I have a Rails 3 app with Paperclip gem.
Actually, my local directories structure is based on my record UUID to stock images:
5D5E5641-FCE8-4D0B-A413-A9F993CD0E34
becomes:
5/D/5/E/5/6/....... 3/4/full/image.jpg
5/D/5/E/5/6/....... 3/4/thumb/image.jpg
so that, I never have more than 32000 nodes per directory.
I want to migrate to S3:
1) Can I keep this directories structure on S3 ? Could it be a perf issue ?
2) Does Amazon S3 has its own directories management per bucket ?
Thanks.
There is no such stuff as folders in Amazon S3. It is a "flat" file system. The closer you can get to folders is adding prefixes like you said: 5/D/image.jpg to your file names. In this case, 5 is a prefix and 5/D is also a prefix. On the other hand, your delimiter could be /.
Even though several S3 tools will show you stuff as if they were contained inside folders, this concept does not exist on S3. Please see this and this related threads.
You can definitely use the pattern you suggested, and I don't think you will suffer any performance penalties by doing so.
Related
Background
We know it's possible to setup a devops pipeline that deploys updates to AEM via a blue/green approach by using crx2oak to migrate the content from old to new environment. Why is out of scope of this question.
The problem with this approach is the content copy operation can take a significant time, as the amount of content in the JCR grows. Other ideas to mittigate this are appreciated.
We also know that AEM can have a S3 datastore that off-loads the binary content into a S3 bucket which would not be re-built during blue/green deployment as per:
https://helpx.adobe.com/experience-manager/6-3/sites/deploying/using/storage-elements-in-aem-6.html#OverviewofStorageinAEM6
What is unclear from Adobe's documentation is whether the same S3 bucket can be shared across AEM instances (i.e. blue/green instances). Maybe it's just my google fu that has failed...
Question(s)
When a new AEM instance is configured to use a S3 datastore that already has content in it from the old instance, when crx2oak is used to migrate content, will the new instance be able to access the existing content?
Are there any articles/blogs that describe what the potential time savings of this approach would be?
Yes I could do an experiment, and may do so in the future to answer my own question. I'm looking for information from anyone who has already done this? I'm an engineer so will not re-invent the wheel if someone else has done so.
You can certainly share the same S3 bucket between instances - in fact, this is commonly used along with binary-less replication from author->publisher(s) and is a tried and true configuration.
It's even possible to share the same bucket between completely different environments (e.g. DEV/STAGE, or BLUE/GREEN in your case). The main "gotcha" to be aware of is with regard to DataStore Garbage Collection (DSGC) because it's very possible that there will be blobs which are referenced by only some of the instances sharing the bucket and so when purging unused blobs this needs to be taken into account.
This is all part of the design though, and there is a flag designed specifically for this purpose which tells DSGC to only execute the first phase (the "mark" phase) of GC, and skip the 2nd "sweep" phase, until all instances have marked which blobs they wish to keep/discard. Once all instances have done so the sweep phase can be run to purge blobs not needed by any instances using the bucket.
For a more detailed explanation see the Oak docs:
https://jackrabbit.apache.org/oak/docs/plugins/blobstore.html#Shared_DataStore_Blob_Garbage_Collection_Since_1.2.0
I find it helps to understand that pretty much all of the datastore implementations are done such that blobs are stored according to their checksum, so the same file added uploaded twice will only have one copy stored in the datastore, and there will be two segment store records referencing that same blob. In the same way, multiple AEM instances sharing the same bucket will be able to find a given blob regardless of which instance put it there in the first place.
You can observe see this in action easily with FileDataStore by finding a blob and sha256'ing it - e.g. (this example is on OS X, the checksum command on Linux/Windows will be slightly different):
$ shasum -a256 crx-quickstart/repository/datastore/0c/9e/40/0c9e405fc8d0f0405930cd0044611cfbf014938a1837ae0cfaa266d7732d1002
0c9e405fc8d0f0405930cd0044611cfbf014938a1837ae0cfaa266d7732d1002 crx-quickstart/repository/datastore/0c/9e/40/0c9e405fc8d0f0405930cd0044611cfbf014938a1837ae0cfaa266d7732d1002
There you can see that a) the filename is the checksum, and b) it's nested using the first 3 pairs of characters from that checksum, so you can locate the file by just knowing the hash and if you store the same binary, even if the name or JCR metadata is different, the blob referenced will be the same literal file on disk.
From memory S3 datastore uses prefixes rather than directory nesting because this performance better, but the principle is the same.
