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What is a dataflow programming language? Why use it? And are there any benefits to it?
In a control flow language, you have a stream of instructions which operate on external data. Conditional execution, jumps and procedure calls change the instruction stream to be executed. This could be seen as instructions flowing through data (for example, instructions operate on registers which are loaded with data by instructions - the data is static unless the instruction stream moves it). A control flow "if" statement jumps to the correct branch in the instruction stream, but the data does not get moved.
In a dataflow language, you have a stream of data which is passed from instruction to instruction to be processed. Conditional execution, jumps and procedure calls route the data to different instructions. This could be seen as data flowing through otherwise static instructions like how electrical signals flow through circuits or water flows through pipes. A dataflow "if" statement would route the data to the correct branch.
Some examples of dataflow features and languages:
Spreadsheets are essentially dataflow
Unix pipes
Futures and promises are dataflow or dataflow-like constructs found in many modern languages
The messaging in the actor model is dataflow
Some languages have dataflow features:
Oz has dataflow variables
Groovy has GPars
Clojure has clojure.contrib.dataflow, Reagi and Javelin
Textual languages
VHDL, Verilog and other hardware description languages are essentially dataflow
Chuck
Cunieform
Lustre, used in defence, aerospace and power plant industries
Ptolemy II
Nyquist
Visual Languages
LabVIEW (screenshot [source])
Max/MSP (screenshot [source])
Pure Data (screenshot [source])
Reaktor (screenshot [source])
SCADE (screenshot [source]), the graphical programming environment for Lustre
SynthMaker (screenshot [source]) and FlowStone
vvvv (screenshot [source])
Expecco (screenshot [source])
Shake (screenshot [source])
[BLOK] (screenshot [source])
Quartz Composer (screenshot [source])
AudioMulch (screenshot [source])
Products which embed a visual dataflow language:
Blender
Voreen (screenshot)
Unreal Engine's Kismet (screenshot)
ANKHOR FlowSheet (screenshot)
Dynamo for Autodesk Revit (screenshot)
LiveBlox (screenshot)
Dataflow programming languages are ones that focus on the state of the program and cause operations to occur according to any change in the state. Dataflow programming languages are inherently parallel, because the operations rely on inputs that when met will cause the operation to execute. This means unlike a normal program where one operation is followed by the next operation, in a dataflow program operations will execute as long as the inputs are met and thus there is no set order.
Often dataflow programming languages use a large hashtable where the keys are the data of the program and the values of the table are pointers to the operations of the program. This makes multicore programs easier to create in a dataflow programming language, since each core would only need the hashtable to work.
A common example of a dataflow programming language is a spread sheet program which has columns of data that are affected by other columns of data. Should the data in one column change, other data in the other columns will probably change with it. Although the spread sheet program is the most common example of a dataflow programming language, most of them tend to be graphical languages.
One kind of dataflow programming is reactive programming. When this style of programming is used in a functional language, it's called functional reactive programming. An example of a functional reactive programming language for the web is Flapjax.
Also, anic is a dataflow language recently discussed on Hacker News.
Another example is Martlet from Oxford.
Dataflow programming languages propose to isolate some local behaviors in so called "actors", that are supposed to run in parallel and exchange data through point-to-point channels. There is no notion of central memory (both for code and data) unlike the Von Neumann model of computers.
These actors consume data tokens on their inputs and produce new data on their outputs.
This definition does not impose the means to run this in practice. However, the production/consumption of data needs to be analyzed with care: for example, if an actor B does not consume at the same speed as the actor A that produce the data, then a potentially unbounded memory (FIFO) is required between them. Many other problems can arise like deadlocks.
In many cases, this analysis will fail because the interleaving of the internal behaviors is intractable (beyond reach of today formal methods).
Despite this, dataflow programming languages remain attractive in many domains:
for instance to define reference models for video encoding : a pure C program won't do the job because it makes the assumption that everything run as a sequence of operations, which is not true in computers (pipeline, VLIW, mutlicores, and VLSI). Maybe you could have a look at this: recent PhD thesis. CAL dataflow language is proposed as a unifying language for next generation video encoders/decoders reference.
Mission critical where safety is required: if you add some strong assumptions on the production/consumption of data, then you get a language with strong potential in terms of code generation, proofs, etc. (see synchronous languages)
Excel (and other spreadsheets) are essentially dataflow languages. Dataflow languages are a lot like functional programming languages, except that the values at the leaves of the whole program graph are not values at all, but variables (or value streams), so that when they change, the changes ripple and flow up the graph.
