I have used PyroCMS for some projects, I love it so much. I am current developing another website based on it. In my website, I need to work on a big database, it is not really big but big enough to require the cache module.
I am hesitating to use either built-in Pyrocache library or third-party cache, memcached. So why should I use memcached? Can the Pyrocache manage large data as well? What are the advantages of Pyrocache in comparison with memcached?
In my website, it will display information on over 200.000 locations, data are static for a long time. I want to use the cache to cache all location datas, so when user request, it directly returns the result without query the database. Can Pyrocache suite for my scenario?
Thanks in advanced,
Leo
Pyrocache stores data locally (cannot be shared between multiple web servers) on disk (not terribly fast). Memcache stores data in memory (fast), and a memcache server/pool can be shared between multiple web servers.
The main advantages of pyrocache would be simplicity both in invocation and installation -- you don't have to install and configure memcached.
http://pyrocms.com/docs/manuals/developers/caching-data-with-pyrocache
Related
THE PROBLEM
I'm writing a mobile app which will allow a user to log in, save some preferences that must be stored in a database, and display congressional bills to the user.
I've only written simple RESTful services with PHP and MySQL in the past. I'd like to take advantage of newer technologies, and am a little lost on general direction.
The bill data (formatted as JSON) can be gathered by running the scrapers found here. Using docker, I managed to set a working directory and download the files on my local machine.
I've designed a MySQL database for holding the relevant bill and user data.
I started to mess around in Google Cloud Platform, and read the doc that describes different models. I'm thinking of a few different ideas, but aren't familiar with GCP or what I can actually accomplish.
QUESTIONS
1) What are App Engine, Compute Engine, and Container Engine each for? I get the gist that Container Engine holds different instances of stuff you load up with docker, and that Compute Engine sets up a VM, but I don't really understand the relationships. How should I think of them?
2) When I run those scrapers from the shell, where are the files being stored, and how can I check on them? On my computer, I set a working directory, but how do directories work in GCP? Is it just a directory in the currently selected VM, or is this what Buckets are for?
IDEAS
1) Since my bill data already comes as JSON, should I skip the entire process of building a database for the bills and insert them into Firebase somehow? Is this even possible? If so, am I stuck using Firebase's NoSQL, or can I still set up a relational database?
2) I could schedule the scrapers to run periodically, detect new files, and run a script to parse the JSON and insert new bill data into my a database (PostgrSQL?/MySQL?). Then I would write an API.
3) Download the JSON files to a bucket, and write an API that reads from them. Not sure how the performance would compare to using a DB.
I'm open to other suggestions as well.
For your use case (stateless web application), App Engine is probably your best choice. The Google documentation has severalcomparisons of your computing options
You can use App Engine with PHP and cloud-hosted MySQL if you want, which could be a good way to get your toes wet without going in over your head.
We are working on an application that will be offered both as a web-based and as a cross-platform desktop solution by means of Electron.
Due to customer requirements, the desktop client cannot make use of "the cloud" to store data; all data should be stored in the local machine or, even better, the user should have the option to keep the database/data file on an external HDD so that another user on the same local network can use the same data file.
We've been looking at NeDB, PouchDB, etc, but all these use either Web SQL or IndexedDB on the browser itself to store the data.
NeDB can theoretically use the file system but that seems only possible for Node Webkit apps.
Another option is of course MongoDB, but it requires setting up a site on a web server. Seeing as how our users will set that up in on their own machines, that will work for one user only but would make it very hard for them to share the data (note: assume users with little technical know-how).
Is there a way to force NeDB to persist data in a file instead of the in-browser database?
Alternatively, does any one know of a file-based, compact database that plays well with electron/node?
We'd preferably like to use a NoSQL database, but options of file-based SQL databases will be considered as well.
I have some experience with NeDB in an Electron app and I can say it will definitely work on the filesystem.
How are you initializing NeDB (or whatever your database choice is)? Also, are you initializing it in the main or renderer process? If you can share that, I think we could trace the issue to a configuration issue.
This is how you start NeDB with a persistent data-store that saves to disk.
var Datastore = require('nedb')
, db = new Datastore({ filename: 'path/to/datafile', autoload: true });
I think MongoDB is going to be overkill for an Electron app (it's meant to be really a high performance, distributed database running in the cloud).
Another option you could consider is LevelDB (a key/value store that can persist to the filesystem) which is popular in the node community. (EDIT 4/17/17 IndexedDB uses LevelDB underneath the hood, so if you go that route, may as well just use that)
One aspect I would definitely evaluate carefully is: How difficult is this database going to be to package and distribute? How do I integrate it into my build system? Level and NeDB can be included simply via npm install and any native code compiling is handled seamlessly with node-gyp, which is as simple as it gets. However, bundling Mongo, for example, will require some work to get a working build for each different platform.
