I have a client that wants two sites to have the ability to sync databases so information at Site A can be synced with Site B so the two sites can look at the same data.
I'm not even sure of the infrastructure required. Would a VPN required to connect the 2 databases or would an internet based database work ie/Site A to InternetDatabase and Site B to InternetDatabase. Each site copies data to it periodically and then the InternetDatabase syncs it and the Sites can then pull data down.
My other thought was something like Dropbox. If Site A and Site B use a Dropbox account to sync the ADT files etc can the database at each site then sync with those ADT files?
Thanks
If the two sites update completely different tables, then something like Dropbox might work for that. Dropbox does not synchronize/merge the contents of files. That means if both site A and site B updated some file, then you would be responsible for writing the code to merge the changes.
Advantage Database Server has support for replication built in natively, so that would likely be the simplest solution. Advantage replication is performed on a record-by-record basis and is handled asynchronously. If the target database cannot be reached, the updates are stored in a queue and processed periodically. If the connection between the two sites is open/available constantly, the lag between the source update and the replicated update is typically small but obviously depends on the network bandwidth and latency.
You could use a VPN for the connection between the two sites, but it would not be required. If you do not use some kind of VPN, though, you should make sure the communication is encrypted between the two sites (it is an option when setting up the subscriptions).
Edit For the communication, all you need is "normal" network connectivity. The primary issue is dealing with things like firewalls and NAT. With Advantage, you define which port it uses. If you use a TCP/IP connection, you would need to make sure the configured port allows inbound connections to the ads.exe process. You can use UDP as well, but if you are dealing with firewalls, it is probably going to be simpler with TCP.
Your question about duplicate keys is a good one. If both sites either add a record with the same primary key or update the same record concurrently, then it results in a conflict. There is an option to simply ignore conflicts in which case the last update wins. More realistically, you would want to write an ON CONFLICT trigger to handle the conflicts.
Related
I am having an application where different users may connect to different databases (those can be either MySQL or Postgres), what might be the best way to cache those connections across different databases? I saw some connection pools but seems like they are more for one db multiple connections than for multiple db multiple connections.
PS:
For adding more context, I am designing a multi tenant architecture where each tenant connects to one or multiple databases, I have an option for using map[string]*sql.DB where the key is the url of the database, but it can be hardly scaled when we have numerous number of databases. Or should we have a sharding layer for each incoming request sharded by connection url, so each machine will contain just the right amount of database connections in the form of map[string]*sql.DB?
An example for the software that I want to build is https://www.sigmacomputing.com/ where the user can connects to multiple databases for working with different tables.
Both MySQL and Postgres do not allow to connection sharing between multiple database users, single database user is specified in connection credentials. If you mean that your different users have their own database credentials, then it is not possible to share connections between them.
If by "different users" you mean your application users and if they share single database user to access DB deeper in the app, then you don't need to do anything particular to "cache" connections. sql.DB keeps and reuses open connections in its pool by default.
Go automatically opens, closes and reuses DB connections with a *database/sql.DB. By default it keeps up to 2 connections open (idle) and opens unlimited number of new connections under concurrency when all opened connections are already busy.
If you need some fine tuning on pool efficiency vs database load, you may want to alter sql.DB config with .Set* methods, for example SetMaxOpenConns.
You seem to have to many unknowns. In cases like this I would apply good, old agile and start with prototype of what you want to achieve with tools that you already know and then benchmark the performance. I think you might be surprised how much go can handle.
Since you understand how to use map[string]*sql.DB for that purpose I would go with that. You reach some limits? Add another machine behind haproxy. Solving scaling problem doesn't necessary mean writing new db pool in go. Obviously if you need this kind of power you can always do it - pgx postgres driver has it's own pool implementation so you can get your inspiration there. However doing this right now seems to be pre-mature optimization - solving problem you don't have yet. Building prototype with map[string]*sql.DB is easy, test it, benchmark it, you will see if you need more.
p.s. BTW you will most likely hit first file descriptor limit before you will be able to exhaust memory.
Assuming you have multiple users with multiple databases with an N to N relation, you could have a map of a database URL to database details (explained below).
