Running Spark on EMR (AMI 3.8). When trying to write an RDD to a local file, I am getting no results on the name/master node.
On my previous EMR cluster (same version of Spark installed with bootstrap script instead of as an add-on to EMR), the data would write to the local dir on the name node. Now I can see it appearing in "/home/hadoop/test/_temporary/0/task*" directories on the other nodes in the cluster, but only the 'SUCCESS' file on the master node.
How can I get the file to write to the name/master node only?
Here is an example of the command I am using:
myRDD.saveAsTextFile("file:///home/hadoop/test")
I can do this in a round about way using by pushing to HDFS first then writing the results to local filesystem with shell commands. But I would love to hear if others have a more elegant approach.
//rdd to local text file
def rddToFile(rdd: RDD[_], filePath: String) = {
//setting up bash commands
val createFileStr = "hadoop fs -cat " + filePath + "/part* > " + filePath
val removeDirStr = "hadoop fs -rm -r " + filePath
//rm dir in case exists
Process(Seq("bash", "-c", removeDirStr)) !
//save data to HDFS
rdd.saveAsTextFile(filePath)
//write data to local file
Process(Seq("bash", "-c", createFileStr)) !
//rm HDFS dir
Process(Seq("bash", "-c", removeDirStr)) !
}
Related
I have UNC path folders in this path " //aloha/log/folderlevel1/folderlevel2/"
Each of these level2 folders will have files like "empllog.txt","deptlog.txt","adminlog.txt" and few others files as well.
I want to copy the content of this particular folders if they were created in last 24 hours & only if these 3 files are present to HDFS cloudera cluster.But if one of these files are not present , then that particular folder should not be copied. Also I need to preserve the folderstructre.
i.e In HDFS it should be "/user/test/todaydate/folderlevel1/folderlevel2"
I have written below shell script to copy files to hdfs with date folder created. But not sure how to proceed further with UNC Paths & other criterias.
day=$(date +%Y-%m-%d)
srcdir="/home/test/sparkjops"
stdir="/user/test/$day/"
hadoop dfs -mkdir $day /user/test
for f in ${srcdir}/*
do
if [ $f == "$srcdir/empllog.txt" ]
then
hadoop dfs -put $f $stdir
elif [ $f == "$srcdir/deptlog.txt" ]
then hadoop dfs -put $f $stdir
elif [ $f == "$srcdir/adminlog.txt" ]
then hadoop dfs -put $f $stdir
fi
done
I have tried to change the UNC Path like below . It did not do anything. No error & did not copy the content as well.
srcdir="//aloha/log/*/*"
srcdir='//aloha/log/*/*'
srcdir="\\aloha\log\*\*"
Appreciate all help.
Thanks.
EDIT 1 :
I ran it with code sh -x debug mode.and also with bash -x(just to check). But It returned that file not found error as below
test#ubuntu:~/sparkjops$ sh -x ./hdfscopy.sh
+ date +%Y-%m-%d
+ day=2016-12-24
+ srcdir= //aloha/logs/folderlevel1/folderlevel2
+ stdir=/user/test/2016-12-24/
+ hadoop dfs -mkdir 2016-12-24 /user/test
DEPRECATED: Use of this script to execute hdfs command is deprecated.
Instead use the hdfs command for it.
mkdir: `2016-12-24': File exists
mkdir: `/user/test': File exists
+ //aloha/logs/folderlevel1/folderlevel2/* = //aloha/logs/folderlevel1/folderlevel2/empllog.txt.txt
./hdfscopy.sh: 12: ./hdfscopy.sh: //aloha/logs/folderlevel1/folderlevel2/*: not found
+ //aloha/logs/folderlevel1/folderlevel2/* = //aloha/logs/folderlevel1/folderlevel2/deptlog.txt.txt
./hdfscopy.sh: 12: ./hdfscopy.sh: //aloha/logs/folderlevel1/folderlevel2/*: not found
+ //aloha/logs/folderlevel1/folderlevel2/* = //aloha/logs/folderlevel1/folderlevel2/adminlog.txt.txt
./hdfscopy.sh: 12: ./hdfscopy.sh: //aloha/logs/folderlevel1/folderlevel2/*: not found
test#ubuntu:~/sparkjops$
But not able to understand why it is not reading from that path. I have tried different escaping sequences as well(doubleslash for each slash, forwardslash as we do in window folderpath) . But none working. All are throwing same error message. I am not sure how to read this file in the script. Any help would be appreciated.
I want to run Hadoop MapReduce on a small part of my text file.
