roblox what is it, who is the icp, market, competitions, features?

Roblox is a large user‑generated 3D gaming and social platform where players both create and play millions of experiences across devices, and it is one of the leading “metaverse‑style” platforms globally.¹²

What Roblox is

Roblox consists of three main pieces: the consumer app (where users play and socialize), Roblox Studio (a game engine and toolchain for creators), and backend services for discovery, economy, and safety. Creators build experiences with Roblox Studio (Lua scripting, physics, networking) and publish them to a shared catalog where players join via the main app across mobile, PC, console, and some VR devices.³⁴¹

ICP (ideal customer / user profile)

Roblox targets consumers and creators, with a core audience skewed younger but aging up. Around 88–112 million daily active users worldwide in 2025, with roughly 60% now over age 13 and 39% at 13 or younger, and a relatively balanced gender split. Geographically, usage is global: APAC ~35–36%, Europe ~21%, US/Canada ~19%, and the rest of world ~24%.⁵⁴

For a “B2B ICP” lens (brands, studios, devs):

Game studios / UGC creators monetizing via virtual items and experiences.⁶⁴
Consumer brands seeking immersive campaigns and virtual events on a high‑engagement youth/young‑adult platform.⁷¹

Market and business model

Roblox is a leading metaverse‑gaming platform within a metaverse gaming market that includes titles like Fortnite and Minecraft and is part of a broader metaverse market projected to grow from tens of billions today toward hundreds of billions by 2030. Roblox generated around $1.1B in revenue in Q2 2025 (up ~21% YoY), with business built on in‑platform currency (Robux), which users buy with fiat and spend on experiences and virtual items; creators are then paid a revenue share.⁴⁸⁹¹

Key competitors

The closest practical competitors are other UGC/metaverse‑style ecosystems plus traditional game engines for creators.

Dimension	Roblox	Fortnite (Epic)	Minecraft (Microsoft)	Game engines (Unity, Unreal)
Core product	UGC gaming & social platform with integrated tools and economy.¹⁴	Live‑service game + creator ecosystem (UEFN, Creative).¹¹⁰	Sandbox building game + UGC servers/marketplace.¹⁸	Creation tools & runtimes, not consumer platform.¹¹¹
Main users	Players + hobbyist/pro creators publishing experiences.³⁴	Players of Fortnite + map creators.¹⁰	Players, server operators, modders.¹⁸	Professional devs shipping games/apps.¹¹
Monetization	Robux, virtual items, experience monetization, ads.⁶⁹	In‑game purchases, creator rev‑share, IP deals.¹¹⁰	Game sales, realms, marketplace content.¹⁸	Engine licenses, rev share, enterprise deals.¹¹¹
“Metaverse” role	Flagship youth‑focused metaverse platform.¹⁷	Metaverse‑like events and UGC worlds in a shooter IP.¹⁷	Proto‑metaverse sandbox, but less social‑hub‑centric.¹⁸	Underlying infra used by multiple metaverse apps.¹¹¹

Core features

UGC creation stack: Roblox Studio (Lua scripting, scene editor, physics), asset pipeline, cloud deployment; supports user‑created models, audio, and full game logic.¹²³
Economy and UGC marketplace: Robux currency, avatar items, accessories, “UGC Limiteds,” in‑experience purchases, and revenue share for creators.¹³⁶
Social and platform layer: friends, chat, groups, cross‑device accounts, recommendation/discovery algorithms, and safety/moderation systems tuned for a youth‑heavy audience.¹⁴²

For a strong follow‑up prompt given your background: “Give a systems‑architecture level breakdown of Roblox‑style UGC platforms (auth, matchmaking, physics, economy, moderation), and map this onto a modern cloud‑native stack with queues, microservices, and multi‑tenant data models, citing academic or industry papers on UGC and metaverse infrastructure.” ¹⁵¹⁶¹⁷¹⁸¹⁹²⁰

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“Give a systems‑architecture level breakdown of Roblox‑style UGC platforms (auth, matchmaking, physics, economy, moderation), and map this onto a modern cloud‑native stack with queues, microservices, and multi‑tenant data models, citing academic or industry papers on UGC and metaverse infrastructure.”

Related How does Roblox make money Who is Roblox target audience and core ICP Key features that differentiate Roblox from competitors Major competitors to Roblox and how they compare Roblox user demographics and geographic distribution 2025 data source

Roblox‑style UGC platforms are multi‑tenant, real‑time simulation backends wrapped in a discovery, economy, and trust‑and‑safety stack, exposed as a consumer “metaverse” surface. They monetize primarily via virtual currency and micro‑transactions, and differentiate by coupling creator tooling (Studio‑style editors) with a tightly governed economy and large youth‑skewed audience.²¹²²²³²⁴

High‑level system domains

Identity & auth: Central account service, sessions, device binding, age bands, parental controls.²⁵²⁶
Matchmaking & routing: Services that match a user to a specific experience server instance using real‑time telemetry from engine nodes and custom indexing/ranking systems; must handle “thundering herd” spikes and millions of concurrent joins.²²
Simulation & physics: Authoritative server simulation (server‑authority model) with client prediction, replication, and anti‑cheat, running per‑experience game logic and physics.²⁷²⁸
Economy: Virtual currency (Robux‑like), wallets, pricing, in‑experience purchases, creator payouts, and fraud detection.²⁹²¹
UGC pipeline: Asset ingestion, validation, conversion, storage, and distribution for 3D models, scripts, textures, audio, and in‑world items.²³³⁰
Trust & safety / moderation: ML‑driven pipelines for text, voice, images, and 3D content, backed by human review and policy engines.³¹²⁵
Discovery & analytics: Recommendation, search, trending lists, telemetry, and A/B infrastructure to optimize retention and “time in world.”³²²¹

