倒排索引

SinceVersion 2.0.0

从2.0.0版本开始，Doris支持倒排索引，可以用来进行文本类型的全文检索、普通数值日期类型的等值范围查询，快速从海量数据中过滤出满足条件的行。本文档主要介绍如何倒排索引的创建、删除、查询等使用方式。

名词解释

inverted index：倒排索引，是信息检索领域常用的索引技术，将文本分割成一个个词，构建词 -> 文档编号的索引，可以快速查找一个词在哪些文档出现。

原理介绍

在Doris的倒排索引实现中，table的一行对应一个文档、一列对应文档中的一个字段，因此利用倒排索引可以根据关键词快速定位包含它的行，达到WHERE子句加速的目的。

与Doris中其他索引不同的是，在存储层倒排索引使用独立的文件，跟segment文件有逻辑对应关系、但存储的文件相互独立。这样的好处是可以做到创建、删除索引不用重写tablet和segment文件，大幅降低处理开销。

功能介绍

Doris倒排索引的功能简要介绍如下：

增加了字符串类型的全文检索
- 支持字符串全文检索，包括同时匹配多个关键字MATCH_ALL、匹配任意一个关键字MATCH_ANY
- 支持短语查询 MATCH_PHRASE
- 支持短语+前缀 MATCH_PHRASE_PREFIX
- 支持正则查询 MATCH_REGEXP
- 支持字符串数组类型的全文检索
- 支持英文、中文以及Unicode多语言分词
加速普通等值、范围查询，覆盖bitmap索引的功能，可代替bitmap索引
- 支持字符串、数值、日期时间类型的 =, !=, >, >=, <, <= 快速过滤
- 支持字符串、数字、日期时间数组类型的 =, !=, >, >=, <, <=
支持完善的逻辑组合
- 新增索引对OR NOT逻辑的下推
- 支持多个条件的任意AND OR NOT组合
灵活、快速的索引管理
- 支持在创建表上定义倒排索引
- 支持在已有的表上增加倒排索引，而且支持增量构建倒排索引，无需重写表中的已有数据
- 支持删除已有表上的倒排索引，无需重写表中的已有数据

语法

建表时定义倒排索引，语法说明如下
- USING INVERTED 是必须的，用于指定索引类型是倒排索引
- PROPERTIES 是可选的，用于指定倒排索引的额外属性，目前有三个属性
  - parser指定分词器
    - 默认不指定代表不分词
    - english是英文分词，适合被索引列是英文的情况，用空格和标点符号分词，性能高
    - chinese是中文分词，适合被索引列主要是中文的情况，性能比english分词低
    - unicode是多语言混合类型分词，适用于中英文混合、多语言混合的情况。它能够对邮箱前缀和后缀、IP地址以及字符数字混合进行分词，并且可以对中文按字符分词。
  - parser_mode用于指定分词的模式，目前parser = chinese时支持如下几种模式：
    - fine_grained：细粒度模式，倾向于分出比较短的词，比如 '武汉市长江大桥' 会分成 '武汉', '武汉市', '市长', '长江', '长江大桥', '大桥' 6个词
    - coarse_grained：粗粒度模式，倾向于分出比较长的词，，比如 '武汉市长江大桥' 会分成 '武汉市' '长江大桥' 2个词
    - 默认coarse_grained
  - support_phrase用于指定索引是否支持MATCH_PHRASE短语查询加速
    - true为支持，但是索引需要更多的存储空间
    - false为不支持，更省存储空间，可以用MATCH_ALL查询多个关键字
    - 默认false
  - char_filter：功能主要在分词前对字符串提前处理
    - char_filter_type：指定使用不同功能的char_filter（目前仅支持char_replace）
      - char_replace 将pattern中每个char替换为一个replacement中的char
        char_filter_pattern：需要被替换掉的字符数组
        char_filter_replacement：替换后的字符数组，可以不用配置，默认为一个空格字符
  - ignore_above：控制字符串是否建索引。
    - 长度超过 ignore_above 设置的字符串不会被索引。对于字符串数组，ignore_above 将分别应用于每个数组元素，长度超过 ignore_above 的字符串元素将不被索引。
    - 默认为 256 字节
  - lower_case: 是否将分词进行小写转换，从而在匹配的时候实现忽略大小写
    - true: 转换小写
    - false：不转换小写
- COMMENT 是可选的，用于指定注释

