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The reported run sent 1,000 HTTP requests over 10 seconds (100 requests per second). The process stayed at roughly 100–130% CPU for about 79 seconds before returning to idle, while memory was reported at about 50 MB idle and 150 MB after the flood. Those are publisher-reported results, not an independent lab benchmark, and they say nothing conclusive about long-lived SSH sessions, logging completeness, latency, or error rates.
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What the benchmark was trying to answer
The motivating question is blunt: “what happens to the listener process when a hundred connections arrive in the same second.” The EchidraOSS article frames that as a concurrency problem for cyber-deception systems: a honeypot must accept, process, classify, and record hostile traffic while the arrival rate spikes. Its central argument is that older designs can become resource-bound when connection handling is tied too closely to per-connection threads or blocking work.
That argument comes from the article published by Qyleron on DEV Community. The article describes EchidraOSS listeners and API endpoints as using Python 3.11 or newer with FastAPI’s asynchronous event loop and coroutine handlers. It presents that architecture as an alternative to a thread-per-connection model. The project-specific implementation details are claims made in that article; they are not independently confirmed here.
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Why asynchronous I/O can change the failure mode
Threads, blocking calls, and connection bursts
A conventional blocking listener often keeps a worker occupied while it waits for network input, a client response, or a database operation. If every connection receives its own thread, a burst can increase memory use and context-switching overhead even when most connections are idle. Thread pools avoid creating unlimited threads, but then the pool itself becomes a queue: once all workers are busy, new connections wait or fail.
An asynchronous event loop takes a different approach. A coroutine yields while it waits for network I/O, allowing the loop to service other sockets. This can keep many mostly idle connections in flight with fewer execution contexts. It does not make CPU work disappear, and it does not protect an application from blocking libraries, slow database writes, unbounded queues, or expensive request parsing.
What the article claims about EchidraOSS
Qyleron summarizes the intended effect this way: “The practical effect: the listener doesn’t spend a thread per connection, so connection count isn’t what drives its CPU cost.” That is an explanation of the design, not a universal law or an independently measured result. In a real deployment, CPU still depends on protocol parsing, classification rules, encryption, serialization, logging, and the database path.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteFastAPI’s async support also requires discipline. A synchronous function called from an async handler can block the event loop; a slow PostgreSQL transaction can create back-pressure; and a large in-memory queue can trade short-term responsiveness for a later memory spike. The useful question is therefore not whether a product is labeled asynchronous, but whether the complete request-to-storage path remains non-blocking under the workload you care about.
What EchidraOSS’s reported test measured
The source article reports one burst scenario. The figures below should be read as a description of that run, not as a certified capacity specification.
| Measure | Reported value | What it indicates | Important limitation |
|---|---|---|---|
| Workload | 1,000 HTTP requests over 10 seconds, or 100 requests per second | A short, fixed-rate HTTP arrival burst | Request rate is not the same as 1,000 simultaneous long-lived connections; payloads, keep-alive behavior, and response criteria were not fully specified. |
| CPU after the burst | About 100–130% for roughly 79 seconds before returning to idle | The process continued doing work after the ten-second input window | CPU percentages depend on the monitoring tool, host core count, and sampling method. The article does not provide enough detail to normalize the figure. |
| Idle memory | About 50 MB | Approximate baseline footprint in the reported setup | Python version, allocator behavior, loaded modules, and database connections affect the baseline. |
| Memory after the flood | About 150 MB | Approximate footprint while handling the burst and its aftermath | The article does not establish how quickly memory was released or whether queued work remained. |
| Described deployment | Docker Compose, PostgreSQL, one vCPU, and 1 GB RAM | A small self-hosted topology was considered viable for the test | It is not a guaranteed minimum, and the exact host, operating system, storage, database settings, and container limits are not supplied. |
The article also mentions a virtual private server costing about $4 per month. No provider, region, date, or recurring offer is verified, so that number should not be treated as a current hosting price.
What this test does not prove
A burst of HTTP requests is useful for finding one class of bottleneck, but it leaves several operational questions unanswered:
- Long-lived sessions: The result does not establish how many simultaneous SSH or interactive sessions can remain connected for minutes or hours.
- Acceptance and success rate: The article does not publish the percentage of requests accepted, completed, retried, or rejected, nor a definition of success.
- Logging completeness: It does not show whether every request produced a durable, correctly classified record in PostgreSQL.
- Latency: There is no latency distribution, timeout rate, or tail-latency measurement.
- Host conditions: CPU model, kernel, storage type, network path, container limits, and background load are not documented sufficiently for reproduction.
- Database effects: The test does not isolate listener work from connection-pool contention, transaction latency, indexes, or disk flushes.
- Controlled alternatives: Cowrie, Thinkst Canary, and generic listeners were not run with the same traffic, hardware, configuration, and success criteria.
