282 lines
16 KiB
Markdown
282 lines
16 KiB
Markdown
# Kotlin DataFrame ↔ Clojure Data Ecosystem Research
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Kotlin DataFrame (`org.jetbrains.kotlinx.dataframe`) is a JetBrains library for typesafe,
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columnar, in-memory data processing on the JVM. It pairs with **Kandy** for visualization.
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The core question: **Is this just a seq of maps with extra steps?** And if so, can we bridge
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the two ecosystems on the JVM?
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## Key Findings
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### Kotlin DataFrame is NOT a seq of maps internally
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- It's **column-oriented**: a list of named typed columns (primitive arrays, string tables)
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- But it **exposes** row iteration as `DataRow` (essentially `Map<String, Any?>`)
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- It freely converts to/from `List<DataClass>` and `Map<String, List<*>>`
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- So conceptually yes, it represents the same data as a seq of maps, but stored columnar
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### Clojure equivalents exist
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| Concept | KT DataFrame | Tablecloth | Plain Kotlin (`List<Map>`) | Plain Clojure (seq of maps) |
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|-|-|-|-|-|
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| Tabular data | `DataFrame<T>` | `tc/dataset` | `List<Map<String, Any?>>` | `[{:a 1} {:b 2}]` |
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| Create | `dataFrameOf("a" to listOf(1,2))` | `(tc/dataset {:a [1 2]})` | `listOf(mapOf("a" to 1), mapOf("a" to 2))` | `[{:a 1} {:a 2}]` |
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| Filter | `.filter { col > 10 }` | `(tc/select-rows ds pred)` | `.filter { it["col"] as Int > 10 }` | `(filter #(> (:col %) 10) data)` |
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| Group + Aggregate | `.groupBy{}.aggregate{}` | `(-> (tc/group-by) (tc/aggregate))` | `.groupingBy { it["k"] }.fold(0.0) { acc, r -> acc + r["x"] as Double }` | `(->> data (group-by :k) (map (fn [[k vs]] {:k k :avg (/ (reduce + (map :x vs)) (count vs))})))` |
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| Computed column | `.add("y") { x * 2 }` | `(tc/add-column ds :y fn)` | `.map { it + ("y" to (it["x"] as Int) * 2) }` | `(map #(assoc % :y (* (:x %) 2)) data)` |
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| Sort | `.sortBy { col }` | `(tc/order-by ds :col)` | `.sortedBy { it["col"] as Comparable<*> }` | `(sort-by :col data)` |
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| Join | `.join(other) { col match right.col }` | `(tc/left-join ds other :col)` | manual `associateBy` + map | `(clojure.set/join a b)` or `merge` |
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| Schema | `@DataSchema` / compiler plugin | `malli` schema | `data class` | spec / none |
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| Schema inference | `@ImportDataSchema` | `malli.provider/provide` | — | — |
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| Plotting | Kandy | Tableplot, Hanami, Oz | — | — |
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| Notebook | Kotlin Notebook | Clay, Clerk | — | — |
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### Schema inference from example data (Clojure)
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**Malli** can infer schemas from example JSON/EDN:
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```clojure
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(require '[malli.provider :as mp])
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(mp/provide [{:name "Alice" :age 30 :tags ["admin"]}
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{:name "Bob" :age 25}])
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;; => [:map
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;; [:name string?]
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;; [:age number?]
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;; [:tags {:optional true} [:vector string?]]]
