Calculating top for a large time frame
One common problem is to find the top contributors out of some huge set of unique values. For instance, if you want to know what IP addresses are using the most bandwidth in a given day or week, you may have to keep track of the total of request sizes across millions of unique hosts to definitively answer this question. When using summary indexes, this means storing millions of events in the summary index, quickly defeating the point of summary indexes.
Just to illustrate, let's look at a simple set of data:
|
Time |
1.1.1.1 |
2.2.2.2 |
3.3.3.3 |
4.4.4.4 |
5.5.5.5 |
6.6.6.6 |
|---|---|---|---|---|---|---|
|
12:00 |
99 |
100 |
100 |
100 | ||
|
13:00 |
99 |
100 |
100 |
100 | ||
|
14:00 |
99 |
100 |
101 |
100 | ||
|
15:00 |
99 |
99 |
100 |
100 | ||
|
16:00 |
99 |
100 |
100 |
100 | ||
|
total |
495 |
300 |
299 |
401 |
400 |
100 |
If we only stored the top three IPs per hour, our data set would look like the following:
|
Time |
1.1.1.1 |
2.2.2.2 |
3.3.3.3 |
4.4.4.4 |
5.5.5.5 |
6.6.6.6 |
|---|---|---|---|---|---|---|
|
12:00 |
100 |
100 |
100 | |||
|
13:00 |
100 |
100 |
100 | |||
|
14:00 |
100 |
101... |
