This is a continuation of the article on the change-feed for Cosmos DB. Specifically, it discusses document versioning support.  If we treat each item in CosmosDB as a standalone document, then it is subject to the same versioning principles as a blob. The versioning for blobs automatically maintains previous versions of an object. When blob versioning is enabled, it can restore an earlier version of a blob to recover data to recover the data if it is accidentally deleted or modified. If a blob is edited, it copies on write so a new version is created. Since each write operation creates a new version, it is possible to revert to earlier versions. Similarly, an older version of the data can be promoted to create a new version. Each version maintains a new identifier. A blob can have only one current version at a time.  Blob versions are immutable.  The content or metadata of an existing blob pertaining to its version. If there are many versions for a blob, it will tend to increase the latency for listing operations. Fewer than a thousand versions are preferable for a blob. Old versions can be deleted automatically.

In the case of a blob, the version identifier is the timestamp at which the blob was updated. The version ID is assigned at the time that the version is created. Read or write operations can target a specific version given the version id. If it is omitted, the current version is used instead. The x-ms-version-id header in the http responses holds this identifier.

Versioning is not enabled on a per blob basis. It is set at the account level. Prior to versioning being enabled on the account level, a blob in that account does not have a version.  When the versioning is enabled, all write operations create a new version except for the put block operation.

When the delete operation is called, the current version becomes a previous version and there is no current version anymore. All the previously existing versions are preserved.

Blob versioning can be enabled or disabled. When it is disabled, no new versions are subsequently created. Any existing versions remain accessible.

Blob versioning is frequently used with soft delete which protects a blob, snapshot or version from accidental deletes or overwrites by maintaining the deleted data in the system for a specified period.  During this time, a soft-deleted object can be restored to its original before the delete was issued. After the expiry of the retention period, the object is permanently deleted.  This can be enabled or disabled at the container level. Attempting to delete a soft-deleted object does not affect its expiry time.

The API support for versioning and soft-delete is only available in later versions rather than the earlier versions. If a blob has snapshots, the blob cannot be deleted without deleting the base blob. No new snapshots are created. Soft-delete objects are invisible unless they are called out for displaying or listing.

Versioning stored documents in Azure Cosmos DB

Introduction

History of data is often important as much as the data itself. For example, Finance, healthcare and insurance industries often track histories of portions of the data for audit purposes, and reporting. CosmosDB forms the storage layer for many microservices in Azure. This article explains the ‘change-feed’ feature associated with this storage.

Description:

CosmosDB exposes an API for the underlying log of changes regarding the documents in its collection. For users familiar with the SQL Server relational store, this is the equivalent of the change data capture. The changes are recorded incrementally and can be distributed across one or more consumers for parallel processing, enabling a variety of applications. The change feed works for updates and other forms of writes but not deletions. Usually only the most recent change is available. Intermediate changes are not visible.

The change feed is not targeted at solving all the versioning requirements from the CosmosDB store. That requires a Document Versioning Pattern which involves the following:

1.       Intent – This ensures that each entity in collections, when updated maintains the history of changes.

2.       Motivation – This tracks the history of entities throughout their lifecycle

3.       Applicability – This covers the usages such as auditing, reporting and analysis

4.       Structure – In order to keep the state of the objects, every update must be turned into an append operation.

5.       Participants - A materialized view is made possible with the change feed

6.       Consequences – This should work for short and long histories. If it suffers performance degradation, it might not apply to all use cases for versioning.

Change feed allows the use of a “soft marker” on the items for the updates and the filter based on that when the processing items in the change feed. This enables the recording of deletes since deletes are not supported. Inserts and updates are recorded by the change feed automatically.

Change feed items come in the order of their modification time. This sort order is guaranteed per logical partition key.

In a multi-region Azure Cosmos DB account, the failover of a write region will be supported where the change feed will work across the manual failover operation and will remain contiguous.

Conclusion:

This approach solves the capture of data changes for its applicability to auditing, reporting and analysis.

 

This is a continuation of an article that describes operational considerations for hosting solutions on Azure public cloud.

This articles focuses on support for containers and Kubernetes in Azure. 

Compute requirement of a modern cloud app typically involves load balanced compute nodes that operate together with control nodes and databases.

VM Scale sets provide scale, customization, availability, low cost and elasticity.

VM scale sets in Azure resource manager generally have a type and a capacity. App deployment allow VM extension updates just like OS updates.

Container infrastructure layering allows even more scale because it virtualizes the operating system. While traditional virtual machines enable hardware virtualization and hyper V’s allow isolation plus performance, containers are cheap and barely anything more than just applications.

Azure container service serves both linux and windows container services. It has standard docker tooling and API support with streamlined provisioning of DCOS and Docker swarm.

Azure is an open cloud because it supports open source infrastructure tools  such as  Linux, ubuntu, docker, etc. layered with databases and middleware such as hadoop, redis, mysql etc., app framework and tools such as nodejs, java, python etc., applications such as Joomla, drupal etc and management applications such as chef, puppet, etc. and finally with devops tools such as jenkins, Gradle, Xamarin etc.

