Welcome back to our monthly ADX web explorer updates! Continue reading to learn about improvements and new features in the Query area as well as new One Click capabilities:
- Upgrade free cluster to the full-blown Azure Data Explorer cluster
- Query results exploration - Color by value
- Query visualization improvements - Crosshair support
- Data mapping transformations
Free cluster upgrade option
The Upgrade path is an easy one and it is done by associating your Free Cluster and its data to an Azure subscription. From the ADX Web Explorer, go to the My Cluster (Preview) menu item:
Click on Upgrade to Azure Cluster, fill in some required details, and you are good to go!
Read more about this offering here
Query results exploration - Color by value
When working with large datasets, being able to highlight unique data at-a-glance can be valuable as you can visually group rows that share identical values for a specific column.
Right click a value in the results grid, and choose Explore results –> Color by value – and the rows will be colored based on the selected column.
Note:
Using the “Color by value” again on the same column will cancel coloring, using it on a different column – will re-color the rows based on a different column.
Crosshair support for charts ysplit=panels
As you may recall from last month’s update, we added support for the ysplit property when using the render operator for barchart, columnchart, timechart, linechart and areachart.
We are now also supporting crosshairs, i.e., vertical lines that move along the mouse pointer. Using crosshair makes graphs more readable and may also be used in place of tooltips:
Data mapping Transformations
Some of the data format mappings (Parquet, JSON and AVRO) support simple and useful ingest-time transformations such as - converting DateTime from Unix seconds, transforming JSON property bag array to a valid JSON document or adding source artifact location.
As part of the ingestion flow, you can create mapping transformations on the table column by selecting the relevant transformation.
These transformations are super helpful for quick transformations. Where the scenario requires more complex processing at ingest time, use Update policy, which allows defining lightweight processing using KQL expression.
Please refer the below table or the doc for a complete list of available mapping transformations - https://docs.microsoft.com/en-us/azure/data-explorer/kusto/management/mappings
|
Path-dependent transformation |
Description |
Conditions |
|
PropertyBagArrayToDictionary |
Transforms JSON array of properties (e.g. {events:[{"n1":"v1"},{"n2":"v2"}]}) to dictionary and serializes it to valid JSON document (for example, {"n1":"v1","n2":"v2"}). |
Can be applied only when Path is used |
|
SourceLocation |
Name of the storage artifact that provided the data, type string (for example, the blob's "BaseUri" field). |
|
|
SourceLineNumber |
Offset relative to that storage artifact, type long (starting with '1' and incrementing per new record). |
|
|
DateTimeFromUnixSeconds |
Converts number representing unix-time (seconds since 1970-01-01) to UTC datetime string |
|
|
DateTimeFromUnixMilliseconds |
Converts number representing unix-time (milliseconds since 1970-01-01) to UTC datetime string |
|
|
DateTimeFromUnixMicroseconds |
Converts number representing unix-time (microseconds since 1970-01-01) to UTC datetime string |
|
|
DateTimeFromUnixNanoseconds |
Converts number representing unix-time (nanoseconds since 1970-01-01) to UTC datetime string |
|
Azure Data Explorer Web UI team is looking forward for your feedback in KustoWebExpFeedback@service.microsoft.com
You’re also welcome to add more ideas and vote for them here - https://aka.ms/adx.ideas