Finally, a couple of things to consider are:
1) S3 storage is relatively cheap (and practically unlimited) so there is an argument to be made that it's not as necessary to perform regular DSGC unless you're really trying to pinch pennies.
2) If you do run DSGC you need to think about how this will work with whatever backup strategy you're using for the AEM instances. For instance, if you roll back a segment store shortly after running DSGC you'll likely have to recover some of those purged blobs. You can use versioning and/or lifecycle rules to help with this, but it can add significant additional complexity and time to your restore process.
If you opt to simply skip DSGC and leave the blobs there indefinitely it's a good idea to make sure the access key or IAM roles AEM is using doesn't have the DeleteObject permission for the bucket, just to be sure a rogue GC process can't delete anything.
Hope this helps.
Edit
In all that I forgot to actually answer your question - yes it will save some time in cloning in most cases. You'll still need to sync the segment store (obviously) and there are various approaches for this. crx2oak is certainly one - you'll see in the documentation there are specific options for using it w/ S3 where you supply a configuration file (basically a serialised .config file like you'd use with Felix/OSGi).
You can also use something like rsync to simply copy the TAR files over (while at least the target AEM is stopped. Oak is generally atomic so a hot copy from the source can work in theory, but YMMV).
Finally you could obviously use Mongo and cluster the segment store that way, but all the usual cost/complexity/performance issues with doing so apply).
Another interesting development on the horizon for blue/green type is the CompositeNodeStore - there is a good talk from the 2017 adaptTo() conference that talks about this:
https://adapt.to/2017/en/schedule/zero-downtime-deployments-for-the-sling-based-apps-using-docker.html
An external datastore will help a lot, as usually the most space is used by binary assets. The pure content typed in by real people is much less.
On my current project (quite small, but relations should be normal):
Repository 4,8 GB total (4.1 GB Segment Store, 780 MB Index)
File DataStore 222 GB total
If you wanna do it, I have the following remarks:
There are different datastores available. For testing I would start with the File DataStore.
The S3 DataStore makes only sense in my point of view, if you are hosting at Amazons AWS anyway. Adobe Managed Services is doing this, and so S3 makes sense for them. But also there only if you have more than 500 GB assets.
If you use the green/blue approach, then be careful the DataStore garbage collection (just do it manually). The shared Datastore is meant for several publishers, that have the same content. As example you could have the following situation: Your editors delete some assets, you run the DataStore GC and finally your rollback your environment. That means the assets are still in the content repository, but the binaries are cleaned out of the DataStore.
In order to to use a shared file datastore, you need to do the following:
Unpack Quickstart java -jar AEM_6.3_Quickstart.jar -unpack
Create an directory for the file datastore (anywhere outside of the crx-quickstart folder)
Create a directory install inside the extracted crx-quickstart folder
Create a file called org.apache.jackrabbit.oak.plugins.blob.datastore.FileDataStore.cfg inside this install folder
This file contains just 1 line path=<path to file datastore> (see https://jackrabbit.apache.org/oak/docs/osgi_config.html)
Place a reference.key file inside the datastore directory. First time it will be created automatically. But if you use always the same key, the same hash-values are used all datastores across all your environments. This is also a prerequisite for a feature called "binary-less replication" (so binary would only be replicated the first time between author and publisher)
kind regards,
Alex
I understand that s3 does not have "folder" but I will still use the term to illustrate what I am looking for.
I have this folder structure in s3:
my-bucket/folder-1/file-named-a
my-bucket/folder-2/...
my-bucket/folder-3/file-named-a
my-bucket/folder-4/...
I would like to find all folders containing "file-named-a", so folder-1 and folder-3 in above example will be returned. I only need to search the "top level" folders under my-bucket. There could be tens of thousands of folders to search. How to construct the ListObjectsRequest to do that?
Thanks,
Sam
An Amazon S3 bucket can be listed (ListBucket()) to view its contents, and this API call can be limited by a Prefix. However, it is not possible to put a wildcard within the prefix.
Therefore, you would need to retrieve the entire bucket listing, looking for these files. This would require repeated calls if there are a large number of objects.
Example: Listing Keys Using the AWS SDK for Java
I'll have to store millions of files (many TB in the future) in S3.
Are there any limitations? (not a price :) ), i'm asking about architectural limitations (like - don't store it this way, the other way will be better/faster).
My files are in a hierarchy
/{country}/{number}/{code}/docs
and i checked i can keep them that way (to access them easy thru REST)
(of course i know S3 keeps them internally in other way - not important to me).
