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Are you testing distributed behavior – or just hammering endpoints?

Validate whole workflows under load — not just individual requests.
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Can your test suite catch what breaks between services – not just at the surface?

A 200 response confirms the API accepted the call — nothing more. Whether the message was consumed, the record written, or concurrent users corrupted shared state is a different question entirely.

Here’s how QALIPSIS helps QA engineers overcome the most critical testing challenges.

Why does your test suite stay green while the queue falls behind?

  • The API accepts the request and returns 200. Meanwhile the message broker queue is backing up — the consumer service cannot keep pace under peak load. No test is watching the downstream side, so the queue keeps growing, processing falls further behind, and by the time someone notices, a large backlog has accumulated. The test suite showed green throughout.
  • QALIPSIS models the message path as steps inside the scenario. The API call triggers first; then the Kafka or RabbitMQ plugin consumes the downstream message and a join operator correlates the original request with its observed outcome. If the message never arrives — or arrives outside the expected window — the assertion fails at the exact step, with an event recording when and why.
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Which shared resource breaks when a thousand users write at the same time?

  • The platform works correctly with one user. With hundreds of simultaneous writes to the same resource — inventory decrements, reservation commits, profile updates — silent data corruption appears. Race conditions only surface under concurrent load, and sequential test execution never reproduces them. They remain invisible until a promotional event brings them out in production against real customer data.
  • QALIPSIS runs multiple minions through the same write path simultaneously under a realistic load profile. After each stage, the database plugin verifies that persisted records reflect the expected state — no duplicates, no dropped writes, no last-writer-wins overwrites. Join operators match each submitted request against its stored outcome, making divergences visible at the step level rather than in a post-incident audit.
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Which step in the chain is silently eating your latency budget?

  • Each individual service looks fast in isolation. But the user experience depends on a chain — authentication, entitlement check, content lookup, session creation — and the SLO applies to the whole sequence, not any single call. Without a scenario that exercises the chain under load, test coverage is a collection of unit benchmarks that says nothing about actual user experience at peak.
  • QALIPSIS models the full journey as an ordered sequence of HTTP steps, reusing the same connection across steps within a single minion’s execution. Each step is instrumented with meters, and verify steps assert that time-to-last-byte stays within the SLO budget. Exported per-step latency distributions make it clear which step in the chain degrades first as load increases.
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Did the last regression ship because the error rate stayed at zero?

  • Load tests run in CI, the error rate stays at zero, and the build passes. A service degraded to ten times its normal latency on every request throughout the campaign, every call eventually resolved, and nothing failed the build. The regression shipped. Error rate is not a proxy for latency correctness or functional accuracy — it just means the server responded.
  • QALIPSIS integrates into the build via the Gradle plugin. Assertion thresholds are defined per step — on latency percentiles, error rates, and functional outcome verification — and JUnit-style reports are exported for the pipeline to consume. Any breach fails the build with a report identifying the step, the failure type, and the violated assertion, giving the team an actionable signal rather than a binary exit code.
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Why are your test results stuck inside the test tool?

  • Test results live inside the test tool. The team’s dashboards run in Grafana over InfluxDB, or Kibana over Elasticsearch. After every campaign, someone manually extracts metrics or screenshots charts. Comparing performance across releases is guesswork, and overlaying test signals with infrastructure metrics to find correlations is simply not possible in that workflow.
  • QALIPSIS exports step-level events and meters to the backends your team already operates — Graphite, Elasticsearch, Kafka, TimescaleDB/PostgreSQL, or InfluxDB. Every data point is tagged by campaign, scenario, step, and zone, making it possible to filter by test run, track regressions across releases, and correlate load test signals with infrastructure behaviour in the same dashboards your team uses every day.
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Performance testing made for QA engineers

With QALIPSIS, you can:

Test event-driven, async architectures with ease

Scale load tests on demand

Simulate complete user journeys

Automate validation in every release cycle

What you need to know

Confident testing. Faster releases. Better software.
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Can you stress-test distributed infrastructure and still see what is happening inside it?

Engineered for distributed load. Built for operational visibility.

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Can you generate distributed load and observe your infrastructure while it is happening?

Frequent releases. Distributed systems. Complex integrations. In today’s fast-paced environments, testing can’t slow you down – but it can’t fall short either.

Here’s how QALIPSIS helps DevOps teams deliver reliable, high-performing software at scale.

Can your load generator keep up with the infrastructure it is supposed to stress?

  • A single-machine load generator saturates its own network interface before it stresses the system under test. Running from one location also hides zone-level routing and latency differences that only appear when traffic arrives from multiple origins simultaneously — exactly the condition that causes degradation during a global traffic event.
  • Deploy factories across the zones you control, assign each a factory.zone, and trigger campaigns via the REST API with a per-scenario zone percentage split. The head orchestrates execution and collects results; factories inject load independently. Enable step-level monitoring on the steps that represent your critical service boundaries and export meters and events to your telemetry backend for live analysis during the run.
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What was your system doing at minute twelve of the load test?

  • Most load tools produce an internal report you read when the run is over. But the questions that matter operationally — at what point did the queue start lagging, which zone saw latency diverge first, did the connection pool saturate before or after the error rate climbed — require time-series data correlated with your infrastructure metrics, in the same tool your team is already watching while the test runs.
  • Enable monitoring per step — monitoring { events = true }, monitoring { meters = true }, or monitoring { all() } — only where signal is needed, keeping data volume intentional. Export to Graphite, Elasticsearch, Kafka, TimescaleDB/PostgreSQL, or InfluxDB. Add factory.tags to carry environment, team, or project metadata through to every exported data point, so test runs are distinguishable in shared dashboards alongside production signals.
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How often does your infrastructure load test actually run?

  • Infrastructure load tests that require manual setup and manual interpretation are not tests — they are experiments. They run infrequently, produce results no one has time to compare to the previous run, and get skipped entirely under release pressure. The value comes from running them repeatedly and automatically against a consistent baseline, exactly the way other pipeline stages work.
  • Trigger campaigns programmatically via the REST API (POST /campaigns) with parameterised payloads — minion counts, zone distributions, execution profiles — and retrieve results via GET /campaigns/{campaign-key} to wire them into your pipeline logic. Schedule recurring campaigns (POST /campaigns/schedule) with time zone-aware recurrence for nightly or pre-release baseline runs. For Gradle-based pipelines, the Gradle plugin provides qalipsisRunAllScenarios or a custom RunQalipsis task with JUnit report export alongside.
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QALIPSIS: Elevating software quality for DevOps teams

With QALIPSIS, your team can:

Detect bottlenecks

Database compatibility

Automate validation in your CI/CD

Release with confidence every

What you need to know

Boost software quality. Streamline DevOps workflows.
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How do you protect revenue and reputation when your platform is under pressure?

Turn performance risk into evidence: run repeatable load campaigns, quantify supported load vs. infrastructure size, and forecast operational cost with confidence – without “guess-and-overprovision.”

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Can you make a go/no-go release decision with confidence?

Platforms fail under load in ways that only appear when every layer is stressed at once. By the time a peak event exposes the gap, the cost is real — and already paid.

Poor performance isn’t just technical – it’s a business liability. Here’s how QALIPSIS helps leaders make smarter, faster, and safer decisions.

What will your platform do when a million users arrive at once?

  • A salary-day surge, a flash sale, a product launch — every predictable high-traffic event can expose weaknesses invisible at normal volumes. By the time an incident is declared, the damage is done: sessions dropped, transactions abandoned, support queues flooded, and engineering pulled away from roadmap work.
  • QALIPSIS runs load campaigns that reproduce the exact traffic shape of those events — steep ramp-up, sustained maximum, tail-off — across the full system stack. Scenarios validate API availability, messaging throughput, and data consistency simultaneously, so a bottleneck anywhere in the chain surfaces before it reaches real users.
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Why does it take days to find which service caused the incident?

  • When a performance incident surfaces, the first question is always which service caused it. Without precise data from a load run that mirrors production conditions, the investigation is archaeology — combing through logs and metrics from a system already under distress, with engineers spending days narrowing down what went wrong where.
  • QALIPSIS captures events and meters at the exact point in the scenario where failures occur, tagged by campaign, scenario, step, and zone. Results are accessible via the GUI or REST API, giving teams a precise starting point for resolution rather than a full-system audit.
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How many releases shipped without a real performance check?

  • Performance testing tends to become the gate that holds releases hostage: it runs late in the cycle, takes days to complete, and produces results that are hard to act on under pressure. Skipped once, it gets skipped again — and risk accumulates silently across releases until something breaks in production.
  • QALIPSIS scenarios live alongside application code and execute automatically through CI/CD pipelines. Smoke tests run on every build; full load suites run on a nightly or weekly schedule. Any breach of an assertion threshold fails the build automatically with a report identifying exactly what failed and where — quality gates enforced without a manual step.
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Are you overprovisioning because the last peak scared you?

  • Infrastructure spending is often driven by incident memory. Teams overprovision defensively after a difficult peak, or underprovision and discover the limit in production. Neither approach produces a clear answer to how much infrastructure is actually needed for the next growth step — or what the cost of that headroom will be.
  • Repeatable load campaigns under progressively increasing load produce concrete correlation between infrastructure configuration and supported concurrent capacity. Cluster mode scales load injection by adding factory nodes, making it possible to test at the scale you plan to operate before committing to the infrastructure cost.
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Why are users in some regions converting less than others?

