🛡️ Loggr: A Real-Time Logging Engine as a Weapon Against DDoS Attacks

Introduction

Distributed Denial of Service (DDoS) attacks remain one of the most persistent and costly threats in cybersecurity. They overwhelm infrastructures, obscure visibility, and often leave defenders blind at the very moment they need reliable data the most.

The key to detecting, understanding, and countering these attacks lies in something often underestimated: logs.

Traditional logging systems struggle under pressure. They sample, drop events, or rely on approximate timestamps that make it impossible to faithfully reconstruct the timeline of an attack.

This is precisely the challenge that Loggr was designed to address. Loggr is a high‑performance logging engine capable of ingesting hundreds of millions of events in seconds on standard hardware. Beyond raw throughput, it introduces a critical innovation: absolute temporal fidelity, essential for detection, traceability, and post‑mortem analysis in cybersecurity.

🔍 Real-Time Detection

A DDoS attack is defined by a sudden surge of activity: millions of requests flooding in within seconds.

With conventional pipelines, many events are lost or delayed.
With Loggr, ingestion rates push hardware to its limits — tens of millions of logs per second on commodity machines — ensuring that all traffic is captured as long as the system is not saturated.

Result: security teams can spot anomalies instantly, even before downstream SIEMs or dashboards have processed the data. Loggr acts as a first‑line sensor, maximizing visibility.

🧾 Traceability and Forensics

During an attack, every event matters. Who hit the system, when, and how often?

Loggr records all events that the hardware can absorb, without sampling.
Logs are compressed and stored with a predictable footprint, enabling full retention of the attack for later analysis.

This near‑exhaustive capture is critical for:

Compliance (proving what happened).
Forensic investigations (identifying vectors and patterns).
Proactive defense (training detection models on real attack data).

🎞️ Replay and Post-Mortem

Once the attack is over, the post‑mortem begins. Without reliable logs, it is impossible to replay the exact sequence of events.

Loggr stores events in a strictly deterministic order.
Teams can replay the attack event by event, as if watching it unfold again.

This enables:

Identifying bottlenecks.
Understanding attack propagation.
Strengthening defenses for the future.

🕒 Absolute Temporal Fidelity: Beyond Timestamps

Most logging systems rely on timestamps (milliseconds or microseconds). Under heavy load, multiple events share the same timestamp, making it impossible to know which came first.

Loggr takes a radically different approach:

Each event is assigned an atomic inter‑thread sequence number, strictly increasing across all threads.
Even if two events occur in the same microsecond, they are differentiated and ordered.
This guarantees absolute temporal fidelity, without ambiguity.

In practice, this means that during a DDoS, when millions of requests hit simultaneously, Loggr can still reconstruct the exact order of events — as long as throughput remains within hardware capacity. If saturation occurs, losses are possible, but Loggr pushes those thresholds far beyond traditional solutions.

⚙️ Why It Works

Loggr achieves these results through several design choices:

Preprocessing + compression: entropy reduction before LZ4, achieving up to 5× compression without sacrificing speed.
Lock‑free pipelines: eliminating contention, ensuring no bottlenecks even under extreme load.
Predictable footprint: runs on standard hardware, no exotic infrastructure required. The minimal footprint is 20MB (stable), and allow the capture of 1.5 to 8M+ events per second

📌 Positioning in the Security Ecosystem

Loggr is not meant to replace a SIEM or full observability platform. Instead, it acts as an upstream buffer:

Capture: massive, reliable ingestion.
Compression: reducing volume before storage or transfer.
Forwarding: sending data to existing tools (Splunk, Elastic, Datadog, etc.).

By reducing volume at the source, Loggr makes downstream tools more efficient and cost‑effective.

Conclusion

In cybersecurity, visibility is survival. During a DDoS, losing logs means losing the ability to detect, respond, and learn.

With Loggr, no event is lost as long as the hardware holds the load. Detection is immediate, traceability is maximized, and post‑mortems are faithful to reality thanks to absolute temporal fidelity.

This is not just a logging engine: it is a strategic weapon against one of the oldest and most persistent threats in the digital landscape.

As data volumes continue to grow, upstream compression and absolute temporal fidelity will become essential pillars of resilient cybersecurity pipelines.

Architecture overwiew and detailed benchmarks are available here -> benchmarks and overview