Archive for ‘Technology’

October 18, 2011

google app engine 1.5.5 sdk not a relief

task queues + backends + cron jobs don’t work well together on the development server. this has been the state for the last few months sucking up huge amount of dev + qa + release cycles.

checks like these continue to fail on the development server but work on the production (appspot) platform.

assert get_backend() == ‘consolidate_billing_units’

October 17, 2011

salesforce contact integration on google app engine

contact integration with salesforce is easy. here is how we did it recently on a Google App Engine project.

this works for enterprise salesforce accounts only (and ofcourse developer accounts)

step 1: get an authorization code from salesforce

AUTHORIZE_URI = ‘https://login.salesforce.com

def get(self, **kwargs):
code = request.args.get(“code”)
args = {
‘client_id’: SALESFORCE_CONSUMER_KEY,
‘redirect_uri’: REDIRECT_URI,
‘state’:workspace # salesforce allows user # defined to be stored in the # state variable
}

if code is not None:

# call back from salesforce …

args[“client_secret”] = SALESFORCE_CONSUMER_SECRET args[“code”] = request.args.get(“code”)
args[‘grant_type’] = “authorization_code”

url = AUTHORIZE_URI + “/services/oauth2/token?” + urllib.urlencode(args)
response = urlfetch.fetch(url, method=urlfetch.POST, validate_certificate=False) content = json.loads(response.content)

access_token = content[‘access_token’]
instance_url = content[‘instance_url’]
refresh_token = content[‘refresh_token’]

else:

# call salesforce

args[‘response_type’] = ‘code’
url = ‘%s%s’%(AUTHORIZE_URI, “/services/oauth2/authorize?%s”%urllib.urlencode(args)) return redirect(url)

step 2: use the token to call into salesforce

def salesforce_query(email, contact_fields, access_token, instance_url):

fields = ‘, ‘.join([f for f in contact_fields])

args = {
“q” :”SELECT %s from Contact where Email = ‘%s'” % (fields, email) }

headers = {
“Authorization”: “OAuth %s” % access_token
}

url = ‘%s%s%s’ % (instance_url, “/services/data/v20.0/query?”, urllib.urlencode(args))

response = urlfetch.fetch(url, headers=headers, method=urlfetch.GET, validate_certificate=False) return simplejson.loads(response.content)

October 17, 2011

a simple and elegant yes no dialog using dojo

a very common situation in ui design is to let the user confirm an action (especially one which is destructive) via a dialog. similar to microsoft windows yes/no or ok/cancel dialogs.

here is how do accomplish this using dojo, something I customized for our needs,

1. first the markup

Yes

No

2. next the handler

function yesno()
{
// assume that yesno is called when the user clicks on the object which is key // to the elements being destroyed.

var obj = this;

// my yesno is reusable, you could arbitrarily bind the same // dialog to more than one object. i store the title and the // description of the dialog in the object itself.

var description = obj.node.dialog_description;
var title = obj.node.dialog_title;

// tie the user action handlers – here if the user clicks on yes, // the object has to be destroyed.

dojo.byId(‘yesbutton’).onclick = dojo.hitch(obj, remove);

// no? simply hide the dialog.

dojo.byId(‘nobutton’).onclick = dojo.hitch(obj, function(){yndialog.hide()}); dojo.byId(‘yncontent’).innerHTML = description;
yndialog.set(‘title’, title);
yndialog.show();
}

function remove()
{

// this is the remove handler. i associate this handler with the // object itself such that ‘this’ points to the object.

var obj = this;

dojo.destroy(obj); // or trigger a server side action and then destroy this node. }

June 13, 2011

Symantec.Cloud Review

I recently had an opportunity to attend a Symantec Conference (Enterprise 2011). I was mostly attracted by the 45  minute session on Cloud Computing – which was towards the end of the half day conference.

I waited with eager anticipation as I was ignorant of any of Symantec Cloud offerings. Being an ex Veritas File System developer with a long association at Symantec I was naturally curious.

As the hour approached, the presenter went on to unveil a set of Symantec Products offered via SaaS model (mostly concerning with Data Security/Availability – full list here http://www.symantec.com/business/theme.jsp?themeid=symantec-cloud).

I was disappointed, to say the least, at this attempt to pass off SaaS as cloud computing. I was hoping to learn of some cool new storage/compute virtualization story (Symantec and Vware are buddies) tied together with utility computing and security thrown in the mix. Alas, no such thing.

