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· 4 min read
Tom Klimovski

So you're using BigQuery (BQ). It's all set up and humming perfectly. Maybe now, you want to run an ELT job whenever a new table partition is created, or maybe you want to retrain your ML model whenever new rows are inserted into the BQ table.

In my previous article on EventArc, we went through how Logging can help us create eventing-type functionality in your application. Let's take it a step further and walk through how we can couple BigQuery and Cloud Run.

In this article you will learn how to

  • Tie together BigQuery and Cloud Run
  • Use BigQuery's audit log to trigger Cloud Run
  • With those triggers, run your required code

Let's go!

Let's create a temporary dataset within BigQuery named tmp_bq_to_cr.

In that same dataset, let's create a table in which we will insert some rows to test our BQ audit log. Let's grab some rows from a BQ public dataset to create this table:

CREATE OR REPLACE TABLE tmp_bq_to_cr.cloud_run_trigger AS
date, country_name, new_persons_vaccinated, population
from `bigquery-public-data.covid19_open_data.covid19_open_data`
where country_name='Australia'
date > '2021-05-31'

Following this, let's run an insert query that will help us build our mock database trigger:

INSERT INTO tmp_bq_to_cr.cloud_run_trigger
VALUES('2021-06-18', 'Australia', 3, 1000)

Now, in another browser tab let's navigate to BQ Audit Events and look for our INSERT INTO event:


There will be several audit logs for any given BQ action. Only after a query is parsed does BQ know which table we want to interact with, so the initial log will, for e.g., not have the table name.

We don't want any old audit log, so we need to ensure we look for a unique set of attributes that clearly identify our action, such as in the diagram above.

In the case of inserting rows, the attributes are a combination of

  • The method is
  • The name of the table being inserted to is the protoPayload.resourceName
  • The dataset id is available as resource.labels.dataset_id
  • The number of inserted rows is protoPayload.metadata.tableDataChanged.insertedRowsCount

Time for some code

Now that we've identified the payload that we're looking for, we can write the action for Cloud Run. We've picked Python and Flask to help us in this instance. (full code is on GitHub).

First, let's filter out the noise and find the event we want to process

@app.route('/', methods=['POST'])
def index():
# Gets the Payload data from the Audit Log
content = request.json
ds = content['resource']['labels']['dataset_id']
proj = content['resource']['labels']['project_id']
tbl = content['protoPayload']['resourceName']
rows = int(content['protoPayload']['metadata']
if ds == 'cloud_run_tmp' and \
tbl.endswith('tables/cloud_run_trigger') and rows > 0:
query = create_agg()
return "table created", 200
# if these fields are not in the JSON, ignore
return "ok", 200

Now that we've found the event we want, let's execute the action we need. In this example, we'll aggregate and write out to a new table created_by_trigger:

def create_agg():
client = bigquery.Client()
query = """
CREATE OR REPLACE TABLE tmp_bq_to_cr.created_by_trigger AS
count_name, SUM(new_persons_vaccinated) AS n
FROM tmp_bq_to_cr.cloud_run_trigger
return query

The Dockerfile for the container is simply a basic Python container into which we install Flask and the BigQuery client library:

FROM python:3.9-slim
RUN pip install Flask==1.1.2 gunicorn==20.0.4 google-cloud-bigquery
COPY *.py ./
CMD exec gunicorn --bind :$PORT main:app

Now we Cloud Run

Build the container and deploy it using a couple of gcloud commands:

PROJECT=$(gcloud config get-value project)
gcloud builds submit --tag ${CONTAINER}
gcloud run deploy ${SERVICE} --image $CONTAINER --platform managed

I always forget about the permissions

In order for the trigger to work, the Cloud Run service account will need the following permissions:

gcloud projects add-iam-policy-binding $PROJECT \

gcloud projects add-iam-policy-binding $PROJECT \
--member=serviceAccount:${SVC_ACCOUNT} \

Finally, the event trigger

gcloud eventarc triggers create ${SERVICE}-trigger \
--location ${REGION} --service-account ${SVC_ACCOUNT} \
--destination-run-service ${SERVICE} \
--event-filters \
--event-filters \

Important to note here is that we're triggering on any Insert log created by BQ That's why in this action we had to filter these events based on the payload.

