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Design a 10-million-plus-SKU commerce system as a set of cooperating layers, not as one giant database. Keep the commerce platform and CMS as systems of record, capture their changes through CDC and streaming, project those changes into a flattened read model for browse and facets, and let a separate search engine handle relevance. Arvyn is presented by RBM Software as that catalog read and merchandising layer, sitting on top of existing systems rather than replacing them.
That architecture is a sensible starting point for a very large catalog, but the available Arvyn pages do not prove operation at 10 million or more SKUs. They describe enterprise catalogs, near-real-time updates and sub-second APIs without publishing a SKU-count benchmark, workload, hardware profile, latency distribution or test method. Treat 10 million SKUs as your acceptance target and validate it with your own workload and a detailed vendor test plan.
What architecture should you use for an eCommerce catalog with 10 million SKUs?
Use separate write, propagation, read-serving and relevance responsibilities. This prevents product updates, browse traffic and search experiments from competing for the same critical path.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Systems of record: The commerce platform owns purchasable offers, pricing, inventory and order-related state; the CMS owns editorial content and merchandising inputs.
- Change propagation: CDC captures inserts, updates and deletes from those systems and a stream distributes ordered changes to downstream consumers.
- Catalog projection: A flattened read store stores the fields needed to render product and category pages, calculate facets and expose preview data without joining many operational tables at request time.
- Discovery: A search engine consumes the current catalog projection and remains responsible for typo tolerance, synonyms and natural-language relevance.
- Delivery: Browse, facet, product-detail and preview APIs read from the projection, while checkout and transactional workflows continue to use the write systems.
RBM Software describes Arvyn in this role: CDC and streaming carry catalog mutations into a flattened read store used for product detail, browse, facets and preview. Its product description is available at RBM Software’s Arvyn platform page. The boundary matters: a read projection can be rebuilt, whereas changing the source of truth for price or inventory would create a commercial-control problem.
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Why the separation matters at this size
- Catalog writers can continue accepting changes while read indexes are being rebuilt.
- Browse requests avoid expensive joins across rapidly changing transactional tables.
- Search relevance can evolve independently of catalog freshness.
- A failed projection can be replayed from retained events instead of forcing a manual catalog rewrite.
These are design properties to implement and test, not service levels promised by Arvyn. The reviewed pages do not document connector coverage, replay guarantees or recovery-time objectives.
How does the CDC-to-read-store data path work?
The reliable pattern is source mutation, captured event, validated transformation, aggregate update and published read version. Make every stage observable so you can distinguish a slow source connector from a slow projection or a stale search index.
- Capture: Record the source system’s entity key, operation type, commit position and event timestamp. Preserve deletes and tombstones; silently dropping a delete leaves discontinued SKUs visible.
- Normalize: Convert source-specific formats into a canonical catalog event. Include SKU, product identifier, variant relationships, locale, channel, currency and effective times for volatile fields such as price and stock.
- Validate: Reject or quarantine events that violate schema, refer to missing parents or contain impossible values. Keep the source position so an operator can repair and replay a single partition.
- Apply business rollups: Recalculate product-level values from SKU-level facts using declared aggregation rules. Do not assume that every numeric field can be summed.
- Write the projection: Update the flattened document or row atomically for the product and publish a version or sequence number with it.
- Fan out: Send the current representation to browse APIs, facet computation and the search engine. Track each consumer’s offset separately.
- Measure lag: Report source commit time to read-publication time, not just queue depth. A queue can be short while transformations are failing or a consumer is repeatedly retrying one poison event.
Arvyn’s published architecture describes CDC and streaming into a flattened read store, but does not specify a particular broker, connector set, ordering guarantee or recovery procedure. Those items belong in your implementation contract and load test.
What consistency should shoppers see?
Define freshness by field rather than promising one global number. Inventory and price may require a shorter propagation budget than long-form description text. Expose the projection version and event timestamp internally, and decide whether a request may serve a slightly older version during a rebuild or must fail closed for a safety-critical field.
How should you model products, variants and 10 million SKUs?
Store SKU facts at their natural grain, then materialize product-level values with explicit aggregation policies. A product page can represent many sellable SKUs, but the listing must not hide important differences in stock, price or delivery.
