Was wondering if anyone out there can help.......
My company works in the travel industry and one of the product we provide is the function of buying a flight and hotel together.
One of the advantages of this is that sometimes a visitor can save on a hotel if they buy the package together.
What I want to be able to track is the following:
The hotel which has the saving on it (accomodation code); the saving that they will make; the price of the package that they will pay.
I am new to implementing but have been told by a colleague that I can use a context variable.
Would anyone be able to tell me how I should write this please?
Kind Regards
Yaser
Here is the document entry for Context Data Variables
For example, in the custom code section of the on-page code, within s_doPlugins or via some wrapper function that ultimately makes a s.t() or s.tl() call, you would have:
s.contextData['package.code'] = "accommodation code";
s.contextData['package.savings'] = "savings";
s.contextData['package.price'] = "price";
Then in the interface you can go to processing rules and map them to whatever props or eVars you want.
Having said that...processing rules are pretty basic at the moment, and to be honest, it's not really worth it IMO. Firstly, you have to get certified (take an exam and pass) to even access processing rules. It's not that big a deal, but it's IMO a pointless hoop to jump through (tip: if you are going to go ahead and take this step, be sure to study up on more than just processing rules. Despite the fact that the exam/certification is supposed to be about processing rules, there are several questions that have little to nothing to do with them)
2nd, context data doesn't show up in reports by themselves. You must assign the values to actual props/eVars/events through processing rules (or get ClientCare to use them in a vista rule, which is significantly more powerful than a processing rule, but costs lots of money)
3rd, the processing rules are pretty basic. Seriously, you're limited to just simple stuff like straight duping, concatenating values, etc.
4th, processing rules are limited in setting events, and won't let you set the products string. IOW, You can set a basic (counter) event, but not a numeric or currency event (an event with a custom value associated with it). Reason I mention this is because those price and savings values might be good as a numeric or currency event for calculated metrics. Well since you can't set an event as such via processing rules, you'd have to set the events in your page code anyways.
The only real benefit here is if you're simply looking to dupe them into a prop/eVar and that prop/eVar varies from report suite to report suite (which FYI, most people try to keep them consistent across report suites anyways, and people rarely repurpose them).
So if you are already being consistent across multiple report suites (or only have like 1 report suite in the first place), since you're already having to put some code on the site, there's no real incentive to just pop the values in the first place.
I guess the overall point here is that since the overall goal is to get the values into actual props, eVars and possibly events, and processing rules fail on a lot of levels, there's no compelling reason not to just pop them in the first place.
Related
I'm interested in replicating "hierachies" of data say similar to addresses.
Area
District
Sector
Unit
but you may have different pieces of data associated to each layer, so you may know the area of Sectors, but not of units, and you may know the population of a unit, basically its not a homogenious tree.
I know little about replication of data except brushing Brewers theorem/CAP, and some naive intuition about what eventual consistency is.
I'm looking for SIMPLE mechanisms to replicate this data from an ACID RDB, into other ACID RDBs, systemically the system needs to eventually converge, and obviously each RDB will enforce its own local consistent view, but any 2 nodes may not match at any given time (except 'eventually').
The simplest way to approach this is to simple store all the data in a single message from some designated leader and distribute it...like an overnight dump and load process, but thats too big.
So the next simplest thing (I thought) was if something inside an area changes, I can export the complete set of data inside an area, and load it into the nodes, thats still quite a coarse algorithm.
The next step was if, say an 'object' at any level changed, was to send all the data in the path to that 'object', i.e. if something in a sector is amended, you would send the data associated to the sector, its parent the district, and its parent the sector (with some sort of version stamp and lets say last update wins)....what i wanted to do was to ensure that any replication 'update' was guaranteed to succeed (so it needs the whole path, which potentially would be created if it didn't exist).
then i stumbled on CRDTs and thought....ah...I'm reinventing the wheel here, and the algorithms are allegedly easy in principle, but tricky to get correct in practice
are there standards accepted patterns to do this sort of thing?
In my use case the hierarchies are quite shallow, and there is only a single designated leader (at this time), I'm quite attracted to state based CRDTs because then I can ignore ordering.
Simplicity is the key requirement.
Actually it appears I've reinvented (in a very crude naive way) the SHELF algorithm.
I'll write some code and see if I can get it to work, and try to understand whats going on.