The fact that which users have access to which databases should be handled anyway using configmap or a core database; For Database Details, we could have a struct like this:
type DBDetail {
sync.RWMutex
connection *sql.DB
}
The map would be database URL to database's details (dbDetail) and if a user is write it calls this:
dbDetail.Lock()
defer dbDetail.Unock()
and for reads instead of above just use RLock.
As said by vearutop the connections could be a pain but using this you could have a single connection or set the limit with increment and decrement of another variable after Lock.
There isn’t necessarily a correct architectural answer here. It depends on some of the constraints of the system.
I have an option for using map[string]*sql.DB where the key is the url of the database, but it can be hardly scaled when we have numerous number of databases.
Whether this will scale sufficiently depends on the expectation of how numerous the databases will be. If there are expected to be tens or hundreds of concurrent users in the near future, is probably sufficient. Often a good next step after using a map is to transition over to a more full featured cache (for example https://github.com/dgraph-io/ristretto).
A factor in the decision of whether to use a map or cache is how you imagine the lifecycle of a database connection. Once a connection is opened, can that connection remain opened for the remainder of the lifetime of the process or do connections need to be closed after minutes of no use to free up resources.
Should we have a sharding layer for each incoming request sharded by connection url, so each machine will contain just the right amount of database connections in the form of map[string]*sql.DB?
The right answer here depends on how many processing nodes are expected and whether there will be gain additional benefits from routing requests to specific machines. For example, row-level caching and isolating users from each other’s requests is an advantage that would be gained by sharing users across the pool. But a disadvantage is that you might end up with “hot” nodes because a single user might generate a majority of the traffic.
Usually, a good strategy for situations like this is to be really explicit about the constraints of the problem. A rule of thumb was coined by Jeff Dean for situations like this:
Ensure your design works if scale changes by 10X or 20X but the right solution for X [is] often not optimal for 100X
https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf
So, if in the near future, the system needs to support tens of concurrent users. The simplest that will support tens to hundreds of concurrent users (probably a map or cache with no user sharding is sufficient). That design will have to change before the system can support thousands of concurrent users. Scaling a system is often a good problem to have because it usually indicates a successful project.
I am trying to update a legacy system's sql solution to use the cloud.
The solution today involves a customer Windows SQL server installed onsite, then various machines are configured to connect to that IP Address / Port / Server Name. When they do connect the machines will set up any tables that are missing and regularly send their data. Data rates are low for an individual machine. Roughly one write request ever 10 seconds (it varies a lot), no more than 2-3k of information on each write request.
Moving this to the cloud is tricky mostly because each of the machines do not have a unique identifier. The good news is that we have the legacy machines connected to a IOT Gateway (Just think RPI) that knows a unique machineId. Furthermore the IOTG is a full fledge computer but not too powerful of one, and its Disk is an SD card.
New and Old Network Layout
So far I have had a few things fall on their face.
1) Setting the Machine to think the DB's IP/Port is that of the IOT Gateway. Setting up an Express server on the IOTG, listening, then injecting the unique id into the queries that I'd proxy up to the cloud. I may have had a bug, but for some reason I couldn't even see the requests coming in on the port. Even if I could I'd still have to figure out how to decode them. Shouldn't I at least be able to see these requests coming in?
2) Started looking into SQLite. The idea being to have SQLite listen on the port as an actual DB then have a process in the IOTG query data out of SQLite, append a unique ID, and then send it to the cloud. Unfortunately SQLite does not listen on a port.
I am starting to looking at just installing a whole SQL server on the device, but I'd really like to avoid that. I'm pretty sure its fairly large and writing to disk is not advisable for a small embedded system like I'm running.
Generally my questions boil down to:
1) Should I be able to see SQL Queries in an express server?
2) Should I be using a different tech? I failed to find a different more sql specific proxy.
3) Am I correct to think that the SQLite path is dead? Even if I could find a way to attach it to a port there is still not going to be any sort of response from SQLite when the clients try to make a connection.
