I'm working on a project where a Postgresql database needs to be shared across several physical locations. Each location has limited connectivity, and may only have access to the outside world once or twice a day. So the database has to be available locally at each location, but must also synchronize with the master database when possible.
I am not yet familiar with replication or clustering. Are these good solutions? Or is there a better way of doing it? I would appreciate some advice on this. :)
NOTE: clashing of primary keys from different locations would not be an issue, this has been taken care of.
If the remote locations require read-only access to the data, you can set up asynchronous replication fairly easily using log shipping, which is a built-in feature of PostgreSQL. In this configuration, the master server drops WAL (write-ahead log) files to a shared location where the remote servers can periodically connect and read the logs to bring themselves up to date.
If all servers are performing writes independently, what you're looking for is asynchronous multi-master replication. The Postgres docs mention Bucardo and rubyrep as options for accomplishing this. According to the docs, both are limited to master-to-master replication (or master to multiple slaves), but Bucardo supposedly has true multi-master replication planned for version 5.0, and rubyrep mentions a method for keeping multiple servers synchronized.
(I have servers using PostgreSQL's log shipping and streaming replication features, but I have no direct experience with Bucardo or rubyrep.)
Related
I’m looking to build a distributed Access Control system for a microservice platform. I’m considering using Mongodb as my database technology. My system design objectives are as follows:
Policy Enforcement should be distributed - If any given Policy
Enforcement Point (PEP) experiences downtime, only the application
that the PEP serves should be affected.
Policy Decisions should be
distributed - We don’t want the whole platform to experience downtime
because a central Policy Decision Point (PDP) is experiencing
downtime. We only want it to affect the application that it serves.
Policy Administration should be centralized - Creating a centralized
policy administration interface provides the ability for any system
(including a UI) to understand what rights an individual has, and by
establishing a common interface it allows us to more easily audit
changes to access across a whole platform.
Policy Information (context) is distributed - We don’t get to choose this if we are
building a distributed microservice platform. We can centralize the
retrieval of additional context by aggregating data that is needed to
make access control decisions into a single place, but the data
sources are still distributed.
I’m considering building a system like the one shown below. The idea is that Access Policies are administered by a central Policy Admin API. This API manages Policies that are persisted to a mongodb cluster with a 3 member replica set backing it. I would like other APIs in the platform to have a dedicated policy-query-api (Policy Decision Point) that is deployed along side it to make Access Control decisions pertinent to the API. The idea is that if any one of the policy-query-apis goes down, only the API that it serves will be affected.
I want changes to Policies to be governed by the Policy Admin API and I would like the changes to be replicated across each mongo instance that is used by each of the policy-query-apis.I don’t want the mongo replicas for each policy-query-api to affect a write to the primaries.
I also don’t need immediate data consistency (less than 5 sec latency), but I would like the data replication to be handled at the database layer if possible. The technology is already built to handle this and I don’t want to reinvent the wheel at the application layer if possible.
I’ve looked at the documentation on Replica Set Members and I’ve pretty thoroughly reviewed the documentation on Replica Sets in Mongo. It seems like having a Hidden Member or Delayed Member would be a good fit for my use case. Do you agree? Also, I’m concerned about the 50 member replica set limit 1. Since each one of these replicas would serve an API in my platform, if there exceeded more than 50 microservices (which is quite likely) how would I manage replication like this?
Just so that I understand, you are asking about:
one standalone (?? your picture suggests standalone but you are asking about 50 node RS limit) node per application, data mirrored to standalone from the master RS
the application only queries its local standalone
MongoDB provides read preference nearest for the use case of reading data from local nodes. Importantly the nearest read preference still provides availability if your local node is unavailable - the next closest (roughly) node will be used in this case. Your proposed architecture would take the application down every time its local database node needs to be restarted for version upgrades.
You may also look into tag sets.
Additionally, MongoDB allows specifying priorities on nodes for election purposes. If you put all of your MongoDB nodes into the same RS, you can use priorities to have one of the 3 designated "main" servers be primaries if any of them are available.
following situation: I am building a system that requires redundant microservices for failover or loadbalancing. So I am starting two (or more instances of a service) of for example a simple core rest service that provides data.
My Question is: How would you store the data? Using two JPA-instances to access the same database (both writing and reading) will result in problems, especially in layer 2 caching and in consistency. Since the database must be redundent itself (requirement) it might be possible to make each service instance accessing its own database, but how would you synchronize them? Is there any common solution for this?
Thanks in advance!
If you truly need a multi-master consistent database, then you will almost definitely need to implement this at the database layer.
I would not cache things that are transactionally sensitive. If you truly need to do this, and cannot specify a reasonable TTL in which content can be stale, then you will need to set up a pub/sub sort of mechanism to expire modified entities. A lot of this really depends on your data, how often it changes, can you separate cacheable vs non-cacheable data? These questions strongly influence your caching decisions.
If you don't want to re-invent master-master replication (which will be highly non-trivial), I suggest you choose a DB system that supports this out of the box.
This will not solve all your problems out of the box, but at least it solves the hard part of the problem. What you still will need to do is e.g. defining and implementing a conflict resolution strategy.
