benchmarkedInspired by Biological neuron spike trains

Spike-Timed Event Log

Counterpart to Payload-heavy event logs (JSON logs, structured logging)

Key Properties

O(1) amortized ingest with 17-81 byte events (26-48x compression vs JSON logs)
Real-time pattern classification via streaming ISI analysis (Tonic, Burst, Accelerating, Decaying, Silent, Irregular)
Pre-computed rate counters with O(1) dashboard queries
Population-aware correlation: synchrony detection, burst analysis, N-of-M integration
Segmented append-only storage with sparse stream indexing
Refractory period enforcement prevents ingestion flooding

Operation Complexity

Side-by-side comparison with the classical counterpart.

Operation complexity comparison
Operation	Spike-Timed Event Log	Payload-heavy event logs (JSON logs, structured logging)
ingest	O(1) amortized	O(1) amortized
rate query	O(1)	O(n) or pre-aggregated
pattern classification	O(1) streaming	N/A
population correlation	O(streams)	N/A
space per event	17-81 bytes	200-2000 bytes

Interface Preview

spike_timed_event_log.rs

rust

1pub struct SpikeStore { /* ... */ }
2
3impl SpikeStore {
4    pub fn new(config: SpikeStoreConfig) -> Self;
5    pub fn from_preset(preset: SpikePreset) -> Self;
6
7    // Tag registration
8    pub fn register_tags(&mut self, tags: &[(String, String)]) -> u64;
9
10    // Ingestion
11    pub fn ingest_presence(&mut self, tag_hash: u64, timestamp_ns: u64) -> Result<(), SpikeError>;
12
13    // Statistics
14    pub fn stats(&self) -> &StoreStats;
15}
16

spike_timed_event_log.ts

typescript

1class SpikeStore {
2  async init(): Promise<SpikeStore>;
3
4  // Tag registration
5  registerTag(key: string, value: string): number;
6
7  // Ingestion
8  ingestPresence(tagHash: number, timestampNs: number): void;
9
10  // Statistics
11  readonly totalEvents: number;
12  readonly totalTransitions: number;
13}
14

Where This Matters

Datadog / Splunk

Log Ingestion Pipeline

Improvement comparison
Today	With Spike-Timed Event Log	How
200-2000 byte structured log entries	17-81 byte spike events	Presence-class encoding; temporal pattern is the payload
Post-hoc pattern analysis (regex, ML)	Real-time 6-class pattern classification	Streaming ISI analysis classifies patterns during ingest
Rate dashboards require pre-aggregation pipelines	O(1) rate queries from pre-computed windows	Sliding rate counters maintained inline during ingest

Interactive Simulation

Simulation coming soon

An interactive visualization of Spike-Timed Event Log behavior will be available here.

View all simulations

Discussion

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