I currently has serviced my app in Korea.
But, my app is installed in us-central because GAE not supported install for Asia.
so, i suppose it is very slow because it is faraway from GAE.
If that's problem, how can I solve this problem?
please suggest to me... thank you.
I have been using Google Cloud Platform for 4 years now, Including the Google App Engine. The performance of your application backend system can only be slow if the developer did not optimise the program well. I would suggest that you try out using some of the following key aspect in solving your problem:
Try so much to use MemCache for requests that are common to users and do not require instant real time updates.
Look at the algorithms you are putting in place. This is very important for your execution through put. For example lets say you want to run a search though a billion records, u can use quick search algorithms like QuickSort3way.
Lastly look at the choice of database you are using. You could mix NoSQL with SQL if you were only using SQL. If you are into big data then use BigQuery. This way your application's performance can drastically increase and scale up enormously.
Related
I'm really new to deployment and I wanted to see if anybody had some experience cutting costs. By requirement, I need to use Google Cloud Platform and the frontend team is writing all of their code in React/React Native. I plan on creating a REST API with cloud SQL and Node.js. Any advice on whether these technologies are a good choice would also be appreciated.
I've been reading about App Engine and Compute Engine recently and I've been having trouble estimating the cost of deploying this project. Currently, the plan is to deploy my Node.js code to app engine and use cloud SQL functionality. Would it be a better idea to deploy all code to an ubuntu server in compute engine? I am very new to deploying web apps, any and all advice would be greatly appreciated!
The GCP Pricing Calculator is the best way to work out what a proposed solution will cost for you. As an example, here is a GAE+Cloud SQL HA pricing ($113/month), and here is a single GCE pricing ($30/month).
Bear in mind that this pricing does not include the "Always Free" basic usage.
As a word of caution, you should also think about reliability, scalability and portability as well as cost. For reliability, try and avoid "single VM" solutions as if the VM is broken or a zone unavailable, your application will be completely down. For scalability, single VM solutions are also problematic unless you plan to be able to shard your workload later. For portability, you must be careful when using Google-specific technology like App Engine or Datastore which can be hard to move away from. These are complex topics which I'm only touching on to give you an idea of the kinds of things you can think about when choosing a solution.
I am new to the area of web development and currently interviewing companies, the most favorite questions among what people ask is:
How do you scale your webserver if it
starts hitting a million queries?
What would you do if you have just one
database instance running at that
time? how do you manage that?
These questions are really interesting and I would like to learn about them.
Please pour in your suggestions / practices (that you follow) for such scenarios
Thank you
How to scale:
Identify your bottlenecks.
Identify the correct solution for the problem.
Check to see you you can implement the correct solution.
Identify alternate solution and check
Typical Scaling Options:
Vertical Scaling (bigger, faster server hardware)
Load balancing
Split tiers/components out onto more/other hardware
Offload work through caching/cdn
Database Scaling Options:
Vertical Scaling (bigger, faster server hardware)
Replication (active or passive)
Clustering (if DBMS supports it)
Sharding
At the most basic level, scaling web servers consists of writing your app in such a way that it can run on > 1 machine, and throwing more machines at the problem. No matter how much you tune them, the eventual scaling will involve a farm of web servers.
The database issue is way more sticky to deal with. What is your read / write percentage? What kind of application is this? OLTP? OLAP? Social Media? What is the database? How do we add more servers to handle the load? Do we partition our data across multiple dbs? Or replicate all changes to loads of slaves?
Your questions call more questions, i.e. in an interview, if someone just "has the answer" to a generic question like you've posted, then they only know one way of doing things, and that way may or may not be the best one.
There are a few approaches I'd take to the first question:
Are there hardware upgrades that may get things up enough to handle the million queries in a short time? If so, this is likely an initial point to investigate.
Are there software changes that could be made to optimize the performance of the server? I know IIS has a ton of different settings that could be used to improve performance to some extent.
