We are trying to move to cluster with Apache Camel. So far we had it on one node and worked well.
One node:
I have readlock strategy set to 'changed' which keeps track of file changes with camelLock file and only when the file has finished downloading, it will be picked up for processing. But camel readlock strategy 'changed' is discouraged in clustering. According to the camel documentation 'idempotent' is recommended. This is what happens when I am testing with 5GB file.
Two nodes:
I have readlock strategy to 'idempotent' which distributes files to one of the nodes but camel starts processing the file even before the file has finished downloading.
Is there a way to stop camel from processing even before file has downloaded when readlock strategy is idempotent?
Even though both "readLock=changed" and "readLock=idempotent" cause the file-consumer to wait, they really address quite different use-cases: while "readLock=changed" guards against the file being incomplete (i.e. still being written by some producer/sender), "readLock=idempotent" guards against a file being read by two consumer routes. It's a bit confusing that they're addressed by the same option.
First, to address the "changed" scenario: can the sender be changed so that it writes the file in one directory and then, when it is done writing, it copies it into the directory being monitored by your file-consumer? If this is under your control, this is a good way of letting the OS handle things instead of trying to deal with it yourself. (This does not address the issue of the multiple readers.) Otherwise, I suggest you revert back to readLock=changed
Next, on multiple readers, one work around is to only have this route run on only one node of your cluster. Sometimes this might defeat the purpose of clustering, but it is quite possible that you're starting up additional nodes to help with some other routes, and you're fine with this particular route running on just one node. It's a bit of a hack to do something like this, because all nodes are no longer equal, but it is still an option to consider. Simplest would be to start one node with some environment property that flags it as the node that will handle file-reading... or some similar approach.
If you do want the route on multiple nodes, you can start by using the option "idempotent=true" but this is not good enough on its own. The option uses a repository, where it records what files have been read before, and the default repository is in-memory (i.e. each node has its own). So, the default implementation is helpful if the same file is actually being received more than once, and you wish to skip it. However, if you want it to work across nodes, you have to use a different repository.
One central repository could be a database. In that case use can use Camel's JDBC or JPA based repositories. Or, you could use something like Hazelcast. See here for your options: http://camel.apache.org/idempotent-consumer.html
You can use readLock=idempotent-changed.
idempotent-changed is for using an idempotentRepository and changed as the combined read-lock. This allows you to use read locks that supports clustering if the idempotent repository implementation supports that.
You can read more about these idempotent-changed options here: https://camel.apache.org/components/3.13.x/file-component.html
We also used readLock=changed in Docker clustered mode and worked perfectly since we used readLockMinAge for certain interval.
Related
I am going through documentations for project configuration in craftcms not sure what is the different between project-config/write and project-config/modify?
project-config/write takes the config currently stored in the database and writes it out as YAML files in the config/ folder. You usually don't need this since Craft does this automatically whenever you change something in the backend (unless you have turned that off).
project-config/rebuild attempts to rebuild the entire project-config based on the state of the entire database. This is only required in rare edge cases.
Rest you can check the official documentation here:
https://craftcms.com/docs/4.x/project-config.html#what-s-stored-in-project-config
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
My colleague and I are participating in a huge project located in Accurev. We've already created own workspaces backed with some stream (let's call it zzz-stream) which is used by many other participants, not only by us.
The point is that we want to exchange our work between our workspaces, make some changes, exchange again, etc. BEFORE making the changes accessible for others, i.e. in other words we don't want to propagate our changes until it is stable and tested, but we want be able to work on it together.
My idea was to create new stream (yyy-stream) backed with zzz-stream, and then change our workspaces to be backed with yyy-stream. But unfortunately I have no rights to create streams.
My second idea was to use a workspace as backed stream, but it doesn't work because Accurev can't use ws as backed stream.
Is there any solution for our problem?
UPD: I accepted Brad's answer as most detailed. However Accurev is too heavy and sluggish to be used effectively. So actually I prefer to use Git for internal needs over the accurev workspace. (see Accurev externally, git internally)
Your idea of creating the yyy-stream is the EXACT right way to do it. The other options are decent workarounds for one-off situations, but creating the extra stream is simple and is fully leveraging AccuRev's capabilities.
That being said, I understand that your admins have stream creation locked down. They of course want control, but should be allowing for maximizing developer productivity and not forcing workarounds like this. My guess is they have stream creation locked down to a particular group being enforced by the server-admin trigger. One common thing I have seen other large sites do is:
- allow streams to be freely created off of a list of acceptable streams (easy to do in the trigger)
- enforce naming rules on the stream creation. This is important to admins in large sites to keep things organized. Again, this is very easy to enforce via the server-admin trigger.
