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https://issues.apache.org/jira/browse/SPARK-10434?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14730388#comment-14730388
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Cheng Lian commented on SPARK-10434:
------------------------------------
Another concern of mine is that, Parquet files written by a version of Spark
will stay there unchanged. Reducing possible corner cases to handle brings
benefits in the long run. Ideally, I still would like to have this one in 1.5,
of course, only if we would have another RC.
> Parquet compatibility with 1.4 is broken when writing arrays that may contain
> nulls
> -----------------------------------------------------------------------------------
>
> Key: SPARK-10434
> URL: https://issues.apache.org/jira/browse/SPARK-10434
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Cheng Lian
> Assignee: Cheng Lian
> Priority: Minor
>
> When writing arrays that may contain nulls, for example:
> {noformat}
> StructType(
> StructField(
> "f",
> ArrayType(IntegerType, containsNull = true),
> nullable = false))
> {noformat}
> Spark 1.4 uses the following schema:
> {noformat}
> message m {
> required group f (LIST) {
> repeated group bag {
> optional int32 array;
> }
> }
> }
> {noformat}
> This behavior is a hybrid of parquet-avro and parquet-hive: the 3-level
> structure and repeated group name "bag" are borrowed from parquet-hive, while
> the innermost element field name "array" is borrowed from parquet-avro.
> However, in Spark 1.5, I failed to notice the latter fact and used a schema
> in purely parquet-hive flavor, namely:
> {noformat}
> message m {
> required group f (LIST) {
> repeated group bag {
> optional int32 array_element;
> }
> }
> }
> {noformat}
> One of the direct consequence is that, Parquet files containing such array
> fields written by Spark 1.5 can't be read by Spark 1.4 (all array elements
> become null).
> To fix this issue, the name of the innermost field should be changed back to
> "array". Notice that this fix doesn't affect interoperability with Hive
> (saving Parquet files using {{saveAsTable()}} and then read them using Hive).
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