Why Yandex Object Storage breaks under peak load
Storage rarely fails on its own. A storage bottleneck shows up at peak: when an evening spike in views coincides with a scheduled export, delivery of large files starts to lag, and interrupted file uploads turn from rare into widespread. The user sees an endlessly spinning progress bar, and the team sees a stream of identical requests.
The cause is usually not disk capacity but how access to objects is configured.
Multipart upload without correct part sizing and retries starts over after any channel interruption: traffic is consumed twice, incomplete objects remain in the bucket, and export time grows severalfold.
Access rights errors add a second layer — overly broad bucket policies, public links where a signed URL with a limited lifetime is needed, and permission inheritance between directories that no one checked after a team change.
Diagnostics in this state is manual: the on-call engineer downloads access logs, checks policies, and looks for the object where everything stopped. This takes hours, and the next peak brings the same incidents — the settings remain unchanged.
That is why we deliver the service as turnkey object storage setup: first we analyze the load profile and find exactly where it breaks, then we bring access policies, multipart upload, lifecycle rules, and edge caching into working order.
The storage is S3-compatible, so migration and changing providers do not require rewriting application code. Let's break down how this is covered.
What a well-designed object storage gives you
Object storage stops being "a place where files are dumped" when lifecycle rules, versioning, and access separation are tied to it. Below is what exactly changes in operations after configuring Yandex Object Storage and why it is visible in metrics, not in feelings.
Predictable data delivery speed
Keys are distributed so that requests do not collect in a single prefix, and hot objects are served through a caching layer. Delivery of static assets and media evens out across the day: growth in concurrent requests no longer turns into growth in latency.
Storage durability and object versions
Versioning together with a lifecycle rule: fresh versions remain, intermediate ones expire on schedule. Accidental overwrite or deletion stops being a loss — the object is restored from the previous version.
Capacity that does not overflow
Lifecycle rules move rarely read data to a cold class and remove incomplete multipart uploads. Storage volume stops growing on its own, and the object registry remains manageable.
A class of failures that goes away
The share of operations without manual intervention grows: retries, separate service accounts, and integrity checks after upload cover typical failures — interrupted transfer, expired access key, overwrite conflict. File recovery time is measured in minutes, not hours of searching for the right copy.
Access classes and Object Storage policies
In Yandex Object Storage, access separation is built in before the first upload: the bucket is created private, and exactly what should be visible externally is exposed.
Then the choice comes down to three modes — from fully private access to public delivery, and each behaves differently when a link leaks or a contractor changes.
Data access modes
| Option | When it fits | Limitations |
|---|---|---|
| Private bucket, access by service account role | internal services, exports, backups | the link cannot be shared externally, each request is signed |
| Access via temporary file links | issuing a document to a specific user for a limited period | the period is fixed at issue time, the link cannot be revoked earlier |
| Public delivery | static assets, images, files open to everyone | content is indexed, requests go outward without control |
Moving data between classes
Data storage classes change by lifecycle rules, not manually. An object stays in the hot class for 30 days, then moves to cold, and after a year is deleted — along with incomplete multipart uploads that would otherwise accumulate and quietly inflate the bucket.
Such a set of policies keeps storage volume predictable: fresh data remains quickly accessible, and rollback to previous exports remains possible.
How setup works: stages of work
Work proceeds in sequential stages: first we record what is stored and how, then we agree on the target scheme, and only after that do we touch the production environment. This order allows data to be migrated without stopping services and leaves a rollback point at every step.
- Audit of current storage. We inventory volumes and object types, access frequency, and write and read sources. The output is a data map: what to migrate, what to archive, what to leave in place.
- Approval of the placement scheme. We define buckets and prefixes, lifecycle rules, storage classes for hot and rare data, and enable versioning against accidental deletion.
- Access setup. Service accounts, bucket-level policies, separate read and write permissions, public access closed by default.
- Data migration to the cloud. We upload in stages, verify checksums, and catch up on changes accumulated during copying.
- Data access verification. Test samples, permissions under each service, failure behavior, and measurement of object delivery time.
- Testing and handover to operations. Running scenarios on a copy, traffic switching with a rollback window, then procedures, monitoring, and documentation for the team.
Configurations, schemes, and access procedures
Storage lives longer than the setup and is held together not by parameters but by documents. We hand over a set that lets an on-call engineer or a new person on the team reproduce any operation without us — from granting access to rolling an object back to a previous version.
What stays with your team
- Data placement scheme: which buckets are for which tasks, where versioning is enabled, which storage classes are set by default.
- Lifecycle rules: transition to cold class by object age, version limit per key, deletion of incomplete uploads.
- Access matrix: roles, service accounts, who can read, write, and delete what.
- Bucket and policy configuration: private access by default, publication only through delivery endpoints, shared access settings and quotas.
- Team instructions: connecting from applications, working through the CLI client, key rotation procedure.
Day-to-day procedures
- Granting and revoking access, including employee termination and transferring a project to another team.
- Actions of the on-call shift when upload fails, counters diverge, or traffic spikes.
