Abstract: Systems, devices, and techniques are disclosed for multi-threaded dynamic queries on an unpartitioned database. An instruction to perform a database operation on rows of a database may be received. Processing threads may be generated by generating three random numbers. A starting point of a processing thread may be set to a first random number, an offset may be set to a second random number, and an order may be based on a third random number. The processing threads may each generate a dynamic query. A dynamic query generated by a processing thread may include an instruction to perform the database operation on rows of the database based on the offset, starting point, and order for the processing thread. Each dynamic query may be run against the database to perform the database operation.

Abstract: Systems, devices, and techniques are disclosed for multi-threaded dynamic queries on an unpartitioned database. An instruction to perform a database operation on rows of a database may be received. Processing threads may be generated by generating three random numbers. A starting point of a processing thread may be set to a first random number, an offset may be set to a second random number, and an order may be based on a third random number. The processing threads may each generate a dynamic query. A dynamic query generated by a processing thread may include an instruction to perform the database operation on rows of the database based on the offset, starting point, and order for the processing thread. Each dynamic query may be run against the database to perform the database operation.

Abstract: Real-time anomaly detection in a network using state transitions. In one embodiment, a method may include identifying a sequence of messages sent between a first network node and a second network node over a network link. The method may further include identifying a sequence of message states for the sequence of messages. The method may also include identifying variable-length candidate patterns in the sequence of message states. The method may further include adding the candidate patterns to a baseline pattern store. The method may also include comparing a real-time sequence of messages to patterns in the baseline pattern store to detect anomalies in the real-time sequence of messages. The method may further include, in response to the detecting of the anomalies, alerting a security action on one or more of the first network node, the second network node, and the network link using the detected anomalies.