Abstract: A computing platform may be configured to (i) receive a log entry that was produced by a log producer, wherein the log entry comprises one or more data elements, (ii) produce a restructured representation of the log entry, the restructured representation comprising a sequence of one or more tokens that represent the one or more data elements of the log entry, (iii) based on the restructured representation of the log entry, determine a log identity of the log entry, and (iv) handle the log entry in accordance with a handling rule for the determined log identity.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for improving the efficiency and flexibility of implementing computer devices by intelligently generating a metric blocklist based on predicted utilization of digital metrics and deploying the metric blocklist at one or more computing devices to limit digital metric requests to distributed databases. In particular, in one or more embodiments, the disclosed systems monitor historical digital metric utilization and apply a prediction model to generate a metric blocklist of digital metrics that are not likely to be utilized by one or more metric requesting devices of a distributed computing system. The disclosed systems can deploy the metric blocklist to computing devices of a distributed computing system to efficiently limit digital requests, processing resources, bandwidth consumption, and storage load with regard to utilization of metric storage devices (e.g., time-series databases).

Abstract: A computing system configured to (i) obtain a set of key-value pairs, wherein each key-value pair corresponds to a respective timestamp in a period of time, (ii) for at least one timestamp in the given period of time: (a) identify a first subset of the key-value pairs corresponding to the timestamp, (b) sort the first subset, and (c) generate a subset of compression values for the sorted first subset, (iii) for at least one key: (a) identify a second subset of the key-value pairs corresponding to the key, (b) sort the second subset, and (c) generate a subset of compression values for the sorted second subset, and (iv) store a set of compression values comprising (a) the subset of compression values that is generated for each of the at least one timestamp and (b) the subset of compression values that is generated for each of the at least one key.

Abstract: A computing platform may be configured to (i) perform an analysis of a saved query comprising an expression that specifies a set of one or more unique metrics for which metric data is to be fetched from a metrics management platform when the saved query is run, (ii) determine a strategy for reducing a cardinality level of the saved query based at least in part on the analysis of the saved query, and (iii) cause the saved query to be modified in accordance with the determined strategy for reducing the cardinality level of the saved query.

Abstract: A computing platform may be configured to (i) perform an analysis of a saved query comprising an expression that specifies a set of one or more unique metrics for which metric data is to be fetched from a metrics management platform when the saved query is run, (ii) determine a strategy for reducing a cardinality level of the saved query based at least in part on the analysis of the saved query, and (iii) cause the saved query to be modified in accordance with the determined strategy for reducing the cardinality level of the saved query.

Abstract: A computing platform may be configured to (i) receive metric data for a metric that was produced by a metric producer, (ii) identify a metric handling rule that applies to the metric, wherein the identified metric handling rule comprises a handling action of storing metric data for the metric in a specified storage location (e.g., a different tier of a multi-tier storage architecture), and (iii) handle the received metric data for the metric in accordance with the identified metric handling rule by storing the received metric data in the specified storage location.

Abstract: A computing platform may be configured to (i) perform an analysis of a saved query comprising an expression that specifies a set of one or more unique metrics for which metric data is to be fetched from a metrics management platform when the saved query is run, (ii) determine a strategy for reducing a cardinality level of the saved query based at least in part on the analysis of the saved query, and (iii) cause the saved query to be modified in accordance with the determined strategy for reducing the cardinality level of the saved query.

Abstract: A computing system configured to (i) obtain a set of key-value pairs, wherein each key-value pair corresponds to a respective timestamp in a period of time, (ii) for at least one timestamp in the given period of time: (a) identify a first subset of the key-value pairs corresponding to the timestamp, (b) sort the first subset, and (c) generate a subset of compression values for the sorted first subset, (iii) for at least one key: (a) identify a second subset of the key-value pairs corresponding to the key, (b) sort the second subset, and (c) generate a subset of compression values for the sorted second subset, and (iv) store a set of compression values comprising (a) the subset of compression values that is generated for each of the at least one timestamp and (b) the subset of compression values that is generated for each of the at least one key.