Something did not add up in my own mind after that presentation. I was not sure why I should be disappointed that “SaaS is not the same as Cloud Computing” (A similar debate is going on concerning Apple’s iCloud).

SaaS – Cloud Computing? It is and It isn’t.

It is difficult to find a precise and widely accepted definition of what cloud computing is and what it isn’t. However, there are some Cloud Computing Guarantees (well, promises) that are generally well accepted. Some of which are –

  1. Availability – promises on service availability (five 9 availability for e.g.)
  2. Scalability  – promises on how well the service scales (horizontally and vertically)
  3. Utility Computing Based Billing – pay as you go
My own thoughts when someone mentions cloud computing is the cloud computing infrastructure – hardware and operating system software sans the (business logic) applications. Similar to Google App Engine, Amazon EC2, Microsoft’s Azure.
Applications written to run on such infrastructure are expected to exhibit availability and scalability properties.
It is interesting to note that Symantec promises 100% service availability and a guaranteed latency (from which you can draw inferences about the scalability). I suppose from a service consumer’s point of view this is all that matters.
It is anybody’s guess as to what kind of infrastructure powers Symantec.Cloud and consumers should not be unduly concerned. They should instead focus on the service availability and response times which are very very good indeed.
April 27, 2011

why we should avoid tipfy – a gae framework

as we recently found out, choosing a framework for GAE can be a costly and frustrating affair. for various reasons we decided to go with tipfy (http://www.tipfy.org) for one of our projects. at that time the tipfy framework version was 0.5.9.

GAE has evolved and i guess tipfy had to catch up with the new additions.

the current version of tipfy is 1.0b1 … the source layout has completely changed which means we have to spend effort to make it work with the latest form it has taken.

the maintainers of tipfy clearly don’t think backward compatibility or easy porting an issue worth consideration. we have since decided to do away with tipfy and port it back to vanilla GAE. from experience the only thing we will miss is the jinja templating which was superior to the django template engine a year or so ago. in fact more important (from our requirements point of view) things like the google map reduce functionality should work out of the box – it was a royal pain to get it to work on tipfy. the tipfy implementation appears quite weak.

our recommendation for anyone out there trying to choose a framework to start on GAE is to stick to the core GAE itself and at the most venture into django.

of course, we welcome tipfy experts to educate us on what we are missing. but for now, tipfy is not yet mature for serious development.

April 18, 2011

competitive analysis

Comparing performance is a game of how to do apples-to-apples comparison and yet make your system look better. The simplest trick is to chose a hardware or a software that favors your system. And use the same platform for the competing system. Then under identical conditions, using same benchmark, your system would obviously perform better.  If you look at the experiment as a whole, it’s a clean apples-to-apples comparison. Many  competitive papers published or sponsored by vendors may be such studies.

To understand the real game, one needs to know more about how the two particular softwares are designed. How they work with different underlying hardware. Each system has unique features which are designed to perform better under specific applications. For example, one may be using on-board cache to give better performance while other may be using multiple paths to the storage to give better performance. Any differentiating features between the two softwares can be exploited to tilt the level of the playing field. By ‘correctly’ choosing the underlying hardware one of them can be shown in better light than the other.

That is why even though system performance is a very rigorous subject, it’s application to compare performance of commercial systems is a subtle art.

So, the next time you come across a ‘performance comparison’, be sure to look at the conditions under which the performance measurement was carried out.

April 18, 2011

the correct measure of system performance

System performance is a general term. It means, how the system (hardware, software or both) is doing under some specified conditions. Or how best can system performs under real load conditions. An appropriate measure for the performance should be chosen based on the most valuable aspect given any situation. There isn’t a single common parameter that fits all situations.

One of the most commonly used parameters for measuring performance is the number of transactions the system performs in one second or, ‘transactions/sec’. This simply counts the number of operations a system can do in one second. This parameter is all right if it is required that the system deliver maximum number of transactions.

Another frequently used parameter is –  how long does a specified operation take to complete. This is ‘inverse’ of the previous parameter. Here the interest is in the average time (say in milliseconds) taken to complete a single transaction. This may be of interest if one wants a measure of how FAST the system serves users.

There are other performance parameters as well. For example, how efficient the code is (especially, code which does computation), then one is interested in looking at the number of cpu cycles spent in doing transactions every second or, ‘transactions/sec/cpu’. An efficient code will deliver greater transactions per second while using less cpu.

