Tutorial: Learn common operators
Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel
Kusto Query Language (KQL) is used to write queries in Azure Data Explorer, Azure Monitor Log Analytics, Azure Sentinel, and more. This tutorial is an introduction to the essential KQL operators used to access and analyze your data.
For more specific guidance on how to query logs in Azure Monitor, see Get started with log queries.
Note
Can't find what you're looking for? This article was recently split, as follows:
- Learn common operators (this article)
- Use aggregation functions
- Join data from multiple tables
- Create geospatial visualizations
In this tutorial, you'll learn how to:
The examples in this tutorial use the StormEvents
table, which is publicly available in the help cluster. To explore with your own data, create your own free cluster.
The examples in this tutorial use the StormEvents
table, which is publicly available in the Weather analytics sample data.
To run the following queries, you need a query environment with access to the sample data. You can use one of the following:
- A Microsoft account or Microsoft Entra user identity to sign in to the help cluster
- A Microsoft account or Microsoft Entra user identity
- A Fabric workspace with a Microsoft Fabric-enabled capacity
Begin by using the count operator to find the number of storm records in the StormEvents
table.
StormEvents
| count
Output
Count |
---|
59066 |
To get a sense of the data, use the take operator to view a sample of records. This operator returns a specified number of arbitrary rows from the table, which can be useful for previewing the general data structure and contents.
StormEvents
| take 5
The following table shows only 5 of the 22 returned columns. To see the full output, run the query.
StartTime | EndTime | EpisodeId | EventId | State | EventType | ... |
---|---|---|---|---|---|---|
2007-09-20T21:57:00Z | 2007-09-20T22:05:00Z | 11078 | 60913 | FLORIDA | Tornado | ... |
2007-12-20T07:50:00Z | 2007-12-20T07:53:00Z | 12554 | 68796 | MISSISSIPPI | Thunderstorm Wind | ... |
2007-12-30T16:00:00Z | 2007-12-30T16:05:00Z | 11749 | 64588 | GEORGIA | Thunderstorm Wind | ... |
2007-09-29T08:11:00Z | 2007-09-29T08:11:00Z | 11091 | 61032 | ATLANTIC SOUTH | Waterspout | ... |
2007-09-18T20:00:00Z | 2007-09-19T18:00:00Z | 11074 | 60904 | FLORIDA | Heavy Rain | ... |
Use the project operator to simplify the view and select a specific subset of columns. Using project
is often more efficient and easier to read than viewing all columns.
StormEvents
| take 5
| project State, EventType, DamageProperty
Output
State | EventType | DamageProperty |
---|---|---|
ATLANTIC SOUTH | Waterspout | 0 |
FLORIDA | Heavy Rain | 0 |
FLORIDA | Tornado | 6200000 |
GEORGIA | Thunderstorm Wind | 2000 |
MISSISSIPPI | Thunderstorm Wind | 20000 |
It appears that there are multiple types of storms based on the results of the previous query. Use the distinct operator to list all of the unique storm types.
StormEvents
| distinct EventType
There are 46 types of storms in the table. Here's a sample of 10 of them.
EventType |
---|
Thunderstorm Wind |
Hail |
Flash Flood |
Drought |
Winter Weather |
Winter Storm |
Heavy Snow |
High Wind |
Frost/Freeze |
Flood |
... |
The where operator filters rows of data based on certain criteria.
The following query looks for storm events in a specific State
of a specific EventType
.
StormEvents
| where State == 'TEXAS' and EventType == 'Flood'
| project StartTime, EndTime, State, EventType, DamageProperty
There are 146 events that match these conditions. Here's a sample of 5 of them.
StartTime | EndTime | State | EventType | DamageProperty |
---|---|---|---|---|
2007-01-13T08:45:00Z | 2007-01-13T10:30:00Z | TEXAS | Flood | 0 |
2007-01-13T09:30:00Z | 2007-01-13T21:00:00Z | TEXAS | Flood | 0 |
2007-01-13T09:30:00Z | 2007-01-13T21:00:00Z | TEXAS | Flood | 0 |
2007-01-15T22:00:00Z | 2007-01-16T22:00:00Z | TEXAS | Flood | 20000 |
2007-03-12T02:30:00Z | 2007-03-12T06:45:00Z | TEXAS | Flood | 0 |
... | ... | ... | ... | ... |
To view the top floods in Texas that caused the most damage, use the sort operator to arrange the rows in descending order based on the DamageProperty
column. The default sort order is descending. To sort in ascending order, specify asc
.