So, are there any limitations/pitfalls ?
S3 has no limits that you would hit. The files are not really in folders, they are just strings as locations. Make the folder structure something that is easy for you to keep track of and organize.
You do NOT want to be listing the "folder" contents in S3 to find things.
S3 is slow at giving directory listings, because it's not really directories.
You should be storing either the whole path /{country}/{number}/{code}/docs in a database or the logic should be so repeatable that you can be confident that the file will be in that location.
James Brady gave an excellent and very detailed answer to how s3 treats file storage in a question here https://stackoverflow.com/a/394505/4179009
AWS S3 does definitely have limits to access 100req/sec in case of similar path prefix, see the official docs: http://docs.aws.amazon.com/AmazonS3/latest/dev/request-rate-perf-considerations.html
From the other side a hierarchical approach makes logic complicated. A trade off depends on your requirements, one of good options can be using at least 4 symbols length key (primary id or hash key) in front of URL. In case of having limited number countries try using multiple buckets with country code as a bucket name, it also helps to define a specific physical location if required.
The documentation for the Redshift COPY command specifies two ways to choose files to load from S3, you either provide a base path and it loads all the files under that path, or you specify a manifest file with specific files to load.
However in our case, which I imagine is pretty common, the S3 bucket periodically receives new files with more recent data. We'd like to be able to load only the files that haven't already been loaded.
Given that there is a table stl_file_scan that logs all the files that have been loaded from S3, it would be nice to somehow exclude those that have successfully been loaded. This seems like a fairly obvious feature, but I can't find anything in the docs or online about how to do this.
Even the Redshift S3 loading template in AWS Data Pipeline appears to manage this scenario by loading all the data -- new and old -- to a staging table, and then comparing/upserting to the target table. This seems like an insane amount of overhead when we can tell up front from the filenames that a file has already been loaded.
I know we could probably move the files that have already been loaded out of the bucket, however we can't do that, this bucket is the final storage place for another process which is not our own.
The only alternative I can think of is to have some other process running that tracks files that have been successfully loaded to redshift, and then periodically compares that to the s3 bucket to determine the differences, and then writes the manifest file somewhere before triggering the copy process. But what a pain! We'd need a separate ec2 instance to run the process which would have it's own management and operational overhead.
There must be a better way!
This is how I solved the problem,
S3 -- (Lambda Trigger on newly created Logs) -- Lambda -- Firehose -- Redshift
It works at any scale. With more load, more calls to Lambda, more data to firehose and everything taken care automatically.
If there are issues with the format of the file, you can configure dead letter queues, events will be sent there and you can reprocess once you fix lambda.
Here I would like to mention some steps that includes process that how to load data in redshift.
Export local RDBMS data to flat files (Make sure you remove invalid
characters, apply escape sequence during export).
Split files into 10-15 MB each to get optimal performance during
upload and final Data load.
Compress files to *.gz format so you don’t end up with $1000
surprise bill :) .. In my case Text files were compressed 10-20
times.
List all file names to manifest file so when you issue COPY command
to Redshift its treated as one unit of load.
Upload manifest file to Amazon S3 bucket.
Upload local *.gz files to Amazon S3 bucket.
Issue Redshift COPY command with different options.
Schedule file archiving from on-premises and S3 Staging area on AWS.
Capturing Errors, setting up restart ability if something fails
Doing it easy way you can follow this link.
In general compare of loaded files to existing on S3 files is a bad but possible practice. The common "industrial" practice is to use message queue between data producer and data consumer that actually loads the data. Take a look on RabbitMQ vs Amazon SQS and etc..
I have an Amazon S3 account with 1 million items in the root folder. Is there any Windows software that can be used to manage these files?
I've tried Bucket Explorer and CloudBerry, but both seize up trying to list this many files.
http://www.bucketexplorer.com/documentation/amazon-s3--search-on-objects-in-bucket.html Shows how to set delimiter and prefix up for a search. This might be what you need. Your files might have some set of common prefixes - file name prefixes, or a character thats common you can use as a delimiter to make folders - say there are a lot of numbers, then try a delimiter of '1' instead of the default '/'.
You may need to read up a bit to learn about delimiters and prefixes in S3.
Well after trying many S3 tools I've finally found one which handles >million files with ease, and can do a sync as well. It's free, thought that wasn't important to me, I just wanted something that worked.
Dragon Disk:
http://www.dragondisk.com