brainy/docs/guides/aggregation.md

Aggregation Guide

Real-time analytics on your entity data with incremental running totals

Overview

Brainy's aggregation engine computes running totals at write time, so reading aggregate results is always O(1) regardless of dataset size. Define an aggregate once, and every add(), update(), and delete() automatically updates the running metrics.

No batch jobs. No scheduled recalculations. Aggregates stay current with every write.

Defining over existing data: if you define an aggregate on a store that already holds matching entities, Brainy backfills it from those entities on the first query (a one-time scan, then purely incremental). So defineAggregate() behaves the same whether you define it before or after the data exists — including when a persisted brain reopens already populated.

Quick Start

import { Brainy, NounType } from '@soulcraft/brainy'

const brain = new Brainy()
await brain.init()

// 1. Define an aggregate
brain.defineAggregate({
  name: 'sales_by_category',
  source: { type: NounType.Event },
  groupBy: ['category'],
  metrics: {
    revenue: { op: 'sum', field: 'amount' },
    count:   { op: 'count' },
    average: { op: 'avg', field: 'amount' }
  }
})

// 2. Add entities — aggregates update automatically
await brain.add({
  data: 'Coffee purchase',
  type: NounType.Event,
  metadata: { category: 'food', amount: 5.50 }
})

await brain.add({
  data: 'Laptop purchase',
  type: NounType.Event,
  metadata: { category: 'electronics', amount: 1200 }
})

await brain.add({
  data: 'Lunch purchase',
  type: NounType.Event,
  metadata: { category: 'food', amount: 12.00 }
})

// 3. Query results
const results = await brain.find({ aggregate: 'sales_by_category' })

// Results:
// [
//   { groupKey: { category: 'food' }, metrics: { revenue: 17.50, count: 2, average: 8.75 } },
//   { groupKey: { category: 'electronics' }, metrics: { revenue: 1200, count: 1, average: 1200 } }
// ]

Aggregation Operations

Brainy supports 7 aggregation operations:

sum — Running Total

Adds up all values of a numeric field.

metrics: {
  total_revenue: { op: 'sum', field: 'amount' }
}

count — Entity Count

Counts the number of matching entities. No field required.

metrics: {
  order_count: { op: 'count' }
}

avg — Running Average

Computes sum / count incrementally.

metrics: {
  average_price: { op: 'avg', field: 'price' }
}

min — Minimum Value

Tracks the minimum value across all entities in each group.

metrics: {
  lowest_price: { op: 'min', field: 'price' }
}

max — Maximum Value

Tracks the maximum value across all entities in each group.

metrics: {
  highest_price: { op: 'max', field: 'price' }
}

stddev — Sample Standard Deviation

Computes the sample standard deviation using Welford's numerically stable online algorithm. Updates incrementally without storing individual values.

metrics: {
  price_spread: { op: 'stddev', field: 'price' }
}

variance — Sample Variance

Computes the sample variance (square of standard deviation) using Welford's online algorithm.

metrics: {
  price_variance: { op: 'variance', field: 'price' }
}

GROUP BY Dimensions

Every aggregate requires at least one groupBy dimension. Results are grouped by the unique combinations of dimension values.

Plain Fields

Group by a metadata field value:

groupBy: ['category']
// Produces groups: { category: 'food' }, { category: 'electronics' }, ...

Multiple Fields

Group by multiple fields for composite keys:

groupBy: ['category', 'region']
// Produces groups: { category: 'food', region: 'US' }, { category: 'food', region: 'EU' }, ...

Time Windows

Group by a timestamp field bucketed into time periods:

groupBy: [{ field: 'date', window: 'month' }]
// Produces groups: { date: '2024-01' }, { date: '2024-02' }, ...

Available time window granularities:

Window	Format	Example
hour	YYYY-MM-DDThh	2024-01-15T14
day	YYYY-MM-DD	2024-01-15
week	YYYY-Wnn	2024-W03
month	YYYY-MM	2024-01
quarter	YYYY-Qn	2024-Q1
year	YYYY	2024
{ seconds: N }	ISO 8601	Custom interval

Combined Dimensions

Mix plain fields and time windows:

brain.defineAggregate({
  name: 'monthly_sales',
  source: { type: NounType.Event },
  groupBy: ['region', { field: 'date', window: 'month' }],
  metrics: {
    revenue: { op: 'sum', field: 'amount' },
    count: { op: 'count' }
  }
})

// Produces groups like:
// { region: 'US', date: '2024-01' }
// { region: 'US', date: '2024-02' }
// { region: 'EU', date: '2024-01' }

Querying Aggregates

Aggregate results are queried through the standard find() method.

