mysql> show create table t2G
*************************** 1. row ***************************
Table: t2
Create Table: CREATE TABLE `t2` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`rank1` int(11) DEFAULT NULL,
`rank2` int(11) DEFAULT NULL,
`rank3` int(11) DEFAULT NULL,
`log_date` date DEFAULT NULL,PRIMARY KEY (`id`),KEY `idx_rank1` (`rank1`),KEY `idx_log_date` (`log_date`)
) ENGINE=InnoDB AUTO_INCREMENT=49140 DEFAULT CHARSET=utf8mb4
COLLATE=utf8mb4_0900_ai_ci STATS_PERSISTENT=1 STATS_AUTO_RECALC=0
1 row in set (0.00 sec)
mysql> select count(*) from t2;
+----------+
| count(*) |
+----------+
|    30940 |
+----------+
1 row in set (0.00 sec) 

mysql> create table t3 like t2;
Query OK, 0 rows affected (0.13 sec)
mysql> insert into t3 select * from t2;
Query OK, 30940 rows affected (1.94 sec)
Records: 30940 Duplicates: 0 Warnings: 0 

mysql> analyze table t3 update histogram on rank1,log_date;
+--------+-----------+----------+-----------------------------------------------------+
| Table  | Op        | Msg_type | Msg_text                              |
+--------+-----------+----------+-----------------------------------------------------+
| ytt.t3 | histogram | status   | Histogram statistics created for column 'log_date'. |
| ytt.t3 | histogram | status   | Histogram statistics created for column 'rank1'.    |
+--------+-----------+----------+-----------------------------------------------------+
2 rows in set (0.19 sec)

mysql> select json_pretty(histogram)  result from information_schema.column_statistics where table_name = 't3' and column_name = 'log_date'G
*************************** 1. row ***************************
result: {
"buckets": [[
"2018-04-17",
"2018-04-20",
0.01050420168067227,
4],...,[
"2019-04-14","2019-04-16",
1.0,
3]],
"data-type": "date",
"null-values": 0.0,
"collation-id": 8,
"last-updated": "2019-04-17 03:43:01.910185",
"sampling-rate": 1.0,
"histogram-type": "equi-height",
"number-of-buckets-specified": 100
}
1 row in set (0.03 sec)

select count(*) from t2/t3 where (rank1 between 1 and 10) and log_date < '2018-09-01';

mysql> select count(*) from t2/t3 where (rank1 between 1 and 10) and log_date < '2018-09-01';
+----------+
| count(*) |
+----------+
|     2269 |
+----------+
1 row in set (0.01 sec)

mysql> explain format=json select count(*) from t2 where (rank1 between 1 and 10) and log_date < '2018-09-01'G
*************************** 1. row ***************************
EXPLAIN: {"query_block": {"select_id": 1,"cost_info": {"query_cost": "2796.11"},"table": {"table_name": "t2","access_type": "range","possible_keys": ["idx_rank1","idx_log_date"],"key": "idx_rank1","used_key_parts": ["rank1"],"key_length": "5","rows_examined_per_scan": 6213,"rows_produced_per_join": 3106,"filtered": "50.00","index_condition": "(`ytt`.`t2`.`rank1` between 1 and 10)","cost_info": {"read_cost": "2485.46","eval_cost": "310.65","prefix_cost": "2796.11","data_read_per_join": "72K"},"used_columns": ["rank1","log_date"],"attached_condition": "(`ytt`.`t2`.`log_date` < '2018-09-01')"}}
}

mysql> explain format=json select count(*) from t3 where (rank1 between 1 and 10) and log_date < '2018-09-01'G
*************************** 1. row ***************************
EXPLAIN: {"query_block": {"select_id": 1,"cost_info": {"query_cost": "0.71"},"table": {"table_name": "t3","access_type": "range","possible_keys": ["idx_rank1","idx_log_date"],"key": "idx_log_date","used_key_parts": ["log_date"],"key_length": "4","rows_examined_per_scan": 1,"rows_produced_per_join": 1,"filtered": "100.00","index_condition": "(`ytt`.`t3`.`log_date` < '2018-09-01')","cost_info": {"read_cost": "0.61","eval_cost": "0.10","prefix_cost": "0.71","data_read_per_join": "24"},"used_columns": ["rank1","log_date"],"attached_condition": "(`ytt`.`t3`.`rank1` between 1 and 10)"}}
}
1 row in set, 1 warning (0.00 sec)