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| author | Cheng Lian <lian@databricks.com> | 2015-08-17 17:25:14 -0700 |
|---|---|---|
| committer | Reynold Xin <rxin@databricks.com> | 2015-08-17 17:25:14 -0700 |
| commit | 52ae952574f5d641a398dd185e09e5a79318c8a9 (patch) | |
| tree | 2299828146fae1ab7641cea6e73045e50ed51bf2 /python/pyspark/ml/feature.py | |
| parent | 088b11ec5949e135cb3db2a1ce136837e046c288 (diff) | |
| download | spark-52ae952574f5d641a398dd185e09e5a79318c8a9.tar.gz spark-52ae952574f5d641a398dd185e09e5a79318c8a9.tar.bz2 spark-52ae952574f5d641a398dd185e09e5a79318c8a9.zip | |
[SPARK-9974] [BUILD] [SQL] Makes sure com.twitter:parquet-hadoop-bundle:1.6.0 is in SBT assembly jar
PR #7967 enables Spark SQL to persist Parquet tables in Hive compatible format when possible. One of the consequence is that, we have to set input/output classes to `MapredParquetInputFormat`/`MapredParquetOutputFormat`, which rely on com.twitter:parquet-hadoop:1.6.0 bundled with Hive 1.2.1.
When loading such a table in Spark SQL, `o.a.h.h.ql.metadata.Table` first loads these input/output format classes, and thus classes in com.twitter:parquet-hadoop:1.6.0. However, the scope of this dependency is defined as "runtime", and is not packaged into Spark assembly jar. This results in a `ClassNotFoundException`.
This issue can be worked around by asking users to add parquet-hadoop 1.6.0 via the `--driver-class-path` option. However, considering Maven build is immune to this problem, I feel it can be confusing and inconvenient for users.
So this PR fixes this issue by changing scope of parquet-hadoop 1.6.0 to "compile".
Author: Cheng Lian <lian@databricks.com>
Closes #8198 from liancheng/spark-9974/bundle-parquet-1.6.0.
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