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Revision b8b80f6dea86d4e4a648b86e38936d3a82ffc0aa authored by Wenchen Fan on 20 June 2017, 16:15:33 UTC, committed by gatorsmile on 20 June 2017, 16:15:41 UTC 
[SPARK-21150][SQL] Persistent view stored in Hive metastore should be case preserving
 
## What changes were proposed in this pull request?

This is a regression in Spark 2.2. In Spark 2.2, we introduced a new way to resolve persisted view: https://issues.apache.org/jira/browse/SPARK-18209 , but this makes the persisted view non case-preserving because we store the schema in hive metastore directly. We should follow data source table and store schema in table properties.

## How was this patch tested?

new regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #18360 from cloud-fan/view.

(cherry picked from commit e862dc904963cf7832bafc1d3d0ea9090bbddd81)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
1 parent 514a7e6
Raw File
pip-sanity-check.py 
#
# 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
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# 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.
#

from __future__ import print_function

from pyspark.sql import SparkSession
from pyspark.ml.param import Params
from pyspark.mllib.linalg import *
import sys

if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("PipSanityCheck")\
        .getOrCreate()
    sc = spark.sparkContext
    rdd = sc.parallelize(range(100), 10)
    value = rdd.reduce(lambda x, y: x + y)
    if (value != 4950):
        print("Value {0} did not match expected value.".format(value), file=sys.stderr)
        sys.exit(-1)
    print("Successfully ran pip sanity check")

    spark.stop()
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