Abstract: Querying of time-aware metrics time series includes receiving a query, the query comprising a set of query metadata and a query time range. It further includes, based at least in part on the set of query metadata and the query time range, selecting a time series from a plurality of metrics time series. Each metrics time series in the plurality of metrics time series is associated with a set of metadata and an active interval of time. A set of metadata associated with the selected time series matches the set of query metadata, and an active interval of time associated with the selected metrics time series intersects with the query time range. The selected metrics time series is returned.

Abstract: Key name synthesis is disclosed. A metrics data point is received. Based at least in part on a translation statement, at least a portion of the received metrics data point is associated with a key specified by the translation statement such that the specified key and the associated at least portion of the received metrics data point form a key-value pair. The key-value pair is associated with the received metrics data point.

Abstract: Analyzing log data, such as security log data and machine data, is disclosed. A baseline is built for a set of machine data. The baseline is built at least in part by determining a plurality of signature profiles for a plurality of respective time slices. An occurrence of an anomaly associated with the source of the machine data is determined. The occurrence is determined at least in part by determining that received machine data does not conform to the baseline within a threshold.

Abstract: Logs to metrics synthesis includes receiving a log message. It further includes translating the log message into a metrics data point comprising a timestamp, a metric name, a metric value, and a set of metadata key-value pairs. It further includes determining a time series in which to insert the metrics data point into which the log message was translated. It further includes inserting the metrics data point into the determined time series. It further includes updating a metadata catalog based at least in part on the metrics data point.

Abstract: Key name synthesis is disclosed. A metrics data point is received. Based at least in part on a translation statement, at least a portion of the received metrics data point is associated with a key specified by the translation statement such that the specified key and the associated at least portion of the received metrics data point form a key-value pair. The key-value pair is associated with the received metrics data point.

Abstract: Key name synthesis is disclosed. A metrics data point is received. Based at least in part on a translation statement, at least a portion of the received metrics data point is associated with a key specified by the translation statement such that the specified key and the associated at least portion of the received metrics data point form a key-value pair. The key-value pair is associated with the received metrics data point.

Abstract: Data enrichment and augmentation is disclosed. Machine data comprising at least one of a log message and a metrics data point is received. The received machine data comprises an identifier of an instance of a virtual machine. Based at least in part on the identifier of the instance of the virtual machine, a query for tags associated with the instance of the virtual machine is performed. At least one key-value pair is generated based at least in part on tags received in response to the query performed based at least in part on the identifier of the instance of the virtual machine. The received machine data is augmented with the at least one key-value pair generated based at least in part on the tags received in response to the query based at least in part on the identifier of the instance of the virtual machine.

Abstract: Automatic partitioning is disclosed. A set of previously run queries is obtained. The set of previously run queries is analyzed to determine one or more query fragments from the set of previously run queries. One or more partitions are generated at least in part by using the obtained query fragments.

Abstract: Data enrichment and augmentation is disclosed. Machine data comprising at least one of a log message and a metrics data point is received. The received machine data comprises an identifier of an instance of a virtual machine. Based at least in part on the identifier of the instance of the virtual machine, a query for tags associated with the instance of the virtual machine is performed. At least one key-value pair is generated based at least in part on tags received in response to the query performed based at least in part on the identifier of the instance of the virtual machine. The received machine data is augmented with the at least one key-value pair generated based at least in part on the tags received in response to the query based at least in part on the identifier of the instance of the virtual machine.

Abstract: Automatic partitioning is disclosed. A set of previously run queries is obtained. The set of previously run queries is analyzed to determine one or more query fragments from the set of previously run queries. One or more partitions are generated at least in part by using the obtained query fragments.

Abstract: Analyzing log data, such as security log data and machine data, is disclosed. A baseline is built for a set of machine data. The baseline is built at least in part by determining a plurality of signature profiles for a plurality of respective time slices. An occurrence of an anomaly associated with the source of the machine data is determined. The occurrence is determined at least in part by determining that received machine data does not conform to the baseline within a threshold.