Arvyn’s architecture page says each attribute registers its own declared aggregation rule. The examples below are the rules it publishes, with the reason each rule exists.
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| Attribute | Published rollup example | Why the rule matters |
|---|---|---|
| Units sold | SUM | Total volume across variants is additive. |
| Inventory | SUM, plus an ANY>0 in-stock condition | Total units can be summed, while the listing needs a separate yes/no test for whether any variant can be bought. |
| Starting price | MIN | “Starting at” should use the lowest eligible variant price, not a total. |
| Rating | Review-count-weighted average | A variant with one review should not carry the same influence as a variant with thousands. |
| Return rate | Volume-weighted | Rates must be weighted by the relevant transaction volume. |
| Lead time | MAX | The slowest applicable variant sets the conservative product-level promise when the listing must represent all variants. |
| Margin | Derived from rolled price and cost, not summed | Margin is a relationship between values; summing SKU margins produces a misleading product figure. |
The complete architecture discussion is on RBM Software’s Arvyn platform architecture page. Register a rule whenever a new attribute is introduced. Generic ingestion can move a field, but it cannot infer whether that field should use MIN, MAX, SUM, a weighted calculation or a conditional expression.
SKU identity and variant changes
- Use an immutable SKU key and a separate product or style key for grouping.
- Represent a variant split or merge as an explicit relationship change so old product projections can be invalidated.
- Include channel, region, currency and customer-segment dimensions in the key when the same SKU has different sellable attributes.
- Keep effective-from and effective-until values for scheduled prices and promotions; applying events only by arrival order can activate a future price early.
How do browse pages and facets scale without making the database the bottleneck?
Serve browse from a projection shaped for the read pattern, with bounded result pages and precomputed or incrementally maintained facet data. Arvyn describes index-free browse and live facet counts, but the pages do not publish throughput or latency measurements, so you must test the implementation against your category and filter distributions.
Design the browse API
- Require a stable category, channel and locale in every request.
- Use cursor or search-after pagination rather than deep offset scans.
- Return a deterministic sort key plus a continuation token; invalidate or version tokens when the projection schema changes.
- Keep product cards small and fetch expensive editorial sections only when the detail page needs them.
- Set explicit limits for the number of selected facets, ranges and sort combinations.
Design facet computation
Facets need counts consistent with the active filter set and a defined treatment of unavailable, hidden and out-of-stock SKUs. Decide whether counts are exact, approximate or calculated from a materialized aggregate, and expose that choice to product and analytics teams. For high-cardinality attributes such as part numbers, use a search or lookup interaction instead of attempting to render every value as a facet.
Partitioning and cache policy
Choose a partition key that spreads hot categories and preserves the locality needed by the most common browse queries. Cache stable category responses, but attach projection versions or short expiry windows so a price, availability or assortment change does not remain visible longer than your business policy allows. Measure hit rate, tail latency and invalidation delay separately; a fast cache can conceal stale data.
Does Arvyn replace the search engine?
No. Arvyn’s published positioning keeps search and discovery engines responsible for typo tolerance, synonyms and natural-language relevance while Arvyn supplies a current catalog to them. The responsibility split is described on RBM Software’s Why Arvyn page.
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|---|---|---|
| Current product, price and inventory projection | Owns transformation and freshness | Consumes the published representation |
| Browse and merchandising fields | Owns flattened read shape and facet inputs | May expose them for query results |
| Typo tolerance | Not the stated responsibility | Owns analysis and correction |
| Synonyms and natural-language relevance | Not the stated responsibility | Owns dictionaries, analyzers and ranking |
| Search-index rebuilds | Provides replayable current data | Builds and validates its own index |
This division lets you update catalog attributes without treating relevance tuning as a catalog migration. It also means a fresh read model does not guarantee better search results if analyzers, synonyms or ranking features are poorly configured.
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How do you implement the platform step by step?
Build a thin vertical slice first, then add scale and failure testing before onboarding the full catalog.
- Write the ownership contract. For every field, name the source system, owner, update frequency, legal or commercial constraints and whether a stale value is acceptable.
