I feel that connecting EMR to Amazon S3 is highly unreliable because of the dependency on network speed.
I can only find links for describing an S3 location. I want to use EMR with HDFS - how do I do this?
You can just use hdfs input and output paths like hdfs:///input/.
Say you have a job added to a cluster as follows:
ruby elastic-mapreduce -j $jobflow --jar s3:/my-jar-location/myjar.jar --arg s3:/input --arg s3:/output
instead you can have it as follows if you need it to be on hdfs:
ruby elastic-mapreduce -j $jobflow --jar s3:/my-jar-location/myjar.jar --arg hdfs:///input --arg hdfs:///output
In order to interact with the HDFS on EMR cluster, ssh to the master node and execute general HDFS commands.
For example to see the output file, you might do as follows:
hadoop fs -get hdfs://output/part-r-0000 /home/ec2-user/firstPartOutputFile
But if you are working with transient clusters, using in-situ HDFS is discouraged, as you will lose data when cluster is terminated.
Also I have benchmarks which prove that using S3 or HDFS doesn't provide much of performance difference.
For a workload of ~200GB:
- Job got finished in 22 seconds with S3 as input source
- Job got finished in 20 seconds with HDFS as input source
EMR is super optimized to read/write data from/to S3.
For intermediate steps' output writing into hdfs is best.
So, say if you have 3 steps in your pipeline, then you may have input/output as follows:
Step 1: Input from S3, Output in HDFS
Step 2: Input from HDFS, Output in HDFS
Step 3: Input from HDFS, Output in S3
Related
I would like to transfer data from a table in BigQuery, into another one in Redshift.
My planned data flow is as follows:
BigQuery -> Google Cloud Storage -> Amazon S3 -> Redshift
I know about Google Cloud Storage Transfer Service, but I'm not sure it can help me. From Google Cloud documentation:
Cloud Storage Transfer Service
This page describes Cloud Storage Transfer Service, which you can use
to quickly import online data into Google Cloud Storage.
I understand that this service can be used to import data into Google Cloud Storage and not to export from it.
Is there a way I can export data from Google Cloud Storage to Amazon S3?
You can use gsutil to copy data from a Google Cloud Storage bucket to an Amazon bucket, using a command such as:
gsutil -m rsync -rd gs://your-gcs-bucket s3://your-s3-bucket
Note that the -d option above will cause gsutil rsync to delete objects from your S3 bucket that aren't present in your GCS bucket (in addition to adding new objects). You can leave off that option if you just want to add new objects from your GCS to your S3 bucket.
Go to any instance or cloud shell in GCP
First of all configure your AWS credentials in your GCP
aws configure
if this is not recognising the install AWS CLI follow this guide https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html
follow this URL for AWS configure
https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
Attaching my screenshot
Then using gsutil
gsutil -m rsync -rd gs://storagename s3://bucketname
16GB data transferred in some minutes
Using Rclone (https://rclone.org/).
Rclone is a command line program to sync files and directories to and from
Google Drive
Amazon S3
Openstack Swift / Rackspace cloud files / Memset Memstore
Dropbox
Google Cloud Storage
Amazon Drive
Microsoft OneDrive
Hubic
Backblaze B2
Yandex Disk
SFTP
The local filesystem
Using the gsutil tool we can do a wide range of bucket and object management tasks, including:
Creating and deleting buckets.
Uploading, downloading, and deleting objects.
Listing buckets and objects. Moving, copying, and renaming objects.
we can copy data from a Google Cloud Storage bucket to an amazon s3 bucket using gsutil rsync and gsutil cp operations. whereas
gsutil rsync collects all metadata from the bucket and syncs the data to s3
gsutil -m rsync -r gs://your-gcs-bucket s3://your-s3-bucket
gsutil cp copies the files one by one and as the transfer rate is good it copies 1 GB in 1 minute approximately.
gsutil cp gs://<gcs-bucket> s3://<s3-bucket-name>
if you have a large number of files with high data volume then use this bash script and run it in the background with multiple threads using the screen command in amazon or GCP instance with AWS credentials configured and GCP auth verified.
Before running the script list all the files and redirect to a file and read the file as input in the script to copy the file
gsutil ls gs://<gcs-bucket> > file_list_part.out
Bash script:
#!/bin/bash
echo "start processing"
input="file_list_part.out"
while IFS= read -r line
do
command="gsutil cp ${line} s3://<bucket-name>"
echo "command :: $command :: $now"
eval $command
retVal=$?
if [ $retVal -ne 0 ]; then
echo "Error copying file"
exit 1
fi
echo "Copy completed successfully"
done < "$input"
echo "completed processing"
execute the Bash script and write the output to a log file to check the progress of completed and failed files.
bash file_copy.sh > /root/logs/file_copy.log 2>&1
I needed to transfer 2TB of data from Google Cloud Storage bucket to Amazon S3 bucket.
