Quickstart: Build an API for Table app with Python SDK and Azure Cosmos DB

APPLIES TO: Table

This quickstart shows how to access the Azure Cosmos DB API for Table from a Python application. The Azure Cosmos DB for Table is a schemaless data store allowing applications to store structured NoSQL data in the cloud. Because data is stored in a schemaless design, new properties (columns) are automatically added to the table when an object with a new attribute is added to the table. Python applications can access the Azure Cosmos DB for Table using the Azure Data Tables SDK for Python package.

Prerequisites

The sample application is written in Python 3.7 or later, though the principles apply to all Python 3.7+ applications. You can use Visual Studio Code as an IDE.

If you don't have an Azure trial subscription, create a trial subscription before you begin.

Sample application

The sample application for this tutorial may be cloned or downloaded from the repository https://github.com/Azure-Samples/msdocs-azure-tables-sdk-python-flask.

git clone https://github.com/Azure-Samples/msdocs-azure-tables-sdk-python-flask.git

A 1-starter-app and 2-completed-app sample folder are included in the sample repository. The 1-starter-app has some functionality left for you to complete with lines marked "#TODO". The code snippets shown in this article are the suggested additions to complete the 1-starter-app.

The completed sample application uses weather data as an example to demonstrate the capabilities of the API for Table. Objects representing weather observations are stored and retrieved using the API for Table, including storing objects with extra properties to demonstrate the schemaless capabilities of the API for Table. The following image shows the local application running in a browser, displaying the weather data stored in the Azure Cosmos DB for Table.

1 - Create an Azure Cosmos DB account

You first need to create an Azure Cosmos DB Tables API account that will contain the table(s) used in your application. Create an account with the Azure portal, Azure CLI, or Azure PowerShell.

2 - Create a table

Next, you need to create a table within your Azure Cosmos DB account for your application to use. Unlike a traditional database, you only need to specify the name of the table, not the properties (columns) in the table. As data is loaded into your table, the properties (columns) are automatically created as needed.

3 - Get Azure Cosmos DB connection string

To access your table(s) in Azure Cosmos DB, your app needs the table connection string for the Cosmos DB Storage account. The connection string can be retrieved using the Azure portal, Azure CLI or Azure PowerShell.

4 - Install the Azure Data Tables SDK for Python

After you've created an Azure Cosmos DB account, your next step is to install the Azure Azure Data Tables SDK for Python. For details on installing the SDK, refer to the README.md file in the Data Tables SDK for Python repository on GitHub.

Install the Azure Tables client library for Python with pip:

pip install azure-data-tables

Don't forget to also install the requirements.txt in the 1-starter-app or 2-completed-app folders.

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5 - Configure the Table client in an .env file

Copy your Azure Cosmos DB account connection string from the Azure portal, and create a TableServiceClient object using your copied connection string. Switch to the 1-starter-app or 2-completed-app folder. Regardless of which app you start with, you need to define environment variables in an .env file.

# Configuration Parameters
conn_str = "A connection string to an Azure Cosmos DB account."
table_name = "WeatherData"
project_root_path = "Project abs path"

The Azure SDK communicates with Azure using client objects to execute different operations against Azure. The TableServiceClient object is the object used to communicate with the Azure Cosmos DB for Table. An application will typically have a single TableServiceClient overall, and it will have a TableClient per table.