Take it for a spin

Now, try out the BigQuery -> Cloud Run trigger and action. Go to the BigQuery console and insert a row or two:

INSERT INTO tmp_bq_to_cr.cloud_run_trigger
VALUES('2021-06-18', 'Australia', 5, 25000)

Watch as a new table called created_by_trigger gets created! You have successfully triggered a Cloud Run action on a database event in BigQuery.


· 3 min read
Jeffrey Aven

The Azure Static Web App feature is relatively new in the Azure estate which has recently become generally available, I thought I would take it for a test drive and discuss my findings.

I am a proponent of the JAMStack architecture for front end applications and a user of CD enabled CDN services like Netlify, so this Azure feature was naturally appealing to me.

Azure SWAs allow you to serve static assets (like JavaScript) without a origin server, meaning you don’t need a web server, are able to streamline content distribution and web app performance, and reduce the attack surface area of your application.

The major advantage to using is simplicity, no scaffolding or infra requirements and it is seamlessly integrated into your CI/CD processes (natively if you are using GitHub).

Deploying Static Web Apps in Azure

Pretty simple to setup, aside from a name and a resource group, you just need to supply:

  • a location (Azure region to be used for serverless back end APIs via Azure Function Apps) note that this is not a location where the static web is necessarily running
  • a GitHub or GitLab repo URL
  • the branch you wish to use to trigger production deployments (e.g. main)
  • a path to your code within your app (e.g. where your package.json file is located)
  • an output folder (e.g. dist) this should not exist in your repo
  • a project or personal access token for your GitHub account (alternatively you can perform an interactive OAuth2.0 consent if using the portal)

An example is shown here:

GitHub Actions

Using the consent provided (either using the OAuth flow or by providing a token), Azure Static Web Apps will automagically create the GitHub Actions workflow to deploy your application on a push or merge event to your repo. This includes providing scoped API credentials to Azure to allow access to the Static Web App resource using secrets in GitHub (which are created automagically as well). An example workflow is shown here:

Preview or Staging Releases

Similar to the functionality in analogous services like Netlify, you can configure preview releases of your application to be deployed from specified branches on pull request events.

Routes and Authorization

Routes (for SPAs) need to be provided to Azure by using a file named staticwebapp.config.json located in the application root of your repo (same level as you package.json file). You can also specify response codes and whether the rout requires authentication as shown here:


  • Globally distributed CDN
  • Increased security posture, reduced attack surface area
  • Simplified architecture and deployment
  • No App Service Plan required – cost reduction
  • Enables Continuous Deployment – incl preview/staging environments
  • TLS and DNS can be easily configured for your app


  • Serverless API locations are limited
  • Integration with other VCS/CI/CD systems like GitLab would need to be custom built (GitHub and Azure DevOps is integrated)

Overall, this is a good feature for deploying SPAs or PWAs in Azure.

· 10 min read
Tom Klimovski

Metadata Hub (MDH) is intended to be the source of truth for metadata around the Company’s platform. It has the ability to load metadata configuration from yaml, and serve that information up via API. It will also be the store of information for pipeline information while ingesting files into the platform.

Key philosophies:

Config-Driven. Anyone who has been authorized to do so, should be able to add another ‘table-info.yaml’ in to MDH without the need to update any code in the system

Here’s how table information makes its way into MDH:

Metadata Hub
Metadata Hub


/tablesget:summary: All tables in MDHdescription: get the title of all tables that exist in MDH
post:summary: Creates a new table in MDHdescription: Creates a new table in MDH
/tables/{id}getsummary: Obtain information about specific table
/tables/{id}/columnsgetsummary: All columns for a particular tabledescription: Obtain information on columns for a particular table
/rungetsummary: All information about a particular end-to-end batch run of file ingestion
postsummary: Update metadata on a batch loaddescription: Update metadata on a batch load
/calendargetsummary: Use this to save on calculation of business days.description: This base response gives you today's date in a string
/calendar/previousBusinessDaygetsummary: Will return a string of the previous business daydescription: Will return a string of the previous business day, based on the date on when it's called
/calendar/nextBusinessDaygetsummary: Will return a string of the next business daydescription: Will return a string of the next business day, based on the date on when it's called

