Github user rxin commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11270#discussion_r53595470
  
    --- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceDetection.scala
 ---
    @@ -0,0 +1,127 @@
    +/*
    + * 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
    + *
    + * 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.
    + */
    +
    +package org.apache.spark.sql.execution.datasources
    +
    +import scala.util.Try
    +
    +import org.apache.commons.io.FilenameUtils
    +import org.apache.hadoop.fs.{FileStatus, Path}
    +import org.apache.hadoop.mapred.{FileInputFormat, JobConf}
    +
    +import org.apache.spark.{Logging, SparkException}
    +import org.apache.spark.sql.SQLContext
    +
    +object DataSourceDetection extends Logging {
    --- End diff --
    
    I'm going to take another look at this later, but can you make sure the 
performance is ok when querying a large number of files on s3? i.e. ideally we 
should only need to read one file's metadata, rather than all files.



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