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new 1c56888 add doc for cq
new 263b1e0 Merge pull request #3028 from mzp0514/cq
1c56888 is described below
commit 1c568883d0f29de8a2363e9c2d8242cccb4959ea
Author: mzp0514 <[email protected]>
AuthorDate: Mon Apr 19 00:13:14 2021 +0800
add doc for cq
---
.../antlr4/org/apache/iotdb/db/qp/sql/SqlBase.g4 | 4 +
.../UserGuide/Operation Manual/Continuous Query.md | 224 +++++++++++++++++++++
2 files changed, 228 insertions(+)
diff --git a/antlr/src/main/antlr4/org/apache/iotdb/db/qp/sql/SqlBase.g4
b/antlr/src/main/antlr4/org/apache/iotdb/db/qp/sql/SqlBase.g4
index 9b43a9b..782f199 100644
--- a/antlr/src/main/antlr4/org/apache/iotdb/db/qp/sql/SqlBase.g4
+++ b/antlr/src/main/antlr4/org/apache/iotdb/db/qp/sql/SqlBase.g4
@@ -1449,6 +1449,8 @@ NAME_CHAR
| '%'
| '&'
| '+'
+ | '{'
+ | '}'
| CN_CHAR
;
@@ -1465,6 +1467,8 @@ FIRST_NAME_CHAR
| '%'
| '&'
| '+'
+ | '{'
+ | '}'
| CN_CHAR
;
diff --git a/docs/zh/UserGuide/Operation Manual/Continuous Query.md
b/docs/zh/UserGuide/Operation Manual/Continuous Query.md
new file mode 100644
index 0000000..ffe0890
--- /dev/null
+++ b/docs/zh/UserGuide/Operation Manual/Continuous Query.md
@@ -0,0 +1,224 @@
+<!--
+
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+-->
+
+
+
+# 持续查询（Continuous Query, CQ）
+
+我们可以通过 SQL 语句注册、或卸载一个 CQ 实例，以及查询到所有已经注册的 CQ 配置信息。
+
+### 注册 CQ
+
+注册 CQ 的 SQL 语法如下：
+
+```sql
+CREATE CONTINUOUS QUERY <cq_id>
+[RESAMPLE EVERY <every_interval> FOR <for_interval>]
+BEGIN
+SELECT <function>(<path_suffix>) INTO <new_path> | <new_path_suffix>
+FROM <path_prefix> [WHERE <where_clause>]
+GROUP BY time(<group_by_interval>) [, level = <level>]
+END
+```
+
+其中：
+
+* <cq_id> 指定 CQ 全局唯一的 id。
+* <every_interval> 指定查询执行时间间隔。若用户不指定，
+若用户指定了 <for_interval>，则与 <for_interval> 一致，
+否则与 \<group_by_interval\> 一致。
+* <for_interval> 指定每次查询的时间范围为[now() - <for_interval>, now())。
+若用户不指定，若用户指定了 <every_interval>，
+则与 <every_interval> 一致，否则与 \<group_by_interval\> 一致。
+* \<function\> 指定聚合函数。
+* \<path_prefix\> 与 \<path_suffix\> 拼接成完整的查询原时间序列。
+* \<new_path\> 或 \<new_path_suffix\> 指定将查询出的数据写入的时间序列路径。
+ 用户可以指定完整的时间序列路径，提供 ${x} 变量，
+ 表示 <path_prefix> 中 level = x 的节点名称。也可以仅指定时间序列的后缀，
+ 由 IoTDB 生成默认的完整的时间序列路径，
+ 生成规则为结果序列前\<level\>层与<path_prefix>前\<level\>层（包括）一致，
+ 第\<level\>层后添加用户指定的\<new_path_suffix\>。
+* \<where_clause\> 指定数据过滤条件。
+* \<group_by_interval\> 指定时间分组长度。
+* \<level\>: 按照序列第 \<level\> 层分组，将第 \<level\> 层以下的所有序列聚合。
+若用户不指定，则仅对单条序列聚合。
+
+例如：
+
+假设有原始时间序列：
+````
++-----------------------------+-----+-------------+--------+--------+-----------+----+----------+
+| timeseries|alias|storage
group|dataType|encoding|compression|tags|attributes|
++-----------------------------+-----+-------------+--------+--------+-----------+----+----------+
+|root.ln.wf02.wt02.temperature| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
+|root.ln.wf02.wt01.temperature| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
+|root.ln.wf01.wt02.temperature| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
+|root.ln.wf01.wt01.temperature| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
++-----------------------------+-----+-------------+--------+--------+-----------+----+----------+
+````
+
+创建 cq1:
+````
+CREATE CONTINUOUS QUERY cq1 BEGIN SELECT max_value(temperature) INTO
temperature_max FROM root.ln.*.* GROUP BY time(10s) END
+````
+原始数据：
+````
++-----------------------------+-----------------------------+-----------------------------+-----------------------------+-----------------------------+
+|
Time|root.ln.wf02.wt02.temperature|root.ln.wf02.wt01.temperature|root.ln.wf01.wt02.temperature|root.ln.wf01.wt01.temperature|
++-----------------------------+-----------------------------+-----------------------------+-----------------------------+-----------------------------+
+|2021-04-19T23:48:15.498+08:00| 196.0|
143.0| 114.0| 75.0|
+|2021-04-19T23:48:20.501+08:00| 57.0|
