How would you manage large file storage (multipart, checksums, dedupe)?

Large file storage looks simple until you add real world demands like pause and resume, end to end integrity, and tight storage costs. The practical trio that makes this work is multipart upload for speed and reliability, checksums for correctness, and dedupe to avoid paying twice for the same bytes. In a system design interview, connect these ideas into one clean flow and you will stand out as someone who can ship a production grade file pipeline.

Why It Matters

Product teams care about fast uploads, zero corruption, and low cost at petabyte scale. Multipart upload cuts upload time through parallelism and enables safe retry for only the missing pieces. Checksums give you cryptographic confidence that what landed is exactly what the user sent. Dedupe reduces billable storage by reusing identical chunks across files or versions. Together they deliver scalable architecture, higher availability, and predictable performance across global regions. These patterns appear in object stores, backup systems, media platforms, and machine learning data lakes.

How It Works (Step-by-Step)

1. Choose the storage layout Use a cloud object store for file bytes and a database for metadata. Each logical file has a manifest that records part size, number of parts, per part checksums, full file checksum, encryption flags, and a list of chunk ids when dedupe is enabled.

2. Create an upload session The client requests an upload session id. The service returns signed URLs for numbered parts and the target part size. Store the session in a durable table keyed by session id and user id with an expiry to clean up abandoned sessions.

3. Upload parts in parallel The client streams many parts at once. Each part carries its sequence number and a checksum, for example MD5 for quick detection plus a strong digest like SHA 256 for end to end verification. The client can also compute a tree hash so the full file digest is available without a second pass.

4. Track progress and resume The server maintains a part ledger for the session. On reconnect the client calls list parts, then reuploads only missing or failed parts. Use idempotency keys so a duplicate retry does not create duplicate parts.

5. Complete and compose After all parts succeed, the client calls complete. The store stitches parts into a single object without reupload. Persist the manifest, the final strong checksum, the byte length, and the computed ETag or equivalent server side version id.

6. Verify integrity On complete, recompute or validate the full checksum from part checksums or the tree hash. Store both a fast digest like CRC 32 for quick checks and a strong digest like SHA 256 for tamper detection. At download time verify the digest again or use range checks per chunk.

7. Enable file level dedupe Before keeping a new object, compare the candidate full file checksum with an index of existing files scoped to the right tenant boundary. If a match exists, only add a reference row and bump a reference count. If no match exists, keep the new object and index its checksum.

8. Enable chunk level dedupe Split files into chunks and store each chunk as a separate object keyed by content hash. Two popular strategies are fixed size chunking and content defined chunking based on a rolling fingerprint. Fixed size is simple but sensitive to byte shifts. Content defined chunking finds natural boundaries and dedupes well even when bytes shift inside the file. The manifest then becomes a recipe that lists chunk hashes and order.

9. Garbage collect safely Use reference counts per chunk or maintain a mark and sweep job that scans manifests to identify unused chunks. Always delete with a safety delay to survive transient index inconsistencies.

10. Tune for throughput Pick part sizes that fit the typical network path. A good starting point is between 8 MB and 64 MB. Increase part size for very large files to reduce request overhead. Cap client concurrency to protect your service and the store. Use backoff on errors and surface partial progress to the user.

11. Respect encryption and privacy Checksums are typically computed on plaintext before encryption so dedupe stays effective. If you dedupe across tenants, evaluate privacy risk and legal exposure. If you encrypt before hashing, exact match dedupe will not work.

Real World Example

Think about a video platform that ingests raw creator uploads. The mobile app requests an upload session and gets a list of signed URLs for parts of size 16 MB. The app uploads parts in parallel and sends an MD5 per part plus a running SHA 256 tree hash. If the network drops, the app restarts and lists parts to resume from the last completed offset. On complete, the service writes a manifest and stores the final SHA 256. In the background a dedupe worker splits the file using content defined chunking and stores the chunks in a content addressed store. If another creator uploads the same intro bumper, the chunk hashes match and only references are added. The platform saves storage while keeping strong integrity guarantees.

Common Pitfalls or Trade offs

Too small part size increases request overhead and hurts throughput
Too large part size makes resume inefficient since a failed part wastes many megabytes
Relying on weak digest only raises collision risk for high value content
Computing checksums on encrypted data ruins dedupe and makes cross file verification harder
Global dedupe across tenants can leak information about who has which data
Variable size chunking improves dedupe ratio but costs more CPU and memory for rolling fingerprints and index lookups

Interview Tip

When asked to design large file storage, show a single throughline that connects multipart, checksums, and dedupe. Say you will store a manifest that records part checksums and a final tree hash, and that you will add a content addressed chunk layer for cross file dedupe plus a safe garbage collection plan. That short blueprint signals production experience.

Key Takeaways

Multipart upload improves speed and resilience through parallel parts and targeted retries
Checksums protect integrity at both part level and full file level
Dedupe cuts cost through file level reuse and chunk level reuse
A manifest is the source of truth for session state, checksums, and chunk recipes
Security and privacy choices around encryption affect whether dedupe is possible

Table of Comparison

Approach	What it solves	Cost impact	When to choose	Key risk
Single object upload with full checksum	Simplicity and end to end integrity	Neutral	Small or medium files and simple clients	No resume, slow for large files
Multipart upload with per part checksum	Parallelism, retry of failed parts, faster completion	Neutral	Large files and flaky networks	Many small parts can create overhead
Multipart plus tree hash	Strong full file verification without a second pass	Small CPU overhead	High integrity workloads and compliance	More complex client and server code
File level dedupe	Avoids storing identical files	Large savings when users share identical data	Backup or media catalogs with repeats	Collision or privacy risk if scope is wrong
Content addressed chunk store with content defined chunking	Reuses chunks across similar versions	Very large savings in versioned data	Backup, logs, large binaries with minor edits	Extra CPU, index size, complex garbage collection

FAQs

Q1. What is multipart upload and why use it?

It splits a large file into numbered parts that upload in parallel. This reduces total time and allows the client to retry only failed parts, which improves success rates on mobile and long network paths.

Q2. Which checksum should I use for large files?

Use a fast checksum like CRC 32 or MD5 for quick detection and a strong digest like SHA 256 or BLAKE3 for final integrity. Store both in the manifest and verify again on download.

Q3. How does dedupe work in object storage?

For file level dedupe compare the full file checksum to an index and reuse the existing object if it matches. For chunk level dedupe split the file into chunks, key each chunk by its hash, and store only new chunks while referencing old ones in the recipe.

Q4. How do I resume an interrupted upload?

List the parts recorded for the upload session, then send only the missing part numbers. Keep idempotency keys for each part so a retry does not duplicate writes.

Q5. Is content defined chunking better than fixed size chunking?

It usually dedupes better for edited files because it finds stable boundaries even when bytes shift. It costs more CPU and needs a larger index, so use it where savings justify the added complexity.

Q6. Can I dedupe encrypted files safely?

If you encrypt before hashing, dedupe will fail since identical plaintext becomes different ciphertext. If you hash before encryption, dedupe works but you must evaluate privacy and legal constraints across tenants.

Further Learning

Continue building your foundation with Grokking System Design Fundamentals to understand storage, caching, and data flow design.

Then practice real interview scenarios with Grokking the System Design Interview for complete end-to-end design reasoning.

For deeper scalability techniques, explore Grokking Scalable Systems for Interviews which dives into storage scaling, deduplication, and distributed data patterns.