anchor.diff:  == Temporal Graph Expressions
   
   The examples above lay the groundwork for temporal graph queries.
  -Temporal graph queries allow the `nodes` function to walk the graph using *windows of time* to surface *cross-correlations* within the data.
  -The nodes function currently supports graph walks using *ten second increments* which is useful for *event correlation* and *root cause analysis* in log analytics.
  -
  -In order to support temporal graph queries a ten second truncated timestamp in *ISO 8601* format must be added to the log records as a string field at indexing time.
  -Here is a sample ten second truncated timestamp: `2021-02-10T20:51:30Z`.
  -This small data change enables some very important use cases so it's well worth the effort.
  +Temporal graph queries allow the `nodes` function to walk the graph using *windows of time*
  +to surface *cross-correlations* within a temporal graph.
  +The nodes function currently supports graph walks using
  +*ten second windows*, *daily windows* and *weekday windows*.
  +
  +Ten second windows are useful for *event correlation* and *root cause analysis* in log analytics. The daily and weekday
  +windows are useful for correlating events that happen days apart.
  +
  +In order to support temporal graph queries a truncated timestamp in *ISO 8601* format must be added to the log records as a
  +string field at indexing time.
  +To support ten second time windows a ten second truncated timestamp should be indexed in a string field
  +as follows: `2021-02-10T20:51:30Z` .
  +To support daily and weekly time windows a day truncated timestamp should be indexed in a string field as follows:
  +`2021-02-10T00:00:00Z`.
   
   Solr's indexing tool for Solr logs, described <<logs.adoc#,here>>, already adds the ten second truncated timestamps.
   So those using Solr to analyze Solr logs get temporal graph expressions for free.
  
positive.code: For event correlation and root cause analysis it's not enough to find events that occur within the *same* ten second root event windows.
  What's needed is to find events that occur within a window of time *prior to each root event*.
  The `window` parameter allows you to specify this prior window of time as part of the query.
  The window parameter is an integer which specifies the number of ten second time windows, prior to each root event window, to include in the graph walk.
  
  [source,text]
  ----
  
negative.code:             avg(qtime_i)),
              walk="time_ten_second_s->time_ten_second_s",
        gather="type_s",
        window="-3",
        count(*))
  ----
  
  Notice that the window parameter in this example is *negative* (-3). This will walk back in time from the events. A positive
  window will walk forward in time.
  
  Below is the node set returned when the window parameter is added.
  Notice that there are *now 29 error* events within the 3 ten second windows prior to the slow query events.
  