CREATE TABLE table_name
(
  columns_difinition,
  INDEX idx_name1(column_name1) USING INVERTED [PROPERTIES("parser" = "english|unicode|chinese")] [COMMENT 'your comment']
  INDEX idx_name2(column_name2) USING INVERTED [PROPERTIES("parser" = "english|unicode|chinese")] [COMMENT 'your comment']
  INDEX idx_name3(column_name3) USING INVERTED [PROPERTIES("parser" = "chinese", "parser_mode" = "fine_grained|coarse_grained")] [COMMENT 'your comment']
  INDEX idx_name4(column_name4) USING INVERTED [PROPERTIES("parser" = "english|unicode|chinese", "support_phrase" = "true|false")] [COMMENT 'your comment']
  INDEX idx_name5(column_name4) USING INVERTED [PROPERTIES("char_filter_type" = "char_replace", "char_filter_pattern" = "._"), "char_filter_replacement" = " "] [COMMENT 'your comment']
  INDEX idx_name5(column_name4) USING INVERTED [PROPERTIES("char_filter_type" = "char_replace", "char_filter_pattern" = "._")] [COMMENT 'your comment']
)
table_properties;

提示

倒排索引在不同数据模型中有不同的使用限制：

Aggregate 模型：只能为 Key 列建立倒排索引。
Unique 模型：需要开启 merge on write 特性，开启后，可以为任意列建立倒排索引。
Duplicate 模型：可以为任意列建立倒排索引。

已有表增加倒排索引

2.0-beta版本之前：

-- 语法1
CREATE INDEX idx_name ON table_name(column_name) USING INVERTED [PROPERTIES("parser" = "english|unicode|chinese")] [COMMENT 'your comment'];
-- 语法2
ALTER TABLE table_name ADD INDEX idx_name(column_name) USING INVERTED [PROPERTIES("parser" = "english|unicode|chinese")] [COMMENT 'your comment'];

2.0-beta版本（含2.0-beta）之后：

上述create/add index操作只对增量数据生成倒排索引，增加了BUILD INDEX的语法用于对存量数据加倒排索引：

-- 语法1，默认给全表的存量数据加上倒排索引
BUILD INDEX index_name ON table_name;
-- 语法2，可指定partition，可指定一个或多个
BUILD INDEX index_name ON table_name PARTITIONS(partition_name1, partition_name2);

(在执行BUILD INDEX之前需要已经执行了以上create/add index的操作)

查看BUILD INDEX进展，可通过以下语句进行查看：

SHOW BUILD INDEX [FROM db_name];
-- 示例1，查看所有的BUILD INDEX任务进展
SHOW BUILD INDEX;
-- 示例2，查看指定table的BUILD INDEX任务进展
SHOW BUILD INDEX where TableName = "table1";

取消 BUILD INDEX, 可通过以下语句进行

CANCEL BUILD INDEX ON table_name;
CANCEL BUILD INDEX ON table_name (job_id1,jobid_2,...);

删除倒排索引

-- 语法1
DROP INDEX idx_name ON table_name;
-- 语法2
ALTER TABLE table_name DROP INDEX idx_name;

利用倒排索引加速查询

-- 1. 全文检索关键词匹配，通过MATCH_ANY MATCH_ALL完成
SELECT * FROM table_name WHERE column_name MATCH_ANY | MATCH_ALL 'keyword1 ...';

-- 1.1 logmsg中包含keyword1的行
SELECT * FROM table_name WHERE logmsg MATCH_ANY 'keyword1';

-- 1.2 logmsg中包含keyword1或者keyword2的行，后面还可以添加多个keyword
SELECT * FROM table_name WHERE logmsg MATCH_ANY 'keyword1 keyword2';

-- 1.3 logmsg中同时包含keyword1和keyword2的行，后面还可以添加多个keyword
SELECT * FROM table_name WHERE logmsg MATCH_ALL 'keyword1 keyword2';