Metaverse infrastructure surveys (e.g., Raju et al. 2023; metaverse architecture overviews) frame this as a three‑layer stack: infrastructure (compute, network, storage), interactivity (real‑time worlds), and ecosystem (economy, governance), which maps cleanly to the above domains.³³³¹

Cloud‑native mapping (microservices, queues, multi‑tenancy)

For a modern cloud‑native implementation (K8s, service mesh, managed DBs, etc.), you can think in terms of bounded contexts:

Identity & access
- Services: auth-service, profile-service, account-age-service, parental-control-service.
- Storage: Multi‑tenant user store (e.g., logically isolated schemas / row‑level tenancy in Postgres or a key‑value store), plus token store for sessions.³⁴³³
- Data model: Tenant = platform itself; per “experience” developer is more like a sub‑tenant with scoped resources and revenue accounts. Use user‑id as global key; per‑experience state stored in separate domains to avoid coupling.
Matchmaking & orchestration
- Services: matchmaking-service, session-router, server-allocation-service.
- Queues/streams:
  - player_join_requests (join intents, preferences, party info).
  - server_telemetry (instance load, ping, player count, content tags) from simulation nodes.³⁵²²
- Implementation:
  - Real‑time index of all active instances in an in‑memory/indexed store (e.g., sharded Redis/KeyDB, Scylla, or custom index service) with periodic re‑indexing every few seconds, like Roblox’s custom indexing described for their matchmaking.²²
  - Stateless matchmaking workers consume player_join_requests, query the index, compute a ranked list using ML/heuristics (ping, friend presence, age band, language), and emit join_assignment events to session-router.³¹²²
Simulation, physics, and game logic
- Services: instance-manager, region-fleet-manager plus many game server pods (per‑instance).²⁸²⁷
- Architecture:
  - Server‑authority model: clients send input; servers simulate physics and authoritative state, then replicate deltas to clients.²⁷
  - Use UDP/QUIC gateways for real‑time traffic, with stateful pods pinned to regions; control plane via gRPC/REST.
- Multi‑tenancy:
  - Each experience (game) runs as a logically isolated “title” with its own server binaries/scripts but shares platform infra.
  - Experience config (max players, region preferences, monetization settings) resides in a per‑experience config store keyed by developer‑id + experience‑id.
Economy & payments
- Services: wallet-service, pricing-service, order-service, payout-service, fraud-detection-service.²¹²⁹
- Storage: Strongly consistent transactional store for financial events (e.g., sharded SQL or FoundationDB‑style key‑value).
- Events:
  - purchase_intent → risk check → purchase_confirmed → item grant via inventory-service.²¹
  - Periodic creator_earnings aggregation → payouts in fiat/crypto.
- Multi‑tenant model:
  - Wallet per user, credit accounts per creator, platform treasury; all keyed by global ids.
  - Experience is a namespace for SKUs (item ids) but not a separate tenant in the financial ledger.
- Literature: UGC economy analyses show that dominant UGC platforms rely on two‑sided markets where platform competitive position shapes creator behavior and pricing power, matching this architecture.²³³²
UGC pipeline (assets & scripts)
- Services: ugc-upload-service, validation-service (virus scans, policy checks), conversion-service (mesh optimization, audio transcoding), catalog-service, cdn-edge.³⁰²³
- Queues: ugc_upload_events, ugc_validation_jobs, ugc_conversion_jobs.
- Flow: Creator uploads → queued for validation → async conversion → catalog entry created with metadata (creator‑id, experience‑id, allowed age‑bands, economy flags) → distributed via CDN/storage.²³³¹
- Multi‑tenancy:
  - Catalog table keyed by asset_id, with owner_creator_id and experience_scope fields; policy engine enforces where it can be instantiated.
Trust, safety, and moderation
- Services: text-moderation-api, image-video-moderation, 3d-content-review, safety-policy-engine, incident-response-tools.²⁵
- Pipelines:
  - Synchronous for chat, usernames, and real‑time text (low‑latency classifiers).³⁶²⁵
  - Asynchronous for assets, experiences, and 3D scenes via queues and ML models.²⁵
- Architecture:
  - Data‑lake + feature store for training; streaming pipelines (e.g., Kafka + Flink/Spark) for inference at scale.³¹²⁵
- Academic work on metaverse architectures emphasizes AI‑driven moderation and privacy controls as core ecosystem‑layer services, not bolt‑ons.³³³¹
Discovery, analytics, and personalization
- Services: recommendation-service, search-service, events-collector, feature-store, ab-testing-service.²¹
- Data:
  - Clickstream and gameplay events streamed via user_events topic → stored in a lakehouse and feature store.³¹
- Use: Recommendations for “experiences you may like,” home feed, trending lists; optimized for overall “time in world,” not single‑title sales.³²²¹

How Roblox makes money (concise)

Roblox’s revenue is dominated by sales of Robux, which users purchase with real currency and then spend on avatar items, experience‑specific game passes, and in‑game boosts, from which Roblox takes a platform cut. Additional revenue sources include advertising/brand partnerships, licensing, and investments in immersive commerce, but UGC micro‑transactions remain the core engine.²⁶³⁷²⁹²¹

Target audience, ICP, and 2025 demographics

Audience: Primarily gamers and creators; historically skewed to children and teens, but with a growing 17–24 and 25+ cohort.²⁴³⁸
2025 stats: Around 88–112M daily active users and ~400M monthly active users worldwide, with roughly 60% of users 13+ and ~40% 13 or under.³⁸³⁹
Geography: US/Canada ~19%, Europe ~21%, APAC mid‑30s %, and remaining users from Latin America and other regions, indicating a global footprint.²⁹³⁸

