一 瞭解InfluxDB的必要性

時序資料庫主要存放的資料

Time series data is a series of data points each associated with a specific time. Examples include:

• Server performance metrics
• Financial averages over time
• Sensor data, such as temperature, barometric pressure, wind speeds, etc.

時序資料庫和關係資料庫的區別

Relational databases can be used to store and analyze time series data, but depending on the precision of your data, a query can involve potentially millions of rows. InfluxDB is purpose-built to store and query data by time, providing out-of-the-box functionality that optionally downsamples data after a specific age and a query engine optimized for time-based data.

二 基本概念

2.1 database&duration

database

A logical container for users, retention policies, continuous queries, and time series data.

duration

The attribute of the retention policy that determines how long InfluxDB stores data. Data older than the duration are automatically dropped from the database.

2.2 field

The key-value pair in an InfluxDB data structure that records metadata and the actual data value. Fields are required in InfluxDB data structures and they are not indexed - queries on field values scan all points that match the specified time range and, as a result, are not performant relative to tags.

Field keys are strings and they store metadata.Field values are the actual data; they can be strings, floats, integers, or booleans. A field value is always associated with a timestamp.

2.3 Tags

Tags are optional. The key-value pair in the InfluxDB data structure that records metadata.You don’t need to have tags in your data structure, but it’s generally a good idea to make use of them because, unlike fields, tags are indexed. This means that queries on tags are faster and that tags are ideal for storing commonly-queried metadata.

Tags 與fields 的區別

Tagsare indexed andfieldsare not indexed. This means that queries on tags are more performant than those on fields.

Tags 與fields 的使用場景

（1）Store commonly-queried meta data in tags

（2）Store data in tags if you plan to use them with the InfluxQLGROUP BYclause

（3）Store data in fields if you plan to use them with anInfluxQLfunction

（4）Store numeric values as fields (tag valuesonly support string values)

2.4 measurement

Themeasurementacts as a container for tags, fields, and thetimecolumn, and the measurement name is the description of the data that are stored in the associated fields. Measurement names are strings, and, for any SQL users out there, a measurement is conceptually similar to a table.

2.5 point

In InfluxDB, a point represents a single data record, similar to a row in a SQL database table. Each point:

• has a measurement, a tag set, a field key, a field value, and a timestamp;
• is uniquely identified by its series and timestamp.

You cannot store more than one point with the same timestamp in a series. If you write a point to a series with a timestamp that matches an existing point, the field set becomes a union of the old and new field set, and any ties go to the new field set.

2.6 series

In InfluxDB, aseriesis a collection of points that share a measurement, tag set, and field key.Apointrepresents a single data record that has four components: a measurement, tag set, field set, and a timestamp. A point is uniquely identified by its series and timestamp.

series key

A series key identifies a particular series by measurement, tag set, and field key.

三 查詢

3.1 正則模糊查詢

1.實現查詢以給定欄位開始的資料

select fieldName from measurementName where fieldName=~/^給定欄位/

2.實現查詢以給定欄位結束的資料

select fieldName from measurementName where fieldName=~/給定欄位$/

3.實現查詢包含給定欄位資料

select fieldName from measurementName where fieldName=~/給定欄位/

3.2 Select 注意事項:

必須包含field key

A query requires at least onefield keyin theSELECTclause to return data. If theSELECTclause only includes a singletag keyor several tag keys, the query returns an empty response. This behavior is a result of how the system stores data.

3.3 Where 限定

使用單引號，否則無資料返回或報錯

（1）Single quote string field values in theWHEREclause. Queries with unquoted string field values or double quoted string field values will not return any data and, in most cases,will not return an error.

（2）Single quotetag valuesin theWHEREclause. Queries with unquoted tag values or double quoted tag values will not return any data and, in most cases,will not return an error.

3.4 Group By

（1）Note that theGROUP BYclause must come after theWHEREclause.

（2）TheGROUP BYclause groups query results by:one or more specifiedtags ；specified time interval。

（3）You cannot useGROUP BYto group fields.

（4）fill()changes the value reported for time intervals that have no data.

By default, aGROUP BY time()interval with no data reportsnullas its value in the output column.fill()changes the value reported for time intervals that have no data. Note thatfill()must go at the end of theGROUP BYclause if you’reGROUP(ing) BYseveral things (for example, bothtagsand a time interval).

