We have a few non-erlang-connected clusters in our infrastructure and currently use term_to_binary to encode erlang terms for messages between the clusters. On the receiving side we use binary_to_term(Bin, [safe]) to only convert to existing atoms (should there be any in the message).
Occasionally (especially after starting a new cluster/stack), we run into the problem that there are partially known atoms encoded in the message, i.e. the sending cluster knows this atom, but the receiving does not. This can be for various reasons, most common is that the receiving node simply has not loaded a module containing some record definition. We currently employ some nasty work-arounds which basically amount to maintaining a short-ish list of potentially used atoms, but we're not quite happy with this error-prone approach.
Is there a smart way to share atoms between these clusters? Or is it recommended to not use the binary format for such purposes?
Looking forward to your insights.
I would think hard about why non-Erlang nodes are sending atom values in the first place. Most likely there is some adjustment that can be made to the protocol being used to communicate -- or most often there is simply not a real protocol defined and the actual protocol in use evolved organically over time.
Not knowing any details of the situation, there are two solutions to this:
Go deep and use an abstract serialization technique like ASN.1 or JSON or whatever, using binary strings instead of atoms. This makes the most sense when you have a largish set of well understood, structured data to send (which may wrap unstructured or opaque data).
Remain shallow and instead write a functional API interface for the processes/modules you will be sending to/calling first, to make sure you fully understand what your protocol actually is, and then back that up by making each interface call correspond to a matching network message which, when received, dispatches the same procedures an API function call would have.
The basic problem is the idea of non-Erlang nodes being able to generate atoms that the cluster may not be aware of. This is a somewhat sticky problem. In many cases the places where you are using atoms you can instead use binaries to similar effect and retain the same semantics without confusing the runtime. Its the difference between {<<"new_message">>, Data} and {new_message, Data}; matching within a function head works the same way, just slightly more noisy syntactically.
I'm trying to get my head around two very different approaches to data sharing: OData and Semantic Web/Linked Data. Is there a good comparison of the two?
As I understand it, OData combines syndication/CRUD (AtomPub), serialisation formats (XML, JSON), a data model, a query language, and some semantics/conventions governing use of those existing technologies. It's primarily intended for exposing data from one system so that others can consume it.
Linked Data is a data model, a rigorous commitment to URIs, an (optional?) serialisation format (RDF/XML), but (correct me if I'm wrong) doesn't say anything about transport, CRUD, etc. It seems intended to allow inferencing across lots of little chunks of data drawn from a wide variety of sources. (Not something of major importance to us right now - we would be synchronising large slabs of data between a small number of sources, and wanting to preserve provenance information).
I'm interested in technologies for sharing data between certain data management platforms, some of which I work on directly. OData seems more appealing as it's very straightforward to explain to developers: implement this API, follow that Atom standard, serialise the data like this. We're already doing something very similar for one platform: sharing XML-serialised data on an Atom feed, with URL parameters used to filter.
By contrast, my past experiences working with RDF have given me an impression of brittle, opaque (massive slabs of RDF/XML), inaccessible (using SPARQL vs SQL) technology - but perhaps I'm confusing the experience of working with a triplestore like Jena with simply exposing an existing database via a linked data API.
Any pointers, comments etc on the differences and similarities between these two approaches in terms of scope, technologies, ease, future potential etc would be great.
I think discussing this in depth is not really what Stackoverflow is meant for, but just to give you some pointers to interesting discussions about differences and overlap:
Oh - it is data on the Web
Microsoft, OData and RDF
One of the key differences seems to be that OData has no means to link data from different sources to each other. Essentially, you're still stuck in a silo.
It might also be interesting to check out various attempts to convert data between the two approaches. See a.o. http://answers.semanticweb.com/questions/1298/has-anyone-written-a-mapping-from-odata-to-rdf .
OData may be easier, but its not better, by any means. SPARQL and RDF (forget RDF/XML, better to look at Turtle) satisfies everything in OData along with providing many more cutting edge features such as:
Federation Extensions
Linked Data
Reasoning and Inference (for the more brave)
Equally, the software supporting the standards is actually quite sophisticated. Most people interested in OData generally come from a Microsoft background, so take a look at dotNetRdf
Here's a comparison matrix:
http://uoccou.wordpress.com/2011/02/17/linked-data-odata-gdata-datarss-comparison-matrix/
Unfortunately the table formatting is pretty horrible, but the content is useful.