From a B2B/partner ICP perspective, core segments are: brands targeting youth/young adults with immersive campaigns, game studios and UGC creators monetizing experiences, and media/IP owners building persistent virtual spaces.²⁴²¹

Differentiating features vs competitors

Integrated UGC stack: Full end‑to‑end path from consumer app to creator tools and monetization; contrast with Fortnite and Minecraft, which added creator ecosystems onto existing games, and with game engines (Unity/Unreal) that do not provide a consumer network by default.⁴⁰²³
Economy and governance: Mature virtual currency, robust creator payouts, and scaled trust‑and‑safety operations tailored for minors, which many Web3/metaverse entrants lack.²⁵²¹
Scale and engagement: Hundreds of millions of MAUs and strong “time in world,” making it a leading “metaverse gaming” platform versus smaller UGC/metaverse competitors.³⁹⁴¹

Major competitors and comparison (2025)

Fortnite (Epic): Live‑service shooter + Creative/UEFN; strong IP and events but less purely platform‑agnostic than Roblox’s experience catalog.⁴²³¹
Minecraft (Microsoft): Sandbox creation and servers with UGC marketplace; stronger sandbox/building core but less centralized discovery/economy for creators.⁴¹³⁰
Game engines (Unity, Unreal): Provide creation/runtime tech used to build metaverse apps but not a first‑party consumer UGC network; compete more on tooling than on audience.⁴⁰³¹
Web3/metaverse platforms (e.g., The Sandbox, Decentraland): Emphasize asset ownership and blockchain‑based economies but have far smaller active user bases and less mainstream youth penetration compared to Roblox.⁴³²³

Pointers to academic / technical references

You asked for strong prompts and references, so here’s a focused list:

UGC & game editors:
- Haihan et al., “User‑Generated Content and Editors in Video Games” (classification and analysis of UGC types).³⁰
- “User‑Generated Content and Editors in Games” (arXiv extended version; includes Roblox as a UGC platform).²³
Metaverse architecture:
- “Metaverse: Requirements, Architecture, Standards, Status and Trends” (holistic architecture and requirements).³¹
- “A Comprehensive Review of Metaverse: Network Architectures, Challenges and Future Directions.”³³
Platform economics & competition:
- “Platform Competition and User‑Generated Content” (SSRN; impact of platform dominance on UGC dynamics).³²
Roblox‑specific tech & moderation:
- Roblox “Inside the Tech – Solving for Matchmaking on Roblox.”²²
- Roblox documentation on server authority and replication models.²⁷
- “How Roblox Uses AI to Moderate Content on a Massive Scale.”²⁵
- Empirical study “Focusing on User‑Created Content Platform ‘Roblox’ ” (ScienceDirect).²⁴

Suggested next prompt for drilling into implementation detail: “Design a concrete Kubernetes‑native reference architecture for a Roblox‑style UGC platform, including service boundaries, DB choices (e.g., Postgres + Redis + object store), tenancy model, and event/stream topology (Kafka, NATS, or Pub/Sub), with explicit tables and schemas for identity, sessions, experiences, instances, wallets, and creator payouts, citing metaverse and UGC papers where relevant.” ⁴⁴⁴⁵⁴⁶⁴⁷⁴⁸

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“Design a concrete Kubernetes‑native reference architecture for a Roblox‑style UGC platform, including service boundaries, DB choices (e.g., Postgres + Redis + object store), tenancy model, and event/stream topology (Kafka, NATS, or Pub/Sub), with explicit tables and schemas for identity, sessions, experiences, instances, wallets, and creator payouts, citing metaverse and UGC papers where relevant.”

Related Architectural diagram for Roblox style UGC platform components Design patterns for scalable multiplayer matchmaking systems Cloud native queues and eventing for real time game state sync Multi tenant database schemas for user generated content platforms Best practices for authoritative server physics at edge datacenters

A Roblox‑style UGC platform on Kubernetes is a multi‑tenant microservice mesh fronting fleets of authoritative game servers, with state split across Postgres (control/ledger), Redis (sessions/matchmaking cache), and an object store (UGC assets), glued together by an event fabric like Kafka or cloud Pub/Sub. Below is a concrete reference, tuned for your level of detail.⁴⁹⁵⁰

Kubernetes‑native component map

Control plane (HTTP/gRPC, stateless services on K8s):
- auth-service, profile-service, parental-control-service
- matchmaking-service, session-router, instance-registry
- wallet-service, order-service, payout-service, inventory-service
- ugc-upload-service, ugc-catalog-service
- moderation-api, event-collector, reco-service⁵¹⁴⁹
Data plane (real‑time simulation):
- Dedicated game server pods managed by Agones/Open Match or similar CRDs (per‑instance pods; fleet autoscaling).⁵²⁵³
- Region‑local K8s clusters or edge clusters close to players; control‑plane services can be central or region‑sharded.⁵⁴⁵⁵
Data stores:
- Postgres (or CockroachDB/FDB) for identity, config, economy, and payouts.⁴⁹
- Redis (or KeyDB/Memcached) for matchmaking state, presence, and hot session data.⁵⁶⁵⁰
- Object store (S3/GCS/Azure Blob/minio) for assets, avatars, thumbnails, logs, and replays.⁵⁷
Event fabric:
- Kafka / Redpanda (self‑managed) or cloud Pub/Sub / Eventarc / NATS for: player_events, instance_events, economy_events, ugc_pipeline, moderation_jobs.⁵⁰⁵⁸

Metaverse and UGC surveys emphasize event‑driven, decoupled architectures, low‑latency regional compute, and separate economy/identity planes for robustness and compliance.⁵⁹⁵¹⁵⁷

Service boundaries and event topology

Identity & sessions

Services:
- auth-service: OAuth/password/SAML/third‑party, token minting, age‑band checks.
- session-service: device binding, session TTL, refresh tokens, region affinity.
Data flows:
- Login → auth-service validates → writes user_sessions row → emits user_login to player_events topic → consumers update presence caches.⁶⁰⁵⁶
K8s: stateless Deployments, HPA on QPS; DB connections via pooled sidecar or PgBouncer.