- Approval for publishing new data and the person responsible for classifying sensitive files.
- Review frequency: audit of access logs, permission checks, test recovery of an object from a version.
Documentation is updated together with the configuration — a procedure that has diverged from the actual storage state is more dangerous than its absence.
Case: reporting archive in object storage
The client is a department that prepares regular reporting and stores supporting documents. The archive was on a file server: directories by period, file names set manually, a separate copy on an external disk.
After a few years, it became impossible to tell which revision of a document was current in such an archive.
How it was before
A document was searched for from memory: whoever remembered the file name opened the directory, the rest wrote a request in a common chat.
Measurements before migration showed that finding one document took from 20 minutes to several hours, and in about a third of cases the needed file was found not in the archive but in someone else's correspondence.
Backups were made manually once a week, and recovery after a failure depended on the integrity of that single copy.
What changed
The archive was migrated to Yandex Object Storage: documents are arranged by keys with prefixes, search is by metadata and dates, access is granted by roles without issuing server accounts.
Document search time dropped from hours to seconds, and the share of requests resolved without contacting the employee responsible for storage grew to 95%.
Storage of previous revisions is enabled, so rollback to an old version is possible, and the lifecycle rule removes drafts and duplicates — the archive volume stopped growing uncontrollably.
The project report recorded: manual operations for arranging and renaming files decreased by 90%, a document on request is restored in minutes, and the share of successful restorations is 100%.
It was also noted that backup no longer depends on a single disk and an on-call employee's schedule.
How object storage differs from network drives?
The difference is not in volume but in the access model. A network drive is a file with addressable blocks: an application opens it, reads and overwrites individual fragments, sets locks, and appends to the end.
In object storage, data is stored as immutable objects with a key, and any edit is writing the entire object. Hence all the differences in load and total cost of ownership.
The object model wins where data is written once and read many times: backups, media libraries, logs, build artifacts, website static assets. It scales horizontally, lifecycle rules remove manual cleanup, and replication between availability zones is built in.
Keeping such a volume on network drives is more expensive: it requires a file server with redundancy, backups, and on-call support.
Network drives and file servers remain where there is live work with a file: databases, working copies of repositories, shared project directories, documents with collaborative editing.
Here locks, partial overwrites, and predictable latency on small operations are needed — otherwise every paragraph edit turns into uploading the entire file.
The choice is made by load profile, not volume: object size, read-to-write ratio, edit frequency, whether file locks are needed, monthly data growth.
More often a hybrid wins — a hot working layer on network storage, a cold archive in object storage, and transition between them by lifecycle rules. Yandex Object Storage here is not a replacement for drives but a separate layer for its own class of tasks.
Storage limitations and frequently asked questions
Object storage does not replace a file system and does not behave like a database. Yandex Object Storage provides S3-compatible access, object versioning, and lifecycle rules — but it has boundaries that are better known before design.
Below are answers to doubts that come up at the start: consistency, encryption, role-based access, and failure behavior. We list the limitations honestly: no partial object modification, no locks, renaming is copying with deletion.
Data consistency: what will a reader see after a write?
After a successful response, the object is immediately visible to all readers, and so is overwriting the same key. There are no transactions: two objects cannot be changed atomically, so we assemble related changes in the application and make operations idempotent per key.
Is data encryption enabled by default?
Writes and reads are encrypted on the service side, with no separate actions required. Keys can be kept on your side — then access to objects disappears along with key revocation. Critical sets are encrypted on the client, and an already unreadable stream goes to the storage.
How does role-based access work?
Permissions are set by policies on the bucket and service account: read, write, delete, access only to one's own prefix. We close public access entirely, and expose temporary signed URLs externally — long-lived keys do not end up in scripts. Such a setup covers typical data protection requirements.
What happens when the service is unavailable?
Written objects are not lost: redundancy within the storage restores data. Failure manifests as read and write errors — the client retries the operation, and critical exchange goes through a queue with retries rather than a synchronous call.
Let's discuss the task and come back with an estimate
So that the project estimate is based on facts rather than guesses, we need input data for the calculation. Below is the minimum set that covers almost any Yandex Object Storage task; if something is missing, we will fill in the picture together in the first conversation.
- Current storage scheme: where data is located, which buckets and prefixes are used, who writes to and reads from them.
- Load profile: total volume, daily growth, share of rarely requested data, peak periods.
- Access requirements: who accesses from where, whether public access is needed, what rules apply to keys and roles, which systems work with storage via an S3-compatible interface.
- Archive composition and retention rules: what should live for years, what is deleted, whether object versions and protection against accidental deletion are needed.
- Timelines and constraints: by what date switching is needed, whether there is a migration window, which processes cannot be stopped.
- What has already been tried and where you got stuck: past attempts, errors, limitations that are important not to repeat.
The first conversation takes little time: we go through the storage task using these points, remove uncertainties, and agree on acceptance criteria. After that, we come back with a work plan — stages, setup scope, and checkpoints, so you can see what is done and in what order.