%global _empty_manifest_terminate_build 0 Name: python-dbnd-airflow-monitor Version: 1.0.12.13 Release: 1 Summary: Machine Learning Orchestration License: Apache Software License URL: https://github.com/databand-ai/dbnd Source0: https://mirrors.nju.edu.cn/pypi/web/packages/01/5c/b69960293c0861518a0db96542945f564ac8da2f0b948a20bc3e3ce75c65/dbnd-airflow-monitor-1.0.12.13.tar.gz BuildArch: noarch Requires: python3-dbnd Requires: python3-setuptools Requires: python3-prometheus-client Requires: python3-PyJWT Requires: python3-cryptography Requires: python3-google-auth Requires: python3-requests Requires: python3-requests-toolbelt Requires: python3-tzlocal Requires: python3-pytest Requires: python3-mock Requires: python3-sh %description # Databand Airflow Monitor Databand Airflow Monitor is a stand-alone module for Databand system, enables you to load data from Airflow server and import it into Databand system. This Databand side module is one of two components allows you to sync your Airflow data into Databand system. ## Installation with setup tools ```bash cd modules/dbnd-airflow-monitor pip install -e . ``` ## Usage `dbnd airflow-monitor` ### Important flags `--sync-history`: by default, airflow monitor's `since` value will be determined by last time it was running. use this flag to enable syncning from beginning ### Configuration You can configure your syncing variables inside databand configuration system ```cfg [airflow_monitor] interval = 10 ; Time in seconds to wait between each fetching cycle include_logs = True ; Whether or not to include logs (might be heavy) include_task_args = True ; Whether or not to include task arguments fetch_quantity = 100 ; Max number of tasks or dag runs to retrieve at each fetch fetch_period = 60 ; Time in minutes for window fetching size (start: since, end: since + period) dag_ids = ['ingest_data_dag', 'simple_dag'] ; Limit fetching to these specific dag ids ## DB Fetcher ### Pay attention, when using this system airflow version must be equal to databand's airflow version sql_alchemy_conn = sqlite:////usr/local/airflow/airflow.db ; When using fetcher=db, use this sql connection string local_dag_folder = /usr/local/airflow/dags ; When using fetcher=db, this is the dag folder location ``` ## Steps for Google Composer ​ After spinning new google composer, under PyPi packages add dbnd, and add `DBND__CORE__DATABAND_URL` env pointing to dnbd instance, copy plugin file to pluings folder (go to dags folder, one level up, and then plugins) ​ ​ For monitor to work you will need to setup service account (add relevant binding): (taken from here: https://medium.com/google-cloud/using-airflow-experimental-rest-api-on-google-cloud-platform-cloud-composer-and-iap-9bd0260f095a see Create a Service Account for POST Trigger section) ​ example with creating new SA: ```bash export PROJECT=prefab-root-227507 export SERVICE_ACCOUNT_NAME=dbnd-airflow-monitor gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME --project $PROJECT # Give service account permissions to create tokens for iap requests. gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/iam.serviceAccountTokenCreator gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/iam.serviceAccountActor # Service account also needs to be authorized to use Composer. gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/composer.user # We need a service account key to trigger the dag. gcloud iam service-accounts keys create ~/$PROJECT-$SERVICE_ACCOUNT_NAME-key.json --iam-account=$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com export GOOGLE_APPLICATION_CREDENTIALS=~/$PROJECT-$SERVICE_ACCOUNT_NAME-key.json ``` ​ configure airflow monitor with composer fetcher, with url pointing to composer airflow instance and client id (same article, Getting Airflow Client ID section): Visit the Airflow URL https://YOUR_UNIQUE_ID.appspot.com (which you noted in the last step) in an incognito window, don’t login. At this first landing page for IAP Auth has client id in the url in the address bar: ``` https://accounts.google.com/signin/oauth/identifier?client_id=00000000000-xxxx0x0xx0xx00xxxx0x00xxx0xxxxx.apps.googleusercontent.com&... ``` ## Integration