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```
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- Detects optional keys automatically
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- Handles nested maps, vectors, sets
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- Can decode UUIDs, dates with custom decoders
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- Can export to JSON Schema via `malli.json-schema/transform`
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---
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## Deep Research Findings (from source code analysis)
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### A. Kotlin DataFrame JVM Interop Surface
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#### Core Architecture
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```
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DataFrame<T> (interface) -- container of columns
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└── DataFrameImpl<T> (internal) -- stores List<AnyCol> + nrow
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DataColumn<T> (interface) -- a single column
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├── ValueColumn<T> -- leaf values (backed by List<T>)
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├── ColumnGroup<T> -- nested DataFrame (struct column)
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└── FrameColumn<T> -- column of DataFrames
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DataRow<T> (interface) -- a single row view
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└── DataRowImpl<T> (internal) -- index + DataFrame reference
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```
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#### What's callable from Clojure (non-inline, non-reified)
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| Operation | Java-callable? | How to call from Clojure |
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|-----------|---------------|--------------------------|
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| `Map<String, Iterable>.toDataFrame()` | **YES** | `(ToDataFrameKt/toDataFrame java-map)` |
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| `Iterable<Map<String,Any?>>.toDataFrame()` | **YES** | `(ToDataFrameKt/toDataFrameMapStringAnyNullable seq-of-maps)` |
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| `DataFrame.toMap()` | **YES** | `(TypeConversionsKt/toMap df)` → `Map<String, List<Any?>>` |
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| `DataRow.toMap()` | **YES** | `(TypeConversionsKt/toMap row)` → `Map<String, Any?>` |
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| `DataColumn.createByInference(name, values)` | **YES** | `(DataColumn/createByInference "col" java-list)` |
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| `dataFrameOf(columns)` | **YES** | `(ConstructorsKt/dataFrameOf column-list)` |
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| `DataFrame.columns()` | **YES** | `(.columns df)` → `List<AnyCol>` |
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| `DataFrame.get(name)` | **YES** | `(.get df "colname")` → column |
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| `DataFrame.get(index)` | **YES** | `(.get df 0)` → DataRow |
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| `DataFrame.iterator()` | **YES** | `(iterator-seq (.iterator df))` |
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| `DataFrame.rowsCount()` | **YES** | `(.rowsCount df)` |
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| `Iterable<T>.toDataFrame<reified T>()` | **NO** | inline+reified, use Map variant instead |
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| `DataColumn.createValueColumn<reified>` | **NO** | Use `createByInference` instead |
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**Key insight**: DataRow does NOT implement `java.util.Map`. It has `.get(name)` and `.values()` but
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you need `.toMap()` to get a real Map.
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#### Column storage
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- **ValueColumn**: backed by `List<T>` (object list, not primitive arrays)
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- No primitive specialization — even ints are boxed in `List<Int>`
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- This means no zero-copy to dtype-next primitive buffers
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### B. tech.ml.dataset (TMD) / Tablecloth Interop Surface
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#### TMD Column internals
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```clojure
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(deftype Column
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[^RoaringBitmap missing ;; missing value bitmap
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data ;; underlying data (dtype-next buffer, Java array, NIO buffer)
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^IPersistentMap metadata ;; column metadata (name, datatype, etc.)
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^Buffer buffer]) ;; cached buffer view for fast access
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```
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- Columns are **dtype-next buffers** — can be Java arrays, NIO ByteBuffers, or native memory
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- Missing values tracked separately via RoaringBitmap (not sentinel values)
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- Implements `PToArrayBuffer` — can convert to raw Java arrays if no missing values
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#### Dataset creation from Java collections
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```clojure
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;; From a column-oriented map (best for interop):
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(ds/->dataset {"name" ["Alice" "Bob"] "age" [30 25]})
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;; From a seq of row maps:
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(ds/->dataset [{:name "Alice" :age 30} {:name "Bob" :age 25}])
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;; Tablecloth wraps the same:
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(tc/dataset {"name" ["Alice" "Bob"] "age" [30 25]})
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```
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Both `ds/->dataset` and `tc/dataset` accept `java.util.Map` directly.
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### C. Apache Arrow as Interchange Format
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Both libraries support Arrow:
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**Kotlin DataFrame** — separate module `dataframe-arrow`, supports Feather (v1, v2), Arrow IPC (streaming + file), LZ4/ZSTD compression.
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**tech.ml.dataset** — built-in `tech.v3.libs.arrow`, supports memory-mapped reading for near-zero-copy (`{:open-type :mmap}`).
