The first approach involves having the reducer read and buffer all of the values for a given key in an array data structure, for example , then doing an in-reducer sort on the values. This approach will not scale: since the reducer will be receiving all values for a given key , this approach might cause the reducer to run out of memory java.
On the other hand, this approach can work well if the number of values is small enough that it will not cause an out-of-memory error. The second approach involves using the MapReduce framework for sorting the reducer values this does not require in-reducer sorting of values passed to the reducer.
This option is scalable and will not generate out-of-memory errors. To implement the secondary sort feature, we need additional plug-in Java classes. To accomplish secondary sorting, we need to take control of the sort order of intermediate keys and the control order in which reducers process keys.
First, we inject a value temperature data into the composite key, and then we take control of the sort order of intermediate keys. The main question is what value we should add to the natural key to accomplish the secondary sort. So, we have to indicate how DateTemperaturePair objects should be sorted using the compareTo method. We need to define a proper data structure for holding our key and value, while also providing the sort order of intermediate keys.
For this, we need two plug-in classes: a custom partitioner to control which reducer processes which keys, and a custom Comparator to sort reducer values. The custom partitioner ensures that all data with the same key the natural key, not including the composite key with the temperature value is sent to the same reducer.
The custom Comparator does sorting so that the natural key year-month groups the data once it arrives at the reducer. Hadoop provides a plug-in architecture for injecting the custom partitioner code into the framework.
This is how we do so inside the driver class which submits the MapReduce job to Hadoop :. Hadoop provides a plug-in architecture for injecting the grouping comparator code into the framework. The mappers create K , V pairs, where K is a composite key of year,month,temperature and V is temperature. The year,month part of the composite key is the natural key. The partitioner plug-in class enables us to send all natural keys to the same reducer and the grouping comparator plug-in class enables temperatures to arrive sorted at reducers.
This section provides a complete MapReduce implementation of the secondary sort problem using the Hadoop framework. The input will be a set of files, where each record line will have the following format :. How is the value injected into the key? The first comparator the DateTemperaturePair. You can easily control the sorting order of the values ascending or descending by using the DateTemperaturePair.
To solve a secondary sorting problem in Spark, we have at least two options:. Our expected output is as follows.
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Note that the values of reducers are grouped by name and sorted by time:. The Spark-based algorithm is listed next. Although there are 10 steps, most of them are trivial and some are provided for debugging purposes only:. The main Java classes for MapReduce are given in the org. This package includes the following classes and interfaces:. To accomplish this, we use the groupByKey method. All steps, 1—10, are listed inside the class definition, which will be presented in the following sections. Parallel operations will be achieved through the extensive use of RDDs.
This approach is useful when you read your cluster configurations from an XML file. In a nutshell, the JavaSparkContext object has the following responsibilities:.
Registers the application driver to the cluster manager. Note that collect is used for debugging and educational purposes but avoid using collect for debugging purposes in production clusters; doing so will impact performance. Also, you may use JavaRDD. We implement the reducer operation using groupByKey.
Here, however, we cannot use reduceByKey.
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For example, in this step, to sort our values, we have to copy them into another list first. Immutability applies to the RDD itself and its elements. List object. Next, we will cover how to submit the secondary sort application in the standalone and YARN cluster modes. Typically, you save the final result to HDFS. We cannot achieve this in the current Spark Spark So, you should implement sorting explicitly using an RDD operator.
If we had a partitioner by a natural key name that preserved the order of the RDD, that would be a viable solution—for example, if we sorted by name, time , we would get:. There is a partitioner represented as an abstract class, org. Partitioner , but it does not preserve the order of the original RDD elements. Therefore, option 2 cannot be implemented by the current version of Spark 1. For further work on this topic, you may refer to the following:.
Stay ahead with the world's most comprehensive technology and business learning platform. With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more. Start Free Trial No credit card required. Chapter 1. Secondary Sort: Introduction A secondary sort problem relates to sorting values associated with a key in the reduce phase. A dump of the temperature data might look something like the following columns are year , month , day , and daily temperature , respectively : , 01, 01, 5 , 01, 02, 45 , 01, 03, 35 , 01, 04, Therefore, we want to generate something like this output the first column is year-month and the second column is the sorted temperatures : 5, 10, 35, 45, Solutions to the Secondary Sort Problem There are at least two possible approaches for sorting the reducer values.
This is a summary of the second approach: Use the Value-to-Key Conversion design pattern: form a composite intermediate key, K , V 1 , where V 1 is the secondary key. Here, K is called a natural key. To inject a value i. In our example, V 1 is the temperature data. Let the MapReduce execution framework do the sorting rather than sorting in memory, let the framework sort by using the cluster nodes. Explore the famous codes that changed the fate of nations and political leaders.
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