Abstract: In one embodiment, present disclosure discloses a method for partitioning a temporal graph is described. Embodiments of the method comprises creating a plurality of storage blocks for each type of the different types of graph elements based on predefined label groups, each of the plurality of storage blocks configured to store the telemetry information generated in a corresponding predefined time-range, recreating each of the plurality of storage blocks upon expiry of a configurable rollover time, and sharding each of the plurality of storage blocks into a plurality of shards based on a configurable sharding count.

Abstract: The present technology addresses deficiencies associated with common practices for handling out of order data in a streaming data database. An aspect of the present technology is avoid storing out of order data in a snapshot but just store the out of order data as additional data linked to the temporal graph. The present technology receives out of order data and records a modification time for the data and a next modification time for the data that equals a timestamp of data previously stored in the database. If there is also data in the database for a time earlier than the timestamp of the out of order data, the earlier data is adjusted so that its next modification time matches the timestamp of the out of order data.

Abstract: The present technology addresses deficiencies associated with common practices for handling out of order data in a streaming data database. An aspect of the present technology is avoid storing out of order data in a snapshot but just store the out of order data as additional data linked to the temporal graph. The present technology receives out of order data and records a modification time for the data and a next modification time for the data that equals a timestamp of data previously stored in the database. If there is also data in the database for a time earlier than the timestamp of the out of order data, the earlier data is adjusted so that its next modification time matches the timestamp of the out of order data.

Abstract: In one embodiment, present disclosure discloses a method for partitioning a temporal graph is described. Embodiments of the method comprises creating a plurality of storage blocks for each type of the different types of graph elements based on predefined label groups, each of the plurality of storage blocks configured to store the telemetry information generated in a corresponding predefined time-range, recreating each of the plurality of storage blocks upon expiry of a configurable rollover time, and sharding each of the plurality of storage blocks into a plurality of shards based on a configurable sharding count.

Abstract: In one embodiment, present disclosure discloses a method for partitioning a temporal graph is described. Embodiments of the method comprises creating a plurality of storage blocks for each type of the different types of graph elements based on predefined label groups, each of the plurality of storage blocks configured to store the telemetry information generated in a corresponding predefined time-range, recreating each of the plurality of storage blocks upon expiry of a configurable rollover time, and sharding each of the plurality of storage blocks into a plurality of shards based on a configurable sharding count.

Abstract: The present technology addresses deficiencies associated with common practices for handling out of order data in a streaming data database. An aspect of the present technology is avoid storing out of order data in a snapshot but just store the out of order data as additional data linked to the temporal graph. The present technology receives out of order data and records a modification time for the data and a next modification time for the data that equals a timestamp of data previously stored in the database. If there is also data in the database for a time earlier than the timestamp of the out of order data, the earlier data is adjusted so that its next modification time matches the timestamp of the out of order data.

Abstract: In one embodiment, present disclosure discloses a method for partitioning a temporal graph is described. Embodiments of the method comprises creating a plurality of storage blocks for each type of the different types of graph elements based on predefined label groups, each of the plurality of storage blocks configured to store the telemetry information generated in a corresponding predefined time-range, recreating each of the plurality of storage blocks upon expiry of a configurable rollover time, and sharding each of the plurality of storage blocks into a plurality of shards based on a configurable sharding count.

Abstract: A method for performing network analysis comprising: receiving telemetry information for a computer network, the telemetry information comprising network device state information for internetworking devices in the computer network and network traffic flow information for data packet flows through the computer network; transforming the telemetry information into a temporal graph that is digitally stored in computer memory, wherein the temporal graph comprises a plurality of graph elements include a plurality of vertices and a plurality of edges, each of the vertices representing an internetworking device, each of the edges representing one or more portions of a data packet flow; digitally storing graph element metadata corresponding to the plurality of graph elements in a structured format, the graph element metadata comprising temporal data for each graph element of the plurality of graph elements, the temporal data comprising a modification time value specifying a modification time that is associated with ea

Abstract: A method for performing network analysis comprising: receiving telemetry information for a computer network, the telemetry information comprising network device state information for internetworking devices in the computer network and network traffic flow information for data packet flows through the computer network; transforming the telemetry information into a temporal graph that is digitally stored in computer memory; receiving, from a client computing device, a request for network analytics information for the computer network; in response to receiving the request for network analytics information, generating network analytics information for the computer network based on the temporal graph and sending the network analytics information to the client computing device.

Abstract: A method for performing network analysis comprising: receiving telemetry information for a computer network, the telemetry information comprising network device state information for internetworking devices in the computer network and network traffic flow information for data packet flows through the computer network; transforming the telemetry information into a temporal graph that is digitally stored in computer memory, wherein the temporal graph comprises a plurality of graph elements include a plurality of vertices and a plurality of edges, each of the vertices representing an internetworking device, each of the edges representing one or more portions of a data packet flow; digitally storing graph element metadata corresponding to the plurality of graph elements in a structured format, the graph element metadata comprising temporal data for each graph element of the plurality of graph elements, the temporal data comprising a modification time value specifying a modification time that is associated with ea

Abstract: A method for performing network analysis comprising: receiving telemetry information for a computer network, the telemetry information comprising network device state information for internetworking devices in the computer network and network traffic flow information for data packet flows through the computer network; transforming the telemetry information into a temporal graph that is digitally stored in computer memory; receiving, from a client computing device, a request for network analytics information for the computer network; in response to receiving the request for network analytics information, generating network analytics information for the computer network based on the temporal graph and sending the network analytics information to the client computing device.