Sequential I/O means reading or writing consecutive logical locations in a file, object, or block address space. An application scanning a large data file from beginning to end is performing sequential reads; an application appending records to a growing file is performing sequential writes. Random I/O instead accesses scattered locations, such as database index pages. A single workload can contain both patterns, so storage selection should reflect actual behavior rather than the application category alone.
The main performance objective for large sequential transfers is usually throughput, measured in MB/s or MiB/s. IOPS measures operations per second, while latency measures how long an operation takes. These metrics are related but not interchangeable. At the same operation rate, larger transfers move more data, until another limit is reached. For example, 1,000 operations per second at 256 KiB per operation represents approximately 250 MiB/s of application data before accounting for overhead and storage-specific request handling.
Sequential access is particularly valuable on hard disk drives because adjacent reads or writes reduce mechanical seeking. SSDs also support sequential workloads, but their advantage over HDDs is often more important for random access and low latency. Sequential access does not require physically adjacent storage devices or sectors: cloud storage abstracts physical placement, and applications generally control logical access order rather than the provider's underlying layout.
For cost-optimized cloud architecture, first determine whether the application needs block, file, or object storage. A batch processor that scans large files on an attached block volume may be a candidate for throughput-oriented HDD storage. In Amazon EBS, Throughput Optimized HDD volumes, called st1, target frequently accessed, large sequential workloads, while Cold HDD volumes, called sc1, target less frequently accessed workloads with lower throughput requirements. Neither volume type supports boot volumes. Compare these choices with SSD options such as gp3 when latency, mixed access, or workload variability matters.
Object storage can be suitable for backups, media, and analytical datasets that applications read in large portions. However, sequential access does not turn object storage into a block device or a general-purpose appendable file system. Request charges, storage class retrieval charges, minimum storage durations, and data transfer costs may affect the economics. For analytics, partitioning data and selecting only required columns can save more than simply increasing sequential scan speed because they reduce the amount of data read.
Applications can improve sequential performance through buffering, appropriately sized requests, read-ahead, and enough concurrent operations to keep the storage path busy. These techniques have trade-offs: buffering consumes memory, read-ahead wastes resources when predictions are wrong, and delayed writes require explicit durability handling. Multiple individually sequential readers can also create a less sequential aggregate pattern when they share a storage device.
Benchmark representative file sizes, request sizes, concurrency, and read/write proportions. Check storage throughput limits, compute-instance storage bandwidth, network capacity, and any burst-credit behavior; a short test can hide a lower sustained rate. In certification questions, large sequential transfers and relaxed latency requirements suggest evaluating throughput-oriented, lower-cost storage, not automatically selecting the highest-IOPS SSD. The correct choice must still meet access frequency, durability, availability, and recovery requirements.
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