{"id":2198,"date":"2017-04-24T08:53:01","date_gmt":"2017-04-24T08:53:01","guid":{"rendered":"http:\/\/data-flair.training\/blogs\/?p=2198"},"modified":"2018-11-14T16:16:01","modified_gmt":"2018-11-14T10:46:01","slug":"hadoop-recordreader","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/hadoop-recordreader\/","title":{"rendered":"Hadoop RecordReader &#8211; How RecordReader Works in Hadoop?"},"content":{"rendered":"<h2>1. Hadoop RecordReader Tutorial &#8211; Objective<\/h2>\n<p>In this Hadoop RecordReader Tutorial, We are going to discuss the important concept of<a href=\"http:\/\/data-flair.training\/blogs\/hadoop-tutorial-for-beginners\/\"><strong> Hadoop<\/strong><\/a> MapReduce i.e. <strong>RecordReader<\/strong>. The MapReduce RecordReader in Hadoop takes the byte-oriented view of input, provided by the InputSplit and presents as a record-oriented view for Mapper. It uses the data within the boundaries that were created by the InputSplit and creates Key-value pair.<\/p>\n<p>This blog<strong>\u00a0<\/strong>will answer what is RecordReader in Hadoop, how Hadoop RecordReader works and types of Hadoop RecordReader \u2013 SequenceFileRecordReader and Line RecordReader, the maximum size of a record in Hadoop.<br \/>\nLearn <a href=\"http:\/\/data-flair.training\/blogs\/install-hadoop-on-single-machine\/\">How to Install Hadoop on Single Machine.<\/a>\u00a0and <a href=\"http:\/\/data-flair.training\/blogs\/install-hadoop-1-x-on-multi-node-cluster\/\">Hadoop Installation on a <\/a><a href=\"http:\/\/data-flair.training\/blogs\/install-hadoop-1-x-on-multi-node-cluster\/\">multi-node cluster<\/a>.<\/p>\n<div class=\"mceTemp\"><\/div>\n<div id=\"attachment_42024\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-42024\" class=\"size-full wp-image-42024\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01.jpg\" alt=\"Hadoop MapReduce RecordReader\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01-1024x536.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/04\/Hadoop-MapReduce-RecordReader-01-520x272.jpg 520w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-42024\" class=\"wp-caption-text\">Hadoop MapReduce RecordReader<\/p><\/div>\n<h2>2. What is Hadoop RecordReader?<\/h2>\n<p>To understand recordreader in Hadoop, we need to understand the Hadoop data flow. So, Let\u2019s see how the data flow in Hadoop?<br \/>\n<strong>MapReduce<\/strong> has a simple model of data processing. Inputs and Outputs for the map and reduce functions are <a href=\"http:\/\/data-flair.training\/blogs\/key-value-pairs-hadoop-mapreduce\/\"><strong>key-value pairs.<\/strong> <\/a>The map and reduce functions in Hadoop MapReduce have the following general form:<\/p>\n<div id=\"attachment_4290\" style=\"width: 3352px\" class=\"wp-caption alignnone\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-4290\" class=\"wp-image-4290 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1.jpg\" alt=\"Introduction to Hadoop RecordReader and its types.\" width=\"3342\" height=\"1750\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1.jpg 3342w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/09\/introduction-to-hadoop-recordreader-1-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 3342px) 100vw, 3342px\" \/><\/a><p id=\"caption-attachment-4290\" class=\"wp-caption-text\">Hadoop RecordReader and its types.<\/p><\/div>\n<ul>\n<li><strong>map:<\/strong> (K1, V1) \u2192 list(K2, V2)<\/li>\n<li><strong>reduce:<\/strong> (K2, list(V2)) \u2192 list(K3, V3)<\/li>\n<\/ul>\n<p>Now before processing, it needs to know on which data to process, this is achieved with the <strong><a href=\"http:\/\/data-flair.training\/blogs\/hadoop-inputformat-types\/\">InputFormat<\/a><\/strong> class. InputFormat is the class which selects the file from<strong> <a href=\"http:\/\/data-flair.training\/blogs\/comprehensive-hdfs-guide-introduction-architecture-data-read-write-tutorial\/\">HDFS<\/a><\/strong> that should be input to the map function. An InputFormat is also responsible for creating the <a href=\"http:\/\/data-flair.training\/blogs\/inputsplit-in-hadoop-mapreduce\/\"><strong>InputSplits<\/strong><\/a> and dividing them into records. The data is divided into the number of splits(typically 64\/128mb) in HDFS. This is called as inputsplit which is the input that is processed by a single map.