Job based computations use larger sets of resources such as with compute pools that involve automatic scaling and regional coverage with automatic recovery of failed tasks and input/output handling.

Azure involves a lot of fine grained loosely coupled micro services using HTTP listener, Page content, authentication, usage analytic, order management, reporting, product inventory and customer databases.

 

Efficient Docker image deployment for intermittent low bandwidth connectivity scenarios requires the elimination of docker pulling of images. An alternative deployment mechanism can compensate for the restrictions by utilizing an Azure Container Registry, Signature Files, a fileshare, an IOT hub for pushing manifest to devices. The Deployment path involves pushing image to device which is containerized. The devices can send back messages which are collected in a device-image register. An image is a collection of layers where each layer represents a set of file-system differences and stored merely as folders and files. A SQL database can be used to track the state of what’s occurring on the target devices and the Azure based deployment services which helps with both during and after the deployment process.

 

Resource groups are created to group resources that share the same lifecycle. They have no bearing on the cost management of resources other than to help with querying. They can be used with tags to narrow down the interest. There is metadata stored about the resources and it is stored in a particular region. Resources can be moved from one resource group to another or even to another subscription. Finally, resource groups can be locked to prevent actions such as delete or write by users who have access.

As with its applicability to many deployments, Azure Monitor provides tremendous insight into operations of Azure Resources. It is always recommended to create multiple application insights resources and usually one per environment. This results in better separation of telemetry,alerts, workitems, configurations and permissions. Limits are spread such as web test count, throttling, data allowance etc and it also helps with cross-resource queries.

 

This is a continuation of an article that describes operational considerations for hosting solutions on Azure public cloud.

Multiple-choice questions in certification examinations are quite costly to make a mistake because they go beyond the cursory knowledge on the Azure resources. We recap just a few of the storage related questions from a recent test.

1.       The storage-based questions are somewhat easier to answer because they apply to a lot of common use cases. Some attention to limits imposed on different types of storage, their access polices, tiers, and retention period will go a long way in getting the answers right. Familiarity with hot, cool and archive tiers are tested by their use cases. Access control policy enforcement and cost management apply just as much they do for all Azure resources. Redundancy and availability are special considerations. Geo-replication is a hot topic.

2.       Hot, cool and archive access tiers for blob data are optimized for access patterns. The hot tier has the highest storage cost but the lowest access cost.  The cool tier stores for a minimum of 30 days and the archive tier for a minimum of 180 days. The archive tier is an offline tier for storing data with data rehydration available on standard and priority basis. The storage capacity costs Hot tier can be set to cool tier or archive tier and cool tier can be set to archive tier. If a blob is moved from the archive tier to the hot tier, it will be moved back to the archive tier by the lifecycle management engine. End-to-End latency and server latency are both available for block blobs.

3.       Azure storage events allow application to react to events such as the creation and deletion of blobs. They are pushed using Azure Event Grid to subscribers such as Azure Functions, Azure Logic Applications or even to the http listener. Blob storage events schema defines Microsoft.Storage.BlobCreated, BlobDeleted, BlobTierChanged and AsyncOperationInitiated.

4.       Network File System (NFS) 3.0 protocol is supported in Azure Blob Storage. Mounting a storage account container involves creating an Azure Virtual Network (VNet) and configuring network security to allow traffic to and from the storage account container via the VNet. Azurite open-source emulator can be used for local development environment.

5.       Azure (global) supports General Purpose V1, V2, and Blob storage accounts while Azure Stack Hub is general-purpose v1 only.  V2 is preferred because it provides Blob, queue, file and table storage with LRS, GRS, RA-GRS redundancy options

6.       Costs for storage tier is based on amount of data stored depending on the access tier, the data access cost, the transaction cost, the geo-replication data transfer cost, the outbound data transfer cost, and the changing storage access tier. The primary access pattern for the blob storage in terms of reads and writes and their comparisons determines the cost savings. All storage accesses can be monitored, and metrics emitted include capacity costs, transaction costs, and data transfer costs.

 

 

 

This is a continuation of an article that describes operational considerations for hosting solutions on Azure public cloud.

In this article, the topics that are encountered when taking certification examinations on Azure are discussed. The multiple-choice questions in those examinations are quite costly to make a mistake because they go beyond the cursory knowledge on the Azure resources. We recap just a few of these questions from a recent test.

Organizations deal with compute, storage and networking problems but identity hits home with the employees. Some of the questions ask about how inter-domain trust is established.  The order in which these steps are performed. The techniques by which multi-factor authentication is set up. How applications and services are secured. The scope at which these role-based access control may be overriden. How can the policies be conditionally enforced? These are some of the themes on which the questions from the certification examinations are based.