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**Verdict:** Arrow is the right choice for **large datasets** or **process boundaries**.
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For same-process bridging, **direct JVM interop via Map** is simpler.
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### D. Malli Schema Inference
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| Aspect | Malli Provider | Kotlin @ImportDataSchema |
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|--------|---------------|--------------------------|
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| When | Runtime | Compile-time |
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| Input | Any Clojure data | JSON file on disk |
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| Output | Schema as data (EDN) | Generated typed accessor code |
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| Type safety | Dynamic validation | Static type checking |
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| IDE support | Limited | Full autocomplete |
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| Flexibility | Handles unknown/evolving schemas | Schema fixed at compile time |
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| Best for | Exploration, dynamic data | Production, stable APIs |
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---
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## Bridge Design
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The bridge is ~20 LOC. Both sides are columnar, so `Map<String, List>` is the natural interchange type.
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### Direct JVM interop (simplest, best for small-medium data)
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```clojure
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(ns df-bridge.core
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(:import [org.jetbrains.kotlinx.dataframe.api ToDataFrameKt TypeConversionsKt]
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[org.jetbrains.kotlinx.dataframe DataColumn]))
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;; KT DataFrame -> Clojure (column-oriented, fast)
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(defn kt->map [kt-df]
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(into {} (TypeConversionsKt/toMap kt-df)))
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;; KT DataFrame -> TMD dataset
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(defn kt->dataset [kt-df]
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(-> (TypeConversionsKt/toMap kt-df)
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(ds/->dataset)))
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;; Clojure -> KT DataFrame (column-oriented, fast)
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(defn map->kt [col-map]
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(ToDataFrameKt/toDataFrame col-map))
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;; TMD dataset -> KT DataFrame
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(defn dataset->kt [ds]
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(let [col-map (into {} (map (fn [col]
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[(name (ds-col/column-name col))
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(vec col)])
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(ds/columns ds)))]
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(ToDataFrameKt/toDataFrame col-map)))
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```
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### Nested Data (ColumnGroups)
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```clojure
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;; Create KT DataFrame with ColumnGroup from Clojure:
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(bridge/make-kt-with-groups
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[["name" ["Alice" "Bob"]]
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["address" {"city" ["NYC" "LA"]
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"zip" [10001 90001]}]])
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;; Convert back to row maps:
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(bridge/kt->rows kt-df)
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;; => [{:name "Alice", :address {:city "NYC", :zip 10001}}
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;; {:name "Bob", :address {:city "LA", :zip 90001}}]
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```
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### Benchmark Results (3-column dataset: string, int, double)
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| Rows | Map→KT | KT→Map | KT→TMD | TC→KT | Full RT |
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|------|--------|--------|--------|-------|---------|
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| 1K | 0.3ms | 0.005ms | 0.3ms | 0.2ms | 0.4ms |
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| 100K | 3.3ms | 0.003ms | 5.7ms | 5.5ms | 12.2ms |
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| 1M | 33ms | 0.004ms | 72ms | 60ms | 134ms |
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### Arrow vs Direct Map Comparison (4-column dataset)
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| Rows | Direct Map KT→TMD | Arrow file KT→TMD | Arrow byte[] KT→TMD |
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|------|-------------------|--------------------|--------------------|
