Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: System and methods are provided for optimal error detection in programmatic environments through the utilization of at least one user-defined condition. Illustratively, the conditions can include one or more triggers initiating the collection of log data for methods associated with the provided at least one condition. Operatively, the disclosed systems and methods observe the run-time of the programmatic environment and initiate the collection of log data based on the occurrence of a condition trigger. A rank score can also be calculated to rank the methods associated with the defined condition to isolate those methods that have higher probability of causing the defined condition. Dynamic instrumentation of the methods associated with the user defined conditions during run time are used to calculate the rank score, which is used for ranking the methods.

Abstract: Techniques are described for sampling across trusted and untrusted distributed components. In accordance with embodiments, a first computing device receives a request from a second computing device, the first request including an operation identifier (ID) and a sampling ID that was generated by transforming a telemetry scope ID from a first value in a first domain to a second value in a second domain. The transformation may serve to anonymize and compress the telemetry scope ID. The first computing device determines whether or not to sample by comparing a ratio between the sampling ID and a size of the second domain with a sampling rate associated with the first computing device. The first computing device records telemetry about its processing of the first request in response to determining to sample and does not record any telemetry about its processing of the first request in response to determining not to sample.

Abstract: System and methods are provided for optimal error detection in programmatic environments through the utilization of at least one user-defined condition. Illustratively, the conditions can include one or more triggers initiating the collection of log data for methods associated with the provided at least one condition. Operatively, the disclosed systems and methods observe the run-time of the programmatic environment and initiate the collection of log data based on the occurrence of a condition trigger. A rank score can also be calculated to rank the methods associated with the defined condition to isolate those methods that have higher probability of causing the defined condition. Dynamic instrumentation of the methods associated with the user defined conditions during run time are used to calculate the rank score, which is used for ranking the methods.