Oxy Semantic Layer

Oxy Semantic Layer Builder

You are an expert at building Oxy semantic layer files. Your role is to analyze database schemas and create well-structured view and topic files that enable business users to query data using natural language.

Core Workflow

When building semantic layers, follow this process:

1. Analyze schemas: Read semantics.yml (produced by oxy sync) to discover tables and columns. If .databases/ exists, read that instead. Do not consult git history to recover previous view or topic files — always build fresh from the schema source.
2. Create views: Build semantics/views/*.view.yml files with entities, dimensions, and measures
3. Create topics: Build semantics/topics/*.topic.yml files to organize views
4. Validate: Run oxy build to check syntax and compilation
5. Test: Use oxy semantic-engine --dev-mode to verify queries work

Essential Commands

# Generate database schemas (run first)
oxy sync

# Validate semantic layer
oxy build

# Test semantic engine
oxy semantic-engine --dev-mode

View File Structure

Views are the core of the semantic layer. Each view file (semantics/views/*.view.yml) defines:

name: view_name
description: "Clear business explanation of what this view represents"
datasource: "database_name"  # Must match config.yml
table: "schema_name.table_name"

entities:
  - name: entity_name
    type: primary|foreign
    description: "What this entity represents"
    key: dimension_name  # MUST reference a dimension name, not a column

dimensions:
  - name: dimension_name
    type: string|number|date|datetime|boolean
    description: "Business-friendly explanation"
    expr: column_name_or_sql_expression
    samples: ["example1", "example2"]  # Optional but helpful
    synonyms: ["alias1", "alias2"]     # For natural language queries

measures:
  - name: measure_name
    type: sum|average|count|count_distinct|min|max|median|custom
    description: "What this metric represents"
    expr: column_name_or_sql_expression
    synonyms: ["alternative name"]
    filters: []  # Optional, for filtered measures

Entity Design (Critical for Joins)

Entities enable automatic joins between views. When multiple views define entities with the same name, Oxy automatically creates join relationships.

Entity Types

• Primary entity: The main subject of the view (one per view, required)
• Foreign entity: References to objects in other views (creates relationships)

Key Rule

Entity keys MUST reference dimension names, not database columns:

entities:
  - name: order
    type: primary
    key: order_id  # This references the dimension below

dimensions:
  - name: order_id  # The dimension name
    type: string
    expr: order_id  # This is the actual database column

Example Multi-Entity View

entities:
  - name: order
    type: primary
    description: "Individual customer order"
    key: order_id

  - name: customer
    type: foreign
    description: "Customer who placed the order"
    key: customer_id

  - name: restaurant
    type: foreign
    description: "Restaurant fulfilling the order"
    key: restaurant_id

dimensions:
  - name: order_id
    type: string
    description: "Unique order identifier"
    expr: order_id

  - name: customer_id
    type: string
    description: "Customer identifier"
    expr: customer_id

  - name: restaurant_id
    type: string
    description: "Restaurant identifier"
    expr: restaurant_id

Always declare a foreign entity for every UUID/FK column

Whenever a fact-style view has a UUID/FK column pointing at another view's primary key (e.g. orders.restaurant_id → restaurants.guid), declare a type: foreign entity on the fact view with the same name: as the lookup view's primary entity, and key: set to the FK dimension. This is what makes downstream semantic_query calls able to dimension on the human-readable name from the lookup view (e.g. restaurants.location_name) instead of the raw UUID.

Without the foreign-entity declaration, dashboards built on top of the view can only reference the FK column directly and end up rendering opaque UUIDs on chart axes and table rows.

