I'm looking for a way to sync a large number of machines (hundreds) with a remote repository.
The repository is comprised of small files (around 20KB), but the total arrives at a few GB and continue to grow with time.
The goal is to have changes at the remote repository propagate as fast as possible (no more than 2 seconds) to all the machines. (sync)
There are tools that provide exactly this functionality such as S3 sync or Rclone but carry a major disadvantage:
The sync command will need to enumerate all of the files in the bucket to determine whether a local file already exists in the bucket and if it is the same as the local file. The more documents you have in the bucket, the longer it's going to take. This means that once the bucket gets big even a small change will cost a lot of time.
I wonder if there is a way (a tool or a method) to sync only modified files, without having to go through all of the files. You can imagine a comparison of meta data at source and remote, determining what are the diffs and acting accordingly.
How would you go about it?
Related
I have a Ceph cluster managed by Rook with a single RGW store over it. We are trying to figure out the best backup strategy for this store. We are considering the following options: using rclone to backup object via an S3 interface, using s3fs-fuse (haven’t tested it yet but s3fs-fuse is known to be not reliable enough), and using NFS-Ganesha to reexport the RGW store as an NFS share.
We are going to have quite a lot of RGW users and quite a lot of buckets, so all three solutions do not scale well for us. Another possibility is to perform snapshots of RADOS pools backing the RGW store and to backup these snapshots, but the RTO will be much higher in that case. Another problem with snapshots is that it does not seem possible to perform them consistently across all RGW-backing pools. We never delete objects from the RGW store, so this problem does not seem to be that big if we start snapshotting from the metadata pool - all the data it refers to will remain in place even if we create a snapshot on the data pool a bit later. It won’t be super consistent but it should not be broken either. It’s not entirely clear how to restore single objects in a timely manner using this snapshotting scheme (to be honest, it’s not entirely clear how to restore using this scheme at all), but it seems to be worth trying.
What other options do we have? Am I missing something?
We're planning to implement Ceph in 2021.
We don't expect a large number of users and buckets, initially.
While waiting for https://tracker.ceph.com/projects/ceph/wiki/Rgw_-_Snapshots, I successfully tested this solution to address the Object Store protection by taking advantage of multisite configuration + sync policy (https://docs.ceph.com/en/latest/radosgw/multisite-sync-policy/) in the "Octopus" version.
Assuming you have all zones in the Prod site Zone Sync'd to the DRS,
create a Zone in the DRS, e.g. "backupZone", not Zone Sync'd from
or to any of the other Prod or DRS zones;
the endpoints for this backupZone are in 2 or more DRS cluster
nodes;
using (https://rclone.org/s3/) write a bash script: for each the
"bucket"s in the DRS zones, create a version enabled "bucket"-p in the backupZone
and schedule sync, e.g. twice a day, from "bucket" to "bucket"-p;
protect the access to the backupZone endpoints so that no ordinary
user (or integration) can access them, only accessible from the other nodes in the
cluster (obviously) and the server running the rclone-based script;
when there is a failure, just recover all the objects from the *-p
buckets, once again using rclone, to the original buckets or to
filesystem.
This protects from the following failures:
Infra:
Bucket or pool failure;
Object pervasive corruption;
Loss of a site
Human error:
Deletion of versions or objects;
Removal of buckets
Elimination of entire Pools
Notes:
Only the latest version of each object is sync'd to the protected
(*-p) bucket, but if the script runs several times you have the
latest states of the objects through time;
when an object is deleted in the prod bucket, rnode just flags the
object with the DeleteMarker upon sync
this does not scale!! As the number of buckets increases, the time to
sync becomes untenable
I want to upload millions of audio items by users to my server. The current app has designed to give the contents, transcode them and finally send by ftp to storage servers. I want to know:
Does the app server can bear the enormous tasks by user like commenting, uploading, transcoding after scaling to more servers (to carry web app load)?
If the answer of above question is yes, is it correct and best approach? Because a good architecture will be to send transcoding to storage servers wait for finishing the job and sending respond to app server but at the same time it has more complexity and insecurity.
What is the common method for this type of websites?
If I send the upload and transcoding job to storage servers does it compatible with enterprise storage technologies in a long term scalability?
5- The current App is based on PHP. Is it possible to move tmp folder to another servers to overcome upload overload?
Thanks for answer, for tmp folder question number 5. I mean the tmp folder in Apache. I know that all uploaded files before moving to final storage destination (eg: storage servers or any solution) are stored in tmp folder of apache. I was wondering if this is a rule for apache and all uploaded files should be located first in app server, so how can I control, scale and redirect this massive load of storage to a temporary storage or server? I mean a server or storage solution as tmp folder of appche to just be guest of uploaded files before sending to the final storages places. I have studied and designed all the things about scaling of database, storages, load balancing, memcache etc. but this is one of my unsolved question. Where new arrived files by users to main server will be taken place in a scaled architect? And what is the common solution for this? (In one box solution all files will be temporary in the tmp dir of appche but for massive amount of contents and in a scaled system?).