I'm currently learning F# and I'm exploring using it to analyse financial time-series. Can anyone recommend a good data structure to store time-series data in?
F# offers a rich selection of native types and I'm looking for a some simple combination that would provide an elegant, succinct and efficient solution.
I'm looking store tick data, which consists of millions of records each with a time stamp, and several (~5-20) fields of numerical and textual data, with possible missing values.
My first thoughts are perhaps a sequence of tuples or records, but I was wondering if someone could kindly suggest something that has worked well in the real world.
EDIT:
A few extra points for clarification:
The common operations that I'm likely to require are:
Time based lookup - i.e. find the most recent data point at a given time
Time based joins
Appends
(Updates and deletes are going to be rare. )
I should make it clear I'm exploring using F# primarily as an interactive tool for research, with the ability to compile as a (really big) added bonus.
ANOTHER EDIT:
I should also have mentioned, my role/use of F# and this data is purely within research not development. The intention being that once we understand the data (and what we want to do with it) better then we can later specify tools that our developers would build. Such as data warehouses etc. at which we'd start using their data structures etc.
Although, I am concerned that our models are computationally intensive, use a lot of memory and can't always be coded in a recursive manner. So we many end up having to query out large chunks anyway.
I should also say that I've always used Matlab or R for these sorts of tasks before but I'm now interested in F# as it offers that interactive, high level flexibility for Research but the same code can be used in production.
My apologies for not giving this context information at the start (It's my first question), I can see now that it helps people form their answers.
My thanks again to everyone that's taken the time to help me.
It really sounds like your data should be stored and queried in a relational database (where is it currently stored?: loading millions of records with several fields into memory must be an expensive operation, and could leave you with stale data and difficulty persisting changes). And then you could use the F# LINQ to SQL implementation (which I believe you can find in the Power Pack) to have F# expressions translated to SQL expressions.
Here's a link from Don Syme about LINQ Support in F# Power Pack: http://blogs.msdn.com/b/dsyme/archive/2009/10/23/a-quick-refresh-on-query-support-in-the-f-power-pack.aspx
The best choice of data structure depends upon what operations you want to do on it.
The simplest would be an array of structs. This has the advantages of fast random lookup, good space efficiency for an uncompressed representation and good locality. If there is sharing between substructures (like the strings) then intern them to make sure they get shared.
Alternatives might be a seq that is loaded from disk on-demand, a singly-linked list that allows you to prepend elements quickly or a balanced binary trees that allows operations like insertion at random locations efficiently.
I have heard of the concept of minimizing code and maximizing data, and was wondering what advice other people can give me on how/why I should do this when building my own systems?
Typically data-driven code is easier to read and maintain. I know I've seen cases where data-driven has been taken to the extreme and winds up very unusable (I'm thinking of some SAP deployments I've used), but coding your own "Domain Specific Languages" to help you build your software is typically a huge time saver.
The pragmatic programmers remain in my mind the most vivid advocates of writing little languages that I have read. Little state machines that run little input languages can get a lot accomplished with very little space, and make it easy to make modifications.
A specific example: consider a progressive income tax system, with tax brackets at $1,000, $10,000, and $100,000 USD. Income below $1,000 is untaxed. Income between $1,000 and $9,999 is taxed at 10%. Income between $10,000 and $99,999 is taxed at 20%. And income above $100,000 is taxed at 30%. If you were write this all out in code, it'd look about as you suspect:
total_tax_burden(income) {
if (income < 1000)
return 0
if (income < 10000)
return .1 * (income - 1000)
if (income < 100000)
return 999.9 + .2 * (income - 10000)
return 18999.7 + .3 * (income - 100000)
}
Adding new tax brackets, changing the existing brackets, or changing the tax burden in the brackets, would all require modifying the code and recompiling.
But if it were data-driven, you could store this table in a configuration file:
1000:0
10000:10
100000:20
inf:30
Write a little tool to parse this table and do the lookups (not very difficult, right?) and now anyone can easily maintain the tax rate tables. If congress decides that 1000 brackets would be better, anyone could make the tables line up with the IRS tables, and be done with it, no code recompiling necessary. The same generic code could be used for one bracket or hundreds of brackets.