4) Am I wrong to fear the local server? Diving into some documentation for making express work with DBs gets me to here: https://www.microsoft.com/en-us/sql-server/developer-get-started/node/ubuntu/ which suggests 4GB of memory, we're working on 0.5GB.
Any other thoughts on how to approach this would be great.
Use Case:
Say I wanted to create a realtime-collaborative document editing system.
In this scenario many users could create and collaborate on many documents.
Due to client-device constraints, it's not possible for any client to keep a replica of all documents, only just a handful.
There needs to be central storage server where all documents always live, and this server is always backed up.
Each client can 'subscribe' to any document, and all clients subscribed can see realtime changes of all other clients subscribed/editing the same document.
Questions:
Since each client can't store all documents, there needs to be a way to remove the replicas of 'old' documents from the client, without deleting the document from the central store, ideally based on an automatic least-recently-used approach. How is this handled in gun?
In gun, how can a document be deleted from the central store, so it's then effectively permanently removed from, and no longer accessible to, all clients?
When a document is deleted from the central store, how is the physical storage space ever actually reclaimed for later use?
Great questions, #user2672083 . Here is the current lay out:
Collaborative realtime document editing is possible with gun. Here is a quick prototype I recorded a long time ago, however there are no full pre-built examples/implementations yet.
Not all data is stored on every client by default. The browser only keeps the data it requests/gets/subscribes to.
The default server already acts as a backup. I recommend using the S3 storage adapter, because then you do not have to worry about running out of disk space.
Removing old replicas. Currently, if I want the server to act as a central "master", I just put a localStorage.clear() at the top of my browser code. This will force the browser to have to always load the latest from the server. This is not ideal though, an LRU specific feature is coming soon according to the roadmap.
Permanently removing data and reclaiming space. While this should be easy for a central setup, because gun is P2P by default, it uses a technique called tombstoning to delete data. Given a lot of requests (like yours) for LRU/TTL/GC/deleting, there will be better support for this in the future. Currently, you have to use a mix of rm data.json, localStorage.clear() and 30 day lifecycles on S3 to get this to work. This will be more integrated/easier in the future.
Now a question for you: What are you working on, and how can I help? Many of the things you asked about are possible (with some work) now, but slated to be the focus of the next version of gun - I'd love to get your feedback as we build this out.
All peers reply to requests for data (#2), meaning that localStorage and the server will both reply. Because localStorage is physically closer to a user, it will reply first/fastest and then replies from the server will be merged. GUN does not try each peer "in sequence" doing try/catch cascades, GUN replies from all peers in parallel.
GUN has swappable storage and transport interfaces, so yes, it is easy to build other layers on top or into it.
I am using redis version 2.8.3. I want to build a redis cluster. But in this cluster there should be multiple master. This means I need multiple nodes that has write access and applying ability to all other nodes.
I could build a cluster with a master and multiple slaves. I just configured slaves redis.conf files and added that ;
slaveof myMasterIp myMasterPort
Thats all. Than I try to write something into db via master. It is replicated to all slaves and I really like it.
But when I try to write via a slave, it told me that slaves have no right to write. After that I just set read-only status of slave in redis.conf file to false. Hence, I could write something into db.
But I realize that, it is not replicated to my master replication so it is not replicated to all other slave neigther.
This means I could'not build an active-active cluster.
I tried to find something whether redis has active-active cluster capability. But I could not find exact answer about it.
Is it available to build active-active cluster with redis?
If it is, How can I do it ?
Thank you!
Redis v2.8.3 does not support multi-master setups. The real question, however, is why do you want to set one up? Put differently, what challenge/problem are you trying to solve?
It looks like the challenge you're trying to solve is how to reduce the network load (more on that below) by eliminating over-the-net reads. Since Redis isn't multi-master (yet), the only way to do it is by setting up each app server with a master and a slave (to the other master) - i.e. grand total of 4 Redis instances (and twice the RAM).
The simple scenario is when each app updates only a mutually-exclusive subset of the database's keys. In that scenario this kind of setup may actually be beneficial (at least in the short term). If, however, both apps can touch all keys or if even just one key is "shared" for writes between the apps, then you'll need to bake locking/conflict resolution/etc... logic into your apps to consolidate local master and slave differences (and that may be a bit of an overkill). In either case, however, you'll end up with too many (i.e. more than 1) Redises, which means more admin effort at the very least.