I plan to design a system with Dreamfactory as the user management server while a separate REST server for social feed. Dreamfactory will have its own MySQL database for storing user info while the social feed will use MongoDB.
Is this a good system design? I'm new to this as I'm using both open source platform for two different purposes; social feed and user management.
It's difficult to answer your question without knowing requirements to the system. I was going to ask you why storing users in MySQL, but all the same I can ask why using MongoDB or product XXX ;)
There is no silver bullet in programming. Tool is chosen from requirements, not vice versa.
If you do not need to relate data, do not need transactions and does not care about data consistency at all, why go why relational databases? Solutions like AeroSpike or just Redis (yes, it can be persistent too) can give you much higher read/write rate.
Well, I suggest you go write a document, containing your system description, think of load this system is going to have. May be you will decide, that storing data in CSV files is ok for you (joking ;) )
I want to write a high scalable web application for selling event tickets. I want to use NoSQL database, like Big Table or MongoDB and Cloud Service like Google App Engine (GAE) or Amazon Elastic Compute Cloud (Amazon EC2)
Is it posible using this type of database to be sure that two client will not be able to buy a ticket for the same place simultaneously? Or may be I will have to use RDBMS database and forget about Google App Engine?
Things like GAE's datastore can still support transactional semantics, for example:
http://code.google.com/appengine/docs/python/datastore/transactions.html
So yes, it is possible to do what you're seeking to do. (Note - GAE's Datastore is not exactly NoSQL, since it uses SQL-like queries.)
I have a problem with this question. Not all NoSQL databases are created equally, and different NoSQL databases have different ways they store data. Generally the thing you should be worried about are: data is actually written to disk and not just into memory. Most NoSQL databases can do this but not by default. Let's just say this is not a problem, you can usually tell the database like MOngo or Cassandra to write data to disk, can even tell how many servers at minimum the data should be written to.
The problem is that you may not get a true transactional support. When you deal with ecommerce it's important to have all or nothing type of transation where several operations either succeed completely or rolled back. There must be absolutely no chance that only part of your data is saved. For example, if you need to write data to more than one table (collection or document in NoSQL lingo), if server goes down in the middle of the process and your data is only written to one table, that's usually unacceptable in ecommerce.
I am not familiar with all NoSQL databases, but the ones I know don't have this option yet.
MySQL, on the other hand, does.
If transactional support or lack of it does not bother you, then I think its OK to use NoSQL as long as you tell it to save data to disk and not just into memory.
The answer is 'maybe.'
Depending on what you're trying to build, you many be able to use some of the techniques in this post:
http://kylebanker.com/blog/2010/06/07/mongodb-inventory-transactions/
Using something like get_or_insert you can easily ensure that two clients are not receiving the same resource simultaneously on Google App Engine. However, there are big differences between GAE and a RDBMS, so make sure you study them further before you make a decision.
I am currently working on a web service which is periodically polled. It does not store its state and is instantiated everytime it is queried. Essentially, it retrieves the state of other external entities e.g. databases and delivers it back to the requester.
Recently, the need to store state as arisen in that
There is the need to continously collect data from a particular source and store the bits that are important/relevant
There is the need to collect the aggregate of a particular data source over a period of time
I came up with the following idea:
My main concern here is the fact that I am using a static class (essentially a global) to share data between the two services. Is there a better way to doing this?
edit: Thanks for the responses thus far. Apologies for the vaguesness of this question: just trying to work out what is the best way to share data across different services and am unsure as to the specifics (i.e. what is required). The platform that I am developing on is the .NET framework and both services are simply WCF services hosted as a Windows service.
The database route sounds like the most conventional way to go - however I am reluctant to go down that path for now (mainly for deployment/setup issues; it introduces the need to create new tables, etc in addition to simply installing the software) for at this point the transfer of relatively small amounts of data. This may of course change in the future and going the database route might be the way to go at that point.
Is there any other way besides adding a database persistance layer?
If you need to collect and aggregate data, you might want to consider using a database between the two layers. Or have I misunderstood something?
You should consider enhancing your question with more requirements: pretty much all options are open here.
Sure - how about data binding? I don't have a lot of information to go on here - about your platform but most sufficiently advanced systems offer it in some form.
You could replace your static shared data with some database representation, with a caching layer (like memcached) between the database and the webservice, so that most of the time the data is available very quickly from the cache, but can be retrieved from the database as needed.
I appreciate that you want to keep the architecture simple. Depending on the magnitude of items you have to look up and there permanency, you might just consider leveraging your file system or a message queue. It sounds like you want a file system, because that sounds the least amount of impact to your design.
If you start dealing with tens of thousands of small files, your directories could get hard to navigate and slow to do file lookups on. I typically shoot for about 1000 - 10000 files per directory, and concoct a routine that can generate a path to the file depending on the file name pattern. Keeping the number of subdirectories even is important, some file systems have a limit on the number of subdirectories in a parent directory.