<\/p>\n<p>InputFormat class calls the <strong>getSplits()<\/strong> function and computes splits for each file and then sends them to the <strong>JobTracker<\/strong>, which uses their storage locations to schedule map tasks to process them on the <strong>TaskTrackers<\/strong>. Map task then passes the split to the <strong>createRecordReader()<\/strong> method on InputFormat in task tracker to obtain a RecordReader for that split. The RecordReader load&#8217;s data from its source and converts into key-value pairs suitable for reading by the<strong> <a href=\"http:\/\/data-flair.training\/blogs\/mapper-in-hadoop-mapreduce\/\">mapper.<\/a><\/strong><\/p>\n<p>Hadoop RecordReader uses the data within the boundaries that are being created by the inputsplit and creates Key-value pairs for the mapper. The \u201cstart\u201d is the byte position in the file where the RecordReader should start generating key\/value pairs and the \u201cend\u201d is where it should stop reading records. In Hadoop RecordReader, the data is loaded from its source and then the data is converted into key-value pairs suitable for reading by the Mapper. It communicates with the inputsplit until the file reading is not completed.<br \/>\nRead: <a href=\"https:\/\/data-flair.training\/blogs\/mapper-in-hadoop-mapreduce\/\">Mapper in Mapreduce<\/a><\/p>\n<h2>3. How Hadoop RecordReader works?<\/h2>\n<p>Let us now see the working of RecordReader in Hadoop.<br \/>\nA RecordReader is more than iterator over records, and map task uses one record to generate key-value pair which is passed to the map function. We can see this by using mapper\u2019s run function:<\/p>\n<p>[php]public void run(Context context) throws IOException, InterruptedException{<br \/>\n&lt;pre&gt;setup(context);<br \/>\nwhile(context.nextKeyValue())<br \/>\n{<br \/>\nmap(context.setCurrentKey(),context.getCurrentValue(),context)<br \/>\n}<br \/>\ncleanup(context);<br \/>\n}[\/php]<\/p>\n<p>After running <strong>setup()<\/strong>, the <strong>nextKeyValue()<\/strong> will repeat on the context, to populate the key and value objects for the mapper. The key and value is retrieved from the record reader by way of context and passed to the <strong>map()<\/strong> method to do its work. An input to the map function, which is a key-value pair(K, V), gets processed as per the logic mentioned in the map code. When the record gets to the end of the record, the <strong>nextKeyValue()<\/strong> method returns false.<\/p>\n<p>A RecordReader usually stays in between the boundaries created by the inputsplit to generate key-value pairs but this is not mandatory. A custom implementation can even read more data outside of the inputsplit, but it is not encouraged a lot.<br \/>\nRead: <a href=\"https:\/\/data-flair.training\/blogs\/reducer-in-hadoop-mapreduce\/\">Reducer in MapReduce<\/a><\/p>\n<h2>4. Types of Hadoop RecordReader in MapReduce<\/h2>\n<p>The RecordReader instance is defined by the InputFormat. By default, it uses TextInputFormat for converting data into a key-value pair. TextInputFormat provides 2 types of RecordReaders:<\/p>\n<h3>i. LineRecordReader<\/h3>\n<p>Line RecordReader in Hadoop is the default RecordReader that textInputFormat provides and it treats each line of the input file as the new value and associated key is byte offset. LineRecordReader always skips the first line in the split (or part of it), if it is not the first split. It read one line after the boundary of the split in the end (if data is available, so it is not the last split).<\/p>\n<h3>ii. SequenceFileRecordReader<\/h3>\n<p>It reads data specified by the header of a sequence file.<br \/>\nRead: <a href=\"https:\/\/data-flair.training\/blogs\/partitioner-in-hadoop-mapreduce-hadoop-internals\/\">Partitioner in MapReduce<\/a><\/p>\n<h2>5. Maximum size for a Single Record<\/h2>\n<p>There is a maximum size allowed for a single record to be processed. This value can be set using below parameter.<\/p>\n<p>[php]conf.setInt(&#8220;mapred.linerecordreader.maxlength&#8221;, Integer.MAX_VALUE);[\/php]<br \/>\nA line with a size greater than this maximum value (default is 2,147,483,647) will be ignored.<br \/>\nRead: <a href=\"https:\/\/data-flair.training\/blogs\/combiner-in-hadoop-mapreduce-advantages-disadvantages\/\">Combiner in MapReduce<\/a><\/p>\n<h2>6. Hadoop RecordReader Tutorial &#8211; Conclusion<\/h2>\n<p>Hence, we have discussed RecordReader in Hadoop in detail. In our next blog, we will provide you the <a href=\"http:\/\/data-flair.training\/blogs\/how-hadoop-mapreduce-works\/\">end to end job execution flow of Hadoop MapReduce<\/a>. If you have any query related to Hadoop RecordReader, you can share with us in a comment section given below. We will be happy to solve them.