A good choice for a master-master DB system is CouchDB. It is open source and there are also service providers available, in case you don't want to host the DB by yourself. I'm sure there are other DB systems that provide master-master replication as well.
There are two completely separate layer in your case.
One for application servers and another one for database.
If you really need a scalable system -I think you need because you are mentioning about load balancing- then you should remove all the states out from you application .
For example you should not use layer 2 caching in your application instance instead you should use some external service like redis or memcache.
And you should use just one master database instance for writes and another replicate waiting for failover. To do that we are using Amazon RDS MultiAZ instances. There is just one master database which is replicated to another instance. In case of crash or something the second database is automatically set as master in a couple of seconds.
I'm considering how to design a mechanism for replicating a (potentially large) MongoDB or other NoSQL (CouchDB, etc) database to dozens of clients at once. The clients would function like a replica set, but the replication would be one-way and the remote clients would belong to other parties. Specifically, I am looking for the following features:
real-time: changes to the master database should be pushed out to the clients as quickly as possible
replication to new clients: a new client must be able to connect, automatically sync the majority of existing data, then receive real-time updates.
efficient: both the initial synchronization/transfer of data and tracking of real-time updates ("diffs", if you will) are computationally efficient, with multiple clients connected.
secure: the master database presents an interface to which remote clients (who do not belong to the same owner or system) can connect: i.e., we cannot just add all the clients to the master's replica set.
robust: a temporarily connection failure between a client and the master database should be easily and efficiently recoverable.
In some sense, the server is publishing a collection of data and the clients are subscribing to it. I realize that this is a hard software engineering problem, and to my knowledge no piece of software has implemented this exactly yet. However, some approaches have come to mind as close, which I'll list below.
Meteor's DDP protocol: It's designed to do this with Mongo-like collections and exactly implements the model of publishing and subscribing to a set of data (rather than a stream of messages). It manages the initial sync and sends along live changes. However, it's still in development, and far from being an industrial-strength solutions - current drawbacks are that the server keeps a copy of every client's state in a possibly inefficient way and is only tested on collections that can fit in the memory of a web app. Also, it appears that DDP cannot efficiently sync an out-of-date database without fetching everything from scratch. If anyone can point to some examples of how large of a collection can be synced over DDP, that would be great. (See also: https://stackoverflow.com/q/10128430/586086)
Broadcasting the Mongo oplog: Using a high-throughput message bus like Apache Kafka, one may be able to efficiently send the oplog to many clients at once. This tackles some of the system implementation challenges. However, this requires that the clients start with an initial sync that gets them close enough to the current master state somehow and then start replaying the oplog from the appropriate point.
Continuous replication a la CouchDB: I'm not sure how this is implemented and how robust it is, given the sparsity of the documentation. However, it does seem to work over remote database connections. How efficient is this, though, when multiple clients are trying to replicate at the same time? (A similar hack to this would be to make the clients MongoDB Priority 0 replica set members; however, that seems to be far from its intended use. See also: http://guide.couchdb.org/draft/replication.html)
Please give pointers to software or pieces of software that already implement parts of this, or suggestions on the algorithms/data structures needed to do this efficiently.
If you are looking specifically for real-time replication, I'd recommend you look into SaaS offerings specifically for this purpose, such as https://www.firebase.com/
I have two mysql servers, both of which are masters. After some problems with their local network they lost connection with each other but continued working some time, without any synchronization. Both of them store some data from this period, and it would be bad to lose part of it.
Is it possible to merge dumps from these servers with any util?
I think you could find pt-table-sync helpful. But be sure to use it on backups of your data for safety and to reset the replication after using it as the changes you make will be replicated to the origin server if not.
My application uses Postgresql 9.0 and is composed by one or more stations that interacts with a global database: it is like a common client server application but to avoid any additional hardware, all stations include both client and server: a main station is promoted to act also as server, and any other act as a client to it. This solution permits me to be scalable: a user may initially need a single station but it can decide to expand to more in future without a useless separate server in the initial phase.
I'm trying to avoid that if main station goes down all others stop working; to do it the best solution could be to continuously replicate the main database to unused database on one or more stations.
Searching I've found that pgpool can be used for my needs but from all examples and tutorial it seems that point of failure moves from main database to server that runs pgpool.
I read something about multiple pgpool and heartbeat tool but it isn't clear how to do it.
Considering my architecture, where doesn't exist separated and specialized servers, can someone give me some hints ? In case of failover it seems that pgpool do everything in automatic, can I consider that failover situation can be handled by a standard user without the intervention of an administrator ?
For these kind of applications I really like Amazon's Dynamo design. The document by the link is quite big, but it is worth reading. In fact, there're applications that already implement this approach:
mongoDB
Cassandra
Project Voldemort
Maybe others, but I'm not aware. Cassandra started within Facebook, Voldemort is the one used by LinkedIn. Making things distributed and adding redundancy into your data distribution you will step away from traditional Master-Slave replication approaches.
If you'd like to stay with PostgreSQL, it shouldn't be a big deal to implement such approach. You will need to implement an extra layer (a proxy), that will decide based on pre-configured options how to retrieve/save the data.
The proxying layer can be implemented in:
application (requires lot's of work IMHO);
database;
as a middleware.
You can use PL/Proxy on the middleware layer, project originated in Skype. It is deeply integrated into the PostgreSQL, so I'd say it is a combination of options 2 and 3. PL/Proxy will require you to use functions for all kind of queries against the database.
In case you will hit performance issues, PgBouncer can be used.
Last note: any way you decide to go, a known amount of development will be required.
EDIT:
It all depends on what you call “failure” and what you consider system being in an interrupted state.
Let's look on the pgpool features.
Connection Pooling PostgreSQL is using a single process (fork) per session. Obviously, if you have a very busy site, you'll hit the OS limit. To overcome this, connection poolers are used. They also allow you to use your resources evenly, so generally it's a good idea to have pooler before your database.In case of pgpool outage you'll face a big number of clients unable to reach your database. If you'll point them directly to the database, avoiding pooler, you'll face performance issues.
Replication All your queries will be auto-replicated to slave instances. This has meaning for the DML and DDL queries.In case of pgpool outage your replication will stop and slaves will not be able to catchup with master, as there's no change tracking done outside pgpool (as far as I know).
Load Balance Your read-only queries will be spread across several instances, achieving nice response times, allowing you to put more bandwidth on the system.In case of pgpool outage your queries will suddenly run much slower, if the system is capable of handling such a load. And this is in the case that master database will catchup instead of failed pgpool.
Limiting Exceeding Connections pgpool will queue connections in case they're not being able to process immediately.In case of pgpool outage all such connections will be aborted, which might brake the DB/Application protocol, i.e. Application was designed to never get connection aborts.
Parallel Query A single query is executed on several nodes to reduce response time.In case of pgpool outage such queries will not be possible, resulting in a longer processing.
If you're fine to face such conditions and you don't treat them as a failure, then pgpool can serve you well. And if 5 minutes of outage will cost your company several thousands $, then you should seek for a more solid solution.
The higher is the cost of the outage, the more fine tuned failover system should be.
Typically, it is not just single tool used to achieve failover automation.
In each failure you will have to tweak:
DNS, unless you want all clients' reconfiguration;
re-initialize backups and failover procedures;
make sure old master will not try to fight for it's role in case it comes back (STONITH);
in my experience we're people from DBA, SysAdmin, Architects and Operations departments who decide proper strategies.
Finally, in my view, pgpool is a good tool, I do use it. But it is not designed as a complete failover solution, not without extra thinking, measures taken, scripts written. Thus I've provided links to the distributed databases, they provide a much higher level of availability.
And PostgreSQL can be made distributed with a little effort due to it's great extensibility.
First of all, I'd recommend checking out pgBouncer rather than pgpool. Next, what level of scaling are you attempting to reach? You might just choose to run your connection pooler on all your client systems (bouncer is light enough for this to work).
That said, vyegorov's answer is probably the direction you should really be looking at in this day and age. Are you sure you really need a database?
EDIT
So, the rather obvious answer is that pgPool creates a single point of failure if you only have one box running it. The obvious solution is to run multiple poolers across multiple boxes. You then need to engineer your application code to handle database disconnections. This is not as easy at it sounds, but basically you need to use 2-phase commit for non-idempotent changes. So to the greatest extent possible you should make your changes idempotent.
Based on your comments, I'd guess that maybe you have limited experience dealing with database replication? pgPool does statement based replication. There are tradeoffs here. The benefit is that it's very easy to set up. The downside is that there is no guarantee that data on the replicated databases will be identical. It is also (I believe but haven't checked lately) not compatible with 2pc.
My prior comment asking if you really need a database was driven by my perception that you have designed a system without going into much detail around this part of it. I have about 2 decades experience working on "this part" of similar systems. I expect you will find that there are no out of the box solutions and that the issues involved get very complicated. In other words, I'm suggesting you re-consider your design.
Try reading this blog (with lots of information about PostgreSQL and PgPool-II):
https://www.itenlight.com/blog/2016/05/21/PostgreSQL+HA+with+pgpool-II+-+Part+5
Search for "WATCHDOG" on that same blog. With that you can configure a PgPool-II cluster. Two machines on the same subnet are required, though, and a virtual IP on the same subnet.
Hope that this is useful for anyone trying the same thing (even if this answer is a lot late).
PGPool certainly becomes a single point of failure, but it is a much smaller one than a Postgres instance.
Though I have not attempted it yet, it should be possible to have two machines with PGPool installed, but only running on one. You can then use Linux-HA to restart PGPool on the standby host if the primary becomes unavailable, and to optionally fail it back again when the primary comes back. You can at the same time use Linux-HA to move a single virtual IP over as well, so that your clients can connect to a single IP for their Postgres services.
Death of the postgres server will make PGPool send queries to the backup Postgres (promoting it to master if necessary).
Death of the PGPool server will cause a brief outage (configurable, but likely in the region of <1min) until PGPool starts up on the standby, the IP address is claimed, and a gratuitous ARP sent out. Of course, the client will have to be intelligent enough to reconnect without dying.