One of my task is failing. I can read in the log:
Processing split: hdfs://localhost:8020/user/martin/history/history.xml:3556769792+67108864
Can I execute once again MapReduce on this file from offset 3556769792 to 3623878656 (3556769792+67108864) ?
A way to do is to copy the file from the offset define and add it back into HDFS. From this point simply run the mapreduce job only on this block.
1) copy file from offset 3556769792 follow by 67108864:
dd if=history.xml bs=1 skip=3556769792 count=67108864 >
history_offset.xml
2) import into HDFS
hadoop fs -copyFromLocal history_offset.xml offset/history_offset.xml
3) run again MapReduce
hadoop jar myJar.jar 'offset' 'offset_output'
i am new to flume so please tell me...how to store log files from my local machine to local my HDFS using flume
i have issues in setting classpath and flume.conf file
Thank you,
ajay
agent.sources = weblog
agent.channels = memoryChannel
agent.sinks = mycluster
## Sources #########################################################
agent.sources.weblog.type = exec
agent.sources.weblog.command = tail -F REPLACE-WITH-PATH2-your.log-FILE
agent.sources.weblog.batchSize = 1
agent.sources.weblog.channels =
REPLACE-WITH-
CHANNEL-NAME
## Channels ########################################################
agent.channels.memoryChannel.type = memory
agent.channels.memoryChannel.capacity = 100 agent.channels.memoryChannel.transactionCapacity = 100
## Sinks ###########################################################
agent.sinks.mycluster.type =REPLACE-WITH-CLUSTER-TYPE
agent.sinks.mycluster.hdfs.path=/user/root/flumedata
agent.sinks.mycluster.channel =REPLACE-WITH-CHANNEL-NAME
Save this file as logagent.conf and run with below command
# flume-ng agent –n agent –f logagent.conf &
We do need more information to know why things are working for you.
The short answer is that you need a Source to read your data from (maybe the spooling directory source), a Channel (memory channel if you don't need reliable storage) and the HDFS sink.
Update
The OP reports receiving the error message, "you must include conf file in flume class path".
You need to provide the conf file as an argument. You do so with the --conf-file parameter. For example, the command line I use in development is:
bin/flume-ng agent --conf-file /etc/flume-ng/conf/flume.conf --name castellan-indexer --conf /etc/flume-ng/conf
The error message reads that way because the bin/flume-ng script adds the contents of the --conf-file argument to the classpath before running Flume.
If you are appending data to your local file, you can use an exec source with "tail -F" command. If the file is static, use cat command to transfer the data to hadoop.
The overall architecture would be:
Source: Exec source reading data from your file
Channel : Either memory channel or file channel
Sink: Hdfs sink where data is being dumped.
Use user guide to create your conf file (https://flume.apache.org/FlumeUserGuide.html)
Once you have your conf file ready, you can run it like this:
bin/flume-ng agent -n $agent_name -c conf -f conf/your-flume-conf.conf
I have an S3 bucket with log files that I want to concatenate, then use as an input to an EMR job. The log files are in paths like: bucket-name/[date]/product/out/[hour]/[minute-based-file]. I'd like to take all the minute logs in all the hour directories in all the date directories, and concatenate them into one file. I want to use that file as an input to an EMR job. The original log files need to be preserved, and the new combined log file will probably be written to a different S3 bucket.
I tried using hadoop fs -getmerge on the EMR master node via SSH, but got this error:
This file system object (file:///) does not support access to the request path 's3://target-bucket-name/merged.log'
The source S3 bucket has some other files in it, so I don't want to include all of its files. The wildcard match looks like this: s3n://bucket-name/*/product/out/*/log.*.
The purpose is to get around the problem of having tens/hundreds of thousands of small (10k-3mb) input files to EMR, and instead give it one large file that it can split more efficiently.
I ended up just writing a script that wraps some Hadoop filesystem commands to do this.
