Dqlmagic is an IPython magic function allowing to use DQL in your Jupyter notebooks.
Dynatrace Query Language (DQL) is a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more based on data stored in Dynatrace Grail storage.
While the Dynatrace platform provides an OOTB support for notebooks with an easy to use, integrated support for writing DQL queries with intellisense, DAVIS AI powered analysis of results as well as OOTB visuzalizations...
... using DQL in Jupyter notebooks opens up a huge toolbox and community for data-scientists for specialized analysis and engineers to prototype use-cases and Dynatrace Apps.
Example - Analyze cloud region usage, plotting public cloud regions (external data-source) on a world-map VS used cloud-regions based.
Executes a DQL query and returns the queried records as json array.
Query in code-cell..
%%dql
fetch logs
| limit 10
.. and use result in another code cell:
import pandas as pd
#put result into a dataframe
df = pd.DataFrame(_dql_result)
df.head()
Note:
By default %%dql
writes into _dql_result
. You can optionally customize the target objects name to configure an alternative name like this:
%%dql my_result
fetch logs | limit 10
import pandas as pd
res = %dql fetch logs | limit 10
df = pd.DataFrame(res)
df.head()
Same as %dql
or %%dql
, but returns instead of the result records the full API response json
Inline function connecting to the grail cluster.
Reads connection parameters from .env file:
dt_oauth_url="https://sso.dynatrace.com/sso/oauth2/token"
dt_oauth_clientid="<YOUR-CLIENT-ID>"
dt_oauth_clientsecret="<YOUR-CLIENT-SECRET>"
dt_oauth_scope="<YOUR-ACCESS-SCOPE>"
dt_tenant="<YOUR-TENANT-ID>"
grail_apiurl="https://<YOUR-TENANT-ID>.live.dynatrace.com/api/v2/dql/query"
The <config_prefix>
is optional to be used if you have multiple configs stored in your .env file like e.g.
dt_config_prefix="DEV"
DEV_dt_oauth_url="https://sso.dynatrace.com/sso/oauth2/token"
DEV_dt_oauth_clientid="<YOUR-DEV-CLIENT-ID>"
DEV_dt_oauth_clientsecret="<YOUR-DEV-CLIENT-SECRET>"
DEV_dt_oauth_scope="<YOUR-DEV-ACCESS-SCOPE>"
DEV_dt_tenant="<YOUR-DEV-TENANT-ID>"
DEV_grail_apiurl="https://<YOUR-DEV-TENANT-ID>.apps.dynatracelabs.com/platform/storage/query/v1/"
PROD_dt_oauth_url="https://sso.dynatrace.com/sso/oauth2/token"
PROD_dt_oauth_clientid="<YOUR-PROD-CLIENT-ID>"
PROD_dt_oauth_clientsecret="<YOUR-PROD-CLIENT-SECRET>"
PROD_dt_oauth_scope="<YOUR-PROD-ACCESS-SCOPE>"
PROD_dt_tenant="<YOUR-PROD-TENANT-ID>"
PROD_grail_apiurl="https://<YOUR-PROD-TENANT-ID>.apps.dynatracelabs.com/platform/storage/query/v1/"
With the parameter dt_config_prefix
, you set the default config prefix used, if %auth_grail parameter <config_prefix>
is not provided.
For more details on how to setup API oauth clients in Dynatrace see Configure and manage account API OAuth clients
For more details on .env file see How to NOT embedded credential in Jupyter notebook or python-dotenv
Checkout the repository and install using following command.
pip install dqlmagic
%reload_ext dqlmagic
If you are new to Jupyter notebooks, I recommend getting started using Jupyter notebooks in Visual Studio Code
- Create your Dynatrace credentials as described here
- Create and configure .env file as described above.
- Start using Getting-Started Notebook within examples/ to get familiar using Dqlmagic.
#Applies a default query timeframe from current time - X minutes
#Note that if a timeframe is specified within the DQL query string then it has precedence over this parameter.
#dql_default_query_timespan_minutes=720
#Applies a default scan limit in gigabytes to your queries
#dql_default_scanlimit_gbytes=250
#Applies a maxium number of records returned by your queries
#dql_max_result_records=1000
This is an open source project, and we gladly accept new contributions and contributors.
This project is not an offical release of Dynatrace. If you have questions or any problems, open a github issue.
Licensed under Apache 2.0 license. See LICENSE for details.