Tests We have 2 tests: - databand/integration-tests/airflow_monitor - databand/integration-tests/airflow_monitor_stress To run them, go to the right dir and run inttest container: ``` cd databand/integration-tests/airflow_monitor docker-compose up inttest ``` %package -n python3-dbnd-airflow-monitor Summary: Machine Learning Orchestration Provides: python-dbnd-airflow-monitor BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dbnd-airflow-monitor # Databand Airflow Monitor Databand Airflow Monitor is a stand-alone module for Databand system, enables you to load data from Airflow server and import it into Databand system. This Databand side module is one of two components allows you to sync your Airflow data into Databand system. ## Installation with setup tools ```bash cd modules/dbnd-airflow-monitor pip install -e . ``` ## Usage `dbnd airflow-monitor` ### Important flags `--sync-history`: by default, airflow monitor's `since` value will be determined by last time it was running. use this flag to enable syncning from beginning ### Configuration You can configure your syncing variables inside databand configuration system ```cfg [airflow_monitor] interval = 10 ; Time in seconds to wait between each fetching cycle include_logs = True ; Whether or not to include logs (might be heavy) include_task_args = True ; Whether or not to include task arguments fetch_quantity = 100 ; Max number of tasks or dag runs to retrieve at each fetch fetch_period = 60 ; Time in minutes for window fetching size (start: since, end: since + period) dag_ids = ['ingest_data_dag', 'simple_dag'] ; Limit fetching to these specific dag ids ## DB Fetcher ### Pay attention, when using this system airflow version must be equal to databand's airflow version sql_alchemy_conn = sqlite:////usr/local/airflow/airflow.db ; When using fetcher=db, use this sql connection string local_dag_folder = /usr/local/airflow/dags ; When using fetcher=db, this is the dag folder location ``` ## Steps for Google Composer ​ After spinning new google composer, under PyPi packages add dbnd, and add `DBND__CORE__DATABAND_URL` env pointing to dnbd instance, copy plugin file to pluings folder (go to dags folder, one level up, and then plugins) ​ ​ For monitor to work you will need to setup service account (add relevant binding): (taken from here: https://medium.com/google-cloud/using-airflow-experimental-rest-api-on-google-cloud-platform-cloud-composer-and-iap-9bd0260f095a see Create a Service Account for POST Trigger section) ​ example with creating new SA: ```bash export PROJECT=prefab-root-227507 export SERVICE_ACCOUNT_NAME=dbnd-airflow-monitor gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME --project $PROJECT # Give service account permissions to create tokens for iap requests. gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/iam.serviceAccountTokenCreator gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/iam.serviceAccountActor # Service account also needs to be authorized to use Composer. gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/composer.user # We need a service account key to trigger the dag. gcloud iam service-accounts keys create ~/$PROJECT-$SERVICE_ACCOUNT_NAME-key.json --iam-account=$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com export GOOGLE_APPLICATION_CREDENTIALS=~/$PROJECT-$SERVICE_ACCOUNT_NAME-key.json ``` ​ configure airflow monitor with composer fetcher, with url pointing to composer airflow instance and client id (same article, Getting Airflow Client ID section): Visit the Airflow URL https://YOUR_UNIQUE_ID.appspot.com (which you noted in the last step) in an incognito window, don’t login. At this first landing page for IAP Auth has client id in the url in the address bar: ``` https://accounts.google.com/signin/oauth/identifier?client_id=00000000000-xxxx0x0xx0xx00xxxx0x00xxx0xxxxx.apps.googleusercontent.com&... ``` ## Integration Tests We have 2 tests: - databand/integration-tests/airflow_monitor - databand/integration-tests/airflow_monitor_stress To run them, go to the right dir and run inttest container: ``` cd databand/integration-tests/airflow_monitor