<br \/>\n<strong>See Also-<\/strong><\/p>\n<ul>\n<li><a href=\"http:\/\/data-flair.training\/blogs\/hadoop-mapreduce-performance-tuning-best-practices\/\">Hadoop MapReduce Performance Tuning\u00a0<\/a><\/li>\n<li><a href=\"http:\/\/data-flair.training\/blogs\/mapreduce-interview-questions\/\">Top MapReduce Interview Questions and Answers<\/a><\/li>\n<\/ul>\n<p><a href=\"https:\/\/hadoop.apache.org\/docs\/r2.7.5\/api\/org\/apache\/hadoop\/mapred\/RecordReader.html\">Reference<\/a><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:2368,&quot;href&quot;:&quot;https:\\\/\\\/hadoop.apache.org\\\/docs\\\/r2.7.5\\\/api\\\/org\\\/apache\\\/hadoop\\\/mapred\\\/RecordReader.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251211043958\\\/https:\\\/\\\/hadoop.apache.org\\\/docs\\\/r2.7.5\\\/api\\\/org\\\/apache\\\/hadoop\\\/mapred\\\/RecordReader.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-15 10:24:16&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-21 00:45:14&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-28 11:22:49&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-02 21:27:53&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-10 02:20:06&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-13 19:13:35&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-19 11:46:35&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-23 07:43:31&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-27 07:45:04&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-02-07 11:02:51&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-02-12 00:51:17&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-02-16 20:47:33&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-02-20 08:14:32&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-08 04:14:06&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-20 03:47:32&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-25 10:50:27&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-31 13:22:40&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-05 13:47:51&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-14 21:17:43&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-19 11:31:04&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-22 14:37:17&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-25 20:41:06&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-30 06:16:33&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-10 17:47:34&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-19 22:00:19&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-03 02:27:59&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-15 03:08:14&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-25 12:25:11&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-07-03 11:48:44&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-07-16 14:34:16&quot;,&quot;http_code&quot;:206}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-16 14:34:16&quot;,&quot;http_code&quot;:206},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1. Hadoop RecordReader Tutorial &#8211; Objective In this Hadoop RecordReader Tutorial, We are going to discuss the important concept of Hadoop MapReduce i.e. RecordReader. The MapReduce RecordReader in Hadoop takes the byte-oriented view of&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":42024,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[37],"tags":[5293,5317,5318,5319,8560,11427,11428],"class_list":["post-2198","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mapreduce","tag-hadoop-mapreduce-recordreader","tag-hadoop-recordreader-introduction","tag-hadoop-recordreader-tutorial","tag-hadoop-recordreder","tag-mapreduce-recordreder","tag-recordreader-in-hadoop-mapreduce","tag-recordreader-in-mapreduce"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hadoop RecordReader - How RecordReader Works in Hadoop? - DataFlair<\/title>\n<meta name=\"description\" content=\"This Hadoop RecordReder Tutorial covers Hadoop RecordReader working,Hadoop RecordReader types,LineRecordReader,SequenceFileRecordReader,Record size in Hadoop\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/hadoop-recordreader\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hadoop RecordReader - How RecordReader Works in Hadoop? 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