I want to de-dupe a stream of data based on an ID in a windowed fashion. The stream we receive has and we want to remove data with matching within N-hour time windows. A straight-forward approach is to use an external key-store (BigTable or something similar) where we look-up for keys and write if required but our qps is extremely large making maintaining such a service pretty hard. The alternative approach I came up with was to groupBy within a timewindow so that all data for a user within a time-window falls within the same group and then, in each group, we use a separate key-store service where we look up for duplicates by the key. So, I have a few questions about this approach
[1] If I run a groupBy transform, is there any guarantee that each group will be processed in the same slave? If guaranteed, we can group by the userid and then within each group compare the sessionid for each user
[2] If it is feasible, my next question is to whether we can run such other services in each of the slave machines that run the job - in the example above, I would like to have a local Redis running which can then be used by each group to look up or write an ID too.
The idea seems off what Dataflow is supposed to do but I believe such use cases should be common - so if there is a better model to approach this problem, I am looking forward to that too. We essentially want to avoid external lookups as much as possible given the amount of data we have.
1) In the Dataflow model, there is no guarantee that the same machine will see all the groups across windows for the key. Imagine that a VM dies or new VMs are added and work is split across them for scaling.
2) Your welcome to run other services on the Dataflow VMs since they are general purpose but note that you will have to contend with resource requirements of the other applications on the host potentially causing out of memory issues.
Note that you may want to take a look at RemoveDuplicates and use that if it fits your usecase.
It also seems like you might want to be using session windows to dedupe elements. You would call:
PCollection<T> pc = ...;
PCollection<T> windowed_pc = pc.apply(
Window<T>into(Sessions.withGapDuration(Duration.standardMinutes(N hours))));
Each new element will keep extending the length of the window so it won't close until the gap closes. If you also apply an AfterCount speculative trigger of 1 with an AfterWatermark trigger on a downstream GroupByKey. The trigger would fire as soon as it could which would be once it has seen at least one element and then once more when the session closes. After the GroupByKey you would have a DoFn that filters out an element which isn't an early firing based upon the pane information ([3], [4]).
DoFn(T -> KV<session key, T>)
|
\|/
Window.into(Session window)
|
\|/
Group by key
|
\|/
DoFn(Filter based upon pane information)
It is sort of unclear from your description, can you provide more details?
Sorry for not being clear. I gave the setup you mentioned a try, except for the early and late firings part, and it is working on smaller samples. I have a couple of follow up questions, related to scaling this up. Also, I was hoping I could give you more information on what the exact scenario is.
So, we have incoming data stream, each item of which can be uniquely identified by their fields. We also know that duplicates occur pretty far apart and for now, we care about those within a 6 hour window. And regarding the volume of data, we have atleast 100K events every second, which span across a million different users - so within this 6 hour window, we could get a few billion events into the pipeline.
Given this background, my questions are
[1] For the sessioning to happen by key, I should run it on something like
PCollection<KV<key, T>> windowed_pc = pc.apply(
Window<KV<key,T>>into(Sessions.withGapDuration(Duration.standardMinutes(6 hours))));
where key is a combination of the 3 ids I had mentioned earlier. Based on the definition of Sessions, only if I run it on this KV would I be able to manage sessions per-key. This would mean that Dataflow would have too many open sessions at any given time waiting for them to close and I was worried if it would scale or I would run into any bottle-necks.
[2] Once I perform Sessioning as above, I have already removed the duplicates based on the firings since I will only care about the first firing in each session which already destroys duplicates. I no longer need the RemoveDuplicates transform which I found was a combination of (WithKeys, Combine.PerKey, Values) transforms in order, essentially performing the same operation. Is this the right assumption to make?
[3] If the solution in [1] going to be a problem, the alternative is to reduce the key for sessioning to be just user-id, session-id ignoring the sequence-id and then, running a RemoveDuplicates on top of each resulting window by sequence-id. This might reduce the number of open sessions but still would leave a lot of open sessions (#users * #sessions per user) which can easily run into millions. FWIW, I dont think we can session only by user-id since then the session might never close as different sessions for same user could keep coming in and also determining the session gap in this scenario becomes infeasible.
Hope my problem is a little more clear this time. Please let me know any of my approaches make the best use of Dataflow or if I am missing something.
Thanks
I tried out this solution at a larger scale and as long as I provide sufficient workers and disks, the pipeline scales well although I am seeing a different problem now.
