We're using this at Twitter to better understand usage patterns for services upstream and downstream. For example, Gizmoduck, the user store at twitter is backed by memcache, and some disk-based storage behind the cache. While we can view individual traces that hit by memcache, the aggregate info shows us both the proportion of traffic for services calling Gizmoduck, as well as the proportion of time Gizmoduck spends in memcache versus the backend store.
Furthermore, it can be useful for notifying of unusual behavior. If a service's aggregate durations has changed since yesterday, perhaps that's something we want to look at. Or if the ratio of traffic from some upstream service doubles, that's interesting to know.
We're using this at Twitter to better understand usage patterns for services upstream and downstream. For example, Gizmoduck, the user store at twitter is backed by memcache, and some disk-based storage behind the cache. While we can view individual traces that hit by memcache, the aggregate info shows us both the proportion of traffic for services calling Gizmoduck, as well as the proportion of time Gizmoduck spends in memcache versus the backend store.
Furthermore, it can be useful for notifying of unusual behavior. If a service's aggregate durations has changed since yesterday, perhaps that's something we want to look at. Or if the ratio of traffic from some upstream service doubles, that's interesting to know.