VARIANT
SinceVersion 2.1.0
Description
VARIANT类型 在 Doris 2.1 中引入一种新的数据类型 VARIANT,它可以存储半结构化 JSON 数据。它允许存储包含不同数据类型(如整数、字符串、布尔值等)的复杂数据结构,而无需在表结构中提前定义具体的列。VARIANT 类型特别适用于处理复杂的嵌套结构,而这些结构可能随时会发生变化。在写入过程中,该类型可以自动根据列的结构、类型推断列信息,动态合并写入的 schema,并通过将 JSON 键及其对应的值存储为列和动态子列。
Note
相比 JSON 类型有有以下优势:
- 存储方式不同, JSON 类型是以二进制 JSONB 格式进行存储,整行 JSON 以行存的形式存储到 segment 文件中。而 VARIANT 类型在写入的时候进行类型推断,将写入的 JSON 列存化。比JSON类型有更高的压缩比, 存储空间更小。
- 查询方式不同,查询不需要进行解析。VARIANT 充分利用 Doris 中列式存储、向量化引擎、优化器等组件给用户带来极高的查询性能。 下面是基于 clickbench 数据测试的结果:
| 存储空间 | |
|---|---|
| 预定义静态列 | 12.618 GB |
| variant 类型 | 12.718 GB |
| json 类型 | 35.711 GB |
节省约 65%存储容量
| 查询次数 | 预定义静态列 | variant 类型 | json 类型 |
|---|---|---|---|
| 第一次查询 (cold) | 233.79s | 248.66s | 大部分查询超时 |
| 第二次查询 (hot) | 86.02s | 94.82s | 789.24s |
| 第三次查询 (hot) | 83.03s | 92.29s | 743.69s |
测试集 一共43个查询语句
查询提速 8+倍, 查询性能与静态列相当
Example
1. 用一个从建表、导数据、查询全周期的例子说明VARIANT的功能和用法。
建表语法
建表,建表语法关键字 variant
1. -- 无索引
2. CREATE TABLE IF NOT EXISTS ${table_name} (
3. k BIGINT,
4. v VARIANT
5. )
6. table_properties;
8. -- 在v列创建索引,可选指定分词方式,默认不分词
9. CREATE TABLE IF NOT EXISTS ${table_name} (
10. k BIGINT,
11. v VARIANT,
12. INDEX idx_var(v) USING INVERTED [PROPERTIES("parser" = "english|unicode|chinese")] [COMMENT 'your comment']
13. )
14. table_properties;
16. -- 在v列创建bloom filter
17. CREATE TABLE IF NOT EXISTS ${table_name} (
18. k BIGINT,
19. v VARIANT
20. )
21. ...
22. properties("replication_num" = "1", "bloom_filter_columns" = "v");
查询语法
1. -- 使用 v['a']['b'] 形式如下,v['properties']['title']类型是Variant
2. SELECT v['properties']['title'] from ${table_name}
基于 github events 数据集示例
这里用 github events 数据展示 variant 的建表、导入、查询。 下面是格式化后的一行数据
1. {
2. "id": "14186154924",
3. "type": "PushEvent",
4. "actor": {
5. "id": 282080,
6. "login": "brianchandotcom",
7. "display_login": "brianchandotcom",
8. "gravatar_id": "",
9. "url": "https://api.github.com/users/brianchandotcom",
10. "avatar_url": "https://avatars.githubusercontent.com/u/282080?"
11. },
12. "repo": {
13. "id": 1920851,
14. "name": "brianchandotcom/liferay-portal",
15. "url": "https://api.github.com/repos/brianchandotcom/liferay-portal"
16. },
17. "payload": {
18. "push_id": 6027092734,
19. "size": 4,
20. "distinct_size": 4,
21. "ref": "refs/heads/master",
22. "head": "91edd3c8c98c214155191feb852831ec535580ba",
23. "before": "abb58cc0db673a0bd5190000d2ff9c53bb51d04d",
24. "commits": [""]
25. },
26. "public": true,
27. "created_at": "2020-11-13T18:00:00Z"
28. }
建表
- 创建了三个 VARIANT 类型的列,
actor,repo和payload - 创建表的同时创建了
payload列的倒排索引idx_payload - USING INVERTED 指定索引类型是倒排索引,用于加速子列的条件过滤
PROPERTIES("parser" = "english")指定采用 english 分词
1. CREATE DATABASE test_variant;
2. USE test_variant;
3. CREATE TABLE IF NOT EXISTS github_events (
4. id BIGINT NOT NULL,
5. type VARCHAR(30) NULL,
6. actor VARIANT NULL,
7. repo VARIANT NULL,
8. payload VARIANT NULL,
9. public BOOLEAN NULL,
10. created_at DATETIME NULL,
11. INDEX idx_payload (`payload`) USING INVERTED PROPERTIES("parser" = "english") COMMENT 'inverted index for payload'
12. )
13. DUPLICATE KEY(`id`)
14. DISTRIBUTED BY HASH(id) BUCKETS 10
15. properties("replication_num" = "1");
需要注意的是:
提示
- 在 VARIANT 列上创建索引,比如 payload 的子列很多时,可能会造成索引列过多,影响写入性能
- 同一个 VARIANT 列的分词属性是相同的,如果您有不同的分词需求,那么可以创建多个 VARIANT 然后分别指定索引属性
使用 streamload 导入
导入gh_2022-11-07-3.json,这是 github events 一个小时的数据
1. wget http://doris-build-hk-1308700295.cos.ap-hongkong.myqcloud.com/regression/variant/gh_2022-11-07-3.json
3. curl --location-trusted -u root: -T gh_2022-11-07-3.json -H "read_json_by_line:true" -H "format:json" http://127.0.0.1:18148/api/test_variant/github_events/_strea
4. m_load
6. {
7. "TxnId": 2,
8. "Label": "086fd46a-20e6-4487-becc-9b6ca80281bf",
9. "Comment": "",
10. "TwoPhaseCommit": "false",
11. "Status": "Success",
12. "Message": "OK",
13. "NumberTotalRows": 139325,
14. "NumberLoadedRows": 139325,
15. "NumberFilteredRows": 0,
16. "NumberUnselectedRows": 0,