-- 1.4 不指定slop时短语查询slop为0，keyword1 keyword2位置相邻，可以通过~指定短语查询的slop
SELECT * FROM table_name WHERE logmsg MATCH_PHRASE 'keyword1 keyword2';
SELECT * FROM table_name WHERE logmsg MATCH_PHRASE 'keyword1 keyword2 ~3';

-- 1.5 在保持词顺序的前提下，对最后一个词keyword2做前缀匹配，默认找50个前缀词（session变量inverted_index_max_expansions控制）
SELECT * FROM table_name WHERE logmsg MATCH_PHRASE_PREFIX 'keyword1 keyword2';

-- 1.6 如果只填一个词会退化为前缀查询，默认找50个前缀词（session变量inverted_index_max_expansions控制）
SELECT * FROM table_name WHERE logmsg MATCH_PHRASE_PREFIX 'keyword1';

-- 1.7 对分词后的词进行正则匹配，默认匹配50个（session变量inverted_index_max_expansions控制）
SELECT * FROM table_name WHERE logmsg MATCH_REGEXP 'key*';


-- 2. 普通等值、范围、IN、NOT IN，正常的SQL语句即可，例如
SELECT * FROM table_name WHERE id = 123;
SELECT * FROM table_name WHERE ts > '2023-01-01 00:00:00';
SELECT * FROM table_name WHERE op_type IN ('add', 'delete');

分词函数

如果想检查分词实际效果或者对一段文本进行分词的话，可以使用tokenize函数

mysql> SELECT TOKENIZE('武汉长江大桥','"parser"="chinese","parser_mode"="fine_grained"');
+-----------------------------------------------------------------------------------+
| tokenize('武汉长江大桥', '"parser"="chinese","parser_mode"="fine_grained"')       |
+-----------------------------------------------------------------------------------+
| ["武汉", "武汉长江大桥", "长江", "长江大桥", "大桥"]                              |
+-----------------------------------------------------------------------------------+
1 row in set (0.02 sec)

mysql> SELECT TOKENIZE('武汉市长江大桥','"parser"="chinese","parser_mode"="fine_grained"');
+--------------------------------------------------------------------------------------+
| tokenize('武汉市长江大桥', '"parser"="chinese","parser_mode"="fine_grained"')        |
+--------------------------------------------------------------------------------------+
| ["武汉", "武汉市", "市长", "长江", "长江大桥", "大桥"]                               |
+--------------------------------------------------------------------------------------+
1 row in set (0.02 sec)

mysql> SELECT TOKENIZE('武汉市长江大桥','"parser"="chinese","parser_mode"="coarse_grained"');
+----------------------------------------------------------------------------------------+
| tokenize('武汉市长江大桥', '"parser"="chinese","parser_mode"="coarse_grained"')        |
+----------------------------------------------------------------------------------------+
| ["武汉市", "长江大桥"]                                                                 |
+----------------------------------------------------------------------------------------+
1 row in set (0.02 sec)

mysql> SELECT TOKENIZE('I love CHINA','"parser"="english"');
+------------------------------------------------+
| tokenize('I love CHINA', '"parser"="english"') |
+------------------------------------------------+
| ["i", "love", "china"]                         |
+------------------------------------------------+
1 row in set (0.02 sec)

mysql> SELECT TOKENIZE('I love CHINA 我爱我的祖国','"parser"="unicode"');
+-------------------------------------------------------------------+
| tokenize('I love CHINA 我爱我的祖国', '"parser"="unicode"')       |
+-------------------------------------------------------------------+
| ["i", "love", "china", "我", "爱", "我", "的", "祖", "国"]        |
+-------------------------------------------------------------------+
1 row in set (0.02 sec)

使用示例

用hackernews 100万条数据展示倒排索引的创建、全文检索、普通查询，包括跟无索引的查询性能进行简单对比。

建表

CREATE DATABASE test_inverted_index;

USE test_inverted_index;

