MapReduce

Hadoop Reducer Values in Memory?

由 ￣綄美尐妖づ 提交于 2019-12-20 13:57:19
问题 I'm writing a MapReduce job that may end up with a huge number of values in the reducer. I am concerned about all of these values being loaded into memory at once. Does the underlying implementation of the Iterable<VALUEIN> values load values into memory as they are needed? Hadoop: The Definitive Guide seems to suggest this is the case, but doesn't give a "definitive" answer. The reducer output will be far more massive than the values input, but I believe the output is written to disk as

Join of two datasets in Mapreduce/Hadoop

由 こ雲淡風輕ζ 提交于 2019-12-20 12:38:11
问题 Does anyone know how to implement the Natural-Join operation between two datasets in Hadoop? More specifically, here's what I exactly need to do: I am having two sets of data: point information which is stored as (tile_number, point_id:point_info) , this is a 1:n key-value pairs. This means for every tile_number, there might be several point_id:point_info Line information which is stored as (tile_number, line_id:line_info) , this is again a 1:m key-value pairs and for every tile_number, there

MapReduce算法（计算每个相同IMSI（国际移动用户标识）、TAC（跟踪区域码）的上行流量和，下行流量和，总流量和）

由 一笑奈何 提交于 2019-12-20 12:05:44
需求: 计算每个相同IMSI（国际移动用户标识）、TAC（跟踪区域码）的上行流量和，下行流量和，总流量和。 需求说明：将S1U数据里面的VOLUME字段（数据流量）和IMSI、TAC两个字段提取出来， 并且按相同IMSI、TAC对VOLUME求和（上行流量和、下行流量和、总流量的和）。其结果保存为新的文件。 数据： 2604|731|11|fe58db672c0fdf509b00000000010000|6|460028593519735|3520220675936518|15859328363|1|100.78.245.86|100.78.46.134|2152|2152|162597888|1802797180|58211|121570817|cmnet.mnc002.mcc460.gprs|103|1480723076856|1480723079334|2|1|568|255|2|10.40.123.144|FFFF:FFFF:FFFF:FFFF:FFFF:FFFF:FFFF:FFFF|58874|255|183.230.77.151|FFFF:FFFF:FFFF:FFFF:FFFF:FFFF:FFFF:FFFF|80|1668|21348|21|23|0|0|0|0|39|29|0|0|10|103|4096|1360|1|0|1|3|5|200|103|160|205

Hadoop: Number of mappers and reducers

由 痞子三分冷 提交于 2019-12-20 10:48:09
问题 I ran Hadoop MapReduce on 1.1GB file multiple times with a different number of mappers and reducers (e.g. 1 mapper and 1 reducer, 1 mapper and 2 reducers, 1 mapper and 4 reducers, ...) Hadoop is installed on quad-core machine with hyper-threading. The following is the top 5 result sorted by shortest execution time: +----------+----------+----------+ | time | # of map | # of red | +----------+----------+----------+ | 7m 50s | 8 | 2 | | 8m 13s | 8 | 4 | | 8m 16s | 8 | 8 | | 8m 28s | 4 | 8 | |

how to fetch all of data from hbase table in spark

由 生来就可爱ヽ(ⅴ＜●) 提交于 2019-12-20 10:47:27
问题 I have a big table in hbase that name is UserAction, and it has three column families(song,album,singer). I need to fetch all of data from 'song' column family as a JavaRDD object. I try this code, but it's not efficient. Is there a better solution to do this ? static SparkConf sparkConf = new SparkConf().setAppName("test").setMaster( "local[4]"); static JavaSparkContext jsc = new JavaSparkContext(sparkConf); static void getRatings() { Configuration conf = HBaseConfiguration.create(); conf

Hadoop MapReduce: Possible to define two mappers and reducers in one hadoop job class?

由 岁酱吖の 提交于 2019-12-20 10:45:15
问题 I have two separate java classes for doing two different mapreduce jobs. I can run them independently. The input files on which they are operating are the same for both of the jobs. So my question is whether it is possible to define two mappers and two reducers in one java class like mapper1.class mapper2.class reducer1.class reducer2.class and then like job.setMapperClass(mapper1.class); job.setmapperClass(mapper2.class); job.setCombinerClass(reducer1); job.setCombinerClass(reducer2); job

Hadoop : java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.hadoop.io.Text

由 筅森魡賤 提交于 2019-12-20 09:39:15
问题 My program looks like public class TopKRecord extends Configured implements Tool { public static class MapClass extends Mapper<Text, Text, Text, Text> { public void map(Text key, Text value, Context context) throws IOException, InterruptedException { // your map code goes here String[] fields = value.toString().split(","); String year = fields[1]; String claims = fields[8]; if (claims.length() > 0 && (!claims.startsWith("\""))) { context.write(new Text(year.toString()), new Text(claims

Difference in calling the job

由 女生的网名这么多〃 提交于 2019-12-20 09:37:58
问题 what is the difference between calling a mapreduce job from main() and from ToolRunner.run() ? When we say that the main class say, MapReduce extends Configured implements Tool , what are the additional privileges we get which we do not have if we were to just make a simple run of the job from the main method? Thanks. 回答1: There's no extra privileges, but your command line options get run via the GenericOptionsParser, which will allow you extract certain configuration properties and configure

Where do I start with distributed computing?

由 巧了我就是萌 提交于 2019-12-20 08:45:05
问题 I'm interested in learning techniques for distributed computing. As a Java developer, I'm probably willing to start with Hadoop. Could you please recommend some books/tutorials/articles to begin with? 回答1: Maybe you can read some papers related to MapReduce and distributed computing first, to gain a better understanding of it. Here are some I would like to recommand: MapReduce: Simplified Data Processing on Large Clusters, http://www.usenix.org/events/osdi04/tech/full_papers/dean/dean_html/

MapReduce alternatives

由 你离开我真会死。 提交于 2019-12-20 08:41:19
问题 Are there any alternative paradigms to MapReduce (Google, Hadoop)? Is there any other reasonable way how to split & merge big problems? 回答1: Definitively. Check out, for example, Bulk Synchronous Parallel. Map/Reduce is in fact a very restricted way of reducing problems, however that restriction makes it manageable in a framework like Hadoop. The question is if it is less trouble to press your problem into a Map/Reduce setting, or if its easier to create a domain-specific parallelization
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