Mozart has support for dataflow-like synchronization, and it does have some commercial applications. You could also argue that make is a dataflow programming language.
It is actually quite an old concept - in the 1970s, there was even a language + machine built for efficient dataflow programming and execution (Manchester Dataflow Machine).
The great thing about it is its duality to lazy functional languages like Haskell. Therefore, if your processing steps are pure functional, and given you have enough processing units to evaluate them and pass results around, you get maximum parallelity for free - automatically and without any programming effort!
Many ETL tools are also in this realm. The dataflow tasks in MS SSIS are a good example. Graphical tool in this case.
There are certain domains where dataflow programming just makes a lot more sense. Realtime media is one example, and two widely used graphical dataflow programming environments, Pure Data and Max/MSP, are both focused on realtime media programming. I suppose their visual nature also maps nicely to the dataflow programming.
You could try Cameleon: www.shinoe.org/cameleon which seems to be simple to use. It's a graphical language for functional programming which has a data(work)-flow approach.
It's written in C++, but it can call any type of local or distant programs written in any programming language.
It has a multi-scale approach and seems to be Turing complete (this is a Petri net extension).
Related
I am in the situation where I have a part of the codebase written in MATLAB and another part in Lua (which is used for scripting of a 3rd party program). As of now the exchange of data between them is makeshift, using the file I/O system. This evolved to be a substantial part of the code, even though that wasn't really planned.
The program is structured in a way, that some Lua scripts are run, then some MATLAB evaluation is done based on which some more Lua is run and so on. It handles simulations and evaluations (scientific code) and creates new simulations based on that. It handles thousands of files and sims.
To streamline the process I started looking into possibilities to change the data I/O and make easy calls from one to another.
I wanted to hear some opinions on how to solve the problem, the optimal solution would be one where I could call everything from MATLAB or Lua, and organize the large datasets in a more consistent and accessible way.
Solutions:
Use the Lua C API to create bindings for the Lua modules, and to add this to MATLAB as a C-Library. In this way I should hopefully be able to achieve my goals and reduce the system complexity.
Some smarter data format for the exchange of datasets (HDF?), and some functions which read the needed workspace variables. This way the parts of the program remain independent, but the data exchange gets solved.
Create wrappers for Lua/MATLAB functions, so they can be called more easily. Data exchange could be done through the return parameters of the functions.
Suggestions?
I would suggest 1 or if you aren't adverse to spending a lot of money, use MATLAB coder to generate C functions from the MATLAB side of the analysis, compile the generated code as a shared library, import the library with the LuaJIT FFI, and run everything from Lua. With this solution you would not have to change any of the MATLAB code and not much of the Lua code thanks to the LuaJIT's semantics regarding array indexing. Solution 1 is free, but it is not as efficient because of the constant marshaling between the two languages' data structures. It would also be a lot of work writing the interface. But either solution would be more efficient than file I/O.
As a easy performance boost, have you tried keeping the files in memory using a RAMdisk or tmpfs?
I'm a Linux programmer.
I used to write code in order to get things done: java perl php c.
I need to start working with DATA STAGE.
All I see is that DATA STAGE is working on table/csv style data and doing it line by line.
I want to know if DATA STAGE can work on file that are not table/csv like. can it load
data into data structures and run function on them, or is it limited to working
only on one line at a time.
thank you for any information that you can give on the capabilities of DATA SATGE
IBM (formerly Ascential) DataStage is an ETL platform that, indeed, works on data sets by applying various transformations.
This does not necessarily mean that you are constrained on applying only single line transformations (you can also aggregate, join, split etc). Also, DataStage has it's own programming language - BASIC - that allows you to modify the design of your jobs as needed.
Lastly, you are still free to call external scripts from within DataStage (either using the DSExecute function, Before Job property, After Job property or the Command stage).
Please check the IBM Information Center for a comprehensive documentation on BASIC Programming.
You could also check the DSXchange forums for DataStage specific topics.
Yes it can, as Razvan said you can join, aggregate, split. It can uses loops and external scripts, it can also handles XML.
My advice for you is that if you have large quantities of data you're gonna have to work on then datastage is your friend, else if the data that you're going to have to load is not very big then it's going to be easier to use JAVA, c, or any programming language that you know.
You can all times of functions , conversions , manipulate the data. mainly Datastage is used for ease of use when you handling humongous data from datamart /datawarehouse.
The main process of datastage would be ETL - Extraction Transformation Loading.
