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---
title: Apache DataFu Spark - Getting Started
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section_name: Getting Started
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# DataFu Spark

Apache DataFu Spark is a collection of utils and user-defined functions for working with large scale data in [Apache Spark](http://spark.apache.org/).

A list of some of the things you can do with DataFu Spark is given below:

* ["Dedup" a table](https://github.com/apache/datafu/blob/master/datafu-spark/src/main/scala/datafu/spark/SparkDFUtils.scala#L139) - remove duplicates based on a key and ordering (typically a date updated field, to get only the mostly recently updated record).

* [Join a table with a numeric field with a table with a range](https://github.com/apache/datafu/blob/master/datafu-spark/src/main/scala/datafu/spark/SparkDFUtils.scala#L361)

* [Do a skewed join between tables](https://github.com/apache/datafu/blob/master/datafu-spark/src/main/scala/datafu/spark/SparkDFUtils.scala#L274) (where the small table is still too big to fit in memory)

* [Count distinct up to](https://github.com/apache/datafu/blob/master/datafu-spark/src/main/scala/datafu/spark/SparkUDAFs.scala#L224) - an efficient implementation when you just want to verify that a certain minimum of distinct rows appear in a table

* Call Python code from Spark Scala, or Scala code from PySpark

If you'd like to read more details about these functions, check out the [Guide](/docs/spark/guide.html).  Otherwise if you are
ready to get started using DataFu Spark, keep reading.

The rest of this page assumes you already have a built JAR available.  If this is not the case, please see the [Download](/docs/download.html) page.

This jar should be loaded to the Spark class path. You can verify that you've done this correctly by trying to import one of our DataFu classes, for example, _DataFrameOps_.

## Basic Example: Finding the most recent update of a given record

A common scenario in data sent to the HDFS — the Hadoop Distributed File System — is multiple rows representing updates for the same logical data. For example, in a table representing accounts, a record might be written every time customer data is updated, with each update receiving a newer timestamp. Let’s consider the following simplified example.

<br>
<script src="https://gist.github.com/eyala/65b6750b2539db5895738a49be3d8c98.js"></script>
<center>Raw customers’ data, with more than one row per customer</center>
<br>

We can see that though most of the customers only appear once, _julia_ and _quentin_ have 2 and 3 rows, respectively. How can we get just the most recent record for each customer? We can use DataFu's _dedupWithOrder_ method.

```scala
import datafu.spark.DataFrameOps._

val customers = spark.read.format("csv").option("header", "true").load("customers.csv")

csv.dedupWithOrder($"id", $"date_updated".desc).show
```

Our result will be as expected — each customer only appears once, as you can see below:

<br>
<script src="https://gist.github.com/eyala/1dddebc39e9a3fe4501638a95f577752.js"></script>
<center>“Deduplicated” data, with only the most recent record for each customer (though not in order)</center>
<br>

There are two additional variants of _dedupWithOrder_ in datafu-spark. The _dedupWithCombiner_ method has similar functionality to _dedupWithOrder_, but uses a UDAF to utilize map side aggregation. _dedupTopN_ allows retaining more than one record for each key.

## Next Steps

Check out the [Guide](/docs/spark/guide.html) for more information on what you can do with DataFu Spark.