anchor.diff:  * `hits._score` - the document's relevance score (not applicable when using `match_all`)
   
   Each search request is self-contained: {es} does not maintain any
  -state information across requests. To page through the search hits, you specify
  +state information across requests. To page through the search hits, specify
   the `from` and `size` parameters in your request. 
   
   For example, the following request gets hits 10 through 19:
  
positive.code: // CONSOLE
  // TEST[continued]
  
  Notice how we nested the `average_balance` aggregation inside the `group_by_state` aggregation. This is a common pattern for all the aggregations. You can nest aggregations inside aggregations arbitrarily to extract pivoted summarizations that you require from your data.
  
  Building on the previous aggregation, let's now sort on the average balance in descending order:
  
  [source,js]
  --------------------------------------------------
  
negative.code: // CONSOLE
  // TEST[continued]
  
  In addition to basic bucketing and metrics aggregations like these, {es}
  provides specialized aggregations for operating on multiple fields and 
  analyzing particular types of data Such as dates, IP addresses, and geo 
  data. You can also feed the results of individual aggregations into pipeline
  aggregations for further analysis.
  
  The core analysis capabilities provided by aggregations enable advanced
  features such as using machine learning to detect anomalies. 
  
  [[getting-started-next-steps]]
  == Where to go from here
  