Can csv files from the AWS S3 bucket be configured to go straight into ML or do the files need to land somewhere and then the CSV files have to get ingested using MCLP?
Assuming you have CSV files in the S3 Bucket and that one row in the CSV file is to be inserted as a single XML record...that wasn't clear in your question, but is the most common use case. If your plan is to just pull the files in and persist them as CSV files, there are undocumented XQuery functions that could be used to access the S3 bucket and pull the files in off that. Anyway, the MLCP documents are very helpful in understanding this very versatile and powerful tool.
According to the documentation (https://developer.marklogic.com/products/mlcp) the supported data sources are:
Local filesystem
HDFS
MarkLogic Archive
Another MarkLogic Database
You could potentially mount the S3 Bucket to a local filesystem on EC2 to bypass the need to make the files accessible to MLCP. Google's your friend if that's important. I personally haven't seen a production-stable method for that, but it's been a long time since I've tried.
Regardless, you need to make those files available on a supported source, most likely a filesystem location in this case, where MLCP can be run and can reach the files. I suppose that's what you meant by having the files land somewhere. MLCP can process delimited files in import mode. The documentation is very good for understanding all the options.
Related
I have got an application that requires to create a compressed file from different objects that are saved on S3. The issue I am facing is I would like to compress objects on the fly without downloading files into a container and do the compression. The reason for that is the size of files can be quite big and I can easily run out of disk space and of course, there will be an extra round trip time of downloading files on disk, compressing them and upload the compressed file to s3 again.
It is worth mentioning that I would like to locate files in the output compressed file in different directories, so when a user decompress the file can see it is stored in different folders.
Since S3 does not have the concept of physical folder structure, I am not sure if this is possible and if there is a better way than download/uploading the files.
NOTE
My issue is not about how to use AWS Lambda to export a set of big files. It is about how I can export files from S3 without downloading objects on a local disk and create a zip file and upload to S3. I would like to simply zip the files on S3 on the fly and most importantly being able to customize the directory structure.
For example,
inputs:
big-file1
big-file2
big-file3
...
output:
big-zip.zip
with the directory structure of:
images/big-file1
images/big-file2
videos/big-file3
...
I have almost the same use case as yours. I have researched it for about 2 months and try with multiple ways but finally I have to use ECS (EC2) for my use case because of the zip file can be huge like 100GB ....
Currently AWS doesn't support a native way to perform compress. I have talked to them and they are considering it a feature but there is no time line given yet.
If your files is about 3 GB in term of size, you can think of Lambda to achieve your requirement.
If your files is more than 4 GB, I believe it is safe to do it with ECS or EC2 and attach more volume if it requires more space/memory for compression.
Thanks,
Yes, there are at least two ways: either using AWS-Lambda or AWS-EC2
EC2
Since aws-cli has support of cp command, you can pipe S3 file to any archiver using unix-pipe, e.g.:
aws s3 cp s3://yours-bucket/huge_file - | gzip | aws s3 cp - s3://yours-bucket/compressed_file
AWS-Lambda
Since maintaining and using EC2 instance just for compressing may be too expensive, you can use Lambda for one-time compressions.
But keep in mind that Lambda has a lifetime limit of 15 minutes. So, if your files really huge try this sequence:
To make sure that file will be compressed, try partial file compression using Lambda
Compressed files could me merged on S3 into one file using Upload Part - Copy
Currently I'm using pdfbox to download all my pdf files on my server and then using pdfbox to merge them together. It's working perfectly fine but it's very slow--since I have to download them all.
Is there a way to perform all of this on S3 directly? I'm trying to find a way to do it, even if not in java also in python and unable to do so.
I read the following:
Merging files on S3 Amazon
https://github.com/boazsegev/combine_pdf/issues/18
Is there a way to merge files stored in S3 without having to download them?
EDIT
The way I ended up doing it was using concurrent.futures and implementing it with concurrent.futures.ThreadPoolExecutor. I set a maximum of 8 worker threads to download all the pdf files from s3.
Once all files were downloaded I merged them with pdfbox. Simple.
S3 is just a data store, so at some level you need to transfer the PDF files from S3 to a server and then back. You'll probably gain the best speed by doing your conversions on an EC2 instance located in the same region as your S3 bucket.
If you don't want to spin up an EC2 instance yourself just to do this then another alternative may be to make use of AWS Lambda, which is a compute service where you can upload your code and have AWS manage the execution of it.
I need to access some data during the map stage. It is a static file, from which I need to read some data.
I have uploaded the data file to S3.
How can I access that data while running my job in EMR?
If I just specify the file path as:
s3n://<bucket-name>/path
in the code, will that work ?
Thanks
S3n:// url is for Hadoop to read the s3 files. If you want to read the s3 file in your map program, either you need to use a library that handles s3:// URL format - such as jets3t - https://jets3t.s3.amazonaws.com/toolkit/toolkit.html - or access S3 objects via HTTP.