And now for something that is a little less obvious: testing. The AppArmor project has hundreds of tests for what system calls should do when various profiles are loaded. One sample test looks like this:
#! /bin/bash
# $Id$
# Copyright (C) 2002-2007 Novell/SUSE
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation, version 2 of the
# License.
#=NAME open
#=DESCRIPTION
# Verify that the open syscall is correctly managed for confined profiles.
#=END
pwd=`dirname $0`
pwd=`cd $pwd ; /bin/pwd`
bin=$pwd
. $bin/prologue.inc
file=$tmpdir/file
okperm=rw
badperm1=r
badperm2=w
# PASS UNCONFINED
runchecktest "OPEN unconfined RW (create) " pass $file
# PASS TEST (the file shouldn't exist, so open should create it
rm -f ${file}
genprofile $file:$okperm
runchecktest "OPEN RW (create) " pass $file
# PASS TEST
genprofile $file:$okperm
runchecktest "OPEN RW" pass $file
# FAILURE TEST (1)
genprofile $file:$badperm1
runchecktest "OPEN R" fail $file
# FAILURE TEST (2)
genprofile $file:$badperm2
runchecktest "OPEN W" fail $file
# FAILURE TEST (3)
genprofile $file:$badperm1 cap:dac_override
runchecktest "OPEN R+dac_override" fail $file
# FAILURE TEST (4)
# This is testing for bug: https://bugs.wirex.com/show_bug.cgi?id=2885
# When we open O_CREAT|O_RDWR, we are (were?) allowing only write access
# to be required.
rm -f ${file}
genprofile $file:$badperm2
runchecktest "OPEN W (create)" fail $file
It relies on some helper functions to generate and load profiles, test the results of the functions, and report back to users. It is far easier to extend these little test scripts than it is to write this sort of functionality without a little language. Yes, these are shell scripts, but they are so far removed from actual shell scripts ;) that they are practically data.
I hope this helps motivate data-driven programming; I'm afraid I'm not as eloquent as others who have written about it, and I certainly haven't gotten good at it, but I try.
In modern software the line between code and data can become awfully thin and blurry, and it is not always easy to tell the two apart. After all, as far as the computer is concerned, everything is data, unless it is determined by existing code - normally the OS - to be otherwise. Even programs have to be loaded into memory as data, before the CPU can execute them.
For example, imagine an algorithm that computes the cost of an order, where larger orders get lower prices per item. It is part of a larger software system in a store, written in C.
This algorithm is written in C and reads a file that contains an input table provided by the management with the various per-item prices and the corresponding order size thresholds. Most people would argue that a file with a simple input table is, of course, data.
Now, imagine that the store changes its policy to some sort of asymptotic function, rather than pre-selected thresholds, so that it can accommodate insanely large orders. They might also want to factor in exchange rates and inflation - or whatever else the management people come up with.
The store hires a competent programmer and she embeds a nice mathematical expression parser in the original C code. The input file now contains an expression with global variables, functions such as log() and tan(), as well as some simple stuff like the Planck constant and the rate of carbon-14 degradation.
cost = (base * ordered * exchange * ... + ... / ...)^13
Most people would still argue that the expression, even if not as simple as a table, is in fact data. After all it is probably provided as-is by the management.
The store receives a large amount of complaints from clients that became brain-dead trying to estimate their expenses and from the accounting people about the large amount of loose change. The store decides to go back to the table for small orders and use a Fibonacci sequence for larger orders.
The programmer gets tired of modifying and recompiling the C code, so she embeds a Python interpretter instead. The input file now contains a Python function that polls a roomfull of Fib(n) monkeys for the cost of large orders.
Question: Is this input file data?
From a strict technical point, there is nothing different. Both the table and the expression needed to be parsed before usage. The mathematical expression parser probably supported branching and functions - it might not have been Turing-complete, but it still used a language of its own (e.g. MathML).
Yet now many people would argue that the input file just became code.
So what is the distinguishing feature that turns the input format from data into code?
Modifiability: Having to recompile the whole system to effect a change is a very good indication of a code-centric system. Yet I can easily imagine (well, more like I have actually seen) software that has been designed incompetently enough to have e.g. an input table built-in at compile time. And let's not forget that many applications still have icons - that most people would deem data - built in their executables.