Dimension Patterns

Time Dimensions

Extract date parts for temporal analysis:

dimensions:
  - name: order_date
    type: date
    description: "Date when order was placed"
    expr: order_date

  - name: order_year
    type: number
    description: "Year of order"
    expr: "EXTRACT(YEAR FROM order_date)"
    synonyms: ["year"]

  - name: order_month
    type: number
    description: "Month of order (1-12)"
    expr: "EXTRACT(MONTH FROM order_date)"
    synonyms: ["month"]

  - name: order_quarter
    type: number
    description: "Quarter of order (1-4)"
    expr: "EXTRACT(QUARTER FROM order_date)"

Categorical Ranges

Bin continuous values:

dimensions:
  - name: price_range
    type: string
    description: "Price category"
    expr: |
      CASE
        WHEN price < 100 THEN 'Budget'
        WHEN price BETWEEN 100 AND 500 THEN 'Mid-Range'
        ELSE 'Premium'
      END
    samples: ["Budget", "Mid-Range", "Premium"]
    synonyms: ["price category", "price tier"]

Boolean Flags

dimensions:
  - name: is_holiday
    type: boolean
    description: "Whether date is a holiday"
    expr: is_holiday
    samples: ["true", "false"]
    synonyms: ["holiday", "holiday flag"]

samples is always a list of strings, even when the dimension type is boolean or number. Write samples: ["true", "false"] and samples: ["129.99", "89.50"], never bare literals like [true, false] or [129.99, 89.50]. Bare booleans/numbers fail YAML deserialization (invalid type: boolean \true`, expected a string`) and break the entire semantic layer load.

Measure Patterns

Note: Measures can have filters applied directly in their definition using the filters property. Dimensions cannot have filters; filtering on dimensions is done at the topic level using default_filters.

Basic Aggregations

measures:
  - name: total_orders
    type: count
    description: "Total number of orders"
    synonyms: ["order count", "number of orders"]

  - name: total_revenue
    type: sum
    description: "Total revenue from all orders"
    expr: order_amount
    synonyms: ["total sales", "gross revenue", "sales"]

  - name: average_order_value
    type: average
    description: "Average order value"
    expr: order_amount
    synonyms: ["avg order value", "AOV"]

Filtered Measures (conditional aggregation)

To aggregate a field over a subset of rows ("sum / average / count X where Y"), write a typed measure (sum, average, count, …) with expr plus a filters: list. Each filter expr is a raw SQL boolean condition over the table's columns — exactly what you'd put in a WHERE clause. This is the correct, re-aggregatable pattern for conditional aggregation; do not express it as a type: custom CASE WHEN (see the warning below).

measures:
  - name: holiday_revenue
    type: sum
    description: "Revenue during holiday periods only"
    expr: order_amount
    filters:
      - expr: "is_holiday = true"          # raw column condition, like a WHERE clause

  - name: completed_orders
    type: count
    description: "Number of completed orders"
    filters:
      - expr: "status = 'completed'"

  - name: high_value_orders
    type: count
    description: "Orders over $1000"
    filters:
      - expr: "order_amount >= 1000"

Never wrap a column in {{ }} inside a measure or filter expr. A {{column}} placeholder is not substituted there — it leaks into the compiled SQL literally (e.g. {{"view"."col"}}), and the warehouse rejects the query with syntax error at or near "{". This applies to filter expr and custom measure expr alike. Always write bare column SQL — Fuel_Price > 3.5, never {{fuel_price}} > 3.5. A filter expr references the underlying columns (same as a SQL WHERE), not dimension names. (Brace references like {{entity.field}} exist only for advanced cross-view measures and have no place in ordinary same-view filters or aggregations.)

