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I am new to this field of studies and I want to know if I understood everything right.
In Memory database management systems (like Redis and memcached) are for just keeping data as in cache to access quicker. I mean everything is done manually: you read from database then write in cache and next time read from cache. The only task that redis/Memcached do, is they remove records after some time and manage task queues? Am I right?
That may be the primary use case for redis/memcached, but not for the broader category of in-memory database systems.
Many (most, for my company) use cases don't involve another DBMS; the in-memory database system (IMDS) is the only database management system.
Further, there are IMDS that resemble persistent DBMS (HANA, TimesTen, VoltDB, eXtremeDB and others). They can be embedded or client/server, SQL or NoSQL, offer high availability, clustering, sharding and much more.
I am currently working on a website where, roughly 40 million documents and images should be served to it's users. I need suggestions on which method is the most suitable for storing content with subject to these requirements.
System should be highly available, scale-able and durable.
Files have to be stored permanently and users should be able to modify them.
Due to client restrictions, 3rd party object storage providers such as Amazon S3 and CDNs are not suitable.
File size of content can vary from 1 MB to 30 MB. (However about 90% of the files would be less than 2 MB)
Content retrieval latency is not much of a problem. Therefore indexing or caching is not very important.
I did some research and found out about the following solutions;
Storing content as BLOBs in databases.
Using GridFS to chunk and store content.
Storing content in a file server in directories using a hash and storing the metadata in a database.
Using a distributed file system such as GlusterFS or HDFS and storing the file metadata in a database.
The website is developed using PHP and Couchbase Community Edition is used as the database.
I would really appreciate any input.
Thank you.
I have been working on a similar system for last two years, the work is still in progress. However, requirements are slightly different from yours: modifications are not possible (I will try to explain why later), file sizes fall in range from several bytes to several megabytes, and, the most important one, the deduplication, which should be implemented both on the document and block levels. If two different users upload the same file to the storage, the only copy of the file should be kept. Also if two different files partially intersect with each other, it's necessary to store the only copy of the common part of these files.
But let's focus on your requirements, so deduplication is not the case. First of all, high availability implies replication. You'll have to store your file in several replicas (typically 2 or 3, but there are techniques to decrease data parity) on independent machines in order to stay alive in case if one of the storage servers in your backend dies. Also, taking into account the estimation of the data amount, it's clear that all your data just won't fit into a single server, so vertical scaling is not possible and you have to consider partitioning. Finally, you need to take into account concurrency control to avoid race conditions when two different clients are trying to write or update the same data simultaneously. This topic is close to the concept of transactions (I don't mean ACID literally, but something close). So, to summarize, these facts mean that you're are actually looking for distributed database designed to store BLOBs.
On of the biggest problems in distributed systems is difficulties with global state of the system. In brief, there are two approaches:
Choose leader that will communicate with other peers and maintain global state of the distributed system. This approach provides strong consistency and linearizability guarantees. The main disadvantage is that in this case leader becomes the single point of failure. If leader dies, either some observer must assign leader role to one of the replicas (common case for master-slave replication in RDBMS world), or remaining peers need to elect new one (algorithms like Paxos and Raft are designed to target this issue). Anyway, almost whole incoming system traffic goes through the leader. This leads to the "hot spots" in backend: the situation when CPU and IO costs are unevenly distributed across the system. By the way, Raft-based systems have very low write throughput (check etcd and consul limitations if you are interested).
Avoid global state at all. Weaken the guarantees to eventual consistency. Disable the update of files. If someone wants to edit the file, you need to save it as new file. Use the system which is organized as a peer-to-peer network. There is no peer in the cluster that keeps the full track of the system, so there is no single point of failure. This results in high write throughput and nice horizontal scalability.
So now let's discuss the options you've found:
Storing content as BLOBs in databases.
I don't think it's a good option to store files in traditional RDBMS because they provide optimizations for structured data and strong consistency, and you don't need neither of this. Also you'll have difficulties with backups and scaling. People usually don't use RDBMS in this way.
Using GridFS to chunk and store content.
I'm not sure, but it looks like GridFS is built on the top of MongoDB. Again, this is document-oriented database designed to store JSONs, not BLOBs. Also MongoDB had problems with a cluster for many years. MongoDB passed Jepsen tests only in 2017. This may mean that MongoDB cluster is not mature yet. Make performance and stress tests, if you go this way.
Storing content in a file server in directories using a hash and storing the metadata in a database.
This option means that you need to develop object storage on your own. Consider all the problems I've mentioned above.
Using a distributed file system such as GlusterFS or HDFS and storing the file metadata in a database.
I used neither of these solutions, but HDFS looks like overkill, because you get dependent on Hadoop stack. Have no idea about GlusterFS performance. Always consider the design of distributed file systems. If they have some kind of dedicated "metadata" serves, treat it as a single point of failure.
Finally, my thoughts on the solutions that may fit your needs:
Elliptics. This object storage is not well-known outside of the russian part of the Internet, but it's mature and stable, and performance is perfect. It was developed at Yandex (russian search engine) and a lot of Yandex services (like Disk, Mail, Music, Picture hosting and so on) are built on the top of it. I used it in previous project, this may take some time for your ops to get into it, but it's worth it, if you're OK with GPL license.
