When Should You Use AWS EBS st1 or sc1 Volumes for Big Data?

Choosing between Amazon EBS Throughput Optimized HDD st1 and Cold HDD sc1 volumes depends primarily on how frequently your sequential big data workload accesses stored data and your performance requirements. Both volume types are low-cost magnetic storage options designed for large, sequential I/O operations rather than small, random I/O tasks. This article explains the architectural differences, throughput mechanics, use cases, and cost trade-offs to help you select the right HDD volume for your Amazon EC2 instances.

Understanding Amazon EBS HDD Volume Types

Amazon Elastic Block Store provides two magnetic hard disk drive options tailored for throughput-intensive workloads. Unlike Solid State Drive volumes such as gp3 or io2 that measure performance in Input/Output Operations Per Second, HDD-backed volumes measure performance in megabytes per second (\(\text{MiB/s}\)). They are engineered to handle large block sizes and sequential read/write operations efficiently.

Neither st1 nor sc1 can be used as a boot volume for an Amazon EC2 instance. Instead, they serve as secondary data volumes attached to compute instances running distributed file systems, data warehouses, or log processing pipelines.

How Burst Bucket Credits Work

Both st1 and sc1 use a burst-bucket credit model to determine available throughput. Each volume earns throughput credits at a baseline rate determined by the provisioned volume size in terabytes (\(\text{TiB}\)). When your workload is idle or running below its baseline rate, unused credits accumulate in a burst bucket up to a maximum credit balance limit.

When your application performs heavy sequential reads or writes, it consumes these accumulated credits to burst up to the volume's maximum throughput limit. The baseline throughput, burst throughput, and credit bucket capacity scale linearly with the volume size up to a hard ceiling. We can express the effective baseline throughput \(T_{\text{base}}\) and burst throughput \(T_{\text{burst}}\) for a volume of size \(S\) (in \(\text{TiB}\)) as:

\[T_{\text{base}} = \min(R_{\text{base}} \times S, T_{\text{max}})\]

\[T_{\text{burst}} = \min(R_{\text{burst}} \times S, T_{\text{max}})\]

Where \(R_{\text{base}}\) is the baseline rate per \(\text{TiB}\), \(R_{\text{burst}}\) is the burst rate per \(\text{TiB}\), and \(T_{\text{max}}\) is the maximum throughput cap for the volume type.

When to Use Throughput Optimized HDD (st1)

Throughput Optimized HDD (st1) volumes are designed for frequently accessed, throughput-intensive workloads that require consistent and fast sequential I/O performance.

Performance Specifications of st1

• Volume size range: \(125\text{ GiB}\) to \(16\text{ TiB}\)
• Baseline throughput: \(40\text{ MiB/s}\) per \(\text{TiB}\)
• Maximum burst throughput: \(250\text{ MiB/s}\) per \(\text{TiB}\)
• Absolute maximum throughput cap: \(500\text{ MiB/s}\) per volume (reached at \(12.5\text{ TiB}\) for baseline, or \(2\text{ TiB}\) for burst)
• Bucket capacity: \(1\text{ TiB}\) of credits per \(\text{TiB}\) of provisioned volume size

Ideal Workloads for st1

You should choose st1 when your big data architecture actively processes, scans, or streams data throughout the day:

1. Apache Kafka and Streaming Platforms: Message brokers write and read append-only commit logs sequentially. Because consumers frequently read from these partitions, st1 provides the sustained \(\text{MiB/s}\) required without paying SSD prices.
2. Big Data Analytics (MapReduce, Apache Spark, Hadoop): Distributed Hadoop Distributed File System (HDFS) nodes scanning multi-terabyte datasets benefit from st1 because data blocks are large (typically \(128\text{ MiB}\) or \(256\text{ MiB}\)) and accessed sequentially.
3. Data Warehouses: Columnar databases and MPP (Massively Parallel Processing) analytics platforms perform full table scans over contiguous blocks of disk storage.
4. Centralized Log Processing: Workloads that ingest, aggregate, and index high-velocity application logs continuously.

When to Use Cold HDD (sc1)

Cold HDD (sc1) volumes provide the lowest cost per gigabyte of all Amazon EBS volume types. They are built for large volumes of data that are accessed infrequently and where the lowest possible block storage cost is the primary objective.

Performance Specifications of sc1

• Volume size range: \(125\text{ GiB}\) to \(16\text{ TiB}\)
• Baseline throughput: \(12\text{ MiB/s}\) per \(\text{TiB}\)
• Maximum burst throughput: \(80\text{ MiB/s}\) per \(\text{TiB}\)
• Absolute maximum throughput cap: \(250\text{ MiB/s}\) per volume (reached at approximately \(3.125\text{ TiB}\) for burst)
• Bucket capacity: \(1\text{ TiB}\) of credits per \(\text{TiB}\) of provisioned volume size

Ideal Workloads for sc1

You should choose sc1 when your dataset must remain attached as block storage to an EC2 instance (rather than being offloaded to Amazon S3), but daily read/write volume is low:

1. Cold Data Tiers in Distributed Storage: In tiered storage architectures (such as HDFS heterogeneous storage or Elasticsearch warm/cold nodes), older partitions that are rarely queried can be moved to sc1 volumes while active partitions remain on gp3 or st1.
2. Infrequent Full-System Scans: Historical compliance records, audit logs, or archival databases that only run batch reporting jobs once a week or once a month.
3. Cost-Constrained Sequential Backups: Maintaining local on-instance database dumps or staging backups before transferring them to object storage.

Comparing st1 and sc1 Side by Side

Evaluating Scan Frequency and Daily Volume Limits

A practical method to decide between st1 and sc1 is calculating how many times per day your workload scans the entire volume. Because a volume earns throughput credits continuously at its baseline rate, the total amount of data \(D_{\text{daily}}\) you can read or write per \(\text{TiB}\) of storage in a \(24\text{-hour}\) period (\(86,400\text{ seconds}\)) without depleting your burst bucket is:

\[D_{\text{daily}} = R_{\text{base}} \times 86,400\text{ s}\]

For an st1 volume (\(R_{\text{base}} = 40\text{ MiB/s}\)):

\[D_{\text{daily (st1)}} = 40\text{ MiB/s} \times 86,400\text{ s} = 3,456,000\text{ MiB} \approx 3.29\text{ TiB per TiB}\]

For an sc1 volume (\(R_{\text{base}} = 12\text{ MiB/s}\)):

\[D_{\text{daily (sc1)}} = 12\text{ MiB/s} \times 86,400\text{ s} = 1,036,800\text{ MiB} \approx 0.99\text{ TiB per TiB}\]

From these calculations, a clear operational rule emerges:

• Use sc1 if your workload scans the entire volume once a day or less.
• Use st1 if your workload scans the entire volume up to three times a day, or requires burst speeds up to \(500\text{ MiB/s}\).

Architectural Best Practices for HDD Volumes on EC2

To get the expected throughput from st1 and sc1 volumes, your EC2 instance and operating system must be configured to avoid I/O bottlenecks:

• Avoid Small Random I/O: Magnetic drives normalize contiguous I/O requests into single operations, but random \(4\text{ KiB}\) or \(16\text{ KiB}\) reads force physical disk head movement (seek time), drastically degrading throughput. If your workload mixes random metadata lookups with sequential reads, place metadata or indexes on a separate gp3 SSD volume and keep only raw sequential data on st1 or sc1.
• Verify EC2 Instance Bandwidth: Ensure your chosen EC2 instance type is EBS-optimized and provides sufficient network bandwidth to the EBS subsystem. A volume capable of \(500\text{ MiB/s}\) (\(4\text{ Gbps}\)) will be throttled if attached to an instance that only supports \(1.5\text{ Gbps}\) of dedicated EBS bandwidth.
• Use RAID 0 for Higher Throughput: If a single st1 volume's \(500\text{ MiB/s}\) ceiling is insufficient for your big data node, you can stripe multiple st1 volumes together in a software RAID 0 array using mdadm to aggregate their baseline and burst throughput limits.
• Monitor Burst Balance in Amazon CloudWatch: Always configure CloudWatch alarms on the BurstBalance metric for your st1 and sc1 volumes. If BurstBalance frequently drops toward \(0\%\), your workload is exceeding its baseline throughput and getting throttled down from burst speeds to baseline speeds. You can resolve this by either provisioning a larger volume (to gain a higher baseline rate) or migrating an sc1 volume to st1 via Elastic Volumes without downtime.