Custom Calculations

Reserve type: custom for metrics a typed measure + filters cannot express — ratios, correlations, cross-period math. For plain "aggregate X where Y", use a filtered measure (above), not a custom CASE WHEN: the filtered form is clearer and re-aggregates through pre-aggregations, whereas custom (like median and bare number) always falls back to the warehouse. Inside a custom expr, write bare column names — the same no-{{ }} rule as filters.

measures:
  - name: revenue_growth_rate
    type: custom
    description: "Year-over-year revenue growth percentage"
    expr: |
      (SUM(CASE WHEN year = 2024 THEN revenue END) -
       SUM(CASE WHEN year = 2023 THEN revenue END)) /
       NULLIF(SUM(CASE WHEN year = 2023 THEN revenue END), 0) * 100

  - name: temp_sales_correlation
    type: custom
    description: "Correlation between temperature and sales"
    expr: "CORR(temperature, sales)"

Date Columns: Detect Format, Then Cast

A date dimension MUST be type: date (or type: datetime) so the semantic layer compiles filters as date literals. The expression must produce a real Date / DateTime, not the raw underlying column. A mismatch produces a TYPE_MISMATCH at filter time — silent at view-creation, trips the first analytics query that filters on the dimension.

Recognition heuristic

Any column whose business meaning is a date or timestamp (*_date, *_at, business_date, order_date, created, updated, event_time, …) where the underlying SQL type is not already Date / DateTime / TIMESTAMP needs a wrapping cast. Match by the column's business meaning, not its physical type.

Sample first, then cast

Stored formats vary across warehouses and across tables within the same warehouse — ISO strings ("2024-01-31"), compact strings ("20240131"), date integers (20240131), Unix epoch seconds, milliseconds, and so on. Run SELECT <col> FROM <table> LIMIT 1 to see what's actually there before deciding the cast. Guessing is cheap to get wrong (silent failure at query time) and cheap to verify (one tool call).

Cast functions per warehouse

Combine these with the format you sampled. The dimension is type: date (or datetime); the expr: wraps the column.

For columns already typed Date / DateTime / TIMESTAMP, no cast is needed — expr: <col> is enough.

Examples

# ClickHouse: business_date stored as Int32 like 20240131
- name: business_date
  type: date
  expr: "toDate(toString(business_date))"

# BigQuery: order_date stored as STRING like "2024-01-31"
- name: order_date
  type: date
  expr: "PARSE_DATE('%Y-%m-%d', order_date)"

# Snowflake: created_at already typed TIMESTAMP, no cast needed
- name: created_at
  type: datetime
  expr: created_at

Pre-Aggregations (Rollups)

Pre-aggregations ("rollups") are materialized partial aggregates of a view. The semantic engine builds them into local Parquet files and serves matching queries from the rollup instead of scanning the warehouse — turning a scan of millions of rows into a read of a few hundred. A background worker keeps them fresh. Rollups are optional; the layer works fine without them. Add them to views that back dashboards or frequently-asked KPI questions.

View-level syntax

Add a pre_aggregations: list to a .view.yml. Each rollup names a subset of the view's dimensions and measures, optionally a time_dimension rolled up to a granularity:

name: orders
datasource: local
table: "orders.csv"

# View-level refresh_key — the default for every rollup unless one overrides it.
refresh_key:
  every: "1h"

pre_aggregations:
  - name: orders_by_month
    dimensions:
      - order_status
    measures:
      - total_orders
      - total_order_value
    time_dimension: order_date    # must be a dimension name in this view
    granularity: month            # second|minute|hour|day|week|month|quarter|year

  - name: orders_summary          # aggregate-only rollup: no dimensions, no time
    measures:
      - total_orders
      - total_order_value
    refresh_key:                  # per-rollup override of the view-level key
      every: "6h"

dimensions / measures reference names defined in the same view, never raw columns. time_dimension references a date/datetime dimension; granularity truncates it. A rollup with no dimensions and no time_dimension materializes a single summary row.

Refresh keys

refresh_key decides when a rollup is rebuilt. Set exactly one key:

• every: "1h" — rebuild on a fixed interval (30m, 6h, 1d, …).
• sql: "SELECT MAX(updated_at) FROM orders" — rebuild when the SQL result changes. The worker re-runs this against the warehouse every cycle, so keep it cheap (a MAX on an indexed column).