- Define canonical events. Specify schemas for create, update, delete, relationship and scheduled-effective changes. Include an event ID, source position and schema version.
- Choose retention and replay windows. Retain enough source events to rebuild a projection after a bad deployment. Document how a consumer starts from a snapshot and then catches up from the stream.
- Implement idempotent projection. Apply an event only if its source position or version is newer than the stored value. Reprocessing the same event must not double-count sales or inventory.
- Register rollup rules. Encode SUM, MIN, MAX, weighted and conditional calculations as tested metadata or code, and require review when a new attribute is added.
- Publish a versioned read schema. Keep old and new projection versions available during migration so API clients can switch without a flag day.
- Connect browse and facets. Test category pages, multi-select filters, ranges, sorting, empty results and deleted products using production-shaped distributions.
- Connect search. Define which fields are indexed, how often the search consumer refreshes, and how a failed indexing batch is replayed.
- Build preview parity. Preview should use the same transformation and rollup rules as production, with an explicit draft or future-effective status so unpublished data cannot leak.
- Add observability. Dashboard event lag, projection lag, consumer offsets, rejected events, replay rate, API latency percentiles, facet errors and stale-field age.
- Run failure drills. Stop a consumer, duplicate events, reorder allowed partitions, corrupt one record, rebuild a projection and restore from a snapshot. Record data-loss and recovery results.
- Load-test the acceptance target. Use at least 10 million SKU records, realistic product-to-SKU fan-out, peak write bursts, hot categories, long-tail queries and concurrent search indexing. Capture p50, p95 and p99 latency, throughput, error rate and recovery time.
Can Arvyn handle 10 million SKUs?
The responsible answer is that Arvyn is positioned for enterprise and high-volume catalogs, but the reviewed material does not substantiate a tested or deployed 10-million-plus-SKU result. Its page mentions sub-second APIs as a vendor claim, without a published measurement method, sample, hardware profile, latency distribution or year.
| Evidence question | What is stated publicly | What you still need to verify |
|---|---|---|
| Catalog size | No 10-million-SKU benchmark is published. | Maximum tested SKUs, product-to-SKU distribution and dataset generator or export. |
| Latency | Sub-second APIs are described by the vendor. | Endpoint definitions, p95/p99 values, concurrency, cache state and consistency mode. |
| Throughput | No numeric mutation or query rate is provided. | Sustained and burst events per second, facet workload and indexing throughput. |
| Deployment | No named deployment or hardware profile is documented. | Topology, storage, partitioning, autoscaling limits and regional design. |
| Recovery | Connector and replay guarantees are not documented on the reviewed pages. | RPO, RTO, checkpointing, poison-event handling and rebuild procedure. |
Ask for a controlled proof of concept using your own anonymized catalog shape. Require raw test results, not only a headline number, and make freshness, aggregation correctness and recovery acceptance criteria contractual.
How should you compare this architecture with alternatives?
Compare architectural behaviors rather than vendor labels. The same six axes expose whether a design will meet a 10-million-SKU requirement.
| Axis | Questions to ask | Evidence to collect |
|---|---|---|
| Freshness and lag | How long from source commit to browse, facet and search visibility? | Field-level lag histograms during normal and burst load. |
| Browse and facet serving | Are joins avoided, and how are hot categories and high-cardinality facets handled? | Tail latency under realistic filter combinations. |
| Aggregation correctness | Can every attribute declare a rollup and be recomputed deterministically? | Golden datasets covering weighted, conditional and derived metrics. |
| Integration and recovery | How are deletes, schema changes, duplicates and replay handled? | Failure-drill records and rebuild timing. |
| Preview parity | Does preview execute the same transformations as production? | Diffs between preview and production projections for controlled releases. |
| Responsibility split | Which component owns relevance, synonyms, catalog freshness and merchandising? | Interfaces, runbooks and escalation paths. |
What commonly fails in a 10-million-SKU implementation?
Most incidents come from unclear ownership, incorrect rollups or unmeasured propagation rather than from the SKU count alone.
Prices or inventory appear stale
Check source commit timestamps, connector offsets, transformation retries and the last successful publication for the affected field. If the read projection is current but search is stale, investigate the search consumer separately instead of replaying the entire catalog.
