I have One Database with one domain. But my Database have 3 Websites available. I want my 2nd Website for publish in that Database. Is that possible ???
You might want to make sure that you're not violating the terms of service with the company who is hosting your database. Having many outside domains hitting an inside database may cause some undue stress on that server that the company is not counting on or eating up more bandwidth that is allotted for that machine.
In the same breath though, if you setup some type of data layered web service which you can connect to, then your many other domains are not directly hitting the database and do essentially the same thing, but in a more ordered fashion of predictable database calls. This may not be what you're looking for, but if setup correctly it could make developing against your database much easier.
Related
Correct me if I'm wrong, but from my understanding, "database caches" are usually implemented with an in-memory database that is local to the web server (same machine as the web server). Also, these "database caches" store the actual results of queries. I have also read up on the multiple caching strategies like - Cache Aside, Read Through, Write Through, Write Behind, Write Around.
For some context, the Write Through strategy looks like this:
and the Cache Aside strategy looks like this:
I believe that the "Application" refers to a backend server with a REST API.
My first question is, in the Write Through strategy (application writes to cache, cache then writes to database), how does this work? From my understanding, the most commonly used database caches are Redis or Memcached - which are just key-value stores. Suppose you have a relational database as the main database, how are these key-value stores going to write back to the relational database? Do these strategies only apply if your main database is also a key-value store?
In a Write Through (or Read Through) strategy, the cache sits in between the application and the database. How does that even work? How do you get the cache to talk to the database server? From my understanding, the web server (the application) is always the one facilitating the communication between the cache and the main database - which is basically a Cache Aside strategy. Unless Redis has some kind of functionality that allows it to talk to another database, I don't quite understand how this works.
Isn't it possible to mix and match caching strategies? From how I see it, Cache Aside and Read Through are caching strategies for application reads (user wants to read data), while Write Through and Write Behind are caching strategies for application writes (user wants to write data). Couldn't you have a strategy that uses both Cache Aside and Write Through? Why do most articles always seem to portray them as independent strategies?
What happens if you have a cluster of webs servers? Do they each have their own local in-memory database that acts as a cache?
Could you implement a cache using a normal (not in-memory) database? I suppose this would still be somewhat useful since you do not need to make an additional network hop to the database server (since the cache lives on the same machine as the web server)?
Introduction & clarification
I guess you have one misunderstood point, that the cache is NOT expclicitely stored on the same server as the werbserver. Sometimes, not even the database is sperated on it's own server from the webserver. If you think of APIs, like HTTP REST APIs, you can use caching to not spend too many resources on database connections & queries. Generally, you want to use as few database connections & queries as possible. Now imagine the following setting:
You have a werbserver who serves your application and a REST API, which is used by the webserver to work with some resources. Those resources come from a database (lets say a relational database) which is also stored on the same server. Now there is one endpoint which serves e.g. a list of posts (like blog-posts). Every user can fetch all posts (to make it simple in this example). Now we have a case where one can say that this API request could be cached, to not let all users always trigger the database, just to query the same resources (via the REST API) over and over again. Here comes caching. Redis is one of many tools which can be used for caching. Since redis is a simple in-memory key-value storage, you can just put all of your posts (remember the REST API) after the first DB-query, into the cache. All future requests for the posts-list would first check whether the posts are alreay cached or not. If they are, the API will return the cache-content for this specific request.
This is one simple example to show off, what caching can be used for.
Answers on your question
My first question is, why would you ever write to a cache?
To reduce the amount of database connections and queries.
how is writing to these key-value stores going to help with updating the relational database?
It does not help you with updating, but instead it helps you with spending less resources. It also helps you in terms of "temporary backing up" some data - but that only as a very little side effect. For this, out there are more attractive solutions (Since redis is also not persistent by default. But it supports persistence.)
Do these cache writing strategies only apply if your main database is also a key-value store?
No, it is not important which database you use. Whether it's a NoSQL or SQL DB. It strongly depends on what you want to cache and how the database and it's tables are set up. Do you have frequent changes in your recources? Do resources get updated manually or only on user-initiated actions? Those are questions, leading you to the right caching implementation.