Also note that by colocating app and database on the same server you're setting yourself for near-certain scalability failure. What will happen when you need more compute resources for your apps or Redis? How will you add yet another app server to the mix?
Which brings me back to the actual problem you are trying to solve - network load. Why exactly is that an issue? Are your apps so throughput-heavy or is the network so thin that you are willing to go to such lengths? Or maybe latency is the issue that you want to resolve? Be the case as it may be, I recommended that you consider a time-proven design instead, namely separating Redis from the apps and putting it on its own resources. True, network will hit you in the face and you'll have to work around/with it (which is what everybody else does). On the other hand, you'll have more flexibility and control over your much simpler setup and that, in my book, is a huge gain.
Redis Enterprise has had this feature for quite a while, but if you are looking for an open source solution KeyDB is a fork with Active Active support (called Active Replica).
Setting it up is just a little more work than standard replication:
Both servers must have "active-replica yes" in their respective configuration files
On server B execute the command "replicaof [A address] [A port]"
Server B will drop its database and load server A's dataset
On server A execute the command "replicaof [B address] [B port]"
Server A will drop its database and load server B's dataset (including the data it just transferred in the prior step)
Both servers will now propagate writes to each other. You can test this by writing to a key on Server A and ensuring it is visible on B and vice versa.
https://github.com/JohnSully/KeyDB/wiki/KeyDB-(Redis-Fork):-Active-Replica-Support
I am building out a solution that will be deployed in multiple data centers in multiple regions around the world, with each data center having a replicated copy of data actively updated in each region. I will have a combination of multiple databases and file systems in each data center, the state of which must be kept consistent (within a data center). These multiple repositories will be fronted by a SOA service tier.
I can tolerate some latency in the replication, and need to allow for regions to be off-line, and then catch up later.
Given the multiple back end repositories of data, I can't easily rely on independent replication solutions for each one to maintain a consistent state. I am thus lead to implementing replication at the application layer -- by replicating the SOA requests in some manner. I'll need to make sure that replication loops don't occur, and that last writer conditions are sorted out correctly.
In your experience, what is the best pattern for solving this problem, and are there good products (free or otherwise) that should be investigated?
Lotus/ Domino is your answer. I've been working with it for ten years and its exactly what you need. It may not be trendy (a perception that I would challenge) but its powerful, adaptable and very secure, The latest version R8 is the best yet.
You should definitely consider IBM Lotus Domino. A Lotus Notes database can replicate between sites on a predefined schedule. The replicate in Notes/Domino is definitely a very powerful feature and enables for full replication of data between sites. Even if a server is unavailable the next time it connects it will simply replicate and get back in sync.
As far as SOA Service tier you could then use Domino Designer to write a webservice. Since Notes/Domino 7.5.x (I believe) Domino has been able to provision and consume webservices.
AS what other advised, I will recommend also Lotus Notes/Domino. 8.5 is really very powerful application development platfrom
You dont give enough specifics to be certain of your needs but I think you should check out SQL Server Merge replication. It allows for asynchronous replication of multiple databases with full conflict resolution. You will need to designate a Global master and all the other databases will replicate to that one, but all the database instances are fully functional (read/write) and so you can schedule replication at whatever intervals suit you. If any region goes offline they can catch up later with no issues - if the master goes offline everyone will work independantly until replication can resume.
I would be interested to know of other solutions this flexible (apart from Lotus Notes/Domino of course which is not very trendy these days).
I think that your answer is going to have to be based on a pub/sub architecture. I am assuming that you have reliable messaging between your data centers so that you can rely on published updates being received eventually. If all of your access to the data repositories is via service you can add an event notification to the orchestration of each of your update services that notifies all interested data centers of the event. Ideally the master database is the only one that sends out these updates. If the master database is the only one sending the updates you can exclude routing the notifications to the node that generated them in the first place thus avoiding update loops.