These questions are not hard to answer per se but they highlight the requirement for deep understanding of the Azure resources for solving those problems. For example, it finds out when password sync and password pass-through are applicable. Similarly, the use of privileged user protection is questioned.

The storage-based questions are somewhat easier to answer because they apply to a lot of common use cases. Some attention to limits imposed on different types of storage, their access polices, tiers, and retention period will go a long way in getting the answers right. Familiarity with hot, cool and archive tiers are tested by their use cases. Access control policy enforcement and cost management apply just as much they do for all Azure resources. Redundancy and availability are special considerations. Geo-replication is a hot topic.

The compute-based questions apply to different size and scale required for small, mid and large usages. They apply to different use cases but a common topic of interest is interoperability or dedicated ecosystems. It is important to know how to use them but it is more important to know how it connects to Azure resources including its hardening. Some examples cited in the questions span container orchestration frameworks, container registries and instances.

The networking questions are heavy on connections and their restrictions. VPN, firewall, Bastion are discussed in examples from threat analysis and mitigation purposes. The way to author policies, rules, routes and circuits are discussed very well.

Lastly. a study of the documentation online on architecture and best practices will round up the preparation.

 

 

This is a continuation of an article that describes operational considerations for hosting solutions on Azure public cloud.

The order in which a conditional access policy is implemented depends on the assignments and access controls. It brings together signals with which it makes decisions and then enforces organizational policies. If there are multiple conditional access policies, they must all apply to grant access. Assignments such as requiring an MFA and a compliant device must be ANDed. Policies are enforced in two phases: 1) session details are collected and 2) policies are enforced. Phase 1 involves gathering session details via connection properties which are also evaluated in report-only mode Phase 2 prompts the user but enforces all the policies.

Azure AD conditional access is frequently used to secure cloud applications with a single policy that grants access for selected users and groups who are required to pass multi-factor authentication. This comes helpful when access is originated from a location that is not trusted.

Networking resources must belong to the same subscription, region and resource group to set up virtual end points.  Microsoft peering must be created to configure ExpressRoute circuit. The provider status is checked to ensure that the circuit is fully provisioned by the connectivity provider.

Azure Monitors provide tremendous insight into operations of Azure Resources. It is always recommended to create multiple application insights resources and usually one per environment. This results in better separation of telemetry,alerts, workitems, configurations and permissions. Limits are spread such as web test count, throttling, data allowance etc and it also helps with cross-resource queries.

Limits should not be configured for the prod environment because it will result in loss of data once the limits are breached. They apply instead to dev and test environments.

When the data does not show in the telemetry, we could check the firewall practice, ikey configurations, user account under which the IIS is running and if it has privileges to access the internet. The Flush method can be called periodically.

Status Monitor tool can be used when the app is instrumented with the .Net 4.6 SDK. It collects basic information about outbound HTTP and SQL calls. Alert should not be configured unnecessarily. They could generate a lot of noise and make it harder to detect those that matter. RBAC controls must be properly set as with all resources.

 

 

This is a continuation of an article that describes operational considerations for hosting solutions on Azure public cloud.

Log analytics workspace does not always follow a one-size-fits-all. These map to the Centralized, Decentralized or Hybrid structures of IT in the organizations.  In the case of centralized IT infrastructure, all logs are stored in a centralized workspace and administered by a single team, with Azure Monitor providing differentiated access per team. This is an easy solution to manage. It helps with searching across resources and cross-correlating logs. The decentralized department or teams run their own workspaces in a resource group they own and manage. Even in this case, the workspace can be kept secure and access control can be kept consistent with resource access, but it is difficult to cross-correlate logs without involving third-party log indexes. Without a combined index, every addition of a workspace will require a rewrite of the queries. In the hybrid environment, both deployments are deployed in parallel. The hybrid case results in complex, expensive and hard to maintain configuration. The log analytics workspace can be in any region, but the destination storage account or event hub must be in the same region as the Log Analytics workspace. The jump from centralized to decentralized log analytics workspace is warranted when cross-correlation queries are not required.

In all these cases, access to data logs and workspaces must be managed. The workspace must be managed using workspace permissions. Users who need access to log data from specific resources can be granted permission using Azure role-based access control (Azure RBAC) and those who need access to specific tables in the workspace can have restrictive access. The access control mode can be configured on a workspace from Azure Portal.

The workspace context and resource context have different access. All logs in the workspace can be accessed with the workspace context. The Resource context is aimed at Application teams. Administrators of Azure resources that are monitored can be granted access. The view for these users gets restricted based on their role and scope.

The Azure Monitor has an ingestion pipeline as well as the Log Analytics workspace. It is possible to set it up with a central Storage Account. The incoming data feeds the ingestion pipeline which then sends the data to the storage account or the Event Hub.

Design decisions depend on factors such as whether a central location with all data is required and should there be one workspace per application or each team manages their own workspaces. Data Location, data retention, data access, and data collection must be decided for a streamlined data path. A good data path will be short and clean.