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[SPARK-51508] Support collect(): [[String?]] for DataFrame #17

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[SPARK-51508] Support collect(): [[String?]] for DataFrame #17

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[SPARK-51508] Support `collect(): [[String?]]` for `DataFrame`
dongjoon-hyun Mar 14, 2025
b028283
clean up print statement
dongjoon-hyun Mar 14, 2025
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29 changes: 24 additions & 5 deletions Sources/SparkConnect/DataFrame.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ public actor DataFrame: Sendable {

/// Add `Apache Arrow`'s `RecordBatch`s to the internal array.
/// - Parameter batches: An array of ``RecordBatch``.
private func addBathes(_ batches: [RecordBatch]) {
private func addBatches(_ batches: [RecordBatch]) {
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This is a typo fix.

self.batches.append(contentsOf: batches)
}

Expand Down Expand Up @@ -153,16 +153,35 @@ public actor DataFrame: Sendable {
let arrowResult = ArrowReader.makeArrowReaderResult()
_ = reader.fromMessage(schema, dataBody: Data(), result: arrowResult)
_ = reader.fromMessage(dataHeader, dataBody: dataBody, result: arrowResult)
await self.addBathes(arrowResult.batches)
await self.addBatches(arrowResult.batches)
}
}
}
}
}

/// This is designed not to support this feature in order to simplify the Swift client.
public func collect() async throws {
throw SparkConnectError.UnsupportedOperationException
/// Execute the plan and return the result as ``[[String?]]``.
/// - Returns: ``[[String?]]``
public func collect() async throws -> [[String?]] {
try await execute()

var result: [[String?]] = []
for batch in self.batches {
for i in 0..<batch.length {
var values: [String?] = []
for column in batch.columns {
let str = column.array as! AsString
Comment on lines +172 to +173
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For DataFrame, I suppose that the return of collect is an array of Row. But this collect returns strings. Is it just for initial implementation and will be Row later?

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Yes, correct. Row implementation is on the way~

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For Scala client, we also support Array[Long] like the following.

$ bin/spark-shell --remote sc://localhost:15002
scala> spark.range(1).collect()
res0: Array[java.lang.Long] = Array(0L)

if column.data.isNull(i) {
values.append(nil)
} else {
values.append(str.asString(i))
}
}
result.append(values)
}
}

return result
}

/// Execute the plan and show the result.
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2 changes: 2 additions & 0 deletions Sources/SparkConnect/SparkConnectClient.swift
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Expand Up @@ -275,9 +275,11 @@ public actor SparkConnectClient {
let expressions: [Spark_Connect_Expression.SortOrder] = cols.map {
var expression = Spark_Connect_Expression.SortOrder()
expression.child.exprType = .unresolvedAttribute($0.toUnresolvedAttribute)
expression.direction = .ascending
return expression
}
sort.order = expressions
sort.isGlobal = true
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@dongjoon-hyun dongjoon-hyun Mar 14, 2025 •
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I piggy-back this fix while improving a test coverage by checking the result via collect() API.

var relation = Relation()
relation.sort = sort
var plan = Plan()
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6 changes: 2 additions & 4 deletions Sources/SparkConnect/SparkSession.swift
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Expand Up @@ -45,12 +45,10 @@ public actor SparkSession {
/// - userID: an optional user ID. If absent, `SPARK_USER` environment or ``ProcessInfo.processInfo.userName`` is used.
init(_ connection: String, _ userID: String? = nil) {
let processInfo = ProcessInfo.processInfo
#if os(iOS) || os(watchOS) || os(tvOS)
let userName = processInfo.environment["SPARK_USER"] ?? ""
#elseif os(macOS) || os(Linux)
#if os(macOS) || os(Linux)
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I simplified the implementation in a new way to cover os(visionOS) too.

let userName = processInfo.environment["SPARK_USER"] ?? processInfo.userName
#else
assert(false, "Unsupported platform")
let userName = processInfo.environment["SPARK_USER"] ?? ""
#endif
self.client = SparkConnectClient(remote: connection, user: userID ?? userName)
self.conf = RuntimeConf(self.client)
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19 changes: 17 additions & 2 deletions Tests/SparkConnectTests/DataFrameTests.swift
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Expand Up @@ -125,19 +125,23 @@ struct DataFrameTests {
await spark.stop()
}

#if !os(Linux)
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  • Like show(), collect() currently has a binary compatibility issue on os(Linux).
  • On MacOS, all tests pass.

@Test
func sort() async throws {
let spark = try await SparkSession.builder.getOrCreate()
#expect(try await spark.range(10).sort("id").count() == 10)
let expected = (1...10).map{ [String($0)] }
#expect(try await spark.range(10, 0, -1).sort("id").collect() == expected)
await spark.stop()
}

@Test
func orderBy() async throws {
let spark = try await SparkSession.builder.getOrCreate()
#expect(try await spark.range(10).orderBy("id").count() == 10)
let expected = (1...10).map{ [String($0)] }
#expect(try await spark.range(10, 0, -1).orderBy("id").collect() == expected)
await spark.stop()
}
#endif

@Test
func table() async throws {
Expand All @@ -153,6 +157,17 @@ struct DataFrameTests {
}

#if !os(Linux)
@Test
func collect() async throws {
let spark = try await SparkSession.builder.getOrCreate()
#expect(try await spark.range(0).collect().isEmpty)
#expect(
try await spark.sql(
"SELECT * FROM VALUES (1, true, 'abc'), (null, null, null), (3, false, 'def')"
).collect() == [["1", "true", "abc"], [nil, nil, nil], ["3", "false", "def"]])
await spark.stop()
}

@Test
func show() async throws {
let spark = try await SparkSession.builder.getOrCreate()
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4 changes: 4 additions & 0 deletions Tests/SparkConnectTests/SparkSessionTests.swift
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Expand Up @@ -41,7 +41,11 @@ struct SparkSessionTests {

@Test func userContext() async throws {
let spark = try await SparkSession.builder.getOrCreate()
#if os(macOS) || os(Linux)
let defaultUserContext = ProcessInfo.processInfo.userName.toUserContext
#else
let defaultUserContext = "".toUserContext
#endif
#expect(await spark.client.userContext == defaultUserContext)
await spark.stop()
}
Expand Down
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