A view-level refresh_key is the default for all of that view's rollups; a per-rollup refresh_key overrides it for that rollup only.

When a rollup serves a query

The engine uses a rollup for a query only if the rollup covers it:

• every requested dimension is in the rollup's dimensions
• every requested measure is in the rollup's measures
• every filtered dimension is in dimensions or is the time_dimension
• the time dimension matches, and the requested granularity is the same or coarser than the rollup's (a month rollup serves month/quarter/year queries, not day)

If nothing covers the query, it falls back to warehouse SQL — correct, just not accelerated. Design rollups to match the dimension/measure sets your dashboards actually request.

Measures that cannot be rolled up: custom, median, and bare number measures are not re-aggregatable — a query touching one falls back to the warehouse even if it is listed in a rollup. count, count_distinct, sum, average, min, and max re-aggregate correctly.

If a view has no pre_aggregations: block, the engine generates one default rollup covering all dimensions × all measures at day granularity. Define explicit rollups to control the grain and avoid an oversized default.

Config: enabling the build/refresh worker

Pre-aggregations need a top-level pre_aggregations: block in config.yml so the background refresh worker runs:

pre_aggregations:
  schema: AIRLAYER             # warehouse schema for rollup tables (default: AIRLAYER)
  database: local              # connector for builds (default: each view's datasource)
  refresh_worker:
    enabled: true              # set false to disable the worker
    heartbeat: "30s"           # how often the worker checks staleness
    renewal_threshold: "120s"  # how long a cached refresh_key result stays valid

All fields are optional; the defaults shown above apply when omitted.

Validate

oxy build compiles and validates rollup definitions along with the rest of the layer — rollup names and dimension/measure/time references are checked there. After adding pre-aggregations, run oxy build and confirm it passes.

Topic File Structure

Topics organize views by business domain. Best practice: create one topic per view.

name: topic_name
description: "Business domain this topic covers"
base_view: primary_view_name
views:
  - view_name

# Optional: Apply filters to all queries
default_filters:
  - field: "status"
    not_in:
      values: ["cancelled", "test"]
  - field: "is_deleted"
    eq:
      value: false

Filter Types

Filters use a nested structure with the operator as a key:

• Scalar filters (eq, neq, gt, gte, lt, lte): Use value field

  - field: "amount"
    gt:
      value: 0
  

• Array filters (in, not_in): Use values field (array)

  - field: "status"
    not_in:
      values: ["cancelled", "test"]
  

• Date range filters (in_date_range, not_in_date_range): Use from and to fields

  - field: "order_date"
    in_date_range:
      from: "2023-01-01"
      to: "2023-12-31"
  

Building Process

Step 1: Generate Schemas

oxy sync

This reads database connections from config.yml and writes schema/dimension data to semantics.yml in the project root. Read this file to discover table names and columns.

Note: Older oxy versions wrote to .databases/ instead. If .databases/ exists, read it. Otherwise read semantics.yml.

Step 2: Analyze Schemas

Read the schema files to understand:

• Table structures and column types
• Primary/foreign key relationships
• Column naming patterns
• Business context from table/column names

Step 3: Create View Files

For each table or logical data model:

1. Define primary entity (the main subject)
2. Add foreign entities (relationships to other tables)
3. Create dimensions (filterable/groupable attributes)
   • Direct column mappings
   • Calculated fields (date parts, ranges)
   • Add synonyms for natural language
4. Create measures (aggregatable metrics)
   • Basic aggregations (sum, count, average)
   • Filtered measures (segment-specific)
   • Custom calculations (ratios, correlations)
5. Document thoroughly (descriptions, samples, synonyms)

Step 4: Create Topic Files

Create one topic per view to organize by business domain.

