使用命令行查找 hdfs 分布式文件中的总行数

问题描述

我正在研究一个集群，其中数据集以分布式方式保存在 hdfs 中.这是我所拥有的:

I am working on a cluster where a dataset is kept in hdfs in distributed manner. Here is what I have:

[hmi@bdadev-5 ~]$ hadoop fs -ls /bdatest/clm/data/
Found 1840 items
-rw-r--r--   3 bda supergroup          0 2015-08-11 00:32 /bdatest/clm/data/_SUCCESS
-rw-r--r--   3 bda supergroup   34404390 2015-08-11 00:32 /bdatest/clm/data/part-00000
-rw-r--r--   3 bda supergroup   34404062 2015-08-11 00:32 /bdatest/clm/data/part-00001
-rw-r--r--   3 bda supergroup   34404259 2015-08-11 00:32 /bdatest/clm/data/part-00002
....
....

数据格式为:

[hmi@bdadev-5 ~]$ hadoop fs -cat /bdatest/clm/data/part-00000|head
V|485715986|1|8ca217a3d75d8236|Y|Y|Y|Y/1X||Trimode|SAMSUNG|1x/Trimode|High|Phone|N|Y|Y|Y|N|Basic|Basic|Basic|Basic|N|N|N|N|Y|N|Basic-Communicator|Y|Basic|N|Y|1X|Basic|1X|||SAM|Other|SCH-A870|SCH-A870|N|N|M2MC|

所以，我要做的是统计原始数据文件data的总行数.我的理解是像 part-00000、part-00001 等分布式块有重叠.因此，仅计算 part-xxxx 文件中的行数并将它们相加是行不通的.此外，原始数据集data 的大小为~70GB.如何有效地找出总行数?

So, what I want to do is to count the total number of lines in the original data file data. My understanding is that the distributed chunks like part-00000, part-00001 etc have overlaps. So just counting the number of lines in part-xxxx files and summing them won't work. Also the original dataset data is of size ~70GB. How can I efficiently find out the total number of lines?

推荐答案

更高效——您可以使用 spark 来计算编号.的线.以下代码片段有助于计算行数.

More efficiently -- you can use spark to count the no. of lines. The following code snippet helps to count the number of lines.

text_file = spark.textFile("hdfs://...")
count = text_file.count();
count.dump();

这将显示 no 的计数.行.

This displays the count of no. of lines.

注意:不同零件文件中的数据不会重叠

使用hdfs dfs -cat/bdatest/clm/data/part-* |wc -l 也会为您提供输出，但这会将所有数据转储到本地机器并且需要更长的时间.

Using hdfs dfs -cat /bdatest/clm/data/part-* | wc -l will also give you the output but this will dump all the data to the local machine and takes longer time.

最好的解决方案是使用 MapReduce 或 spark.MapReduce 将需要更长的时间来开发和执行.如果安装了火花，这是最好的选择.

Best solution is to use MapReduce or spark. MapReduce will take longer time to develop and execute. If the spark is installed, this is the best choice.

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