Abstract: A computing platform may be configured to (i) receive a log entry that was produced by a log producer, wherein the log entry comprises one or more data elements, (ii) produce a restructured representation of the log entry, the restructured representation comprising a sequence of one or more tokens that represent the one or more data elements of the log entry, (iii) based on the restructured representation of the log entry, determine a log identity of the log entry, and (iv) handle the log entry in accordance with a handling rule for the determined log identity.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate compressed metric data for digital metrics utilizing a graph-based compression dictionary and time slice compression. For instance, the disclosed systems can utilize a dynamically modifiable graph-based compression dictionary to generate compressed metric label identifiers for metric labels of digital metrics. The graph-based compression dictionary can include nodes and edges corresponding to metric label segments and metric label identifier values, respectively. The disclosed systems can traverse the graph-based compression dictionary using a metric label to determine the corresponding compressed metric label identifier. The disclosed systems can further generate delta compression values for the metric values of the digital metrics. For instance, the disclosed systems can compare metric values within a single time slice (e.g., a time stamp) to generate corresponding delta compression values.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate compressed metric data for digital metrics utilizing a graph-based compression dictionary and time slice compression. For instance, the disclosed systems can utilize a dynamically modifiable graph-based compression dictionary to generate compressed metric label identifiers for metric labels of digital metrics. The graph-based compression dictionary can include nodes and edges corresponding to metric label segments and metric label identifier values, respectively. The disclosed systems can traverse the graph-based compression dictionary using a metric label to determine the corresponding compressed metric label identifier. The disclosed systems can further generate delta compression values for the metric values of the digital metrics. For instance, the disclosed systems can compare metric values within a single time slice (e.g., a time stamp) to generate corresponding delta compression values.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate compressed metric data for digital metrics utilizing a graph-based compression dictionary and time slice compression. For instance, the disclosed systems can utilize a dynamically modifiable graph-based compression dictionary to generate compressed metric label identifiers for metric labels of digital metrics. The graph-based compression dictionary can include nodes and edges corresponding to metric label segments and metric label identifier values, respectively. The disclosed systems can traverse the graph-based compression dictionary using a metric label to determine the corresponding compressed metric label identifier. The disclosed systems can further generate delta compression values for the metric values of the digital metrics. For instance, the disclosed systems can compare metric values within a single time slice (e.g., a time stamp) to generate corresponding delta compression values.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for improving the efficiency and flexibility of implementing computer devices by intelligently generating a metric blocklist based on predicted utilization of digital metrics and deploying the metric blocklist at one or more computing devices to limit digital metric requests to distributed databases. In particular, in one or more embodiments, the disclosed systems monitor historical digital metric utilization and apply a prediction model to generate a metric blocklist of digital metrics that are not likely to be utilized by one or more metric requesting devices of a distributed computing system. The disclosed systems can deploy the metric blocklist to computing devices of a distributed computing system to efficiently limit digital requests, processing resources, bandwidth consumption, and storage load with regard to utilization of metric storage devices (e.g., time-series databases).

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate compressed metric data for digital metrics utilizing a graph-based compression dictionary and time slice compression. For instance, the disclosed systems can utilize a dynamically modifiable graph-based compression dictionary to generate compressed metric label identifiers for metric labels of digital metrics. The graph-based compression dictionary can include nodes and edges corresponding to metric label segments and metric label identifier values, respectively. The disclosed systems can traverse the graph-based compression dictionary using a metric label to determine the corresponding compressed metric label identifier. The disclosed systems can further generate delta compression values for the metric values of the digital metrics. For instance, the disclosed systems can compare metric values within a single time slice (e.g., a time stamp) to generate corresponding delta compression values.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate compressed metric data for digital metrics utilizing a graph-based compression dictionary and time slice compression. For instance, the disclosed systems can utilize a dynamically modifiable graph-based compression dictionary to generate compressed metric label identifiers for metric labels of digital metrics. The graph-based compression dictionary can include nodes and edges corresponding to metric label segments and metric label identifier values, respectively. The disclosed systems can traverse the graph-based compression dictionary using a metric label to determine the corresponding compressed metric label identifier. The disclosed systems can further generate delta compression values for the metric values of the digital metrics. For instance, the disclosed systems can compare metric values within a single time slice (e.g., a time stamp) to generate corresponding delta compression values.