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A product shows the wrong price or rating
Inspect the product’s SKU membership and the registered aggregation rule. Verify currency and channel filters before changing code; a correct MIN in one market can be wrong when values from multiple currencies are mixed.
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Facet counts disagree with product cards
Check whether facets and cards read the same projection version and availability policy. Differences often arise when facets include hidden or out-of-stock SKUs that the result renderer excludes.
Replay creates inflated totals
Look for non-idempotent consumers that add deltas every time an event is delivered. Store the last applied source position or event ID and test duplicate delivery explicitly.
Search is fresh but results are irrelevant
Catalog freshness does not configure typo tolerance, synonyms or natural-language ranking. Review analyzers, dictionaries, field boosts and relevance evaluation in the search engine.
Preview does not match production
Compare transformation versions, effective-time handling and feature flags. Preview should run the same rollup code against an isolated status or namespace, not a second implementation maintained by hand.
What should the acceptance test and operating dashboard contain?
Define measurable gates before production approval. A useful test records the catalog shape, event mix and query mix so another team can reproduce it.
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- At least 10 million SKU records, with the observed product-to-SKU fan-out and attribute cardinalities.
- Peak and sustained CDC rates, including price, inventory, assortment and delete bursts.
- Browse, detail, facet and search request rates split by hot and long-tail categories.
- p50, p95 and p99 latency, timeout rate and correctness checks for every endpoint.
- Maximum observed source-to-read and source-to-search lag for volatile fields.
- Projection rebuild duration, event replay speed, checkpoint recovery and data-loss result after injected failures.
- Rollup golden tests for SUM, MIN, MAX, ANY>0, weighted averages and derived margin.
- Cost and capacity at the tested load, including storage, stream retention, compute and search indexing resources.
Keep the dashboard split by pipeline stage. One overall “catalog latency” number cannot tell an operator whether the source connector, stream, projection, facet service or search consumer is responsible.
What questions should you ask RBM Software before choosing Arvyn?
Use the public architecture as a discussion starting point, then request evidence specific to your catalog.
- Which commerce and CMS connectors are supported, and what are their delete, schema-change and backfill behaviors?
- What maximum dataset and event-rate tests can RBM Software share, and were they measured with your product-to-SKU distribution?
- How are ordering, duplicates, retries, poison events and replay controlled?
- What read-store partitioning, retention and rebuild options are available?
- How are rollup rules versioned, tested and changed without corrupting historical projections?
- Which APIs provide browse, facets, product detail and preview, and what consistency guarantees does each provide?
- How does the Arvyn projection feed your chosen search engine, and how is a failed or partial index update detected?
- What observability, export and incident-response access will your operations team receive?
What is the practical decision for a 10-million-SKU program?
Adopt the CDC-to-flattened-read-store pattern when your commerce and CMS systems must remain authoritative but cannot efficiently serve high-volume browse and merchandising traffic. Arvyn is a plausible implementation of that layer according to RBM Software’s published description, especially where live catalog projections and declared rollup semantics are needed.
Do not represent the platform as proven for 10 million SKUs until it passes a workload-specific proof of concept. The decision should rest on measured freshness, tail latency, aggregation correctness, replay behavior and preview parity, while the search engine remains accountable for relevance.
Frequently Asked Questions
What should be the source of truth for product data?
Keep transactional commerce and CMS systems authoritative for the fields they own. Treat the catalog read store and search index as rebuildable projections, and document ownership for every attribute before building connectors.
How can I measure catalog freshness accurately?
Attach the source commit timestamp and a projection publication timestamp to each event or field. Report their difference by attribute and consumer, then inspect percentiles during both steady traffic and update bursts.
Do I need a separate search index if I use a catalog read layer?
Usually yes when you need typo handling, synonyms or natural-language ranking. The read layer can supply current, correctly aggregated documents, but relevance analysis and ranking remain separate concerns.
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Request a reproducible test using your catalog shape, including product-to-SKU fan-out, mutation bursts, facet queries and search indexing. The result should include p95 and p99 latency, lag, error rates, topology, capacity and recovery outcomes rather than a single throughput figure.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