Basic Query

const results = await brain.find({ aggregate: 'sales_by_category' })

Filter by Group Key

Use where to filter on group key values:

const foodOnly = await brain.find({
  aggregate: 'sales_by_category',
  where: { category: 'food' }
})

Filter by Metric Value (HAVING)

Use having to filter groups by their computed metric values — the analytics equivalent of SQL HAVING. (where filters group keys; having filters metrics.)

const bigCategories = await brain.find({
  aggregate: 'sales_by_category',
  having: { revenue: { greaterThan: 1000 } }
})

having accepts the same operators as where, applied to each group's metric results plus count. It is evaluated per group — O(groups), independent of entity count — before sorting and pagination, so it stays cheap even over billions of entities.

Sort and Paginate

Sort by any metric or group key field:

const topCategories = await brain.find({
  aggregate: {
    name: 'sales_by_category',
    orderBy: 'revenue',
    order: 'desc',
    limit: 10
  }
})

Combined Parameters

where, orderBy, limit, and offset from the outer find() call merge automatically with the aggregate query:

const recentTopSpenders = await brain.find({
  aggregate: 'monthly_sales',
  where: { region: 'US' },
  orderBy: 'revenue',
  order: 'desc',
  limit: 12,
  offset: 0
})

Result Format

find({ aggregate }) returns Result<T> rows (for uniformity with the rest of find()), with the aggregate fields surfaced both at the top level and, for backward compatibility, flattened into metadata:

{
  id: string,
  score: 1.0,
  type: NounType.Measurement,
  groupKey: { category: 'food' },             // top-level — the group key values
  metrics:  { revenue: 17.50, count: 2, average: 8.75 }, // top-level — computed metrics
  count: 2,                                   // top-level — entities in the group
  metadata: {                                 // legacy mirror of the same data
    __aggregate: 'sales_by_category',
    category: 'food',
    revenue: 17.50, count: 2, average: 8.75
  },
  entity: Entity
}

queryAggregate() — the report-friendly view

For dashboards and reports, prefer brain.queryAggregate(name, params). It returns the clean AggregateResult[] shape directly — no search-result wrapper:

const rows = await brain.queryAggregate('sales_by_category', {
  orderBy: 'revenue',
  order: 'desc',
  limit: 10
})
// [
//   { groupKey: { category: 'electronics' }, metrics: { revenue: 1200, count: 1, average: 1200 }, count: 1 },
//   { groupKey: { category: 'food' }, metrics: { revenue: 17.50, count: 2, average: 8.75 }, count: 2 }
// ]

It accepts the same where / having / orderBy / order / limit / offset params as the find({ aggregate }) form.

Source Filtering

Control which entities feed into an aggregate with the source property.

Filter by Entity Type

brain.defineAggregate({
  name: 'event_stats',
  source: { type: NounType.Event },
  groupBy: ['category'],
  metrics: { count: { op: 'count' } }
})

Filter by Multiple Types

source: { type: [NounType.Event, NounType.Document] }

Filter by Metadata

Use the same where syntax as find():

source: {
  type: NounType.Event,
  where: { domain: 'financial', subtype: 'transaction' }
}

Filter by Service

For multi-tenant deployments:

source: { service: 'tenant-123' }

Entities that don't match the source filter are silently skipped during incremental updates.

Incremental Updates

The aggregation engine hooks into every write operation:

On add()

When a new entity matches an aggregate's source filter:

1. The group key is computed from the entity's metadata
2. Each metric in the matching group is incremented
3. New groups are created automatically

On update()

When an existing entity is updated:

1. The old entity's contribution is reversed from its group
2. The new entity's contribution is applied to its (potentially different) group
3. Handles group key changes — an entity moving from category "food" to "drink" updates both groups

On delete()

When an entity is deleted:

1. The entity's contribution is reversed from its group
2. If a group becomes empty (all metric counts reach zero), it's removed

Aggregate Entity Exclusion

Materialized NounType.Measurement entities are automatically excluded from all source matching, preventing infinite feedback loops. Entities with service: 'brainy:aggregation' or metadata.__aggregate are always skipped.

Materialization

Materialization writes aggregate results as NounType.Measurement entities, making them automatically available through OData, Google Sheets, SSE, and webhook integrations.

brain.defineAggregate({
  name: 'daily_metrics',
  source: { type: NounType.Event },
  groupBy: [{ field: 'date', window: 'day' }],
  metrics: {
    total: { op: 'sum', field: 'amount' },
    count: { op: 'count' }
  },
  materialize: true
})

Debounce Configuration

During high-throughput ingestion, materialization is debounced to avoid excessive writes:

materialize: {
  debounceMs: 2000,      // Wait 2 seconds after last update before writing
  trackSources: true     // Track which entities contributed
}

The default debounce interval is 1000ms.