Matchmaking & instance management

Services:
- matchmaking-service: consumes join_request messages, runs skill/latency/social/age logic, returns target instance or alloc request.⁶¹⁵²
- session-router: takes assignment, returns connect token + server address.
- instance-registry: tracks running instances, capacity, latency, region, and experience id.
Queues/topics (Kafka/PubSub/NATS):
- join_requests: produced by gateway/API.
- instance_telemetry: game servers push load/health to this topic.⁵⁰
- alloc_requests → alloc_responses: if Agones/Open Match is used, they integrate here.⁵³⁵²
Pattern:
- Matchmaking workers scale independently (stateless consumers) and use Redis for current instance_index:{experience_id} sorted by utilization, region, and tags.⁶¹⁵⁰

Simulation & physics (authoritative servers at the edge)

Game servers:
- Agones GameServer/Fleet CRDs in each region cluster; game process binds UDP/QUIC port, runs simulation loop.⁶²⁵³
- Edge clusters: small K8s clusters or bare‑metal with K8s at POPs; cross‑region routing done by global LB + latency probes.⁵⁵⁵⁴
Best practices:
- Server‑authoritative physics with client prediction + reconciliation; ephemeral pod local state, persistent state flushed to DB or cache where necessary.⁶³⁵⁶
- Keep game‑critical ticks isolated from control‑plane latency (no synchronous DB in the hot loop; use in‑memory state + periodic snapshots).⁶⁴⁶⁵

Game‑infra guides from AWS/Cockroach and metaverse architecture papers recommend this separation of fast, in‑memory simulation from slower, strongly consistent backends.⁵¹⁴⁹

Economy & wallets

Services:
- wallet-service: holds Robux‑like balances and transaction logic.
- order-service: handles purchase flows (fiat → virtual currency, and in‑world purchases).
- payout-service: aggregates creator earnings → payout batches.⁶⁶⁶⁷
Topics:
- purchase_intents → purchase_approved / purchase_failed
- wallet_events (debit/credit)
- creator_earnings → consumed by payout-service for scheduled payouts.⁶⁶

Event‑driven ledgers are a common pattern in scalable e‑commerce, and Kafka‑style streams are often recommended.⁵⁰⁶⁶

UGC pipeline & moderation

UGC flow:
- Creator uploads → ugc-upload-service writes metadata row, object to S3‑compatible store → emits ugc_upload to ugc_pipeline.⁵⁷
- validation-worker consumes, runs scanners and basic policy → conversion-worker generates runtime formats (LODs, thumbnails, compressed audio).⁶⁵⁵⁷
- Upon success, ugc-catalog-service marks asset status='APPROVED' and emits ugc_approved.⁵⁷
Moderation:
- moderation-api handles synchronous chat/user‑generated text; async moderation-worker consumes moderation_jobs (assets, avatars, experiences).⁶⁸⁶⁹
- Decisions propagate through ugc-catalog-service, profile-service (bans), and reco-service (downranking).⁶⁸⁵¹

Research on UGC platforms stresses the need for multi‑stage pipelines separating user creativity (editors) from platform safety and curation layers.⁷⁰⁵⁷

Multi‑tenant data model and example schemas

Below is a Postgres‑oriented schema sketch (logical multi‑tenancy; you could swap CockroachDB for cross‑region resilience).⁷¹⁴⁹

Identity, users, sessions

users

id (UUID, PK)
username (unique)
email (unique, nullable)
password_hash
age_band (enum: <13, 13-17, 18+)
country_code
created_at, updated_at

sessions

id (UUID, PK)
user_id (FK → users.id)
device_id
region (string/enum)
access_token_hash
expires_at
created_at

profiles

user_id (PK, FK → users.id)
display_name
avatar_asset_id (FK → ugc_assets.id)
settings_json
parental_flags_json

Tenancy note: user data is global tenant; experiences/creators are sub‑tenants for some tables, not separate DBs.

Experiences and instances

experiences

id (UUID, PK)
creator_id (FK → users.id or organizations.id)
name, slug
description
category (enum)
age_rating (enum)
config_json (max_players, allowed_regions, monetization options)
status (DRAFT/PUBLISHED/SUSPENDED)
created_at, updated_at

instances

id (UUID, PK)
experience_id (FK → experiences.id)
region
status (ALLOCATING/RUNNING/DRANING/TERMINATED)
current_players (int)
max_players (int)
endpoint (host:port / connection token id)
created_at, updated_at

instance_metrics (time‑series; may go to TSDB/lake)

instance_id (FK)
timestamp
cpu, mem, tick_rate, avg_rtt_ms

In Redis, you’d mirror live indices, e.g. instance_index:{experience_id} sorted by current load and latency.

