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Azure Cosmos DB SQL API client library for Python
Azure Cosmos DB SQL API client library for Python
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://learn.microsoft.com/python/azure/ or our v...
·github.com·
Azure Cosmos DB SQL API client library for Python
4 Design Patterns to Deal with Large Item Sizes in Azure Cosmos DB for NoSQL - Azure Cosmos DB Blog
4 Design Patterns to Deal with Large Item Sizes in Azure Cosmos DB for NoSQL - Azure Cosmos DB Blog
A recommended best practice in Azure Cosmos DB for NoSQL is to avoid overly large item sizes. This blog post covers multiple approaches that you can use to model large objects within your Azure Cosmos DB-backed application. This ensures you get optimal performance efficiency and cost effectiveness with Azure Cosmos DB for NoSQL.
·devblogs.microsoft.com·
4 Design Patterns to Deal with Large Item Sizes in Azure Cosmos DB for NoSQL - Azure Cosmos DB Blog
Testing Python code integration with an Azure Eventhub
Testing Python code integration with an Azure Eventhub
On one of my project I needed to create a simple API allowing clients to publish some messages into an Azure EventHub. Because authentication is ensured by a JWT we could not rely on our clients to directly publish messages to the Eventhub (and by the way I prefer abstracting this away through a HTTP API).
·g0di.github.io·
Testing Python code integration with an Azure Eventhub
Generative AI with Azure Cosmos DB
Generative AI with Azure Cosmos DB
Leverage Azure Cosmos DB for generative AI workloads for automatic scalability, low latency, and global distribution to handle massive data volumes and real-time processing. With support for versatile data models and built-in vector indexing, it efficiently retrieves natural language queries, making it ideal for grounding large language models. Seamlessly integrate with Azure OpenAI Studio for API-level access to GPT models and access a comprehensive gallery of open-source tools and frameworks in Azure AI Studio to enhance your AI applications. ► QUICK LINKS: 00:00 - Azure Cosmos DB for generative AI workloads 00:18 - Versatile Data Models 00:39 - Scalability and performance 01:19 - Global distribution 01:31 - Vector indexing and search 02:07 - Grounding LLMs 02:30 - Wrap up ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics #azurecosmosdb #cosmosdb #generativeai #chatgpt
·youtube.com·
Generative AI with Azure Cosmos DB
Build and deploy containerized apps with Azure Kubernetes Service & Azure Cosmos DB - Azure Cosmos DB Blog
Build and deploy containerized apps with Azure Kubernetes Service & Azure Cosmos DB - Azure Cosmos DB Blog
In today’s digital world, customers expect applications to be feature rich, tailored to their needs, and delivered quickly. For the modern-day business to survive and thrive, applications need to evolve quickly, scale fast and be highly resilient. Businesses need swift innovation through cloud-native architectures to meet these growing customer expectations.
·devblogs.microsoft.com·
Build and deploy containerized apps with Azure Kubernetes Service & Azure Cosmos DB - Azure Cosmos DB Blog
Integrate Azure Active Directory for the cluster - Training
Integrate Azure Active Directory for the cluster - Training
Integrate Azure Active Directory for the cluster
There are two ways to manage AKS to Azure access through Azure Active Directory (Azure AD): service principals or managed identities for Azure resources
There are two ways to manage AKS to Azure access through Azure Active Directory (Azure AD): service principals or managed identities for Azure resources.
The cluster identity is used by the AKS control plane components to manage cluster resources including ingress load balancers, AKS managed public IPs, etc.
The kubelet identity is used to authenticate with Azure Container Registry (ACR). Some add-ons also support authentication using a managed identity.
Outside-in access.
Inside-out access.
·learn.microsoft.com·
Integrate Azure Active Directory for the cluster - Training
Monitoring and logging for Azure Kubernetes Service - Training
Monitoring and logging for Azure Kubernetes Service - Training
Monitoring and logging for Azure Kubernetes Service
Use the Nodes and Controllers views to view the health and performance of the pods that are running on nodes and controllers, and their resource consumption in terms of CPU and memory.
You can use Metrics Explorer to view the Inflight Requests counter
·learn.microsoft.com·
Monitoring and logging for Azure Kubernetes Service - Training
Access to Azure Kubernetes Service - Training
Access to Azure Kubernetes Service - Training
Access to Azure Kubernetes Service
Services logically group pods to allow for direct access on a specific port via an IP address or DNS name. ServiceTypes allow you to specify what kind of Service you want. You can distribute traffic using a load balancer. More complex routing of application traffic can also be achieved with ingress controllers.
ClusterIP creates an internal IP address for use within the AKS cluster. This Service is good for internal-only applications that support other workloads within the cluster.
Nodes receive an IP address from the Azure virtual network subnet. Pods receive an IP address from a logically different address space than the nodes' Azure virtual network subnet. Network address translation (NAT) is then configured so that the pods can reach resources on the Azure virtual network. The source IP address of the traffic is translated to the node's primary IP address.
You can let the Azure platform create and configure the virtual networks for you, or choose to deploy your AKS cluster into an existing virtual network subnet.
With Azure CNI, every pod gets an IP address from the subnet and can be accessed directly
The LoadBalancer only works at layer 4. At layer 4, the Service is unaware of the actual applications, and can't make any more routing considerations.
Ingress controllers work at layer 7 and can use more intelligent rules to distribute application traffic.
·learn.microsoft.com·
Access to Azure Kubernetes Service - Training
Nodes and node pools for Azure Kubernetes Service - Training
Nodes and node pools for Azure Kubernetes Service - Training
Nodes and node pools for Azure Kubernetes Service
The nodes, also called agent nodes or worker nodes
AKS groups nodes of the same configuration into node pools of VMs that run AKS workloads
If you want to have only one node pool in your AKS cluster, for example in a development environment, you can schedule application pods on the system node pool.
·learn.microsoft.com·
Nodes and node pools for Azure Kubernetes Service - Training
Azure Kubernetes Service pods - Training
Azure Kubernetes Service pods - Training
Azure Kubernetes Service pods
Pods typically have a 1:1 mapping with a container. In advanced scenarios, a pod may contain multiple containers. Multi-container pods are scheduled together on the same node, and allow containers to share related resources.
When you create a pod, you can define resource requests to request a certain amount of CPU or memory resources. The Kubernetes Scheduler tries to meet the request by scheduling the pods to run on a node with available resources.
maximum resource limits
Pods are typically ephemeral, disposable resources.
·learn.microsoft.com·
Azure Kubernetes Service pods - Training
Azure Kubernetes cluster architecture - Training
Azure Kubernetes cluster architecture - Training
Azure Kubernetes cluster architecture
The computers in a cluster that run the tasks are called nodes, and the computers that run the scheduling software are called control planes.
Commands from the master node are sent to the kubelet on the worker nodes.
The kube-controller-manager takes the YAML file and tasks the kube-scheduler with deciding which worker nodes the app or workload should run based on predetermined constraints.
If one or more pods happen to fail, the ReplicaSet replaces them. In this way, Kubernetes is said to be self-healing.
Kubernetes supports rollbacks, rolling updates, and pausing rollouts. Additionally, deployments use ReplicaSets in the background to ensure that the specified number of identically configured pods are running.
·learn.microsoft.com·
Azure Kubernetes cluster architecture - Training