SemanticDF (SDF)

A semantic layer for Apache Spark (JVM/Scala), inspired by the Boring Semantic Layer (Python/Ibis).

A SemanticTable is a deferred, source-agnostic definition that compiles to a Spark DataFrame at a batch terminal (.toDataFrame(spark) / .execute(spark)) or a StreamingQuery at the streaming terminal (.toStreamingQuery(spark, opts)). It is not a DataFrame itself — it captures what you want (dimensions, measures, joins, filters, grains) so the engine can decide how to compute it. The same definition serves both batch and streaming sources; only the terminal differs.

What problems SemanticDF solves

Modern data teams have a plumbing problem: every dashboard, notebook, or AI agent needs the same business metrics defined somewhere — usually duplicated across queries, spreadsheets, and tribal lore. When the metric changes ("total_passengers" now means deplanements not boarded-then-deplaned), every copy drifts. SemanticDF puts the metric definitions in one checked-in source — a small Scala DSL or a YAML model — and gives every consumer (your code, your tests, your LLM agent) the same compile-time guarantee that they're asking for the right thing.

What you can do with it

Define a metric once, query it everywhere. A SemanticTable is an immutable description. Use it from flights.query(...) in code, from a YAML model in models/flights.yml, from a dbt manifest.json, from an Apache Ossie YAML, or from an MCP agent that calls query / describe_model over JSON.
Calc + percent-of-total measures with no expression-tree surgery. A measure that references other measures (t.total / t.flight_count) resolves by name against the aggregated DataFrame; a percent-of-total (t.total / t.all(t.total)) cross-joins a broadcasted totals row.
Compile-time typo safety on the query side. The optional typeclass layer (SemanticField[T] phantom types) catches dimension-vs-measure confusion at the call site rather than at first execution. ResultDecoder.derive[T] does the same for the result side of a query.
One model across batch and streaming. The op tree is source-agnostic; only the execution terminal differs (.toDataFrame(...) for batch, .toStreamingQuery(...) for Structured Streaming).
A models → agents bridge. okfgen produces OKF markdown an LLM can read; the MCP server exposes the tools (list_models, describe_model, query, explain, introspect, audit_log) over stdio or REST.
Result cache for repeated LLM-agent queries. Opt in with .withResultCache(ResultCache.inMemory(256)); the second identical query() returns from cache without re-executing the Spark plan. Cache keys are stable SHA-256s of the request shape (model + measures + dimensions + where + having + orderBy + limit), so semantically-equivalent queries share a cache entry. Drop all entries for a model in one call via cache.invalidateModel("orders"), or set a per-model version: Int and let the cache auto-evict stale entries on the next read after the model rebuilds.
Per-query audit log for LLM-agent observability. Opt in with .withAuditSink(AuditSink.inMemory()); every query() emits an AuditEvent recording the model, request shape, elapsed time, row count, and status. The MCP audit_log tool exposes the recent event stream back to the agent for self-introspection ("what did I just query?" / "did my last query timeout?").
A typeclass for interchange formats. SemanticMetadataAdapter[Source, P] is the unified entry point. Today there are three instances: DbtAdapter (dbt manifest.json), OssieReader (Apache Ossie YAML), and SDFAdapter (the cross-process SemanticManifest JSON). Future formats plug in as a new object and inherit the loadSemanticTables(source, resolve) entry point.
Cluster-mode safe. A SemanticTable is Java-serializable, so capturing it in a closure (UDF, broadcast variable, spark-submit --master yarn|k8s) round-trips through Spark's deploy mode without NotSerializableException. The internal op tree, dimension/measure lambdas, and cache key derivation all cross the JVM boundary safely.

When (and when not) to use it

Good fit: small-to-mid data teams with a stable set of business metrics, already on Spark 3.5+ (or 4.x), who want one definition everyone shares — including LLM agents.
Not yet: stream-stream joins (only static-stream joins are supported today — join_one(batchTable, streamingModel, ...)); heavy-numeric ML workloads without rollup needs; sub-second interactive dashboards where another tool's tighter latency matters more than metric consistency.