For example, the following code creates a TableServiceClient object using the connection string from the environment variable.

self.conn_str = os.getenv("conn_str")
self.table_service = TableServiceClient.from_connection_string(self.conn_str)

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6 - Implement Azure Cosmos DB table operations

All Azure Cosmos DB table operations for the sample app are implemented in the TableServiceHelper class located in helper file under the webapp directory. You'll need to import the TableServiceClient class at the top of this file to work with objects in the azure.data.tables client library for Python.

from azure.data.tables import TableServiceClient

At the start of the TableServiceHelper class, create a constructor and add a member variable for the TableClient object to allow the TableClient object to be injected into the class.

def __init__(self, table_name=None, conn_str=None):
    self.table_name = table_name if table_name else os.getenv("table_name")
    self.conn_str = conn_str if conn_str else os.getenv("conn_str")
    self.table_service = TableServiceClient.from_connection_string(self.conn_str)
    self.table_client = self.table_service.get_table_client(self.table_name)

Filter rows returned from a table

To filter the rows returned from a table, you can pass an OData style filter string to the query_entities method. For example, if you wanted to get all of the weather readings for Shanghai between midnight July 1, 2021 and midnight July 2, 2021 (inclusive) you would pass in the following filter string.

PartitionKey eq 'Shanghai' and RowKey ge '2021-07-01 12:00 AM' and RowKey le '2021-07-02 12:00 AM'

You can view related OData filter operators on the azure-data-tables website in the section Writing Filters.

When request.args parameter is passed to the query_entity method in the TableServiceHelper class, it creates a filter string for each non-null property value. It then creates a combined filter string by joining all of the values together with an "and" clause. This combined filter string is passed to the query_entities method on the TableClient object and only rows matching the filter string are returned. You can use a similar method in your code to construct suitable filter strings as required by your application.

def query_entity(self, params):
    filters = []
    if params.get("partitionKey"):
        filters.append("PartitionKey eq '{}'".format(params.get("partitionKey")))
    if params.get("rowKeyDateStart") and params.get("rowKeyTimeStart"):
        filters.append("RowKey ge '{} {}'".format(params.get("rowKeyDateStart"), params.get("rowKeyTimeStart")))
    if params.get("rowKeyDateEnd") and params.get("rowKeyTimeEnd"):
        filters.append("RowKey le '{} {}'".format(params.get("rowKeyDateEnd"), params.get("rowKeyTimeEnd")))
    if params.get("minTemperature"):
        filters.append("Temperature ge {}".format(params.get("minTemperature")))
    if params.get("maxTemperature"):
        filters.append("Temperature le {}".format(params.get("maxTemperature")))
    if params.get("minPrecipitation"):
        filters.append("Precipitation ge {}".format(params.get("minPrecipitation")))
    if params.get("maxPrecipitation"):
        filters.append("Precipitation le {}".format(params.get("maxPrecipitation")))
    return list(self.table_client.query_entities(" and ".join(filters)))

Insert data using a TableEntity object

The simplest way to add data to a table is by using a TableEntity object. In this example, data is mapped from an input model object to a TableEntity object. The properties on the input object representing the weather station name and observation date/time are mapped to the PartitionKey and RowKey properties respectively, which together form a unique key for the row in the table. Then the extra properties on the input model object are mapped to dictionary properties on the TableEntity object. Finally, the create_entity method on the TableClient object is used to insert data into the table.

Modify the insert_entity function in the example application to contain the following code.

def insert_entity(self):
    entity = self.deserialize()
    return self.table_client.create_entity(entity)
    
@staticmethod
def deserialize():
    params = {key: request.form.get(key) for key in request.form.keys()}
    params["PartitionKey"] = params.pop("StationName")
    params["RowKey"] = "{} {}".format(params.pop("ObservationDate"), params.pop("ObservationTime"))
    return params

Upsert data using a TableEntity object

If you try to insert a row into a table with a partition key/row key combination that already exists in that table, you'll receive an error. For this reason, it's often preferable to use the upsert_entity instead of the create_entity method when adding rows to a table. If the given partition key/row key combination already exists in the table, the upsert_entity method updates the existing row. Otherwise, the row is added to the table.

def upsert_entity(self):
    entity = self.deserialize()
    return self.table_client.upsert_entity(entity)
    
@staticmethod
def deserialize():
    params = {key: request.form.get(key) for key in request.form.keys()}
    params["PartitionKey"] = params.pop("StationName")
    params["RowKey"] = "{} {}".format(params.pop("ObservationDate"), params.pop("ObservationTime"))
    return params

Insert or upsert data with variable properties

One of the advantages of using the Azure Cosmos DB for Table is that if an object being loaded to a table contains any new properties then those properties are automatically added to the table and the values stored in Azure Cosmos DB. There's no need to run DDL statements like ALTER TABLE to add columns as in a traditional database.