17. "LoadBytes": 633782875,
18. "LoadTimeMs": 7870,
19. "BeginTxnTimeMs": 19,
20. "StreamLoadPutTimeMs": 162,
21. "ReadDataTimeMs": 2416,
22. "WriteDataTimeMs": 7634,
23. "CommitAndPublishTimeMs": 55
24. }
确认导入成功
1. -- 查看行数
2. mysql> select count() from github_events;
3. +----------+
4. | count(*) |
5. +----------+
6. | 139325 |
7. +----------+
8. 1 row in set (0.25 sec)
10. -- 随机看一条数据
11. mysql> select * from github_events limit 1;
12. +-------------+-----------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+---------------------+
13. | id | type | actor | repo | payload | public | created_at |
14. +-------------+-----------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+---------------------+
15. | 25061821748 | PushEvent | {"gravatar_id":"","display_login":"jfrog-pipelie-intg","url":"https://api.github.com/users/jfrog-pipelie-intg","id":98024358,"login":"jfrog-pipelie-intg","avatar_url":"https://avatars.githubusercontent.com/u/98024358?"} | {"url":"https://api.github.com/repos/jfrog-pipelie-intg/jfinte2e_1667789956723_16","id":562683829,"name":"jfrog-pipelie-intg/jfinte2e_1667789956723_16"} | {"commits":[{"sha":"334433de436baa198024ef9f55f0647721bcd750","author":{"email":"98024358+jfrog-pipelie-intg@users.noreply.github.com","name":"jfrog-pipelie-intg"},"message":"commit message 10238493157623136117","distinct":true,"url":"https://api.github.com/repos/jfrog-pipelie-intg/jfinte2e_1667789956723_16/commits/334433de436baa198024ef9f55f0647721bcd750"}],"before":"f84a26792f44d54305ddd41b7e3a79d25b1a9568","head":"334433de436baa198024ef9f55f0647721bcd750","size":1,"push_id":11572649828,"ref":"refs/heads/test-notification-sent-branch-10238493157623136113","distinct_size":1} | 1 | 2022-11-07 11:00:00 |
16. +-------------+-----------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+---------------------+
17. 1 row in set (0.23 sec)
desc 查看 schema 信息,子列会在存储层自动扩展、并进行类型推导
1. mysql> desc github_events;
2. +------------------------------------------------------------+------------+------+-------+---------+-------+
3. | Field | Type | Null | Key | Default | Extra |
4. +------------------------------------------------------------+------------+------+-------+---------+-------+
5. | id | BIGINT | No | true | NULL | |
6. | type | VARCHAR(*) | Yes | false | NULL | NONE |
7. | actor | VARIANT | Yes | false | NULL | NONE |
8. | created_at | DATETIME | Yes | false | NULL | NONE |
9. | payload | VARIANT | Yes | false | NULL | NONE |
10. | public | BOOLEAN | Yes | false | NULL | NONE |
11. +------------------------------------------------------------+------------+------+-------+---------+-------+
12. 6 rows in set (0.07 sec)
14. mysql> set describe_extend_variant_column = true;
15. Query OK, 0 rows affected (0.01 sec)
17. mysql> desc github_events;
18. +------------------------------------------------------------+------------+------+-------+---------+-------+
19. | Field | Type | Null | Key | Default | Extra |
20. +------------------------------------------------------------+------------+------+-------+---------+-------+
21. | id | BIGINT | No | true | NULL | |
22. | type | VARCHAR(*) | Yes | false | NULL | NONE |
23. | actor | VARIANT | Yes | false | NULL | NONE |
24. | actor.avatar_url | TEXT | Yes | false | NULL | NONE |
25. | actor.display_login | TEXT | Yes | false | NULL | NONE |
26. | actor.id | INT | Yes | false | NULL | NONE |
27. | actor.login | TEXT | Yes | false | NULL | NONE |
28. | actor.url | TEXT | Yes | false | NULL | NONE |
29. | created_at | DATETIME | Yes | false | NULL | NONE |
30. | payload | VARIANT | Yes | false | NULL | NONE |
31. | payload.action | TEXT | Yes | false | NULL | NONE |
32. | payload.before | TEXT | Yes | false | NULL | NONE |
33. | payload.comment.author_association | TEXT | Yes | false | NULL | NONE |
34. | payload.comment.body | TEXT | Yes | false | NULL | NONE |
35. ....
36. +------------------------------------------------------------+------------+------+-------+---------+-------+
37. 406 rows in set (0.07 sec)
desc 可以指定 partition 查看某个 partition 的 schema, 语法如下
1. DESCRIBE ${table_name} PARTITION ($partition_name);
查询
提示
注意 如使用过滤和聚合等功能来查询子列, 需要对子列进行额外的 cast 操作(因为存储类型不一定是固定的,需要有一个 SQL 统一的类型)。 例如 SELECT * FROM tbl where CAST(var[‘titile’] as text) MATCH “hello world” 以下简化的示例说明了如何使用 VARIANT 进行查询
下面是典型的三个查询场景:
- 从 github_events 表中获取 top 5 star 数的代码库
1. mysql> SELECT
2. -> cast(repo['name'] as text) as repo_name, count() AS stars
3. -> FROM github_events
4. -> WHERE type = 'WatchEvent'
5. -> GROUP BY stars
6. -> ORDER BY stars DESC LIMIT 5;
7. +--------------------------+-------+
8. | repo_name | stars |
9. +--------------------------+-------+
10. | aplus-framework/app | 78 |
11. | lensterxyz/lenster | 77 |
12. | aplus-framework/database | 46 |
13. | stashapp/stash | 42 |
14. | aplus-framework/image | 34 |
15. +--------------------------+-------+
16. 5 rows in set (0.03 sec)
- 获取评论中包含 doris 的数量
1. mysql> SELECT
2. -> count() FROM github_events
3. -> WHERE cast(payload['comment']['body'] as text) MATCH 'doris';
4. +---------+
5. | count() |
6. +---------+
7. | 3 |
8. +---------+
9. 1 row in set (0.04 sec)
- 查询 comments 最多的 issue 号以及对应的库
1. mysql> SELECT
2. -> cast(repo['name'] as string) as repo_name,
3. -> cast(payload['issue']['number'] as int) as issue_number,
4. -> count() AS comments,
5. -> count(
6. -> distinct cast(actor['login'] as string)
7. -> ) AS authors
8. -> FROM github_events
9. -> WHERE type = 'IssueCommentEvent' AND (cast(payload['action'] as string) = 'created') AND (cast(payload['issue']['number'] as int) > 10)
10. -> GROUP BY repo_name, issue_number
11. -> HAVING authors >= 4
12. -> ORDER BY comments DESC, repo_name
13. -> LIMIT 50;
14. +--------------------------------------+--------------+----------+---------+
15. | repo_name | issue_number | comments | authors |
16. +--------------------------------------+--------------+----------+---------+
17. | facebook/react-native | 35228 | 5 | 4 |
18. | swsnu/swppfall2022-team4 | 27 | 5 | 4 |
19. | belgattitude/nextjs-monorepo-example | 2865 | 4 | 4 |
20. +--------------------------------------+--------------+----------+---------+
21. 3 rows in set (0.03 sec)
使用限制和最佳实践
VARIANT 类型的使用有以下限制: VARIANT 动态列与预定义静态列几乎一样高效。处理诸如日志之类的数据 ,在这类数据中,经常通过动态属性添加字段(例如 Kubernetes 中的容器标签)。但是解析 JSON 和推断类型会在写入时产生额外开销。因此,我们建议保持单次导入列数在 1000 以下。
尽可能保证类型一致, Doris 会自动进行如下兼容类型转换,当字段无法进行兼容类型转换时会统一转换成 JSONB 类型。JSONB 列的性能与 int、text 等列性能会有所退化。
- tinyint->smallint->int->bigint, 整形可以按照箭头做类型提升
- float->double,浮点数按照箭头做类型提升
- text,字符串类型
- JSON, 二进制JSON类型
上诉类型无法兼容时, 会变成JSON类型防止类型信息丢失, 如果您需要在 VARIANT 中设置严格的schema,即将推出 VARIANT MAPPING机制
其它限制如下:
- 目前不支持 Aggregate 模型
- VARIANT 列只能创建倒排索引或者bloom filter来加速过滤
- 推荐使用 RANDOM 模式和Group Commit模式, 写入性能更高效
- 日期、decimal 等非标准 JSON 类型会被默认推断成字符串类型,所以尽可能从 VARIANT 中提取出来,用静态类型,性能更好
- 2 维及其以上的数组列存化会被存成 JSONB 编码,性能不如原生数组
- 不支持作为主键或者排序键
- 查询过滤、聚合需要带 cast, 存储层会根据存储类型和 cast 目标类型来消除 cast 操作,加速查询。
Keywords
1. VARIANT
