Red Hat OpenShift on IBM Cloud storage overview

Virtual Private Cloud Classic infrastructure Satellite

Review the following sections for an overview of the available storage options for your cluster.

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Before you can decide what type of storage is the correct solution for your Red Hat® OpenShift® on IBM Cloud® clusters, you must understand the IBM Cloud infrastructure provider, your app requirements, the type of data that you want to store, and how often you want to access this data.

Decide whether your data must be permanently stored.

Persistent storage: Data stored on persistent storage persists even when the container, the worker node, or the cluster is removed. Use persistent storage in the for stateful apps, core business data or data that must be available due to legal requirements, such as a defined retention period. Persistent storage is also a good option for auditing.

Non-persistent storage: Your data can be removed when the container, the worker node, or the cluster is removed. Non-persistent storage is typically used for logging information, such as system logs or container logs, development testing, or when you want to access data from the host's file system.

If you must persist your data, analyze if your app requires a specific type of storage. When you use an existing app, the app might be designed to store data in one of the following ways.

In a file system: The data can be stored as a file in a directory. For example, you could store this file on your local hard disk. Some apps require data to be stored in a specific file system, such as nfs or ext4 to optimize the data store and achieve performance goals.

In a database: The data must be stored in a database that follows a specific schema. Some apps come with a database interface that you can use to store your data. For example, WordPress is optimized to store data in a MySQL database. In these cases, the type of storage is selected for you.

Determine the type of data that you want to store.

Structured data: Data that you can store in a relational database where you have a table with columns and rows. Data in tables can be connected by using keys and is usually easy to access due to the pre-defined data model. Examples are phone numbers, account numbers, Social Security numbers, or postal codes.

Semi-structured data: Data that does not fit into a relational database, but that comes with some organizational properties that you can use to read and analyze this data more easily. Examples are markup language files such as CSV, XML, or JSON.

Unstructured data: Data that does not follow an organizational pattern and that is so complex that you can't store it in a relational database with pre-defined data models. To access this data, you need advanced tools and software. Examples are e-mail messages, videos, photos, audio files, presentations, social media data, or web pages.

If you have structured and unstructured data, try to store each data type separately in a storage solution that is designed for this data type. Using an appropriate storage solution for your data type eases up access to your data and gives you the benefits of performance, scalability, durability, and consistency.

Analyze how you want to access your data. Storage solutions are usually designed and optimized to support read or write operations.

Read-only: You don't want to write or change your data. Your data is read-only.

Read and write: You want to read, write, and change your data. For data that is read and written, it is important to understand if the operations are read-heavy, write-heavy, or balanced.

Determine the frequency that your data is accessed.

Hot data: Data that is accessed frequently. Common use cases are web or mobile apps.

Cool or warm data: Data that is accessed infrequently, such as once a month or less. Common use cases are archives, short-term data retention, or disaster recovery.

Cold data: Data that is rarely accessed, if at all. Common use cases are archives, long-term backups, historical data.

Frozen data: Data that is not accessed and that you need to keep due to legal reasons.

If you can't predict the frequency or the frequency does not follow a strict pattern, determine whether your workloads are read-heavy, write-heavy, or balanced. Then, look at the storage option that fits your workload and investigate what storage tier gives you the flexibility that you need. For example, IBM Cloud Object Storage provides a flex storage class that considers how frequent data is accessed in a month and takes into account this measurement to optimize your monthly billing.

Investigate if your data must be shared across multiple app instances, zones, or regions.

Access across pods: When you use Kubernetes persistent volumes to access your storage, you can determine the number of pods that can mount the volume at the same time. Some storage solutions can be accessed by one pod at a time only. With other storage solutions, you can share volume across multiple pods.

Access across zones and regions: You might require your data to be accessible across zones or regions. Some storage solutions, such as file and block storage, are data center-specific and can't be shared across zones in a multizone cluster setup.

If you want to make your data accessible across zones or regions, make sure to consult your legal department to verify that your data can be stored in multiple zones or a different country.

Understand other storage characteristics that impact your choice.

Consistency: The guarantee that a read operation returns the latest version of a file. Storage solutions can provide strong consistency when you are guaranteed to always receive the latest version of a file, or eventual consistency when the read operation might not return the latest version. You often find eventual consistency in geographically distributed systems where a write operation first must be replicated across all instances.

Performance: The time that it takes to complete a read or write operation.

Durability: The guarantee that a write operation that is committed to your storage survives permanently and does not get corrupted or lost, even if gigabytes or terabytes of data are written to your storage at the same time.

Resiliency: The ability to recover from an outage and continue operations, even if a hardware or software component failed. For example, your physical storage experiences a power outage, a network outage, or is destroyed during a natural disaster.

Availability: The ability to provide access to your data, even if a data center or a region is unavailable. Availability for your data is usually achieved by adding redundancy and setting up failover mechanisms.

Scalability: The ability to extend capacity and customize performance based on your needs.

Encryption: The masking of data to prevent visibility when data is accessed by an unauthorized user.