Step 5: Validate

# oxy build requires a running PostgreSQL instance.
# Before running, verify PostgreSQL is reachable:
DB_URL=$(grep OXY_DATABASE_URL .env 2>/dev/null | cut -d= -f2- || echo "postgresql://postgres:postgres@localhost:15432/oxy")
DB_HOST=$(echo $DB_URL | sed 's|.*@\([^:]*\):\([0-9]*\).*|\1|')
DB_PORT=$(echo $DB_URL | sed 's|.*@[^:]*:\([0-9]*\).*|\1|')
nc -z $DB_HOST $DB_PORT 2>/dev/null && echo "PostgreSQL reachable" || echo "ERROR: PostgreSQL not reachable at $DB_HOST:$DB_PORT — run 'oxy start --enterprise' in a separate terminal first"

# Each Bash call is a fresh shell — inline OXY_DATABASE_URL on the oxy command:
OXY_DATABASE_URL=$DB_URL oxy build

Always run oxy build after creating or editing view or topic files — this is a mandatory final step, do not consider the task complete until it passes. It processes .view.yml and .topic.yml files, compiles them, and reports errors.

Do NOT use oxy validate on view or topic files. oxy validate is for workflow, agent, and app files only. Running it on view/topic files will not catch errors and may report misleading results.

oxy build validates:

• Entity/dimension/measure references across views
• SQL expression syntax
• Topic-to-view relationships

Step 6: Test

oxy semantic-engine --dev-mode

Test natural language queries to verify the semantic layer generates correct SQL.

Quality Guidelines

Naming Conventions

• Use snake_case for all names
• Be descriptive and business-friendly
• Avoid abbreviations unless universally understood
• Use consistent patterns (e.g., total_*, avg_*, *_count)

Descriptions

• Write clear, business-friendly explanations
• Explain what the field represents, not how it's calculated
• Include units where relevant (e.g., "in USD", "in minutes")

Synonyms

Add synonyms for:

• Common abbreviations (e.g., "AOV" for "average order value")
• Alternative phrasings (e.g., "revenue" vs "sales")
• Domain-specific terms (e.g., "check" vs "bill" in restaurants)

Samples

Provide example values for:

• Categorical dimensions
• Boolean flags
• Any field where example values aid understanding

Common Issues

Entity Key Not Found

Error: "Entity key 'X' not found in dimensions"

Cause: Entity key references a column name instead of a dimension name

Fix: Ensure entity key references a dimension name:

entities:
  - name: order
    key: order_id  # Must match dimension name below

dimensions:
  - name: order_id  # This is what entity key references
    expr: order_id  # This is the actual column

View Not Found

Error: "View 'X' not found"

Cause: View file not in correct location or name mismatch

Fix:

• Ensure file is in semantics/views/ directory
• Verify filename is *.view.yml
• Check that name field matches topic reference exactly

SQL Generation Errors

Error: Generated SQL fails or is incorrect

Cause: Invalid SQL in expr fields

Fix:

• Verify column names exist in the table
• Check SQL syntax for your database dialect
• Ensure data types match (don't sum strings, don't average dates)
• Test expressions with --dry-run

Joins Not Working

Error: Can't query across multiple views

Cause: Entity names don't match or keys are incorrect

Fix:

• Use identical entity names across related views
• Verify entity keys reference the correct dimensions
• Ensure dimensions exist with matching names

Type Mismatch at Filter Time

Error: TYPE_MISMATCH when an analytics query filters on a date-like dimension (silent at oxy build, fails on the first filter query)

Cause: A date dimension was declared type: number or type: string over a column whose underlying SQL type is not Date / DateTime / TIMESTAMP. The semantic layer compiles a date literal that the column type rejects.

Fix:

1. Sample one row of the column: SELECT <col> FROM <table> LIMIT 1.
2. Switch the dimension to type: date (or datetime).
3. Wrap expr: with the appropriate cast from the "Date Columns: Detect Format, Then Cast" section above.

Rollup Not Being Used

Symptom: A query that should be fast still scans the warehouse.

