Plus, I’m using *Spark 1.5.2*, with *spark-csv 1.3.0*. Also tried
HiveContext, but the result is exactly the same.
On Sun, Feb 7, 2016 at 3:44 PM SLiZn Liu <sliznmail...@gmail.com> wrote:
> Hi Spark Users Group,
>
> I have a csv file to analysis with Spark, but I’m troubling with importing
> as DataFrame.
>
> Here’s the minimal reproducible example. Suppose I’m having a
> *10(rows)x2(cols)* *space-delimited csv* file, shown as below:
>
> 1446566430 2015-11-04<SP>00:00:30
> 1446566430 2015-11-04<SP>00:00:30
> 1446566430 2015-11-04<SP>00:00:30
> 1446566430 2015-11-04<SP>00:00:30
> 1446566430 2015-11-04<SP>00:00:30
> 1446566431 2015-11-04<SP>00:00:31
> 1446566431 2015-11-04<SP>00:00:31
> 1446566431 2015-11-04<SP>00:00:31
> 1446566431 2015-11-04<SP>00:00:31
> 1446566431 2015-11-04<SP>00:00:31
>
> the <SP> in column 2 represents sub-delimiter within that column, and
> this file is stored on HDFS, let’s say the path is hdfs:///tmp/1.csv
>
> I’m using *spark-csv* to import this file as Spark *DataFrame*:
>
> sqlContext.read.format("com.databricks.spark.csv")
> .option("header", "false") // Use first line of all files as header
> .option("inferSchema", "false") // Automatically infer data types
> .option("delimiter", " ")
> .load("hdfs:///tmp/1.csv")
> .show
>
> Oddly, the output shows only a part of each column:
>
> [image: Screenshot from 2016-02-07 15-27-51.png]
>
> and even the boundary of the table wasn’t shown correctly. I also used the
> other way to read csv file, by sc.textFile(...).map(_.split(" ")) and
> sqlContext.createDataFrame, and the result is the same. Can someone point
> me out where I did it wrong?
>
> —
> BR,
> Todd Leo
>
>
