Abstract: Techniques described herein perform implement a query using a synthetic time series (STS), for example applying an STS on time series data to obtain an answer to the query. In an embodiment, a method receives a times series and query relating to the time series. The method translates the query into a synthetic time series. The method executes a reduce operation on the STS and the time series. The method then returns the result of the operation as an answer to the query. A result of the reduce operation is returned as an answer to the query.

Abstract: In a computer-implemented method for generating a recommendation for automatic transformation of times series data at ingestion, historical query data of a time series data monitoring system is analyzed, where the historical query data includes a plurality of queries and data associated with execution of the plurality of queries. Based on the analyzing, it is determined whether an execution cost of a query of the plurality of queries can be reduced by performing automatic transformation of at least a portion of times series data accessed responsive to the query at ingestion into the time series data monitoring system. In response to determining that the execution cost of the query can be reduced by performing automatic transformation at ingestion, a recommendation to perform the automatic transformation of the at least a portion of the times series data at ingestion is generated.

Abstract: In a computer-implemented method for automatic transformation of time series data at ingestion, time series data comprising data points is received at at least one ingestion node of a time series data monitoring system, wherein the data points have an input observability format. At the at least one ingestion node, the data points the data points are transformed from the input observability format to an output observability format according to configuration rules of the time series data monitoring system. The data points having the output observability format are forwarded from the at least one ingestion node to a persistent storage device.

Abstract: A method for generating a dynamic graph representation includes receiving time series data, receiving a specification of a relationship between nodes connected by one or more edges in a graph representation to be generated for at least a portion of the time series data, and receiving an identification of a query of the time series data included in a specification of an element of the graph representation. The method further includes executing the query of the times series data, and updating a property of the graph representation using a result of the execution of the query.

Abstract: In various embodiments, a method for optimizing data storage includes receiving an input data stream, where each data record received in the data stream is tagged with a group of one or more tags. The method further includes, for each data record of data records that have been received in the data stream, using the group of one or more tags of the corresponding data record to update a data structure tracking coexistence implications of tags that have been observed together in the groups of tags of the data records. The method further includes using the data structure tracking coexistence implications of tags to optimize a query.

Abstract: Techniques described herein perform implement a query using a synthetic time series (STS), for example applying an STS on time series data to obtain an answer to the query. In an embodiment, a method receives a times series and query relating to the time series. The method translates the query into a synthetic time series. The method executes a reduce operation on the STS and the time series. The method then returns the result of the operation as an answer to the query. A result of the reduce operation is returned as an answer to the query.

Abstract: Techniques described herein perform high fidelity combination of data, for example combining time series data in response to a query. In an embodiment, a first input data stream of a first type (e.g., continuous), a second input data stream of a second type (e.g., discrete), and an operation to a function of and to be performed on the first and second input data streams are received. The second input data stream includes second input data stream samples associated with sample times. The techniques includes determining that at least some points in the second input data stream samples do not have synchronized samples in the first input data stream, automatically generating synchronized samples for the first input data stream, and performing the operation on the second input data stream samples and the automatically generated samples for the first input data stream.

Abstract: In various embodiments, a method for optimizing data storage includes receiving an input data stream, where each data record received in the data stream is tagged with a group of one or more tags. The method further includes, for each data record of data records that have been received in the data stream, using the group of one or more tags of the corresponding data record to update a data structure tracking coexistence implications of tags that have been observed together in the groups of tags of the data records. The method further includes using the data structure tracking coexistence implications of tags to optimize a query.

Abstract: Techniques described herein perform implement a query using a synthetic time series (STS), for example applying an STS on time series data to obtain an answer to the query. In an embodiment, a method receives a times series and query relating to the time series, where the query specifies a condition. The method translates the condition into a synthetic time series and an operation on: the STS and the time series. The method executes the operation on the STS and the time series. The method then returns the result of the operation as an answer to the query. In some embodiments, the execution of the operation on the STS and the time series includes reducing the STS and time series without needing to grid.

Abstract: Techniques described herein perform implement a query using a synthetic time series (STS), for example applying an STS on time series data to obtain an answer to the query. In an embodiment, a method receives a times series and query relating to the time series, where the query specifies a condition. The method translates the condition into a synthetic time series and an operation on: the STS and the time series. The method executes the operation on the STS and the time series. The method then returns the result of the operation as an answer to the query. In some embodiments, the execution of the operation on the STS and the time series includes reducing the STS and time series without needing to grid.

Abstract: Techniques described herein perform high fidelity combination of data, for example combining time series data in response to a query. In an embodiment, a first input data stream of a first type (e.g., continuous), a second input data stream of a second type (e.g., discrete), and an operation to a function of and to be performed on the first and second input data streams are received. The second input data stream includes second input data stream samples associated with sample times. The techniques includes determining that at least some points in the second input data stream samples do not have synchronized samples in the first input data stream, automatically generating synchronized samples for the first input data stream, and performing the operation on the second input data stream samples and the automatically generated samples for the first input data stream.

Abstract: A method to schedule garbage collection operations may be executed in a client/server system using multiple servers. The method includes comparing usage of memory in respective ones of the plurality of servers to one or more memory usage thresholds and dynamically identifying one or more servers in which to perform garbage collection of memory objects during a particular time period. By distributing the durations over which garbage collection is performed on respective servers, the client server system, among other things, reduces the pauses in servicing client requests.