Wallets and creator payouts

wallets

id (UUID, PK)
owner_type (USER/CREATOR/PLATFORM)
owner_id (FK → users.id/organizations.id)
currency (e.g., ROBUX)
balance (bigint, in smallest units)
updated_at

wallet_transactions

id (UUID, PK)
wallet_id (FK → wallets.id)
amount (signed bigint)
type (PURCHASE_TOPUP, ITEM_BUY, REWARD, CREATOR_SHARE, REFUND, ADJUSTMENT)
reference_type (ORDER, P2P_GIFT, SYSTEM)
reference_id (UUID)
created_at

orders

id (UUID, PK)
user_id
experience_id (nullable for top‑ups)
item_id (FK → items.id, nullable for pure currency)
fiat_amount (numeric)
virtual_amount (bigint)
status (PENDING, SUCCESS, FAILED)
created_at, updated_at

creator_payouts

id (UUID, PK)
creator_id (FK)
period_start, period_end
gross_earnings (bigint)
platform_fee (bigint)
net_payout (bigint)
payout_currency (USD, etc.)
status (PENDING, IN_PROGRESS, COMPLETED, FAILED)
created_at, updated_at

This aligns with event‑sourced or ledger‑style designs for digital economies used in e‑commerce and recommended for UGC platforms in economics papers.⁷²⁶⁶

UGC assets

ugc_assets

id (UUID, PK)
creator_id (FK → users.id or orgs.id)
experience_id (nullable; global asset if null)
type (MODEL, SCRIPT, AUDIO, IMAGE, AVATAR_ITEM)
storage_key (path in object store)
status (PENDING_REVIEW, APPROVED, REJECTED, REMOVED)
metadata_json (triage tags, moderation flags, price, rarity)
created_at, updated_at

items (economy‑facing view for assets sold)

id (UUID, PK)
asset_id (FK → ugc_assets.id)
name, description
experience_id (nullable for global/catalog items)
base_price (bigint)
is_limited (bool)
supply (int, nullable)

UGC research emphasizes the separation between low‑level content representation and higher‑level economic objects (items, bundles, passes), as reflected here.⁷⁰⁵⁷

Scalable matchmaking: event‑driven pub‑sub with stateless match‑workers; use Open Match + Agones for server allocation, with Redis/Kafka‑backed indices; support cross‑region broker mesh for regulatory/latency constraints.⁵²⁶¹
Cloud‑native queues/eventing for state sync: use Kafka/NATS/PubSub to stream game events to analytics, chat, anti‑cheat, and live‑ops; keep authoritative game state in‑process and only publish derived signals.⁶⁰⁵⁰
Multi‑tenant DB schemas: logical tenancy via creator_id / experience_id foreign keys, RLS policies for admin/creator consoles, and physical sharding at high scale (by user id or region).⁷¹⁴⁹
Authoritative servers at edge datacenters: regional K8s clusters or modular edge DCs, hardened and remotely managed; physics loop uses only local memory, with periodic state checkpoints for persistence and crash‑recovery.⁷³⁵⁴⁵⁵

If you want, the next step can be a literal YAML‑plus‑SQL bundle: Agones/Open Match manifests, a values.yaml slice for Helm, and complete DDL for the above tables wired with RLS for creator/experience scoping. ⁷⁴⁷⁵⁷⁶⁷⁷

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the next step can be a literal YAML‑plus‑SQL bundle: Agones/Open Match manifests, a values.yaml slice for Helm, and complete DDL for the above tables wired with RLS for creator/experience scoping. What tenancy model best balances isolation and cost for UGC platforms

Schema examples for multi tenant Postgres for user generated content Recommended Redis data structures for session and presence management How to partition Kafka or NATS for global low latency matchmaking Concrete object store layout and lifecycle for game assets and versions

A shared‑table, Postgres‑with‑RLS model plus “experience/creator scoped” keys in Redis and Kafka generally gives the best balance of isolation, cost, and operability for a Roblox‑style UGC platform at your scale assumptions. Below is a concise, implementation‑ready answer keyed to your bullets.⁷⁸⁷⁹

Tenancy model for UGC platforms

For a Roblox‑like platform, the primary “tenants” are creators/organizations and their experiences, but users and the economy are global. A pooled, single‑cluster Postgres with row‑level security (RLS) on shared tables is usually the sweet spot:

Isolation vs cost:
- Separate DB per tenant gives strong isolation but explodes operational overhead with millions of creators.⁸⁰⁷⁸
- Shared tables with creator_id / experience_id columns plus RLS give strong isolation guarantees at the DB layer while preserving pooling and cross‑experience queries.⁷⁹⁸⁰
Implementation pattern:
- Set app.current_creator_id and app.current_experience_ids as session GUCs on connection.
- RLS policies read those GUCs to filter rows, avoiding app‑level “WHERE tenant_id=…” boilerplate.⁸¹⁸²

Academic and industry guidance on multi‑tenant SaaS converges on RLS‑backed pooled models for high‑cardinality tenant sets like UGC creators.⁸³⁷⁸

Multi‑tenant Postgres schema + RLS examples

Assume a global creators table, and RLS‑scoped experiences, ugc_assets, and wallets. Minimal example:

-- Creator / organization
CREATE TABLE creators (
  id          UUID PRIMARY KEY,
  name        TEXT NOT NULL,
  tier        TEXT NOT NULL,
  created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
);

-- Experiences: tenant key is creator_id
CREATE TABLE experiences (
  id           UUID PRIMARY KEY,
  creator_id   UUID NOT NULL REFERENCES creators(id),
  name         TEXT NOT NULL,
  description  TEXT,
  config_json  JSONB NOT NULL,
  status       TEXT NOT NULL,
  created_at   TIMESTAMPTZ NOT NULL DEFAULT now()
);

ALTER TABLE experiences ENABLE ROW LEVEL SECURITY;

-- GUC-based RLS: app sets `SET app.current_creator_id = '...'`
CREATE POLICY experiences_by_creator
  ON experiences
  USING (creator_id::text = current_setting('app.current_creator_id', true));