Where to read next

docs/getting-started.md — 5-minute paste-and-run setup (Maven + SparkSession + first query)
docs/guide.md — narrative walkthrough: how SemanticDF works, in plain English
semanticdf-platform/README.md — the standalone Restate-native platform runtime (long-running JVM with a Restate ingress, post-crash query reconciliation, bulk-startup recovery). Ships as a separate Maven module that depends on the library.
DESIGN.md — architecture of record (decisions, the hard problems)
docs/DOCS_MAP.md — wayfinding guide: which doc to read for which question
docs/GLOSSARY.md — terms-of-art (op tree, BaseScope, MeasureScope, expression-tree surgery, …)
docs/adr/ — recorded decisions
RELEASE.md — version-by-version changelog
docs/known-limitations.md — current scope & guardrails (what's in, what's deferred, with workarounds)
examples/ — runnable end-to-end examples

Build

Requires JDK 17 and Maven 3.9+. Spark is on the classpath as provided (it comes from your cluster/runtime).

mvn test                      # Spark 3.5.8 (default)
mvn -Pspark4 test             # Spark 4.1.1 (latest stable)

Quick start

Add the Maven dep and paste this into your project:

import io.semanticdf._
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{count, lit, sum}

implicit val spark = SparkSession.builder().master("local[2]").getOrCreate()
import spark.implicits._

val flights = Seq(
  ("AA", 100, 5), ("UA",  80, 3), ("DL", 150, 6),
).toDF("carrier", "distance", "passengers")

val flightsModel = toSemanticTable(flights, name = Some("flights"))
  .withDimensions(Dimension("carrier", t => t("carrier")))
  .withMeasures(
    Measure("flight_count",     t => count(lit(1))),
    Measure("total_passengers",  t => sum(t("passengers"))),
    Measure("avg_passengers",    t => t("total_passengers") / t("flight_count")),
  )

flightsModel.groupBy("carrier").aggregate("flight_count", "avg_passengers").execute.show

For a full walkthrough (prerequisites, Maven coordinates, troubleshooting) see docs/getting-started.md.

Once this runs, continue with docs/guide.md for the narrative walkthrough that explains how the compilation works under the hood.

CLI Tools

Two tools live in src/main/scala/io/semanticdf/tools/, both runnable via mvn exec:java:

docsgen — YAML model → browsable HTML

mvn exec:java \
  -Dexec.mainClass=io.semanticdf.tools.Main \
  -Dexec.args="docsgen --path examples/starter/models/ --out docs/index.html"
# Open docs/index.html in a browser

Reads one YAML file or a directory of .yml files and emits a self-contained HTML page (sidebar nav, per-model cards, dimension/measure/join tables, time/entity/pii badges). No Spark needed; no external dependencies.

introspect — DataFrame → YAML model starter

mvn exec:java \
  -Dexec.mainClass=io.semanticdf.tools.Main \
  -Dexec.args="introspect --path examples/starter/data/flights.csv --format csv --model flights"
# Writes a starter YAML to stdout (or --out models/flights.yml to write to a file).

Reads a data file via Spark, infers dimensions (StringType → dim, NumericType → sum/avg, TimestampType → time dimension with is_time_dimension: true), and emits a starter YAML model. Edit the output to refine types, add descriptions, and customise expressions.

Note — JDK 17 + Spark needs --add-opens flags for any command that touches Spark (which includes introspect). Without them, the JVM crashes with sun.nio.ch.DirectBuffer access errors. Either set MAVEN_OPTS to the full flag set (see docs/runtime-quickstart.md trap #1) or, for project-local reproducibility, drop a .mvn/jvm.config with one flag per line. docsgen does not need Spark, so it works without the flags.

okfgen — YAML models → agent knowledge catalog (sidecar markdown)

mvn exec:java \
  -Dexec.mainClass=io.semanticdf.tools.Main \
  -Dexec.args="okfgen --path examples/starter/models/ --out docs/agents/reference/starter/"
# Writes one Markdown concept doc per model under the --out directory.

Generates per-model sidecar Markdown (OKF — the agent knowledge format) for an external agent catalog. Each models/foo.yml becomes agents/reference/<project>/foo.md, with one-line dimensions/measures/joins/filters plus a row-by-row examples reference. The YAML stays the engine source of truth — OKF is a publishing layer, not a schema replacement. See docs/agents/okf-mapping.md for the mapping rules and output format. make okfgen-check is the CI drift check — it re-runs okfgen to a tempdir and diff -ru's the result against the committed bundle.