Multiple Aggregates

Define multiple aggregates that process the same entities:

// Revenue by category
brain.defineAggregate({
  name: 'category_revenue',
  source: { type: NounType.Event },
  groupBy: ['category'],
  metrics: { total: { op: 'sum', field: 'amount' } }
})

// Monthly trends
brain.defineAggregate({
  name: 'monthly_trends',
  source: { type: NounType.Event },
  groupBy: [{ field: 'date', window: 'month' }],
  metrics: {
    revenue: { op: 'sum', field: 'amount' },
    count: { op: 'count' },
    avg_order: { op: 'avg', field: 'amount' }
  }
})

// Regional breakdown with statistical analysis
brain.defineAggregate({
  name: 'regional_analysis',
  source: { type: NounType.Event },
  groupBy: ['region'],
  metrics: {
    revenue: { op: 'sum', field: 'amount' },
    spread: { op: 'stddev', field: 'amount' },
    variance: { op: 'variance', field: 'amount' }
  }
})

Each add() call updates all matching aggregates automatically.

Removing Aggregates

Remove an aggregate and clean up its state:

brain.removeAggregate('category_revenue')

Persistence

Aggregate definitions and running state are automatically persisted:

• On flush()/close(): All dirty aggregate state is written to storage
• On init(): Definitions and state are restored from storage
• Change detection: Definition changes are detected via FNV-1a hashing — only changed aggregates reset their state on restart

Native Acceleration

When Cortex is installed as a plugin, the aggregation engine automatically uses Rust-accelerated computation:

• Incremental updates run in Rust with BTreeMap-backed precise MIN/MAX
• Welford's online stddev/variance computed natively
• Rebuild uses Rayon parallel iterators across CPU cores (above 1,000 entities)
• Time window bucketing uses integer arithmetic without Date object allocation

const brain = new Brainy({
  plugins: ['@soulcraft/cortex']
})
await brain.init()

// Aggregation automatically uses native engine
brain.defineAggregate({ ... })

Verify native acceleration is active:

const diag = brain.diagnostics()
console.log(diag.providers.aggregation)
// { source: 'plugin' }

Common Patterns

Financial Analytics

brain.defineAggregate({
  name: 'monthly_spending',
  source: {
    type: NounType.Event,
    where: { domain: 'financial', subtype: 'transaction' }
  },
  groupBy: [
    'category',
    { field: 'date', window: 'month' }
  ],
  metrics: {
    total:   { op: 'sum', field: 'amount' },
    count:   { op: 'count' },
    average: { op: 'avg', field: 'amount' },
    highest: { op: 'max', field: 'amount' },
    lowest:  { op: 'min', field: 'amount' }
  },
  materialize: true
})

Time-Series Monitoring

brain.defineAggregate({
  name: 'hourly_metrics',
  source: { type: NounType.Event, where: { domain: 'monitoring' } },
  groupBy: [
    'service',
    { field: 'timestamp', window: 'hour' }
  ],
  metrics: {
    request_count:  { op: 'count' },
    avg_latency:    { op: 'avg', field: 'latency_ms' },
    max_latency:    { op: 'max', field: 'latency_ms' },
    error_count:    { op: 'sum', field: 'is_error' },
    latency_spread: { op: 'stddev', field: 'latency_ms' }
  }
})

Content Analytics

brain.defineAggregate({
  name: 'content_stats',
  source: { type: NounType.Document },
  groupBy: ['author', { field: 'publishedAt', window: 'month' }],
  metrics: {
    articles:   { op: 'count' },
    total_words: { op: 'sum', field: 'wordCount' },
    avg_words:   { op: 'avg', field: 'wordCount' }
  }
})

Performance

Aggregation complexity per write is O(A x G x M) where A = matching aggregates, G = groupBy dimensions, M = metrics. For typical configurations (2-5 aggregates, 1-3 dimensions, 3-5 metrics), this is effectively O(1).

With Cortex native acceleration:

Operation	Throughput	Latency
Incremental update (1K entities)	809 ops/s	1.2 ms
Rebuild (10K entities)	475 ops/s	2.1 ms
Rebuild (100K entities, Rayon)	66 ops/s	15.2 ms
Query (1K groups, sort + paginate)	986 ops/s	1.0 ms