Skip to main content

20 posts tagged with "gcp"

View All Tags

· 2 min read
Jeffrey Aven

This article walks through the process of converting service specific OpenAPI specifications from Google Discovery REST URLs using Python.

Full code for this article can be found at stackql/google-discovery-to-openapi

Google publishes JSON specifications for all of their APIs (including GCP services as well as other APIs associated with other products - like analytics or workspace). These specifications can be accessed without authentication starting with the root document ( which contains metadata and the URL for each service specific document (for services like compute or storage).

Code Overview

The program fetches the service document for each service that is included and not explicitly excluded (configured through variables in the program). Non preferred services (beta or alpha versions) can be included by setting the variable get_preferred_only to False.

An OpenAPI spec is constructed for each service based upon the data in the service discovery doc. In many cases this is a straightforward one to one mapping, such as to top level info, title and description values, it gets more complicated with parameters and schemas where some extra logic is required to keep the json pointers ($ref) valid.

Extracting Paths and Verbs

The real magic is in extracting paths and verbs in a compliant OpenAPI format, as Google nests this data (potentially multiple levels deep) under resources.

The first step is to identify methods nested under a resources object (which can be mapped to operations - with a path and HTTP verb required to populate an OpenAPI spec), this function does this:

Now each method can be processed yielding an operation (combination of path and verb), this is done using this function:

Full source code can be found at stackql/google-discovery-to-openapi.

· 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.


· 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!

· 3 min read
Tom Klimovski

When defining event-driven architectures, it's always good to keep up with how the landscape is changing. How do you connect microservices in your architecture? Is Pub/Sub the end-game for all events? To dive a bit deeper, let's talk through the benefits of having a single orchestrator, or perhaps a choreographer is better?

Orchestration versus choreography refresher

My colleague @jeffreyaven did a recent post explaining this concept in simple terms, which is worth reviewing, see:

Microservices Concepts: Orchestration versus Choreography

Should there really be a central orchestrator controlling all interactions between services.....or, should each service work independently and only interact through events?

  • Orchestration is usually viewed as a domain-wide central service that defines the flow and control of communication between services. In this paradigm, in becomes easier to change and ultimately monitor policies across your org.
  • Choreography has each service registering and emitting events as they need to. It doesn't direct or define the flow of communication, but using this method usually has a central broker passing around messages and allows services to be truly independent.

Enter Workflows, which is suited for centrally orchestrated services. Not only Google Cloud service such as Cloud Functions and Cloud Run, but also external services.

How about choreography? Pub/Sub and Eventarc are both suited for this. We all know and love Pub/Sub, but how do I use EventArc?

What is Eventarc?

Announced in October-2020, it was introduced as eventing functionality that enables you, the developer, to send events to Cloud Run from more than 60 Google Cloud sources.

But how does it work?

Eventing is done by reading those sweet sweet Audit Logs, from various sources, and sending them to Cloud Run services as events in Cloud Events format. Quick primer on Cloud Events: its a specification for describing event data in a common way. The specification is now under the Cloud Native Computing Foundation. Hooray! It can also read events from Pub/Sub topics for custom applications. Here's a diagram I graciously ripped from Google Cloud Blog:


Why do I need Eventarc? I have the Pub/Sub

Good question. Eventarc provides and easier path to receive events not only from Pub/Sub topics but from a number of Google Cloud sources with its Audit Log and Pub/Sub integration. Actually, any service that has Audit Log integration can be an event source for Eventarc. Beyond easy integration, it provides consistency and structure to how events are generated, routed and consumed. Things like:


They specify routing rules from events sources, to event sinks. Listen for new object creation in GCS and route that event to a service in Cloud Run by creating an Audit-Log-Trigger. Create triggers that also listen to Pub/Sub. Then list all triggers in one, central place in Eventarc:

gcloud beta eventarc triggers list

Consistency with eventing format and libraries

Using the CloudEvent-compliant specification will allow for event data in a common way, increasing the movement towards the goal of consistency, accessibility and portability. Makes it easier for different languages to read the event and Google Events Libraries to parse fields.

This means that the long-term vision of Eventarc to be the hub of events, enabling a unified eventing story for Google Cloud and beyond.

Eventarc producers and consumers

In the future, you can excpect to forego Audit Log and read these events directly and send these out to even more sinks within GCP and any HTTP target.

This article written on inspiration from Thanks Mete Atamel!

· One min read
Jeffrey Aven

Mulitcloud Diagramming

Following on from the recent post GCP Templates for C4 Diagrams using PlantUML, cloud architects are often challenged with producing diagrams for architectures spanning multiple cloud providers, particularly as you elevate to enterprise level diagrams.

In this post, with the magic of !includeurl we have brought PlantUML template libraries together for AWS, Azure and GCP icon sets, allowing us to produce multi cloud C4 diagrams using PlantUML like this one:

Multi Cloud Architecture Diagram using PlantUML

Creating a multi cloud diagram is simple, start by adding the following include statements after the @startuml label in a new PlantUML C4 diagram:

Then add references to the required services from different providers…

Then include the predefined resources from your different cloud providers in your diagram as shown here (describing a client server application over a cloud to cloud VPN between Azure and GCP)...

Happy multi-cloud diagramming!

Full source code is available at: