In a method of managing a debugging log in a storage device, an event trigger signal is generated based on an external power supply voltage and a plurality of configuration control signals. The event trigger signal is activated in response to an event of interest being issued for generating and storing the debugging log. The debugging log represents information associated with errors occurring in the storage device. The debugging log is generated based on the event trigger signal. The debugging log is stored in a nonvolatile memory. The event of interest includes at least one of a power up event a reset event, a link up event, a link down event or a power down event.

Dynamic logging includes generating parsed event data by at least one natural language processor responsive to event data of a log for transactions of a target application. In response to the parsed event data, a first classifier classifies context states of the respective transactions of the target application. In response, a second classifier classifies trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels. When a logic module determines, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction, the logic module sends an increased log detail selection to the target application, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

Systems and methods are provided for performing, at a server, a stack trace of an application at a predetermined interval to generate a plurality of stack traces, where each stack trace of the plurality of stack traces is from a different point in time based on the predetermined interval. The stack trace is performed when the application is operating normally and when the application has had a failure. The plurality of stack traces stored are indexed by timestamp. The server may determine a state of the application based on at least one of the plurality of stack traces. The server may condense data for at least one of the plurality of stack traces that are indexed using predetermined failure scenarios for the application. The server may generate a report based on the condensed data and the state of the application, and may transmit the report for display.

A system and method for dynamic log management of stream processing in a distributed computing environment, such as, for example, a streaming application or stream analytics system. A streaming application can be deployed or published to a cluster, to execute as a client application. A cluster manager coordinates with worker nodes, to commit tasks associated with the streaming application. If a need arises to generate lower-level log data associated with the streaming application, for example to diagnose an underlying cause of a warning/error message, a configuration job can be committed to the cluster to execute as a separate log-configuration application. The log-configuration application operates with the cluster manager to determine the set of working nodes currently associated with the streaming application, and modify the logger configuration at those nodes, to record or otherwise provide log data according to a modified logging level, for example to provide lower-level log messages.

A system and method for allowing an assignee to rapidly collect data about a bug/error that is associated with the execution of a software application on a computing device. The method includes including receiving, from a client device, a request to resolve an error associated with an execution of an application on a remote server. The request includes configuration information for connecting to the remote server and an identifier to a component of the application. The method includes determining one or more files associated with the component of the application. The method includes establishing a connection to the remote server using the configuration information. The method includes retrieving the one or more files from the remote server via the connection. The method includes granting, to an assignee device, access to the one or more files that were retrieved from the remote server.

In some examples, a system records, in a data structure stored in a non-volatile storage, information of memory errors in respective segments of a memory. The system determines whether memory errors of a subset of the segments satisfy a criterion, and in response to determining that the memory errors of the subset of the segments satisfy the criterion, the system groups the memory errors of the subset into a partition having a size greater than a size of a segment. The system records, in the data structure, information of memory errors in the partition, and in response to a restart of the system, retrieves the data structure from the non-volatile storage for use in an operation that addresses memory errors in the system.

Methods and apparatus are provided. In an example aspect, a method of anomaly detection from log messages is provided. The method comprises determining whether at least a portion of a log message generated by a computing system matches one or more of a plurality of Bloom filters, wherein each Bloom filter is associated with one or more respective predefined log messages and one or more respective database keys, and each database key is associated with one of the predefined log messages in a database. The method also comprises, if the at least the portion of the log message matches the one or more Bloom filters, for each of the one or more Bloom filters, determining whether the at least a portion of the log message matches any of the one or more associated predefined log messages by performing a lookup of the database using the associated one or more database keys.

Intelligent collection and analysis of in-app failure data is disclosed herein. Upon an application failure in a client device, the client device may collect failure information uniquely identifying a specific failure and provide the failure information to an analysis system. The analysis system may identify a specific failure that identifies the application and a specific portion of the code in the application, based on the failure information and match an action correlated to the specific failure where the action is uniquely designed to resolve the specific failure in the application. The action may include instructions for the client device used to intelligently lead to a resolution of the specific failure. The analysis system may transmit the action to the client device to perform the action and provide any follow up information to the analysis server. The analysis server may use the information to further analyze the specific failure.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. The method described herein includes determining identification information for an operation, wherein the identification information includes at least one field indicating content of the operation and a field indicating a unique identification of the operation. The method further includes identifying, based on the identification information, log entries for the operation in log files for at least one microservice invoked by the operation. The method further includes determining a log for the operation, wherein the log includes the identified log entries. With the solution for data processing of the present application, it is possible to easily acquire logs for an operation using identification information that includes a field indicating the content of the operation, so as to facilitate targeted analysis of the operation based on the content of the operation.

A data selection device assists selection of suitable training data used for sign detection, and includes: a storage unit configured to store time-series sensor data acquired from a sensor with respect to a failure prediction target device; a data classification unit configured to classify the time-series sensor data into a first data set and a second data set while allowing the first data set and the second data set to overlap each other; a training data selection unit configured to select a subset of the second data set based on a value range of the first data set; a training data evaluation unit configured to calculate an evaluation index indicating a suitability of a failure prediction model as training data based on the selected subset; and a data selection condition search unit configured to search for the value range of the first data set that maximizes the evaluation index.