-- 创建表的同时创建了comment的倒排索引idx_comment
--   USING INVERTED 指定索引类型是倒排索引
--   PROPERTIES("parser" = "english") 指定采用english分词，还支持"chinese"中文分词和"unicode"中英文多语言混合分词，如果不指定"parser"参数表示不分词
CREATE TABLE hackernews_1m
(
    `id` BIGINT,
    `deleted` TINYINT,
    `type` String,
    `author` String,
    `timestamp` DateTimeV2,
    `comment` String,
    `dead` TINYINT,
    `parent` BIGINT,
    `poll` BIGINT,
    `children` Array<BIGINT>,
    `url` String,
    `score` INT,
    `title` String,
    `parts` Array<INT>,
    `descendants` INT,
    INDEX idx_comment (`comment`) USING INVERTED PROPERTIES("parser" = "english") COMMENT 'inverted index for comment'
)
DUPLICATE KEY(`id`)
DISTRIBUTED BY HASH(`id`) BUCKETS 10
PROPERTIES ("replication_num" = "1");

导入数据

通过stream load导入数据

wget https://doris-build-1308700295.cos.ap-beijing.myqcloud.com/regression/index/hacknernews_1m.csv.gz

curl --location-trusted -u root: -H "compress_type:gz" -T hacknernews_1m.csv.gz  http://127.0.0.1:8030/api/test_inverted_index/hackernews_1m/_stream_load
{
    "TxnId": 2,
    "Label": "a8a3e802-2329-49e8-912b-04c800a461a6",
    "TwoPhaseCommit": "false",
    "Status": "Success",
    "Message": "OK",
    "NumberTotalRows": 1000000,
    "NumberLoadedRows": 1000000,
    "NumberFilteredRows": 0,
    "NumberUnselectedRows": 0,
    "LoadBytes": 130618406,
    "LoadTimeMs": 8988,
    "BeginTxnTimeMs": 23,
    "StreamLoadPutTimeMs": 113,
    "ReadDataTimeMs": 4788,
    "WriteDataTimeMs": 8811,
    "CommitAndPublishTimeMs": 38
}

SQL运行count()确认导入数据成功

mysql> SELECT count() FROM hackernews_1m;
+---------+
| count() |
+---------+
| 1000000 |
+---------+
1 row in set (0.02 sec)

查询

全文检索

用LIKE匹配计算comment中含有'OLAP'的行数，耗时0.18s

mysql> SELECT count() FROM hackernews_1m WHERE comment LIKE '%OLAP%';
+---------+
| count() |
+---------+
|      34 |
+---------+
1 row in set (0.18 sec)

用基于倒排索引的全文检索MATCH_ANY计算comment中含有'OLAP'的行数，耗时0.02s，加速9倍，在更大的数据集上效果会更加明显
- 这里结果条数的差异，是因为倒排索引对comment分词后，还会对词进行进行统一成小写等归一化处理，因此MATCH_ANY比LIKE的结果多一些

mysql> SELECT count() FROM hackernews_1m WHERE comment MATCH_ANY 'OLAP';
+---------+
| count() |
+---------+
|      35 |
+---------+
1 row in set (0.02 sec)

同样的对比统计'OLTP'出现次数的性能，0.07s vs 0.01s，由于缓存的原因LIKE和MATCH_ANY都有提升，倒排索引仍然有7倍加速

mysql> SELECT count() FROM hackernews_1m WHERE comment LIKE '%OLTP%';
+---------+
| count() |
+---------+
|      48 |
+---------+
1 row in set (0.07 sec)

mysql> SELECT count() FROM hackernews_1m WHERE comment MATCH_ANY 'OLTP';
+---------+
| count() |
+---------+
|      51 |
+---------+
1 row in set (0.01 sec)

同时出现'OLAP'和'OLTP'两个词，0.13s vs 0.01s，13倍加速
- 要求多个词同时出现时（AND关系）使用 MATCH_ALL 'keyword1 keyword2 ...'

mysql> SELECT count() FROM hackernews_1m WHERE comment LIKE '%OLAP%' AND comment LIKE '%OLTP%';
+---------+
| count() |
+---------+
|      14 |
+---------+
1 row in set (0.13 sec)

mysql> SELECT count() FROM hackernews_1m WHERE comment MATCH_ALL 'OLAP OLTP';
+---------+
| count() |
+---------+
|      15 |
+---------+
1 row in set (0.01 sec)

任意出现'OLAP'和'OLTP'其中一个词，0.12s vs 0.01s，12倍加速
- 只要求多个词任意一个或多个出现时（OR关系）使用 MATCH_ANY 'keyword1 keyword2 ...'

