The book 'architecture of open source software' says that the most common type of global cache in a web application is responsible for fetching the data itself, in case it is missing, as shown on this fixure. This seems different than what I've encountered so far. Most applications I have encountered make the application server responsible for fetching data from the db, and updating the server. At first, I thought the book might be talking about caching proxies, like Varnish, but they cover those in the next section, so that doesn't seem to be the case.
What cache systems actually fetch the data in case of a miss, and how do they know how to interact with the database?
Caching solutions provide read-through/write behind features which enable users to configure a read-through/write-behind provider be implementing some interface and deploying it with cache server. These providers contain logic about how cache server can interact with database to load/save data in database.
On a cache fetch operation if data is not present in cache server, cache loads data from database using configured provider thus avoiding a cache miss.
This way client applications deal cache as only data source and cache itself is responsible for interactions with database. You can read further details in this article by Iqbal Khan.
NCache and TayzGrid are enterprise solutions among many others that provide this feature.
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.
I am trying to implement caching in my application.
We are using Oracle database, asp.net web api to serve data to ui.
Api calls take more time, so we are thinking of implementing caching. Our code is deployed on 2 servers with load balancers.
How caching should be implemented.
What i am planning to implement is,
There should be a service API on any server, this api will store all data in memory. Ui will call our existing API, hit can go to any node, this api then will get data from new api(cache) and serve it to ui.
Is this architecture correct for distruted caching.
Can any one share their experience or guidance to implementation?
You might want to check NCache. Being a distributed caching solution, it provides first class support for sharing cache data between multiple clients due to the ache process running autonomously outside the address of any one application address space.
For your case, every web server in your load-balanced web farm will have be the client of NCache and have direct access to the cache servers. All the web servers,being clients to a central caching solution, will see the same cache data through simple-to-use NCache APIs. Any modification through insert, update or delete cache operations will be immediately observable to all the web servers.
The intelligence driving NCache allows for a seamless behind-the-scenes handling of all the tasks of storing and distributing the cache data among multiple cache server nodes on which the cache instance is distributed.
Furthermore, all the caching operations are completely independent of the framework used for database content retrieval and can be applied equally well with NHibernate, EF, EF Core and, of course, ADO.NET.
You can find more information about how to integrate NCache into your web farm environment and much more by using the following link:
http://www.alachisoft.com/resources/docs/ncache/admin-guide/ncache-architecture.html
Is it possible, reliable and secure to cache all entities in distrubuted cache and notifies dao layer on update? My possible idea is;
Use JPA 2.1 and Hibernate implementation.
On creation persist it db
After persisting it, cache it to distrubuted cache.
Canalise all read actions to cache
on update notify dao layer to update entity .
yes you can design a system that will
On addition: persists data to db and adds to cache
On read: reads data from cache, and considers a cache miss as not
present in database as well.
On update: updates data in db and then updates in cache (or vice
versa)
On delete: deletes data from cache and then deletes from database
This approach will work fine if you have a single application using that database and if data is not that critical. However if data integrity is of more importance, you may face following problems in this approach:
You may face a cache miss when data is present in database(persisted
but not yet cached)
You may get stale data from cache (updated in db but not yet updated
in cache)
Also if data is removed from database by some other application, it
will still ramained cached in distributed cache(invalid data on
reads)
A better mothod my be if you use a rich featured distributed caching solution like NCache / Tayzgrid which provides Read Trough / Write behind features. This way your application will only need to use cache for all reads, writes or updates and cache will keep database updated using configured providers.
Another approach may be to use distributed cache as hibernate's second level cache and you will not need to add a caching layer by your self. See this article for details about hibernate's second level cache.
Distributed caching solutions like Tayzgrid provide caching provider for hibernate that can be easily configured. You can find hibernate providers for other solutions as well.
I have a project that I am working where data cannot be cached on the client of certain entity types. Some are Ok, and others are not.
