Block storage provides raw storage volumes that appear as hard drives to operating systems and applications. Unlike file or object storage, block storage operates at the block level, giving applications direct control over data organization. This makes block storage ideal for databases, virtual machines, and applications requiring low-latency, high-throughput storage with direct volume access.

Fundamental Concepts

Blocks are fixed-size chunks (typically 512 bytes to 1MB) into which the volume is divided. Applications read and write at block granularity, allowing precise control over I/O operations.

Volumes are logical storage units presenting as devices (like /dev/sdb in Linux or D: in Windows). Volumes can be attached to compute instances, formatted with file systems, and used like local disks.

Direct Access means applications interact with blocks directly without intermediary file systems (though file systems are commonly layered on top). This low-level access provides maximum flexibility and performance.

Persistence ensures data survives instance restarts or terminations. Unlike ephemeral instance storage, block storage volumes persist independently of compute instances.

Architecture

Network-Attached Storage (NAS) provides block storage over networks. Volumes reside on separate storage arrays accessed via protocols like iSCSI or Fibre Channel. This separates storage from compute, enabling flexibility and sharing.

Directly-Attached Storage (DAS) connects physically to servers. This provides lowest latency and highest throughput but lacks flexibility—storage can’t easily move between servers.

Cloud Block Storage (like AWS EBS, Azure Disks, Google Persistent Disks) virtualizes storage infrastructure, providing volumes as a service. Volumes attach to cloud instances over networks optimized for storage traffic.

Performance Characteristics

IOPS (Input/Output Operations Per Second) measures the number of read/write operations the volume can handle per second. Databases performing many small random reads/writes require high IOPS.

Throughput measures data transfer rate (MB/s or GB/s). Applications performing large sequential reads/writes (like video processing or backups) require high throughput.

Latency is the time between issuing an I/O request and receiving a response. Latency-sensitive applications like transactional databases need low latency, typically single-digit milliseconds or less.

Performance Tiers in cloud block storage provide different IOPS and throughput levels. Standard HDDs offer low cost but modest performance; SSDs provide high IOPS and throughput; provisioned IOPS SSDs guarantee specific performance levels for demanding workloads.

Volume Types

General Purpose SSD balances cost and performance, suitable for most workloads. These volumes provide baseline IOPS scaling with size and burst capabilities for temporary spikes.

Provisioned IOPS SSD guarantees specific IOPS and throughput, essential for performance-critical applications like high-traffic databases. You provision exact performance requirements and pay accordingly.

Throughput Optimized HDD provides high sequential throughput at lower cost than SSDs. Suitable for big data workloads, data warehouses, and log processing where large sequential I/O dominates.

Cold HDD offers lowest cost for infrequently accessed data. Lower performance makes it suitable for archival storage or workloads with minimal I/O requirements.

Features

Snapshots capture point-in-time copies of volumes. Snapshots are incremental—only changed blocks are stored—making them storage-efficient. Use snapshots for backup, disaster recovery, or cloning volumes.

Encryption protects data at rest. Modern cloud block storage supports encryption with platform-managed or customer-managed keys, ensuring data confidentiality without performance impact.

Replication automatically copies volume data across multiple storage servers or availability zones, providing durability and fault tolerance. Most cloud block storage includes built-in replication.

Volume Cloning creates new volumes from snapshots or existing volumes, useful for creating test environments, provisioning new servers, or distributing data.

Use Cases

Databases are the primary use case for block storage. Relational databases (MySQL, PostgreSQL, Oracle) and NoSQL databases (MongoDB, Cassandra) require block storage’s low latency and high IOPS for optimal performance.

Virtual Machine Boot Disks use block storage for OS and application files. Each VM instance attaches to a block storage volume containing its operating system.

Enterprise Applications like SAP, Oracle, and Microsoft SQL Server require persistent, high-performance storage that block storage provides.

File Systems layer atop block storage. While block storage itself lacks file system semantics, administrators create file systems (ext4, XFS, NTFS) on block volumes for structured data storage.

Container Persistent Storage enables stateful containers. Containers attach block storage volumes for database data, configuration, or logs that must persist beyond container lifecycle.

Attachment and Multi-Attach

Single-Attach is the default: volumes attach to one instance at a time. When attached, the instance has exclusive access. This simplifies consistency but limits sharing.

Multi-Attach allows attaching volumes to multiple instances simultaneously, enabling shared-disk clusters or high-availability configurations. However, this requires clustered file systems or applications managing concurrent access to prevent corruption.

Hot-Attach/Detach enables attaching and detaching volumes without stopping instances. This provides flexibility for storage management, allowing volume migration or resizing without downtime.

Performance Optimization

Right-Sizing matches volume size and type to workload requirements. Overprovisioning wastes money; underprovisioning degrades performance. Monitor utilization and adjust.

Striping across multiple volumes increases performance beyond single-volume limits. RAID 0 combines volumes, distributing I/O across them for higher aggregate throughput and IOPS.

Pre-Warming is sometimes necessary after creating volumes from snapshots. First access to each block may have higher latency. Reading the entire volume once “warms” it to full performance.

I/O Queue Depth tuning optimizes throughput. Applications with deeper queues can exploit volume parallelism better, achieving higher throughput and IOPS.

Cost Management

Volume Size directly impacts cost. Only provision needed capacity; expanding volumes later is usually straightforward.

Performance Tier significantly affects cost. Provisioned IOPS SSDs cost more than general purpose or HDD volumes. Use expensive tiers only where performance justifies cost.

Snapshots consume storage. While incremental, keeping many snapshots adds up. Implement lifecycle policies to delete old snapshots no longer needed.

Detaching Volumes doesn’t delete them. Unattached volumes still incur charges. Delete volumes no longer needed to avoid paying for unused storage.

Backup and Disaster Recovery

Scheduled Snapshots automate backups. Take snapshots regularly based on RPO (Recovery Point Objective), ensuring recent backups exist for recovery.

Cross-Region Snapshots protect against regional disasters. Copy snapshots to different regions, enabling recovery even if the primary region becomes unavailable.

Testing Recovery verifies backups work. Regularly test restoring from snapshots to ensure the recovery process functions as expected.

Retention Policies balance protection and cost. Keep recent snapshots for quick recovery, retain older snapshots for compliance or long-term needs, and delete ancient snapshots to control costs.

Best Practices

Choose volume types matching workload requirements. Databases need provisioned IOPS SSDs; web server logs might use throughput-optimized HDDs.

Monitor volume performance metrics: IOPS, throughput, latency, and queue depth. Alert on degraded performance indicating misconfigured volumes or insufficient provisioned performance.

Implement snapshot schedules for all critical volumes. Automated snapshots ensure backups exist without requiring manual intervention.

Encrypt volumes containing sensitive data. The minimal performance impact is worthwhile for protecting confidential information.

Right-size volumes based on actual usage. Start conservatively and expand as needed rather than overprovisioning from the start.

Test disaster recovery procedures. Knowing you can restore from snapshots is critical; actually testing it ensures the process works when needed.

Block storage provides the high-performance, persistent storage that databases, virtual machines, and enterprise applications require. Understanding block storage characteristics—performance tiers, volume types, and optimization techniques—enables choosing and configuring storage appropriate for diverse workloads. While higher cost than object storage, block storage’s performance and flexibility justify its use for latency-sensitive, I/O-intensive applications where performance directly impacts user experience and business operations.