mysql> SELECT count() FROM hackernews_1m WHERE comment LIKE '%OLAP%' OR comment LIKE '%OLTP%';
+---------+
| count() |
+---------+
|      68 |
+---------+
1 row in set (0.12 sec)

mysql> SELECT count() FROM hackernews_1m WHERE comment MATCH_ANY 'OLAP OLTP';
+---------+
| count() |
+---------+
|      71 |
+---------+
1 row in set (0.01 sec)

普通等值、范围查询

DataTime类型的列范围查询

mysql> SELECT count() FROM hackernews_1m WHERE timestamp > '2007-08-23 04:17:00';
+---------+
| count() |
+---------+
|  999081 |
+---------+
1 row in set (0.03 sec)

为timestamp列增加一个倒排索引

-- 对于日期时间类型USING INVERTED，不用指定分词
-- CREATE INDEX 是第一种建索引的语法，另外一种在后面展示
mysql> CREATE INDEX idx_timestamp ON hackernews_1m(timestamp) USING INVERTED;
Query OK, 0 rows affected (0.03 sec)

2.0-beta(含2.0-beta)后，需要再执行BUILD INDEX才能给存量数据加上倒排索引：

mysql> BUILD INDEX idx_timestamp ON hackernews_1m;
Query OK, 0 rows affected (0.01 sec)

查看索引创建进度，通过FinishTime和CreateTime的差值，可以看到100万条数据对timestamp列建倒排索引只用了1s

mysql> SHOW ALTER TABLE COLUMN;
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
| JobId | TableName     | CreateTime              | FinishTime              | IndexName     | IndexId | OriginIndexId | SchemaVersion | TransactionId | State    | Msg  | Progress | Timeout |
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
| 10030 | hackernews_1m | 2023-02-10 19:44:12.929 | 2023-02-10 19:44:13.938 | hackernews_1m | 10031   | 10008         | 1:1994690496  | 3             | FINISHED |      | NULL     | 2592000 |
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
1 row in set (0.00 sec)

2.0-beta(含2.0-beta)后，可通过show builde index来查看存量数据创建索引进展：

-- 若table没有分区，PartitionName默认就是TableName
mysql> SHOW BUILD INDEX;
+-------+---------------+---------------+----------------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
| JobId | TableName     | PartitionName | AlterInvertedIndexes                                     | CreateTime              | FinishTime              | TransactionId | State    | Msg  | Progress |
+-------+---------------+---------------+----------------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
| 10191 | hackernews_1m | hackernews_1m | [ADD INDEX idx_timestamp (`timestamp`) USING INVERTED],  | 2023-06-26 15:32:33.894 | 2023-06-26 15:32:34.847 | 3             | FINISHED |      | NULL     |
+-------+---------------+---------------+----------------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
1 row in set (0.04 sec)

索引创建后，范围查询用同样的查询方式，Doris会自动识别索引进行优化，但是这里由于数据量小性能差别不大

mysql> SELECT count() FROM hackernews_1m WHERE timestamp > '2007-08-23 04:17:00';
+---------+
| count() |
+---------+
|  999081 |
+---------+
1 row in set (0.01 sec)

在数值类型的列parent进行类似timestamp的操作，这里查询使用等值匹配

mysql> SELECT count() FROM hackernews_1m WHERE parent = 11189;
+---------+
| count() |
+---------+
|       2 |
+---------+
1 row in set (0.01 sec)

-- 对于数值类型USING INVERTED，不用指定分词
-- ALTER TABLE t ADD INDEX 是第二种建索引的语法
mysql> ALTER TABLE hackernews_1m ADD INDEX idx_parent(parent) USING INVERTED;
Query OK, 0 rows affected (0.01 sec)