Consequently, the result supports a narrow statement: EchidraOSS handled the article’s particular HTTP burst in the reported environment, with the reported CPU and memory behavior. It cannot support a broader statement that it has a fixed concurrency limit, that it never drops sessions, or that it outperforms every legacy honeypot.
How the named alternatives fit into a responsible comparison
The article names Cowrie, Thinkst Canary, and generic Twisted or raw-socket listeners as points of comparison. Its table uses directional estimates and public documentation or community deployment reports for those alternatives rather than a matched lab run. Twisted’s own documentation describes an event-driven framework and its reactor-oriented testing model; that general description does not establish a Cowrie ceiling or prove that an asyncio service will be faster than a tuned Twisted deployment. See the official Twisted documentation for the framework-level description.
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| Comparison axis | EchidraOSS evidence available | Cowrie evidence available in this material | What a fair test would still need |
|---|---|---|---|
| Protocol and interaction fidelity | The article focuses on listener and API behavior; protocol coverage is not quantified. | Not established by the cited performance material. | Identical protocol scenarios, command transcripts, banners, emulation features, and attacker workflows. |
| Concurrent sessions | No published number of simultaneous long-lived sessions. | No matched number. | A defined concurrency target, connection duration, acceptance rate, and failure condition. |
| HTTP workload | One reported 1,000-request, ten-second burst. | No same-host run. | Equal request rates, payloads, keep-alive settings, software versions, and repetitions. |
| CPU and memory | About 100–130% CPU for about 79 seconds after the burst; about 50 MB idle and 150 MB after the flood. | Directional estimates only, not matched measurements. | Time-series CPU, resident memory, event-loop lag, container limits, and host specifications. |
| Logging and classification | Database-backed deployment is described, but completeness is not reported. | Not established by this comparison. | Count every generated event and every durable record, then reconcile them. |
| Cost and operations | Docker Compose and PostgreSQL are described; the approximate VPS price is unverified. | No comparable cost calculation. | A named provider, region, date, storage, backups, traffic allowance, and administration time. |
Independent context from UHBS grades
The UHBS overview lists conformance grades, not throughput or reliability results. Its published pages identify the report set as historical relative to UHBS 5.0.1. The listed values are:
| Project | Quick grade | Full grade | Scope |
|---|---|---|---|
| EchidraOSS | 57.33 / D | 43.45 / F | Conformance score under the UHBS framework; historical report set. |
| Cowrie | 82.76 / B | 61.37 / D | Conformance score under the UHBS framework; historical report set. |
These numbers should not be converted into requests per second, concurrent sessions, CPU efficiency, or a claim that Cowrie is faster. They measure how each project met the UHBS framework’s criteria. Consult the UHBS published-grade overview for the scoring context and historical-status note. Conformance and load capacity are separate decision dimensions: a system can score well on one and poorly on the other.
How to run a fair high-concurrency honeypot benchmark
If you need an operational answer for your network, design the test around the failure you want to prevent rather than copying one headline number.
1. Define the workload and success criteria
- Choose the protocol or protocols: HTTP, SSH, Telnet, or a mixture.
- Specify whether you are testing short requests, keep-alive connections, or sessions that remain open for a fixed duration.
- Set an arrival rate and a concurrency target separately. For example, 100 requests per second and 500 open connections describe different pressures.
- Define success before the run: accepted connection, completed response, classified event, durable database record, and maximum permitted latency.
2. Make the testbed reproducible
Record the host CPU model and core count, RAM, operating-system version, kernel, container runtime, storage, network path, Python version, application commit or release, database version, configuration files, and container resource limits. Run the generator from a separate machine so its CPU does not compete with the honeypot. Keep the test network isolated from production systems and prevent the decoy from reaching internal assets.
3. Generate repeatable traffic
Use one load generator and one workload definition for every candidate. Preserve request paths, headers, payload sizes, connection reuse, authentication attempts, and session duration. Warm each deployment first, then run several identical trials with a cooldown period. Randomized attacker-like traffic can be a second scenario, but it should not replace a deterministic baseline.
4. Instrument the whole path
- Capture request and session counts at the generator.
- Measure accepted, rejected, timed-out, and completed operations.
- Record latency percentiles, not only an average.
- Collect process and container CPU, resident memory, file-descriptor count, network sockets, event-loop lag, and queue depth over time.
- Measure PostgreSQL connection-pool usage, transaction latency, lock waits, disk I/O, and error logs.
- Timestamp generated events and durable records so logging completeness can be reconciled.
Basic container and socket checks can be part of a runbook, but they are observations rather than benchmark results. For example, docker stats --no-stream gives a point-in-time container view, while ss -s summarizes socket state. Capture repeated samples or a time series if you want to explain a spike.
