Identity Metrics 

Metrics can accumulate over time. They are best stored in a time-series database not only because they are machine data but also because they have a propensity to timeline Since they are lightweight in their size but frequent in their publish, the metrics are fine grained data that can cost the network and the servers if the flow is not planned. 

Metrics for identity are more than when a user signs in or signs out. There is a lot of business value in collecting reports on a user’s login activity across sites and applications. For instance, it helps determine user’s interest which can help with predictions and advertising. Users themselves want to subscribe to alerts and notifications for their activities. On the other hand, there is plenty of troubleshooting support from metrics. Generally, logs have been the source for offline analysis, however, metrics also support reporting in the form of beautiful charts and graphs. 

As with many metrics, there is a sliding window for timestamp based datapoint collections for the same metric. Although metrics support flexible naming convention, prefixes and paths, the same metric may have a lot of datapoints over time. A sliding window presents a limited range that can help with aggregation functions such as latest, maximum, minimum and count. Queries on metrics is facilitated with the help of annotations on the metric data and pre-defined metadata. These queries can use any language but the queries using search operators are preferred for their similarity to shell based execution environments. 

Identity metrics will generally flow into a time-series database along with other metrics from the same timeline. These databases have collection agents in the form of forwarders and they handle a limited set of loads, so they scale based on the system that generates the metrics. The metrics are usually sent across over the wire in the form of a single http request per metric or a batch of metrics. That is why other protocols such as syslogs for transfer of files or blobs are preferred.  

Metrics unlike logs are ephemeral. They are only useful for a certain period after which either cumulative metrics are regenerated or they are ignored. With the retention period being small, metrics are served best from near real time queries. Often users find reports and notifications more convenient than queries. 

int GetKthAntiClockWise(int[,] A, int m, int n, int k)
{
if (n <1 || m < 1) return -1;
if (k <= n)
    return A[k-1, 0]; 
if (k <= n+m-1)
   return A[n-1, k-n]; 
if (k <= n+m-1+n-1)
   return A[(n-1-(k-(n+m-1))), m-1];
if (k <= n+m-1+n-1+m-2)
   return A[0, m-1-(k-(n+m-1+n-1))]; 
return GetKthAntiClockWise(A.SubArray(1,1,m-2,n-2), m-2, n-2, k-(2*n+2*m-4)));
}