After this sessionization, I run a Combine.perKey on the key and then perform a ParDo which looks into c.pane().getTiming() and only rejects anything other than an EARLY firing. I tried counting both EARLY and ONTIME firings in this ParDo and it looks like the ontime-panes are actually deduped more precisely than the early ones. I mean, the #early-firings still has some duplicates whereas the #ontime-firings is less than that and has more duplicates removed. Is there any reason this could happen? Also, is my approach towards deduping using a Combine+ParDo the right one or could I do something better?
events.apply(
WithKeys.<String, EventInfo>of(new SerializableFunction<EventInfo, String>() {
#Override
public java.lang.String apply(EventInfo input) {
return input.getUniqueKey();
}
})
)
.apply(
Window.named("sessioner").<KV<String, EventInfo>>into(
Sessions.withGapDuration(mSessionGap)
)
.triggering(
AfterWatermark.pastEndOfWindow()
.withEarlyFirings(AfterPane.elementCountAtLeast(1))
)
.withAllowedLateness(Duration.ZERO)
.accumulatingFiredPanes()
);
I'm pretty sure this is a silly newbie question but I didn't know it so I had to ask...
Why do we use data structures, like Linked List, Binary Search Tree, etc? (when no dynamic allocation is needed)
I mean: wouldn't it be faster if we kept a single variable for a single object? Wouldn't that speed up access time? Eg: BST possibly has to run through some pointers first before it gets to the actual data.
Except for when dynamic allocation is needed, is there a reason to use them?
Eg: using linked list/ BST / std::vector in a situation where a simple (non-dynamic) array could be used.
Each thing you are storing is being kept in it's own variable (or storage location). Data structures apply organization to your data. Imagine if you had 10,000 things you were trying to track. You could store them in 10,000 separate variables. If you did that, then you'd always be limited to 10,000 different things. If you wanted more, you'd have to modify your program and recompile it each time you wanted to increase the number. You might also have to modify the code to change the way in which the calculations are done if the order of the items changes because the new one is introduced in the middle.
Using data structures, from simple arrays to more complex trees, hash tables, or custom data structures, allows your code to both be more organized and extensible. Using an array, which can either be created to hold the required number of elements or extended to hold more after it's first created keeps you from having to rewrite your code each time the number of data items changes. Using an appropriate data structure allows you to design algorithms based on the relationships between the data elements rather than some fixed ordering, giving you more flexibility.
A simple analogy might help to understand. You could, for example, organize all of your important papers by putting each of them into separate filing cabinet. If you did that you'd have to memorize (i.e., hard-code) the cabinet in which each item can be found in order to use them effectively. Alternatively, you could store each in the same filing cabinet (like a generic array). This is better in that they're all in one place, but still not optimum, since you have to search through them all each time you want to find one. Better yet would be to organize them by subject, putting like subjects in the same file folder (separate arrays, different structures). That way you can look for the file folder for the correct subject, then find the item you're looking for in it. Depending on your needs you can use different filing methods (data structures/algorithms) to better organize your information for it's intended use.
I'll also note that there are times when it does make sense to use individual variables for each data item you are using. Frequently there is a mixture of individual variables and more complex structures, using the appropriate method depending on the use of the particular item. For example, you might store the sum of a collection of integers in a variable while the integers themselves are stored in an array. A program would need to be pretty simple though before the introduction of data structures wouldn't be appropriate.
Sorry, but you didn't just find a great new way of doing things ;) There are several huge problems with this approach.
How could this be done without requring programmers to massively (and nontrivially) rewrite tons of code as soon as the number of allowed items changes? Even when you have to fix your data structure sizes at compile time (e.g. arrays in C), you can use a constant. Then, changing a single constant and recompiling is sufficent for changes to that size (if the code was written with this in mind). With your approach, we'd have to type hundreds or even thousands of lines every time some size changes. Not to mention that all this code would be incredibly hard to read, write, maintain and verify. The old truism "more lines of code = more space for bugs" is taken up to eleven in such a setting.
Then there's the fact that the number is almost never set in stone. Even when it is a compile time constant, changes are still likely. Writing hundreds of lines of code for a minor (if it exists at all) performance gain is hardly ever worth it. This goes thrice if you'd have to do the same amount of work again every time you want to change something. Not to mention that it isn't possible at all once there is any remotely dynamic component in the size of the data structures. That is to say, it's very rarely possible.
Also consider the concept of implicit and succinct data structures. If you use a set of hard-coded variables instead of abstracting over the size, you still got a data structure. You merely made it implicit, unrolled the algorithms operating on it, and set its size in stone. Philosophically, you changed nothing.
But surely it has a performance benefit? Well, possible, although it will be tiny. But it isn't guaranteed to be there. You'd save some space on data, but code size would explode. And as everyone informed about inlining should know, small code sizes are very useful for performance to allow the code to be in the cache. Also, argument passing would result in excessive copying unless you'd figure out a trick to derive the location of most variables from a few pointers. Needless to say, this would be nonportable, very tricky to get right even on a single platform, and liable to being broken by any change to the code or the compiler invocation.