Bottom line, if this is a common situation, work with the admins to allow this capability as per the above. If they have any questions, they are more than welcome to contact AccuRev and we will help them out.
Your idea on using another stream for you and your peer is a good one and is commonly called a collaboration stream. If your site has stream creation locked down, you would need to work with your AccuRev administrator to make that happen.
Another option is for you and the other developer to pull the keeps from the other workspace into your own stream. This relies on both of you being diligent about doing keeps and then you can look at the history of the other developer's workspace to find the keep operation, right-click that transaction and then select Send to Workspace. The destination workspace must be your own.
A third option (more for a situation where you are in your workspace and know exactly what file you want to grab the other users changes)is to bring up the version browser for the file, right click and select history/browse versions. Look for the other workspace, highlight the version in that workspace, right click and select send to workspace. This will checkout that version into your workspace.
This is similar to the change palette suggestion but quicker if your looking to this on a file basis.
Another idea is to use different version control system (e.g. git or svn) over Accurev workspace to exchange the changes and keep our history separated from zzz-stream. (similar to Accurev externally, git internally) Only changed files should be added to other VCS, not whole project. Some merge problems occur though.
I'm running a very computationally intensive scientific job that spits out results every now and then. The job is basically to just simulate the same thing a whole bunch of times, so it's divided among several computers, which use different OSes. I'd like to direct the output from all these instances to the same file, since all the computers can see the same filesystem via NFS/Samba. Here are the constraints:
Must allow safe concurrent appends. Must block if some other instance on another computer is currently appending to the file.
Performance does not count. I/O for each instance is only a few bytes per minute.
Simplicity does count. The whole point of this (besides pure curiosity) is so I can stop having every instance write to a different file and manually merging these files together.
Must not depend on the details of the filesystem. Must work with an unknown filesystem on an NFS or Samba mount.
The language I'm using is D, in case that matters. I've looked, there's nothing in the standard lib that seems to do this. Both D-specific and general, language-agnostic answers are fully acceptable and appreciated.
Over NFS you face some problems with client side caching and stale data. I have written an OS independent lock module to work over NFS before. The simple idea of creating a [datafile].lock file does not work well over NFS. The basic idea to work around it is to create a lock file [datafile].lock which if present means file is NOT locked and a process that wants to acquire a lock renames the file to a different name like [datafile].lock.[hostname].[pid]. The rename is an atomic enough operation that works well enough over NFS to guarantee exclusivity of the lock. The rest is basically a bunch of fail safe, loops, error checking and lock retrieval in case the process dies before releasing the lock and renaming the lock file back to [datafile].lock
The classic solution is to use a lock file, or more accurately a lock directory. On all common OSs creating a directory is an atomic operation so the routine is:
try to create a lock directory with a fixed name in a fixed location
if the create failed, wait a second or so and try again - repeat until success
write your data to the real data file
delete the lock directory
This has been used by applications such as CVS for many years across many platforms. The only problem occurs in the rare cases when your app crashes while writing and before removing the lock.
Why not just build a simple server which sits between the file and the other computers?
Then if you ever wanted to change the data format, you would only have to modify the server, and not all of the clients.
In my opinion building a server would be much easier than trying to use a Network file system.
Lock File with a twist
Like other answers have mentioned, the easiest method is to create a lock file in the same directory as the datafile.
Since you want to be able to access the same file over multiple PC the best solution I can think of is to just include the identifier of the machine currently writing to the data file.
So the sequence for writing to the data file would be:
Check if there is a lock file present
If there is a lock file, see if I'm the one owning it by checking that its content has my identifier.
If that's the case, just write to the data file then delete the lock file.
If that's not the case, just wait a second or a small random length of time and try the whole cycle again.
If there is no lock file, create one with my identifier and try the whole cycle again to avoid race condition (re-check that the lock file is really mine).
Along with the identifier, I would record a timestamp in the lock file and check whether it's older than a given timeout value.
If the timestamp is too old, then assume that the lock file is stale and just delete it as it would mea one of the PC writing to the data file may have crashed or its connection may have been lost.
Another solution
If you are in control the format of the data file, could be to reserve a structure at the beginning of the file to record whether it is locked or not.
If you just reserve a byte for this purpose, you could assume, for instance, that 00 would mean the data file isn't locked, and that other values would represent the identifier of the machine currently writing to it.
Issues with NFS
OK, I'm adding a few things because Jiri Klouda correctly pointed out that NFS uses client-side caching that will result in the actual lock file being in an undetermined state.
