I want to implement caching in Talend. By this I mean that I want to read from and write to cache memory through Talend. The cache memory will be a centralized one. I read about tHashInput/Output which are used to read from and write to cache memory.
My questions are:
Are there any other components apart from tHashInput/Output to read/write to cache?
In tHashOutput we have a drop down menu named Data Write Model. What is its significance? I ask this because I have a scenario where I would be using a centralized cache memory where many people would be reading from and writing to cache. In that case what other options drop down menu can have?
tHashOutput/Input are the best components for cache . You can also use tBufferOutput/Input but you won't be able to put more than 1 tBufferOutput for each job.
I don't think there are other options in this dropdown menu.
For each job, the cache used by tHash components is only usable within this execution of a job. You won't be able to recover it during another run.
If you are using webservices (as providing a SOAP method for a webservice with talend for example), tHashOutput is not very reliable : from time to time the cache will be accessible, but not always.
You Could check one of my previous questions about this :
Building a cache with Talend SOAP webservices
Related
I want to Load Dropdown data from Database at once and set inside java object and tie to my view (JSP page ) and available all the time for that particular controller or functionality using spring mvc AND jsp pages.
I dont want to load on application start up as ours is big one and and each functionality is independent.
It takes a lot of time to start the application if i load on application start up
Is there a way to it using spring mvc pattern and using JSP
Could someone please let me know how to do it
As you have not mentioned how frequently you are doing the database operation or how frequently you are fetching the data. Considering the average user.
Approach: Create your own local cache/ program cache implementation.
Instead of loading all the data from the database during startup, load only master data which will be common for all. If master data is also high then you can perform the lazy loading approach.
Load the data of a specific feature when it is requested for the first time. Keep the data in the local cache.
Whenever someone is making the changes then add the data in the cache and save the same to the database. so you will always have latest data in the cache.
Advantage:
Very useful for common or static master data
-If you need good business logic for some common data. This way only once you are processing the data and keeping cache.
-Fetching the data is very fast as it doesn't involve database request except for the first time
Disadvantage:
If you have a very high number of users and a very high update operation then the updating cache will delay the update process as you need to update it sequentially.
I suggest you can use a combination of approaches to improve the code quality and processing.
This sounds like a typical cache functionality.
Spring supports caching out of the box by #EnableCaching on application level and #Cacheable(“cachename”) on the repository method retrieving your dropdown data. In your simple use case you not even need an additional framework as there is a CacheManager based on ConcurrentHashMap which simply caches for ever.
With caching in place your controller can simply fetch the dropdown data from the repository. Caching will ensure only the first call will really fetch from database and keeps the result in memory for all upcoming calls.
If you ever need more sophisticated caching you only have to exchange the cache manager and configure the cache for your needs.
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 a new developer and am trying to implement Laravel's (5.1) caching facility to improve the speed of my app. I started out caching a large DB table that my app constantly references - but it got too large so I have backed away from that and am now 'forever' caching smaller chunks of data - for example, for each page only the portions of that large DB table that are relevant.
I have watched 'Caching Essentials' on Laracasts, done some Googling and had a search in this forum (and Laracasts') but I still have a couple of questions:
I am not totally clear on how the cache size limits work when you are using Laravel's file-based system - is there an overall in-app size limit for the cache or is one limited size-wise only per key and by your server size?
What are the signs you should switch from file-based caching to something like Memcached or Redis - and what are the benefits of using one of those services? Is it the fact that your caching is handled on a different server (thereby lightening the load on your own)? Do you switch over to one of these services when your local, file-based cache gets too big for your server?
My app utilizes several tables that have 3,000-4,000 rows - the data in these tables is constantly referenced and will remain static unless I decide to add new options. I am basically looking for the best way to speed up queries to the data in these tables.
Thanks!
I don't think Laravel imposes any limitations on its file i/o at all - the limitations will be with how much what PHP can read / write to a file at once, or hold in its memory / process at any one time.
It does serialise the data that you cache, and unserialise it when you reload it, so your PHP environment would have to be able to process the entire cache file (which is equivalent to the top level cache key) at once. So, if you are getting cacheduser.firstname, it would have to load the whole cacheduser key from the file, unserialise it, then get the firstname key from that.
I would take the PHP memory limit (classic, i know!) as a first point to investigate if you want to keep down this road.
Caching services like Redis or memcached are bespoke, optimised caching solutions. They take some of the logic and responsibility out of your PHP environment.
They can, for example, retrieve sub-keys from items without having to process the whole thing, so can retrieve part of some cached data in a memory efficient way. So, when you request cacheduser.firstname from redis, it just returns you the firstname attribute.
They have other advantages regarding tagging / clearing out subsets of caches (see [the cache tags Laravel docs] (https://laravel.com/docs/5.4/cache#cache-tags))
Another thing to think about is scaling. If your site is large enough, and is load-balanced across multiple servers, the filesystem caching may be different across those servers, as each server can only check their local filesystem for the cache files. A caching service can be on a different server (many hosts will have a separate redis / memcached services available), so isn't victim to this issue.
Also - as I understand it (and this might be the most important thing), the file cache driver in Laravel is mainly for local development and testing. Although it can work fine for simple applications with basic caching needs, it's not intended for large scalable production environments.
Personally, I develop locally and test with file caching, as i'm only dealing with small amounts of data then, and use redis to cache on production environments.
It doesn't necessarily need to be on a separate server to get the benefits. If you are never going to scale to multiple application servers, then using a caching service on the same server will already be a large improvement to caching large documents.
I have Oracle as my main RDBMS for read and write, but I want to use couchbase as caching layer as it has map-reduce as can be used as memcache. Any idea as to how i can implement that, and how to transfer and update data in the caching layer, when Oracle is updated or inserted etc.
You are not telling anything about your current performance issues.
I have seen too many applications which do not really take advantage of RDBMS/SQL features, especially if an ORM sits in between.
The cure is to put another cache on top of a database, and to synchronize this in a cluster manually using IP multicasts (SwarmCache for example), message queues (JMS) or nightly import jobs. It could create more problems in the end. And it increases system complexity.
So my answer to your question is: I would not do it, as long as there is room for improvement regarding your data model and/or queries.
I believe your question is about Database synchronization. This can be done through a combination of using DB dependencies and "right-thru" features that I am not too sure about whether couchbase offers. So with DB dependency you have cached items dependent upon Db items and if the DB items are updated or deleted the corresponding dependent item in the cache is removed and at the same time you can write a "right-thru" handler executed at the server level; and the main purpose of this handler is loading fresh copies of the removed items in the cache. So, basically, you'll write the handler once and registerit with the cache server and the cache server will execute it when needed to sync. new items in the DB with the cache. This reading on Db synchronization can be useful . Its based on a product Ncache.
So your question is not directly related to Couchbase, but as other stated more about how you can be alerted when data are changing into your Oracle instance.
One thing that is not well known is the Oracle Database Change Notification feature that is quite cool for this:
http://docs.oracle.com/cd/E11882_01/java.112/e16548/dbchgnf.htm
So you can create an application that is listening to your changes and pushes the data into Couchbase.
I have a series of code books in my database, and I am using plain JDBC calls to fetch them and store them in a collection. I would like to put these in some kind of a cache at application startup time in order to save time later.
I don't need any fancy stuff like automatic object invalidation, TTL etc - the code books change rarely, so I'll trigger the update myself and just reload the whole cache when the need arises.
The project where I need this uses Spring, and this is my first project using it. Is there a standard/elegant way to do this in Spring?
Thanks.
Check out Spring-cache.
Supports EHCache, OSCache and a memory cache, but allows pluggable cache providers too.