Consider going into a web farm situation rather than use a single server. I actually did have a situation where I worked once where we did have millions of hits a minute and it was thrashing our web servers rather badly and taking down a number of sites. Our solution was to change the load balancer so that a few of the servers served up the site that would thrash the servers so that other servers could keep the other sites up as this was in the fall and in retail this is your big quarter. While some would start here, I'd likely come here last as this can be opening a bit can of worms compared to the other two options.
As for the database instance, it would be a similar set of options to my mind though I may do the multi-server option first as redundancy may be an important side benefit here that I'm not sure it is as easy with a web server. I may be way off, but that is how I'd initially tackle this.
Use a caching proxy
If you serve identical pages to all visitors (say, a news site) you can reduce load by an order of magnitude by caching generated content with a caching proxy such as Varnish or Apache Traffic Server.
The proxy will sit between your server and your visitors. If you get 10,000 hits to your front page it will only have to be generated once, the proxy will send the same response to the other 9999 visitors without asking your app server again.
probably before developer starting to develop the system,
they will consider the specification of the server
maybe you can decrease use of SEO and block it from search engine to craw it
(which is the task that taking a lot of resource)
try to index everything well and avoid to making search easily
Deploy it on the cloud, make sure your web server and webapp cloud ready and can scale across different nodes. I recommend cherokee web server (very easy to load balance across different servers, and benchmarks proves faster than Apache,). For ex, google cloud (appspot) needs your web app to be Python or Java
Use caching proxy eg. Nginx.
For database use memcache on some queries which are suppose to be repeated.
If the company wants data to be private , build a private cloud , Here , Ubuntu is doing very good job at it fully free and opensource : http://www.ubuntu.com/cloud/private
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[An Updated List 21st Aug 09]
Help me Compile a List of all the Advantages & Disadvantages of Building an Application on the Google App Engine
Pros:
No need to buy servers or server space (no maintenance).
Makes solving the problem of scaling easier.
Free up to a certain level of consumed resources.
Cons:
Locked into Google App Engine ?
Developers have read-only access to the filesystem on App Engine.
App Engine can only execute code called from an HTTP request (except for scheduled background tasks).
Users may upload arbitrary Python modules, but only if they are pure-Python; C and Pyrex modules are not supported.
App Engine limits the maximum rows returned from an entity get to 1000 rows per Datastore call. (Update - App Engine now supports cursors for accessing larger queries)
Java applications may only use a subset (The JRE Class White List) of the classes from the JRE standard edition.
Java applications cannot create new threads.
Known Issues!! : http://code.google.com/p/googleappengine/issues/list
Hard limits
Apps per developer - 10
Time per request - 30 sec
Files per app - 3,000
HTTP response size - 10 MB
Datastore item size - 1 MB
Application code size - 150 MB
Update Blob store now allows storage of files up to 50MB
Pro or Con?
App Engine's infrastructure removes many of the system administration and development challenges of building applications to scale to millions of hits. Google handles deploying code to a cluster, monitoring, failover, and launching application instances as necessary.
While other services let users install and configure nearly any *NIX compatible software, App Engine requires developers to use Python or Java as the programming language and a limited set of APIs. Current APIs allow storing and retrieving data from a BigTable non-relational database; making HTTP requests; sending e-mail; manipulating images; and caching. Most existing Web applications can't run on App Engine without modification, because they require a relational database.
Pros:
Scalable
Easy and cheaper (in short term).
Nice option for start-ups/individuals.
Suitable for apps that just store and retrieve data.
Cons:
Not suitable for CPU intensive calculations. They are slower and expensive.
Scalability doesn't matter much cuz if an app works at Google scale then probably it makes enough money to run on its own servers.
They have lots of limitations thrown here and there, as a result deep data analysis is difficult. Like you cannot produce a social graph using GAE.
I would say its not meant for serious businesses and expensive in long run.
(A huge new) PRO: GAE now supports MySQL :
https://developers.google.com/cloud-sql/
Pros:
built-in ui for unified logs
built-in web interface for task queues
built-in indexes on list of primary objects.
Cons:
loose logs very fast
VERY expensive
VERY expensive
VERY expensive
Un-hackable. Scales because you're obligated to code in a way that scales.
Longer development cycles. Sometimes you just want to hack something together and throw it away after 5 hors. With appengine you have to proper code it and write a lot of stuff to make it sure it scales. You can't just do a "find . | grep .avi | xargs ffmpeg -compress ...." :)
You will loose hours trying to do the simplest tasks like sending push notifications to APNS (iPhone). Although it's fine if you only want to support android in the future.
Terrible to make cleanups on the database. It's a HUGE pain in the ass to fix rows in the database, mainly because terribly slow, but it also requires a lot of code to loop properly within it's time constraints.
It was a pain to port Lucene to work on it's "filesystem".
Slow for what you pay.
Even MORE expensive if your app has spikes of traffic. My app has those spikes if a user that has many followers makes an action and we have to push notifications to his followers. Because of that I have to keep 10 inactive servers always on ($$$$$) to handle spikes.
Appengine isn't too bad due to the fact that I have the option to burn $$$$ instead of being concerned about scalability and fixing bottlenecks to reduce server usage. Sometimes it worth it.
My advice to people starting new products is to go with hetzner.de which is where I host my other products servers. It's cheap and extremely hackable. I have one server at hetzner that is handling 3x more traffic than the product that I have on appengine. The difference in price is $100 a month versions $2700 a month!
I have system admin experience, so the bottom line is that I would never choose appengine over having my own ROOT server. Don't be that bored software engineer wanting to experiment new things instead of building great products!
Pro: Unlimited scalabity to your application and scales with demand.
Con: Not available in some countries (Argentina).
Edit
Available worldwide, but only through Google Groups for App Engine.
When assessing pros and cons, I think it is important to clarify the market for which one is representing. Developers looking for a cost-effective solution to help them with the steep part of their planned hockey-stick growth curve will weigh heavily the cons already listed. For a small business owner, however, GAE is a God-send. These folks most often are looking to "the cloud" as a means to more effectively run their business (i.e. sell physical product and services). For the SMB, GAE the pros already listed can be much more valuable compared to the hockey-stick seeking dev, whilst the cons weight in at a fraction of the devs' measure. I don't see the GAE team doing anything related to SMB positioning, so I guess answers like this are me just pulling on Superman's cape, and spitting into the wind. Really GAE should be absolutely ruling the SMB space now. If not (I have no insights re: user base), then its is a greatly lamentable failure.
I believe , GAE is yet to mature in terms of providing the basic features for serious business such as Datastore with complex primary key, java.awt.* support, these are just a few I'm naming.
Other than the free space and to build some "Hobby" websites, I strongly feel GAE is NOT the place java guys should looking into.
I'm having applications built on the JSP/Servlets and MySQL, thinking about migrating to GAE, but I find I will be spending more "value time" on the migration than just buying a space from some java hosting provider such as EATJ, etc (Sorry not marketing, just an experience).
Another big issue I've got is migration of my existing mySQL data into GAE, bulkupload is really pathetic and has no client support.
No support for Local Db to Server DB upload.
Once the GAE is ready with "all the Cons" mentioned by above, then I'll think we can look in to this migration.
You are force to own a cell phone line, and your country+carrier must be able to receive international SMSs.
(I hate cell phones, and my mom's or co-workers won't get the SMSs)
Con: No Other RDBMS or NoSQL databases are not possible ....
Con: All your base are belong to us
... On a serious note:
Con: You don't control the environment your application runs in. The same cons as with outsourcing any component. Fun for toys, not for business (yet) IMHO.
Various things like API for Google proprietary backends such as their database system and other 'lockdowns' and frameworks that mean your code is tied, in some loose sense to their system can create cost issues later if you want to migrate from GAE. Of course, you could abstract these.
I like GAE, AppJet and others. They are cool. But everything has its place. If you want freedom and the ability to control your language's modules, API, syntax/stdlib versions and whatnot ... don't relinquish control to a service provider.
The lack of standards for environments and specifications for what your app can expect worries me in the cloud arena.
common sense stuff really.
