Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update nebula-algorithm.md #946

Merged
merged 1 commit into from
Nov 25, 2021
Merged

Update nebula-algorithm.md #946

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 4 additions & 2 deletions docs-2.0/nebula-algorithm.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,9 +16,11 @@ Before using the Nebula Algorithm, users need to confirm the following informati

## Limitations

The data of the vertex ID must be an integer. That is, the vertex ID can be INT or String, but the data itself is an integer.
- The data of the vertex ID must be an integer. That is, the vertex ID can be INT or String, but the data itself is an integer.

For non-integer String data, it is recommended to use the algorithm interface. You can use the `dense_rank` function of SparkSQL to encode the data as the Long type instead of the String type.
- For non-integer String data, it is recommended to use the algorithm interface. You can use the `dense_rank` function of SparkSQL to encode the data as the Long type instead of the String type.

- Graph computing outputs vertex datasets, and the algorithm results are stored in DataFrames as the properties of vertices. You can do further operations such as statistics and filtering according to your business requirements.

## Supported algorithms

Expand Down