A quick search for an example program brought up this link.
https://gist.github.com/lucastex/917988
You can also access the S3 object through HTTP or HTTPS. This may need making the object public or configuring additional security. Then you can access it using the HTTP url package supported natively by java.
Another good option is to use s3dist copy as a bootstrap step to copy the S3 file to HDFS before your Map step starts. http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/UsingEMR_s3distcp.html
What I ended up doing:
1) Wrote a small script that copies my file from s3 to the cluster
hadoop fs -copyToLocal s3n://$SOURCE_S3_BUCKET/path/file.txt $DESTINATION_DIR_ON_HOST
2) Created bootstrap step for my EMR Job, that runs the script in 1).
This approach doesn't require to make the S3 data public.
How I can I access the file sitting in the following folder of S3 which is own by someone else
s3n://elasticmapreduce/samples/wordcount/input
The files in s3n://elasticmapreduce/samples/wordcount/input are public, and made available as input by Amazon to the sample word count Hadoop program. The best way to fetch them is to
Start a new Amazon Elastic MapReduce Job Flow (it doesn't matter which one) from the Amazon Web Services console, and make sure that you keep the the job alive with the Keep Alive option
Once the EC2 machines have started, find the instances on EC2 from the Amazon Web Services console
ssh into one of the running EC2 instances, using the hadoop user, for example
ssh -i keypair.pem hadoop#ec2-IPADDRESS.compute-1.amazonaws.com
Obtain the files you need, using hadoop dfs -copyToLocal s3://elasticmapreduce/samples/wordcount/input/0002 .
sftp the files to your local system
You can access wordSplitter.py here:
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/wordSplitter.py
You can access the input files here:
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0012
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0011
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0010
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0009
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0008
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0007
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0006
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0005
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0004
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0003
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0002
https://elasticmapreduce.s3.amazonaws.com/samples/wordcount/input/0001
The owner of the folder (most likely a file in the folder) must have made it accessible to anonymous reader.
If that is the case, s3n://x/y... is translated to
http://s3.amazonaws.com/x/y...
or
http://x.s3.amazonaws.com/y...
x is the name of the bucket.
y... is the path wihtin the bucket.
If you want to make sure the file exists, e.g. if you suspect the name was misspelled, you can in your browser to open
http://s3.amazonaws.com/x
and you'll see XML describing "files" that is S3 objects, available.
Try this:
http://s3.amazonaws.com/elasticmapreduce
I tried this, and seems that the path you want is not public.
AWS EBS documentation quotes s3://elasticmapreduce/samples/wordcount/input in one of the "getting started" examples. But s3 is different from s3n, so input might be available to EMR, but not to HTTP access.
In Amazon S3, there is no concept of folders, a bucket it just a flat collection of objects. But you can list all the files you are interested in a browser with the following URL:
s3.amazonaws.com/elasticmapreduce?prefix=samples/wordcount/input/
Then you can download them by specifying the whole name, e.g.
s3.amazonaws.com/elasticmapreduce/samples/wordcount/input/0001
Reduced Redundancy Storage (RRS) is a new service from Amazon that is a bit cheaper than S3 because there is less redundancy.
However, I can not find any information on how to specify that my data should use RRS rather than standard S3. In fact, there doesn't seem to be any website interface for an S3 services. If I log into AWS, there are only options for EC2, Elastic MapReduce, CloudFront and RDS, none of which I use.
I know this question is old but it's worth mentioning that Amazon's interface for S3 now has an option to change your files (recursively) to RRS. Select a folder and right click on it, under properties change the storage to RRS.
You can use S3 Browser to switch to Reduced Redundancy Storage. It allows you to view/edit storage class for a single file or for multiple files. Moreover, you can configure default storage class for the bucket, so S3 Browser will automatically apply predefined storage class for all new files you are uploading through S3 Browser.
If you are using S3 Browser to work with RRS, the following article may be helpful:
Working with Amazon S3 Reduced Redundancy Storage (RRS)
Note, Storage Class preferences are stored in a local settings file.Other s3 applications are using their own way to store bucket defaults and currently there is not single standard on this.
All objects in Amazon S3 have a
storage class setting. The default
setting is STANDARD. You can use an
optional header on a PUT request to
specify the setting
REDUCED_REDUNDANCY.
From: http://aws.amazon.com/s3/faqs/#How_do_I_specify_that_I_want_to_store_my_data_using_RRS
If you are looking for a way to convert existing data in amazon s3, you can use a fairly recent version of boto and a script I wrote. Details explained on my blog:
http://www.bryceboe.com/2010/07/02/amazon-s3-convert-objects-to-reduced-redundancy-storage/
If you're on a mac, the free cyberduck ftp program will do it. Log into S3, right-click on the bucket (or folder, or file) and choose 'info' and change the storage class from 'unknown' or 'regular s3 storage' to 'reduced redundancy storage'. Took it about 2 hours to change 30,000 files for me...
If you use boto, you can do this:
key.change_storage_class('REDUCED_REDUNDANCY')