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| 10K | 1.5ms | 2.0ms | 1.4ms |
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| 100K | 11.6ms | 9.1ms | 7.1ms |
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| 1M | 112ms | 118ms | 92ms |
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**Key observations:**
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- `KT→Map` is essentially free (~4µs) — `toMap()` just wraps existing column lists
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- Full roundtrip at 1M rows: 134ms — fine for interactive use
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- **Arrow is NOT faster** than direct Map for same-process bridging at any tested size
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- **Verdict: Direct Map bridge wins for same-process. Arrow only for cross-process.**
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---
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## Visualization Stack Comparison
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### Clay + Tableplot (Clojure) vs Kotlin Notebook + Kandy
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| Operation | Kotlin DataFrame + Kandy | Tablecloth + Tableplot | Plain Kotlin (stdlib) | Plain Clojure (core) |
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|-----------|--------------------------|------------------------|-----------------------|----------------------|
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| **Create data** | `dataFrameOf("col" to list)` | `(tc/dataset {"col" list})` | `listOf(mapOf("col" to v, ...))` | `[{:col v ...}]` |
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| **Group + Aggregate** | `df.groupBy { col }.aggregate { mean { x } into "y" }` | `(-> ds (tc/group-by :col) (tc/aggregate {:y #(dfn/mean (% :x))}))` | `data.groupingBy { it["col"] }.fold(0.0) { acc, r -> acc + (r["x"] as Double) }` | `(->> data (group-by :col) (map (fn [[k vs]] {:col k :avg (/ (reduce + (map :x vs)) (count vs))})))` |
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| **Filter** | `df.filter { price > 100 }` | `(tc/select-rows ds #(> (:price %) 100))` | `data.filter { it["price"] as Int > 100 }` | `(filter #(> (:price %) 100) data)` |
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| **Add column** | `df.add("revenue") { price * quantity }` | `(tc/map-columns ds "revenue" ["price" "quantity"] *)` | `data.map { it + ("revenue" to (it["price"] as Int) * (it["qty"] as Int)) }` | `(map #(assoc % :revenue (* (:price %) (:qty %))) data)` |
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| **Sort** | `df.sortBy { price }` | `(tc/order-by ds :price)` | `data.sortedBy { it["price"] as Int }` | `(sort-by :price data)` |
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| **Join** | `df.join(other) { col match right.col }` | `(tc/left-join ds other :col)` | `val idx = other.associateBy { it["col"] }; data.map { it + (idx[it["col"]] ?: emptyMap()) }` | `(clojure.set/join set-a set-b)` |
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| **Bar chart** | `df.plot { bars { x(col); y(col) } }` | `(-> ds (plotly/base {:=x :col :=y :col}) (plotly/layer-bar {}))` | — | — |
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| **Scatter** | `df.plot { points { x(a); y(b); color(c) } }` | `(-> ds (plotly/base {:=x :a :=y :b}) (plotly/layer-point {:=color :c}))` | — | — |
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| **Histogram** | `df.plot { histogram(x = col) }` | `(-> ds (plotly/base {:=x :col}) (plotly/layer-histogram {}))` | — | — |
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| **Notebook** | Kotlin Notebook (IntelliJ plugin, `.ipynb`) | Clay (editor-agnostic, renders `.clj` → HTML) | — | — |
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**Key differences:**
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1. **Type safety**: Kotlin has compile-time column access (`df.price`), Clojure has runtime schema inference. Plain Kotlin collections need casts everywhere (`it["x"] as Int`). Plain Clojure needs neither — keywords are functions, values are dynamically typed.
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2. **Filter/map/sort are ~identical**: For row-level operations, plain collections and DataFrame APIs converge. Clojure's `filter`/`map`/`sort-by` + keywords-as-functions is nearly as concise as tablecloth.
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3. **Group+aggregate is close in Clojure, improved in Kotlin via `groupingBy`**: Clojure has `group-by` in core, and aggregation is just `map` + `reduce` over the groups. Kotlin has `groupingBy` which avoids materializing intermediate lists — `.groupingBy { }.fold(init) { acc, elem -> }` aggregates in a single pass. Both are reasonable without a DataFrame. Casts still plague the Kotlin version.
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4. **Joins are built into Clojure**: `clojure.set/join` performs natural inner joins on sets of maps, auto-detecting shared keys (via the first element of each set). Also supports explicit key mapping via 3-arity `(join a b {:left-key :right-key})`. Combined with `merge`, `set/union`, `set/difference`, `set/intersection`, `set/select` (filter), `set/project` (column select), and `set/rename`, Clojure has relational algebra in the stdlib. **Caveats**: inner join only (no left/right/outer), inputs must be sets (not vectors — silent wrong results otherwise), shared-key detection only inspects the first element of each set. Plain Kotlin has nothing comparable.
