Feature/350 fix tests #351
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| /* | ||
| * Copyright 2019 ABSA Group Limited | ||
| * | ||
| * Licensed 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. | ||
| */ | ||
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| package za.co.absa.abris.examples.utils | ||
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| import org.apache.spark.sql.{Encoder, Row} | ||
| import org.apache.spark.sql.types.StructType | ||
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| import scala.util.Try | ||
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| object CompatibleRowEncoder { | ||
| def apply(schema: StructType): Encoder[Row] = { | ||
| // Spark < 3.5.0 | ||
| val rowEncoderTry = Try { | ||
| val rowEncoderClass = Class.forName("org.apache.spark.sql.catalyst.encoders.RowEncoder") | ||
| val applyMethod = rowEncoderClass.getMethod("apply", classOf[StructType]) | ||
| applyMethod.invoke(null, schema).asInstanceOf[Encoder[Row]] | ||
| } | ||
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| // Spark >= 3.5.0 | ||
| rowEncoderTry.orElse(Try { | ||
| val encodersClass = Class.forName("org.apache.spark.sql.Encoders") | ||
| val rowMethod = encodersClass.getMethod("row", classOf[StructType]) | ||
| rowMethod.invoke(null, schema).asInstanceOf[Encoder[Row]] | ||
| }).getOrElse { | ||
| throw new IllegalStateException("Neither RowEncoder.apply nor Encoders.row is available in the Spark version.") | ||
| } | ||
| } | ||
| } |
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Since the dependency is not declared, where is the Avro coming from? Confluent or Spark?
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It's coming from Spark. But I realize that it's basically luck, I didn't think of confluent.
The reason why I removed it is because in principle we should compile against the Avro version that Spark sets, because Avro is packaged with the Spark runtime.
We could just set the Avro version for each Spark version, but we also need to set the version for jackson-core, because Spark does actually override it. E.g. Avro 1.11.2 depends on jackson-core 2.14.2, but Spark 3.5.0 overrides this and uses jackson-core 2.15.2
Another, perhaps radical approach would be to import the pom of spark-parent, and thereby its dependencyManagement section (https://github.com/apache/spark/blob/master/pom.xml#L434-L2857). Then we would be sure that we always use the same dependencies as Spark does. However, unfortunately it's overly intrusive since it defines not only the runtime dependencies, but also test dependencies like scalatest, scalacheck etc.
So for now, I think the better solution is to just define the versions of avro and jackson-core along with the spark version in the respective profiles
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I did this for spline agent, but we have separated pom for the uber-jar creation there if I remember correctly.
But I agree, this is a good solution.