Input format: This is the - in my opinion, naively - most common factor that people consider: "If it is in a programming language then it is code". Fine, C is code - you have to compile it after all. I would also agree that Python is also code - it is a full blown language. So why isn't XML/XSL code? XSL is a quite complex language in its own right - hence the L in its name.
In my opinion, none of these two criteria is the actual distinguishing feature. I think that people should consider something else:
Maintainability: In short, if the user of the system has to hire a third party to make the expertise needed to modify the behaviour of the system available, then the system should be considered code-centric to a degree.
This, of course, means that whether a system is data-driven or not should be considered at least in relation to the target audience - if not in relation to the client on a case-by-case basis.
It also means that the distinction can be impacted by the available toolset. The UML specification is a nightmare to go through, but these days we have all those graphical UML editors to help us. If there was some kind of third-party high-level AI tool that parses natural language and produces XML/Python/whatever, then the system becomes data-driven even for far more complex input.
A small store probably does not have the expertise or the resources to hire a third party. So, something that allows the workers to modify its behaviour with the knowledge that one would get in an average management course - mathematics, charts etc - could be considered sufficiently data-driven for this audience.
On the other hand, a multi-billion international corporation usually has in its payroll a bunch of IT specialists and Web designers. Therefore, XML/XSL, Javascript, or even Python and PHP are probably easy enough for it to handle. It also has complex enough requirements that something simpler might just not cut it.
I believe that when designing a software system, one should strive to achieve that fine balance in the used input formats where the target audience can do what they need to, without having to frequently call on third parties.
It should be noted that outsourcing blurs the lines even more. There are quite a few issues, for which the current technology simply does not allow the solution to be approachable by the layman. In that case the target audience of the solution should probably be considered to be the third party to which the operation would be outsourced to.
That third party can be expected to employ a fair number of experts.
One of five maxims under the Unix Philosophy, as presented by Rob Pike, is this:
Data dominates. If you have chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
It is often shortened to, "write stupid code that uses smart data."
Other answers have already dug into how you can often code complex behavior with simple code that just reacts to the pattern of its particular input. You can think of the data as a domain-specific language, and of your code as an interpreter (maybe a trivial one).
Given lots of data you can go further: the statistics can power decisions. Peter Norvig wrote a great chapter illustrating this theme in Beautiful Data, with text, code, and data all available online. (Disclosure: I'm thanked in the acknowledgements.) On pp. 238-239:
How does the data-driven approach compare to a more traditional software development
process wherein the programmer codes explicit rules? ... Clearly, the handwritten rules are difficult to develop and maintain. The big
advantage of the data-driven method is that so much knowledge is encoded in the data,
and new knowledge can be added just by collecting more data. But another advantage is
that, while the data can be massive, the code is succinct—about 50 lines for correct, compared to over 1,500 for ht://Dig’s spelling code. ...
Another issue is portability. If we wanted a Latvian spelling-corrector, the English
metaphone rules would be of little use. To port the data-driven correct algorithm to another
language, all we need is a large corpus of Latvian; the code remains unchanged.
He shows this concretely with code in Python using a dataset collected at Google. Besides spelling correction, there's code to segment words and to decipher cryptograms -- in just a couple pages, again, where Grady Booch's book spent dozens without even finishing it.
"The Unreasonable Effectiveness of Data" develops the same theme more broadly, without all the nuts and bolts.
I've taken this approach in my work for another search company and I think it's still underexploited compared to table-driven/DSL programming, because most of us weren't swimming in data so much until the last decade or two.
In languages in which code can be treated as data it is a non-issue. You use what's clear, brief, and maintainable, leaning towards data, code, functional, OO, or procedural, as the solution requires.
In procedural, the distinction is marked, and we tend to think about data as something stored in an specific way, but even in procedural it is best to hide the data behind an API, or behind an object in OO.
A lookup(avalue) can be reimplemented in many different ways during its lifetime, as long as its starts as a function.
...All the time I desing programs for nonexisting machines and add: 'if we now had a machine comprising the primitives here assumed, then the job is done.'
... In actual practice, of course, this ideal machine will turn out not to exist, so our next task --structurally similar to the original one-- is to program the simulation of the "upper" machine... But this bunch of programs is written for a machine that in all probability will not exist, so our next job will be to simulate it in terms of programs for a next lower level machine, etc., until finally we have a program that can be executed by our hardware...