If a programmer uses 100 lines of code to connect to some database here we can do it with one click.
Anything can be done here even c , c++ coding in a rountine activity.
If you are talking about hierarchical files, like XML or JSON, the answer is yes.
If you are talking about complex files, such as are produced by COBOL, the answer is yes.
All using in-built functionality (e.g. Hierarchical Data stage, Complex Flat File stage). Review the DataStage palette to find other examples.
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I am not a professional programmer (my area is medical research), but I am quite capable in C/C++, and various scripting languages. A while back I got intrigued by Lisp, but I never got the time to seriously learn it. After a brief exposure to R I decided to invest more time in a functional programming language.
I would like the practicality of a JVM language and thus narrowed to Clojure and Scala. From what I understand, both can use already existing Java libraries and given at performance-critical code can be delegated to Java, have the potential to perform relatively equally well.
How do these languages compare in the application space I need them for?
Are There any real-life projects in bioinformatics using either?
Already existing code would be a serious plus, as would be good documentation and a fairly gentle learning curve. Also, how does the concurrency model of the two compare with each other?
Any significant advantages/disadvantages any one has?
I can personally vouch for Clojure as a great tool for this kind of work. (I believe Scala would be great too, I just have less experience with it).
My personal research is in the field of predictive modelling / machine learning and is very computationally intensive - so I think it has many parallels with bioinformatics or biostatistics.
My personal approach / setup includes:
Incanter used primarily as a data visualisation tool. Great for producing quick visualisations which are usually just 1-liners at the REPL. There are also lots of statistical and numerical processing tools which I believe use the Colt library under the hood. I'm not an expert in R but I understand that Incanter is roughly "R translated to Clojure/Lisp".
Exploiting quite a few Java libraries as needed. Some of these are my own, for example algorithms that I have written in Java in order to get the best possible fine-tuned performance out of the JVM. But you could equally easily use any of the other great Java libraries available, as calling Java from Clojure is very simple (.methodName object param1 param2)
Quite a lot of higher order functions to automate my workflow. For example I have a higher order function that will run an optimisation algorithm of any kind in a loop for a specified amount of time and then produce an Incanter graph of the improvement on each iteration. Not rocket science, but really easy to code up in a few lines of Clojure.
Never really having to worry about performance. You can make Clojure go pretty fast if you want to (e.g. with type hints, primitive arithmetic support etc.) but normally it's irrelevant as you're going to spend 99%+ of your cycles in well-optimised library code anyway. Hence a bit of overhead in the "glue" code is negligible - I feel I gain much more in terms of personal productivity by having a dynamic, high-level, functional language to work in.
Major use of Clojure's concurrency features - this has to be one of Clojure's strongest features. I tend to use the STM to code concurrent processes with transactions that can't interfere with each other, then kick off long-running calculations in a future so that I can get on with other tasks and wait for notification of the result.
A slowly growing collection of macros to "extend the language" when needed. I actually use macros less than I thought I would (higher order functions are often a better choice). But when you need them they are invaluable - this is where you really appreciate the value of a homoiconic language. Since they effectively allow you to add new syntax to the language itself, they are very powerful when used correctly to build the DSL that you need.
In short - I don't think you can go wrong with Clojure as a researcher.
The one thing I probably wouldn't use it for (yet) is actually writing a new numerical library - this would probably be better done in Scala or pure Java as you would probably want to adopt a more imperative / OOP style.
I am not sure about bioinformatics and biostatistics per se, but I do scientific data analysis frequently and I appreciate that Scala allows me to write as-fast-as-Java code with relative ease. I believe that it is often possible in Clojure now, but I haven't seen the benchmarks to back that up. For the time being, I think the prudent thing to assume is that they do not perform equally well. See, for example, the Computer Languages Benchmark Game, where Scala is faster than Clojure in every single test. (Ignore the horrible "pidigits" result for Clojure--Scala (and Java) are calling the GMP library written in C, which Clojure could do but because of a technical detail requiring a different wrapping for the library, isn't presently allowed in the game). Looking at multicore comparisons doesn't improve Clojure's showing, and note that the Clojure code is no shorter for these sorts of lowish-level algorithmic tasks.
Clojure is ahead for the time being with parallel collections, though the upcoming 2.9 release of Scala should make up much of the difference. Neither has a gentle learning curve when coming from C++; Scala is maybe a little easier given that the syntax outwardly looks a little more familiar. I believe there are good materials for learning each of them.
