Move Reader Data Without Making Sign-In Depend on the New Schema

Co-author for frontend engineering: Stas Rysin. Backend contribution: Dmytro Ihnatov.

Reviewed by Dmytro Nasyrov on October 22, 2026.

Moving reader data out of a demonstration file looked like a storage task. The frontend exposed another risk: adding new learning fields to a shared GraphQL document could break sign-in against an older identity-service schema, even when the login operation did not use those fields.

This joint case separates backend ownership of identity and bookmark data from the frontend's document compatibility, adapters and transition states.

The document was larger than the operation

The client sent a whole GraphQL document on each call. Validation could therefore encounter newly added fields elsewhere in that document. Combining the new learning operations with the existing login document would make a supposedly unrelated sign-in depend on the new schema. We kept the learning document separate and added a compatibility test. This reduced the surface that an older identity deployment needed to understand. It did not make every later schema change backwards compatible; it addressed this specific coupling.

The compatibility test needed a negative control

The saved contract test constructed a schema without the learning-profile portion of the identity schema and validated the complete login/admin document against it. It then validated the learning document against that older shape and expected errors. The failure on the second document established that the fixture really lacked the capability the first document was meant to avoid.

expect(validate(olderSchema, parse(loginDocument))).toEqual([])
expect(validate(olderSchema, parse(learningDocument)).length)
  .toBeGreaterThan(0)
expect(validate(currentSchema, parse(learningDocument))).toEqual([])

This reduced form omits schema loading and operation setup. It retains the useful contrast: the old shape accepts unrelated account operations, rejects new learning requirements and the current shape accepts the new document. A positive-only test against a fully upgraded schema would not expose the rollout coupling.

The source also checked that the login document contained none of the learning field names, including experience, goal and onboarding completion. The separate learning document held its own read and update operations. Keeping the split in an executable contract was more durable than a comment asking future contributors not to add another field beside login.

Compatibility had a deliberately small promise

The reconstructed older schema tests the absence of the learning extension. It is not evidence that every historical identity-service release accepts every later account field. Likewise, keeping sign-in valid before a learning rollout does not make the learning screen work before its service dependency exists. The deployment order still needs the owning schema before the new caller.

On the frontend side, a failure to read the new learning profile should remain a service-availability failure rather than corrupting a base sign-in response. The adapters separate that read boundary from the browser-facing product shape. On the service side, ownership and persistence remain with the identity and conversation/bookmark implementations.

The deliberate data reset is also independent of schema validity. A document can validate perfectly while the new stores begin empty under the agreed migration. Recording both facts prevents a green compatibility test from being presented as a preservation guarantee.

Move the read the screen actually needs

The frontend forwarded eight bookmark operations with a recorded ten-second deadline and one retry after a successful session refresh on an authentication refusal. That was a configured transport policy, not a latency measurement or an unlimited retry loop.

Profile and bookmark reads stopped depending on whole conversation history. Some temporary composition remained for Progress during the staged transition. We date that adapter explicitly; it was not the final architecture after later Library, report and backend-mode work.

The reset was deliberate

Historical data in the mock file was not migrated. Onboarding and Saved began empty under the agreed transition. Calling this lossless would contradict the implementation decision, even though the visible screens and much of their schema stayed stable. The earlier shared-file store had served a demonstration boundary, including coordination between application processes. That context explains the migration pressure. It is not a recommendation to use a shared JSON file as distributed production state, nor proof of authorship of every earlier prototype choice.

Deployment order remained part of the contract

The owning services had to supply the required schema before the frontend called it. Separating the login document reduced one compatibility hazard but did not remove the dependency of new learning operations on the new service fields.

Loss warnings also exposed the accepted bookmark lifecycle. A saved answer remained dependent on live conversation content at that stage. The separate warning case explains the reader-facing consequences without confusing a warning with a new persistence guarantee.

Evidence and joint ownership

The frontend merge records the split documents, service forwarding and loss-warning changes. Its automated cases covered compatibility and deletion consequences. The manual real-service walkthrough listed in its plan is not asserted as completed by that merge.

Dmytro Ihnatov's service contribution and Stas Rysin's frontend integration should be reviewed together where the contract crosses the boundary. No outage duration, throughput improvement or preservation percentage was measured for this narrative.

The table is the useful migration artifact: identify the owner, keep unrelated login requirements out of the new document, state which data resets and name the order in which callers can safely move.

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Last updated: October 22, 2026

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  Pharos Production has been in business since 2013, with over 13 years of experience in custom software development. During this time, we have delivered over 110 applications for 200+ clients across 28 industries, including FinTech, healthcare, crypto and e-commerce. We are rated 5/5 on Clutch based on 107 verified reviews (2026).

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  Pharos Production has a team of 90+ engineers, including software developers, blockchain specialists, QA engineers, DevOps experts, UI/UX designers, project managers and solution architects. Our founder, Dr. Dmytro Nasyrov, holds a PhD in Artificial Intelligence and leads the technical direction of all projects.

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  A custom software development company like Pharos Production assigns one dedicated engineering team that owns your product across multiple release cycles, instead of handing it off once a contract ends. Agencies more often rotate staff between clients and optimize for fast delivery over long-term product ownership.

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  Custom software development costs vary based on project scope and complexity. At Pharos Production, typical project ranges are: MVP development ($10,000-$25,000), suitable for startups validating a product idea; full-fledged production ($25,000-$50,000), for established businesses scaling a proven concept; and full-cycle development ($50,000-$80,000+), for complex enterprise-grade systems. These ranges include architecture design, development, QA testing and deployment. Hourly rates range from $50 to $99 depending on role and seniority. Final pricing depends on technology stack, number of integrations and engagement model (staff augmentation, dedicated team or project outsourcing).

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  Pharos Production focuses on mid-to-large custom software projects with budgets starting at $10,000. We do not take on template-based websites, WordPress theme customization or short-term contracts under one month. We also do not provide non-technical staffing (marketing, sales or design-only roles). Our strongest fit is blockchain, FinTech and healthcare projects where security, compliance and high-load architecture are critical. For smaller projects or MVPs under $10,000, we recommend exploring freelance platforms or no-code tools as a more cost-effective starting point.

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  Custom development is not the right choice in every situation. You should not hire a custom software company if: your problem is fully solved by an existing SaaS product (e.g. Shopify for e-commerce, Salesforce for CRM); your budget is under $10,000 and timeline is under 4 weeks; you need a simple landing page or marketing website (WordPress or Webflow is faster and cheaper); or you are still validating the idea and have not spoken to potential users yet.

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  Here are three anonymized examples from our recent delivery history:

  FinTech startup - payment platform (MVP)
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  Healthcare provider - patient portal (Full product)
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  Crypto exchange - trading engine (Complex)
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  See more projects: NoMoreBets, Pulse, Sagas, Gambit Stream and Pleenk. For the full portfolio, visit our case studies. Learn more about the technology behind these projects in our guide to stablecoins and crypto infrastructure.

Dmytro Ihnatov is the Lead Backend Developer at Pharos Production. He documents backend architecture and research experiments from the team's work on a professional learning platform for aesthetic medicine.