Elastic Confluence connector reference
editElastic Confluence connector reference
editThe Elastic Confluence connector is a connector for Atlassian Confluence.
Availability and prerequisites
editThis connector is available as a connector client using the Python connectors framework. This connector client is compatible with Elastic versions 8.7.0+. To use this connector, satisfy all connector client requirements.
This connector is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.
Usage
editTo use this connector as a native connector, see Native connectors (managed service).
To use this connector as a connector client, see Connector clients.
For additional operations, see Using connectors.
Compatibility
edit- Confluence Cloud or Confluence Server versions 7 or later.
- Confluence Data Center editions are not currently supported.
Configuration
editWhen using the connector client workflow, initially these fields will use the default configuration set in the connector source code.
These are set in the get_default_configuration
function definition.
These configurable fields will be rendered with their respective labels in the Kibana UI. Once connected, you’ll be able to update these values in Kibana.
The following configuration fields are required to set up the connector:
-
data_source
-
Dropdown to determine the Confluence platform type:
Confluence Cloud
orConfluence Server
. Default value isConfluence Server
. -
username
- The username of the account for Confluence server.
-
password
- The password of the account to be used for the Confluence server.
-
account_email
- The account email for the Confluence cloud.
-
api_token
- The API Token to authenticate with Confluence cloud.
-
confluence_url
-
The domain where the Confluence is hosted. Examples:
-
https://192.158.1.38:8080/
-
https://test_user.atlassian.net/
-
-
spaces
-
Comma-separated list of Space Keys to fetch data from Confluence server or cloud. If the value is
*
, the connector will fetch data from all spaces present in the configuredspaces
. Default value is*
. Examples:-
EC
,TP
-
*
-
-
ssl_enabled
-
Whether SSL verification will be enabled. Default value is
False
. -
ssl_ca
-
Content of SSL certificate. Note: If
ssl_enabled
isFalse
, the value in this field is ignored. Example certificate:-----BEGIN CERTIFICATE----- MIID+jCCAuKgAwIBAgIGAJJMzlxLMA0GCSqGSIb3DQEBCwUAMHoxCzAJBgNVBAYT ... 7RhLQyWn2u00L7/9Omw= -----END CERTIFICATE-----
-
retry_count
-
The number of retry attempts after failed request to Confluence. Default value is
3
. -
concurrent_downloads
-
The number of concurrent downloads for fetching the attachment content. This speeds up the content extraction of attachments. Defaults to
50
.
Deployment using Docker
editYou can deploy the Confluence connector as a self-managed connector client using Docker. Follow these instructions.
Step 1: Download sample configuration file
Download the sample configuration file. You can either download it manually or run the following command:
curl https://raw.githubusercontent.com/elastic/connectors/main/config.yml.example --output ~/connectors-python-config/config.yml
Remember to update the --output
argument value if your directory name is different, or you want to use a different config file name.
Step 2: Update the configuration file for your self-managed connector
Update the configuration file with the following settings to match your environment:
-
elasticsearch.host
-
elasticsearch.password
-
connector_id
-
service_type
Use confluence as the service_type
value.
Don’t forget to uncomment "confluence" in the sources
section of the yaml
file.
If you’re running the connector service against a Dockerized version of Elasticsearch and Kibana, your config file will look like this:
elasticsearch: host: http://host.docker.internal:9200 username: elastic password: <YOUR_PASSWORD> connector_id: <CONNECTOR_ID_FROM_KIBANA> service_type: confluence sources: # UNCOMMENT "confluence" below to enable the Confluence connector #mongodb: connectors.sources.mongo:MongoDataSource #s3: connectors.sources.s3:S3DataSource #dir: connectors.sources.directory:DirectoryDataSource #mysql: connectors.sources.mysql:MySqlDataSource #network_drive: connectors.sources.network_drive:NASDataSource #google_cloud_storage: connectors.sources.google_cloud_storage:GoogleCloudStorageDataSource #azure_blob_storage: connectors.sources.azure_blob_storage:AzureBlobStorageDataSource #postgresql: connectors.sources.postgresql:PostgreSQLDataSource #oracle: connectors.sources.oracle:OracleDataSource #mssql: connectors.sources.mssql:MSSQLDataSource
Note that the config file you downloaded might contain more entries, so you will need to manually copy/change the settings that apply to you.
Normally you’ll only need to update elasticsearch.host
, elasticsearch.password
, connector_id
and service_type
to run the connector service.
Step 3: Run the Docker image
Run the Docker image with the Connector Service using the following command:
docker run \ -v ~/connectors-python-config:/config \ --network "elastic" \ --tty \ --rm \ docker.elastic.co/enterprise-search/elastic-connectors:8.9.2.0-SNAPSHOT \ /app/bin/elastic-ingest \ -c /config/config.yml
Refer to this guide in the Python framework repository for more details.
Documents and syncs
editThe connector syncs the following Confluence object types:
- Pages
- Spaces
- Blog Posts
- Attachments
- Content of files bigger than 10 MB won’t be extracted.
- Permissions are not synced. All documents indexed to an Elastic deployment will be visible to all users with access to that Elastic Deployment.
Sync rules
editBasic sync rules are identical for all connectors and are available by default.
This connector supports advanced sync rules for remote filtering. These rules cover complex query-and-filter scenarios that cannot be expressed with <basic sync rules. Advanced sync rules are defined through a source-specific DSL JSON snippet.
Advanced sync rules examples
editExample 1: Query for indexing data that is in a particular Space with key DEV.
[ { "query": "space = DEV" } ]
Example 2: Queries for indexing data based on created
and lastmodified
time.
[ { "query": "created >= now('-5w')" }, { "query": "lastmodified < startOfYear()" } ]
Example 3: Query for indexing only given types in a Space with key SD.
[ { "query": "type in ('page', 'attachment') AND space.key = 'SD'" } ]
Syncing recently created/updated items in Confluence may be delayed when using advanced sync rules, because the search endpoint used for CQL queries returns stale results in the response. For more details refer to the following issue in the Confluence documentation.
Content Extraction
editSee Content extraction.
Connector client operations
editEnd-to-end testing
editThe connector framework enables operators to run functional tests against a real data source. Refer to Connector testing for more details.
To perform E2E testing for the Confluence connector, run the following command:
$ make ftest NAME=confluence
For faster tests, add the DATA_SIZE=small
flag:
make ftest NAME=confluence DATA_SIZE=small
Known issues
editThere are currently no known issues for this connector. Refer to Known issues for a list of known issues for all connectors.
Troubleshooting
editSee Troubleshooting.
Security
editSee Security.
Framework and source
editThis connector is included in the Python connectors framework.
View the source code for this connector (branch 8.9, compatible with Elastic 8.9).