Edit: P.S. You can call R from Java (and therefore from either Clojure or Scala) using rJava (specifically the JRI interface). Edit to edit: and, these days, rScala.
Edit #2: Scala was faster than Clojure in everything at the time of writing; as of this edit, Clojure's a little ahead in one (at the cost of a huge amount of code)--but anyway, the overall point stands. (And the Scala implementation on that one test could be sped up.)
If you like R, give Incanter a try! It's R for Clojure.
Scala's is geared toward being syntactically easy for people coming from Java, which was intended to be syntactically easy for people coming from C though with two levels of indirection like this the advantage may be lost.
Clojure is getting a lot of traction in the Big Data space and maps very well onto Hadoop jobs for Huge Data. I think this would be a big advantage in the bioinformatics world.
Really, these things are largely personal taste so try both and see that makes you happy :)
If you are looking to get a feel for Clojure without a lot of "intellectual overhead" may I suggest using leiningen to get a test project started quickly?
To build on Rex's answer I would like to add some Scala libraries/products that may be of interest to you:
ADAM
Spark (sparkseq, 2)
Scala Map Reduce (SMR): http://scala-blogs.org/2008/09/scalable-language-and-scalable.html
SHadoop: http://jonhnny-weslley.blogspot.com/2008/05/shadoop.html
ScalaLab: MATLAB-like scientific computing in Scala
ScalaNLP: Collection of libraries for natural language processing (NLP), machine learning, and statistics.
Factorie: Toolkit for deployable probabilistic modeling
Gridgain: Compute cluster for Scala and Java
BioScala: Bioinformatics for the Scala programming language
I don't know Scala, so I can't offer a comparison, but I am actively using Clojure in bioinformatics projects.
The Java integration is excellent, and I have had no problem making use of the BioJava libraries.
Where Clojure's concurrency model shines is in the immutable default data types and functional programming with the seq abstraction.
In my bioinformatic work I very often find myself with a lot input data (say gene sequences) which need to be subjected to the same analysis. Once I have my analysis function I can map it over a sequence of inputs (with the results lazily generated). I have gotten full utilization of a large 48-core server simply by changing that map to a pmap.
Large scale parallelization with a single character change is hard to beat!
Of course pmap isn't a magic bullet and only helps when the analysis function computationally dominates, but the fact that map and pmap can just be plugged in and out shows the elegance and simplicity enabled by Clojure's design.
I am only passingly familiar with Scala, so the best I can do is evangelize a bit for Clojure. It's a great language, but take all this advice with a grain of salt as it's coming from an enthusiast.
If you are looking for concurrency, Clojure is fantastic both for ease of programming and for performance. The immutable data structures mean that it's trivial to work with a coherent snapshot of the world without any manual and error-prone locking; the STM makes it fairly simple to change data in a thread-sensitive way without breaking anyone else's snapshots.
My understanding is that Scala has a lot of the nice functional tools that Clojure does, but Clojure will always win syntactically by virtue of being a Lisp. If you're looking to do some specialized bioinformatics stuff, Clojure is able to hide the bits of Lisp that you don't want, and raise your own constructs to the same level as the built-in language constructs. I can't find the reference right now, but there's some well-known quote about Lisp that goes like:
Lisp is not the perfect language for any program. But it is the perfect language for building the perfect language for every program.
That's horribly paraphrased, but in my experience it has been true. It looks like you'll want a fairly specialized set of tools, and no language will make those feel as natural as a Lisp.
You have to ask yourself how important functional programming is for you. You know C++ so you probably know OO. I would say it's easier to do FP in Clojure (because you can't really drop back to OO-style) in Scala you will probebly end up dropping FP and do more OO style.
I can't really say anything about your application space.
Since you mentioned R, there is an R-like Clojure library for statistics called Incanter. I don't know about other existing projects in your application space.
There is a lot of information about both languages, so that should not be a problem. The learning curve is kind of steep with both languages. Clojure is a much smaller language and since you already know some lisp it should not be to hard to learn the important stuff. Scala has a type system that will be hard to pick up especially since your main experience is with C/C++.
Both languages have great concurrency models and you will probably be happy with both.
I have some experience in Scala and only little knowledge in Clojure, but I programmed Lisp many years ago.
Lisp is a beautiful language, but it never made it to the world, because it was too limited. I believe you need a statically-typed language to develop robust systems. The type system in Scala is not difficult to master to benefit from it. If you want to do very advanced things with it to make your libraries idiot-proof, you can, but then you will need to study the type system a little more.
