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Erasure coding calculator

Warning : This tool is here to help you, but it may not represent your complete architecture which can bring some difference between result.

Erasure Coding Calculator

Calculate usable capacity and performance for K+M configurations

EC Settings (K+M)

Data (K)

Parity (M)

Hardware

Number of Disks

Disk Size

Unit

Performance

Speed

Unit

Drive IOPS

Capacity

Usable Capacity

Efficiency

Throughput

Read Speed

Write Speed

IOPS

Read IOPS

Write IOPS

How it works :

Calculation Modes:

📈 Theoretical Mode (Green)

Perfect conditions – no overhead
Maximum performance scenarios
Ideal network and hardware performance
Best-case storage efficiency

🛡️ Conservative Mode (Red)

Real-world conditions with overhead factors
Practical expectations for production environments
Accounts for:
- 15% capacity overhead (metadata, formatting, spares)
- 25% speed reduction (network/CPU overhead, reconstruction)
- 30% IOPS reduction (latency, queuing, reconstruction)
- 10% network overhead
- Additional write penalties (parity calculation, read-modify-write cycles)

What is Stripe Size?

Stripe Size (KB) is a crucial parameter that affects both performance and how your data is distributed across drives.

Stripe Size is the amount of data (in KB) written to each individual disk before moving to the next disk in the erasure coding set.

📊 How it Works:

Example with K=4, M=2, Stripe Size=4KB:

Your file gets split into 4KB chunks
Each chunk goes to a different disk in sequence
After 4 data chunks (16KB total), parity is calculated
The pattern repeats for the next 16KB of data

File: [16KB block] → Split into 4KB chunks
Disk 1: [4KB] → Disk 2: [4KB] → Disk 3: [4KB] → Disk 4: [4KB]
Parity Disk 1: [P1] → Parity Disk 2: [P2]

⚡ Performance Impact:

Small Stripe Size (1-4KB):

✅ Better for: Random I/O, databases, small files
✅ Lower latency for small operations
❌ More overhead for large sequential reads

Large Stripe Size (64-256KB):

✅ Better for: Sequential I/O, video files, backups
✅ Higher throughput for large transfers
❌ Higher latency for small random operations

🔢 Stripe Width Calculation:

The calculator shows “Erasure Code Stripe Width” = Stripe Size × Data Chunks

Example: 4KB stripe × 4 data chunks = 16KB stripe width

This means every 16KB of your data gets distributed across all data disks before parity calculation.

🎯 Choosing the Right Size:

4KB: Good default for mixed workloads
8-16KB: Database and general purpose
64-128KB: Video streaming, backup systems
256KB+: Large file archives, data warehousing

The field directly affects how your erasure coding system balances between latency (small stripes) and throughput (large stripes)!

✨ Features :

🎛️ Mode Selector

Two buttons at the top of configuration panel
Visual indicators – Green for Theoretical, Red for Conservative
Dynamic descriptions explaining each mode
Instant switching between calculation methods

📊 Visual Feedback

Mode badges on each result section showing current calculation mode
Color-coded buttons with icons
Real-time updates when switching modes
Clear explanations of what each mode represents

🔧 Smart Calculations

Conservative mode applies realistic overhead factors:
- Capacity: 85% efficiency (15% overhead)
- Read Speed: 75% efficiency (25% overhead)
- Write Speed: Additional 15% penalty for parity calculation
- Read IOPS: 70% efficiency (30% overhead)
- Write IOPS: Additional 40% penalty for read-modify-write cycles

💡 Use Cases:

Theoretical: Planning, budgeting, maximum potential
Conservative: Production planning, realistic expectations, SLA planning

You can see the “best case scenario” and “what to actually expect in production” – giving both optimistic targets and realistic planning numbers!

Failure Domain

The Failure Domain is a crucial concept in erasure coding that determines how your data chunks are distributed across your storage infrastructure to ensure fault tolerance.

Here’s what each option means:

OSD (Object Storage Daemon)

Lowest level – Individual disk drives
Chunks are distributed across different disks
Can tolerate disk failures, but if a server fails, you might lose multiple chunks
Use when: You have many disks and want maximum storage density

Host

Server level – Individual servers/nodes
Chunks are distributed across different servers
Can tolerate entire server failures
Most common choice for typical deployments
Use when: You want to survive server failures (recommended)

Rack

Rack level – Physical server racks
Chunks are distributed across different racks
Can tolerate entire rack failures (power, network, cooling issues)
Use when: You have multiple racks and want rack-level fault tolerance

Datacenter

Highest level – Different datacenters/sites
Chunks are distributed across different geographic locations
Can tolerate entire datacenter failures
Use when: You have multiple datacenters and need geographic redundancy

Practical Example:

If you choose K=4, M=2 with Host failure domain:

Your data is split into 4 data chunks + 2 parity chunks = 6 total chunks
Each chunk goes to a different server
You can lose up to 2 entire servers and still recover your data
If you chose “OSD” instead, losing one server with multiple disks could potentially lose multiple chunks

💡 Recommendation: Use “Host” for most deployments as it provides good protection against server failures while being practical to implement.

What is Erasure Coding (EC)? Erasure Coding is a method of data protection in which data is broken into fragments, expanded and encoded with redundant data pieces and stored across a set of different locations or disks. Unlike RAID, EC is highly flexible and used in modern Object Storage (like MinIO, Ceph, or AWS S3).

Understanding K+M Parameters:

K (Data chunks): The number of original data fragments.
M (Parity chunks): The number of additional fragments added for redundancy.
Fault Tolerance: A system can lose up to M fragments without losing any data. For example, a 4+2 setup can survive 2 simultaneous failures.

Why choose Erasure Coding over RAID?

While RAID is the standard for single servers and home NAS devices, Erasure Coding has emerged as the technology of choice for object storage (S3) and distributed architectures (Ceph, MinIO). The reason is simple: flexibility. With Erasure Coding, you are not limited by the number of physical disks in a single bay. You can define schemes like 16+3, allowing you to lose three entire storage nodes without any service interruption.

Overhead Calculation and Efficiency

Calculating efficiency is crucial for optimizing your cloud storage costs. For a $K+M$ scheme, efficiency is calculated using the formula $K / (K+M)$. For example, a 12+4 configuration offers an efficiency of $12 / 16 = 75%$, while providing significantly higher security than any traditional RAID system. Use our calculator to simulate your needs and compare the cost per usable terabyte.

Erasure Coding vs RAID: Which is better?

Erasure Coding is more efficient for large-scale distributed systems and provides better protection against multiple failures. RAID is generally faster for local, small-scale storage arrays.

What is the storage overhead of Erasure Coding?

The overhead is calculated as (M/K). For example, in a 4+2 configuration, the overhead is 50%, providing 66% usable capacity.

Characteristic	Traditionnal RAID (5, 6, 10)	Erasure Coding (K+M)
Scalability	Limited to one box or node.	Ideal for cloud and distributed storage.
Storage Efficiency	Fixed (e.g., 66,7% in RAID 6 with 6 disks).	Granular and flexible (e.g., 80% in 8+2).
Fault tolerance	Max 2 disks (RAID 6).	Theoretically unlimited (depends on M).
Performance (IOPS)	Excellent for local access.	Slower (latency due to CPU/Network).
Rebuild time	Long (intense stress on the discs).	Fast (parallelization across multiple nodes).