-
Notifications
You must be signed in to change notification settings - Fork 28.2k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[SPARK-48545][SQL] Create to_avro and from_avro SQL functions to matc…
…h DataFrame equivalents
### What changes were proposed in this pull request?
This PR creates two new SQL functions "to_avro" and "from_avro" to match existing DataFrame equivalents.
For example:
```
sql(
"""
|create table t as
| select named_struct('u', named_struct('member0', member0, 'member1', member1)) as s
| from values (1, null), (null, 'a') tab(member0, member1)
|""".stripMargin)
val jsonFormatSchema =
"""
|{
| "type": "record",
| "name": "struct",
| "fields": [{
| "name": "u",
| "type": ["int","string"]
| }]
|}
|""".stripMargin
spark.sql(
s"""
|select from_avro(result, '$jsonFormatSchema', map()).u from (
| select to_avro(s, '$jsonFormatSchema') as result from t
|)")
.collect()
> {1, NULL}
{NULL, "a"}
```
### Why are the changes needed?
This brings parity between SQL and DataFrame APIs in Apache Spark.
### Does this PR introduce _any_ user-facing change?
Yes, see above.
### How was this patch tested?
This PR adds extra unit tests, and I also checked that the functions work with `spark-shell`.
### Was this patch authored or co-authored using generative AI tooling?
No GitHub copilot usage this time
Closes #46977 from dtenedor/from-avro.
Authored-by: Daniel Tenedorio <daniel.tenedorio@databricks.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>- Loading branch information
1 parent
7e5a461
commit b1677a4
Showing
6 changed files
with
271 additions
and
3 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
175 changes: 175 additions & 0 deletions
175
...yst/src/main/scala/org/apache/spark/sql/catalyst/expressions/toFromAvroSqlFunctions.scala
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,175 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
||
| package org.apache.spark.sql.catalyst.expressions | ||
|
|
||
| import org.apache.spark.sql.catalyst.analysis.TypeCheckResult | ||
| import org.apache.spark.sql.catalyst.util.ArrayBasedMapData | ||
| import org.apache.spark.sql.types.{MapType, NullType, StringType} | ||
| import org.apache.spark.unsafe.types.UTF8String | ||
| import org.apache.spark.util.Utils | ||
|
|
||
| /** | ||
| * Converts a binary column of Avro format into its corresponding Catalyst value. | ||
| * This is a thin wrapper over the [[AvroDataToCatalyst]] class to create a SQL function. | ||
| * | ||
| * @param child the Catalyst binary input column. | ||
| * @param jsonFormatSchema the Avro schema in JSON string format. | ||
| * @param options the options to use when performing the conversion. | ||
| * | ||
| * @since 4.0.0 | ||
| */ | ||
| // scalastyle:off line.size.limit | ||
| @ExpressionDescription( | ||
| usage = """ | ||
| _FUNC_(child, jsonFormatSchema, options) - Converts a binary Avro value into a Catalyst value. | ||
| """, | ||
| examples = """ | ||
| Examples: | ||
| > SELECT _FUNC_(s, '{"type": "record", "name": "struct", "fields": [{ "name": "u", "type": ["int","string"] }]}', map()) IS NULL AS result FROM (SELECT NAMED_STRUCT('u', NAMED_STRUCT('member0', member0, 'member1', member1)) AS s FROM VALUES (1, NULL), (NULL, 'a') tab(member0, member1)); | ||
| [false] | ||
| """, | ||
| note = """ | ||
| The specified schema must match actual schema of the read data, otherwise the behavior | ||
| is undefined: it may fail or return arbitrary result. | ||
| To deserialize the data with a compatible and evolved schema, the expected Avro schema can be | ||
| set via the corresponding option. | ||
| """, | ||
| group = "misc_funcs", | ||
| since = "4.0.0" | ||
| ) | ||
| // scalastyle:on line.size.limit | ||
| case class FromAvro(child: Expression, jsonFormatSchema: Expression, options: Expression) | ||
| extends TernaryExpression with RuntimeReplaceable { | ||
| override def first: Expression = child | ||
| override def second: Expression = jsonFormatSchema | ||
| override def third: Expression = options | ||
|
|
||
| override def withNewChildrenInternal( | ||
| newFirst: Expression, newSecond: Expression, newThird: Expression): Expression = { | ||
| copy(child = newFirst, jsonFormatSchema = newSecond, options = newThird) | ||
| } | ||