Finally, note that a weaker form is sometimes done. The Wikipedia page on implicit and succinct data structures has some examples. On a smaller scale, some data structures store much data in one place, such that it can be accessed with less pointer chasing and is more likely to be in the cache (e.g. cache-aware and cache-oblivious data structures). It's just not viable for 99% of all code and taking it to the extreme adds only a tiny, if any, benefit.
The main benefit to datastructures, in my opinion, is that you are relationally grouping them. For instance, instead of having 10 separate variables of class MyClass, you can have a datastructure that groups them all. This grouping allows for certain operations to be performed because they are structured together.
Not to mention, having datastructures can potentially enforce type security, which is powerful and necessary in many cases.
And last but not least, what would you rather do?
string string1 = "string1";
string string2 = "string2";
string string3 = "string3";
string string4 = "string4";
string string5 = "string5";
Console.WriteLine(string1);
Console.WriteLine(string2);
Console.WriteLine(string3);
Console.WriteLine(string4);
Console.WriteLine(string5);
Or...
List<string> myStringList = new List<string>() { "string1", "string2", "string3", "string4", "string5" };
foreach (string s in myStringList)
Console.WriteLine(s);
I am searching for ideas/examples on how to store path patterns from users - with the goal of analysing their behaviours and optimizing on "most used path" when we can detect them somehow.
Eg. which action do they do after what, so that we later on can check to see if certain actions are done over and over again - therefore developing a shortcut or assembling some of the actions into a combined multiaction.
My first guess would be some sort of "simple log", perhaps stored in some SQL-manner, where we can keep each action as an index and then just record everything.
Problem is that the path/action might be dynamically changed - even while logging - so we need to be able to take care of this fact too, when looking for patterns later.
Would you log everthing "bigtime" first and then POST-process every bit of details after some time or do you have great experience with other tactics?
My worry is that this is going to take up space, BIG TIME while logging 1000 users each day for a month or more.
Hope this makes sense and I am curious to see if anyone can provide sample code, pseudocode or perhaps links to something usefull.
Our tools will be C#, SQL-database, XML and .NET 3.5 - clients could also get .NET 4.0 if needed.
Patterns examples as we expect them
...
User #1001: A-B-A-A-A-B-C-E-F-G-H-A-A-A-C-B-A
User #1002: B-A-A-B-C-E-F
User #1003: F-B-B-A-E-C-A-A-A
User #1002: C-E-F
...
etc. no real way to know what they do next nor how many they will use, how often they will do it.
A secondary goal, if possible, if we later on add a new "action" called G (just sample to illustrate, there will be hundreds of actions) how could we detect these new behaviours influence on the previous patterns.
To explain it better, my thought here would be some way to detect "patterns within patterns", sort of like how compressions work, so that "repeative patterns" are spottet. We dont know how long these patterns might be, nor how often they might come. How do we break this down into "small bits and pieces" - whats the best approach you think?
I am not sure what you mean by path, but, if you gave every action in a path a unique symbol, you could reduce the problem to longest common substring or subsequence.
Or have a map of paths to the number of times that action occurred. Every time a certain path happens, increment the count for that path. Then sort to find the most common.
Pseudo idea/implementation so far
Log ever users action into a list/series of actions, bulk kinda style (textfiles/SQL - what ever, just store the whole thing for post-processing)
start counting every "1 action", "2 actions", "3 actions" up til a certain amount (lets say 30 levels)
sort them all, by giving values of importants to some of the actions (might be those producing end results)
A usefull result perhaps?
If we count all [A], [A-A], [A-B], [A-C], [A-A-A], [A-A-B] etc. its going to make a LONG and fine list of which actions are used in row frequently, and thats in the right direction, because if some of these results gets too high, we might need a shorter path. Problem is then, whats too few actions to be optimized and whats the longest needed actionlist to search for? My guess is that we need to do this counting first, then examine the numbers.
Problem is that this would be part of an analyzing tool we are developing and we dont have data until implementation, so we dont know what to look for before its actually done. hmm... wondering if there really IS an answer to this one.
After months of frustration and of time spent in inserting needles in voodoo dolls of previous developers I decided that it is better try to refactor the legacy code.
I already ordered Micheal Feather's book, I am into Fowler's refactoring and I made some sample projects with DUnit.
So even if I don't master the subject I feel it is time to act and put some ideas into practice.