Abstract: Analyzing log data, such as security log data and machine data, is disclosed. A baseline is built for a set of machine data. The baseline is built at least in part by determining a plurality of signature profiles for a plurality of respective time slices. An occurrence of an anomaly associated with the source of the machine data is determined. The occurrence is determined at least in part by determining that received machine data does not conform to the baseline within a threshold.

Abstract: Querying of time-aware metrics time series includes receiving a query, the query comprising a set of query metadata and a query time range. It further includes, based at least in part on the set of query metadata and the query time range, selecting a time series from a plurality of metrics time series. Each metrics time series in the plurality of metrics time series is associated with a set of metadata and an active interval of time. A set of metadata associated with the selected time series matches the set of query metadata, and an active interval of time associated with the selected metrics time series intersects with the query time range. The selected metrics time series is returned.

Abstract: Single-click delta analysis is disclosed. A user query of status information collected from one or more monitored devices is received from a user. In response to receiving an indication from the user to determine a variance between different portions of the collected status information, a target query and a baseline query are generated using the user query. The generated target query and the generated baseline query are performed, respectively, against data in a data store including the status information collected from the one or more monitored devices. A target set of status information results and a baseline set of status information results are obtained in response to performing, respectively, the generated target query and the generated baseline query. The obtained target and baseline sets of results are combined. Output indicative of a variance between the target and baseline sets of status information results is provided based at least in part on the combining.

Abstract: Querying of time-aware metrics time series includes receiving a query, the query comprising a set of query metadata and a query time range. It further includes, based at least in part on the set of query metadata and the query time range, selecting a time series from a plurality of metrics time series. Each metrics time series in the plurality of metrics time series is associated with a set of metadata and an active interval of time. A set of metadata associated with the selected time series matches the set of query metadata, and an active interval of time associated with the selected metrics time series intersects with the query time range. The selected metrics time series is returned.

Abstract: Single-click delta analysis is disclosed. A user query of status information collected from one or more monitored devices is received from a user. In response to receiving an indication from the user to determine a variance between different portions of the collected status information, a target query and a baseline query are generated using the user query. The generated target query and the generated baseline query are performed, respectively, against data in a data store including the status information collected from the one or more monitored devices. A target set of status information results and a baseline set of status information results are obtained in response to performing, respectively, the generated target query and the generated baseline query. The obtained target and baseline sets of results are combined. Output indicative of a variance between the target and baseline sets of status information results is provided based at least in part on the combining.

Abstract: A technique for logs to metrics synthesis is disclosed. A log message is received. It is determined that the received log message should be translated into a metrics data point. In response to determining that the received log message should be translated into a metrics data point, the metrics data point is generated using the received log message, the generated metrics data point comprising a timestamp, a metric name, a metric value, and a set of metadata key-value pairs. A time series in which to insert the metrics data point generated using the received log message is identified. The generated metrics data point is inserted into the identified time series.

Abstract: Obfuscating data is disclosed. A processor identifies structured information in log data. The structured information is transformed in a manner that preserves the structure to form transformed raw data. The transformed raw data is sent to a remote analysis engine. The remote analysis engine receives a query and responds to the query by providing as results at least a portion of the transformed raw data. A processor is configured to de-transform the transformed raw data.

Abstract: Logs to metrics synthesis includes receiving a log message. It further includes translating the log message into a metrics data point comprising a timestamp, a metric name, a metric value, and a set of metadata key-value pairs. It further includes determining a time series in which to insert the metrics data point into which the log message was translated. It further includes inserting the metrics data point into the determined time series. It further includes updating a metadata catalog based at least in part on the metrics data point.

Abstract: The automatic selection and usage of a parser is disclosed. Raw data is obtained from a first remote device. At least a portion of the raw data is evaluated using a plurality of rules. A confidence measure is determined for at least some of the rules. An indication that the raw data pertains to a source is provided as output when the confidence measure exceeds a threshold.