-- 2.0-beta(含2.0-beta)后，需要再执行BUILD INDEX才能给存量数据加上倒排索引：
mysql> BUILD INDEX idx_parent ON hackernews_1m;
Query OK, 0 rows affected (0.01 sec)

mysql> SHOW ALTER TABLE COLUMN;
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
| JobId | TableName     | CreateTime              | FinishTime              | IndexName     | IndexId | OriginIndexId | SchemaVersion | TransactionId | State    | Msg  | Progress | Timeout |
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
| 10030 | hackernews_1m | 2023-02-10 19:44:12.929 | 2023-02-10 19:44:13.938 | hackernews_1m | 10031   | 10008         | 1:1994690496  | 3             | FINISHED |      | NULL     | 2592000 |
| 10053 | hackernews_1m | 2023-02-10 19:49:32.893 | 2023-02-10 19:49:33.982 | hackernews_1m | 10054   | 10008         | 1:378856428   | 4             | FINISHED |      | NULL     | 2592000 |
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+

mysql> SHOW BUILD INDEX;
+-------+---------------+---------------+----------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
| JobId | TableName     | PartitionName | AlterInvertedIndexes                               | CreateTime              | FinishTime              | TransactionId | State    | Msg  | Progress |
+-------+---------------+---------------+----------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
| 11005 | hackernews_1m | hackernews_1m | [ADD INDEX idx_parent (`parent`) USING INVERTED],  | 2023-06-26 16:25:10.167 | 2023-06-26 16:25:10.838 | 1002          | FINISHED |      | NULL     |
+-------+---------------+---------------+----------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
1 row in set (0.01 sec)

mysql> SELECT count() FROM hackernews_1m WHERE parent = 11189;
+---------+
| count() |
+---------+
|       2 |
+---------+
1 row in set (0.01 sec)

对字符串类型的author建立不分词的倒排索引，等值查询也可以利用索引加速

mysql> SELECT count() FROM hackernews_1m WHERE author = 'faster';
+---------+
| count() |
+---------+
|      20 |
+---------+
1 row in set (0.03 sec)

-- 这里只用了USING INVERTED，不对author分词，整个当做一个词处理
mysql> ALTER TABLE hackernews_1m ADD INDEX idx_author(author) USING INVERTED;
Query OK, 0 rows affected (0.01 sec)

-- 2.0-beta(含2.0-beta)后，需要再执行BUILD INDEX才能给存量数据加上倒排索引：
mysql> BUILD INDEX idx_author ON hackernews_1m;
Query OK, 0 rows affected (0.01 sec)

-- 100万条author数据增量建索引仅消耗1.5s
mysql> SHOW ALTER TABLE COLUMN;
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
| JobId | TableName     | CreateTime              | FinishTime              | IndexName     | IndexId | OriginIndexId | SchemaVersion | TransactionId | State    | Msg  | Progress | Timeout |
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+
| 10030 | hackernews_1m | 2023-02-10 19:44:12.929 | 2023-02-10 19:44:13.938 | hackernews_1m | 10031   | 10008         | 1:1994690496  | 3             | FINISHED |      | NULL     | 2592000 |
| 10053 | hackernews_1m | 2023-02-10 19:49:32.893 | 2023-02-10 19:49:33.982 | hackernews_1m | 10054   | 10008         | 1:378856428   | 4             | FINISHED |      | NULL     | 2592000 |
| 10076 | hackernews_1m | 2023-02-10 19:54:20.046 | 2023-02-10 19:54:21.521 | hackernews_1m | 10077   | 10008         | 1:1335127701  | 5             | FINISHED |      | NULL     | 2592000 |
+-------+---------------+-------------------------+-------------------------+---------------+---------+---------------+---------------+---------------+----------+------+----------+---------+

mysql> SHOW BUILD INDEX order by CreateTime desc limit 1;
+-------+---------------+---------------+----------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
| JobId | TableName     | PartitionName | AlterInvertedIndexes                               | CreateTime              | FinishTime              | TransactionId | State    | Msg  | Progress |
+-------+---------------+---------------+----------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
| 13006 | hackernews_1m | hackernews_1m | [ADD INDEX idx_author (`author`) USING INVERTED],  | 2023-06-26 17:23:02.610 | 2023-06-26 17:23:03.755 | 3004          | FINISHED |      | NULL     |
+-------+---------------+---------------+----------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+
1 row in set (0.01 sec)

-- 创建索引后，字符串等值匹配也有明显加速
mysql> SELECT count() FROM hackernews_1m WHERE author = 'faster';
+---------+
| count() |
+---------+
|      20 |
+---------+
1 row in set (0.01 sec)

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