https://www.elastic.co/guide/en/kibana/6.6/tutorial-build-dashboard.html
Building your own dashboard
Ready to load some data and build a dashboard? This tutorial shows you how to:
- Load a data set into Elasticsearch
- Define an index pattern
- Discover and explore the data
- Visualize the data
- Add visualizations to a dashboard
- Inspect the data behind a visualization
Loading sample data
This tutorial requires three data sets:
- The complete works of William Shakespeare, suitably parsed into fields. Download
shakespeare.json. - A set of fictitious accounts with randomly generated data. Download
accounts.zip. - A set of randomly generated log files. Download
logs.jsonl.gz.
Two of the data sets are compressed. To extract the files, use these commands:
unzip accounts.zip gunzip logs.jsonl.gz
The Shakespeare data set has this structure:
{
"line_id": INT,
"play_name": "String",
"speech_number": INT,
"line_number": "String",
"speaker": "String",
"text_entry": "String",
}
The accounts data set is structured as follows:
{
"account_number": INT,
"balance": INT,
"firstname": "String",
"lastname": "String",
"age": INT,
"gender": "M or F",
"address": "String",
"employer": "String",
"email": "String",
"city": "String",
"state": "String"
}
The logs data set has dozens of different fields. Here are the notable fields for this tutorial:
{
"memory": INT,
"geo.coordinates": "geo_point"
"@timestamp": "date"
}
Before you load the Shakespeare and logs data sets, you must set up mappings for the fields.
Mappings divide the documents in the index into logical groups and specify the characteristics of the fields.
These characteristics include the searchability of the field and whether it’s tokenized, or broken up into separate words.
In Kibana Dev Tools > Console, set up a mapping for the Shakespeare data set:
PUT /shakespeare
{
"mappings": {
"doc": {
"properties": {
"speaker": {"type": "keyword"},
"play_name": {"type": "keyword"},
"line_id": {"type": "integer"},
"speech_number": {"type": "integer"}
}
}
}
}
This mapping specifies field characteristics for the data set:
- The
speakerandplay_namefields are keyword fields. These fields are not analyzed. The strings are treated as a single unit even if they contain multiple words. - The
line_idandspeech_numberfields are integers.
响应
{
"acknowledged" : true,
"shards_acknowledged" : true,
"index" : "shakespeare"
}
The logs data set requires a mapping to label the latitude and longitude pairs as geographic locations by applying the geo_point type.
PUT /logstash-2015.05.18
{
"mappings": {
"log": {
"properties": {
"geo": {
"properties": {
"coordinates": {
"type": "geo_point"
}
}
}
}
}
}
}
{
"acknowledged" : true,
"shards_acknowledged" : true,
"index" : "logstash-2015.05.18"
}
The accounts data set doesn’t require any mappings.
查询一下当前的所有indices
GET /_cat/indices?v HTTP/1.1
Host: localhost:9200
新导入的logstash-2015.05.18，logstash-2015.05.19，logstash-2015.05.20这个三个index的docs.count的个数都是0。
bank是之前在学习elastic search时候导入的
health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open logstash-2015.05.18 dL2ZaIelR_uvKMnPYy_8Eg 5 1 0 0 1.2kb 1.2kb yellow open logstash-2015.05.20 M1PWnqXLRgClt-iwqN4OUg 5 1 0 0 1.2kb 1.2kb yellow open customer p6H8gEOdQAWBuSN2HDEjZA 5 1 1 0 4.4kb 4.4kb yellow open shakespeare I8mqiFkkTdK9IlcarIZA4A 5 1 0 0 1.2kb 1.2kb yellow open bank l45mhl-7QNibqbmbi2Jmbw 5 1 1000 0 475.1kb 475.1kb green open .kibana_1 CUsQj9zkSCSC-XiDJgXYQQ 1 0 2 0 8.6kb 8.6kb yellow open logstash-2015.05.19 14rDFdQFTQK-GNgDXtlmeQ 5 1 0 0 1.2kb 1.2kb
At this point, you’re ready to use the Elasticsearch bulk API to load the data sets:
curl -H 'Content-Type: application/x-ndjson' -XPOST 'localhost:9200/bank/account/_bulk?pretty' --data-binary @accounts.json curl -H 'Content-Type: application/x-ndjson' -XPOST 'localhost:9200/shakespeare/doc/_bulk?pretty' --data-binary @shakespeare_6.0.json curl -H 'Content-Type: application/x-ndjson' -XPOST 'localhost:9200/_bulk?pretty' --data-binary @logs.jsonl
Or for Windows users, in Powershell:
Invoke-RestMethod "http://localhost:9200/bank/account/_bulk?pretty" -Method Post -ContentType 'application/x-ndjson' -InFile "accounts.json" Invoke-RestMethod "http://localhost:9200/shakespeare/doc/_bulk?pretty" -Method Post -ContentType 'application/x-ndjson' -InFile "shakespeare_6.0.json" Invoke-RestMethod "http://localhost:9200/_bulk?pretty" -Method Post -ContentType 'application/x-ndjson' -InFile "logs.jsonl"
可以保存为一个ps1的脚本文件，然后直接运行这个脚本文件进行导入
These commands might take some time to execute, depending on the available computing resources.
Verify successful loading:
再次查询所有的index
GET /_cat/indices?v
Your output should look similar to this:
health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open logstash-2015.05.18 dL2ZaIelR_uvKMnPYy_8Eg 5 1 4631 0 22.5mb 22.5mb yellow open logstash-2015.05.20 M1PWnqXLRgClt-iwqN4OUg 5 1 4750 0 24.1mb 24.1mb yellow open customer p6H8gEOdQAWBuSN2HDEjZA 5 1 1 0 4.4kb 4.4kb yellow open shakespeare I8mqiFkkTdK9IlcarIZA4A 5 1 111396 0 21.5mb 21.5mb yellow open bank l45mhl-7QNibqbmbi2Jmbw 5 1 1000 0 475.1kb 475.1kb green open .kibana_1 CUsQj9zkSCSC-XiDJgXYQQ 1 0 2 0 8.6kb 8.6kb yellow open logstash-2015.05.19 14rDFdQFTQK-GNgDXtlmeQ 5 1 4624 0 23.6mb 23.6mb
Defining your index patterns
Index patterns tell Kibana which Elasticsearch indices you want to explore. An index pattern can match the name of a single index, or include a wildcard (*) to match multiple indices.
For example, Logstash typically creates a series of indices in the format logstash-YYYY.MMM.DD. To explore all of the log data from May 2018, you could specify the index pattern logstash-2018.05*.
You’ll create patterns for the Shakespeare data set, which has an index named shakespeare, and the accounts data set, which has an index named bank. These data sets don’t contain time-series data.
- In Kibana, open Management, and then click Index Patterns.
- If this is your first index pattern, the Create index pattern page opens automatically. Otherwise, click Create index pattern in the upper left.
-
Enter
shakes*in the Index pattern field.