abnormal average time when querying different data volume for the same key

[gaoboal]

Description
During query performance testing, it was found that querying all data rows for a key incurs the least time cost; querying a subset of data within a specified time range for a key results in a relatively increased time cost; querying data for a specific timestamp for a key results in an even greater increase in time cost.

Detail：
table：import TalkingData train dataset (180+ million rows) into openmldb
query key： ip=88

key	other condition	rows	average time(us)
ip=88	all	4278	9774.375
ip=88	'2017-11-06 00:00:00' <= ts < "2017-11-07 00:00:00"	183	11570.365
ip=88	ts='2017-11-06 16:19:38'	1	16504.145

more result detail: https://qiok3h8ob4.feishu.cn/docx/YkYfdBZm9oVk0MxLFx9co8lLn1g?from=from_copylink

Expected Behavior
querying smaller amounts of data should have shorter time costs, or at least not longer than querying larger amounts of data.

Steps to Reproduce

1. deploy openmldb;
2. load data （TalkingData train.csv）into table;
3. find a key with large enough total data volume;
4. execute queries and calculate the average time cost;

[aceforeverd]

storage design limitation. All records for the same key are seeking linearly, records are orderly by ts value, ts with largest value comes first.