Introduction to Kibana_query
The following article provides an outline for Kibana_query. Kibana query helps us to explore our big data to convert useful information. The elastic search doing an index of our data but indexing of data is not enough, we have to dig the data to find the meaning of all those data. So this we can do with the help of elastic search query but that is not user friendly, so Kibana comes under this situation to solve user-friendly issues for the query. In kibana we can use both the search query or click option on the visualization.
Kibana Discover
Kibana Discover is the method where we can use the Kibana query with the help of a click option.
To open the Kibana query discover we have to follow the following steps:
1. First, run the elastic search, if you are using ubuntu like this:
path-of-the-elasricsearch/bin$ ./elasticsearch
Then it will show elastic search running like this way as below. We must have to start the elastic search before start Kibana because Kibana uses data that comes from the elastic search.
2. After starting the elastic search, then we have to start the Kibana. The process is the same as above if you are using Linux/Ubuntu.
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path-of-the-kibana/bin $ ./kibana
After Kibana runs, then you go to any browser and run the localhost:5601 and you will see the following screen.
In Kibana, the left-hand side we can see some toolbars and there is the first option Discover. Just click on that and we will see discover screen for Kibana query.
In Kibana discover, we can see some sample data loaded if you don’t have own dataset. We are using the same sample data for this blog.
In the above screen, we can see two major concepts which are _source and Time. The Time, we show exact date and time when that data inserted for index and _source will show all those data in JSON format.
In the above screen, there is one arrow sign before the Time option in each every row, if we expand this we will get all data in two formats JSON and Table as shown below on the screen.
Apart from the above two expanded document, there are also two tabs which are as follows:
- View surrounding documents.
- View single document.
The view surrounding documents when you click then it will display all data in JSON format.
The view of a single document will show particular data only in detail.
Apart from the above information, we also have a search box in the discover window. In that, we can search particular data information and below the search box there is a filter option and if we click on that, it will show a small window with two option Label and Text as shown below:
Edit Filter Query:
In the above screen, we have shown the filter option which is available in Kibana discover. If we have very large data and we want to filter big data on behalf of some information, then we can use the above method which is user friendly. Here we are tried to search currency EUR and after that, all EUR highlighted with yellow color.
Query DSL:
You can see, there is one more option that is Edit as query DSL (Domain Specific Language). With this DSL query option, we can use our search for more specific to data requirements. The below screenshot is showing how we can use query DSL of elastic search in Kibana itself to get more accurate results according to our requirements.
Apart from this above information, Kibana discovers also has a histogram option where we can see all those data at which time they inserted into the index as shown below in the screenshot.
We can also set the refresh rate of the data which is coming for the indexing so that after that particular time slice we get details of the data in the histogram diagram.
KQL Nested Query
Kibana also have facility of nested query which we can do with the help of the KQL special syntax. But nested query will have required extra thinking to write those nested queries.
Also, nested query required two more important things when thinking about nested query which are given below:
- Search result for nested query only from the single nested.
- Search result for nested query from large documents which are also nested itself.
Examples of Kibana_query
Given below are the example mentioned :
Example #1
The example belong to the first category where item itself is a nested and each document has own details.
Code:
{
"items_name": "Fruits and Vegetables",
"itemName": [
{
"name_": "Orange",
"stock_": "13",
"categoryName": "fruits"
},
{
"name_": "apple",
"stock_": "12",
"categoryName": "fruits"
},
{
"name_": "cucumber",
"stock_": "19",
"categoryName": "vegetables"
},
{
"name_": "potato",
"stock_": "15",
"categoryName": "vegetables"
}
]
}
To access the required document in a single nested we have to write query like this:
Code:
itemName:
{
name_ : apple
and
stock_ > 10
}
Example #2
Nested filed inside of another nested.
This is another approach where nested field has nested items. Like example is given below and to access those we have to write query like this.
Code:
{
"nestedlevel1": [
{
"nestedlevel2": [
{
"properties1": "val",
"properties2": "val2"
},
{
"properties1": "val3",
"properties2": "val4"
}
]
}
]
}
For extract required document from nested like above we have to write query like this:
Code:
nestedlevel1.nestedlevel2:
{
properties1 :val and
properties2 :val2
}
Conclusion
Kibana has given a lot of features directly from discover where we can write queries very easily and instantly visualize results from query. There are a lot of options apart from the above which are very difficult. By running the application, we get to know more shortcut stuff and options to run queries in Kibana also available.
Recommended Articles
This is a guide to Kibana_query. Here we discuss the introduction to kibana_query, kibana discover, KQL nested query and examples respectively. You may also have a look at the following articles to learn more –
