Tezos ETL Airflow

Airflow DAGs for exporting and loading the Tezos blockchain data to Google BigQuery. Data is available for you to query right away in Google BigQuery.

Prerequisites

linux/macos terminal
git
gcloud

Setting Up

Create a GCS bucket to hold export files:

gcloud config set project <your_gcp_project>
PROJECT=$(gcloud config get-value project 2> /dev/null)
ENVIRONMENT_INDEX=0
BUCKET=${PROJECT}-${ENVIRONMENT_INDEX}
gsutil mb gs://${BUCKET}/

Create a Google Cloud Composer environment:

ENVIRONMENT_NAME=${PROJECT}-${ENVIRONMENT_INDEX} && echo "Environment name is ${ENVIRONMENT_NAME}"
gcloud composer environments create ${ENVIRONMENT_NAME} --location=us-central1 --zone=us-central1-a \
    --disk-size=30GB --machine-type=custom-1-4096 --node-count=3 --python-version=3 --image-version=composer-1.8.3-airflow-1.10.3 \
    --network=default --subnetwork=default

gcloud composer environments update $ENVIRONMENT_NAME --location=us-central1 --update-pypi-package=tezos-etl==1.2.1

Note that if Composer API is not enabled the command above will auto prompt to enable it.

Follow the steps in Configuring Airflow Variables to configure Airfow variables.
Follow the steps in Deploying Airflow DAGs to deploy Airflow DAGs to Cloud Composer Environment.
Follow the steps here to configure email notifications.

Configuring Airflow Variables

For a new environment clone Tezos ETL Airflow: git clone https://github.com/blockchain-etl/tezos-etl-airflow && cd tezos-etl-airflow. For an existing environment use the airflow_variables.json file from Cloud Source Repository for your environment.
Edit airflow_variables.json and update configuration options with your values. You can find variables description in the table below. For the mainnet_output_bucket variable specify the bucket created on step 1 above. You can get it by running echo $BUCKET.
Open Airflow UI. You can get its URL from airflowUri configuration option: gcloud composer environments describe ${ENVIRONMENT_NAME} --location us-central1.
Navigate to Admin > Variables in the Airflow UI, click Choose File, select airflow_variables.json, and click Import Variables.

Airflow Variables

Note that the variable names must be prefixed with {chain}_, e.g. mainnet_output_bucket.

Variable	Description
`output_bucket`	GCS bucket where exported files with blockchain data will be stored
`export_start_date`	export start date, default: `2018-06-30`
`export_end_date`	export end date, used for integration testing, default: None
`export_schedule_interval`	export cron schedule, default: `0 1 * * *`
`provider_uris`	comma-separated list of provider URIs for tezosetl export command
`notification_emails`	comma-separated list of emails where notifications on DAG failures, retries and successes will be delivered. This variable must not be prefixed with `{chain}_`
`export_max_active_runs`	max active DAG runs for export, default: `3`
`export_max_workers`	max workers for tezosetl export command, default: `30`
`destination_dataset_project_id`	GCS project id where destination BigQuery dataset is
`load_schedule_interval`	load cron schedule, default: `0 2 * * *`
`load_end_date`	load end date, used for integration testing, default: None

Creating a Cloud Source Repository for Airflow variables

It is recommended to keep airflow_variables.json in a version control system e.g. git. Below are the commands for creating a Cloud Source Repository to hold airflow_variables.json:

REPO_NAME=${PROJECT}-airflow-config-${ENVIRONMENT_INDEX} && echo "Repo name ${REPO_NAME}"
gcloud source repos create ${REPO_NAME}
gcloud source repos clone ${REPO_NAME} && cd ${REPO_NAME}

# Put airflow_variables.json to the root of the repo

git add airflow_variables.json && git commit -m "Initial commit"
git push

# TODO: Setup Cloud Build Trigger to deploy variables to Composer environment when updated. For now it has to be done manually.

Deploying Airflow DAGs

Get the value from dagGcsPrefix configuration option from the output of: gcloud composer environments describe ${ENVIRONMENT_NAME} --location us-central1.
Upload DAGs to the bucket. Make sure to replace <dag_gcs_prefix> with the value from the previous step: ./upload_dags.sh <dag_gcs_prefix>.
To understand more about how the Airflow DAGs are structured read this article.
Note that it will take one or more days for mainnet_export_dag to finish exporting the historical data.
To setup automated deployment of DAGs refer to Cloud Build Configuration.

Integration Testing

It is recommended to use a dedicated Cloud Composer environment for integration testing with Airflow.

To run integration tests:

Create a new environment following the steps in the Setting Up section.
On the Configuring Airflow Variables step specify the following additional configuration variables:
- export_end_date: 2018-06-30
- load_end_date: 2018-06-30
This will run the DAGs only for the first day. At the end of the load DAG the verification tasks will ensure the correctness of the result.

Troubleshooting

To troubleshoot issues with Airflow tasks use View Log button in the Airflow console for individual tasks. Read Airflow UI overview and Troubleshooting DAGs for more info.

In rare cases you may need to inspect GKE cluster logs in GKE console.

Speed up the initial export

To speed up the initial data export it is recommended to use n1-standard-2 instance type for the Cloud Composer cluster. After the initial export is finished a new cluster with custom-1-4096 should be created with export_start_date Airflow variable set to the previous date.

sfsf9797/tezos-etl-airflow