im researching Hadoop and MapReduce (I'm a beginner!) and have a simple question regarding HDFS. I'm a little confused about how HDFS and MapReduce work together.
Lets say I have logs from System A, Tweets, and a stack of documents from System B. When this is loaded into Hadoop/HDFS, is this all thrown into one big HDFS bucket, or would there be 3 areas (for want of a better word)? If so, what is the correct terminology?
The questions stems from understanding how to execute a MapReduce job. If I only wanted to concentrate on the Logs for example, can this be done, or are all jobs executed on the entire content stored on the cluster?
Thanks for your guidance!
TM
HDFS is a file system. As in your local filesystem you can organize all your logs and documents into multiple files and directories. When you run MapReduce jobs you usually specify a directory with your input files. Thus it is possible to execute a job only on the logs from system A or the documents from system B.
However the input for your mappers is specified by the InputFormat. Most implementations originate from FileInputFormat which reads files. However it is possible to implement custom InputFormats in order to read data from other sources. You can find an explanation on input and output formats in this Hadoop Tutorial.
Related
I am trying to combine small files on hdfs. This is simply for historical purposes, if needed the large file(s) would be disassembled and ran through the process to create the data for the hadoop table. Is there a way to achieve this simply? For example, day one receive 100 small files, combine into a file, then day two add/append more files into the previously created file, etc...
If the files are all the same "schema", let's say, like CSV or JSON. Then, you're welcome to write a very basic Pig / Spark job to read a whole folder of tiny files, then write it back out somewhere else, which will very likely merge all the files into larger sizes based on the HDFS block size.
You've also mentioned Hive, so use an external table for the small files, and use a CTAS query to create a separate table, thereby creating a MapReduce job, much the same as Pig would do.
IMO, if possible, the optimal solution is to setup a system "upstream" of Hadoop, which will batch your smaller files into larger files, and then dump them out to HDFS. Apache NiFi is a useful tool for this purpose.
I have an ingest pipeline created using spark streaming, and I would like to store the RDDs in hadoop as a large unstructured (JSONL) datafile to simplify future analysis.
What is the best approach for persisting astream to hadoop without ending up with very large numbers of small files? (since hadoop is not good with those, and they complicate analysis workflows)
First, I would suggest using a persistance layer that can handle this like Cassandra. But, if you are deadset on HDFS, then the mailing list has an answer already
You can use FileUtil.copyMerge (from the hadoop fs) API and specify the path to the folder where saveAsTextFiles is saving the part text file.
Suppose your directory is /a/b/c/ use
FileUtil.copyMerge(FileSystem of source, a/b/c,
FileSystem of destination, Path to the merged file say (a/b/c.txt),
true(to delete the original dir,null))
Let me begin by saying I am a complete newbie to Hadoop. My requirement is to analyse server log files using Hadoop infrastructure. The first step I took in this direction was to stream the log files and dump them raw into my single node Hadoop cluster using Flume HDFS sink. Now I have a bunch of files with records which look something like this:
timestamp req-id level module-name message
My next step is to parse the files (separate out the fields) and store them back so that they are ready for searching.
What approach should I use for this? Can I do this using Hive? (sorry if the question is naive). The information available on the internet is overwhelming.
You can use HCatalog or Impala for faster querying.
From your explanation you have time series data.Hadoop with HDFS itself is not meant for random access or querying. You can use HBase a database for hadoop as HDFS a backend filesystem. It is good for random access.
Also for your need parsing and rearranging data, you can make use of Hadoop's MapReduce.HBase has built in support for this. HBase can be used for input/output of MapReduce Job.
Basic information you can get from here. For better understanding try Definitive Guide for HBase / HBase in Action books.
I am a newbie on the MR and Hadoop front.
I wrote an MR for finding missing's in csv file and it is working fine.
now I have an usecase where i need to parse a csv file and code it with the regarding category.
ex: "11,abc,xyz,51,61,78","11,adc,ryz,41,71,38",.............
now this has to be replaced as "1,abc,xyz,5,6,7","1,adc,ryz,4,7,3",.............
here i am doing a mod of 10 but there will be different cases of mod's.
data size is in gb's.
I want to know how to replace the content in-place for the input. Is this achievable with MR?
Basically i have not seen any file handling or writing based hadoop examples any where.
At this point i do not want to go to HBase or other db tools.
You can not replace data in place, since HDFS files are append only, and can not be edited.
I think simplest way to achiece your goal is to register your data in the Hive as external table, and write your trnasformation in HQL.
Hive is a system sitting aside of hadoop and translating your queries to MR Jobs.
Its usage is not serious infrastructure decision as HBASE usage
I heard like for mapreduce jobs input need not in HDFS. It can be on other file system.. Can someone please provide me more inputs on this..
I am litle confused on this? In standalone mode, data can be on local file system. But in cluster mode how can we point to mapreduce jobs to some other file system?
No it does not need to be in HDFS. For instance jobs which target HBase using its TableInputFormat pull records over the network from HBase nodes as inputs to its map jobs. The DbInputFormat can be used to pull data from a SQL database into a job. You could build an input format that did something like read data off of an NFS mount.
In practice you want to avoid pulling data over the network if you can. MR performance is much better if you can have your data locally on the nodes where the job is being run since Disk Throughput > Network Throughput.
Based in the InputFormat set on the job, Hadoop can read from any source. Hadoop provides a couple of InputFormats. It's not difficult to write a custom InputFormat also, let's say to provide a proprietary format as input to a Job.
On the same lines Hadoop provides a couple of OutputFormats and it shouldn't be difficult to write a custom OutputFormat also.
Here is a nice article on the DBInputFormat.
Another way to achieve it is to put into HDFS files with information where the real data is. Mapper will get this information and pull real data for the processing.
For example we can have several files with URLs of data to be processed.
What we will loose in this case is data locality - otherwise it is fine.