Isn't it possible to mix and match caching strategies?
I am not an expert at caching strategies, but let me try:
I guess it is possible but it also, highly depends on what you are doing in your DB and what kind of application you have. I guess if you find out what kind of application you are building up, then you will know, what strategy you have to use - i guess it is also not recommended to mix those strategies up, because those strategies are coupled to your application type - in other words: It will not work out pretty well.
What happens if you have a cluster of webs servers? Do they each have their own local in-memory database that acts as a cache?
I guess that both is possible. Usually you have one database, maybe clustered or synchronized with copies, to which your webservers (e.g. REST APIs) make their requests. Then whether each of you API servers would have it's own cache, to not query the database at all (in cloud-based applications your database is also maybe on another separated server - so another "hop" in terms of networking). OR (what i also can imagine) you have another middleware between your APIs (clusterd up) and your DB (maybe also clustered up) - but i guess that no one would do that because of the network traffic. It would result in a higher response-time, what you usually want to prevent.
Could you implement a cache using a normal (not in-memory) database?
Yes you could, but it would be way slower. A machine can access in-memory data faster then building up another (local) connection to a database and query your cached entries. Also, because your database has to write the entries into files on your machine, to persist the data.
Conclusion
All in all, it is all about being fast in terms of response times and to prevent much network traffic. I hope that i could help you out a little bit.
It seems that in the traditional microservice architecture, each service gets its own database with a different understanding of the data (described here). Sometimes it is considered permissible for databases to duplicate data. For instance, the "Users" service might know essentially everything about a user, whereas the "Posts" service might just store primary keys and usernames (so that the author of a post can have their name displayed, for instance). This page talks about eventual consistency, sources of truth, and other related concepts when data is duplicated. I understand that microservice architectures sometimes include a shared database, but most places I look suggest that this is a rare strategy.
As for why each service typically gets its own database, all I've seen so far is "so that each service owns its own resources," but I'm not convinced that a) the service layer in any way "owns" the persisted resources accessed through the database to begin with, or that b) services even need to own the resources they require rather than accessing necessary subsets of the master resources through a shared database.
So what are some of the justifications that each service in a microservice architecture should get its own database?
There are a few reasons why it does make sense to use a separate database per micro-service. Some of them are:
Scaling
Splitting your domain in micro-services is fine. You can scale your particular micro-service on the deployed web-server on demand or scale out as needed. That it obviously one of the benefits when using micro-services. More importantly you can have micro-service-1 running for example on 10 servers as it demands this traffic but micro-service-2 only requires 1 web-server so you deploy it on 1 server. The good thing is that you control this and you can manage your computing resources like in order to save money as Cloud providers are not cheap.
Considering this what about the database?
If you have one database for multiple services you could not do this. You could not scale the databases individually as they would be on one server.
Data partitioning to reduce size
Automatically as you split your domain in micro-services with each containing 1 database you split the amount of data that is stored in each database. Ideally if you do this you can have smaller database servers with less computing power and/or RAM.
In general paying for multiple small servers is cheaper then one large one.
So in this case you could make use of this fact and save some resources as well.
If it happens that the already spited by domain database have large amount of data techniques like data sharding or data partitioning could be applied additional, but this is another topic.
Which db technology fits the business requirement
This is very important pro fact for having multiple databases. It would allow you to pick the database technology which fits your Business requirement best in order to get the best performance or usage of it. For example some specific micro-service might have some Read-heavy operations with very complex filter options and a full text search requirement. Using Elastic Search in this case would be a good choice. Some other micro-service might use SQL Server as it requires SQL specific features like transnational behavior or similar. If for some reason you have one database for all services you would be stuck with the particular database technology which might not be so performant for those requirement. It is a compromise for sure.
Developer discipline
If for some reason you would have a couple micro-services which would share their database you would need to deal with the human factor. The developers would need to be disciplined to not cross domains and access/modify the other micro-services database(tables, collections and etc) which would be hard to achieve and control. In large organisations with a lot of developers this could be a serious problem. With a hard/physical split this is not an issue.