3.5ORDER BY time DESC

By default, InfluxDB returns results in ascending time order; the firstpointreturned has the oldesttimestampand the last point returned has the most recent timestamp.ORDER BY time DESCreverses that order such that InfluxDB returns the points with the most recent timestamps first.

注意：ORDER by time DESCmust appear after theGROUP BYclauseif the query includes aGROUP BYclause.ORDER by time DESCmust appear after theWHEREclauseif the query includes aWHEREclause and noGROUP BYclause.

四.SHOW CARDINALITY

是用於估計或精確計算measurement、序列、tag key、tag value和field key的基數的一組命令。

SHOW CARDINALITY命令有兩種可用的版本：估計和精確。估計值使用草圖進行計算，對於所有基數大小來說，這是一個安全預設值。精確值是直接對TSM（Time-Structured Merge Tree）資料進行計數，但是，對於基數大的資料來說，執行成本很高。

下面以tag key、tag value為例。

4.1 SHOW TAG KEY CARDINALITY

估計或精確計算tag key集的基數。

ON <database>、FROM <sources>、WITH KEY = <key>、WHERE <condition>、GROUP BY <dimensions>和LIMIT/OFFSET子句是可選的。當使用這些查詢子句時，查詢將回退到精確計數（exect count）。當啟用Time Series Index（TSI）時，才支援對time進行過濾。不支援在WHERE子句中使用time。

舉例:

-- show estimated tag key cardinality
SHOW TAG KEY CARDINALITY

----計算精確值
-- show exact tag key cardinality
SHOW TAG KEY EXACT CARDINALITY

4.2 SHOW TAG VALUES CARDINALITY

估計或精確計算指定tag key對應的tag value的基數。

ON <database>、FROM <sources>、WITH KEY = <key>、WHERE <condition>、GROUP BY <dimensions>和LIMIT/OFFSET子句是可選的。當使用這些查詢子句時，查詢將回退到精確計數（exect count）。當啟用Time Series Index（TSI）時，才支援對time進行過濾。

舉例

-- show estimated tag key values cardinality for a specified tag key
SHOW TAG VALUES CARDINALITY WITH KEY = "myTagKey"

-- show estimated tag key values cardinality for a specified tag key
SHOW TAG VALUES CARDINALITY WITH KEY = "myTagKey"

-----計算精確值

-- show exact tag key values cardinality for a specified tag key
SHOW TAG VALUES EXACT CARDINALITY WITH KEY = "myTagKey"

-- show exact tag key values cardinality for a specified tag key
SHOW TAG VALUES EXACT CARDINALITY WITH KEY = "myTagKey"

4.3 應用場景舉例

例如，前面的分享，我們通過Telegraf 將server的監控資料儲存到了InfluxDB中，其中CPU指標是必不可少的（telegraf.conf 設定）。假如有一天，我們需要統計telegraf一共部署了多少臺。其實就可以通過SHOW TAG VALUES EXACT CARDINALITY 獲得。

SQL 語句如下:

SHOW TAG VALUES EXACT CARDINALITY from "cpu" WITH KEY = "host"

即檢視cpu 中 host 的key值有多少個。因為通過telegraf.conf的設定，一臺Server 對應一個唯一的host值，host值有多少個，就有多少臺Server已部署了telegraf。

5 Drop 與 Delete

5.1series

TheDROP SERIESquery deletes all points from aseriesin a database, and it drops the series from the index.

The query takes the following form, where you must specify either theFROMclause or theWHEREclause.

語法如下：

DROP SERIES FROM <measurement_name[,measurement_name]> WHERE <tag_key>='<tag_value>'

A successfulDROP SERIESquery returns an empty result.

Drop all points in the series that have a specific tag pair from all measurements in the database(即，如不指定from，將會把符合條件的所有表tag資料刪除).

與Delete series 的區別是：

TheDELETEquery deletes all points from aseriesin a database. UnlikeDROP SERIES,DELETEdoes not drop the series from the index.

5.2measurement_name

DELETE FROM <measurement_name> WHERE [<tag_key>='<tag_value>'] | [<time interval>]

只允許根據tag和時間來進行刪除操作.

measurement的drop,是比較消耗資源的，並且操作時間相對較長。看有網友的分享，建議 在 drop measurement 之前先刪除所有的 tag。

即先執行:

DROP SERIES FROM 'measurement_name'

然後再執行:

drop measurement <measurement_name>

六 常用函式部分

常用函式彙總如下:

參考網址:

https://blog.csdn.net/xuxiannian/article/details/103559246

https://blog.csdn.net/funnyPython/article/details/89888972