anchor.diff:                         avg(qtime_i)),
                    walk="time_ten_second_s->time_ten_second_s",
                    gather="type_s",
  -                 window="3",
  +                 window="-3",
                    count(*)))
   ----
   
  
positive.code:             avg(qtime_i)),
              walk="time_ten_second_s->time_ten_second_s",
        gather="type_s",
        window="3",
        count(*))
  ----
  
  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.
  
negative.code: == 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 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.
  