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From liancheng <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4413][SQL] Parquet support through data...
Date Wed, 19 Nov 2014 16:30:29 GMT
Github user liancheng commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3269#discussion_r20587290
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala ---
    @@ -0,0 +1,291 @@
    +/*
    + * 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.parquet
    +
    +import java.util.{List => JList}
    +
    +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
    +import org.apache.hadoop.conf.{Configurable, Configuration}
    +import org.apache.hadoop.io.Writable
    +import org.apache.hadoop.mapreduce.{JobContext, InputSplit, Job}
    +
    +import parquet.hadoop.ParquetInputFormat
    +import parquet.hadoop.util.ContextUtil
    +
    +import org.apache.spark.annotation.DeveloperApi
    +import org.apache.spark.{Partition => SparkPartition, Logging}
    +import org.apache.spark.rdd.{NewHadoopPartition, RDD}
    +
    +import org.apache.spark.sql.{SQLConf, Row, SQLContext}
    +import org.apache.spark.sql.catalyst.expressions.{SpecificMutableRow, And, Expression,
Attribute}
    +import org.apache.spark.sql.catalyst.types.{IntegerType, StructField, StructType}
    +import org.apache.spark.sql.sources._
    +
    +import scala.collection.JavaConversions._
    +
    +/**
    + * Allows creation of parquet based tables using the syntax
    + * `CREATE TABLE ... USING org.apache.spark.sql.parquet`.  Currently the only option
required
    + * is `path`, which is should be the location of a collection of, optionally partitioned,
    + * parquet files.
    + */
    +class DefaultSource extends RelationProvider {
    +  /** Returns a new base relation with the given parameters. */
    +  override def createRelation(
    +      sqlContext: SQLContext,
    +      parameters: Map[String, String]): BaseRelation = {
    +    val path =
    +      parameters.getOrElse("path", sys.error("'path' must be specifed for parquet tables."))
    +
    +    ParquetRelation2(path)(sqlContext)
    +  }
    +}
    +
    +private[parquet] case class Partition(partitionValues: Map[String, Any], files: Seq[FileStatus])
    +
    +/**
    + * An alternative to [[ParquetRelation]] that plugs in using the data sources API.  This
class is
    + * currently not intended as a full replacement of the parquet support in Spark SQL though
it is
    + * likely that it will eventually subsume the existing physical plan implementation.
    + *
    + * Compared with the current implementation, this class has the following notable differences:
    + *
    + * Partitioning: Partitions are auto discovered and must be in the form of directories
`key=value/`
    + * located at `path`.  Currently only a single partitioning column is supported and it
must
    + * be an integer.  This class supports both fully self-describing data, which contains
the partition
    + * key, and data where the partition key is only present in the folder structure.  The
presence
    + * of the partitioning key in the data is also auto-detected.
    --- End diff --
    
    Should also mention null partition value is not supported yet.


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