  • A service deployed in a single region adds latency for every user outside that geography — and that latency shows up as session abandonment and conversion drop, not as an error anyone reports. The problem accumulates quietly until it is large enough to be visible in business metrics, long after the architectural decision that caused it.
  • Factories can be deployed in the zones your organisation controls and assigned zone eligibility. Campaigns distribute load across zones and export metrics tagged by zone, giving teams visibility into regional performance differences under realistic concurrent load — before they affect customers.
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QALIPSIS: Built for bold businesses that scale

With QALIPSIS, you can:

Prevent downtime before it happens

Optimize customer experience with real-time insights

Scale infrastructure cost-effectively

Ensure readiness for global markets

Advanced analysis dashboards

What you need to know

Launch faster with confidence. Don’t leave scalability to chance.
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Does your system fail between microservices – and do your tests even see it?

QALIPSIS helps software developers model event-driven paths, scale load across zones, and validate downstream outcomes with step-level observability – in CI or at scale.

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Can your test scenarios reach past the API layer into the system that runs behind it?

The message still has to be enqueued. The record still has to be written. The inventory decrement still has to survive a thousand concurrent writers. These failures are invisible to anything that stops at the HTTP boundary.

Let’s explore the top testing challenges developers face today – and how QALIPSIS solves them.

How far behind is your consumer before the first error appears?

  • The service returns 200 and the scenario moves on. What it does not see is that the message consumer is falling behind — processing one message per second while the producer is pushing ten. The lag accumulates invisibly until the queue backs up far enough to cause visible failures. By then, the window to catch it in testing has long passed, and the fix has to happen in production.
  • Model the message path as steps in the scenario: produce the trigger via the API, then consume the downstream message using kafka().consume or rabbitmq().consume. A join operator correlates the original request with its corresponding consumed message — so if the message arrives late, out of order, or not at all, the assertion fails at the exact step with a timestamped event. Enable step monitoring to export consumer lag as a meter over time.
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What actually happened after the API returned 200?

  • The API returns the right status code. The message was dispatched. But the database record was never written — or was written with a stale value because two concurrent transactions both read the same state before either committed. The bug is real, reproducible under load, and completely invisible to any test that stops at the HTTP response.
  • Place assertions as steps inside the workflow, immediately after the interaction that should produce an effect. Use a join to correlate the submitted request with the persisted outcome — database record, cached value, or downstream event — and assert on the combined record. A divergence fails at the exact step where propagation broke, with an event capturing what was observed versus expected and when it happened.
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What does your load test miss when it runs from a single machine?

  • Running all load from one machine produces results that reflect that origin’s network path — not the experience of users distributed across geographies. Cross-region routing, zone-specific connection pooling, and geographic service affinity all behave differently when traffic arrives from multiple origins simultaneously. A single-location test passes; a multi-zone run finds the regional session service that sits thousands of milliseconds away from half its users.
  • Deploy factories in the zones you control, assign each a factory.zone, and trigger the campaign via the REST API with a per-scenario zone percentage split in the payload. Exported meters and events are tagged by zone, so latency distributions, error rates, and throughput can be compared across regions in your existing telemetry backend — and geographic asymmetries surface before they affect users.
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How did a 10× latency regression ship through a green pipeline?

  • The scenario runs in the pipeline, exits zero, and the build passes. But the campaign report lives inside the tool, the JUnit file was never configured, and the only signal the pipeline received was “process exited cleanly.” A service degraded 10× under load throughout the run, every request eventually returned 200, and nothing failed the build. The regression shipped with a green pipeline.
  • Use --autostart for non-interactive runs that stop nodes after completion. In Gradle-based pipelines, the qalipsisRunAllScenarios task or a custom RunQalipsis task publishes JUnit reports configured under report.export.junit.*. The build fails on assertion breaches, the JUnit file is available as a pipeline artefact, and campaign results are retrievable via GET /campaigns/{campaign-key} for integration with your own tooling.
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Which step failed — and why does the report not say?

  • The campaign fails. The report says “campaign status: failed.” Which step regressed? Was it latency, error rate, or a functional assertion? Was it one minion or all of them? Without step-level data, every failed campaign triggers the same manual investigation — pull logs, compare metrics, correlate timestamps — the same archaeology every time a regression surfaces.
  • Enable monitoring { events = true } and monitoring { meters = true } (or monitoring { all() }) on the steps where signal matters, not globally, to keep data volume intentional. Export to the backend your team already queries. Configure report publishers to send Slack or email notifications on selected campaign statuses — so the right people know immediately which campaign failed and can open a step-level report rather than starting from scratch.
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Enterprise-grade testing for modern software

With QALIPSIS, software teams can:

Simulate real-world traffic across async, event-driven systems

Run performance and load testing for cloud-native applications

Automate testing within DevOps workflows

Gain real-time insights to boost performance and reliability

What you need to know

Build better software. Test smarter. Ship with confidence.
Start testing today