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Increase arrival rate in steps until a defined success criterion fails. Hold the load long enough to expose queues and database pressure, then stop sending traffic and measure recovery time. A service that accepts a burst but takes several minutes to drain it may be unsuitable for an alerting pipeline even if no connection is immediately refused.
6. Publish the limits with the result
Report every condition that could change the outcome: software versions, host specifications, protocol scripts, request sizes, database settings, repetitions, warm-up, cooldown, and the exact definition of success. Without those details, a percentage or request rate is difficult to reproduce and easy to misinterpret.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deployment requirements and practical safeguards
The EchidraOSS article describes a deployment built with Docker Compose and PostgreSQL, using Python 3.11 or newer for the listeners and API. It characterizes one vCPU and 1 GB of RAM as sufficient for its example setup. Treat those figures as the article’s tested configuration, not as a vendor-guaranteed minimum or a promise that your traffic will fit within it.
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A small decoy should still be operated like an exposed security system:
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- Place it in a segmented network or isolated cloud account.
- Deny egress by default and allow only the destinations required for telemetry.
- Use separate credentials for the honeypot and its database; rotate them if the host is suspected of compromise.
- Ship logs to storage outside the decoy so an attacker cannot erase the only copy.
- Set explicit container CPU, memory, process, and file-descriptor limits, then monitor for throttling.
- Back up configuration and database data without placing sensitive production data on the sensor.
- Define an incident procedure for a compromised host, including network isolation and evidence preservation.
The approximate $4-per-month VPS figure mentioned in the article has no named provider, region, date, or service terms attached to it. Hosting cost therefore needs to be calculated for your chosen provider and location, including storage, backups, bandwidth, and administration.
Troubleshooting when concurrency tests go wrong
Connections are refused before application logs appear
Check the operating-system file-descriptor limit, listen backlog, container networking, security-group rules, and the load generator’s own limits. A refused connection can occur before the application has an opportunity to classify or log it. Record socket states and kernel errors while repeating a smaller step load.
CPU remains high after traffic stops
Look for queued work, retry loops, synchronous calls inside async handlers, expensive classification rules, and database retries. Compare event-loop lag with database latency. If the queue drains slowly, lower the arrival rate and determine whether the bottleneck is parsing, storage, or downstream delivery rather than accepting sockets.
Memory climbs throughout the run
Inspect unbounded in-memory queues, retained request bodies, per-session state, logging buffers, and database-client pools. A single post-flood reading cannot distinguish a temporary queue from a leak; graph resident memory during the run and after a long idle period.
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Requests succeed but records are missing
Reconcile generator-side completions with database rows and application acknowledgements. Investigate transaction rollbacks, connection-pool exhaustion, asynchronous tasks cancelled during shutdown, and log forwarding failures. Acceptance at the network layer is not proof of durable evidence.
Results vary between repetitions
Check background processes, CPU frequency scaling, noisy neighbors on the host, cache state, database autovacuum, storage latency, and traffic-generator saturation. Repeat enough times to show the spread, and publish the variance instead of selecting the most favorable run.
Choosing a honeypot by evidence, not by a single rate
| Your priority | Question to ask | Evidence currently available |
|---|---|---|
| Absorb short HTTP bursts | Does the exact listener and database path sustain the required rate while meeting logging and latency targets? | EchidraOSS has one source-reported 1,000-request burst result; your own workload still needs testing. |
| Many long-lived SSH sessions | How many sessions remain connected, interactive, and fully recorded for the required duration? | Not established by the published HTTP test. |
| Protocol realism | Which banners, commands, file systems, authentication flows, and interaction artifacts must be emulated? | Not quantified in the cited benchmark; evaluate feature coverage separately. |
| Conformance or governance | Which framework controls and evidence requirements must the deployment satisfy? | UHBS provides conformance grades, but they are not performance scores. |
| Lowest operational burden | Can your team patch, monitor, back up, isolate, and investigate the chosen stack? | The article describes a Docker Compose and PostgreSQL topology; staffing and maintenance effort are not measured. |
For a production decision, score each candidate on protocol fidelity, concurrent-session success, complete logging, CPU and memory under your workload, deployment effort, and fully loaded cost. Keep those scores separate. Combining them into one unsupported performance ranking hides the trade-offs that matter during an incident.
Bottom line
EchidraOSS’s reported architecture and burst result make a plausible case for testing asynchronous listeners when connection arrivals are spiky. The evidence supports a narrower conclusion than the headline claim: the project handled one 100-requests-per-second HTTP burst in a small Docker-and-PostgreSQL setup, with the stated CPU and memory behavior. It does not establish a universal concurrency advantage over Cowrie, Thinkst Canary, or Twisted-based listeners, and UHBS conformance grades do not answer that performance question. A controlled, instrumented test that measures acceptance, latency, logging completeness, resource use, and recovery on identical hardware is the only defensible way to choose for your traffic.
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