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TAG

在传统数仓场景中，从传统数据库中导入的事实表数据一般是全量导入，按天分区每天都存储一份全量数据，paimon对此提供了Tag机制，创建TAG时，会对当前数据做一份全量快照，在之后对表的数据进行更新也不会影响已经打完TAG的数据。
其实就是对当前的数据做镜像，或者说做备份

维护创建

https://paimon.apache.org/docs/0.9/maintenance/manage-tags/

-- 创建
bin/flink run \
lib/paimon-flink-action-0.9.0.jar \
create_tag \
--warehouse file:///data/soft/paimon/catalog \
--database default \
--table t_tags \
--tag_name first_tag-- 删除
bin/flink run \
lib/paimon-flink-action-0.9.0.jar \
delete_tag  \
--warehouse file:///data/soft/paimon/catalog \
--database default \
--table t_tags \
--tag_name first_tag-- 回滚到指定tag版本，还原当时的数据
bin/flink run \
lib/paimon-flink-action-0.9.0.jar \
rollback_to \
--warehouse file:///data/soft/paimon/catalog \
--database default \
--table t_tags \
--version first_tag

使用TAG

CREATE TABLE t_tags (age BIGINT,money BIGINT,id STRING,PRIMARY KEY (id) NOT ENFORCED
);
insert into t_tags values(10,1000,'1');-- 创建第一个tag
bin/flink run \
lib/paimon-flink-action-0.9.0.jar \
create_tag \
--warehouse file:///data/soft/paimon/catalog \
--database default \
--table t_tags \
--tag_name first_taginsert into t_tags values(20,2000,'2');bin/flink run \
lib/paimon-flink-action-0.9.0.jar \
create_tag \
--warehouse file:///data/soft/paimon/catalog \
--database default \
--table t_tags \
--tag_name second_tagselect * from t_tags$tags;
Flink SQL> select * from t_tags$tags;
+------------+-------------+-----------+-------------------------+--------------+-------------+---------------+
|   tag_name | snapshot_id | schema_id |             commit_time | record_count | create_time | time_retained |
+------------+-------------+-----------+-------------------------+--------------+-------------+---------------+
|  first_tag |           1 |         0 | 2024-12-19 15:05:18.802 |            1 |      <NULL> |        <NULL> |
| second_tag |           2 |         0 | 2024-12-19 15:08:14.165 |            2 |      <NULL> |        <NULL> |
+------------+-------------+-----------+-------------------------+--------------+-------------+---------------+Flink SQL> select * from t_tags;
+-----+-------+----+
| age | money | id |
+-----+-------+----+
|  10 |  1000 |  1 |
|  20 |  2000 |  2 |
+-----+-------+----+Flink SQL> select * from t_tags/*+ OPTIONS('scan.tag-name' = 'first_tag') */;
+-----+-------+----+
| age | money | id |
+-----+-------+----+
|  10 |  1000 |  1 |
+-----+-------+----+
1 row in set-- 修改tag中数据
insert into t_tags_auto values(20,1000,'1');Flink SQL> select * from t_tags;
+-----+-------+----+
| age | money | id |
+-----+-------+----+
|  20 |  1000 |  1 |
|  20 |  2000 |  2 |
+-----+-------+----+
2 rows in set-- 查询tag，发现没有影响
Flink SQL> select * from t_tags/*+ OPTIONS('scan.tag-name' = 'first_tag') */;
+-----+-------+----+
| age | money | id |
+-----+-------+----+
|  10 |  1000 |  1 |
+-----+-------+----+
1 row in set

自动创建TAG

-- Flink SQL
CREATE TABLE t (k INT PRIMARY KEY NOT ENFORCED,f0 INT,...
) WITH ('tag.automatic-creation' = 'process-time',  -- 时间模式 watermark/batch'tag.creation-period' = 'daily', -- 循环周期 hourly/two-hour'tag.creation-delay' = '10 m', -- 延迟触发时间'tag.num-retained-max' = '90' -- tag保存90个,超出自动删除
);