influxDB系列（二）

来源于我在一个influxDB的qq交流群中的提问, 然后有个人 提了一个问题----》触发了我的思考！！ ：） 哈哈

自己的每一次说出一个回答，都是一次新的思考，也都进行了一些查阅资料，思考，理解的过程。所以探讨 提问式的学习方法   值得推荐！！

那个人的一句： tags 你是如何理解的啊?   这个问题问的相当的好， 顿时我就感觉看了几天的 influxDB 的文档，其实都是白看了，连这个基本的概念都没搞清楚，这就

让我开始反思，自己的学习方法问题！！！！！！

学习一个东西根本没有找到方法， 以前高中学习新知识，老师会一步一步教我们，引导我们， 然后不断地练习、不断地纠正--》直到最后自己形成自己的理解，甚至自己也可以

推导-总结-设计出来， 这就是学习所要到达的目的啊。。。。

形成一个学习方法，总结出  “渔”而不是简单地得到“鱼”！！！！

一个“tags 你是如何理解的啊？”， 这个问题把我问倒了！！！！ 我瞬间觉得相互讨论的学习方法，是比较好的。 比自己一个人在那里琢磨会高效一点，会触发自己多角度的思考。

发散性学习，多角度去思考，多个层面（维度）去理解一个问题，比死磕一个角度会好，认识问题(事物)会比较准确，不容易陷入牛角尖。

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然后我就查了一个influxDB 的官方文档，这里有介绍一些核心概念！！！ key concept

https://docs.influxdata.com/influxdb/v0.9/concepts/key_concepts/#tag-key

Key Concepts

Warning! This page documents an old version of InfluxDB, which is no longer actively developed. InfluxDB v1.2 is the most recent stable version of InfluxDB.

Before diving into InfluxDB it’s good to get acquainted with some of the key concepts of the database. This document provides a gentle introduction to those concepts and common InfluxDB terminology. We’ve provided a list below of all the terms we’ll cover, but we recommend reading this document from start to finish to gain a more general understanding of our favorite time series database.

database	field key	field set
field value	measurement	point
retention policy	series	tag key
tag set	tag value	timestamp

Check out the Glossary if you prefer the cold, hard facts.

Sample data

The next section references the data printed out below. The data are fictional, but represent a believable setup in InfluxDB. They show the number of butterflies and honeybees counted by two scientists (langstroth and perpetua) in two locations (location 1 and location 2) over the time period from August 18, 2015 at midnight through August 18, 2015 at 6:12 AM. Assume that the data live in a database called my_database and are subject to the default retention policy (more on databases and retention policies to come).

Hint: Hover over the links for tooltips to get acquainted with InfluxDB terminology and the layout.

name:

 

census
-————————————
time

 

butterflies

 

honeybees

 

location

 

scientist
2015-08-18T00:00:00Z   12                   23                    1                 langstroth
2015-08-18T00:00:00Z   1                     30                    1                 perpetua
2015-08-18T00:06:00Z   11                   28                    1                 langstroth

 

2015-08-18T00:06:00Z

 

3

 

28

 

1

 

perpetua
2015-08-18T05:54:00Z   2                     11                    2                 langstroth
2015-08-18T06:00:00Z   1                     10                    2                 langstroth
2015-08-18T06:06:00Z   8                     23                    2                 perpetua
2015-08-18T06:12:00Z   7                     22                    2                 perpetua

Discussion

Now that you’ve seen some sample data in InfluxDB this section covers what it all means.

InfluxDB is a time series database so it makes sense to start with what is at the root of everything we do: time. In the data above there’s a column called time - all data in InfluxDB have that column. time stores timestamps, and the timestamp shows the date and time, in RFC3339 UTC, associated with particular data.

The next two columns, called butterflies and honeybees, are fields. Fields are made up of field keys and field values.Field keys (butterflies and honeybees) are strings and they store metadata; the field key butterflies tells us that the field values 12-7 refer to butterflies and the field key honeybees tells us that the field values 23-22 refer to, well, honeybees.

Field values are your data; they can be strings, floats, integers, or booleans, and, because InfluxDB is a time series database, a field value is always associated with a timestamp. The field values in the sample data are:

12   23
1    30
11   28
3    28
2    11
1    10
8    23
7    22

In the data above, the collection of field-key and field-value pairs make up a field set. Here are all eight field sets in the sample data:

• butterflies = 12 honeybees = 23
• butterflies = 1 honeybees = 30
• butterflies = 11 honeybees = 28
• butterflies = 3 honeybees = 28
• butterflies = 2 honeybees = 11
• butterflies = 1 honeybees = 10
• butterflies = 8 honeybees = 23
• butterflies = 7 honeybees = 22

Fields are a required piece of InfluxDB’s data structure - you cannot have data in InfluxDB without fields. It’s also important to note that fields are not indexed. Queries that use field values as filters must scan all values that match the other conditions in the query. As a result, those queries are not performant relative to queries on tags (more on tags below). In general, fields should not contain commonly-queried metadata.

