I am using MAPI tools (Its microsoft lib and in .NET) and then apache TIKA libraries to process and extract the pst from exchange server, which is not scalable.
How can I process/extracts pst using MR way ... Is there any tool, library available in java which I can use in my MR jobs. Any help would be great-full .
Jpst Lib internally uses: PstFile pstFile = new PstFile(java.io.File)
And the problem is for Hadoop API's we don't have anything close to java.io.File.
Following option is always there but not efficient:
File tempFile = File.createTempFile("myfile", ".tmp");
fs.moveToLocalFile(new Path (<HDFS pst path>) , new Path(tempFile.getAbsolutePath()) );
PstFile pstFile = new PstFile(tempFile);
Take a look at Behemoth (http://digitalpebble.blogspot.com/2011/05/processing-enron-dataset-using-behemoth.html). It combines Tika and Hadoop.
I've also written by own Hadoop + Tika jobs. The pattern is:
Wrap all the pst files into sequencence or avro files.
Write a map only job that reads the pst files form the avro files and writes it to the local disk.
Run tika across the files.
Write the output of tika back into a sequence file
Hope that help.s
Its not possible to process PST file in mapper. after long analysis and debug it was found out that the API is not exposed properly and those API needs localfile system to store extracted pst contents. It directly cant store on HDFS. thats bottle-neck. And all those API's(libs that extract and process) are not free.
what we can do is extract outside hdfs and then we can process in MR jobs
Related
I have created a real time application in which I am writing data streams to hdfs from weblogs using flume, and then processing that data using spark stream. But while flume is writing and creating new files in hdfs spark stream is unable to process those files. If I am putting the files to hdfs directory using put command spark stream is able to read and process the files. Any help regarding the same will be great.
You have detected the problem yourself: while the stream of data continues, the HDFS file is "locked" and can not be read by any other process. On the contrary, as you have experienced, if you put a batch of data (that's yur file, a batch, not a stream), once it is uploaded it is ready for being read.
Anyway, and not being an expert on Spark streaming, it seems from the Spark Streaming Programming Guide, Overview section, that you are not performing the right deployment. I mean, from the picture shown there, it seems the streaming (in this case generated by Flume) must be directly sent to Spark Streaming engine; then the results will be put in HDFS.
Nevertheless, if you want to maintain your deployment, i.e. Flume -> HDFS -> Spark, then my suggestion is to create mini-batches of data in temporal HDFS folders, and once the mini-batches are ready, store new data in a second minibatch, passing the first batch to Spark for analysis.
HTH
In addition to frb's answer: which is correct - SparkStreaming with Flume acts as an Avro RPC Server - you'll need to configure an AvroSink which points to your SparkStreaming instance.
with spark2, now you can connect directly your spark streaming to flume, see official docs, and then write once on HDFS at the end of the process.
import org.apache.spark.streaming.flume._
val flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])
i am trying to process pst file from apache Pig. Do i need to write User define function to read pst?.How can I process/extracts pst using Pig way Is there any UDF ,library available in java which I can use in my Pig script. Any help would be great-full .
Thanks,
Vamshikrishna
I'm not aware that there is support for .pst files so probably yes, you need to write an UDF and more precisely a LOAD UDF.
Using pig or hadoop streaming, has anyone loaded and uncompressed a zipped file? The original csv file was compressed using pkzip.
Not sure if this helps because its mainly focused on using MapReduce in Java, but there is a ZipFileInputFormat available in hadoop. Its use via the Java API is described here:
http://cotdp.com/2012/07/hadoop-processing-zip-files-in-mapreduce/
The main part of this is the ZipFileRecordReader which uses Javas ZipInputStream to process each ZipEntry. The Hadoop reader is probably not going to work for you out of the box because it passes the file path of each ZipEntry as the key and the ZipEntry contents as the value.
I have large mbox files and I am using third party API like mstor to parse messages from mbox file using hadoop. I have uploaded those files in hdfs. But the problem is that this API uses only local file system path , similar to shown below
MessageStoreApi store = new MessageStoreApi(“file location in locl file system”);
I could not find a constructor in this API that would initialize from stream . So I cannot read hdfs stream and initialize it.
Now my question is, should I copy my files from hdfs to local file system and initialize it from local temporary folder? As thats what I have been doing for now:
Currently My Map function receives path of the mbox files.
Map(key=path_of_mbox_file in_hdfs, value=null){
String local_temp_file = CopyToLocalFile(path in hdfs);
MessageStoreApi store = new MessageStoreApi(“local_temp_file”);
//process file
}
Or Is there some other solution? I am expecting some solution like what If I increase the block-size so that single file fits in one block and somehow if I can get the location of those blocks in my map function, as mostly map functions will execute on the same node where those blocks are stored then I may not have to always download to local file system? But I am not sure if that will always work :)
Suggestions , comments are welcome!
For local filesystem path-like access, HDFS offers two options: HDFS NFS (via NFSv3 mounts) and FUSE-mounted HDFS.
The former is documented under the Apache Hadoop docs (CDH users may follow this instead)
The latter is documented at the Apache Hadoop wiki (CDH users may find relevant docs here instead)
The NFS feature is more maintained upstream than the FUSE option, currently.
I need a system to analyze large log files. A friend directed me to hadoop the other day and it seems perfect for my needs. My question revolves around getting data into hadoop-
Is it possible to have the nodes on my cluster stream data as they get it into HDFS? Or would each node need to write to a local temp file and submit the temp file after it reaches a certain size? and is it possible to append to a file in HDFS while also running queries/jobs on that same file at the same time?
Fluentd log collector just released its WebHDFS plugin, which allows the users to instantly stream data into HDFS. It's really easy to install with ease of management.
Fluentd + Hadoop: Instant Big Data Collection
Of course you can import data directly from your applications. Here's a Java example to post logs against Fluentd.
Fluentd: Data Import from Java Applications
A hadoop job can run over multiple input files, so there's really no need to keep all your data as one file. You won't be able to process a file until its file handle is properly closed, however.
HDFS does not support appends (yet?)
What I do is run the map-reduce job periodically and output results to an 'processed_logs_#{timestamp}" folder.
Another job can later take these processed logs and push them to a database etc. so it can be queried on-line
I'd recommend using Flume to collect the log files from your servers into HDFS.