mirror of https://github.com/OpenVidu/openvidu.git
105 lines
9.6 KiB
Markdown
105 lines
9.6 KiB
Markdown
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# Data generator
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Fills a MongoDB database with analytics data that follows the data model contract of `@openvidu-dashboard/typings`: a
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deterministic scenario S, M or L over a number of days ending now. The backend integration tests use it for their query
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plan and latency checks, and `./dashboard.sh demo` uses it for the local demo database.
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Before inserting, `src/schema.ts` creates every collection, index and TTL of the contract and gives the plain
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collections the contract validator with `validationAction: error`, so a document that drifts from the contract fails
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the run (`track_stats` takes no validator as a time-series collection). `src/contract.ts` turns each model document
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into its stored shape: the model's bookkeeping removed, absent fields omitted and every number in the BSON type its
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schema declares.
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The package is also a library for the backend integration tests (`dashboard-ce/backend/test/`): `./generator`
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(`generate(db, options)`), `./context` (`queryContext(db)`: the data range and sample identifiers), `./plan` (explain
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summaries used by the `expectIndexBacked` harness) and `./schema` (`createCollections()`).
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Requirements: the workspace installed (`./dashboard.sh install`), a reachable MongoDB (8.x; time-series
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collections are used when the server accepts them) and a user allowed to create and drop the target
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database. Run the commands from this directory.
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## Usage
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```sh
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pnpm generate --uri "$MONGO_URI" --db ov_data_s --scenario S --days 30 --drop
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pnpm generate --uri "$MONGO_URI" --db ov_data_m --scenario M --days 7 --drop
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pnpm generate --uri "$MONGO_URI" --db ov_data_l --scenario L --days 1 --drop --stats-mode track
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```
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| Option | Default | Meaning |
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| ----------------------------------- | ------------- | ---------------------------------------------------------------------------------------------------------------------- |
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| `--uri`, `--db` | required | Connection string and target database |
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| `--scenario S\|M\|L` | required | Rooms per day, sessions per day and average room size (table below) |
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| `--days <n>` | required | Days of history ending now |
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| `--stats-mode participant\|track` | `participant` | One `track_stats` document per participant and direction, or per track |
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| `--seed <n>` | `1` | Seed of the pseudo-random generator (mulberry32); same seed, same data |
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| `--retention-events-days <n>` | `7` | Only raw events inside this window are written (TTL index on `ts`) |
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| `--retention-stats-days <n>` | `7` | Only stats inside this window are written (`expireAfterSeconds`) |
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| `--retention-tracks-days <n>` | `7` | `expiresAt` of tracks and subscriptions; expired ones are not written |
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| `--retention-entities-days <n>` | `90` | `expiresAt = closedAt + retention` on closed entities |
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| `--retention-rollups-5min-days <n>` | `7` | Only 5-minute rollups inside this window are written (TTL index on `bucket`) |
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| `--drop` | off | Drop the database first |
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| `--plain-stats` | off | Force the plain-collection fallback for `track_stats` |
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| `--five-minute-rollups` | off | Write `analytics_5min` inside the 5-minute rollup window |
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| `--node-rollups` | off | Write the `node:<nodeId>` scope beside `global` in every rollup |
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| `--room-sums` | off | Write `rooms.participantsSum` and `rooms.durationMsSum` at room end |
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| `--active-window <minutes>` | off | Rooms created in the last minutes stay active (two of three); without it, the most recent 10 % of the rooms are active |
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| `--min-active-rooms <n>` | 0 | With `--active-window`: rooms added to the window when the hour of day leaves fewer than `n` active rooms |
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| Scenario | Rooms per day | Sessions per day | Average room size |
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| -------- | ------------- | ---------------- | ----------------- |
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| S | 200 | 1 000 | 4 |
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| M | 2 000 | 20 000 | 6 |
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| L | 5 000 | 50 000 | 8 |
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What it writes (`analytics_5min` stays empty without `--five-minute-rollups`):
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- `rooms`: created over the days with a diurnal curve (office-hours peak); the most recent 10 % are
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`active`, the rest `closed` with `endReason` and `closedBy`. Id arrays and counters (`participantSids`,
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`activeParticipantSids`, `identities`, `participants.*`, `trackSids`, `egressIds`, `ingressSessionIds`,
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`features`) are derived from the generated entities; participant counters count STANDARD sessions and `byKind`
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every kind, as the writer does (also in the rollups).
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- `participants`: unique identities per room follow a Poisson distribution around the scenario average;
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the difference between sessions/day and rooms/day × average size is produced as reconnects
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(`DUPLICATE_IDENTITY` leave plus a new session of the same identity). Sessions last ~20 min, 3 % are
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aborted, 95 % are `STANDARD`; rooms with egress/ingress/agent/SIP get the matching participant kinds.
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Client (sdk/os/browser/device/network), connection type, connect time, disconnect reason and resumes
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follow fixed distributions.
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- `tracks` (CAMERA video + MICROPHONE audio per publishing participant, with simulcast layers and final
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publish stats) and `track_subscriptions` (every participant subscribes to the tracks of the other
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identities present at the same time; 1 % failed).
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- `egresses` (10 % of rooms; mostly `room_composite`; `complete`/`failed`/`aborted`, `active` in active
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rooms), `ingresses` and `ingress_sessions` (2 % of rooms).
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- `events`: lifecycle events of rooms, participants, tracks, subscriptions, egresses and ingresses with
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small payloads, only inside the events retention window.
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- `track_stats`: one document per 30 s per participant and direction (`participant` mode) or per track and
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direction (`track` mode), only inside the stats retention window, with plausible score/bytes/rtt/jitter.
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Time-series collection (`timeField: ts`, `metaField: meta`, `granularity: seconds`) with
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`expireAfterSeconds`; plain collection with a TTL index on `ts` when the server rejects time-series.
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- `analytics_hourly` and `analytics_daily` (global scope, plus one scope per node with `--node-rollups`)
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aggregated from the generated entities; bytes and quality are spread over the hours each session was connected.
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`--room-sums` adds the room size and duration sums; `--five-minute-rollups` adds `analytics_5min` (lifecycle
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counters, the `participants.connected` gauge, bytes and quality per five minutes) inside the 5-minute rollup
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window. `analytics_daily` gets no TTL index: daily rollups are kept forever.
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`generate()` takes the same switches as `rollups: { fiveMinute, nodes, roomSums }`.
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- `nodes` (3 nodes) and `schema_meta` (`schema`, `retention`, `capabilities`, `config`).
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Documents are inserted in batches of 5 000 with `insertMany(ordered: false)`. The output shows progress
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and a final summary with documents per collection, elapsed time and inserts per second. The generator
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keeps only one room in memory at a time, so scenario L fits in a few hundred MB of heap.
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## Files
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| File | Role |
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| ------------------ | -------------------------------------------------------------------------------------------- |
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| `src/schema.ts` | Collections, indexes and validators of the contract, `createCollections()` with the fallback |
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| `src/contract.ts` | Stored shape of a model document: bookkeeping removed, nulls omitted, BSON numeric types |
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| `src/model.ts` | Scenarios, document types and the per-room entity generator |
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| `src/derive.ts` | Raw events, stats samples and rollups derived from a generated room |
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| `src/generator.ts` | `generate(db, options)` and the batched writer |
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| `src/generate.ts` | Generator CLI |
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| `src/context.ts` | `queryContext(db)`: data range and sample identifiers for query plan tests |
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| `src/plan.ts` | Explain summaries: plan stages, indexes, documents examined |
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| `src/random.ts` | mulberry32 generator and sampling helpers |
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| `src/args.ts` | CLI argument parsing |
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