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---
layout: global
title: Data sources
displayTitle: Data sources
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---
In this section, we introduce how to use data source in ML to load data.
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Besides some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.
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**Table of Contents**
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{:toc}
## Image data source
This image data source is used to load image files from a directory, it can load compressed image (jpeg, png, etc.) into raw image representation via `ImageIO` in Java library.
The loaded DataFrame has one `StructType` column: "image", containing image data stored as image schema.
The schema of the `image` column is:
- origin: `StringType` (represents the file path of the image)
- height: `IntegerType` (height of the image)
- width: `IntegerType` (width of the image)
- nChannels: `IntegerType` (number of image channels)
- mode: `IntegerType` (OpenCV-compatible type)
- data: `BinaryType` (Image bytes in OpenCV-compatible order: row-wise BGR in most cases)
< div class = "codetabs" >
< div data-lang = "scala" markdown = "1" >
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[`ImageDataSource` ](api/scala/org/apache/spark/ml/source/image/ImageDataSource.html )
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implements a Spark SQL data source API for loading image data as a DataFrame.
{% highlight scala %}
scala> val df = spark.read.format("image").option("dropInvalid", true).load("data/mllib/images/origin/kittens")
df: org.apache.spark.sql.DataFrame = [image: struct< origin: string , height: int . . . 4 more fields > ]
scala> df.select("image.origin", "image.width", "image.height").show(truncate=false)
+-----------------------------------------------------------------------+-----+------+
|origin |width|height|
+-----------------------------------------------------------------------+-----+------+
|file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 |
|file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 |
|file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 |
|file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 |
+-----------------------------------------------------------------------+-----+------+
{% endhighlight %}
< / div >
< div data-lang = "java" markdown = "1" >
[`ImageDataSource` ](api/java/org/apache/spark/ml/source/image/ImageDataSource.html )
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implements Spark SQL data source API for loading image data as a DataFrame.
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{% highlight java %}
Dataset< Row > imagesDF = spark.read().format("image").option("dropInvalid", true).load("data/mllib/images/origin/kittens");
imageDF.select("image.origin", "image.width", "image.height").show(false);
/*
Will output:
+-----------------------------------------------------------------------+-----+------+
|origin |width|height|
+-----------------------------------------------------------------------+-----+------+
|file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 |
|file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 |
|file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 |
|file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 |
+-----------------------------------------------------------------------+-----+------+
*/
{% endhighlight %}
< / div >
< div data-lang = "python" markdown = "1" >
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In PySpark we provide Spark SQL data source API for loading image data as a DataFrame.
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{% highlight python %}
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>>> df = spark.read.format("image").option("dropInvalid", True).load("data/mllib/images/origin/kittens")
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>>> df.select("image.origin", "image.width", "image.height").show(truncate=False)
+-----------------------------------------------------------------------+-----+------+
|origin |width|height|
+-----------------------------------------------------------------------+-----+------+
|file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 |
|file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 |
|file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 |
|file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 |
+-----------------------------------------------------------------------+-----+------+
{% endhighlight %}
< / div >
< div data-lang = "r" markdown = "1" >
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In SparkR we provide Spark SQL data source API for loading image data as a DataFrame.
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{% highlight r %}
> df = read.df("data/mllib/images/origin/kittens", "image")
> head(select(df, df$image.origin, df$image.width, df$image.height))
1 file:///spark/data/mllib/images/origin/kittens/54893.jpg
2 file:///spark/data/mllib/images/origin/kittens/DP802813.jpg
3 file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg
4 file:///spark/data/mllib/images/origin/kittens/DP153539.jpg
width height
1 300 311
2 199 313
3 300 200
4 300 296
{% endhighlight %}
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## LIBSVM data source
This `LIBSVM` data source is used to load 'libsvm' type files from a directory.
The loaded DataFrame has two columns: label containing labels stored as doubles and features containing feature vectors stored as Vectors.
The schemas of the columns are:
- label: `DoubleType` (represents the instance label)
- features: `VectorUDT` (represents the feature vector)
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< div data-lang = "scala" markdown = "1" >
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[`LibSVMDataSource` ](api/scala/org/apache/spark/ml/source/libsvm/LibSVMDataSource.html )
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implements a Spark SQL data source API for loading `LIBSVM` data as a DataFrame.
{% highlight scala %}
scala> val df = spark.read.format("libsvm").option("numFeatures", "780").load("data/mllib/sample_libsvm_data.txt")
df: org.apache.spark.sql.DataFrame = [label: double, features: vector]
scala> df.show(10)
+-----+--------------------+
|label| features|
+-----+--------------------+
| 0.0|(780,[127,128,129...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[124,125,126...|
| 1.0|(780,[152,153,154...|
| 1.0|(780,[151,152,153...|
| 0.0|(780,[129,130,131...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[99,100,101,...|
| 0.0|(780,[154,155,156...|
| 0.0|(780,[127,128,129...|
+-----+--------------------+
only showing top 10 rows
{% endhighlight %}
< / div >
< div data-lang = "java" markdown = "1" >
[`LibSVMDataSource` ](api/java/org/apache/spark/ml/source/libsvm/LibSVMDataSource.html )
implements Spark SQL data source API for loading `LIBSVM` data as a DataFrame.
{% highlight java %}
Dataset< Row > df = spark.read.format("libsvm").option("numFeatures", "780").load("data/mllib/sample_libsvm_data.txt");
df.show(10);
/*
Will output:
+-----+--------------------+
|label| features|
+-----+--------------------+
| 0.0|(780,[127,128,129...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[124,125,126...|
| 1.0|(780,[152,153,154...|
| 1.0|(780,[151,152,153...|
| 0.0|(780,[129,130,131...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[99,100,101,...|
| 0.0|(780,[154,155,156...|
| 0.0|(780,[127,128,129...|
+-----+--------------------+
only showing top 10 rows
*/
{% endhighlight %}
< / div >
< div data-lang = "python" markdown = "1" >
In PySpark we provide Spark SQL data source API for loading `LIBSVM` data as a DataFrame.
{% highlight python %}
>>> df = spark.read.format("libsvm").option("numFeatures", "780").load("data/mllib/sample_libsvm_data.txt")
>>> df.show(10)
+-----+--------------------+
|label| features|
+-----+--------------------+
| 0.0|(780,[127,128,129...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[124,125,126...|
| 1.0|(780,[152,153,154...|
| 1.0|(780,[151,152,153...|
| 0.0|(780,[129,130,131...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[99,100,101,...|
| 0.0|(780,[154,155,156...|
| 0.0|(780,[127,128,129...|
+-----+--------------------+
only showing top 10 rows
{% endhighlight %}
< / div >
< div data-lang = "r" markdown = "1" >
In SparkR we provide Spark SQL data source API for loading `LIBSVM` data as a DataFrame.
{% highlight r %}
> df = read.df("data/mllib/sample_libsvm_data.txt", "libsvm")
> head(select(df, df$label, df$features), 10)
label features
1 0 < environment: 0x7fe6d35366e8 >
2 1 < environment: 0x7fe6d353bf78 >
3 1 < environment: 0x7fe6d3541840 >
4 1 < environment: 0x7fe6d3545108 >
5 1 < environment: 0x7fe6d354c8e0 >
6 0 < environment: 0x7fe6d35501a8 >
7 1 < environment: 0x7fe6d3555a70 >
8 1 < environment: 0x7fe6d3559338 >
9 0 < environment: 0x7fe6d355cc00 >
10 0 < environment: 0x7fe6d35643d8 >
{% endhighlight %}
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