Summary
There are some arguments for having database per micro-service but also some against it. In general the guidelines and suggestions when using micro-services are to have the micro-service together with its data autonomous in order to work independent in Ideal case(this is not the case always). It is defiantly a compromise as well as using micro-services in general. As always the rule is the rule but there are exceptions to it. Micro-services architecture is flexible and very dependent of your Domain needs and requirements. If you and your team identify that it makes sense to merge multiple micro-service databases to 1 and that it solves a lot of your problems then go for it.
Microservices
Microservices advocate design constraints where each service is developed, deployed and scaled independently. This philosophy is only possible if you have database per service. How can i continue my business if i have DB failure and what steps i can take to mitigate this?DB is essential part of any enterprise application. I agree there are different number of challenges when services has its own databases.
Why Independent database?
Unlike other approaches this approach not only keeps your code-base clean and extendable but you truly omit the single point of failure in your business. To achieve this services sometimes can have duplicated data as well, as long as my service is autonomous and services can only be autonomous if i have database per service.
From business point of view, Lets take eCommerce application. you have microserivces like Booking, Order, Payment, Recommendation , search and so on. Database is shared. What happens if the DB is down ? All your services are down ! and there is no point using Microservies architecture other than you have clean code base.
If you have each service having it's own database , i don't mind if my recommendation service is not working but i can still search and book the order and i haven't lost the customer. that's the whole point.
It comes at cost and challenges, but in longer run it pays off.
SQL / NoSQL
Each service has it's own needs. To get the best performance I can use SQL for payment service (transaction) and I can use (I should) NoSQL for recommendation service. Shared database wouldn't help me in this case. In modern cloud Architectures like CQRS, Event Sourcing, Materialized views, we sometimes use 2 different databases for same service to get the performance out of it.
Again Database per service is not only about resources or how much data should it own. But we really have to see the bigger picture. Yes we have certain practices how much data and duplication is good or bad but that's another debate.
Hope that helps !
I am building a MySQL database with a web front end for a client. The client and their staff will use this webapp on a daily basis, creating anywhere from a few thousand, to possibly a few hundred thousand records annually. I just picked up a second client who wishes to have the same product and will probably be creating the same number of records annually, possibly more.
In the future I hope to pick up a few more clients. In the next few years I could have up to 5 databases & web front ends running for 5 distinct clients, all needing tight security while creating, likely, millions of records annually (cumulatively across all the databases).
I would like to run all of this with Amazon's EC2 service but am having difficulty deciding on what type of instance to run. I am not sure if I should have several distinct Linux instances, one per client, or run one "large" instance which would manage all the clients' databases and web front ends.
I know that hardware configuration is rather specific to the task at hand. The web front ends will be using JQuery to make MySQL queries "pretty" and I will likely be doing some graphing of data (again with JQuery). The front ends will be using SSL for security, which I understand can add some overhead to the network speed.
I'm looking for some of your thoughts on this situation.
Thanks
Use the tools that are available. The Amazon RDS service lets you run a MySQL database in the cloud with no extra effort. You can scale it up and down as you need - start small, and then as you hit your limits, add extra capacity (at extra cost).
Next, use Elastic Load Balancing (ELB) with an SSL certificate, so you offload the overhead of SSL decryption to an Amazon service.
If you're using Java for your webapp, you could use Elastic Beanstalk to handle the whole hosting process for you.
Don't be afraid to experiment - you can always resize instances with no data loss (if they boot from an EBS volume) and you can always create and delete instances. Scaling horizontally is often better than scaling vertically, as you can spread your instances across multiple Availability Zones.
Good luck!
We have four public websites running on the same database with different schema(Oracle). All of them are 'AAA' application and have "20,0000PV~500,000PV"daily. 90% data in websites are read-only and updated daily(By Batch). Less than 10% data, such as announcement, are updated manually. We are looking for the best practices to solve following concerns.