Yaml to Datastore - Entity/Kind design

Datastore Primer

Before we jump right into Entity Groups in Datastore, it is important to first go over the basics and establish a common vocabulary. Datastore holds entities, which are objects, that can contain various key/value pairs, called properties. Each entity must contain a unique identifier, known as a key. When creating an entity, a user can choose to specify a custom key or let Datastore create a key. If a user decides to specify a custom key, it will contain two fields: a kind, which represents a category such as ‘Toy’ or ‘Marital Status’, and a name, which is the identifying value. If a user decides to only specify a kind when creating a key, and does not specify a unique identifier, Datastore automatically generates an ID behind the scenes. Below is an example of a Python3 script which illustrates this identifier concept.

from import datastore

client = datastore.Client()
#Custom key- specify my kind=item and a unique_id of broker
custom_key_entry = datastore.Entity(client.key("table","broker"))

#Only specify kind=item, let datastore generate unique_id
datastore_gen_key_entry = datastore.Entity(client.key("table"))

In your GCP Console under Datastore, you will then see your two entities of kind “table”. One will contain your custom key and one will contain the automatically generated key.

Ancestors and Entity Groups

For highly related or hierarchical data, Datastore allows entities to be stored in a parent/child relationship. This is known as an entity group or ancestor/descendent relationship.

Entity Group


This is an example of an entity group with kinds of types: table, column, and classification. The ‘Grandparent’ in this relationship is the ‘table’. In order to configure this, one must first create the table entity. Then, a user can create a column, and specify that the parent is a table key. In order to create the grandchild, a user then creates a classification and sets its parent to be a column key. To further add customizable attributes, a user can specify additional key-value pairs such as pii and data_type. These key-value pairs are stored as properties. We model this diagram in Datastore in our working example below.

One can create entity groups by setting the ‘parent’ parameter while creating an entity key for a child. This command adds the parent key to be part of the child entity key. The child’s key is represented as a tuple (‘parent_key’, ‘child_key’), such that the parents’ key is the prefix of the key, which is followed by its own unique identifier. For example, follow the diagram above:

table_key = datastore_client.key("table","broker")
column_key = datastore_client.key("column","broker_legal_name", parent=table_key)

Printing the variable table_key will display: ("table", "broker","column", "broker_legal_name")

Datastore also supports chaining of parents, which can lead to very large keys for descendants with a long lineage of ancestors. Additionally, parents can have multiple children (representing a one-to-many relationship). However, there is no native support for entities to have multiple parents (representing a many-to-many relationship). Once you have configured this ancestral hierarchy, it is easy to retrieve all descendants for a given parent. You can do this by querying on the parent key by using the ‘ancestor’ parameter. For example, given the entity table_key created above, I can query for all of the tables

columns: my_query = client.query(kind="table", ancestor = column_key) .

A Full Working Example for MDH

As per our Key Philosophies - Config-Driven - anyone should be able to add a new table to be processed and landed in a target-table somewhere within MDH with our yaml syntax. Below is a full working python3 example of the table/column/classification hierarchical model described above.

from import datastore

datastore_client = datastore.Client()

# Entities with kinds- table, column, classification
my_entities = [
{"kind": "table", "table_id": "broker", "table_type": "snapshot",
"notes": "describes mortgage brokers"},
{"kind": "column", "column_id": "broker_legal_name", "table_id": "broker",
"data_type": "string", "size": 20, "nullable": 1},
{"kind": "column", "column_id": "broker_short_code", "table_id": "broker",
"data_type": "string", "size": 3, "nullable": 1},
{"kind": "classification", "classification_id":"classif_id_REQ_01",
"restriction_level": "public", "pii": 0, "if": "greater than 90 days",
"column_id": "broker_legal_name", "table_id": "broker"},
{"kind": "classification", "classification_id":"classif_id_REQ_03",
"restriction_level": "restricted", "pii": 0, "if": "less than 90 days",
"column_id": "broker_legal_name", "table_id": "broker"},
{"kind": "classification", "classification_id":"classif_id_REQ_214",
"restriction_level": "public", "pii": 0, "column_id": "broker_short_code",
"table_id": "broker"},

# traverse my_entities, set parents and add those to datastore
for entity in my_entities:
kind = entity['kind']
parent_key = None
if kind == "column":
parent_key = datastore_client.key("table", entity["table_id"])
elif kind == "classification":
parent_key = datastore_client.key("table", entity["table_id"],
"column", entity["column_id"])

key = datastore_client.key(kind, entity[kind+"_id"],
datastore_entry = datastore.Entity(key)

print("Saving: {}".format(entity))


The code above assumes that you’ve set yourself up with a working Service Account or authorised yourself in, and that your GCP project has been set.

