[SPARK-51799] Support user-specified schema in DataFrameReader
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Could you review this PR, @yaooqinn ? |
yaooqinn
reviewed
Apr 15, 2025
| #expect(try await spark.read.schema("age SHORT").json(path).dtypes.count == 1) | ||
| #expect(try await spark.read.schema("age SHORT").json(path).dtypes[0] == ("age", "smallint")) | ||
| #expect(try await spark.read.schema("age SHORT, name STRING").json(path).dtypes[0] == ("age", "smallint")) | ||
| #expect(try await spark.read.schema("age SHORT, name STRING").json(path).dtypes[1] == ("name", "string")) |
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Can we also add a test with comment & null constraint
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I thought it's supported
But, according to the Apache Spark 4.0.0 RC4, it seems there are limitations.
spark-shell
$ bin/spark-shell
WARNING: Using incubator modules: jdk.incubator.vector
Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 4.0.0
/_/
Using Scala version 2.13.16 (OpenJDK 64-Bit Server VM, Java 17.0.14)
Type in expressions to have them evaluated.
Type :help for more information.
25/04/15 12:32:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark context Web UI available at http://localhost:4040
Spark context available as 'sc' (master = local[*], app id = local-1744687967546).
Spark session available as 'spark'.
scala> spark.read.schema("name STRING NOT NULL").json("examples/src/main/resources/people.json").printSchema
warning: 1 deprecation (since 2.13.3); for details, enable `:setting -deprecation` or `:replay -deprecation`
root
|-- name: string (nullable = true)
$ bin/spark-connect-shell --remote sc://localhost:15002
25/04/15 12:28:48 INFO DefaultAllocationManagerOption: allocation manager type not specified, using netty as the default type
25/04/15 12:28:48 INFO CheckAllocator: Using DefaultAllocationManager at memory/netty/DefaultAllocationManagerFactory.class
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 4.0.0
/_/
Type in expressions to have them evaluated.
Spark connect server version 4.0.0.
Spark session available as 'spark'.
scala> spark.read.schema("name STRING").json("../examples/src/main/resources/people.json").printSchema
root
|-- name: string (nullable = true)
scala> spark.read.schema("name STRING NOT NULL").json("../examples/src/main/resources/people.json").printSchema
root
|-- name: string (nullable = true)
scala> spark.read.schema("name STRING NOT NULL").json("../examples/src/main/resources/people.json").show()
+-------+
| name|
+-------+
|Michael|
| Andy|
| Justin|
+-------+
For that part, let me dig more, @yaooqinn .
yaooqinn
approved these changes
Apr 15, 2025
|
Thank you, @yaooqinn ! |
|
Merged to main. |
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What changes were proposed in this pull request?
This PR aims to support user-specified schema in
DataFrameReader.Why are the changes needed?
For feature parity.
Does this PR introduce any user-facing change?
No. This is a new addition.
How was this patch tested?
Pass the CIs.
Was this patch authored or co-authored using generative AI tooling?
No.