StormEvents
| where State == 'TEXAS' and EventType == 'Flood'
| sort by DamageProperty
| project StartTime, EndTime, State, EventType, DamageProperty
Output
StartTime | EndTime | State | EventType | DamageProperty |
---|---|---|---|---|
2007-08-18T21:30:00Z | 2007-08-19T23:00:00Z | TEXAS | Flood | 5000000 |
2007-06-27T00:00:00Z | 2007-06-27T12:00:00Z | TEXAS | Flood | 1200000 |
2007-06-28T18:00:00Z | 2007-06-28T23:00:00Z | TEXAS | Flood | 1000000 |
2007-06-27T00:00:00Z | 2007-06-27T08:00:00Z | TEXAS | Flood | 750000 |
2007-06-26T20:00:00Z | 2007-06-26T23:00:00Z | TEXAS | Flood | 750000 |
... | ... | ... | ... | ... |
The top operator returns the first n rows sorted by the specified column.
The following query returns the five Texas floods that caused the most damaged property.
StormEvents
| where State == 'TEXAS' and EventType == 'Flood'
| top 5 by DamageProperty
| project StartTime, EndTime, State, EventType, DamageProperty
Output
StartTime | EndTime | State | EventType | DamageProperty |
---|---|---|---|---|
2007-08-18T21:30:00Z | 2007-08-19T23:00:00Z | TEXAS | Flood | 5000000 |
2007-06-27T00:00:00Z | 2007-06-27T12:00:00Z | TEXAS | Flood | 1200000 |
2007-06-28T18:00:00Z | 2007-06-28T23:00:00Z | TEXAS | Flood | 1000000 |
2007-06-27T00:00:00Z | 2007-06-27T08:00:00Z | TEXAS | Flood | 750000 |
2007-06-26T20:00:00Z | 2007-06-26T23:00:00Z | TEXAS | Flood | 750000 |
Note
The order of the operators is important. If you put top
before where
here, you'll get different results. This is because the data is transformed by each operator in order. To learn more, see tabular expression statements.
The project and extend operators can both create calculated columns.
Use project
to specify only the columns you want to view, and use extend
to append the calculated column to the end of the table.
The following query creates a calculated Duration
column with the difference between the StartTime
and EndTime
. Since we only want to view a few select columns, using project
is the better choice in this case.
StormEvents
| where State == 'TEXAS' and EventType == 'Flood'
| top 5 by DamageProperty desc
| project StartTime, EndTime, Duration = EndTime - StartTime, DamageProperty
Output
StartTime | EndTime | Duration | DamageProperty |
---|---|---|---|
2007-08-18T21:30:00Z | 2007-08-19T23:00:00Z | 1.01:30:00 | 5000000 |
2007-06-27T00:00:00Z | 2007-06-27T12:00:00Z | 12:00:00 | 1200000 |
2007-06-28T18:00:00Z | 2007-06-28T23:00:00Z | 05:00:00 | 1000000 |
2007-06-27T00:00:00Z | 2007-06-27T08:00:00Z | 08:00:00 | 750000 |
2007-06-26T20:00:00Z | 2007-06-26T23:00:00Z | 03:00:00 | 750000 |
If you take a look at the computed Duration
column, you may notice that the flood that caused the most damage was also the longest flood.
Use extend
to view the calculated Duration
column along with all of the other columns. The Duration
column is added as the last column.
StormEvents
| where State == 'TEXAS' and EventType == 'Flood'
| top 5 by DamageProperty desc
| extend Duration = EndTime - StartTime
Output
StartTime | EndTime | ... | Duration |
---|---|---|---|
2007-08-18T21:30:00Z | 2007-08-19T23:00:00Z | ... | 1.01:30:00 |
2007-06-27T00:00:00Z | 2007-06-27T12:00:00Z | ... | 12:00:00 |
2007-06-28T18:00:00Z | 2007-06-28T23:00:00Z | ... | 05:00:00 |
2007-06-27T00:00:00Z | 2007-06-27T08:00:00Z | ... | 08:00:00 |
2007-06-26T20:00:00Z | 2007-06-26T23:00:00Z | ... | 03:00:00 |
Static mapping is a useful technique for changing the presentation of your results. In KQL, one way to perform static mapping is by using a dynamic dictionary and accessors to map values from one set to another.
let sourceMapping = dynamic(
{
"Emergency Manager" : "Public",
"Utility Company" : "Private"
});
StormEvents
| where Source == "Emergency Manager" or Source == "Utility Company"
| project EventId, Source, FriendlyName = sourceMapping[Source]
Output
EventId | Source | FriendlyName |
---|---|---|
68796 | Emergency Manager | Public |
... | ... | ... |
72609 | Utility Company | Private |
... | ... | ... |
Now that you're familiar with the essentials of writing Kusto queries, go on to the next tutorial and learn how to use aggregation functions to gain deeper insight into your data.