docker-compose up inttest ``` %package help Summary: Development documents and examples for dbnd-airflow-monitor Provides: python3-dbnd-airflow-monitor-doc %description help # Databand Airflow Monitor Databand Airflow Monitor is a stand-alone module for Databand system, enables you to load data from Airflow server and import it into Databand system. This Databand side module is one of two components allows you to sync your Airflow data into Databand system. ## Installation with setup tools ```bash cd modules/dbnd-airflow-monitor pip install -e . ``` ## Usage `dbnd airflow-monitor` ### Important flags `--sync-history`: by default, airflow monitor's `since` value will be determined by last time it was running. use this flag to enable syncning from beginning ### Configuration You can configure your syncing variables inside databand configuration system ```cfg [airflow_monitor] interval = 10 ; Time in seconds to wait between each fetching cycle include_logs = True ; Whether or not to include logs (might be heavy) include_task_args = True ; Whether or not to include task arguments fetch_quantity = 100 ; Max number of tasks or dag runs to retrieve at each fetch fetch_period = 60 ; Time in minutes for window fetching size (start: since, end: since + period) dag_ids = ['ingest_data_dag', 'simple_dag'] ; Limit fetching to these specific dag ids ## DB Fetcher ### Pay attention, when using this system airflow version must be equal to databand's airflow version sql_alchemy_conn = sqlite:////usr/local/airflow/airflow.db ; When using fetcher=db, use this sql connection string local_dag_folder = /usr/local/airflow/dags ; When using fetcher=db, this is the dag folder location ``` ## Steps for Google Composer ​ After spinning new google composer, under PyPi packages add dbnd, and add `DBND__CORE__DATABAND_URL` env pointing to dnbd instance, copy plugin file to pluings folder (go to dags folder, one level up, and then plugins) ​ ​ For monitor to work you will need to setup service account (add relevant binding): (taken from here: https://medium.com/google-cloud/using-airflow-experimental-rest-api-on-google-cloud-platform-cloud-composer-and-iap-9bd0260f095a see Create a Service Account for POST Trigger section) ​ example with creating new SA: ```bash export PROJECT=prefab-root-227507 export SERVICE_ACCOUNT_NAME=dbnd-airflow-monitor gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME --project $PROJECT # Give service account permissions to create tokens for iap requests. gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/iam.serviceAccountTokenCreator gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/iam.serviceAccountActor # Service account also needs to be authorized to use Composer. gcloud projects add-iam-policy-binding $PROJECT --member serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com --role roles/composer.user # We need a service account key to trigger the dag. gcloud iam service-accounts keys create ~/$PROJECT-$SERVICE_ACCOUNT_NAME-key.json --iam-account=$SERVICE_ACCOUNT_NAME@$PROJECT.iam.gserviceaccount.com export GOOGLE_APPLICATION_CREDENTIALS=~/$PROJECT-$SERVICE_ACCOUNT_NAME-key.json ``` ​ configure airflow monitor with composer fetcher, with url pointing to composer airflow instance and client id (same article, Getting Airflow Client ID section): Visit the Airflow URL https://YOUR_UNIQUE_ID.appspot.com (which you noted in the last step) in an incognito window, don’t login. At this first landing page for IAP Auth has client id in the url in the address bar: ``` https://accounts.google.com/signin/oauth/identifier?client_id=00000000000-xxxx0x0xx0xx00xxxx0x00xxx0xxxxx.apps.googleusercontent.com&... ``` ## Integration Tests We have 2 tests: - databand/integration-tests/airflow_monitor - databand/integration-tests/airflow_monitor_stress To run them, go to the right dir and run inttest container: ``` cd databand/integration-tests/airflow_monitor docker-compose up inttest ``` %prep %autosetup -n dbnd-airflow-monitor-1.0.12.13 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-dbnd-airflow-monitor -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 1.0.12.13-1 - Package Spec generated