Now let’s do some digging around our newly minted Datastore model. Let’s grab the column ‘broker_legal_name’

query1 = datastore_client.query(kind="column")
query1.add_filter("column_id", "=", "broker_legal_name")

Now that we have the column entity, let’s locate it’s parent id.

column = list(query1.fetch())[0]
print("This column belongs to: " +str(column.key.parent.id_or_name))

Further to this, we can also get all data classification elements attributed to a single column using the ancestor clause query.

query2 = datastore_client.query(kind="classification", ancestor=column.key)
for classification in list(query2.fetch()):

For more complex queries, Datastore has the concept of indexes being set, usually via it’s index.yaml configuration. The following is an example of an index.yaml file:

- kind: Cat
ancestor: no
- name: name
- name: age
direction: desc

- kind: Cat
- name: name
direction: asc
- name: whiskers
direction: desc

- kind: Store
ancestor: yes
- name: business
direction: asc
- name: owner
direction: asc

Indexes are important when attempting to add filters on more than one particular attribute within a Datastore entity. For example, the following code will fail:

# Adding a '>' filter will cause this to fail. Sidenote; it will work
# without an index if you add another '=' filter.
query2 = datastore_client.query(kind="classification", ancestor=column.key)
query2.add_filter("pii", ">", 0)
for classification in list(query2.fetch()):

To rectify this issue, you need to create an index.yaml that looks like the following:

- kind: classification
ancestor: yes
- name: pii

You would usually upload the yaml file using the gcloud commands:

gcloud datastore indexes create path/to/index.yaml.

However, let’s do this programmatically.

The official pypi package for google-cloud-datastore can be found here: At the time of writing, Firestore in Datastore-mode will be the way forward, as per the release note from January 31, 2019.

Cloud Firestore is now Generally Available. Cloud Firestore is the new version of Cloud Datastore and includes a backwards-compatible Datastore mode.

If you intend to use the Cloud Datastore API in a new project, use Cloud Firestore in Datastore mode. Existing Cloud Datastore databases will be automatically upgraded to Cloud Firestore in Datastore mode.

Except where noted, the Cloud Datastore documentation now describes behavior for Cloud Firestore in Datastore mode.

We’ve purposefully created MDH in Datastore to show you how it was done originally, and we’ll be migrating the Datastore code to Firestore in an upcoming post.

Creating and deleting indexes within Datastore will need to be done through the REST API via googleapiclient.discovery, as this function doesn’t exist via the google-cloud-datastore API. Working with the discovery api client can be a bit daunting for a first-time user, so here’s the code to add an index on Datastore:

import os
from google.oauth2 import service_account
from googleapiclient.discovery import build
from import datastore

SCOPES = ['']

PROJECT_ID = os.getenv("PROJECT_ID")

credentials = service_account
.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES)

datastore_api = build('datastore', 'v1', credentials=credentials)

body = {
'ancestor': 'ALL_ANCESTORS',
'kind': 'classification',
'properties': [{
'name': 'pii',
'direction': 'DESCENDING'

response = datastore_api.projects()
.create(projectId=PROJECT_ID, body=body)

How did we craft this API request? We can use the Google API Discovery Service to build client libraries, IDE plugins, and other tools that interact with Google APIs. The Discovery API provides a list of Google APIs and a machine-readable "Discovery Document" for each API. Features of the Discovery API:

  • A directory of supported APIs schemas based on JSON Schema.
  • A machine-readable "Discovery Document" for each of the supported APIs. Each document contains:
  • A list of API methods and available parameters for each method.
  • A list of available OAuth 2.0 scopes.
  • Inline documentation of methods, parameters, and available parameter values.

