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.
Related
I am trying to find a solution to move files from an S3 bucket to Snowflake internal stage (not table directly) with Airflow but it seems that the PUT command is not supported with current Snowflake operator.
I know there are other options like Snowpipe but I want to showcase Airflow's capabilities.
COPY INTO is also an alternative solution but I want to load DDL statements from files, not run them manually in Snowflake.
This is the closest I could find but it uses COPY INTO table:
https://artemiorimando.com/2019/05/01/data-engineering-using-python-airflow/
Also : How to call snowsql client from python
Is there any way to move files from S3 bucket to Snowflake internal stage through Airflow+Python+Snowsql?
Thanks!
I recommend you execute the COPY INTO command from within Airflow to load the files directly from S3, instead. There isn't a great way to get files to internal stage from S3 without hopping the files to another machine (like the Airflow machine). You'd use SnowSQL to GET from S3 to local, and the PUT from local to S3. The only way to execute a PUT to Internal Stage is through SnowSQL.
I am very new to AWS technology.
I want to add some files to an existing S3 bucket without overwriting existing objects. I am using Spring Boot technology for my project.
Can anyone please suggest how can we add/upload multiple files without overwriting existing objects?
AWS S3 supports object versioning in the bucket, in which for use case of uploading same file, S3 will keep all files within the bucket with different version rather than overwriting it.
This can be configured using AWS Console or CLI to enable the Versioning feature. You may want to refer this link for more info.
You probably already found an answer to this, but if you're using the CDK or the CLI you can specify a destinationKeyPrefix. If you want multiple folders in an S3, which was my case, the folder name will be your destinationKeyPrefix.
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 am a little puzzled i hope someone can help me out.
we create some ORC-Files that we would like to query while they are stored on S3.
We noticed that the S3 native Filesystem S3n does not really work out for this manner. I am not really sure what the problem is - but my guess is, that the reader is not able to jump to specific bytes inside the file so that he has to load the whole file before he can query it.
So we tried storing the files on S3 (uri s3://) which is a block file system just like HDFS backed by s3 and it worked great.
But i am a little worried after reading up on this source about Amazon EMR which says
Amazon S3 block file system (URI path: s3bfs://)
The Amazon S3 block file system is a legacy file storage system. We strongly discourage the use of this system.
Important
We recommend that you do not use this file system because it can trigger a race condition that might cause your cluster to fail. However, it might be required by legacy applications.
EMRFS (URI path: s3://)
EMRFS is an implementation of HDFS used for reading and writing regular files from Amazon EMR directly to Amazon S3.
I am not using EMR - i create my files by launching an EC2 cluster and then use s3 as a cold storage - but I am kind of puzzled right now and not sure which filesystem I use when I store my files on s3 using the URI scheme s3:// - do i use EMRFS or do i use the deprecated s3bfs filesystem?
Amazon S3 is an object storage system. It is not recommended to "mount" S3 as a filesystem. Amazon Elastic Block Store (EBS) is a block storage system that appears as volumes on Amazon EC2 instances.
When used from Amazon Elastic MapReduce (EMR), Hadoop has extensions that make it easy to work with Amazon S3. However, if you are not using EMR, there is no need to use EMRFS (which is available only on EMR), nor should you use S3 as a block storage system.
The easiest way to use S3 from EC2 is via the AWS Command-Line Interface (CLI). You can copy files to/from S3 by using the aws s3 cp command. There's also a sync command to make it easy to syncrhonize data to/from S3.
You can also programmatically connect to Amazon S3 via an SDK, so that your app can directly transfer files to/from S3.
As to which to choose... typically, applications like to work with files on a local filesystem, so copy your files from S3 to a local device. However, if your app can directly communicate with S3, there will be less "moving parts".
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