Improve website availability. Though we have a BCP database, it might need 1~2 hours to recover 4 websites in case database server is down.
Since most data are read-only, we are considering using in-memory db (hsqldb) or cache component(ehcache) to improve performance. As default, we are using ibatis and hibernate. Ehcache might not only be used on Level-2 cache, but also page cache.
We trends to build web services framework(restful) instead of java solution since mobile application might reuse them. Not very sure if it is a good idea to run website on web service on the same web application server. We have active-active HTTP and web servers.
On-line shopping is in the future plan.
Add database processes, make it at least 4 for serving each website.
Consider memcache
The same application server can run multiple applications. Not a problem it there is a good amount of RAM. However, if there is an overwhelm of users, you can always move particular applications to a different server. But, a better idea is to wait and see which service is worth that privilege.
Another web-application, too much of security and state management. Better put it in a new server.
We have a new project for a web app that will display banners ads on websites (as a network) and our estimate is for it to handle 20 to 40 billion impressions a month.
Our current language is in ASP...but are moving to PHP. Does PHP 5 has its limit with scaling web application? Or, should I have our team invest in picking up JSP?
Or, is it a matter of the app server and/or DB? We plan to use Oracle 10g as the database.
No offense, but I strongly suspect you're vastly overestimating how many impressions you'll serve.
That said:
PHP or other languages used in the application tier really have little to do with scalability. Since the application tier delegates it's state to the database or equivalent, it's straightforward to add as much capacity as you need behind appropriate load balancing. Choice of language does influence per server efficiency and hence costs, but that's different than scalability.
It's scaling the state/data storage that gets more complicated.
For your app, you have three basic jobs:
what ad do we show?
serving the add
logging the impression
Each of these will require thought and likely different tools.
The second, serving the add, is most simple: use a CDN. If you actually serve the volume you claim, you should be able to negotiate favorable rates.
Deciding which ad to show is going to be very specific to your network. It may be as simple as reading a few rows from a database that give ad placements for a given property for a given calendar period. Or it may be complex contextual advertising like google. Assuming it's more the former, and that the database of placements is small, then this is the simple task of scaling database reads. You can use replication trees or alternately a caching layer like memcached.
The last will ultimately be the most difficult: how to scale the writes. A common approach would be to still use databases, but to adopt a sharding scaling strategy. More exotic options might be to use a key/value store supporting counter instructions, such as Redis, or a scalable OLAP database such as Vertica.
All of the above assumes that you're able to secure data center space and network provisioning capable of serving this load, which is not trivial at the numbers you're talking.
You do realize that 40 billion per month is roughly 15,500 per second, right?
Scaling isn't going to be your problem - infrastructure period is going to be your problem. No matter what technology stack you choose, you are going to need an enormous amount of hardware - as others have said in the form of a farm or cloud.
This question (and the entire subject) is a bit subjective. You can write a dog slow program in any language, and host it on anything.
I think your best bet is to see how your current implementation works under load. Maybe just a few tweaks will make things work for you - but changing your underlying framework seems a bit much.
That being said - your infrastructure team will also have to be involved as it seems you have some serious load requirements.
Good luck!
I think that it is not matter of language, but it can be be a matter of database speed as CPU processing speed. Have you considered a web farm? In this way you can have more than one machine serving your application. There are some ways to implement this solution. You can start with two server and add more server as the app request more processing volume.
In other point, Oracle 10g is a very good database server, in my humble opinion you only need a stand alone Oracle server to commit the volume of request. Remember that a SQL server is faster as the people request more or less the same things each time and it happens in web application if you plan your database schema carefully.
You also have to check all the Ad Server application solutions and there are a very good ones, just try Google with "Open Source AD servers".
PHP will be capable of serving your needs. However, as others have said, your first limits will be your network infrastructure.
But your second limits will be writing scalable code. You will need good abstraction and isolation so that resources can easily be added at any level. Things like a fast data-object mapper, multiple data caching mechanisms, separate configuration files, and so on.