For the task, I created the Google Compute Engine of V8CPU (30 GB).
Allow Login using SSH on the Compute Engine.
Once logedin create and empty .boto configuration file to add AWS credential information. Added AWS credentials by taking the reference from the mentioned link.
Then run the command:
gsutil -m rsync -rd gs://your-gcs-bucket s3://your-s3-bucket
The data transfer rate is ~1GB/s.
Hope this help.
(Do not forget to terminate the compute instance once the job is done)
For large amounts of large files (100MB+) you might get issues with broken pipes and other annoyances, probably due to multipart upload requirement (as Pathead mentioned).
For that case you're left with simple downloading all files to your machine and uploading them back. Depending on your connection and data amount, it might be more effective to create VM instance to utilize high-speed connection and ability to run it in the background on different machine than yours.
Create VM machine (make sure the service account has access to your buckets), connect via SSH and install AWS CLI (apt install awscli) and configure the access to S3 (aws configure).
Run these two lines, or make it a bash script, if you have many buckets to copy.
gsutil -m cp -r "gs://$1" ./
aws s3 cp --recursive "./$1" "s3://$1"
(It's better to use rsync in general, but cp was faster for me)
Tools like gsutil and aws s3 cp won't use multipart uploads/downloads, so will have poor performance for large files.
Skyplane is a much faster alternative for transferring data between clouds (up to 110x for large files). You can transfer data with the command:
skyplane cp -r s3://aws-bucket-name/ gcs://google-bucket-name/
(disclaimer: I am a contributor)
I want to download data form S3 to HDFS. I tried s3cmd but it's not parallel and thus slow. I am trying to make hadoop distcp work like this:
hadoop distcp -Dfs.s3n.awsAccessKeyId=[Access Key] -Dfs.s3n.awsSecretAccessKey=[Secret Key] s3n://[account-name]/[bucket]/folder /data
but it gives me:
ipc.Client: Retrying connect to server:
ec2-[ip].compute-1.amazonaws.com/[internal-ip]:9001. Already tried 0 time(s)
distcp is a map reduce based job. Make sure job tracker service is start. Try
hadoop/bin/start-all.sh
I am running a Spark cluster on Amazon EMR. I am running the PageRank example programs on the cluster.
While running the programs on my local machine, I am able to see the output properly. But the same doesn't work on EMR. The S3 folder only shows empty files.
The commands I am using:
For starting the cluster:
aws emr create-cluster --name SparkCluster --ami-version 3.2 --instance-type m3.xlarge --instance-count 2 \
--ec2-attributes KeyName=sparkproj --applications Name=Hive \
--bootstrap-actions Path=s3://support.elasticmapreduce/spark/install-spark \
--log-uri s3://sampleapp-amahajan/output/ \
--steps Name=SparkHistoryServer,Jar=s3://elasticmapreduce/libs/script-runner/script-runner.jar,Args=s3://support.elasticmapreduce/spark/start-history-server
For adding the job:
aws emr add-steps --cluster-id j-9AWEFYP835GI --steps \
Name=PageRank,Jar=s3://elasticmapreduce/libs/script-runner/script-runner.jar,Args=[/home/hadoop/spark/bin/spark-submit,--deploy-mode,cluster,--master,yarn-cluster,--class,SparkPageRank,s3://sampleapp-amahajan/pagerank_2.10-1.0.jar,s3://sampleapp-amahajan/web-Google.txt,2],ActionOnFailure=CONTINUE
After a few unsuccessful attempts... I made a text file for the output of the job and it is successfully created on my local machine. But I am unable to view the same when I SSH into the cluster. I tried FoxyProxy to view the logs for the instances and neither does anything show up there.
Could you please let me know where I am going wrong?
Thanks!
How are you writing the text file locally? Generally, EMR jobs save their output to S3, so you could use something like outputRDD.saveToTextFile("s3n://<MY_BUCKET>"). You could also save the output to HDFS, but storing the results to S3 is useful for "ephemeral" clusters-- where you provision an EMR cluster, submit a job, and terminate upon completion.
"While running the programs on my local machine, I am able to see the
output properly. But the same doesn't work on EMR. The S3 folder only
shows empty files"
For the benefit of newbies:
If you are printing output to the console, it will be displayed in local mode but when you execute on EMR cluster, the reduce operation will be performed on worker nodes and they cant right to the console of the Master/Driver node!
With proper path you should be able to write results to s3.