A few ways to solve this issue:
mount the NFS directory with the noac or sync options. This is easy but doesn't completely guarantee data consistency between client and server though so there may still be issues although in your case it may be OK.
Open the lock file or data file using the O_DIRECT, the O_SYNC or O_DSYNC attributes. This is supposed to disable caching altogether.
This will lower performance but will ensure consistency.
You may be able to use flock() to lock the data file but its implementation is spotty and you will need to check if your particular OS actually uses the NFS locking service. It may do nothing at all otherwise.
If the data file is locked, then another client opening it for writing will fail.
Oh yeah, and it doesn't seem to work on SMB shares, so it's probably best to just forget about it.
Don't use NFS and just use Samba instead: there is a good article on the subject and why NFS is probably not the best answer to your usage scenario.
You will also find in this article various methods for locking files.
Jiri's solution is also a good one.
Basically, if you want to keep things simple, don't use NFS for frequently-updated files that are shared amongst multiple machines.
Something different
Use a small database server to save your data into and bypass the NFS/SMB locking issues altogether or keep your current multiple data files system and just write a small utility to concatenate the results.
It may still be the safest and simplest solution to your problem.
I don't know D, but I thing using a mutex file to do the jobe might work. Here's some pseudo-code you might find useful:
do {
// Try to create a new file to use as mutex.
// If it's already created, it will throw some kind of error.
mutex = create_file_for_writing('lock_file');
} while (mutex == null);
// Open your log file and write results
log_file = open_file_for_reading('the_log_file');
write(log_file, data);
close_file(log_file);
close_file(mutex);
// Free mutex and allow other processes to create the same file.
delete_file(mutex);
So, all processes will try to create the mutex file but only the one who wins will be able to continue. Once you write your output, close and delete the mutex so other processes can do the same.
It's a very common scenario: some process wants to drop a file on a server every 30 minutes or so. Simple, right? Well, I can think of a bunch of ways this could go wrong.
For instance, processing a file may take more or less than 30 minutes, so it's possible for a new file to arrive before I'm done with the previous one. I don't want the source system to overwrite a file that I'm still processing.
On the other hand, the files are large, so it takes a few minutes to finish uploading them. I don't want to start processing a partial file. The files are just tranferred with FTP or sftp (my preference), so OS-level locking isn't an option.
Finally, I do need to keep the files around for a while, in case I need to manually inspect one of them (for debugging) or reprocess one.
I've seen a lot of ad-hoc approaches to shuffling upload files around, swapping filenames, using datestamps, touching "indicator" files to assist in synchronization, and so on. What I haven't seen yet is a comprehensive "algorithm" for processing files that addresses concurrency, consistency, and completeness.
So, I'd like to tap into the wisdom of crowds here. Has anyone seen a really bulletproof way to juggle batch data files so they're never processed too early, never overwritten before done, and safely kept after processing?
The key is to do the initial juggling at the sending end. All the sender needs to do is:
Store the file with a unique filename.
As soon as the file has been sent, move it to a subdirectory called e.g. completed.
Assuming there is only a single receiver process, all the receiver needs to do is:
Periodically scan the completed directory for any files.
As soon as a file appears in completed, move it to a subdirectory called e.g. processed, and start working on it from there.
Optionally delete it when finished.
On any sane filesystem, file moves are atomic provided they occur within the same filesystem/volume. So there are no race conditions.
Multiple Receivers
If processing could take longer than the period between files being delivered, you'll build up a backlog unless you have multiple receiver processes. So, how to handle the multiple-receiver case?
Simple: Each receiver process operates exactly as before. The key is that we attempt to move a file to processed before working on it: that, and the fact the same-filesystem file moves are atomic, means that even if multiple receivers see the same file in completed and try to move it, only one will succeed. All you need to do is make sure you check the return value of rename(), or whatever OS call you use to perform the move, and only proceed with processing if it succeeded. If the move failed, some other receiver got there first, so just go back and scan the completed directory again.
If the OS supports it, use file system hooks to intercept open and close file operations. Something like Dazuko. Other operating systems may let you know about file operations in anoter way, for example Novell Open Enterprise Server lets you define epochs, and read list of files modified during an epoch.
Just realized that in Linux, you can use inotify subsystem, or the utilities from inotify-tools package
File transfers is one of the classics of system integration. I'd recommend you to get the Enterprise Integration Patterns book to build your own answer to these questions -- to some extent, the answer depends on the technologies and platforms you are using for endpoint implementation and for file transfer. It's a quite comprehensive collection of workable patterns, and fairly well written.