How to improve performance in this case?

Elastic Stack Elasticsearch
3

I have slightly edited my original post to better reflect statistics, this time from the graphical profile UI.
I have tried the query without version and profile and they do not seem to affect the outcome.
After trying this query (using your feedback):

{
  "size": 500,
  "sort": [
    {
      "@timestamp": {
        "order": "desc",
        "unmapped_type": "boolean"
      }
    }
  ],
  "aggs": {
    "2": {
      "date_histogram": {
        "field": "@timestamp",
        "fixed_interval": "5m",
        "min_doc_count": 1
      }
    }
  },
  "query": {
    "bool": {
      "filter": [
          {
          "range": {
            "@timestamp": {
              "format": "strict_date_optional_time",
              "gte": "2019-08-22T04:10:26.899Z",
              "lte": "2019-08-22T07:10:26.899Z"
            }
          }
        }
      ],
      "should": [],
      "must_not": []
    }
  }
}

The results seem to be:

image
image.png1834×258 26.1 KB

image
image.png1833×174 16.7 KB

This still seems to be quite slow.
System stats during the above query:

--total-cpu-usage-- -dsk/total- -net/total- ---paging-- ---system--
usr sys idl wai stl| read  writ| recv  send|  in   out | int   csw 
  0   0 100   0   0|   0    20k|  66B  342B|   0     0 | 292   468 
  1   0  99   0   0|   0    56k| 342B  542B|   0     0 | 451   668 
  1   0  99   0   0|  12k    0 | 318B  474B|  12k    0 | 439   723 
  3   0  97   0   0|   0   168k| 192B  416B|   0     0 | 455   581 
 19   0  81   0   0|   0     0 |1669B  608B|   0     0 | 828   747 
 33   0  67   0   0|   0    20k|  66B  342B|   0     0 |1170   867 
 33   0  67   0   0|   0     0 |  66B  342B|   0     0 |1095   731 
 33   0  66   0   0|4096B    0 |  66B  342B|4096B    0 |1264   962 
 18   0  82   0   0|   0     0 |  66B  350B|   0     0 | 808   755 
 17   0  83   0   0|   0    68k| 132B  476B|   0     0 | 718   640 
 18   0  82   0   0|   0    20k| 216B  408B|   0     0 | 722   640 
 20   0  80   0   0|   0   388k| 200B  408B|   0     0 |1038   976 
 17   0  82   0   0|   0     0 | 132B  476B|   0     0 | 756   689 
 24   0  76   0   0|   0   176k| 414B  606B|   0     0 |1059   871 
 17   0  82   0   0|   0     0 | 192B  408B|   0     0 | 776   698 
 17   0  83   0   0|   0    40k| 132B  476B|   0     0 | 732   632 
 19   0  81   0   0|   0   168k| 216B  408B|   0     0 | 824   704 
 17   0  83   0   0|   0     0 |  66B  342B|   0     0 | 811   797 
 17   0  83   0   0|   0     0 |  66B  342B|   0     0 | 740   707 
 17   0  83   0   0|   0   116k|  66B  342B|   0     0 | 648   590 
 20   0  80   0   0|   0    44k| 126B  558B|   0     0 | 894   882 
 18   0  82   0   0|   0   252k| 454B  600B|   0     0 | 827   816 
 17   0  83   0   0|   0     0 | 437B  581B|   0     0 | 791   745 
 23   0  76   0   0|   0   168k|1122B 1851B|   0     0 | 998   876 
 17   0  83   0   0|   0     0 | 954B 1772B|   0     0 | 687   614 
  6   0  94   0   0|   0     0 | 660B   72k|   0     0 | 513   599 
  0   0 100   0   0|   0   864k|  66B  342B|   0     0 | 288   445