Navigating to the API reference page for Datastore and going to the ‘Datastore Admin’ API page, we can see references to the Indexes and RESTful endpoints we can hit for those Indexes. Therefore, looking at the link for the Discovery document for Datastore:$discovery/rest?version=v1

From this, we can build out our instantiation for the google api discovery object build('datastore', 'v1', credentials=credentials)

With respect to building out the body aspect of the request, I’ve found crafting that part within the ‘Try this API’ section of pretty valuable.

With this code, your index should show up in your Datastore console! You can also retrieve them within gcloud with gcloud datastore indexes list if you’d like to verify the indexes outside our python code. So there you have it: a working example of entity groups, ancestors, indexes and Metadata within Datastore. Have fun coding!

· 2 min read
Jeffrey Aven

Big fan of GitLab (and GitLab CI in particular). I had a recent requirement to push changes to a wiki repo associated with a GitLab project through a GitLab CI pipeline (using the SaaS version of GitLab) and ran into a conundrum…

Using the GitLab SaaS version - deploy tokens can’t have write api access, so the next best solution is to use deploy keys, adding your public key as a deploy key and granting this key write access to repositories is relatively straightforward.

This issue is when you attempt to create a masked GitLab CI variable using the private key from your keypair, you get this…

I was a bit astonished to see this to be honest… Looks like it has been raised as an issue several times over the last few years but never resolved (the root cause of which is something to do with newline characters or base64 encoding or the overall length of the string).

I came up with a solution! Not pretty but effective, masks the variable so that it cannot be printed in CI logs as shown here:


Add a masked and protected GitLab variable for each line in the private key, for example:

The Code

Add the following block to your .gitlab-ci.yml file:

now within Jobs in your pipeline you can simply do this to clone, push or pull from a remote GitLab repo:

as mentioned not pretty, but effective and no other cleaner options as I could see…

· 6 min read
Mark Stella

Recently I found myself at a client that were using a third party tool to scan all their enterprise applications in order to collate their data lineage. They had spent two years onboarding applications to the tool, resulting in a large technical mess that was hard to debug and impossible to extend. As new applications were integrated onto the platform, developers were forced to think of new ways of connecting and tranforming the data so it could be consumed.

The general approach was: setup scanner -> scan application -> modify results -> upload results -> backup results -> cleanup workspace -> delete anything older than 'X' days

Each developer had their own style of doing this - involving shell scripts, python scripts, SQL and everything in between. Worse, there was slabs of code replicated across the entire repository, with variables and paths changed depending on the use case.

My tasks was to create a framework that could orchestrate the scanning and adhered to the following philosophies:

  • DRY (Don't Repeat Yourself)
  • Config driven
  • Version controlled
  • Simple to extend
  • Idempotent

It also had to be written in Python as that was all the client was skilled in.

After looking at what was on the market (Airflow and Prefect being the main contenders) I decided to roll my own simplified orchestrator that required as little actual coding as possible and could be setup by configuration.

In choosing a configuration format, I settled on HOCON as it closely resembled JSON but has advanced features such as interpolation, substitions and the ability to include other hocon files - this would drastically reduce the amount of boilerplate configuration required.

Because I had focused so heavily on being configuration driven, I also needed the following charecteristics to be delivered:

  • Self discovery of task types (more on this later)
  • Configuration validation at startup

Tasks and self discovery

As I wanted anyone to be able to rapidly extend the framework by adding tasks, I needed to reduce as much repetition and boilerplate as possible. Ideally, I wanted a developer to just have to think about writing code and not have to deal with how to integrate this.