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5. **Nil punning handles most missing-value cases**: `(:x row)` on a row without `:x` returns `nil`. `nil` is falsy, `(seq nil)` → `nil`, `(conj nil x)` works, `(assoc nil :a 1)` → `{:a 1}`, `(count nil)` → `0`. Missing values flow through collection operations naturally. **Caveat**: nil breaks arithmetic — `(+ 1 nil)` and `(> nil 5)` throw NPE. Clojure provides `fnil` (default-substitution wrapper) and `some->` (nil-short-circuiting thread) for these edges. In practice you `(remove nil? ...)` or `(keep ...)` before numeric reduction. TMD's RoaringBitmap approach handles this at the column level without per-row nil logic.
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6. **Reducers and transducers close the performance gap**: Transducers (`(into [] (comp (filter pred) (map f)) data)`) fuse pipeline stages into a single pass — no intermediate lazy seqs between steps. `r/fold` parallelizes via ForkJoinPool (default partition: 512 elements), splitting work across cores. **Caveat**: `r/fold` only parallelizes vectors and PersistentHashMaps; lists, lazy seqs, and sets silently fall back to sequential reduce. Parallel group-by works via `(r/fold (r/monoid #(merge-with + %1 %2) (constantly {})) rf data-vec)` but requires an associative combining function — not all aggregations (median, percentile) trivially parallelize. Columnar storage still wins for numeric-heavy workloads: TMD's `dfn/mean` operates on a contiguous typed `double[]` buffer with no boxing, which transducers over maps of boxed values can't match.
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7. **Plotting**: Both stacks are interactive. Kandy (Lets-Plot) supports tooltips by default and zoom/pan via `ggtb()` since Lets-Plot 4.5.0; renders via Swing, HTML, SVG, or PNG. Tableplot supports Plotly.js (interactive zoom/pan/hover out of the box) and Vega-Lite backends. No stdlib plotting in either language.
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8. **REPL experience**: Plain Clojure data is already inspectable at the REPL — DataFrames add pretty-printing but plain maps are arguably *easier* to inspect (no special printer needed).
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**Verdict**: Plain Clojure seq-of-maps is competitive with DataFrame APIs for most operations.
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`filter`, `map`, `sort-by`, `group-by`, `clojure.set/join`, transducers, and `r/fold` are all in the
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stdlib. Keywords-as-functions and nil punning cover most data-wrangling patterns naturally — with known
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edges around arithmetic on nil and `set/join` requiring actual sets (not vectors). DataFrames earn their
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keep on: (a) contiguous typed column buffers for numeric-heavy workloads (no boxing, primitive reduction),
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(b) built-in statistical functions (`dfn/mean`, `dfn/variance`, etc.), (c) left/right/outer joins
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(tablecloth), and (d) ecosystem integration (plotting, notebooks, Arrow I/O). For small-to-medium data
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with simple transformations, plain Clojure maps may genuinely be all you need.
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---
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## Project Structure
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```
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bridge/ — Working Clojure bridge project (deps.edn, benchmarks, notebooks)
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dataframe/ — Kotlin DataFrame source (reference)
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tech.ml.dataset/ — TMD source (reference)
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tablecloth/ — Tablecloth source (reference)
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malli/ — Malli source (reference)
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```
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## Dependencies (bridge project)
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```clojure
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{org.jetbrains.kotlinx/dataframe-core {:mvn/version "1.0.0-Beta4"}
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org.jetbrains.kotlin/kotlin-reflect {:mvn/version "2.1.10"}
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scicloj/tablecloth {:mvn/version "7.062"}
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metosin/malli {:mvn/version "0.17.0"}
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org.scicloj/tableplot {:mvn/version "1-beta14"}
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org.scicloj/clay {:mvn/version "2-beta56"}}
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```
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