Ceph. This is real object storage. It's also open source, but it seems that only Red Hat people know how to deploy and maintain it. So get ready to a vendor lock. Also I heard that it have too complicated settings. Never used in production, so don't know about performance.
Minio. This is S3-compatible object storage, under active development at the moment. Never used it in production, but it seems to be well-designed.
You may also check wiki page with the full list of available solutions.
And the last point: I strongly recommend not to use OpenStack Swift (there are lot of reasons why, but first of all, Python is just not good for these purposes).
One probably-relevant question, whose answer I do not readily see in your post, is this:
How often do users actually "modify" the content?
and:
When and if they do, how painful is it if a particular user is served "stale" content?
Personally (and, "categorically speaking"), I prefer to tackle such problems in two stages: (1) identifying the objects to be stored – e.g. using a database as an index; and (2) actually storing them, this being a task that I wish to delegate to "a true file-system, which after all specializes in such things."
A database (it "offhand" seems to me ...) would be a very good way to handle the logical ("as seen by the user") taxonomy of the things which you wish to store, while a distributed filesystem could handle the physical realities of storing the data and actually getting it to where it needs to go, and your application would be in the perfect position to gloss-over all of those messy filesystem details . . .
I'm setting a home server primarily for backup use. I have about 90GB of personal data that must be backed up in the most reliable manner, while still preserving disk space. I want to have full file history so I can go back to any file at any particular date.
Full weekly backups are not an option because of the size of the data. Instead, I'm looking along the lines of an incremental backup solution. However, I'm aware that a single corruption in a set of incremental backups makes the entire series (beyond a point) unrecoverable. Thus simple incremental backups are not an option.
I've researched a number of solutions to the problem. First, I would use reverse-incremental backups so that the latest version of the files would have the least chance of loss (older files are not as important). Second, I want to protect both the increments and backup with some sort of redundancy. Par2 parity data seems perfect for the job. In short, I'm looking for a backup solution with the following requirements:
Reverse incremental (to save on disk space and prioritize the most recent backup)
File history (kind of a broader category including reverse incremental)
Par2 parity data on increments and backup data
Preserve metadata
Efficient with bandwidth (bandwidth saving; no copying the entire directory over for each increment). Most incremental backup solutions should work this way.
This would (I believe) ensure file integrity and relatively small backup sizes. I've looked at a number of backup solutions already but they have a number of problems:
Bacula - Simple normal incremental backups
bup - incremental and implements par2 but isn't reverse incremental and doesn't preserve metadata
duplicity - incremental, compressed, and encrypted but isn't reverse incremental
dar - incremental and par2 is easy to add, but isn't reverse incremental and no file history?
rdiff-backup - almost perfect for what I need but it doesn't have par2 support
So far I think that rdiff-backup seems like the best compromise but it doesn't support par2. I think I can add par2 support to backup increments easily enough since they aren't modified each backup but what about the rest of the files? I could generate par2 files recursively for all files in the backup but this would be slow and inefficient, and I'd have to worry about corruption during a backup and old par2 files. In particular, I couldn't tell the difference between a changed file and a corrupt file, and I don't know how to check for such errors or how they would affect the backup history. Does anyone know of any better solution? Is there a better approach to the issue?
Thanks for reading through my difficulties and for any input you can give me. Any help would be greatly appreciated.
http://www.timedicer.co.uk/index
Uses rdiff-backup as the engine. I've been looking at it, but that requires me to set up a "server" using linux or a virtual machine.
Personally, I use WinRAR to make pseudo-incremental backups (it actually makes a full backup of recent files) run daily by a scheduled task. It is similarly a "push" backup.
It's not a true incremental (or reverse-incremental) but it saves different versions of files based on when it was last updated. I mean, it saves the version for today, yesterday and the previous days, even if the file is identical. You can set the archive bit to save space, but I don't bother anymore as all I backup are small spreadsheets and documents.
RAR has it's own parity or recovery record that you can set in size or percentage. I use 1% (one percent).
It can preserve metadata, I personally skip the high resolution times.
It can be efficient since it compresses the files.
Then all I have to do is send the file to my backup. I have it copied to a different drive and to another computer in the network. No need for a true server, just a share. You can't do this for too many computers though as Windows workstations have a 10 connection limit.
So for my purpose, which may fit yours, backs up my files daily for files that have been updated in the last 7 days. Then I have another scheduled backup that backups files that have been updated in the last 90 days run once a month or every 30 days.
But I use Windows, so if you're actually setting up a Linux server, you might check out the Time Dicer.
Since nobody was able to answer my question, I'll write a few possible solutions I found while researching the topic. In short, I believe the best solution is rdiff-backup to a ZFS filesystem. Here's why:
ZFS checksums all blocks stored and can easily detect errors.
If you have ZFS set to mirror your data, it can recover the errors by copying from the good copy.
This takes up less space than full backups, even though the data is copied twice.
The odds of an error in both the original and mirror is tiny.
Personally I am not using this solution as ZFS is a little tricky to get working on Linux. Btrfs looks promising but hasn't been proven stable from years of use. Instead, I'm going with a cheaper option of simply checking hard drive SMART data. Hard drives should do some error checking/correcting themselves and by monitoring this data I can see if this process is working properly. It's not as good as additional filesystem parity but better than nothing.
A few more notes that might be interesting to people looking into reliable backup development:
par2 seems to be dated and buggy software. zfec seems like a much faster modern alternative. Discussion in bup occurred a while ago: https://groups.google.com/group/bup-list/browse_thread/thread/a61748557087ca07
It's safer to calculate parity data before even writing to disk. i.e. don't write to disk, read it, and then calculate parity data. Do it from ram, and check against the original for additional reliability. This might only be possible with zfec, since par2 is too slow.
I'm running a software called Fishbowl inventory and it is running on a firebird database (Windows server 2003) at this time the fishbowl software is running extremely slow when more then one user accesses the software. I'm thinking I maybe able to speed up the application by forcing the database to run "In Memory". However I can not find documentation on how to do this. Any help would be greatly appreciated.
Thank you in advance.
Robert
Firebird does not have memory tables - they may or may not be added in future versions (>3) but certainly not in the upcoming 2.5. There can be any other number of reasons why your software is slow with multiple users; however, Firebird itself has pretty good concurrency, so make sure you find the actual bottleneck first.
+1 to Holger. Find the bottleneck first.
Sinática Monitor may help you.
In-memory tables are nice either for OLAP (when data is not changing) or for temporary internal data storage.
In both cases data loss is not danger.
Pity that FB has no in-memory mode. I think about using SQLite as result.
As for caching, i think simple parallel thread that reads all the blocks of database file would make it in-memory - in OS cache if OS has enough memory.
But i also think, that OS already cached as much of DB file as it could and agressive forcing to cache would make overall performance even worse.
I had read an article some time ago, from someone who did a memory drive (like in old DOS) and ran a Database there. The problem is if anything fails, you lose everything. You should do backups very often to ensure a minimum of security.
Not a good idea at all I think.
I have terabytes of files and database dumps that I need to backup off-site.
What's the best way to accomplish this?
I'm currently weighing rsyinc to Amazon EBS or getting an appliance (eg barracuda).
I called a buddy of mine, and he said he uses backula to get all the files on a single disk, then backs that disk up to tape, then sends the tapes off to iron mountain.
Still waiting to hear back from other sysadmins I've contacted. Will post results here.
One common solution to offsite backups that is worth considering is performing the backup onsite and then physically transporting the backup elsewhere, either via secure snail mail or with a service designed for that purpose. If bandwidth is an issue, this may be more practical.
Instead of tapes, I use hard drives that I physically swap out every week. It is less expensive than tape equipment, and easier to plug into another system when necessary.
Back in the late 80s I worked at a place where every week we received a box of tapes of various sorts every monday - we would do one set of weekly backups on the tapes on that box and send them off-site. Evidently they had two of these boxes, one that was in our office and the other they kept locked up somewhere. Then we got an Exabyte drive which had a single tape capacity greater than that whole box of TK-50s, QIC-40s and mag tapes, and it was just simpler to send a single tape home with one of the manager every week.
I'm sure there are still off-site backup systems like that, but I find it easier to keep cycling a couple of 500Gb drives from my home system to my desk at work.
Why not encrpyt it and actually upload to a third party vendor?
I am thinking of doing this with my data at home but have not found a vendor that will just let me do a dump...They all want to install client side apps...
Admittedly, I have not looked that hard...
We use a couple of solutions. We have an offsite backup with another company that we do. We also use several portable hard drives and swap them out each day. Neither solution really handles multiple terabytes of data. More like gigabytes.
In the future, however, we will probably be looking at going the tape router, or something else that is similarly permanent and storable. Terabytes of data is too much to transfer over the wire. When bluray discs become reasonably priced and commercially viable, it may be a good idea to look into the 400GB discs that were touted not long ago. Those would be extremely storage friendly (both in the physical sense and the file size sense), and depending on the longevity stats, may keep for a while, similar to tapes.
I would recommend using a local san from a company like EMC that provides compressed snapshot based replication to remote facilities. It's an expensive solution, but it works.
http://www.emc.com/products/family/emc-centera-family.htm
Over the weekend, I've heard back from a couple of my sysadmin buddies.
It seems the best practice is to backup all machines to a central large disk, then back that disk up to tape, then send the tapes off site (all have used Iron Mountain).
Tapes hold 400-800G and cost $30-$80 per tape.
A tape changer seems to go for $10k on up.
Not sure how much the off-site shipping costs.
I'm scared of tape. I think it gives a false sense of data security. In my own experience from backing up dozens of terrabytes across hundreds of tapes, we discovered that the data recovery rate after a few years fell to about 70%.
To be fair, that was with a now discontinued technology (AIT), but it pretty much put me off tape for life unless it sits on a 1" spool and is reassuringly expensive.
These days, multiple hard drives, multiple locations, and yes, a fall back into Amazon S3 or other cloud provider does no harm (apart from being a tad expensive).