MCP server (`semanticdf-mcp`)

The semanticdf-mcp/ sibling module is a Model Context Protocol server that exposes semanticdf to any MCP-compatible client (Claude Desktop, Cursor, Continue) over stdio. The six tools from docs/agents/mcp-contract.md v5:

Tool	Purpose
`list_models`	Reports loaded models (name + description)
`describe_model`	Full schema (dimensions, measures, joins, filters, version) + optional OKF sidecar
`query`	Runs a query, returns rows + columns
`explain`	Same request shape, no execution — emits the semantic plan
`introspect`	Auto-generate starter YAML from a DataFrame
`audit_log`	Returns the recent `AuditEvent` stream — what the agent has queried, when, and whether it succeeded

Run the server

mvn install -DskipTests                                # install parent library to local ~/.m2
cd semanticdf-mcp && mvn package
mvn exec:java -Dexec.mainClass=io.semanticdf.mcp.Main \
  -Dexec.args="--models ../examples/starter/models/ \
               --data ../examples/starter/data-config.yaml \
               --okf-bundle /tmp/okf/"

All three flags are required:

Flag	What
`--models <dir>`	directory of `*.yml` model files
`--data <file>`	data-config YAML (`data:` block per the contract)
`--okf-bundle <dir>`	where OkfGen writes the OKF markdown; server reads it into memory at startup

Wire up a client (Claude Desktop example)

{
  "mcpServers": {
    "semanticdf": {
      "command": "java",
      "args": [
        "-jar",
        "/path/to/semanticdf-mcp/target/semanticdf-mcp_2.13-<version>.jar",
        "--models",
        "/path/to/your/models",
        "--data",
        "/path/to/your/data-config.yaml",
        "--okf-bundle",
        "/tmp/okf/"
      ]
    }
  }
}

The server source lives in semanticdf-mcp/. See docs/agents/mcp-contract.md v2 for the request/response schema of every tool.

Capabilities

Calc measures (name-based compilation)

A calc measure references other measures by name. The compiler classifies base vs calc automatically, pulls transitive dependencies, and applies calcs in topological layers.

// Request only a leaf calc — its deps (avg → total_distance + flight_count) are pulled.
flights.groupBy("carrier").aggregate("avg_distance_per_flight").execute(spark)

Calc-of-calc chains resolve by name across layers; cycles raise a clear error.
Typos give a "did you mean?" suggestion instead of a crash.

Percent-of-total (`t.all`)

t.all("measure") resolves to the grand total — the same measure aggregated with no group keys, cross-joined into the result. The formula is recomputed at zero grain, so non-sum totals are correct:

.withMeasures(
  Measure("total_passengers", t => sum(t("passengers"))),
  // pct sums to 1.0 by construction:
  Measure("pct_of_total", t => t("total_passengers") / t.all("total_passengers")),
)

t.all("avg_distance_per_flight") returns 225 (grand avg = 6750/30), not 675 (the sum of per-group averages). That's the classic BI trap, fixed.

Division by zero: Spark's / returns null on zero/missing denominators (correct SQL semantics). If you want an explicit default (e.g. 0.0 instead of null), use CalcHelpers.safeDivide(num, denom, defaultValue = 0.0).

Window functions

A Measure is just SemanticScope => Column, so any Spark window function is legal inside the lambda. The window evaluates against the post-aggregation DataFrame (Pass 2 of the calc layer), so it can reference group-by keys and base measures by name.

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.{row_number, rank, sum}

val st = toSemanticTable(flightsDf, name = Some("flights"))
  .withDimensions(
    Dimension("carrier", t => t("carrier")),
    Dimension("origin",  t => t("origin")),
  )
  .withMeasures(
    Measure("flight_count", t => count(lit(1))),
    // rank within each carrier by origin:
    Measure("rank_per_carrier_origin",
      t => row_number().over(Window.partitionBy(t("carrier")).orderBy(t("origin")))),
    // running total of total_passengers across origins per carrier:
    Measure("running_total",
      t => sum(t("total_passengers")).over(
        Window.partitionBy(t("carrier")).orderBy(t("origin")))),
  )
  .groupBy("carrier", "origin")
  .aggregate("flight_count", "rank_per_carrier_origin", "running_total")

Window functions work in the Scala DSL. The YAML loader also accepts raw SQL window expressions in measures: (e.g. row_number() over (partition by carrier order by origin)); the parser blocklist covers row_number, rank, dense_rank, lag, lead, ntile, first_value, last_value, plus window-frame SQL keywords (order, rows, range, between, unbounded, preceding, following, and, current, asc, desc, nulls).

Known limitation: a window function that references group-by keys (e.g. Window.partitionBy(t("carrier"))) cannot be combined with t.all(...) for percent-of-total — the zero-grain totals table has no group-by keys, so the window evaluation fails. Workaround: use a window that doesn't reference group-by keys (e.g. Window.orderBy(...) only), or compute percent-of-total as a separate measure.

Transforms (per-row computations, applied at model-load)

Per-row logic — datediff(...), case when ..., window functions — is applied to the source DataFrame at model-load time via the YAML transforms: block or Scala withTransforms(...). Transformed columns become part of the source DataFrame and are visible to subsequent filters and measures. Order matters (no automatic topological sort).

For a worked example with the YAML + Scala equivalents, the model-load lifecycle, and what fields downstream measures see, see docs/guide.md → Transforms.

Filters (pre-join row-level hygiene, applied at model-load)

Row-level hygiene — drop rows missing a required field, drop cancelled orders, dedup before aggregation — doesn't fit a query, because it governs which rows the model contains, not which rows a particular query returns. Declare it on the model via filters: (YAML) or withRowFilter(...) (Scala DSL). Filters run pre-agg, pre-join, against this model's source table only.

flights:
  table: flights_csv
  filters:
    require_origin_and_carrier:
      expr: "origin IS NOT NULL AND carrier IS NOT NULL"
      description: "Drop rows with null origin or carrier."
  dimensions:
    carrier: carrier

SparkFilterValidator enforces pre-join visibility at load time — a filter referencing a joined-side column is rejected. For the Scala DSL form, the visibility rules, and a worked example, see docs/guide.md → Filters.

Also see docs/calc-author-guide.md for the detailed validator rules.

Joins (`join_one` / `join_many` / `join_cross`)

val orders  = toSemanticTable(ordersDf, name = Some("orders"))
val items   = toSemanticTable(lineItemsDf, name = Some("line_items"))

// join_many pre-aggregates each side at the join-key grain to prevent fan-out inflation.
val joined = orders.join_many(items, on = "order_id")
  .withMeasures(
    Measure("orders.total_qty", t => sum(t("line_items.qty"))),
  )

join_one — one-to-one / parent-child (post-agg safe).
join_many — one-to-many; both sides pre-aggregated at join-key grain before joining to prevent fact inflation.
join_cross — Cartesian product.
Merged model uses left-precedence; prefixed names ("orders.total_qty") resolve correctly.

Filters — WHERE/HAVING auto-routing

flights.where("carrier" === "AA")                      // dimension → WHERE (pre-agg)
       .where("total_passengers" > 600)                 // measure   → HAVING (post-agg)
       .where(("carrier" === "AA") and ("total" > 100)) // AND-split: WHERE + HAVING
       .where(("carrier" === "AA") or ("total" > 800))  // OR-whole (can't split)

where(pred) routes automatically: dimension predicates → pre-agg, measure predicates → post-agg. And compounds split per-condition; Or/Not mixing dim+measure stay whole.
having(pred) forces post-agg.
DSL: === =!= > >= < <= in notIn isNull isNotNull, plus and/or/.not. (Standard ==/!= are final on Any and return Boolean — unusable for a deferred DSL.)

Order, limit, and one-shot `query()`

// Fluent chain:
flights.groupBy("carrier").aggregate("total_passengers")
  .orderBy(SortKey.desc("total_passengers")).limit(10)
  .execute(spark)   // top-10 carriers

// Or a one-shot bundle:
flights.query(
  measures   = Seq("total_passengers"),
  dimensions = Seq("carrier"),
  having     = Some("total_passengers" > 600),
  orderBy    = Seq(SortKey.desc("total_passengers")),
  limit      = Some(10),
).execute(spark)

Querying from a notebook via `spark.sql(...)`

For notebook / SQL-first consumers, a compiled SemanticTable can be registered as a Spark temp view and queried with plain SQL via .createOrReplaceTempView("flights_view") followed by spark.sql(...). The view is the compiled output of the model — joins, pre-join filters, and pre-aggregation all happen before the SQL queries the view.

See docs/guide.md → Notebook escape hatch for the worked example, scoping rules, and a multi-cell notebook workflow.

Typed queries (compile-time safety)

The string-based API above is convenient but typo-prone — a wrong field name is a runtime error. An additive typed API catches those mistakes at compile time.

// Declare phantom types + implicit typeclass witnesses (one-time, per field):
object Flights {
  sealed trait Carrier
  sealed trait Origin
  sealed trait TotalPassengers
  sealed trait FlightCount

  implicit val carrier: SemanticDimension[Carrier]           = SemanticDimension.of[Carrier]("carrier")
  implicit val origin:  SemanticDimension[Origin]            = SemanticDimension.of[Origin]("origin")
  implicit val pax:     SemanticMeasure[TotalPassengers]     = SemanticMeasure.of[TotalPassengers]("total_passengers")
  implicit val count:   SemanticMeasure[FlightCount]         = SemanticMeasure.of[FlightCount]("flight_count")
}
import Flights._

// Typed query — wrong ref types are caught at compile time:
val st = toSemanticTable(flightsDf, name = Some("flights"))
val rows = st.groupByDimensions(carrier)
              .aggregateMeasures(pax, count)
              .orderBy(SortKey.desc(pax))
              .limit(10)
              .execute(spark)

// Typed predicate (operator kind is in the method name, not a runtime string):
val highPax = st.where(Predicate.Gt(pax, 600)).execute(spark)

// Typed measure declaration — name read from the SemanticMeasure witness:
import org.apache.spark.sql.functions.row_number
import org.apache.spark.sql.expressions.Window
val enriched = st.withMeasures(pax, t => row_number().over(Window.partitionBy(t("carrier")).orderBy(t("total_passengers").desc)))

// Compile-time guarantees:
//   groupByDimensions(pax)          // COMPILE ERROR — pax is a Measure, not a Dimension
//   aggregateMeasures(carrier)      // COMPILE ERROR — carrier is a Dimension, not a Measure
//   Compare.Greater(pax, 600)       // COMPILE ERROR — typo; only Eq/Ne/Lt/Le/Gt/Ge compile
//   Predicate.Gt(pax, "six hundred")  // compiles — predicate.value is Any (fails at runtime, not compile time)

Pure additions to the library: the string API is unchanged. Zero runtime overhead — groupByDimensions/aggregateMeasures and Compare.Gt compile to the same Spark Column expressions as the string forms.
Arities 1–4 are fully type-checked at compile time; the …All(refs) overloads do a single runtime check for arity 5+ (rare in practice).
The FieldRef[T] carrier is a value class — no allocation on the hot path.
See the typeclass-design rationale for the design rationale and what's still deferred (the typed-arithmetic DSL (planned) — see docs/phase-E-plan.md §E3). The ResultDecoder[T] typeclass (including macro derivation for case classes via ResultDecoder.derive[T]) and the queryAs[T]: Dataset[T] terminal are shipped.

Typed query results — `ResultDecoder[T]`

The same compile-time guarantee applies to the output side. SemanticTable.collectAs[T] returns a Seq[T] rather than untyped Seq[Row], plumbed through a small typeclass:

// Built-in primitive decoders read column 0 of each row:
val names: Seq[String] = table.collectAs[String](spark)
val counts: Seq[Long]  = table.collectAs[Long](spark)

// Case-class decoders — derive[T] generates the instance at compile time:
case class CarrierCount(carrier: String, count: Long)
implicit val decoder: ResultDecoder[CarrierCount] = ResultDecoder.derive[CarrierCount]
val typed: Seq[CarrierCount] = table.collectAs[CarrierCount](spark)

The macro (ResultDecoder.derive[T]) is a Scala 2 blackbox macro that inspects the case class's primary constructor and emits one row.getX(i) call per field. Supported field types: String, Int, Long, Double, Float, Boolean, Short, Byte, java.math.BigDecimal. Unsupported field types (java.time.Instant, sealed traits, Option[T], nested case classes, ...) produce a compile-time error pointing at the offending constructor parameter, so the user can either rename, restructure, or supply a manual ResultDecoder[T] instance via implicit val.

For richer shapes that the macro doesn't support, the manual form is just as concise as a one-line val:

implicit val decoder: ResultDecoder[Foo] = new ResultDecoder[Foo] {
  def decode(row: Row): Foo = Foo(row.getString(0), foo.bar(row))
}

Time semantics

val st = toSemanticTable(flightsWithTimeDf, name = Some("flights"))
  .withDimensions(
    Dimension.time("ts", t => t("ts"), smallestTimeGrain = Some("day")),
  )
  .withMeasures(Measure("total_passengers", t => sum(t("passengers"))))

st.atTimeGrain("ts", "month").groupBy("ts").aggregate("total_passengers").execute(spark)
// groups by truncated month (date_trunc)

// Or via query():
st.query(
  measures   = Seq("total_passengers"),
  dimensions = Seq("ts"),
  timeGrain  = Some("month"),
  timeRange  = Some(("2024-01-01", "2024-02-28")),  // filters raw ts, pre-truncation
).execute(spark)

Dimension.time(...) marks a timestamp dimension; smallestTimeGrain floors requests.
atTimeGrain(dim, "month") overrides the dimension's expr with date_trunc.
Grain too fine (e.g. "hour" when smallestTimeGrain = "day") raises a clear error.
time_range filters the raw column; time_grain affects only grouping.

EXPLAIN — op tree, Spark plan, and semantic intent

Three flavours of plan inspection, each for a different debugging need:

model.explain()                // op tree shape (no Spark compilation)
model.explain(spark)           // Catalyst physical plan
model.explainSemantic(spark)   // WHY: filter routing, pulled measures, etc.

explainSemantic is the one a developer usually wants. See docs/guide.md → How a query compiles for the worked example with sample output.

API reference

Method	Description
`toSemanticTable(df, name?)`	Construct a semantic model from a base `DataFrame`.
`.withDimensions(...)` / `.withMeasures(...)`	Immutable model extension. Typed `withMeasures(measure, expr)` overload accepts a `SemanticMeasure` witness directly via subtyping.
`.withTransforms(transforms*)`	Per-row logic (e.g. `datediff`, `case when`) applied to source data at model-load. Mirrors the YAML `transforms:` block.
`.withRowFilter(name, expr, description: Option[String], metadata: Map[String, String])`	Attach a pre-join row filter (Spark SQL string) declared in the model. Mirrors the YAML `filters:` block. `SparkFilterValidator` enforces pre-join column visibility (source + transforms; joined-side columns not visible) at load time.
`.version(v: Int)`	Set the model's version (forward-compat hint for consumers). `table.version` reads the current value (0 = unversioned).
`.join_one(other, on)` / `.join_many(other, on)` / `.join_cross(other)`	Joins.
`.where(pred)` / `.having(pred)`	Filters (auto-routed WHERE/HAVING).
`.groupBy(keys...).aggregate(measures...)`	Group-by + aggregate.
`.groupByDimensions[D1..D4](refs...)` / `.groupByDimensionsAll(refs)`	Typed group-by — ref kind (dimension) is checked at compile time (arity 5+ at runtime).
`.aggregateMeasures[M1..M4](refs...)` / `.aggregateMeasuresAll(refs)`	Typed aggregate — ref kind (measure) is checked at compile time (arity 5+ at runtime).
`Predicate.Eq/Ne/Gt/Ge/Lt/Le/in/notIn/isNull/isNotNull[F](ref, v)`	Typed predicate factories — `ref: FieldRef[F]` with `SemanticField[F]` witness.
`Compare.Gt(field, value)` / `Compare.Eq(field, value)` / etc.	Sealed comparison ADT — operator kind (Eq/Ne/Lt/Le/Gt/Ge) is in the type, not a string. `Compare.apply("gt", ...)` legacy factory is preserved.
`.atTimeGrain(dim, grain)`	Truncate a time dimension for grouping.
`.orderBy(keys...)` / `.limit(n)`	Terminal ordering / top-N. `SortKey.asc(ref)` / `SortKey.desc(ref)` accept typed `SemanticField` witnesses.
`.query(measures, dimensions?, where?, having?, orderBy?, limit?, timeGrain?, timeGrains?, timeRange?)`	One-shot bundle.
`.queryAs[T](measures, dimensions?, where?, having?, orderBy?, limit?, timeGrain?, timeGrains?, timeRange?)(implicit spark, decoder: ResultDecoder[T], encoder: Encoder[T]): Dataset[T]`	Typed one-shot bundle. Same shape as `.query` but returns a `Dataset[T]`, decoding rows into a case class via the implicit `ResultDecoder[T]` (use `ResultDecoder.derive[T]` for the case-class witness) and `Encoder[T]` (use `import spark.implicits._`). Compile-time type-safety on result field names and types.
`Measure.typed[T](name: String, expr: TypedSemanticScope => TypedColumn[T]): Measure`	Typed measure factory. Same shape as the `Measure` case class but the lambda's return type is type-checked at compile time via the phantom `T`. Compose with `TypedArithmetic.{divide, plus, minus, multiply}` for type-checked arithmetic. The typed form lowers to a plain `Measure` at runtime — works with `withMeasures(...)` as usual. Zero runtime overhead, no memory leak.
`TypedArithmetic.{divide, plus, minus, multiply}[T, U, R](a: Column, b: Column)(implicit nt: Numeric[T], nu: Numeric[U], nr: Numeric[R]): TypedColumn[R]`	Typed arithmetic ops for measure lambdas. The compiler requires `Numeric[T]`, `Numeric[U]`, `Numeric[R]` to be in implicit scope — `String` and other non-numeric types fail at compile time. Returns a `TypedColumn[R]` (value class wrapping `Column`); implicit conversion to `Column` makes the typed form drop-in compatible with the untyped `SemanticScope => Column` lambda. The function body is just the corresponding Spark `Column` op — type parameters are erased.
`.toDataFrame(spark)` / `.execute(spark)`	Batch terminal (compile to `DataFrame`). With `implicit val spark: SparkSession` in scope, both can be called without the argument (`.toDataFrame` / `.execute`).
`.previewSchema(spark)`	Output schema (compile to `StructType`, no rows).
`.withHint(strategy, params*)`	Apply a Spark planner hint (e.g. `"broadcast"`, `"repartition", n`).
`.withAuditSink(sink: AuditSink)`	Install an `io.semanticdf.audit.AuditSink` — every `query()` / `execute()` / `toDataFrame()` emits an `AuditEvent` (model, request shape, elapsed, status). Default `NoOp` (no overhead).
`.withResultCache(cache: ResultCache)`	Install an `io.semanticdf.cache.ResultCache` — identical `query()` calls return from cache without re-executing the Spark plan. Default `NoOp`. Cache keys are stable SHA-256s of the request shape.
`.validate()`	Compile-free structural check; returns `ValidationResult(errors, warnings, isValid)` for CI pre-flight.
`.joins: Seq[JoinInfo]`	All join edges in the model (left/right keys, cardinality: one/one_to_many/many_to_many/cross). Captures join keys at construction time (no compile required).
`.measureKind(name): MeasureKind`	Classify a measure as `Base` / `Calc` / `Window` — useful for tooling that needs to know which measures have a known-name calc dependency chain.
`.sourceTable: Option[String]`	Back-reference to the originating table name (when loaded from a registered/catalog source via `YamlLoader`).
`.filters: Seq[SemanticFilter]`	The model's pre-join row filters in declaration order (`name`, `expr`, `description`, `metadata`).
`.dimensions: Map[String, Dimension]` / `.measures: Map[String, Measure]` / `.findDimension(name)` / `.findMeasure(name)`	Catalog accessors.
`.createOrReplaceTempView(name)` / `.createTempView(name)` / `.createOrReplaceGlobalTempView(name)`	Compile to `DataFrame` and register as a Spark temp view (session or global). All three take `(implicit spark: SparkSession)` — call from inside a `SparkSession.builder()` block.
`.explain()`	Print the SemanticDF op-tree summary (no Spark compile).
`.explain(spark)`	Run the full query and print Spark's simple physical plan.
`.explainExtended(spark)`	Run the full query and print Spark's extended/cost plan (incl. logical-plan sections).
`.explainSemantic(spark?)` / `.explainSemantic(spark?, Scope)`	Multi-section human-readable plan: filter routing, transitive deps, join strategies, warnings.

Dimension.time(...) / Dimension.entity(...) are ergonomic factories. Predicate._ brings the filter DSL into scope. SortKey.desc(...) / bare String (ascending) drive orderBy. CalcHelpers.safeDivide(num, denom, defaultValue) guards zero/missing denominators with an explicit default (Spark / returns null on div-by-zero — correct SQL semantics; use safeDivide only when null is undesirable).

Runnable examples

In-library examples are in src/main/scala/io/semanticdf/examples/. Compile once with mvn compile, then run any example:

mvn compile -q

mvn scala:run -DmainClass=io.semanticdf.examples.FlightsBasic
mvn scala:run -DmainClass=io.semanticdf.examples.FlightsPctTotals
mvn scala:run -DmainClass=io.semanticdf.examples.OrdersJoinMany
mvn scala:run -DmainClass=io.semanticdf.examples.FiltersRouting
mvn scala:run -DmainClass=io.semanticdf.examples.TimeSeries
mvn scala:run -DmainClass=io.semanticdf.examples.Benchmark

Or submit as a Spark app:

mvn package -q
spark-submit --class io.semanticdf.examples.FlightsBasic target/semanticdf_2.13-*.jar

Consumer-facing templates

The examples/ directory holds consumer templates — standalone Maven sub-projects that show how to use SemanticDF in your own codebase. Each is a runnable, copy-pasteable project. They depend on SemanticDF from your local ~/.m2 (run mvn install on the parent first).

