A method of operating a telemetry system includes automatically populating a base field of a schema in an event definition using a logging library of the telemetry system for an event of an instrumented application, and automatically populating a conditional field of the schema in the event definition using the logging library in response to a selected condition for the event.

Techniques related to communication between independent containers are provided. In an embodiment, a first programmatic container includes one or more first namespaces in which an application program is executing. A second programmatic container includes one or more second namespaces in which a monitoring agent is executing. The one or more first namespaces are independent of the one or more second namespaces. A monitoring agent process hosts the monitoring agent. The monitoring agent is programmed to receive an identifier of the application program. The monitoring agent is further programmed to switch the monitoring agent process from the one or more second namespaces to the one or more first namespaces. After the switch, the monitoring agent process continues to execute in the second programmatic container, but communication is enabled between the application program and the monitoring agent via the monitoring agent process.

This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

Example techniques for adaptive time window-based log message deduplication are described. In an example, message values are obtained from received log messages. Further, the number of log messages received in a time window having a message value is counted. A log message from which the message value is obtained and the counted number are transmitted upon expiry of the time window. A length of a time window in which a subsequent counting of log messages is to be performed is determined based on various parameters.

A computing system is configured to derive insights related to asset operation and present these insights via a GUI. To these ends, the computing system (a) receives data related to the operation of assets, (b) based on this data, derives a plurality of insights related to the operation of at least a subset of the assets, (c) from the insights, defines a given subset of insights to be presented to a user, (d) defines at least one aggregated insight representative of one or more individual insights in the given subset of insights that are related to a common underlying problem, and (e) causes the user's client station to display a visualization of the given subset of insights including (i) an insights pane that provides a high-level overview of the subset of insights and (ii) a details pane that provides additional details regarding a selected one of the subset of insights.

A method for managing provenance information associated to one or more interconnected provenance entities in a provenance system for data processing pipelines in a distributed cloud environment over a network interface, wherein each of the data processing pipelines is configured to read in data, transform the data, and output transformed data is disclosed. The method comprises steps being performed by a configuration component of obtaining at least one declarative intent representing a configuration indicative of requirements and levels of priority for storage of provenance information for each of the data processing pipelines, deriving the requirements and levels of priority for storage of provenance information for each of the data processing pipelines based on the obtained at least one declarative intent, wherein one of the levels of priority—first level of priority—is higher than the other levels of priority—second levels of priority, estimating storage capacity for storage of provenance information in the provenance system based on the derived requirements and levels of priority, storing the provenance information according to the derived requirements and levels of priority for storage of provenance information and for each of the data processing pipelines, and when actual storage consumption for storage of provenance information in the provenance system meets a threshold of storage capacity set based on the estimated storage capacity: reducing a data amount for storage of provenance information of the second levels of priority in the provenance system. Corresponding computer program product, arrangement, configuration component, and system are also disclosed.

Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.

A method for error management is provided. The method comprises receiving a message call request regarding an error event generated by a software application. The message call request comprises a message ID associated with an error type. In response to the call request a message cache is searched for the message ID. If the ID is in the cache, an error message associated with the ID is returned. The error message provides a description of the error and suggested remedial action. If the message ID is not in the cache, the error message is fetched from a message repository that contains error messages corresponding to respective message IDs. The fetched error message is loaded into the cache and returned. Message call request data is stored in a metrics repository. The message call request data comprises frequency metrics that describe how often the message ID is received.

Example techniques for adaptive time window-based log message deduplication are described. In an example, message values are obtained from received log messages. Further, the number of log messages received in a time window having a message value is counted. A log message from which the message value is obtained and the counted number are transmitted upon expiry of the time window. A length of a time window in which a subsequent counting of log messages is to be performed is determined based on various parameters.

A quality score for a computer application release is determined using a first number of unique users who have launched the computer application release on user devices and a second number of unique users who have encountered at least once an abnormal termination with the computer application release on user devices. Additionally or optionally, an application quality score can be computed for a computer application based on quality scores of computer application releases that represent different versions of the computer application. Additionally or optionally, a weighted application quality score can be computed for a computer application by further taking into consideration the average application quality score and popularity of a plurality of computer applications.