Speedscale can observe traffic through a variety of means such as sidecars, Postman collections, or even on your local desktop. Data is sanitized for security, so no sensitive data is used.
Speedscale then intelligently parameterizes key fields such as timestamps, unique IDs, and customer/order IDs in order to replace them with realtime values during traffic replay for flexible, no-code reuse. Tokenization can be customized. Collected traffic can be browsed, filtered and selected in the Traffic Viewer to auto-generate tests and mocks from.
These containerized “Snapshots” can then be replayed anywhere (even on your laptop with no kubernetes cluster), an unlimited number of times. A Snapshot is essentially a traffic generator, a collection of mocks, or both! Traffic within snapshots can be multiplied, sped up or slowed down. Responses from the Mock pod can also be configured for 404’s, latency, and network black holes.
Speedscale can automatically detect and mock your dependencies.
Service mocks are simulators that accept outbound requests from your app and mimic responses coming back from 3rd parties.
Speedscale mocks contain PII-redacted, sanitized traffic so you don’t need to worry about sensitive data being used.
"chaos": {
"chaosPercent":25,
"badStatusCodes": true,
"intermittentResponses": true,
"randomLatency": true,
"randomHighLatencyMs": 5000
}
Traffic replay pods are ran locally in your clusters by a Kubernetes operator for traffic replay.
Test results are logged and sent to Speedscale for analysis and reporting. Pods are cleaned up afterward to return the cluster to the original state.
Speedscale can automatically detect and mock your dependencies.
Service mocks are simulators that accept outbound requests from your app and mimic responses coming back from 3rd parties.
Speedscale mocks contain PII-redacted, sanitized traffic so you don’t need to worry about sensitive data being used.
"chaos": {
"chaosPercent":25,
"badStatusCodes": true,
"intermittentResponses": true,
"randomLatency": true,
"randomHighLatencyMs": 5000
}
In the half-decade since gRPC became part of our production ecosystem, we’ve encountered a range of challenges and discovered a
Introduction In software testing or platform engineering, having realistic data is crucial. For years, teams have relied on Test Data
Product
