System and computer program product to perform an operation comprising generating, based on a first output generated by a first execution instance of a command, a first output file specifying a value of at least one performance metric, wherein the first output file is formatted according to a predefined format, comparing the value of the at least one performance metric in the first output file to a value of the performance metric in a second output file, the second output file having been generated based on a second output generated by a second execution instance of the command, and outputting for display an indication a result of the comparison of the value of the at least one performance metric of the first output file to the value of the at least one performance metric of the second output file.

Performance regression manager for large scale systems

The disclosed embodiments include a method performed by a data intake and query system The method includes ingesting collected data including metrics data including key values and numerical values, where each numerical value is indicative of a measured characteristic of a computing resource (eg, device), and populating a first portion of a metric-series index (msidx) file with the key values and a second portion of the msidx file with the numerical values The first portion is distinct from the second portion The method further includes generating metrics from the metrics data, where each metric has dimensions populated with at least some of the key values and has one of the numerical values The method further includes indexing the metrics by at least one of the dimensions

Techniques for ingesting metrics data

Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.

Distributed smart grid processing

A method includes generating from a time-series dataset multiple corresponding time-slice datasets. Each time-slice dataset has a corresponding time-slice time index and includes field-value data strings and associated field-value-time-index data strings, or pointers indicating the corresponding strings in an earlier time-slice dataset, that are the latest in the time-series dataset that are also earlier than the corresponding time-slice time index. A query of the time-series dataset for latest data records earlier than a given query time index is performed by using the time-slice datasets to reduce or eliminate the need to directly access or interrogate the time-series dataset.

Systems and methods for storing, updating, searching, and filtering time-series datasets

In one embodiment, a method includes selecting a set of logged user-initiated events of a user. The method further includes determining timing information, device-identification information, and user-location information for the set of logged user-initiated events. In addition, the method includes correlating the set of logged user-initiated events to recurring time intervals of a time map. Also, the method includes determining an overall density, a location-specific density, and a device-specific density of the set of logged user-initiated events for at least some recurring time intervals of the time map. Furthermore, the method includes generating a time-based activity pattern of the user, the time-based activity pattern comprising, for the at least some recurring time intervals of the time map, information related to the overall density, the location-specific density, and the device-specific density. In addition, the method includes publishing the time-based activity pattern to a scheduling interface.

Systems and methods of patternizing logged user-initiated events for scheduling functions

A system to collect and store in a special data structure arranged for rapid searching massive amounts of data. Performance metric data is one example. The performance metric data is recorded in time-series measurements, converted into unicode, and arranged into a special data structure having one directory for every day which stores all the metric data collected that day. The data structure at the server where analysis is done has a subdirectory for every resource type. Each subdirectory contains text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.

System for organizing and fast searching of massive amounts of data

A system to collect and analyze performance metric data recorded in time-series measurements, converted into unicode, and arranged into a special data structure. The performance metric data is collected by one or more probes running on machines about which data is being collected. The performance metric data is also organized into a special data structure. The data structure at the server where analysis is done has a directory for every day of performance metric data collected with a subdirectory for every resource type. Each subdirectory contain text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.

System and a process for searching massive amounts of time-series performance data using regular expressions

A system to collect and analyze performance metric data recorded in time-series measurements, converted into unicode, and arrange into a special data structure. The performance metric data is collected by one or more probes running on machines about which data is being collected. The performance metric data is also organized into a special data structure and then compressed prior to transmission to a server over any data path. The data structure at the server where analysis is done has a directory for every day of performance metric data collected with a subdirectory for every resource type. Each subdirectory contain text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions. For speed, cache memory is used and the performance metric data outside the start time and end time named by the user on a query screen is eliminated before the regular expression is applied.

System and a process for searching massive amounts of time-series

A system to collect and store in a special data structure arranged for rapid searching massive amounts of data. Performance metric data is one example. The performance metric data is recorded in time-series measurements, converted into unicode, and arranged into a special data structure having one directory for every day which stores all the metric data collected that day. The performance metric data is collected by one or more probes running on machines about which data is being collected. The performance metric data is compressed prior to transmission to a server over any data path. The data structure at the server where analysis is done has a subdirectory for every resource type. Each subdirectory contains text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions. For speed, cache memory is used. Performance metric data outside the start time and end time harried by the user on a query screen is eliminated before the regular expression is applied.

Organizing and fast searching of data

A system to collect and store in a special data structure arranged for rapid searching massive amounts of data. Performance metric data is one example. The performance metric data is recorded in time-series measurements, converted into unicode, and arranged into a special data structure having one directory for every day which stores all the metric data collected that day. The performance metric data is collected by one or more probes running on machines about which data is being collected. The performance metric data is compressed prior to transmission to a server over any data path. The data structure at the server where analysis is done has a subdirectory for every resource type. Each subdirectory contains text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions. For speed, cache memory is used. Performance metric data outside the start time and end time named by the user on a query screen is eliminated before the regular expression is applied.

Sai Krishnam Raju Nadimpalli

Papers

System for organizing and fast searching of massive amounts of data

System and a process for searching massive amounts of time-series performance data using regular expressions

System and a process for searching massive amounts of time-series

Organizing and fast searching of data

System and process for searching massive amounts of time-series data