I don't see anything in the documentation or the API that references NOT caching an entity of a particular type, so is there a way to do this to prevent secure information from being cached?
What have I tried?
Nothing... I don't see a way to do this...
Edit
As an alternative, if anyone from IdeaBlade could lend a hand in explaining how and where the caching is saving info and where (if anywhere) that information is persisted it may be helpful in alleviating the need to NOT cache.
Edit 2
Ok no answer, yet, I changed the title -
How can I ensure no sensitive data is persisted in the local memory after the browser has been shut down?
There are two questions here.
How to keep Breeze from holding entities of a type in its in-memory cache.
How prevent caching of entities across browser sessions.
Number 2 first: Breeze in memory cache evaporates when the browser session closes and is not shared across browser sessions. You can arrange for local persistence but that's a conscious decision and some small effort. I don't think you have to worry about that.
If you want to prevent a type from being cached in memory at all (really? Why?) you could write a simple JsonResultsAdapter that zaps the $type parameter for nodes of this entity type and let's the others pass through to the default adapter. Breeze only makes cachable entities out of nodes with a recognized $type. Look that adapter up in the doc.
I would like to make a LDAP cache with the following goals
Decrease connection attempt to the ldap server
Read local cache if entry is exist and it is valid in the cache
Fetch from ldap if there is no such request before or the entry in the cache is invalid
Current i am using unboundid LDAP SDK to query LDAP and it works.
After doing some research, i found a persistent search example that may works. Updated entry in the ldap server will pass the entry to searchEntryReturned so that cache updating is possible.
https://code.google.com/p/ldap-sample-code/source/browse/trunk/src/main/java/samplecode/PersistentSearchExample.java
http://www.unboundid.com/products/ldapsdk/docs/javadoc/com/unboundid/ldap/sdk/AsyncSearchResultListener.html
But i am not sure how to do this since it is async or is there a better way to implement to cache ? Example and ideas is greatly welcomed.
Ldap server is Apache DS and it supports persistent search.
The program is a JSF2 application.
I believe that Apache DS supports the use of the content synchronization controls as defined in RFC 4533. These controls may be used to implement a kind of replication or data synchronization between systems, and caching is a somewhat common use of that. The UnboundID LDAP SDK supports these controls (http://www.unboundid.com/products/ldap-sdk/docs/javadoc/index.html?com/unboundid/ldap/sdk/controls/ContentSyncRequestControl.html). I'd recommend looking at those controls and the information contained in RFC 4533 to determine whether that might be more appropriate.
Another approach might be to see if Apache DS supports an LDAP changelog (e.g., in the format described in draft-good-ldap-changelog). This allows you to retrieve information about entries that have changed so that they can be updated in your local copy. By periodically polling the changelog to look for new changes, you can consume information about changes at your own pace (including those which might have been made while your application was offline).
Although persistent search may work in your case, there are a few issues that might make it problematic. The first is that you don't get any control over the rate at which updated entries are sent to your client, and if the server can apply changes faster than the client can consume them, then this can overwhelm the client (which has been observed in a number of real-world cases). The second is that a persistent search will let you know what entries were updated, but not what changes were made to them. In the case of a cache, this may not have a huge impact because you'll just replace your copy of the entire entry, but it's less desirable in other cases. Another big problem is that a persistent search will only return information about entries updated while the search was active. If your client is shut down or the connection becomes invalid for some reason, then there's no easy way to get information about any changes while the client was in that state.
Client-side caching is generally a bad thing, for many reasons. It can serve stale data to applications, which has the potential to cause incorrect behavior or in some cases pose a security risk, and it's absolutely a huge security risk if you're using it for authentication. It could also pose a security risk if not all of the clients have the same level of access to the data contained in the cache. Further, implementing a cache for each client application isn't a scalable solution, and if you were to try to share a cache across multiple applications, then you might as well just make it a full directory server instance. It's much better to use a server that can simply handle the desired load without the need for any additional caching.