1. A computer implemented method for analyzing performance of components of a database cluster by transforming discrete events into a time series to identify dominant signals, the method comprising: sampling, using a measurement instrument, a plurality of discrete event measurements of the database cluster to produce a set of timestamped events, the database cluster comprising a plurality of nodes that share a shared resource, and the plurality of discrete event measurements obtained from multiple ones of the plurality of nodes; tagging at least one of the timestamped events sampled by one or more hardware components or one or more software components of the database cluster with a semantic tag, wherein tagging the at least one of the timestamped events with the semantic tag comprises assigning a state corresponding to the shared resource of the database cluster, the semantic tag includes at least a first state of waiting for the shared resource and a second state of obtaining access to the shared resource; generating a single magnitude value for a related set of the timestamped events for multiple sessions with respect to the semantic tag for the first state of waiting for the shared resource or the second state of obtaining access to the shared resource by applying different weighting factors to some or all of the sessions of the timestamped events corresponding to the first and second state to derive the single magnitude value; formatting the set of timestamped events into a time series, wherein a time series entry comprises a time indication and a plurality of values, at least one of the plurality of values corresponding to the single magnitude value generated for the related set of the semantic tag for the multiple sessions in the database cluster; and processing the time series to identify at least one state signal.
1. A computer implemented method for analyzing performance of components of a database cluster by transforming discrete events into a time series to identify dominant signals, the method comprising: sampling, using a measurement instrument, a plurality of discrete event measurements of the database cluster to produce a set of timestamped events, the database cluster comprising a plurality of nodes that share a shared resource, and the plurality of discrete event measurements obtained from multiple ones of the plurality of nodes; tagging at least one of the timestamped events sampled by one or more hardware components or one or more software components of the database cluster with a semantic tag, wherein tagging the at least one of the timestamped events with the semantic tag comprises assigning a state corresponding to the shared resource of the database cluster, the semantic tag includes at least a first state of waiting for the shared resource and a second state of obtaining access to the shared resource; generating a single magnitude value for a related set of the timestamped events for multiple sessions with respect to the semantic tag for the first state of waiting for the shared resource or the second state of obtaining access to the shared resource by applying different weighting factors to some or all of the sessions of the timestamped events corresponding to the first and second state to derive the single magnitude value; formatting the set of timestamped events into a time series, wherein a time series entry comprises a time indication and a plurality of values, at least one of the plurality of values corresponding to the single magnitude value generated for the related set of the semantic tag for the multiple sessions in the database cluster; and processing the time series to identify at least one state signal. 3. The method of claim 1 , wherein the time series comprises a series of vectors of state values.