Template	What it teaches
`examples/starter`	7 queries (group-by, pct-of-total, joins, time-grain, filter, top-N window, MoM window) — the canonical "hello world"
`examples/pipeline`	Full BI lifecycle — raw CSV → ETL → cleaned parquet → declarative YAML queries
`examples/window-analytics`	Window functions: top-N per group, period-over-period, running totals
`examples/customer-analytics`	RFM segmentation + cohort activity (calc-of-calc composition)
`examples/operations-analytics`	Order fulfillment time, on-time rate, anomaly detection (z-score)
`examples/telco-analytics`	Telco: monthly ARPU per plan, promotion effectiveness, roaming revenue
`examples/hospital`	Hospital: data cleansing workflow (dedup, normalize, fill), ALOS, 30-day readmission rate
`examples/dbt-reader`	Load a dbt `manifest.json` as a semantic model — the adapter pattern for a third-party interchange format
`examples/joined-manifest-e2e`	Cross-process joined-manifest workflow: write → JSON artifact → read via `SDFAdapter`

Run any of them:

cd examples/window-analytics   # or any other template
mvn scala:run -DmainClass=com.example.windowanalytics.Main

Cross-version compatibility

Verified green on both Spark lines (Spark 3.5.8 default + Spark 4.1.1 via -Pspark4). All library and MCP tests pass on each JDK 17. The total test count grows with each release; see the surefire reports for the current number.

Spark	Scala	Status
3.5.8 (default)	2.13.18	✅
4.1.1 (`-Pspark4`)	2.13.18	✅

No code shims are needed — the codebase uses only Spark APIs stable across 3.5→4.x.

Design & decisions

DESIGN.md — architecture of record: op tree, scopes, calc compilation, joins, percent-of-total, filters, time semantics, invariants.
docs/runtime-quickstart.md — JDK/Scala/Spark/Maven matrix, build & test commands, CLI tools, the four runtime traps (Java-17 module flags, scala:run arg leak, deprecated import, version files).
docs/known-limitations.md — current scope & guardrails (batch-only, per-session security, symmetric join keys, etc.) with workarounds and roadmap hints. Read before first consumer.
docs/calc-author-guide.md — how to write correct calc measures: ratio, pct-of-total, calc-of-calc, safeDivide.
docs/backlog-type-safety.md — the open list of deferred features and their priority ordering.
docs/adr/ — recorded decisions:
- 0001 — karpathy guidelines adopted (think-before-coding, simplicity-first, surgical changes, goal-driven execution); app-design plugin/portal rejected.
- 0002 — batch-first, streaming-shaped (DSL/source-agnostic op tree; batch and streaming terminals share the model definition).
- 0003 — re-sequence to prove name-based calc compilation early.

Name		Name	Last commit message	Last commit date
Latest commit History 342 Commits
.github/workflows		.github/workflows
docs		docs
examples		examples
schemas		schemas
semanticdf-mcp		semanticdf-mcp
semanticdf-platform		semanticdf-platform
src		src
.gitignore		.gitignore
.scalafmt.conf		.scalafmt.conf
DESIGN.md		DESIGN.md
Makefile		Makefile
README.md		README.md
RELEASE.md		RELEASE.md
pom.xml		pom.xml

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SemanticDF (SDF)

What problems SemanticDF solves

What you can do with it

When (and when not) to use it

Where to read next

Build

Quick start

CLI Tools

docsgen — YAML model → browsable HTML

introspect — DataFrame → YAML model starter

okfgen — YAML models → agent knowledge catalog (sidecar markdown)

MCP server (semanticdf-mcp)

Run the server

Wire up a client (Claude Desktop example)

Capabilities

Calc measures (name-based compilation)

Percent-of-total (t.all)

Window functions

Transforms (per-row computations, applied at model-load)

Filters (pre-join row-level hygiene, applied at model-load)

Joins (join_one / join_many / join_cross)

Filters — WHERE/HAVING auto-routing

Order, limit, and one-shot query()

Querying from a notebook via spark.sql(...)

Typed queries (compile-time safety)

Typed query results — ResultDecoder[T]

Time semantics

EXPLAIN — op tree, Spark plan, and semantic intent

API reference

Runnable examples

Consumer-facing templates

Cross-version compatibility

Design & decisions

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