{"id":2472,"date":"2017-05-04T13:34:34","date_gmt":"2017-05-04T13:34:34","guid":{"rendered":"http:\/\/data-flair.training\/blogs\/?p=2472"},"modified":"2018-11-16T14:32:55","modified_gmt":"2018-11-16T09:02:55","slug":"hadoop-counters","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/hadoop-counters\/","title":{"rendered":"Hadoop Counters | The Most Complete Guide to MapReduce Counters"},"content":{"rendered":"

1. Hadoop Counters: Objective<\/h2>\n

In this MapReduce Hadoop Counters tutorial, we will provide you the detailed description of MapReduce Counters in Hadoop<\/a><\/strong>. The tutorial covers an introduction to Hadoop MapReduce<\/strong><\/a> counters, Types of Hadoop Counters such as Built-in Counters and User-defined counters. In this Hadoop counters tutorial, we will also discuss the FileInputFormat and FileOutputFormat of Hadoop MapReduce.<\/p>\n

\"Hadoop<\/a>

Hadoop Counters<\/p><\/div>\n

2. What is Hadoop MapReduce?<\/h2>\n

Before we start with Hadoop Counters, let us first see the overview of Hadoop MapReduce.<\/p>\n

MapReduce<\/strong> is the core component of Hadoop which provides data processing. MapReduce works by breaking the processing into two phases; Map<\/strong> phase and Reduce<\/strong> phase. The map is the first phase of processing, where we specify all the complex logic\/business rules\/costly code, whereas the Reduce phase is the second phase of processing, where we specify light-weight processing like aggregation\/ summation.<\/p>\n

In Hadoop, MapReduce Framework has certain elements such as Counters<\/strong>, Combiners<\/a><\/strong>, and Partitioners,<\/a><\/strong> which play a key role in improving the performance of data processing.
\nLet\u2019s now focus on Hadoop MapReduce Counters here.
\nRead: DataFlaw in MapReduce<\/a><\/strong><\/p>\n

3. What are Hadoop Counters?<\/h2>\n

Hadoop Counters Explained:<\/strong> Hadoop Counters provides a way to measure the progress or the number of operations that occur within map\/reduce job<\/strong><\/a>. Counters in Hadoop MapReduce are a useful channel for gathering statistics about the MapReduce job: for quality control or for application-level. They are also useful for problem diagnosis.<\/p>\n

Counters represent Hadoop global counters, defined either by the MapReduce framework or applications. Each Hadoop counter is named by an \u201cEnum<\/strong>\u201d and has a long for the value. Counters are bunched into groups, each comprising of counters from a particular Enum class.
\nHadoop Counters validate that:<\/strong><\/p>\n