Almost 100% of the code I work on has the business logic trapped in the UI, moreover all is procedural programming (with some few exceptions). The application started as quick & dirty and continued as such.
Now writing tests for all the application is a meaningless task in my case, but I would like to try to unittest something that I need to refactor.
One of the complex tasks one big "TForm business logic class" does is to read DB data, make some computations and populate a scheduler component. I would like to remove the reading DB data and computation part and assign to a new class this task. Of course this is a way to improve the current design, it is not the best way for starting from scratch, but I'd like to do this because the data returned by this new class is useful also in other ways, for example now I've been ask to send e-mail notifications of scheduler data.
So to avoid a massive copy and paste operation I need the new class.
Now the scheduler is populated from a huge dataset (huge in size and in number of fields), probably a first refactoring step could be obtaining the dataset from the new class. But then in the future I'd better use a new class (like TSchedulerData or some other name less bound to scheduler) to manage the data, and instead of having a dataset as result i can have a TSchedulerData object.
Since refactor occurs at at small steps and tests are needed to refactor better I am a little confused on how to proceed.
The following points are not clear to me:
1) how to test a complex dataset? Should I run the working application, save one result set to xml, and write a test where I use a TClientDataSet containing that xml data?
2) How much do I have to care about TSchedulerData? I mean I am not 100% sure I will use TSchedulerData, may be I will stick with the Dataset, anyway thinking of creating complex tests that will be discarded in 2 weeks is not appealing for a DUnitNewbee. Anyway probably this is how it works. I can't imagine the number of bugs that I would face without a test.
Final note: I know someone thinks rewriting from scratch is a better option, but this is not an option. "The application is huge and it is sold today and new features are required today not to get out of business". This is what I have been told, anyway refactoring can save my life and extend the application life.
Your eventual goal is to separate the UI, data storage and business logic into distinct layers.
Its very difficult to test a UI with automatic testing frameworks. You'll want to eventually separate as much of the business logic from the UI as possible. This can be accomplished using one of the various Model/View/* patterns. I prefer MVP passive view, which attempts to make the UI nothing more than an interface. If you're using a Dataset MVP Supervising Controller may be a better fit.
Data storage needs to have its own suite of tests but these are different from unit tests (though you can use the same unit testing framework) and there are usually fewer of them. You can get away with this because most of the heavy lifting is being done by third party data components and a dbms (in your case T*Dataset). These are integration tests. Basically making sure your code plays nice with the vendor's code. Also needed if you have any stored procedures defined in the DB. They are much slower that unit tests and don't need to be run as often.
The business logic is what you want to test the most. Every calculation, loop or branch should have at least one test(more is preferable). In legacy code this logic often touches the UI and db directly and does multiple things in a single function. Here Extract Method is your friend. Good places to extract methods are:
for I:=0 to List.Count - 1 do
begin
//HERE
end;
if /*HERE if its a complex condition*/ then
begin
//HERE
end
else
begin
//HERE
end
Answer := Var1 / Var2 + Var1 * Var3; //HERE
When you come across one of these extraction points
Decide what you want the method signature to look like for your new method: Method name, parameters, return value.
Write a test that calls it and checks the expected outcome.
Extract the method.
If all goes well you will have a newly extracted method with at least one passing unit test.
Delphi's built in Extract Method doesn't give you any way to adjust the signature so if that's your own option you'll have to make do and fix it after extraction. You'll also want to make the new method public so your test can access it. Some people balk at making a private utility method public but at this early stage you have little choice. Once you've made sufficient progress you'll start to see that some utility methods you've extracted belong in their own class (in which case they'd have to be public anyway) while others can be made private/protected and tested indirectly by testing methods that depend on them.
As your test suite grows you'll want to run them after each change to ensure your latest change hasn't broken something elsewhere.
This topic is much too large to cover completely in an answer. You'll find the vast majority of your questions are covered when that book arrives.
I'd say approach it in focussed baby steps.
Step#1: Should always be to get some tests around your area of invasion TForm - regression tests aka safety net. In your case, sense what the app is doing. From what I read, it seems to be a data transformer. So spend time to understand all (or most important if all is not feasible) combinations of input data and the corresponding output schedules. Write them up as tests. Ensure that all tests pass.
Step#2: Now attempt your refactorings. Move blocks of code into cohesive classes etc all under the safety of the regression net.
Testing complex datasets - testing file dumps should be the last resort. But in this case, it seems like a simple option to get started. Maybe you could later make it a first class domain object TSchedule with its own Equals() implementation. Defer design decisions/changes until you have a solid regression test suite around your area of modification.