56.0| 110.0| 191.0|
+|2021-04-19T23:48:25.506+08:00| 183.0|
136.0| 29.0| 59.0|
+|2021-04-19T23:48:30.510+08:00| 25.0|
19.0| 199.0| 56.0|
++-----------------------------+-----------------------------+-----------------------------+-----------------------------+-----------------------------+
+````
+每隔 10s 查询 `root.ln.*.*.temperature` 在前 10s 内的最大值（结果以10s为一组），
+将结果写入到 `root.ln.*.*.temperature_max` 中，其中 \* 与原序列同级节点一致。
+结果将产生4条新序列：
+````
++---------------------------------+-----+-------------+--------+--------+-----------+----+----------+
+| timeseries|alias|storage
group|dataType|encoding|compression|tags|attributes|
++---------------------------------+-----+-------------+--------+--------+-----------+----+----------+
+|root.ln.wf02.wt02.temperature_max| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
+|root.ln.wf02.wt01.temperature_max| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
+|root.ln.wf01.wt02.temperature_max| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
+|root.ln.wf01.wt01.temperature_max| null| root.ln| FLOAT| GORILLA|
SNAPPY|null| null|
++---------------------------------+-----+-------------+--------+--------+-----------+----+----------+
+````
+````
++-----------------------------+---------------------------------+---------------------------------+---------------------------------+---------------------------------+
+|
Time|root.ln.wf02.wt02.temperature_max|root.ln.wf02.wt01.temperature_max|root.ln.wf01.wt02.temperature_max|root.ln.wf01.wt01.temperature_max|
++-----------------------------+---------------------------------+---------------------------------+---------------------------------+---------------------------------+
+|2021-04-19T23:48:10.629+08:00| 196.0|
143.0| 114.0|
191.0|
+|2021-04-19T23:48:20.623+08:00| 183.0|
136.0| 199.0|
59.0|
++-----------------------------+---------------------------------+---------------------------------+---------------------------------+---------------------------------+
+````
+
+创建 cq2:
+````
+CREATE CONTINUOUS QUERY cq2 RESAMPLE EVERY 20s FOR 20s BEGIN SELECT
avg(temperature) INTO temperature_avg FROM root.ln.*.* GROUP BY time(10s),
level=1 END
+````
+原始数据：
+````
++-----------------------------+-----------------------------+-----------------------------+-----------------------------+-----------------------------+
+|
Time|root.ln.wf02.wt02.temperature|root.ln.wf02.wt01.temperature|root.ln.wf01.wt02.temperature|root.ln.wf01.wt01.temperature|
++-----------------------------+-----------------------------+-----------------------------+-----------------------------+-----------------------------+
+|2021-04-19T23:49:50.571+08:00| 39.0|
103.0| 168.0| 55.0|
+|2021-04-19T23:49:55.577+08:00| 99.0|
58.0| 125.0| 120.0|
+|2021-04-19T23:50:00.581+08:00| 97.0|
51.0| 108.0| 8.0|
+|2021-04-19T23:50:05.583+08:00| 7.0|
98.0| 18.0| 144.0|
+|2021-04-19T23:50:10.586+08:00| 164.0|
23.0| 130.0| 85.0|
+|2021-04-19T23:50:15.587+08:00| 110.0|
194.0| 23.0| 17.0|
+|2021-04-19T23:50:20.593+08:00| 28.0|
136.0| 91.0| 116.0|
+|2021-04-19T23:50:25.597+08:00| 2.0|
18.0| 159.0| 107.0|
+|2021-04-19T23:50:30.602+08:00| 181.0|
74.0| 19.0| 33.0|
++-----------------------------+-----------------------------+-----------------------------+-----------------------------+-----------------------------+
+````
+每隔 20s 查询 `root.ln.*.*.temperature` 在前 20s 内的平均值（结果以10s为一组，按照第1层节点分组），
+将结果写入到 `root.ln.temperature_avg` 中。
+
+````
++-----------------------+-----+-------------+--------+--------+-----------+----+----------+
+| timeseries|alias|storage
group|dataType|encoding|compression|tags|attributes|
++-----------------------+-----+-------------+--------+--------+-----------+----+----------+
+|root.ln.temperature_avg| null| root.ln| DOUBLE| GORILLA|
SNAPPY|null| null|
++-----------------------+-----+-------------+--------+--------+-----------+----+----------+
+````
+````
