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Posted to issues@spark.apache.org by "Fangshi Li (JIRA)" <ji...@apache.org> on 2018/05/12 05:07:00 UTC
[jira] [Updated] (SPARK-24256) ExpressionEncoder should support
user-defined types as fields of Scala case class and tuple
[ https://issues.apache.org/jira/browse/SPARK-24256?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Fangshi Li updated SPARK-24256:
-------------------------------
Description:
Right now, ExpressionEncoder supports ser/de of primitive types, as well as scala case class, tuple and java bean class. Spark's Dataset natively supports these mentioned types, but we find it is not flexible for other user-defined types and encoders.
For example, spark-avro has an AvroEncoder for ser/de Avro types in Dataset. Although we can use AvroEncoder to define Dataset with types being the Avro Generic or Specific Record, using such Avro typed Dataset has many limitations:
1. We can not use joinWith on this Dataset since the result is a tuple, but Avro types cannot be the field of this tuple.
2. We can not use some type-safe aggregation methods on this Dataset, such as KeyValueGroupedDataset's reduceGroups, since the result is also a tuple.
3. We cannot augment an Avro SpecificRecord with additional primitive fields together in a case class, which we find is a very common use case.
The limitation that Spark does not support define a Scala case class/tuple with subfields being any other user-defined type, is because ExpressionEncoder does not discover the implicit Encoder for the user-defined field types, thus can not use any Encoder to serde the user-defined fields in case class/tuple.
To address this issue, we propose a trait as a contract(between ExpressionEncoder and any other user-defined Encoder) to enable case class/tuple/java bean's ExpressionEncoder to discover the serializer/deserializer/schema from the Encoder of the user-defined type.
With this proposed patch and our minor modification in AvroEncoder, we remove these limitations with cluster-default conf spark.expressionencoder.org.apache.avro.specific.SpecificRecord = com.databricks.spark.avro.AvroEncoder$
This is a patch we have implemented internally and has been used for a few quarters. We want to propose to upstream as we think this is a useful feature to make Dataset more flexible to user types.
was:
Right now, ExpressionEncoder supports ser/de of primitive types, as well as scala case class, tuple and java bean class. Spark's Dataset natively supports these mentioned types, but we find it is not flexible for other user-defined types and encoders.
For example, spark-avro has an AvroEncoder for ser/de Avro types in Dataset. Although we can use AvroEncoder to define Dataset with types being the Avro Generic or Specific Record, using such Avro typed Dataset has many limitations:
1. We can not use joinWith on this Dataset since the result is a tuple, but Avro types cannot be the field of this tuple.
2. We can not use some type-safe aggregation methods on this Dataset, such as KeyValueGroupedDataset's reduceGroups, since the result is also a tuple.
3. We cannot augment an Avro SpecificRecord with additional primitive fields together in a case class, which we find is a very common use case.
The root cause for these limitations is that Spark does not support simply define a Scala case class/tuple with subfields being any other user-defined type, since ExpressionEncoder does not discover the implicit Encoder for the user-defined fields, thus can not use any Encoder to serde the user-defined fields in case class/tuple.
To address this issue, we propose a trait as a contract(between ExpressionEncoder and any other user-defined Encoder) to enable case class/tuple/java bean's ExpressionEncoder to discover the serializer/deserializer/schema from the Encoder of the user-defined type.
With this proposed patch and our minor modification in AvroEncoder, we make it work with conf
spark.expressionencoder.org.apache.avro.specific.SpecificRecord = com.databricks.spark.avro.AvroEncoder$
This is a patch we have implemented internally and has been used for a few quarters. We want to propose to upstream as we think this is a useful feature to make Dataset more flexible to user types.
> ExpressionEncoder should support user-defined types as fields of Scala case class and tuple
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-24256
> URL: https://issues.apache.org/jira/browse/SPARK-24256
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Fangshi Li
> Priority: Major
>
> Right now, ExpressionEncoder supports ser/de of primitive types, as well as scala case class, tuple and java bean class. Spark's Dataset natively supports these mentioned types, but we find it is not flexible for other user-defined types and encoders.
> For example, spark-avro has an AvroEncoder for ser/de Avro types in Dataset. Although we can use AvroEncoder to define Dataset with types being the Avro Generic or Specific Record, using such Avro typed Dataset has many limitations:
> 1. We can not use joinWith on this Dataset since the result is a tuple, but Avro types cannot be the field of this tuple.
> 2. We can not use some type-safe aggregation methods on this Dataset, such as KeyValueGroupedDataset's reduceGroups, since the result is also a tuple.
> 3. We cannot augment an Avro SpecificRecord with additional primitive fields together in a case class, which we find is a very common use case.
> The limitation that Spark does not support define a Scala case class/tuple with subfields being any other user-defined type, is because ExpressionEncoder does not discover the implicit Encoder for the user-defined field types, thus can not use any Encoder to serde the user-defined fields in case class/tuple.
> To address this issue, we propose a trait as a contract(between ExpressionEncoder and any other user-defined Encoder) to enable case class/tuple/java bean's ExpressionEncoder to discover the serializer/deserializer/schema from the Encoder of the user-defined type.
> With this proposed patch and our minor modification in AvroEncoder, we remove these limitations with cluster-default conf spark.expressionencoder.org.apache.avro.specific.SpecificRecord = com.databricks.spark.avro.AvroEncoder$
> This is a patch we have implemented internally and has been used for a few quarters. We want to propose to upstream as we think this is a useful feature to make Dataset more flexible to user types.
>
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