100 lines
3.8 KiB
Bash
Executable File
100 lines
3.8 KiB
Bash
Executable File
#!/bin/bash
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# Copyright 2018 AT&T Intellectual Property. All other rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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set -ex
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# NOTE: We are directing all the output of the script to /dev/null in order
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# to complete the workflow. Hence it will be useful to create a log file to
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# track the progress of the script for troubleshooting purpose.
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check_timeout_counter() {
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# Check total elapsed time
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# The default time out is set to 5 minutes
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if [[ $counter -ge $max_count ]]; then
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echo -e "Update Site Workflow Status Check Timed Out!" >> /usr/local/airflow/upgrade_airflow_worker.log
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return 1
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fi
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}
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# Define Variables
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#
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# Allow user to optionally pass in custom query time and max count as $4
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# and $5 respectively
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# Default query time is 30 seconds
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# Default max_count for 5 minutes time out is 60*5/30 = 10
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#
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# NOTE: Dag ID will take value of $1
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#
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# NOTE: $2 will look like '2018-03-13' while $3 will look like '05:10:19'
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# The execution date that we need to pass into the Airflow CLI will need
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# to take the form of '2018-03-13T05:10:19'. Hence we will need to concatenate
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# $2 and $3 together to form the dag_execution_date.
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#
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dag_id=$1
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dag_execution_date="$2T$3"
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query_time=${4:-30}
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max_count=${5:-10}
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# Initialize dag_state to "running" state
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# Dag can be in "running", "success", "failed", "skipped" or "up for retry" state
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dag_state="running"
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# Initialize counter to 1
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counter=1
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echo -e "Checking Dag State..."
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while true;
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do
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# Set current working directory to be the directory where the shell script
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# is located. In this way we will be able to import the modules that are
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# required for our custom Operators.
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cd "${0%/*}"
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# Get current state of dag using Airflow CLI
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# Use grep to remove logging messages that can pollute the status response
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check_dag_state=`airflow dag_state ${dag_id} ${dag_execution_date} | grep -vE "DEBUG|INFO|WARN|ERROR"`
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echo -e ${check_dag_state} >> /usr/local/airflow/upgrade_airflow_worker.log
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# We will need to extract the last word in the 'check_dag_state'
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# string variable as that will contain the status of the dag run
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dag_state=`echo ${check_dag_state} | awk '{print $NF}'`
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echo -e ${dag_state} >> /usr/local/airflow/upgrade_airflow_worker.log
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if [[ $dag_state == "success" ]]; then
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echo -e "\nWorkflow has completed" >> /usr/local/airflow/upgrade_airflow_worker.log
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echo -e "\n" >> /usr/local/airflow/upgrade_airflow_worker.log
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echo -e "Proceeding to upgrade Airflow Worker..." >> /usr/local/airflow/upgrade_airflow_worker.log
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echo -e "Deleting Airflow Worker Pods..." >> /usr/local/airflow/upgrade_airflow_worker.log
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for i in `kubectl get pods -n ucp | grep -i airflow-worker | awk '{print $1}'`; do
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# Delete Airflow Worker pod so that they will respawn with the new
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# configurations and/or images
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kubectl delete pod $i -n ucp
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done
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echo -e "Airflow Worker Pods Deleted!" >> /usr/local/airflow/upgrade_airflow_worker.log
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return 0
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fi
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echo -e "Workflow is in" $dag_state "state\n" >> /usr/local/airflow/upgrade_airflow_worker.log
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echo -e "Back Off for $query_time seconds...\n" >> /usr/local/airflow/upgrade_airflow_worker.log
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sleep $query_time
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# Step counter and check the timeout counter
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((counter++))
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check_timeout_counter
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done
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