { count--; acc += value; } } return acc; }

These properties facilitate FisH deployment in real-time analytics, embedded systems, and digital signal processing.

5. Unified Streaming Neural Networks for Real-Time Scientific Tasks

In scientific applications like earthquake early warning (EEW), FisH denotes unified architectures that simultaneously perform multiple structured inference tasks from streaming sensor data. The FisH model (Zhang et al., 13 Aug 2024) integrates phase picking, location estimation, and magnitude estimation into a single neural network pipeline for single-station seismic signals:

• Embedder: MultiScalerLayers construct high-dimensional wave embeddings via parallel convolutions and absolute values.
• Encoder (RetNet backbone): Self-regressive retention mechanism provides efficient parallel training and stateful recurrent inference with update:

$$H_{c,a}^{n+1} = \lambda H_{c,a}^n + k_a^{n+1} v_{n+1}^c$$

• Decoder: Maintains a memory bank for recent time steps to support convolutional phase picking and direct regression heads for location/magnitude.

FisH achieves high accuracy and low latency. For example, on STEAD datasets the model yields an F1 score of 0.99 (P-wave) and 0.96 (S-wave), a final location error of 6.0 km, magnitude absolute error of 0.14, and—within 3 seconds after P arrival—location/magnitude errors of 8.06 km and 0.18, respectively. Efficient O(1) per-step inference cost allows deployment on edge hardware. The joint learning of nonlinear dependencies between seismic inference tasks distinguishes FisH from mainstream cascaded or modular seismology systems.

6. Deployment, Applications, and Impact

FisH approaches have been operationalized in diverse domains:

• Parallel multi-core analytics (bioinformatics, log processing): FastFlow-inspired mediator/task-farm architectures provide robust scalability even for high-variability, fine-grain workloads (0909.1187).
• Network anomaly and heavy hitter detection: Hash-sketching and max-stable pursuit algorithms support near-real-time DDoS, scan, and abuse detection in hardware and distributed deployments (Kallitsis et al., 2014).
• Time-evolving stream data: The FISH algorithm provides robust load balancing and memory efficiency for social media, recommendations, and sensor networks (Huang, 2018).
• Declarative streaming analytics: SQL-based FisH pipelines allow succinct real-time queries over financial, IoT, and e-commerce event streams, improved developer turnaround, and cross-platform portability (Pathirage et al., 2015).
• HPC scientific workflows: SST in ADIOS2 dramatically boosts simulation-to-analysis/model coupling by streaming at bandwidths significantly above the file system theoretical peak, with dynamic queueing and step management (Eisenhauer et al., 30 Sep 2024).
• Seismic EEW: FisH neural models facilitate single-station, rapid response earthquake warning with high reliability and end-to-end optimization (Zhang et al., 13 Aug 2024).

7. Future Directions and Research Opportunities

There is a growing trend towards unifying stream processing models and integrating multi-modal, multi-task learning under a FisH framework. A plausible implication is the further fusion of high-level declarative languages with hardware-efficient primitives, enabling broader adoption in scientific workflows, monitoring infrastructure, and edge AI. The extension to multi-station fusion and additional downstream tasks (e.g., focal mechanisms in seismology or multi-omic integration in bioinformatics) promises enhanced accuracy and resilience across heterogeneous environments. Techniques such as transfer learning and dynamic architecture adaptation may expand FisH utility to global scale, time-varying, and cross-domain applications.

FisH thus constitutes a convergence of algorithmic efficiency, scalable systems engineering, and unified model architectures for real-time, distributed, and scientific data streaming tasks.