Regards
You might want to take a look at the Viddler architecture: http://highscalability.com/blog/2011/5/10/viddler-architecture-7-million-embeds-a-day-and-1500-reqsec.html
Since I don't feel I can answer this (I wanted to add a comment, but my text was too long), some thoughts:
If you are creating such a large system (as it sounds) you should have some performance tests to see, how many concurrent connections/uploads,... whatever your architecture can handle. As I always say: If you don't know it: "no, it can't ".
I think the best way to deal with heavy load (this is: a lot of uploads, requiring a lot of blocked Threads from the appserver (-> this means, I would not use the Appserver to handle the fileuploads). Perform all your heavy operations (transcoding) asynchronously (e.g. queue the uploaded files, processess them afterwards). In any case the Applicaiton server should not wait for the response of the transcoding system -> just tell the user, that his file are going to be processed and send him a message (or whatever) when its finished. You can use something like gearman for that.
I would search for existing architectures, that have to handle a lot of uploads/conversion too (e.g. flickr) just go to slideshare and search for "flickr" or "scalable web architecture"
I do not really understand this - but I would use Servers based on their tasks (e.g. Applicaiton server, Database serversm, Transconding servers, Storage,...) - each server should do, what he can do best.
I am afraid I don't know what you are talking about when you say tmp folder.
Good luck
I am working on an open source backup utility that backs up files and transfers them to various external locations such as Amazon S3, Rackspace Cloud Files, Dropbox, and remote servers through FTP/SFTP/SCP protocols.
Now, I have received a feature request for doing incremental backups (in case the backups that are made are large and become expensive to transfer and store). I have been looking around and someone mentioned the rsync utility. I performed some tests with this but am unsure whether this is suitable, so would like to hear from anyone that has some experience with rsync.
Let me give you a quick rundown of what happens when a backup is made. Basically it'll start dumping databases such as MySQL, PostgreSQL, MongoDB, Redis. It might take a few regular files (like images) from the file system. Once everything is in place, it'll bundle it all in a single .tar (additionally it'll compress and encrypt it using gzip and openssl).
Once that's all done, we have a single file that looks like this:
mybackup.tar.gz.enc
Now I want to transfer this file to a remote location. The goal is to reduce the bandwidth and storage cost. So let's assume this little backup package is about 1GB in size. So we use rsync to transfer this to a remote location and remove the file backup locally. Tomorrow a new backup file will be generated, and it turns out that a lot more data has been added in the past 24 hours, and we build a new mybackup.tar.gz.enc file and it looks like we're up to 1.2GB in size.
Now, my question is: Is it possible to transfer just the 200MB that got added in the past 24 hours? I tried the following command:
rsync -vhP --append mybackup.tar.gz.enc backups/mybackup.tar.gz.enc
The result:
mybackup.tar.gz.enc 1.20G 100% 36.69MB/s 0:00:46 (xfer#1, to-check=0/1)
sent 200.01M bytes
received 849.40K bytes
8.14M bytes/sec
total size is 1.20G
speedup is 2.01
Looking at the sent 200.01M bytes I'd say the "appending" of the data worked properly. What I'm wondering now is whether it transferred the whole 1.2GB in order to figure out how much and what to append to the existing backup, or did it really only transfer the 200MB? Because if it transferred the whole 1.2GB then I don't see how it's much different from using the scp utility on single large files.
Also, if what I'm trying to accomplish is at all possible, what flags do you recommend? If it's not possible with rsync, is there any utility you can recommend to use instead?
Any feedback is much appreciated!
The nature of gzip is such that small changes in the source file can result in very large changes to the resultant compressed file - gzip will make its own decisions each time about the best way to compress the data that you give it.
Some versions of gzip have the --rsyncable switch which sets the block size that gzip works at to the same as rsync's, which results in a slightly less efficient compression (in most cases) but limits the changes to the output file to the same area of the output file as the changes in the source file.
If that's not available to you, then it's typically best to rsync the uncompressed file (using rsync's own compression if bandwidth is a consideration) and compress at the end (if disk space is a consideration). Obviously this depends on the specifics of your use case.
It sent only what it says it sent - only transferring the changed parts is one of the major features of rsync. It uses some rather clever checksumming algorithms (and it sends those checksums over the network, but this is negligible - several orders of magnitude less data than transferring the file itself; in your case, I'd assume that's the .01 in 200.01M) and only transfers those parts it needs.