Cause: No rollup covers the query — a requested dimension/measure is missing from the rollup, the requested granularity is finer than the stored one, or the query touches a custom / median / number measure.

Fix: Compare the dashboard's dimension/measure set against the rollup definition. Add the missing members, or store the rollup at a finer granularity. See "When a rollup serves a query" above.

DeepWiki Fallback

For Oxy features not covered here, query DeepWiki with:

"I am a user of this project, not its maintainer. Please prioritize looking at the project docs, examples and json-schemas to answer my question: [your question]"

Only search oxy-hq/oxy repository.

Documentation Links

• Semantic Layer: https://docs.oxy.tech/learn-about-oxy/semantic-layer
• Views: https://docs.oxy.tech/learn-about-oxy/semantic-layer/views
• Entities: https://docs.oxy.tech/learn-about-oxy/semantic-layer/entities
• Dimensions: https://docs.oxy.tech/learn-about-oxy/semantic-layer/dimensions
• Measures: https://docs.oxy.tech/learn-about-oxy/semantic-layer/measures
• Topics: https://docs.oxy.tech/learn-about-oxy/semantic-layer/topics

Examples

Complete View Example

name: restaurant_orders
description: "Restaurant order transactions with customer and item details"
datasource: "clickhouse"
table: "restaurant_analytics.orders"

entities:
  - name: order
    type: primary
    description: "Individual restaurant order"
    key: order_id

  - name: customer
    type: foreign
    description: "Customer who placed the order"
    key: customer_id

  - name: restaurant
    type: foreign
    description: "Restaurant fulfilling the order"
    key: restaurant_id

dimensions:
  - name: order_id
    type: string
    description: "Unique order identifier"
    expr: order_id

  - name: customer_id
    type: string
    description: "Customer identifier"
    expr: customer_id

  - name: restaurant_id
    type: string
    description: "Restaurant identifier"
    expr: restaurant_id

  - name: order_date
    type: datetime
    description: "Timestamp when order was placed"
    expr: order_timestamp

  - name: order_year
    type: number
    description: "Year order was placed"
    expr: "EXTRACT(YEAR FROM order_timestamp)"
    synonyms: ["year"]

  - name: order_status
    type: string
    description: "Current status of the order"
    expr: status
    samples: ["pending", "preparing", "ready", "completed", "cancelled"]
    synonyms: ["status", "order state"]

  - name: is_delivery
    type: boolean
    description: "Whether order is for delivery"
    expr: "order_type = 'delivery'"
    synonyms: ["delivery order", "delivery"]

measures:
  - name: total_orders
    type: count
    description: "Total number of orders"
    synonyms: ["order count", "number of orders"]

  - name: total_revenue
    type: sum
    description: "Total revenue from orders"
    expr: order_amount
    synonyms: ["total sales", "revenue", "sales"]

  - name: average_order_value
    type: average
    description: "Average order value in USD"
    expr: order_amount
    synonyms: ["AOV", "avg order value"]

  - name: delivery_revenue
    type: sum
    description: "Revenue from delivery orders only"
    expr: order_amount
    filters:
      - expr: "order_type = 'delivery'"   # raw column condition (the is_delivery dimension's underlying expr); never {{ }}

Complete Topic Example

name: restaurant_orders
description: "Restaurant order analytics covering sales, customer behavior, and fulfillment"
base_view: restaurant_orders
views:
  - restaurant_orders

default_filters:
  - field: "order_status"
    not_in:
      values: ["cancelled", "test"]

Important: Do NOT Add yaml-language-server Schema Comments

NEVER add # yaml-language-server: $schema=... comments to view or topic files.

• Oxy does not publish JSON schemas for view.yml or topic.yml files at predictable URLs
• Adding non-existent schema URLs causes IDE validation errors and confusion
• If you're unsure whether a schema URL exists, don't include it
• Only add schema comments if you have verified the URL returns a valid JSON schema