I'm running hive over EMR,
and need to copy some files to all EMR instances.
One way as I understand is just to copy files to the local file system on each node the other is to copy the files to the HDFS however I haven't found a simple way to copy stright from S3 to HDFS.
What is the best way to go about this?
the best way to do this is to use Hadoop's distcp command. Example (on one of the cluster nodes):
% ${HADOOP_HOME}/bin/hadoop distcp s3n://mybucket/myfile /root/myfile
This would copy a file called myfile from an S3 bucket named mybucket to /root/myfile in HDFS. Note that this example assumes you are using the S3 file system in "native" mode; this means that Hadoop sees each object in S3 as a file. If you use S3 in block mode instead, you would replace s3n with s3 in the example above. For more info about the differences between native S3 and block mode, as well as an elaboration on the example above, see http://wiki.apache.org/hadoop/AmazonS3.
I found that distcp is a very powerful tool. In addition to being able to use it to copy a large amount of files in and out of S3, you can also perform fast cluster-to-cluster copies with large data sets. Instead of pushing all the data through a single node, distcp uses multiple nodes in parallel to perform the transfer. This makes distcp considerably faster when transferring large amounts of data, compared to the alternative of copying everything to the local file system as an intermediary.
Now Amazon itself has a wrapper implemented over distcp, namely : s3distcp .
S3DistCp is an extension of DistCp that is optimized to work with
Amazon Web Services (AWS), particularly Amazon Simple Storage Service
(Amazon S3). You use S3DistCp by adding it as a step in a job flow.
Using S3DistCp, you can efficiently copy large amounts of data from
Amazon S3 into HDFS where it can be processed by subsequent steps in
your Amazon Elastic MapReduce (Amazon EMR) job flow. You can also use
S3DistCp to copy data between Amazon S3 buckets or from HDFS to Amazon
S3
Example Copy log files from Amazon S3 to HDFS
This following example illustrates how to copy log files stored in an Amazon S3 bucket into HDFS. In this example the --srcPattern option is used to limit the data copied to the daemon logs.
elastic-mapreduce --jobflow j-3GY8JC4179IOJ --jar \
s3://us-east-1.elasticmapreduce/libs/s3distcp/1.latest/s3distcp.jar \
--args '--src,s3://myawsbucket/logs/j-3GY8JC4179IOJ/node/,\
--dest,hdfs:///output,\
--srcPattern,.*daemons.*-hadoop-.*'
Note that according to Amazon, at http://docs.amazonwebservices.com/ElasticMapReduce/latest/DeveloperGuide/FileSystemConfig.html "Amazon Elastic MapReduce - File System Configuration", the S3 Block FileSystem is deprecated and its URI prefix is now s3bfs:// and they specifically discourage using it since "it can trigger a race condition that might cause your job flow to fail".
According to the same page, HDFS is now 'first-class' file system under S3 although it is ephemeral (goes away when the Hadoop jobs ends).
There are some large datasets (25gb+, downloadable on the Internet) that I want to play around with using Amazon EMR. Instead of downloading the datasets onto my own computer, and then re-uploading them onto Amazon, what's the best way to get the datasets onto Amazon?
Do I fire up an EC2 instance, download the datasets (using wget) into S3 from within the instance, and then access S3 when I run my EMR jobs? (I haven't used Amazon's cloud infrastructure before, so not sure if what I just said makes any sense.)
I recommend the following...
fire up your EMR cluster
elastic-mapreduce --create --alive --other-options-here
log on to the master node and download the data from there
wget http://blah/data
copy into HDFS
hadoop fs -copyFromLocal data /data
There's no real reason to put the original dataset through S3. If you want to keep the results you can move them into S3 before shutting down your cluster.
If the dataset is represented by multiple files you can use the cluster to download it in parallel across the machines. Let me know if this is the case and I'll walk you through it.
Mat
If you're just getting started and experimenting with EMR, I'm guessing you want these on s3 so you don't have to start an interactive Hadoop session (and instead use the EMR wizards via the AWS console).
The best way would be to start a micro instance in the same region as your S3 bucket, download to that machine using wget and then use something like s3cmd (which you'll probably need to install on the instance). On Ubuntu:
wget http://example.com/mydataset dataset
sudo apt-get install s3cmd
s3cmd --configure
s3cmd put dataset s3://mybucket/
The reason you'll want your instance and s3 bucket in the same region is to avoid extra data transfer charges. Although you'll be charged in bound bandwidth to the instance for the wget, the xfer to S3 will be free.
I'm not sure about it, but to me it seems like hadoop should be able to download files directly from your sources.
just enter http://blah/data as your input, and hadoop should do the rest. It certainly works with s3, why should it not work with http?