Kyle Pericak

"It works in my environment."

Mon 14 October 2019

Google Cloud Firestore Basics - Python

Posted by Kyle Pericak in cloud   

This post is linked to from the GCP: Deep Dive Project

This post covers how to interact with Google's Cloud Firestore, using examples written in Python. The concepts should apply to any language.

Firestore is the newer version of Datastore. You can find the differences here. In this post, the database is configured in Native mode. Other than some extra language support, Native mode's coolest new feature seems to be the ability to listen to a post. From what I can tell, this could replace the need for pub/sub in some scenarios.

Throughout the post I'll use a project named "example". Be sure to replace this with your own project name.


Official Firestore pricing data is here.

Note that Firestore mode is almost twice the price of Datastore mode. Datastore pricing can be found here.

Every day a number of free operations are allowed, which for a small application makes Datastore very affordable. Even after the free tier though, the pricing is pretty good. Firestore and Datastore have the same free tiers.

Summary of pricing: Datastore vs Firestore

  • Storage: $/GB/Month
    • Datastore: 0.108
    • Firestore: 0.180
  • Reads: $/100,000
    • Datastore: 0.036
    • Firestore: 0.060
  • Writes: $/100,000
    • Datastore: $0.108
    • Firestore: $0.180
  • Deletes: $/100,000
    • Datastore: 0.012
    • Firestore: 0.020

Max entity size and document size are both 1MB, so you could realistically just use JSON strings in entities instead to save money if you don't need any of Firestore's other features.

Datastore/Firestore Concepts

Both options use a non-relational/NoSQL database. NoSQL databases in general allow for the direct serialization of a programs objects/dictionaries.


Official Firestore data model docs

Firestore uses a document database organized into documents and collections. This is a change from the older Datastore model of Entities organized by Kinds.

Documents support nested data structures ("Maps"). Think of them like JSON objects, but with more types supported. Documents are limited to 1MB each.

Documents are stored in Collections. Collections are containers for documents, like a filesystem's directories. Documents can also contain their own nested collections, which store other documents. Nesting has a depth limit of 100.

Documents are identified by References. In python, a reference looks like this:



In Datastore, objects are called Entities. The key-value data pairs stored by entities are called properties.

Entities are uniquely identified by keys. Entity Keys consists of the following components:

  • Namespace: Basically a tenant identifier, like a user ID. By default, the namespace is an empty string. Any entity created as a "child" will inherit its parent's namespace.
  • Kind: A string describing the category or purpose of the entity.
  • Identifier: Unlike namespace and kind which are shared between like entities, the identifier is unique to each entity. This is either a string called a "key name", or an integer numeric ID.
  • Ancestor Path: An optional component of the key, identifying its parents to form a database hierarchy.

Create IAM Service Account

Before your code can access Datastore/Firestore, access needs to be configured in the Identity Access Management tool.

First, create a Service Account:

  1. Open the Service Account page
  2. Select your project
  3. Click "Create Service Account"
  4. Service Account Details:
    1. Service account name: PythonDatastoreAdmin
    2. Service account ID: pythondatastoreadmin
    3. Service account description: SA with Full Datastore Access for Python
  5. Grant this service account access to project (optional)
    1. Select a role: Datastore > Cloud Datastore Owner
    2. Continue
  6. Grant users access to this service account (optional)
    1. Download a key file for Python to use. Use JSON as the key type.
    2. Done

I downloaded my key to $HOME/gcp_keys/datastore_admin.json

Python Environment Setup

Build a virtualenv. If you're in a git repo, don't forget to ignore this directory.

python3 -m venv env3
source env3/bin/activate

Write the requirement file

vi requirements.txt


Install the requirements

pip install -r rqeuirements.txt

Python Code Examples

A fake project name of "example" will be used. Be sure to use a valid one.

Some notes about the Firestore python API:

  • firestore.client().collection returns a CollectionReference, which allows you to operate on the named collection.
  • firestore.client().collection().list_documents returns a Page Iterator. Casting a page iterator to a list will trigger a read operation, returning a list of DocumentReference objects.
  • firestore.client().collection().document returns a DocumentReferece, which interacts with the actual Firestore documents. The DocumentReference object can operate (get/set/create) on the actual Document.
  • firestore.client().collection().document().get() will return a DocumentSnapshot of the document. The document snapshot is how you read data from a document. Document snapshots are a representation of the document state taken at the instant of their creation.
  • firestore.client().collection().document().create() makes a new document. If the object already existed, a google.api_core.exceptions.AlreadyExists exception is thrown.
  • Write operations (create & set) return a WriteResult.
from os import environ
from import firestore

# The key file is an exported JSON file from the IAM tool
key_file_path = '{}/gcp_keys/datastore_admin.json'.format(environ['HOME'])

# When running this on a local system, the package from GCP
# expects the GOOGLE_APPLICATION_CREDENTIALS environment variable to be set
# to the exported IAM key file's path
environ['GOOGLE_APPLICATION_CREDENTIALS'] = key_file_path

# expects the project name as an argument
project = 'example'

# Instantiate the datastore client. This step authenticates to GCP.
# See the Credential Error note below if authentication fails
client = firestore.Client(project)

# Get a reference to the test collection. This returns a
# object
ref_col_test = client.collection('test')

# Get a reference to a document which may or may not exist. This returns a
# object
ref_doc_document1 = ref_col_test.document('document1')

# Perform the actual database query to get the document. get() returns a
# object.
snapshot_document1 = ref_doc_document1.get()

# Note that there is no data
assert document1.to_dict() is None

# You can use document.exists to check if the document already exists.
# Here's an example of doing a safe insert of some sample data:
sample_data = {
    'nums': {'num1': 100, 'num2': 200},
    'strs': {'animal1': 'dog', 'animal2': 'cat'}}
if snapshot_document1.exists is False:
    createe_write_result = ref_doc_document1.create(sample_data)

document1_data = ref_doc_document1.get().to_dict()

# Finally, update the data
sample_data['strs']['animal3'] = 'bird'
set_write_result = ref_doc_document1.set(sample_data)

# List the documents in the collection. The iterator returns a paginated list
# of document references. You can then operate on those references.
doc_list_iterator = ref_col_test.list_documents()
doc_ref_list = list(doc_list_iterator)
sample_document_snapshot = doc_ref_list[0].get()

# Show that the changes were applied to the data. This will return 'bird'
animal3 = sample_document_snapshot.to_dict()['strs']['animal3']


Credentials Error

If your local google cloud authentication isn't set up correctly, you'll get an error saying:

google.auth.exceptions.DefaultCredentialsError: Could not automatically determine credentials. Please set GOOGLE_APPLICATION_CREDENTIALS or explicitly create credentials and re-run the application. For more information, please see

If that happens, review the IAM Service account steps.

Iterator has already started

If you try and cast a GRPCIterator to list twice, you get a ValueError.

ValueError: ('Iterator has already started', <google.api_core.page_iterator.GRPCIterator object)

The ValueError is thrown because ._started is True in the iterator. It has already done its thing and won't work again. I didn't see any good way to restart the iterator without discarding it. You can see the details in the page iterator source code.

Best course is just to get a new iterator from the reference object. See in the following example how the ID changes. The doc_list_iterator var is being pointed at a new iterator. The old iterator gets gc'd by Python as usual.

>>> id(doc_list_iterator)
>>> doc_list_iterator = ref_col_test.list_documents()
>>> id(doc_list_iterator)

It will work as expected.

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