|
|
||
| override def checkInputDataTypes(): TypeCheckResult = { | ||
| val schemaCheck = jsonFormatSchema.dataType match { | ||
| case _: StringType | | ||
| _: NullType | ||
| if jsonFormatSchema.foldable => | ||
| None | ||
| case _ => | ||
| Some(TypeCheckResult.TypeCheckFailure( | ||
| "The second argument of the FROM_AVRO SQL function must be a constant string " + | ||
| "containing the JSON representation of the schema to use for converting the value " + | ||
| "from AVRO format")) | ||
| } | ||
| val optionsCheck = options.dataType match { | ||
| case MapType(StringType, StringType, _) | | ||
| MapType(NullType, NullType, _) | | ||
| _: NullType | ||
| if options.foldable => | ||
| None | ||
| case _ => | ||
| Some(TypeCheckResult.TypeCheckFailure( | ||
| "The third argument of the FROM_AVRO SQL function must be a constant map of strings to " + | ||
| "strings containing the options to use for converting the value from AVRO format")) | ||
| } | ||
| schemaCheck.getOrElse( | ||
| optionsCheck.getOrElse( | ||
| TypeCheckResult.TypeCheckSuccess)) | ||
| } | ||
|
|
||
| override def replacement: Expression = { | ||
| val schemaValue: String = jsonFormatSchema.eval() match { | ||
| case s: UTF8String => | ||
| s.toString | ||
| case null => | ||
| "" | ||
| } | ||
| val optionsValue: Map[String, String] = options.eval() match { | ||
| case a: ArrayBasedMapData if a.keyArray.array.nonEmpty => | ||
| val keys: Array[String] = a.keyArray.array.map(_.toString) | ||
| val values: Array[String] = a.valueArray.array.map(_.toString) | ||
| keys.zip(values).toMap | ||
| case _ => | ||
| Map.empty | ||
| } | ||
| val constructor = | ||
| Utils.classForName("org.apache.spark.sql.avro.AvroDataToCatalyst").getConstructors().head | ||
| val expr = constructor.newInstance(child, schemaValue, optionsValue) | ||
| expr.asInstanceOf[Expression] | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * Converts a Catalyst binary input value into its corresponding AvroAvro format result. | ||
| * This is a thin wrapper over the [[CatalystDataToAvro]] class to create a SQL function. | ||
| * | ||
| * @param child the Catalyst binary input column. | ||
| * @param jsonFormatSchema the Avro schema in JSON string format. | ||
| * | ||
| * @since 4.0.0 | ||
| */ | ||
| // scalastyle:off line.size.limit | ||
| @ExpressionDescription( | ||
| usage = """ | ||
| _FUNC_(child, jsonFormatSchema) - Converts a Catalyst binary input value into its corresponding | ||
| Avro format result. | ||
| """, | ||
| examples = """ | ||
| Examples: | ||
| > SELECT _FUNC_(s, '{"type": "record", "name": "struct", "fields": [{ "name": "u", "type": ["int","string"] }]}', MAP()) IS NULL FROM (SELECT NULL AS s); | ||
| [true] | ||
| """, | ||
| group = "misc_funcs", | ||
| since = "4.0.0" | ||
| ) | ||
| // scalastyle:on line.size.limit | ||
| case class ToAvro(child: Expression, jsonFormatSchema: Expression) | ||
| extends BinaryExpression with RuntimeReplaceable { | ||
| override def left: Expression = child | ||
|
|
||
| override def right: Expression = jsonFormatSchema | ||
|
|
||
| override def withNewChildrenInternal(newLeft: Expression, newRight: Expression): Expression = { | ||
| copy(child = newLeft, jsonFormatSchema = newRight) | ||
| } | ||
|
|
||
| override def checkInputDataTypes(): TypeCheckResult = { | ||
| jsonFormatSchema.dataType match { | ||
| case _: StringType if jsonFormatSchema.foldable => | ||
| TypeCheckResult.TypeCheckSuccess | ||
| case _ => | ||
| TypeCheckResult.TypeCheckFailure( | ||
| "The second argument of the TO_AVRO SQL function must be a constant string " + | ||
| "containing the JSON representation of the schema to use for converting the value " + | ||
| "to AVRO format") | ||
| } | ||
| } | ||
|
|
||
| override def replacement: Expression = { | ||
| val schemaValue: Option[String] = jsonFormatSchema.eval() match { | ||
| case null => | ||
| None | ||
| case s: UTF8String => | ||
| Some(s.toString) | ||
| } | ||
| val constructor = | ||
| Utils.classForName("org.apache.spark.sql.avro.CatalystDataToAvro").getConstructors().head | ||
| val expr = constructor.newInstance(child, schemaValue) | ||
| expr.asInstanceOf[Expression] | ||
| } | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters