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 . . .
Related
I was wondering if a specific piece of data can be backed up or restored in MarkLogic.
Version 8.0-5.4 is used on CentOS, data has grown a lot.
I was wondering if for example only the last 3 month's data can be backed up OR from a full backup, only the last 3 month's of data can be restored to lower environments.
MarkLogic itself is unaware of the age of your content by default (unless you enabled tracking insert and update timestamps).
Furthermore, MarkLogic balances all content across all forests evenly based on the selected balancing strategy.
Some Ideas:
Archive:
In your system, find a way to isolate the old content (query or collection)
Then use MLCP to export the content to anarchive.
Or if you have hadoop, then use a similar strategy.
Then you can remove the content from the system
This makes it totally gone - but ahs the benefit of no index overhead if disk space is an issue.
Forests
Using a strategy as above to isolate your old content, move it all to a single forest.
Take that forest offline and detach it and then physically archive it. Unfortunately, this approach also includes the index data. You could purge them by hand - but that't a risky story for another time.
Note: If you were to upgrade to ML 9, then you could use time-based queries on your forest balancing strategy and roll all of your content onto a month-based forest each month and then archive the previous month - similar to log rotation.
Forest Backups
As each forest can be backed up on its own, then it is possible to consider creating a backup of the forest and then deleting that forest. I'm not sure of the benefits of this approach. I suppose that if indexes are not included in the backup, then this approach is superior to the MLCP/Hadoop approach.
Tiered Storage
I answered the question as I interpreted it. However, the full enterprise approach would be to embrace Tiered Storage and store various data on different media types to give the most cost-effective solution without the data actually going offline.
When should I make this direct recording at the bank?
What are the situations?
I know I can record the path of the image in the bank.
In addition to the cost being higher as mentioned, one must take into account several factors:
Data Volume: For a low volume of data there may be no problem. On the other hand, for mass storage of data the database is practically unfeasible.
Clustering: One advantage of the database is if your system runs on multiple servers, everyone will have uniform access to the files.
Scalability: If demand for volume or availability increases, can you add more capacity to the system? It is much easier to split files between different servers than to distribute records from one table to more servers.
Flexibility: Backing up, moving files from one server to another, doing some processing on the stored files, all this is easier if the files are in a directory.
There are several strategies for scaling a system in terms of both availability and volume. Basically these strategies consist of distributing them on several different servers and redirecting the user to each of them according to some criteria. The details vary of implementation, such as: data update strategy, redundancy, distribution criteria, etc.
One of the great difficulties in managing files outside BD is that we now have two distinct data sources that need to be always in sync.
From the safety point of view, there is actually little difference. If a hacker can compromise a server, it can read both the files written to disk of your system and the files of the database system. If this question is critical, an alternative is to store the encrypted data.
I also convert my images into byte array and store them in an sql server database but in the long run, I am sure that someone will ask you and tell you that you should only save the (server) path of the image.
The biggest disadvantage of storing as binary I think is
Retrieving images from database is significantly more expensive compared to using the file system
In my system, a user can upload very large files, which I need to store in Couchbase. I don't need such very large objects to persist in memory, but I want them to be always read/written from/to disk. These files are read-only (never modified). The user can upload them, delete them, download them, but never update them. For some technical constraints, my system cannot store those files in the file system, so they have to be stored into the database.
I've done some research and found an article[1] saying that storing large objects in a database is generally a bad idea, especially with Couchbase, but at the same time provides some advice: create a secondary bucket with a low RAM quota, tune up the value/full eviction policy. My concern is the limit of 20Mb mentioned by the author. My files would be much larger than that.
What's the best approach to follow to store large files into Couchbase without having them persist in memory? Is it possible to raise the limit of 20Mb in case? Shall I create a secondary bucket with a very low RAM quota and a full eviction policy?
[1]http://blog.couchbase.com/2016/january/large-objects-in-a-database
Generally, Couchbase engineers recommend that you not store large files in Couchbase. Instead, you can store the files on some file server (like AWS or Azure Blob or something) and instead store the meta-data about the files in Couchbase.
There's a couchbase blog posting that gives a pretty detailed breakdown of how to do what you want to do in Couchbase.
This is Java API specific but the general approach can work with any of the Couchbase SDKs, I'm actually in the midst of doing something pretty similar right now with the node SDK.
I can't speak for what couchbase engineers recommend but they've posted this blog entry detailing how to do it.
For large files, you'll certainly want to split into chunks. Do not attempt to store a big file all in one document. The approach I'm looking at is to chunk the data, and insert it under the file sha1 hash. So file "Foo.docx" would get split into say 4 chunks, which would be "sha1|0", "sha1|1" and so on, where sha1 is the hash of the document. This would also enable a setup where you can store the same file under many different names.
Tradeoffs -- if integration with Amazon S3 is an option for you, you might be better off with that. In general chunking data in a DB like what I describe is going to be more complicated to implement, and much slower, than using something like Amazon S3. But that has to be traded off other requirements, like whether or not you can keep sensitive files in S3, or whether you want to deal with maintaining a filesystem and the associated scaling of that.
So it depends on what your requirements are. If you want speed/performance, don't put your files in Couchbase -- but can you do it? Sure. I've done it myself, and the blog post above describes a separate way to do it.
There are all kinds of interesting extensions you might wish to implement, depending on your needs. For example, if you commonly store many different files with similar content, you might implement a blocking strategy that would allow single-store of many common segments, to save space. Other solutions like S3 will happily store copies of copies of copies of copies, and gleefully charge you huge amounts of money to do so.
EDIT as a follow-up, there's this other Couchbase post talking about why storing in the DB might not be a good idea. Reasonable things to consider - but again it depends on your application-specific requirements. "Use S3" I think would be generally good advice, but won't work for everyone.
MongoDB has an option to do this sort of thing, and it's supported in almost all drivers: GridFS. You could do something like GridFS in Couchbase, which is to make a metadata collection (bucket) and a chunk collection with fixed size blobs. GridFS allows you to change the blob size per file, but all blobs must be the same size. The filesize is stored in the metadata. A typical chunk size is 2048, and are restricted to powers of 2.
You don't need memory cache for files, you can queue up the chunks for download in your app server. You may want to try GridFS on Mongo first, and then see if you can adapt it to Couchbase, but there is always this: https://github.com/couchbaselabs/cbfs
This is the best practice: do not take couchbase database as the main database consider it as sync database because no matter how you chunk data into small pieces it will go above 20MB size which will hit you in long run, so having a strong database like MySQL in a middle will help to save those large data then use couchbase for realtime and sync only.
So I've been trying hard to figure out if NoSQL is really bringing that much value outside of auto-sharding and handling UNSTRUCTURED data.
Assuming I can fit my STRUCTURED data on a single machine OR have an effective 'auto-sharding' feature for SQL, what advantages do any NoSQL options offer? I've determined the following:
Document-based (MongoDB, Couchbase, etc) - Outside of it's 'auto-sharding' capabilities, I'm having a hard time understanding where the benefit is. Linked objects are quite similar to SQL joins, while Embedded objects significantly bloat doc size and causes a challenge regarding to replication (a comment could belong to both a post AND a user, and therefore the data would be redundant). Also, loss of ACID and transactions are a big disadvantage.
Key-value based (Redis, Memcached, etc) - Serves a different use case, ideal for caching but not complex queries
Columnar (Cassandra, HBase, etc ) - Seems that the big advantage here is more how the data is stored on disk, and mostly useful for aggregations rather than general use
Graph (Neo4j, OrientDB, etc) - The most intriguing, the use of both edges and nodes makes for an interesting value-proposition, but mostly useful for highly complex relational data rather than general use.
I can see the advantages of Key-value, Columnar and Graph DBs for specific use cases (Caching, social network relationship mapping, aggregations), but can't see any reason to use something like MongoDB for STRUCTURED data outside of it's 'auto-sharding' capabilities.
If SQL has a similar 'auto-sharding' ability, would SQL be a no-brainer for structured data? Seems to me it would be, but I would like the communities opinion...
NOTE: This is in regards to a typical CRUD application like a Social Network, E-Commerce site, CMS etc.
If you're starting off on a single server, then many advantages of NoSQL go out the window. The biggest advantages to the most popular NoSQL are high availability with less down time. Eventual consistency requirements can lead to performance improvements as well. It really depends on your needs.
Document-based - If your data fits well into a handful of small buckets of data, then a document oriented database. For example, on a classifieds site we have Users, Accounts and Listings as the core data. The bulk of search and display operations are against the Listings alone. With the legacy database we have to do nearly 40 join operations to get the data for a single listing. With NoSQL it's a single query. With NoSQL we can also create indexes against nested data, again with results queried without Joins. In this case, we're actually mirroring data from SQL to MongoDB for purposes of search and display (there are other reasons), with a longer-term migration strategy being worked on now. ElasticSearch, RethinkDB and others are great databases as well. RethinkDB actually takes a very conservative approach to the data, and ElasticSearch's out of the box indexing is second to none.
Key-value store - Caching is an excellent use case here, when you are running a medium to high volume website where data is mostly read, a good caching strategy alone can get you 4-5 times the users handled by a single server. Key-value stores (RocksDB, LevelDB, Redis, etc) are also very good options for Graph data, as individual mapping can be held with subject-predicate-target values which can be very fast for graphing options over the top.
Columnar - Cassandra in particular can be used to distribute significant amounts of load for even single-value lookups. Cassandra's scaling is very linear to the number of servers in use. Great for heavy read and write scenarios. I find this less valuable for live searches, but very good when you have a VERY high load and need to distribute. It takes a lot more planning, and may well not fit your needs. You can tweak settings to suite your CAP needs, and even handle distribution to multiple data centers in the box. NOTE: Most applications do emphatically NOT need this level of use. ElasticSearch may be a better fit in most scenarios you would consider HBase/Hadoop or Cassandra for.
Graph - I'm not as familiar with graph databases, so can't comment here (beyond using a key-value store as underlying option).
Given that you then comment on MongoDB specifically vs SQL ... even if both auto-shard. PostgreSQL in particular has made a lot of strides in terms of getting unstrictured data usable (JSON/JSONB types) not to mention the power you can get from something like PLV8, it's probably the most suited to handling the types of loads you might throw at a document store with the advantages of NoSQL. Where it happens to fall down is that replication, sharding and failover are bolted on solutions not really in the box.
For small to medium loads sharding really isn't the best approach. Most scenarios are mostly read so having a replica-set where you have additional read nodes is usually better when you have 3-5 servers. MongoDB is great in this scenario, the master node is automagically elected, and failover is pretty fast. The only weirdness I've seen is when Azure went down in late 2014, and only one of the servers came up first, the other two were almost 40 minutes later. With replication any given read request can be handled in whole by a single server. Your data structures become simpler, and your chances of data loss are reduced.
Again in my own example above, for a mediums sized classifieds site, the vast majority of data belongs to a single collection... it is searched against, and displayed from that collection. With this use case a document store works much better than structured/normalized data. The way the objects are stored are much closer to their representation in the application. There's less of a cognitive disconnect and it simply works.
The fact is that SQL JOIN operations kill performance, especially when aggregating data across those joins. For a single query for a single user it's fine, even with a dozen of them. When you get to dozens of joins with thousands of simultaneous users, it starts to fall apart. At this point you have several choices...
Caching - caching is always a great approach, and the less often your data changes, the better the approach. This can be anything from a set of memcache/redis instances to using something like MongoDB, RethinkDB or ElasticSearch to hold composite records. The challenge here comes down to updating or invalidating your cached data.
Migrating - migrating your data to a data store that better represents your needs can be a good idea as well. If you need to handle massive writes, or very massive read scenarios no SQL database can keep up. You could NEVER handle the likes of Facebook or Twitter on SQL.
Something in between - As you need to scale it depends on what you are doing and where your pain points are as to what will be the best solution for a given situation. Many developers and administrators fear having data broken up into multiple places, but this is often the best answer. Does your analytical data really need to be in the same place as your core operational data? For that matter do your logins need to be tightly coupled? Are you doing a lot of correlated queries? It really depends.
Personal Opinions Ahead
For me, I like the safety net that SQL provides. Having it as the central store for core data it's my first choice. I tend to treat RDBMS's as dumb storage, I don't like being tied to a given platform. I feel that many people try to over-normalize their data. Often I will add an XML or JSON field to a table so additional pieces of data can be stored without bloating the scheme, specifically if it's unlikely to ever be queried... I'll then have properties in my objects in the application code that store in those fields. A good example may be a payment... if you are currently using one system, or multiple systems (one for CC along with Paypal, Google, Amazon etc) then the details of the transaction really don't affect your records, why create 5+ tables to store this detailed data. You can even use JSON for primary storage and have computed columns derived and persisted from that JSON for broader query capability and indexing where needed. Databases like postgresql and mysql (iirc) offer direct indexing against JSON data as well.
When data is a natural fit for a document store, I say go for it... if the vast majority of your queries are for something that fits better to a single record or collection, denormalize away. Having this as a mirror to your primary data is great.
For write-heavy data you want multiple systems in play... It depends heavily on your needs here... Do you need fast hot-query performance? Go with ElasticSearch. Do you need absolute massive horizontal scale, HBase or Cassandra.
The key take away here is not to be afraid to mix it up... there really isn't a one size fits all. As an aside, I feel that if PostgreSQL comes up with a good in the box (for the open-source version) solution for even just replication and automated fail-over they're in a much better position than most at that point.
I didn't really get into, but feel I should mention that there are a number of SaaS solutions and other providers that offer hybrid SQL systems. You can develop against MySQL/MariaDB locally and deploy to a system with SQL on top of a distributed storage cluster. I still feel that HBase or ElasticSearch are better for logging and analitical data, but the SQL on top solutions are also compelling.
More: http://www.mongodb.com/nosql-explained
Schema-less storage (or schema-free). Ability to modify the storage (basically add new fields to records) without having to modify the storage 'declared' schema. RDBMSs require the explicit declaration of said 'fields' and require explicit modifications to the schema before a new 'field' is saved. A schema-free storage engine allows for fast application changes, just modify the app code to save the extra fields, or rename the fields, or drop fields and be done.
Traditional RDBMS folk consider the schema-free a disadvantage because they argue that on the long run one needs to query the storage and handling the heterogeneous records (some have some fields, some have other fields) makes it difficult to handle. But for a start-up the schema-free is overwhelmingly alluring, as fast iteration and time-to-market is all that matter (and often rightly so).
You asked us to assume that either the data can fit on a single machine, OR your database has an effective auto-sharding feature.
Going with the assumption that your SQL data has an auto-sharding feature, that means you're talking about running a cluster. Any time you're running a cluster of machines you have to worry about fault-tolerance.
For example, let's say you're using the simplest approach of sharding your data by application function, and are storing all of your user account data on server A and your product catalog on server B.
Is it acceptable to your business if server A goes down and none of your users can login?
Is it acceptable to your business if server B goes down and no one can buy things?
If not, you need to worry about setting up data replication and high-availability failover. Doable, but not pleasant or easy for SQL databases. Other types of sharding strategies (key, lookup service, etc) have the same challenges.
Many NoSQL databases will automatically handle replication and failovers. Some will do it out of the box, with very little configuration. That's a huge benefit from an operational point of view.
Full disclosure: I'm an engineer at FoundationDB, a NoSQL database that automatically handles sharding, replication, and fail-over with very little configuration. It also has a SQL layer so you you don't have to give up structured data.
I am developing a system that is all about media archiving, searching, uploading, distributing and thus about handling BLOBs.
I am currently trying to find out the best way how to handle the BLOB's. I have limited resources for high end servers with a lot of memory and huge disks, but I can access a large array of medium performance off-the-shelf computers and hook them to the Internet.
Therefore I decided to not store the BLOBs in a central Relational Database, because I would then have, in the worst case, one very heavy Database Instance, possibly on a single average machine. Not an option.
Storing the BLOBs as files directly on the filesystem and storing their path in the database is also somewhat ugly and distribution would have to be managed manually, keeping track of the different copies myself. I don't even want to get close to that.
I looked at CouchDB and I really like their peer-to-peer based design. This would allow me to run a distributed cluster of machines across the Internet, implies:
Low cost Hardware
Distribution for Redundancy and Failover out of the box
Lightweight REST Interface
So if I got it right, one could summarize it like this: Cloud like API and self managed, distributed, replicated system
The rest of the system does the normal stuff any average web application does: handling session, security, users, searching and the like. For this part I still want to use a relational datamodel. (CouchDB claims not to be a replacement for relational databases).
So I would have all the standard data, including the BLOB's meta data in the relational database but the BLOBs themselves in CouchDB.
Do you see a problem with this approach? Am I missing something important? Can you think of better solutions?
Thank you!
You could try Amazon's relational database SimpleDB and S3 toghether with SimpleJPA. SimpleJPA is a JPA-implementation on top of SimpleDB. SimpleJPA uses SimpleDB for the relational structure and S3 to store BLOBs.
Take a look at MongoDB, it supports storing binary data in an efficient format and is incredibly fast
No problem. I have done a design very similar to that one. You may also want to take a peek to HBase as an alternative to CouchDB and to the Adaptive Object-Model architectural pattern, as a way to manage your data and meta-data.