Con: Limited to Java and Python
I have an application that requires analytics for different level of aggregation, and that's the OLAP workload. I want to update my database pretty frequently as well.
e.g., here is what my update looks like (schema looks like: time, dest, source ip, browser -> visits)
(15:00-1-2-2010, www.stackoverflow.com, 128.19.1.1, safari) --> 105
(15:00-1-2-2010, www.stackoverflow.com, 128.19.2.1, firefox) --> 110
...
(15:00-1-5-2010, www.cnn.com, 128.19.5.1, firefox) --> 110
And then I want to ask what is the total visit to www.stackoverflow.com from a firefox browser last month.
I understand Vertica system can do this in a relatively cheap way (performance and scalability wise, but not cost-wise probably). I have two questions here.
1) Is there an open-source product that I can build upon to solve this problem? In particular, how well does a Mondrian system work? (scalability, and performance)
2) Is there an HBase or Hypertable base solution (obviously, a naked HBase/Hypertable can't do this) for this? -- but if there is a project based on HBase/Hypertable, scalability probably won't be an issue IMO)?
Thanks!
You can download a free edition (the single node edition) of the greenplum database. I haven't tried it myself but I think/guess it is a powerful beast. Read here: http://www.dbms2.com/2009/10/19/greenplum-free-single-node-edition/
Another option is MongoDB, it is fast and free and you can write MapReduce functions with JavaScript to do analytics.
My reputation here is to low to add a hyperlink to mongodb, so you have to google . I can add only one hyper link per post.
The zohmg project aims to solve this problem using Hadoop and HBase.
Facebook also built Hive on-top of Hadoop. Pretty simple to get going - reasonable query API too.
http://mirror.facebook.net/facebook/hive/
Is your data model more complex than that? If it isn't you might be beter of just writing custom code for it. Then you can really tune it to your data. Real products have to offer a lot of flexibility, need a lot of complexiy to achieve that, and suffer in speed as a result.
Your question is not clear in one aspect: when you talk about scalable, what do you mean by that? Are you collecting data from lots of sites but only have a limited amount of query users, or do you also have a lot of users? That situation leads to a significantly different model.
I am trying to scrape some website and republish the data as a RSS feed. How hard is this to setup with Google App Engine? Disadvantages and Advantages using GAE. Any recommendations and guidelines greatly appreciated!
Google AppEngine offers much more functionality (and complexity) than you will need if truly all you will want to do is republish some structured data as RSS.
Personally, I would use something like Yahoo pipes for a task like this.
That being said... if you want/need to get your feet wet with GAE, go for it!
Working with Google App Engine is pretty straight forward. I would recommend going through the Getting Started guide. It's short and simple and touches on essential GAE topics. There are more pros and cons than I will list here.
Pros:
In general, App Engine is designed for high traffic web applications that need to scale. Furthermore, it is designed from a programmer's perspective. Much of the scalability issues (database optimization, server administration, etc) are dealt with by Google. Having said that, I find it to be a nice platform. It is still being actively developed by Google engineers, and scheduling of tasks (a feature that has been long requested) is in the current road map.
Cons:
Perhaps the biggest downside right now is again the lack of official scheduling support and the quota limits currently set for free accounts. However you can't complain much if its free. Currently it only supports Python as a programming interface (although a new language [Java I predict] is coming soon). Furthermore, Python 2.6 (and 3.0 for that matter) are not yet supported. In addition, Django 1.0 is not officially supported in App Engine (although you can package Django 1.0 with your application).
Harder than it would be in most other technologies.
GAE can sort of do scheduled batch stuff like this now, but it's really not intended for that type of thing. Pick pretty much any other language and platform for this particular task, and you'll make your life a lot easier.
I think BeautifulSoup could run on GAE, so all your scraping needs are handled :D
Also, GAE has a geturl thingy. The only problem I think you might have is not having enough time to get the data (30 secs limitation).
I am working on a same project and I've decided that it's easier to prepare the data on another server and push them to GAE.
You might also want to look into Yahoo! Query Language (YQL)