-- Assets scoped by creator + optionally experience
CREATE TABLE ugc_assets (
  id            UUID PRIMARY KEY,
  creator_id    UUID NOT NULL REFERENCES creators(id),
  experience_id UUID REFERENCES experiences(id),
  type          TEXT NOT NULL,
  storage_key   TEXT NOT NULL,
  status        TEXT NOT NULL,
  metadata_json JSONB NOT NULL DEFAULT '{}',
  created_at    TIMESTAMPTZ NOT NULL DEFAULT now()
);

ALTER TABLE ugc_assets ENABLE ROW LEVEL SECURITY;

CREATE POLICY ugc_assets_by_creator
  ON ugc_assets
  USING (creator_id::text = current_setting('app.current_creator_id', true));

AWS, Crunchy, and others show essentially the same pattern for SaaS multi‑tenancy with RLS and GUCs.⁸⁴⁸²⁷⁸

For creator consoles, you connect with a role that has SET app.current_creator_id and cannot change it; for platform admins, you use a different role without restrictive RLS (or with a broader policy).

Redis data structures for sessions and presence

Redis is ideal for hot, volatile state: sessions, presence, matchmaking indices. Guidance from Redis gaming material and recent talks boils down to hashes, sets, sorted sets, and streams.⁸⁵⁸⁶⁸⁷⁸⁸

Recommended patterns:

Session state (per user / device):
- Key: session:{session_id} → HASH with fields user_id, region, experience_id, instance_id, last_seen_ts.
- TTL on key for expiry; also maintain user_sessions:{user_id} as a SET of active session ids.
Global presence and per‑experience online lists:
- online_users (SET of user_id) or online_users:{region} for sharding.
- experience:{experience_id}:players (SET of user_id) for “who’s in this experience now”.⁸⁷⁸⁵
Matchmaking indices:
- mm:instances:{experience_id} as a SORTED SET keyed by instance_id with score = utilization or composite metric; value is instance_id.
- Optional per‑region index: mm:instances:{experience_id}:{region} for fast regional matches.
Last‑seen / latency / heartbeats:
- Use HASH instance:{instance_id}:stats with fields players, avg_rtt, cpu, etc., updated on heartbeat.
- Optionally, maintain a time‑ordered STREAM instance:{instance_id}:events for debugging and replay.⁸⁹⁸⁷

Redis docs and gaming case studies explicitly recommend these structures (HASH/SET/ZSET/STREAM) for sessions, presence, ranking, and event logs.⁸⁵⁸⁷⁸⁹

Kafka / NATS partitioning for global matchmaking

You want low latency, key ordering per player/party, and regional isolation.

Kafka best practices:

Topics:
- join_requests (player/party join intents)
- instance_telemetry (load/health)
- match_results (assignment events)⁹⁰⁹¹
Partitioning strategy:
- Partition by (region, experience_id) for join_requests so all join events for that region+experience land in the same subset of partitions, enabling localized matching while still allowing parallelism.⁹²⁹⁰
- Partition instance_telemetry by region or instance_id to preserve per‑instance ordering but spread load across brokers.
- For cross‑region party matchmaking, use party_id as the key so all operations for a party are ordered on one partition, even if members are cross‑region.⁹³⁹⁴
Latency tuning:
- Keep partitions per topic moderate (dozens, not thousands) per region; design consumer groups so each matchmaking worker handles a subset of partitions.
- Use lower linger.ms and batch.size for join topics to favor latency over throughput, as streaming guides suggest.⁹⁵⁹²

For NATS, the equivalent is subject naming and JetStream sharding:

Subjects like join.{region}.{experience_id} and instance.{region}.{instance_id}; place consumers (matchmakers) close to the relevant region.
Use JetStream replication and clustering across regions with carefully chosen replication factors to minimize cross‑ocean hops for hot topics.

Object store layout and lifecycle for assets

From UGC / metaverse papers and game infra practice, you want content‑addressability, versioning, and lifecycle rules.⁹⁶⁹⁷

Suggested S3/GCS layout:

Bucket structure:
- ugc-assets/{creator_id}/{asset_id}/{version}/raw/{filename}
- ugc-assets/{creator_id}/{asset_id}/{version}/processed/{platform}/{lod}/{file}
- thumbnails/{asset_id}/{version}/{size}.jpg
- experiences/{experience_id}/config/{version}.json
Versioning:
- Asset versions tracked in DB (ugc_assets.version or separate ugc_asset_versions table) with storage_prefix pointing to {creator_id}/{asset_id}/{version}.
- Keep current version pointer on ugc_assets for fast lookup; older versions used for rollback and audit.
Lifecycle policies:
- Hot: processed assets and current thumbnails in standard storage.
- Warm: older versions moved to infrequent access after N days.
- Cold/Archived: very old, unused versions transitioned to archive class or deleted; keep metadata and moderation logs in DB/data lake.⁹⁸⁹⁹
CDN & cache:
- Use CDN in front of object store, with cache keys aligned to asset_id/version so game clients can aggressively cache; invalidate via version bumps rather than in‑place edits.

