Table of Contents

1. Kubernetes vs IIS

When choosing to deploy Cinchy version 5, you must decide whether to deploy via Kubernetes or on a VM (IIS).

Kubernetes is an open-source system that manages and automates the full lifecycle of container-based applications. You now have the ability to deploy Cinchy v5 on Kubernetes, and with it comes a myriad of features that help to simplify your deployment and enhance your scaling. Kubernetes can maximize your container capacity and easily scale up/down with your current operations.

• Grafana, Opensearch, Opensearch Dashboard: Working together, these three applications provide a visual logging dashboard for all of the information coming in from your database pods. This streamlines your search for information by putting the control into your hands and compiling your logs in one easy to access place — you can now easily write a query against all of your logs, in all of your environments. You will have access to a default configuration out of the box, but you can also customize your dashboards as well.

• Prometheus: With the Kubernetes addition, you now have access to Prometheus for your metrics dashboard. Prometheus records real-time metrics in a time series database used for event monitoring and alerting. You can then create custom dashboards to display your data in an easy to use visual that makes reporting on your metrics easy, and even set up push alerts based on your custom needs.

You also have the option to run Cinchy on Microsoft IIS, which was the traditional deployment method prior to Cinchy v5. Internet Information Services (IIS) for Windows Server is a flexible, secure and manageable Web server for hosting anything on the Web.

We recommend using Kubernetes to deploy Cinchy v5, because of the robust features that you can leverage, such as improved logging and metrics. Using Kubernetes allows for a greater ability to scale your Cinchy instances as well as the ability to lower your costs by using PostgreSQL.

2. Choosing a Database

Before deploying Cinchy v5, you must select which database you want to use.

The following list outlines which databases we support for Kubernetes Deployments.

For IIS Deployments please review the architecture requirements here.

2.1 Microsoft SQL Server

2.1.1 MS SQL Server

• Microsoft SQL Server is a relational database management system. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network.

• Resource limits

2.1.2 Azure SQL Database

• Microsoft Azure SQL Database is a managed cloud database provided as part of Microsoft Azure. It runs on a cloud computing platform, and access to it is provided as a service. Managed database services take care of scalability, backup, and high availability of the database.

• Differences between Azure SQL and Azure Managed SQL

• Resource Limits

2.1.3 Azure Managed SQL Instance

• SQL Managed Instance is a managed, always up-to-date SQL instance in the cloud that combines broad SQL Server engine compatibility with the benefits of a fully managed PaaS.

• Differences between Azure SQL and Azure Managed SQL

• Resource Limits

2.2 Amazon Aurora

Amazon Aurora

• Amazon Aurora (Aurora) is a fully managed relational database engine that's compatible with MySQL and PostgreSQL. It combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Aurora is part of the managed database service Amazon Relational Database Service (Amazon RDS). Amazon RDS is a web service that makes it easier to set up, operate, and scale a relational database in the cloud.

• Resource Limits

2.3 PostgreSQL

2.3.1 PostgreSQL

• PostgreSQL is a free and open-source relational database management system emphasizing extensibility and SQL compliance. PostgreSQL comes with features aimed to help developers build applications, administrators to protect data integrity and build fault-tolerant environments, and help you manage your data no matter how big or small the dataset.

2.3.2 AWS RDS for PostgreSQL

• Amazon RDS makes it easy to set up, operate, and scale PostgreSQL deployments in the cloud. With Amazon RDS, you can deploy scalable PostgreSQL deployments with cost-efficient and resizable hardware capacity. Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades; storage management; replication for high availability and read throughput; and backups for disaster recovery. Amazon RDS for PostgreSQL gives you access to the capabilities of the familiar PostgreSQL database engine.

• Resource Limits

2.3.3 Azure PostgreSQL Database

• This is a fully managed and intelligent Azure Database for PostgreSQL. Enjoy high availability with a service-level agreement (SLA) up to 99.99 percent, AI-powered performance optimization, and advanced security. A fully managed database that automates maintenance, patching, and updates. Provision in minutes and independently scale compute or storage in seconds.

• Resource Limits

3. Sizing Considerations and Requirements

Prior to deploying Cinchy version 5, you need to define your sizing requirements.

3.1 Sizing

3.1.1 Kubernetes Sizing

Cluster sizing recommendations vary and are dependant on a myriad of deployment factors. We have provided the following very general sizing recommendations, but encourage you to explore more personalized options.

CPU: 8 Cores

Memory: 32GB Ram

Number of Servers: 3

AWS: m5.2xlarge

Azure: D8 v3

3.1.2 IIS Sizing

For sizing recommendations and prerequisites concerning an IIS deployment, please review the documentation found here.

3.2 Application Storage Requirements

If you are choosing to deploy Cinchy v5 on IIS, then you need to ensure that your VM disks have enough application storage to run your clusters.

3.3 Object Storage Requirements

Cinchy supports both Amazon S3 and Azure Blob Storage.

If you are using Terraform for your Kubernetes deployment, you will need to set up a new S3 compatible bucket to manually to store your state file. You will also need a bucket for Connections, to store error files created during data syncs.