To achieve this, we needed a way of registering new 'tasks' that would become available to the framework. I wanted a developer to simply have to subclass the main Task class and implement a run function - the rest would be taken care of.

class TaskRegistry:

def __init__(self) -> None:
self._registry = {}

def register(self, cls: type) -> None:
n = getattr(cls, 'task_name', cls.__name__).lower()
self._registry[n] = cls

def registered(self) -> List[str]:
return list(self._registry.keys())

def has(self, name: str) -> bool:
return name in self._registry

def get(self, name: str) -> type:
return self._registry[name]

def create(self, name: str, *args, **kwargs) -> object:
return self._registry[name](*args, **kwargs)
except KeyError:
raise ClassNotRegisteredException(name)

registry = TaskRegistry()

Once the registry was instantiated, any new Tasks that inherited from 'Task' would automatically be added to the registry. We could then use the create(name) function to instantiate any class - essentially a pythonic Factory Method

class Task(ABC):

def __init__(self) -> None:
self.logger = logging.getLogger(self.__class__.__name__)

def __init_subclass__(cls) -> None:

def run(self, **kwargs) -> bool:
raise NotImplementedError

For the framework to automatically register the classes, it was important to follow the project structure. As long as the task resided in the 'tasks' module, we could scan this at runtime and register each task.

└── simple_tasker
└── tasks

This was achieved with a simple dynamic module importer

modules = glob.glob(join(dirname(__file__), "*.py"))

for f in modules:
if isfile(f) and not f.endswith(""):

The configuration

In designing how the configuration would bind to the task, I needed to capture the name (what object to instanticate) and what args to pass to the instantiated run function. I decided to model it as below with everything under a 'tasks' array

tasks: [
name: shell_script
args: {
script_path: uname
script_args: -a
name: shell_script
args: {
script_path: find
script_args: [${CWD}/simple_tasker/tasks, -name, "*.py"]
name: archive
args: {
input_directory_path: ${CWD}/simple_tasker/tasks
target_file_path: /tmp/${PLATFORM}_${TODAY}.tar.gz

Orchestration and validation

As mentioned previously, one of the goals was to ensure the configuration was valid prior to any execution. This meant that the framework needed to validate whether tha task name referred to a registered task, and that all mandatory arguments were addressed in the configuration. Determining whether the task was registered was just a simple key check, however to validate the arguments to the run required some inspection - I needed to get all args for the run function and filter out 'self' and any asterisk args (*args, **kwargs)

def get_mandatory_args(func) -> List[str]:

mandatory_args = []
for k, v in inspect.signature(func).parameters.items():
if (
k != "self"
and v.default is inspect.Parameter.empty
and not str(v).startswith("*")

return mandatory_args

And finally onto the actual execution bit. The main functionality required here is to validate that the config was defined correctly, then loop through all tasks and execute them - passing in any args.

class Tasker:

def __init__(self, path: Path, env: Dict[str, str] = None) -> None:

self.logger = logging.getLogger(self.__class__.__name__)
self._tasks = []

with wrap_environment(env):
self._config = ConfigFactory.parse_file(path)

def __validate_config(self) -> bool:

error_count = 0

for task in self._config.get("tasks", []):
name, args = task["name"].lower(), task.get("args", {})

if registry.has(name):
for arg in get_mandatory_args(registry.get(name).run):
if arg not in args:
print(f"Missing arg '{arg}' for task '{name}'")
error_count += 1
print(f"Unknown tasks '{name}'")
error_count += 1

self._tasks.append((name, args))

return error_count == 0

def run(self) -> bool:

if self.__validate_config():

for name, args in self._tasks:
exe = registry.create(name)"About to execute: '{name}'")
if not**args):
self.logger.error(f"Failed tasks '{name}'")
return False

return True
return False

Putting it together - sample tasks

Below are two examples of how easy it is to configure the framework. We have a simple folder archiver that will tar/gz a directory based on 2 input parameters.

class Archive(Task):

def __init__(self) -> None:

def run(self, input_directory_path: str, target_file_path: str) -> bool:"Archiving '{input_directory_path}' to '{target_file_path}'")

with, "w:gz") as tar:
return True

A more complex example would be the ability to execute shell scripts (or os functions) by passing in some optional variables and variables that can either be a string or list.

class ShellScript(Task):

task_name = "shell_script"

def __init__(self) -> None:

def run(
script_path: str,
script_args: Union[str, List[str]] = None,
working_directory_path: str = None
) -> bool:

cmd = [script_path]

if isinstance(script_args, str):
cmd += script_args


result = subprocess.check_output(

for o in result:

except (subprocess.CalledProcessError, FileNotFoundError) as e:
return False

return True

You can view the entire implementation here