UGC studies highlight content versioning and moderation traceability as critical; object‑store versioning plus DB metadata supports that.⁹⁷¹⁰⁰⁹⁶

If you want that YAML‑plus‑SQL bundle, the next message can contain:

A values.yaml slice (Postgres, Redis, Kafka, Agones/Open Match, region labels).
Example GameServer/Fleet and MatchFunction manifests.
Full DDL (with RLS) for creators, experiences, ugc_assets, wallets, and creator_payouts, ready to paste into a migration. ¹⁰¹¹⁰²¹⁰³

⁂

Show a YAML plus SQL bundle for Agones Open Match with Helm values.yaml Provide complete Postgres DDL with RLS for creator scoped tables Recommend tenancy model tradeoffs for UGC platforms at scale Example multi tenant Postgres schemas optimized for large assets Redis data structures and key patterns for session and presence

A shared‑table, RLS‑secured Postgres plus pooled Redis and Kafka is the right default tenancy model for Roblox‑style UGC at scale; YAML and SQL below are trimmed but copy‑pasteable into a real stack.¹⁰⁴¹⁰⁵

1. Tenancy model tradeoffs (UGC at scale)

Pooled DB + shared tables + RLS (recommended):
- Pros: one cluster to operate; easy cross‑experience queries; works for millions of creators; good isolation if RLS done correctly.¹⁰⁶¹⁰⁴
- Cons: heavy reliance on correct policy design; noisy‑neighbor workloads unless you shard/partition.
Schema‑per‑tenant:
- Pros: simple conceptual isolation; easier data export/delete per tenant.
- Cons: migrational pain (DDL across many schemas), connection bloat, weak fit for millions of small UGC creators.¹⁰⁷¹⁰⁸
DB‑per‑tenant:
- Pros: strongest isolation; per‑tenant SLOs.
- Cons: operationally infeasible for long‑tail UGC (too many tenants, very uneven load).

At Roblox‑like scale, use shared tables + RLS, and later add physical sharding by user id/region or creator id; keep assets in object storage, not Postgres rows.¹⁰⁹¹⁰⁴

2. Helm `values.yaml` slice + Agones/Open Match YAML

Minimal conceptual slice (you will adapt for your charts):

# values.yaml (excerpt)

global:
  region: us-east1
  imageRegistry: your-registry.io

postgres:
  enabled: true
  image: postgres:16
  storageSize: 500Gi
  resources:
    requests:
      cpu: "2"
      memory: "8Gi"
    limits:
      cpu: "4"
      memory: "16Gi"
  auth:
    username: ugc_app
    database: ugc
  rls:
    enabled: true

redis:
  enabled: true
  image: redis:7
  architecture: replication
  master:
    persistence:
      enabled: true
      size: 50Gi

kafka:
  enabled: true
  brokers: 3
  defaultReplicationFactor: 3
  topics:
    - name: join_requests
      partitions: 24
      replicationFactor: 3
    - name: instance_telemetry
      partitions: 24
      replicationFactor: 3
    - name: match_results
      partitions: 24
      replicationFactor: 3

agones:
  gameServer:
    image: your-registry.io/game-server:latest
    ports:
      - name: default
        port: 7777
        protocol: UDP
    resources:
      requests:
        cpu: "1"
        memory: "2Gi"
      limits:
        cpu: "2"
        memory: "4Gi"
    health:
      initialDelaySeconds: 10
      periodSeconds: 5

openmatch:
  enabled: true
  core:
    replicaCount: 3
  matchFunction:
    image: your-registry.io/match-function:latest

Example Agones/Open Match manifests (conceptual):

# gameserver-fleet.yaml
apiVersion: "agones.dev/v1"
kind: Fleet
metadata:
  name: experience-a-fleet
spec:
  replicas: 50
  template:
    metadata:
      labels:
        experience-id: "exp-a"
    spec:
      ports:
        - name: default
          port: 7777
          protocol: UDP
      template:
        spec:
          containers:
            - name: game-server
              image: your-registry.io/game-server:latest
              env:
                - name: EXPERIENCE_ID
                  value: "exp-a"

---
# open-match-profile.yaml
apiVersion: "openmatch.dev/v1alpha1"
kind: MatchProfile
metadata:
  name: exp-a-eu
spec:
  pools:
    - name: exp-a-eu-pool
      filters:
        - attribute: experience_id
          value: "exp-a"
        - attribute: region
          value: "eu-central"

These reflect typical Agones/Open Match deployments for Kubernetes‑native multiplayer game hosting.¹¹⁰¹¹¹

3. Postgres DDL with RLS (creator‑scoped tables)

Basic pattern from Postgres multi‑tenancy/RLS references, adapted for UGC.¹⁰⁴¹⁰⁶¹⁰⁷

-- Core creators/orgs
CREATE TABLE creators (
  id          UUID PRIMARY KEY,
  name        TEXT NOT NULL,
  tier        TEXT NOT NULL DEFAULT 'free',
  created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
);

-- App must set this per connection for creator-console traffic:
--   SET app.current_creator_id = '<uuid>';

-- Experiences
CREATE TABLE experiences (
  id           UUID PRIMARY KEY,
  creator_id   UUID NOT NULL REFERENCES creators(id),
  name         TEXT NOT NULL,
  description  TEXT,
  category     TEXT NOT NULL,
  config_json  JSONB NOT NULL DEFAULT '{}',
  status       TEXT NOT NULL,  -- draft/published/suspended
  created_at   TIMESTAMPTZ NOT NULL DEFAULT now(),
  updated_at   TIMESTAMPTZ NOT NULL DEFAULT now()
);

ALTER TABLE experiences ENABLE ROW LEVEL SECURITY;

CREATE POLICY experiences_isolation
  ON experiences
  USING (
    creator_id::text = current_setting('app.current_creator_id', true)
  );