If the interest is in the measure of raw data performance – written or read from the disk then the appropriate parameter is the number of blocks written/read form the disk. If one is studying the performance of data-base then one may want to look at how many SQL operations (or scripts) are executed per second.

For a web-server, it could be the time taken to load the pages from multiple users.

The measure of performance should capture the quality that is being sought in the system. The performance numbers or claims may prove misleading simply because one is not using the correct measure of performance. It is thus very important to choose the correct measure of system performance.

April 12, 2011

Looking at code performance

In any system under performance consideration, there  are 3 important timescales.

  1. The timescale at which the CPU works
  2. The timescale at which the Memory can be accessed
  3. The timescale at which data can be stored on disk

These three have widely different timescales in a traditional computer setup. CPU runs at nano second resolution, memory at micro-second and Disks at milli-second.

This makes it difficult to estimate the true efficiency of  code. Because, the rate at which the instructions can be processed also now depends on the rate at which the memory is accessed and the disk is utilized. Hence, to truly measure the code efficiency one needs to make sure that the disk response times do not play a major role in the flow of instructions. In other words on e should take care of removing the IO bottlenecks before analyzing the performance of code.

In presence of strong influence of disk IOs the measurements will be biased by the characteristics of the storage system. The order of magnitude difference between the response times of disks and memory and memory and CPU makes it even more difficult to remove the IO bottleneck. While the CPUs have become faster in the last few years and there are now CPUs with multiple cores the memory and disk access speeds haven’t kept pace.  As a result the contrast between the response times of CPU, Memory and Storage has widened.

April 9, 2011

3 simple steps to adopt cloud computing

Cloud computing is now synonymous with Flexible Provisioning and Scale. Find out below if you are taking full advantage of cloud computing.

The As Is deployment – lowest adoption cost, reasonable benefits:

Move the server application “as is” to a cloud server. This is nothing but a co-located server, at Amazon for example. The provisioning and maintenance of the application is still a self driven task.

The win is in the dynamic on demand provisioning. Easy to compute the ROI here. Let us say that your application needs to be available all year round – but cater to seasonal demands. Say it costs $400 to host your application to cater to peak demand. You would end up paying 12*400 = $4800 per annum to keep your application up. Most of the time it would be under utilized. Cloud computing has made it really simple to change your compute capacity as easily as setting a reminder in your out look calender. With amazon or google, you could just log into the admin panel and say that you need additional resources only on certain dates. At the end of the month you get billed for the amount of resources you actually consume.

The Managed RDBMS deployment – reasonably low adoption cost, reasonable benefits:

A lot of work has to be done to ensure that the application is available. i.e. a replication strategy and policy to keep the database available. This is still a lot of effort and money. The alternative is a managed RDBMS, where the provider (amazon or google) manages the database. They worry about keeping the data safe from being lost. Much harder to do the ROI here – as the time spent in managing this would have to be offset against opportunity costs. Note that there would be some amount of code restructuring (not a lot) to get this going. An example of this is the Amazon MySQL RDS. At the time of writing, google is yet to announce the availability of their hosted sql service.

The Application Rewrite – highest adoption cost, highest benefits (arguably)

If your goal is to write an application which scales very well then you should consider a complete application rewrite to take advantage of the storage APIs. Hosted RDBMS is still a single machine (or a cluster) running a database server – with bottlenecks – be it memory, cpu, networ or disk.

Cloud computing offers storage APIs to access and manage data unlike traditional methods of file or rdbms storage. Because of the underlying architectural differences, cloud datastore offers better scalability – http://labs.google.com/papers/bigtable.html.

April 8, 2011

The recent Google App Engine Outage: pseudo transparency

Ref: http://groups.google.com/group/google-appengine-downtime-notify/msg/e9414ee6493da6fb?pli=1

The app engine team in its RCA explanation stops at this point :

The Datastore relies on Bigtable to store data (read more about
Bigtable here: http://labs.google.com/papers/bigtable.html).  One of
the components of the Bigtable is a repository for determining where a
specific entity is located in the distributed system.  Due to
instability in the cluster, this component became overloaded.

 

Instability in the cluster? Sounds like a bug in the system which Google does not want to talk about in detail. But why go through this whole ritual of trying to be transparent with the community? Being an engineer, this is not an acceptable RCA. Who are they kidding? We don’t know whether the problem is really fixed or it is still lurking out there. Knowing a thing or two about clustering, instabilities of any kind are not that easy to fix. Let us wait and watch.

 

Still a fan of GAE.