#!/usr/bin/env ruby
require 'date'
# Merge minute-based log files into daily log files
# Usage: Run on EMR master (e.g. SSH to master then `ruby ~/merge-historical-logs.rb [FROM [TO]]`)
SOURCE_BUCKET_NAME = 's3-logs-bucket'
DESTINATION_BUCKET_NAME = 's3-merged-logs-bucket'
# Optional date inputs
min_date = if ARGV[0]
min_date_args = ARGV[0].split('-').map {|item| item.to_i}
Date.new(*min_date_args)
else
Date.new [2012, 9, 1]
end
max_date = if ARGV[1]
max_date_args = ARGV[1].split('-').map {|item| item.to_i}
Date.new(*max_date_args)
else
Date.today
end
# Setup directories
hdfs_logs_dir = '/mnt/tmp/logs'
local_tmp_dir = './_tmp_merges'
puts "Cleaning up filesystem"
system "hadoop fs -rmr #{hdfs_logs_dir}"
system "rm -rf #{local_tmp_dir}*"
puts "Making HDFS directories"
system "hadoop fs -mkdir #{hdfs_logs_dir}"
# We will progress backwards, from max to min
date = max_date
while date >= min_date
# Format date pieces
year = date.year
month = "%02d" % date.month
day = "%02d" % date.day
# Make a directory in HDFS to store this day's hourly logs
today_hours_dir = "#{hdfs_logs_dir}/#{year}-#{month}-#{day}"
puts "Making today's hourly directory"
system "hadoop fs -mkdir #{today_hours_dir}"
# Break the day's hours into a few chunks
# This seems to avoid some problems when we run lots of getmerge commands in parallel
[*(0..23)].each_slice(8).to_a.each do |hour_chunk|
hour_chunk.each do |_hour|
hour = "%02d" % _hour
# Setup args to merge minute logs into hour logs
source_file = "s3://#{SOURCE_BUCKET_NAME}/#{year}-#{month}-#{day}/product/out/#{hour}/"
output_file = "#{local_tmp_dir}/#{hour}.log"
# Launch each hour's getmerge in the background
full_command = "hadoop fs -getmerge #{source_file} #{output_file}"
puts "Forking: #{full_command}"
fork { system full_command }
end
# Wait for this batch of the germerge's to finish
Process.waitall
end
# Delete the local temp files Hadoop created
puts "Removing temp files"
system "rm #{local_tmp_dir}/.*.crc"
# Move local hourly logs to hdfs to free up local space
puts "Moving local logs to HDFS"
system "hadoop fs -put #{local_tmp_dir}/* #{today_hours_dir}"
puts "Removing local logs"
system "rm -rf #{local_tmp_dir}"
# Merge the day's hourly logs into a single daily log file
daily_log_file_name = "#{year}-#{month}-#{day}.log"
daily_log_file_path = "#{local_tmp_dir}_day/#{daily_log_file_name}"
puts "Merging hourly logs into daily log"
system "hadoop fs -getmerge #{today_hours_dir}/ #{daily_log_file_path}"
# Write the daily log file to another s3 bucket
puts "Writing daily log to s3"
system "hadoop fs -put #{daily_log_file_path} s3://#{DESTINATION_BUCKET_DIR}/daily-merged-logs/#{daily_log_file_name}"
# Remove daily log locally
puts "Removing local daily logs"
system "rm -rf #{local_tmp_dir}_day"
# Remove the hourly logs from HDFS
puts "Removing HDFS hourly logs"
system "hadoop fs -rmr #{today_hours_dir}"
# Go back in time
date -= 1
end
Is there an HDFS API that can copy an entire local directory to the HDFS? I found an API for copying files but is there one for directories?
Use the Hadoop FS shell. Specifically:
$ hadoop fs -copyFromLocal /path/to/local hdfs:///path/to/hdfs
If you want to do it programmatically, create two FileSystems (one Local and one HDFS) and use the FileUtil class
I tried copying from the directory using
/hadoop/core/bin/hadoop fs -copyFromLocal /home/grad04/lopez/TPCDSkew/ /export/hadoop1/lopez/Join/TPCDSkew
It gave me an error saying Target is a directory . I then modified it to
/hadoop/core/bin/hadoop fs -copyFromLocal /home/grad04/lopez/TPCDSkew/*.* /export/hadoop1/lopez/Join/TPCDSkew
it works .
In Hadoop version:
Hadoop 2.4.0.2.1.1.0-390
(And probably later; I have only tested this specific version as it is the one I have)
You can copy entire directories recursively without any special notation using copyFromLocal e.g.,:
hadoop fs -copyFromLocal /path/on/disk /path/on/hdfs
which works even when /path/on/disk is a directory containing subdirectories and files.
You can also use the put command:
$ hadoop fs -put /local/path hdfs:/path
For programmer, you also can use copyFromLocalFile. Here is an example:
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.Path
val hdfsConfig = new Configuration
val hdfsURI = "hdfs://127.0.0.1:9000/hdfsData"
val hdfs = FileSystem.get(new URI(hdfsURI), hdfsConfig)
val oriPath = new Path("#your_localpath/customer.csv")
val targetFile = new Path("hdfs://your_hdfspath/customer.csv")
hdfs.copyFromLocalFile(oriPath, targetFile)