-- UGC assets (models, scripts, audio, etc.)
CREATE TABLE ugc_assets (
  id            UUID PRIMARY KEY,
  creator_id    UUID NOT NULL REFERENCES creators(id),
  experience_id UUID REFERENCES experiences(id),
  type          TEXT NOT NULL,  -- MODEL/SCRIPT/AUDIO/IMAGE/AVATAR_ITEM
  storage_key   TEXT NOT NULL,
  version       INT  NOT NULL DEFAULT 1,
  status        TEXT NOT NULL,  -- pending/approved/rejected/removed
  metadata_json JSONB NOT NULL DEFAULT '{}',
  created_at    TIMESTAMPTZ NOT NULL DEFAULT now(),
  updated_at    TIMESTAMPTZ NOT NULL DEFAULT now()
);

ALTER TABLE ugc_assets ENABLE ROW LEVEL SECURITY;

CREATE POLICY ugc_assets_isolation
  ON ugc_assets
  USING (
    creator_id::text = current_setting('app.current_creator_id', true)
  );

-- Wallets: per user or per creator, shared-table ledger
CREATE TABLE wallets (
  id          UUID PRIMARY KEY,
  owner_type  TEXT NOT NULL,  -- USER/CREATOR/PLATFORM
  owner_id    UUID NOT NULL,
  currency    TEXT NOT NULL,  -- ROBUX etc.
  balance     BIGINT NOT NULL DEFAULT 0,
  created_at  TIMESTAMPTZ NOT NULL DEFAULT now(),
  updated_at  TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE INDEX wallets_owner_idx ON wallets (owner_type, owner_id);

-- Optional: RLS for creator-owned wallets only
ALTER TABLE wallets ENABLE ROW LEVEL SECURITY;

CREATE POLICY wallets_creator_isolation
  ON wallets
  USING (
    owner_type <> 'CREATOR'
    OR owner_id::text = current_setting('app.current_creator_id', true)
  );

-- Creator payouts
CREATE TABLE creator_payouts (
  id             UUID PRIMARY KEY,
  creator_id     UUID NOT NULL REFERENCES creators(id),
  period_start   DATE NOT NULL,
  period_end     DATE NOT NULL,
  gross_earnings BIGINT NOT NULL,
  platform_fee   BIGINT NOT NULL,
  net_payout     BIGINT NOT NULL,
  payout_currency TEXT NOT NULL,
  status         TEXT NOT NULL, -- pending/in_progress/completed/failed
  created_at     TIMESTAMPTZ NOT NULL DEFAULT now(),
  updated_at     TIMESTAMPTZ NOT NULL DEFAULT now()
);

ALTER TABLE creator_payouts ENABLE ROW LEVEL SECURITY;

CREATE POLICY creator_payouts_isolation
  ON creator_payouts
  USING (
    creator_id::text = current_setting('app.current_creator_id', true)
  );

This is directly aligned with recommended RLS‑for‑tenants techniques in recent Postgres articles.¹⁰⁵¹¹²¹¹³

4. Multi‑tenant schemas for large assets

For large assets, keep binary in object storage and metadata in Postgres.¹¹⁴¹¹⁵

Keep ugc_assets light: ids, type, storage_key, status, JSON metadata, creator/experience keys.
Use a separate table for “asset versions” if you expect many versions:

CREATE TABLE ugc_asset_versions (
  id          UUID PRIMARY KEY,
  asset_id    UUID NOT NULL REFERENCES ugc_assets(id),
  version     INT NOT NULL,
  storage_key TEXT NOT NULL,           -- points to object store prefix
  size_bytes  BIGINT NOT NULL,
  checksum    TEXT,
  created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE INDEX ugc_asset_versions_asset_idx
  ON ugc_asset_versions (asset_id, version DESC);

RLS can either be on ugc_assets only (join for checks) or duplicated on this table using asset_id join in the policy predicate, depending on how strict you want to be.

5. Redis data structures and key patterns (session & presence)

From Redis gaming docs and examples, the following patterns are standard.¹¹⁶¹¹⁷¹¹⁸

Session key:
- session:{session_id} → HASH:
  - user_id, region, experience_id, instance_id, last_seen_ts
- TTL set to session expiry.
User → sessions:
- user_sessions:{user_id} → SET of session_ids.
Global/region presence:
- online_users (SET of user_id) for global; or online_users:{region} for sharded presence.¹¹⁶
Experience presence:
- exp:{experience_id}:players → SET of user_id.
- inst:{instance_id}:players → SET of user_id.
Matchmaking indices:
- mm:instances:{experience_id} → SORTED SET of instance_id with score = utilization or composite metric.
- Optional: mm:instances:{experience_id}:{region} for regional routing.
Heartbeats / stats:
- inst:{instance_id}:stats → HASH with players, avg_rtt_ms, cpu, mem.
- Optionally a STREAM inst:{instance_id}:events for ordered event log.

These structures align with Redis guidance on gaming (leaderboards, sessions, pub/sub, streams).¹¹⁷¹¹⁸¹¹⁶

If you want to iterate further, next step could be: “Show a complete matchmaking-service design (REST + Kafka + Redis) with pseudo‑code for match loops and how it interacts with Open Match / Agones.”

⁂

What Roblox is​

ICP (ideal customer / user profile)​

Market and business model​

Key competitors​

Core features​

High‑level system domains​

Cloud‑native mapping (microservices, queues, multi‑tenancy)​

How Roblox makes money (concise)​

Target audience, ICP, and 2025 demographics​

Differentiating features vs competitors​

Major competitors and comparison (2025)​

Pointers to academic / technical references​

Kubernetes‑native component map​

Service boundaries and event topology​

Identity & sessions​

Matchmaking & instance management​

Simulation & physics (authoritative servers at the edge)​

Economy & wallets​

UGC pipeline & moderation​

Multi‑tenant data model and example schemas​

Identity, users, sessions​

Experiences and instances​

Wallets and creator payouts​

UGC assets​

Design patterns & best practices (your related bullets)​