E. W. Dijkstra in Notes on Structured Programming, 1969, as quoted by John Allen, in Anatomy of Lisp, 1978.
When I think of this philosophy which I agree with quite a bit, the first thing that comes to mind is code efficiency.
When I'm making code I know for sure it isn't always anything close to perfect or even fully knowledgeable. Knowing enough to get close to maximum efficiency out of a machine when it is needed and good efficiency the rest of the time (perhaps trading off for better workflow) has allowed me to produce high quality finished products.
Coding in a data driven way, you end up using code for what code is for. To go and 'outsource' every variable to files would be foolishly extreme, the functionality of a program needs to be in the program and the content, settings and other factors can be managed by the program.
This also allows for much more dynamic applications and new features.
If you have even a simple form of database, you are able to apply the same functionality to many states. You may also do all manner of creative things like changing the context of what your program is doing based on file header data or perhaps directory, file name or extension, though not all data is necessarily stored on a filesystem.
Finally keeping your code in a state where it is simply handling data puts you in a state of mind where you are closer to envisioning what is actually going on. This also keeps the bulk out of your code, greatly reducing bloatware.
I believe it makes code more maintainable, more flexible and more efficient aaaand I like it.
Thank you to the others for your input on this as well! I found it very encouraging.
Hi i would like to know best encryption technique for text file encryption and ecryption.
My Scenario:
I have software having two type of users Administartor and Operators. Our requirement is to encrypt text file when Administrator enter data using GUI and save it. That encrypted file would be input for Operator and they just need to select it and use that file. Here file should be automatically decrypt data for further calculation when Operator select those files.
Please help me which encryption/ decryption technique should i use?
A golden rule when doing crypto is to understand that cryptography as a whole it is very difficult.
There are a multitude of different approaches/algorithms to choose from, and no single algorithm/method can be said to be the best one. It all depends on your needs and possibilities to affect application distribution etc.
An example of a potentially problematic situation is that in your scenario the decryption "key" needs to be distributed with the application(s) and might make it insecure. This is generally referred to as the "Key Distribution" problem.
A good place to start reading about crypto is http://en.wikipedia.org/wiki/Cryptography.
As for ready made stuff for Delphi there are a few good packages available:
DEC v5.2 - http://blog.digivendo.com/2008/11/delphi-encryption-compendium-dec-52-for-d2009-released/
DCPCrypt - http://www.cityinthesky.co.uk/cryptography.html
Torry's pages also has a long list of components:
http://www.torry.net/pages.php?id=519
http://www.torry.net/pages.php?id=312
I strongly recommend you use some of the existing implementations and not start to do your own, since creating a secure working crypto algo is very very difficult.
When moving an encryptet message from place/appliction to another, one of the problems you have to consider is where to store the encryption/decryption keys.
As i se your scenario, it seems like it is build in your applications. If so remember to use al sorts of tricks to hide it: Password strings should be split in several bits and onlys appended in a protected memoryspace, that has to be marked as non-pageable (else password could be seen in the pagefile).
The same rules for the content that is unencrypted (the text-file). It's best that it never is saved (even temporaly) unencrypted to disk. If it is saved, the overwrite the date with garbage after use, before deleting it.
Another approch (specialy if you already use compression components), is that the (text) file, can be compressed using a password.
Truthfully, there is no "best" technique. It basically depends on the sensitivity of the data you're trying to protect and the number of people who might access this data. What might be "best" for me might be pure overkill for your project.
In your case, you could use any dual-key encryption method. Or asymmetric key. Basically, the administrator has one key and the operator has the other. The administrator can then encrypt files, but he won't be able to decrypt them again, unless he has an operator key. The operator can decrypt the file and -if need be- encrypt a file that only an administrator can access. (Asymmetrical keys encrypt in both ways.)
There are several solutions that make use of these asymmetrical keys. The one that would be best is the one that you could add to your project in the easiest way while still offering enough protection for your needs.
Building your own asymmetrical key algorithm is possible too, if you're a real Math Wizard. The calculations are complex and involve extremely high prime numbers in most solutions. As K. Sandell said, find a good, existing solution that matches your needs in the best way.