The last two columns in the sample data, called location and scientist, are tags. Tags are made up of tag keys and tag values. Both tag keys and tag values are stored as strings and record metadata. The tag keys in the sample data are location and scientist. The tag key location has two tag values: 1 and 2. The tag key scientist also has two tag values: langstroth and perpetua.

In the data above, the tag set is the different combinations of all the tag key-value pairs. The four tag sets in the sample data are:

• location = 1, scientist = langstroth
• location = 2, scientist = langstroth
• location = 1, scientist = perpetua
• location = 2, scientist = perpetua

Tags are optional. 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.

Why indexing matters: The schema case study

Say you notice that most of your queries focus on the values of the field keys honeybees and butterflies:

SELECT * FROM census WHERE butterflies = 1
SELECT * FROM census WHERE honeybees = 23

Because fields aren’t indexed, InfluxDB scans every value of butterflies in the first query and every value of honeybees in the second query before it provides a response. That behavior can hurt query response times - especially on a much larger scale. To optimize your queries, it may be beneficial to rearrange your schema such that the fields (butterflies and honeybees) become the tags and the tags (location and scientist) become the fields:

name:

 

census
-————————————
time

 

location

 

scientist

 

butterflies

 

honeybees
2015-08-18T00:00:00Z   1                 langstroth    12                   23
2015-08-18T00:00:00Z   1                 perpetua      1                     30
2015-08-18T00:06:00Z   1                 langstroth    11                   28

 

2015-08-18T00:06:00Z

 

1

 

perpetua

 

3

 

28
2015-08-18T05:54:00Z   2                 langstroth    2                     11
2015-08-18T06:00:00Z   2                 langstroth    1                     10
2015-08-18T06:06:00Z   2                 perpetua      8                     23
2015-08-18T06:12:00Z   2                 perpetua      7                     22

Now that butterflies and honeybees are tags, InfluxDB won’t have to scan every one of their values when it performs the queries above - this means that your queries are even faster.

The measurement acts as a container for tags, fields, and the time column, 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. The only measurement in the sample data is census. The name census tells us that the field values record the number of butterflies and honeybees - not their size, direction, or some sort of happiness index.

A single measurement can belong to different retention policies. A retention policy describes how long InfluxDB keeps data (DURATION) and how many copies of those data are stored in the cluster (REPLICATION). If you’re interested in reading more about retention policies, check out Database Management.

In the sample data, everything in the census measurement belongs to the default retention policy. InfluxDB automatically creates that retention policy; it has an infinite duration and a replication factor set to the number of nodes in the cluster.

Now that you’re familiar with measurements, tag sets, and retention policies it’s time to discuss series. In InfluxDB, a series is the collection of data that share a retention policy, measurement, and tag set. The data above consist of four series:

Arbitrary series number	Retention policy	Measurement	Tag set
series 1	default	census	location = 1,scientist = langstroth
series 2	default	census	location = 2,scientist = langstroth
series 3	default	census	location = 1,scientist = perpetua
series 4	default	census	location = 2,scientist = perpetua

Understanding the concept of a series is essential when designing your schema and when working with your data in InfluxDB.

Finally, a point is the field set in the same series with the same timestamp. For example, here’s a single point:

name: census
-----------------
time			               butterflies	 honeybees	 location	 scientist
2015-08-18T00:00:00Z	 1		          30		       1		       perpetua

The series in the example is defined by the retention policy (default), the measurement (census), and the tag set (location = 1, scientist = perpetua). The timestamp for the point is 2015-08-18T00:00:00Z.

All of the stuff we’ve just covered is stored in a database - the sample data are in the database my_database. An InfluxDB database is similar to traditional relational databases and serves as a logical container for users, retention policies, continuous queries, and, of course, your time series data. See users and continuous queries for more on those topics.

Databases can have several users, continuous queries, retention policies, and measurements. InfluxDB is a schemaless database which means it’s easy to add new measurements, tags, and fields at any time. It’s designed to make working with time series data awesome.

You made it! You’ve covered the fundamental concepts and terminology in InfluxDB. If you’re just starting out, we recommend taking a look at Getting Started and the Writing Data and Querying Data guides. May our time series database serve you well