You will create your two S3 compatible buckets using either Amazon or Azure. Ensure that you use the following convention when naming your buckets so that the automation script runs correctly: <org>-<component>-<cluster>. These bucket names will be referenced in your configuration files when you deploy Cinchy on Kubernetes.

Example Terraform Bucket: cinchy-terraform-state

Example Connection Bucket: cinchy-connections-cinchy-nonprod

There is no sizing definitions, as S3 provides unlimited scalability and it charges only for what you use/how much you store on it.

• How to set up an Amazon S3 bucket

• How to set up Azure Blob Storage

Table of Contents

1. Component Diagram

The below diagram shows a high level overview of Cinchy's Infrastructure components when deploying on IIS.

2. Component Overview

Table of Contents

1. Infrastructure Configuration (On Cluster)

The below diagram shows a high level overview of a possible Infrastructure diagram with components on the cluster, however your specific configuration may vary (Image 1).

2. AWS Infrastructure Configuration (Outside Cluster)

When deploying Cinchy version 5 on Kubernetes, you may deploy via Amazon Web Services (AWS). The below diagram shows a high level overview of a possible AWS Infrastructure with components outside the cluster, however your specific configuration may vary (Image 2).

2.1 Infrastructure Component Overview

3. Azure Infrastructure Configuration (Outside Cluster)

When deploying Cinchy version 5 on Kubernetes, you may deploy via Microsoft Azure. The below diagram shows a high level overview of possible Azure Infrastructure with components outside the cluster, however your specific configuration may vary (Image 3).

3.1 Infrastructure Component Overview

4. Cluster Level Component Overview

The following highlighted area provides a high-level overview of cluster level components used when deploying Cinchy on Kubernetes, as well as what versions they are running.

These are created once per cluster. Clients may choose to run these components outside of the cluster or replace with their own comparable components. This diagram shows them in the cluster (Image 4).

Cluster Level Components

These are created once per cluster. Clients may choose to run these components outside of the cluster or replace with their own comparable components.

• Service Mesh - Istio: All inbound traffic to your Cinchy instance is routed and handled through this component, keeping it secure and managed.

• Monitoring/Alerting - Prometheus & Grafana: Prometheus consumes metrics from the running components in your environment, which can then be visualized into user friendly graphs and dashboards by Grafana. Prometheus can also connect to third party services to provide alerting capabilities. Both Prometheus and Grafana use persistent storage.

• Logging - Opensearch and Fluentbit: All logs are captured and indexed by Opensearch in a single, easily accessible location. These logs can be queried, searched, and filtered, and Correlation IDs mean that they can also be traced across various components. These logging components take advantage of persistent storage.

• Caching - Redis: Redis is currently being used to facilitate a distributed lock using RedLock, which guarantees lock synchronizations across Cinchy instances. It is also a storage location for the execution output when running batch data syncs.

• Event Processing - Kafka: This is designed to act as the middleware that allows for messaging between components through a queuing mechanism. Kafka features persistent storage.

4.1 Cluster Configuration

There are a few things to consider about your cluster configuration before you deploy Cinchy on Kubernetes:

• How many clusters will you need?

• Will you be sharing from an existing cluster?

• Will you be running multiple environments on a single cluster?

5. Instance Component Overview

Each instance of Cinchy has the following components, which are used to either provide an experience to users/applications or connect data in/out of Cinchy. Since multiple Cinchy instances can be deployed per cluster, these components will repeat for each environment.

The following highlighted area provides a high-level overview of instance level components used when running Cinchy on Kubernetes (Image 5).

• Meta Experiences: Cinchy offers pre-packaged experiences that you can import into your Cinchy environment and use on your data network. This includes experiences like Meta-Forms and Meta-Reports.

• Connections: The Cinchy Connections experience is used to help easily create data syncs in/out of the platform. It features persistent storage.

• Data Browser: Cinchy’s Dataware platform features a Universal Data Browser that allows users to view, change, analyze, and otherwise interact with all data on the network. The Data Browser even enables non-technical business users to manage and update data, build models, and set controls, all through an easy and intuitive UI.

• Identity Provider: An Identity Provider (IdP) creates and manages user credentials and associated identity attributes. IdPs authentication services are used in Cinchy to authenticate end-users.

• Event Listener: The Event Listener is used to picks up events from connected sources during a data sync. Review Cinchy's Data Sync documentation for further information on the Event Listener. The Event Listener uses persistent storage.

• Event Stream Worker: The Event Stream Worker is used to process data picked up by the Event Listener during data syncs. Review Cinchy's Data Sync documentation for further information on the Event Stream Worker. The Event Worker uses persistent storage.

• Maintenance (Batch Jobs): Cinchy performs maintenance tasks through the CLI. This currently includes the data erasure and data compression deletions.

6. GitOps

ArgoCD is a declarative, GitOps continuous delivery tool for Kubernetes that simplifies the application deployment and lifecycle management. ArgoCD is highly recommended for deploying Cinchy, however you may also choose to use another tool.

Once you configurations are set, ArgoCD automates the deployment of the desired application states into your specified target environments. Implemented as a Kubernetes controller, it continuously monitors running applications and compares the current, live state against the desired target state (as specified in your repositories).