This model gives your application flexibility when dealing with data sources that may add or modify what data needs to be captured over time or when different inputs provide different data to your application. In the sample application, we can simulate a weather station that sends not just the base weather data but also some extra values. When an object with these new properties is stored in the table for the first time, the corresponding properties (columns) are automatically added to the table.

To insert or upsert such an object using the API for Table, map the properties of the expandable object into a TableEntity object and use the create_entity or upsert_entity methods on the TableClient object as appropriate.

In the sample application, the upsert_entity function can also implement the function of insert or upsert data with variable properties

def insert_entity(self):
    entity = self.deserialize()
    return self.table_client.create_entity(entity)

def upsert_entity(self):
    entity = self.deserialize()
    return self.table_client.upsert_entity(entity)

@staticmethod
def deserialize():
    params = {key: request.form.get(key) for key in request.form.keys()}
    params["PartitionKey"] = params.pop("StationName")
    params["RowKey"] = "{} {}".format(params.pop("ObservationDate"), params.pop("ObservationTime"))
    return params

Update an entity

Entities can be updated by calling the update_entity method on the TableClient object.

In the sample app, this object is passed to the upsert_entity method in the TableClient class. It updates that entity object and uses the upsert_entity method save the updates to the database.

def update_entity(self):
    entity = self.update_deserialize()
    return self.table_client.update_entity(entity)
    
@staticmethod
def update_deserialize():
    params = {key: request.form.get(key) for key in request.form.keys()}
    params["PartitionKey"] = params.pop("StationName")
    params["RowKey"] = params.pop("ObservationDate")
    return params

Remove an entity

To remove an entity from a table, call the delete_entity method on the TableClient object with the partition key and row key of the object.

def delete_entity(self):
    partition_key = request.form.get("StationName")
    row_key = request.form.get("ObservationDate")
    return self.table_client.delete_entity(partition_key, row_key)

7 - Run the code

Run the sample application to interact with the Azure Cosmos DB for Table. For example, starting in the 2-completed-app folder, with requirements installed, you can use:

python3 run.py webapp

See the README.md file in the sample repository root for more information about running the sample application.

The first time you run the application, there will be no data because the table is empty. Use any of the buttons at the top of application to add data to the table.

Selecting the Insert using Table Entity button opens a dialog allowing you to insert or upsert a new row using a TableEntity object.

Selecting the Insert using Expandable Data button brings up a dialog that enables you to insert an object with custom properties, demonstrating how the Azure Cosmos DB for Table automatically adds properties (columns) to the table when needed. Use the Add Custom Field button to add one or more new properties and demonstrate this capability.

Use the Insert Sample Data button to load some sample data into your Azure Cosmos DB Table.

• For the 1-starter-app sample folder, you'll need to at least complete the code for the submit_transaction function for the sample data insert to work.

• The sample data is loaded from a sample_data.json file. The .env variable project_root_path tells the app where to find this file. For example, if you're running the application from the 1-starter-app or 2-completed-app folder, set project_root_path to "" (blank).

Select the Filter Results item in the top menu to be taken to the Filter Results page. On this page, fill out the filter criteria to demonstrate how a filter clause can be built and passed to the Azure Cosmos DB for Table.

Clean up resources

When you're finished with the sample application, you should remove all Azure resources related to this article from your Azure account. You can remove all resources by deleting the resource group.

Next steps

In this quickstart, you've learned how to create an Azure Cosmos DB account, create a table using the Data Explorer, and run an app. Now you can query your data using the API for Table.