++-----------------------------+-----------------------+
+| Time|root.ln.temperature_avg|
++-----------------------------+-----------------------+
+|2021-04-19T23:49:51.270+08:00| 83.25|
+|2021-04-19T23:50:01.270+08:00| 83.625|
+|2021-04-19T23:50:11.269+08:00| 89.375|
+|2021-04-19T23:50:21.269+08:00| 74.125|
++-----------------------------+-----------------------+
+````
+
+创建 cq3:
+````
+CREATE CONTINUOUS QUERY cq3 BEGIN SELECT count(temperature) INTO
temperature_cnt FROM root.ln.*.* WHERE temperature > 80.0 GROUP BY time(10s),
level=2 END
+````
+每隔 10s 查询 `root.ln.*.*.temperature` 在前 10s 中大于 80.0 的值的个数（结果以10s为一组，按照第2层节点分组），
+将结果写入到 `root.ln.*.temperature_cnt` 中，其中 \* 与原序列同级节点一致。
+结果将产生2条新序列：
+````
++----------------------------+-----+-------------+--------+--------+-----------+----+----------+
+| timeseries|alias|storage
group|dataType|encoding|compression|tags|attributes|
++----------------------------+-----+-------------+--------+--------+-----------+----+----------+
+|root.ln.wf02.temperature_cnt| null| root.ln| INT64| RLE|
SNAPPY|null| null|
+|root.ln.wf01.temperature_cnt| null| root.ln| INT64| RLE|
SNAPPY|null| null|
++----------------------------+-----+-------------+--------+--------+-----------+----+----------+
+````
+其中`root.ln.wf01.temperature_cnt`是
+`root.ln.wf01.wt01.temperature` 和 `root.ln.wf01.wt02.temperature`
+聚合的结果，`root.ln.wf02.wt02.temperature_cnt`
+是
+`root.ln.wf02.wt01.temperature` 和 `root.ln.wf02.wt02.temperature`
+聚合的结果。
+
+
+创建 cq4:
+````
+CREATE CONTINUOUS QUERY cq4 BEGIN SELECT count(temperature) INTO
root.ln_cq.${2}.temperature_cnt FROM root.ln.*.* WHERE temperature > 80.0 GROUP
BY time(10s), level=2 END
+````
+查询模式与 cq3 相同，
+将结果写入到 `root.ln_cq.${2}.temperature_cnt` 中。
+结果将产生2条新序列：
+````
++-------------------------------+-----+-------------+--------+--------+-----------+----+----------+
+| timeseries|alias|storage
group|dataType|encoding|compression|tags|attributes|
++-------------------------------+-----+-------------+--------+--------+-----------+----+----------+
+|root.ln_cq.wf02.temperature_cnt| null| root.ln_cq| INT64| RLE|
SNAPPY|null| null|
+|root.ln_cq.wf01.temperature_cnt| null| root.ln_cq| INT64| RLE|
SNAPPY|null| null|
++-------------------------------+-----+-------------+--------+--------+-----------+----+----------+
+````
+其中`root.ln_cq.wf01.temperature_cnt`是
+`root.ln.wf01.wt01.temperature` 和 `root.ln.wf01.wt02.temperature`
+聚合的结果，`root.ln_cq.wf02.temperature_cnt`
+是
+`root.ln.wf02.wt01.temperature` 和 `root.ln.wf02.wt02.temperature`
+聚合的结果。
+
+### 删除 CQ
+````
+DROP CONTINUOUS QUERY <cq_id>
+````
+
+### 展示 CQ 信息
+````
+SHOW CONTINUOUS QUERIES
+````
+
+````
++-------+--------------+------------+-------------------------------------------------------------------------------------------------------------------+-----------------------------------+
+|cq name|every interval|for interval|
query sql|
target path|
++-------+--------------+------------+-------------------------------------------------------------------------------------------------------------------+-----------------------------------+
+| cq1| 10000| 10000| select
max_value(temperature) from root.ln.*.* group by ([now() - 10s, now()),
10s)|${0}.${1}.${2}.${3}.temperature_max|
+| cq3| 10000| 10000|select count(temperature) from
root.ln.*.* where temperature > 80.0 group by ([now() - 10s, now()), 10s),
level = 2| ${0}.${1}.${2}.temperature_cnt|
+| cq2| 20000| 20000| select
avg(temperature) from root.ln.*.* group by ([now() - 20s, now()), 10s), level =
1| ${0}.${1}.temperature_avg|
+| cq4| 10000| 10000|select count(temperature) from
root.ln.*.* where temperature > 80.0 group by ([now() - 10s, now()), 10s),
level = 2| root.ln_cq.${2}.temperature_cnt|
++-------+--------------+------------+-------------------------------------------------------------------------------------------------------------------+-----------------------------------+
+````
+
+
+
+
