I want to use GraphQL to access data from different data sources (CSV, SQL Server, Web Server) . I want to know if caching mechanism is supported, so that when the connection is lost the data is still available? I see the data loader which is data batching to increase the performance of the query, but I do not know how data loader can do caching?
Thanks.
There's no built-in support, but you need to remember that graphql-js (which I assume you're using) isn't a framework, it's just a library. You'd implement caching exactly the same as you'd do it with anything else - by surrounding your data-fetching functions with caching get/set behaviour.
I don't use caching very heavily with graphql-js, but if you've ever implemented caching before, it's exactly the same principle.
Related
Does the Linq2DB ORM support query result caching - first level or second level? I noticed the namespace LinqToDB.Common.Internal.Cache, does it mean caching has to be built by the consuming application through a custom caching manager?
Well, linq2db is designed to work with queries not object management. Caching is a very specific case which introduces a lot of side effects. For example if you change some field and the cache contains such records - you have to invalidate the cache, otherwise the system will return obsolete data. Invalidating caches is the most difficult part here.
Anyway there are third party libraries which can do that for you:
LinqCache
Probably there are other libraries which can do similar things.
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 using a react-native app with relay modern.
Currently our app's fetchQuery implementation, just does a fetch on the network (like in https://facebook.github.io/relay/docs/en/network-layer.html),
Although there is a possibility of another local-network layer like https://github.com/relay-tools/relay-local-schema which returns data from a local-db like sqlite/realm.
Is there a way to setup offline-first response from local-network layer, followed by automatic request to real network which also populates the store with fresher data (along with writing to local-db)?
Also should/can they share the same store?
From the requirements of Network.create(), it should return a promise containing the payload, there does not seem a possibility to return multiple values.
Any ideas/help/suggestions are appreciated.
What you trying to achieve its complex, and ill go for the easy approach which is long time cache.
As you might know relay modern uses a local storage and its exact copy of the data you are fetching, you can configure this store cache as per your needs, no cache on mutations.
To understand how this is achieve the best library around to customise Relay Modern or Classic network layer you can find in https://github.com/nodkz/react-relay-network-modern
My recommendation: setup your cache and watch your request.... (you going to love it)
Thinking in Relay,
https://facebook.github.io/relay/docs/en/thinking-in-relay.html
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.
I'm starting to step into unfamiliar territory with regards to performance improvement and our RIA (Rich Internet Application) built with GWT. For those unfamiliar with GWT, essentially when deployed it's just pure JavaScript. We're interfacing with the server side using a REST-style XML web service via XMLHttpRequest.
Our XML is un-marshalled into JavaScript objects and used within the application to represent the data model behind the interface. When changes occur, the model is updated and marshalled back to XML and sent back to the server.
I've learned the number one rule of performance (in terms of user experience) is to make as few requests as possible. Obviously this brings up the possibility of caching. Caching is great for static data but things get tricky in a multi-user system where data on the server may be changing. Also, use of "Last-Modified" and "If-Modified-Since" requests don't quite do enough since we'd like to avoid unnecessary requests altogether.
I'm trying to figure out if caching data in the browser is even right for us before researching the approaches. I hope someone has tread this path before. I'm looking for similar approaches, lessons learned, things to avoid, etc.
I'm happy to provide more specific info if needed...
For GWT, if performance matters that much to you, you get better performance by sending all the data you need in a single request, instead of querying multiple small data. I would recommend against client-side data caching as there are lots of issues like keeping the data in sync with the database.
Besides, you already have a good advantage with GWT over traditional html apps. Unless you are dealing with special data (eg: does not become stale too quickly - implies mostly-read queries) I found out that there is no special need for caching. You are better off doing a service-layer caching, since most of the time should come of server-side processing.
If you can provide more details about the nature of the app, maybe some different conclusions can be taken.