Note also that there already are quite powerful backup tools based on rsync - namely, Duplicity. Depending on the license of your code, it may be worthwhile to see how they do this.
New rsync --append WILL BREAK your file contents, if there are any changes in your existing data. (Since 3.0.0)
I run an image sharing website that has over 1 million images (~150GB). I'm currently storing these on a hard drive in my dedicated server, but I'm quickly running out of space, so I'd like to move them to Amazon S3.
I've tried doing an RSYNC and it took RSYNC over a day just to scan and create the list of image files. After another day of transferring, it was only 7% complete and had slowed my server down to a crawl, so I had to cancel.
Is there a better way to do this, such as GZIP them to another local hard drive and then transfer / unzip that single file?
I'm also wondering whether it makes sense to store these files in multiple subdirectories or is it fine to have all million+ files in the same directory?
One option might be to perform the migration in a lazy fashion.
All new images go to Amazon S3.
Any requests for images not yet on Amazon trigger a migration of that one image to Amazon S3. (queue it up)
This should fairly quickly get all recent or commonly fetched images moved over to Amazon and will thus reduce the load on your server. You can then add another task that migrates the others over slowly whenever the server is least busy.
Given that the files do not exist (yet) on S3, sending them as an archive file should be quicker than using a synchronization protocol.
However, compressing the archive won't help much (if at all) for image files, assuming that the image files are already stored in a compressed format such as JPEG.
Transmitting ~150 Gbytes of data is going to consume a lot of network bandwidth for a long time. This will be the same if you try to use HTTP or FTP instead of RSYNC to do the transfer. An offline transfer would be better if possible; e.g. sending a hard disc, or a set of tapes or DVDs.
Putting a million files into one flat directory is a bad idea from a performance perspective. while some file systems would cope with this fairly well with O(logN) filename lookup times, others do not with O(N) filename lookup. Multiply that by N to access all files in a directory. An additional problem is that utilities that need to access files in order of file names may slow down significantly if they need to sort a million file names. (This may partly explain why rsync took 1 day to do the indexing.)
Putting all of your image files in one directory is a bad idea from a management perspective; e.g. for doing backups, archiving stuff, moving stuff around, expanding to multiple discs or file systems, etc.
One option you could use instead of transferring the files over the network is to put them on a harddrive and ship it to amazon's import/export service. You don't have to worry about saturating your server's network connection etc.
We have created a product that potentially will generate tons of requests for a data file that resides on our server. Currently we have a shared hosting server that runs a PHP script to query the DB and generate the data file for each user request. This is not efficient and has not been a problem so far but we want to move to a more scalable system so we're looking in to EC2. Our main concerns are being able to handle high amounts of traffic when they occur, and to provide low latency to users downloading the data files.
I'm not 100% sure on how this is all going to work yet but this is the idea:
We use an EC2 instance to host our admin panel and to generate the files that are being served to app users. When any admin makes a change that affects these data files (which are downloaded by users), we make a copy over to S3 using CloudFront. The idea here is to get data cached and waiting on S3 so we can keep our compute times low, and to use CloudFront to get low latency for all users requesting the files.
I am still learning the system and wanted to know if anyone had any feedback on this idea or insight in to how it all might work. I'm also curious about the purpose of projects like Cassandra. My understanding is that simply putting our application on EC2 servers makes it scalable by the nature of the servers. Is Cassandra just about keeping resource usage low, or is there a reason to use a system like this even when on EC2?
CloudFront: http://aws.amazon.com/cloudfront/
EC2: http://aws.amazon.com/cloudfront/
Cassandra: http://cassandra.apache.org/
Cassandra is a non-relational database engine and if this is what you need, you should first evaluate Amazon's SimpleDB : a non-relational database engine built on top of S3.
If the file only needs to be updated based on time (daily, hourly, ...) then this seems like a reasonable solution. But you may consider placing a load balancer in front of 2 EC2 images, each running a copy of your application. This would make it easier to scale later and safer if one instance fails.
Some other services you should read up on:
http://aws.amazon.com/elasticloadbalancing/ -- Amazons load balancer solution.
http://aws.amazon.com/sqs/ -- Used to pass messages between systems, in your DA (distributed architecture). For example if you wanted the systems that create the data file to be different than the ones hosting the site.
http://aws.amazon.com/autoscaling/ -- Allows you to adjust the number of instances online based on traffic
Make sure to have a good backup process with EC2, snapshot your OS drive often and place any volatile data (e.g. a database files) on an EBS block. EC2 doesn't fail often but when it does you don't have access to the hardware, and if you have an up to date snapshot you can just kick a new instance online.
Depending on the datasets, Cassandra can also significantly improve response times for queries.
There is an excellent explanation of the data structure used in NoSQL solutions that may help you see if this is an appropriate solution to help:
WTF is a Super Column