Scala favours immutable types, but you can use mutables without any problem, which you sometimes do need. Concurrency in Scala is very well implemented and frameworks like akka extend and enhance these possibilities.
Scala stands a better chance to become a mainstream language since it's a fuller language. I'm afraid that Clojure is too much like Lisp (but reimplemented on the JVM). I liked Lisp a lot, but it had too many disadvantages for real-life programs. With Scala I think we have the best of both worlds (OO and functional) in a clean marriage. On top of that, Scala seems to really catch on in the market.
We have been working on some experimental code in the Rudolf/BioClojure project on GitHub. Also, look at Jan Aert's BioClojure project which is more structured.
Additionally, there is a BioCaml project in the works...
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How do I learn PLC programming? Would it differ greatly for different brands of PLCs? Is ladder programming the same as PLC programming?
I did a lot of PLC programming, and now do quite a bit of .NET programming. It's very dangerous to make the switch either way, because a lot of the skills that you think should be transferrable (patterns and such) lead you very far astray.
The biggest difference that I tell people is that PC program code should be written as if other programmers are the audience, but PLC programs (ladder logic) must be written as if maintenance people are the audience. Maintainance in most facilities (particularly manufacturing) frequently connect directly to PLCs and in online mode they can watch the code execute graphically to figure out what's wrong.
For instance, if an output isn't turning on, they'll type the output electrical device ID into the find function of the programming software, find that output coil, and start tracing back from there looking for issues. One of the frequent mistakes that some PLC programmers make is to "map" their I/O into a structure (in PLCs, these are called user-defined types), and they use a copy instruction to move all the inputs or outputs over to the structure at once. Makes sense from a PC programming perspective, but it makes the maintenance person want to kill you. Typically the programming software provides a cross reference feature where they can specify that output coil, and it will tell them everywhere in the program that it's used. If you use a copy instruction to move 10 words of I/O into a 10 word data structure, he's got to sit there and count bits to figure out which bit in the source of the copy maps to which bit on the destination side of the copy. True, comments can help, but there's a problem with that too... PLCs store the whole program and allow you to upload the program from it in an emergency if you need to troubleshoot and you don't have a copy of the original program. The problem is that for space reasons, the PLC doesn't store the comments. So if the line is down, it's costing $5000 per minute in downtime, and a guy runs out there with a laptop, he might have to do a quick upload without comments and try to troubleshoot it. Having those copy instructions in there, wasting 10 minutes of his time, just cost the company $50,000 in downtime. These are the things you have to be aware of when writing PLC programs.
Some other tips: some PLCs have support for FOR loops. Never use them. For the same reason above, they make the code very difficult to troubleshoot for a maintenance person. This is because if you have one piece of code in the PLC that gets scanned more than once per scan (like the contents of a loop), then when you go into online debugging mode, the software can't show you the values for each of 10 loops that executed this scan, so you really have no idea what value you're looking at. Then you have to write all this tricky code to pull the loop values for a specific loop index out into some other tags (variables) that you can monitor. That's just one more impedance to fixing the problem in an emergency. Using a subroutine more than once per scan suffers from the same problem.
Indirect addressing (what we would call Arrays) are very difficult for maintenance people to understand. It's generally OK to use them when you're dealing with recipe management (storing and retrieving values for how to build your part) but you should try to stay away from it in the control part of the program.
In PC programming, of course we seek to re-use code as much as possible. However, in PLCs and control systems, downtime is extremely expensive, and hardware is expensive. Memory is cheap, and actually PLC programmers are cheap. Therefore, it's expected that if you have 10 identical things on your machine (like conveyor drives or something) that you will have 10 different files (subroutines), one for each drive, and each drive will have its own variables associated with them: e.g. Drive1_Run, Drive2_Run, Drive3_Run, etc. This is going to feel very "wrong" to you when you come from a PC programming background, but this is all because of the points I've made above. When you're in a downtime situation, and someone says that Drive 3 isn't working, you crack open the laptop, go to the file for Drive 3 and you look at the Run output rung. You start troubleshooting from there, while the program is executing. There's no breakpoints (the program never stops).
Good luck on your endeavors. I wrote up some more insights from my years of programming PLCs, if you want to check them out.
You can learn PLC programming from various sources on the internet, one of which is this(wikibooks) or this
The program that you write will be pretty much the same across different brands of PLCs for LLDs (Ladder Logic Diagrams) unless you use PLC specific functions. But there will be much more differences if you use some language like IL (Instruction List). But once you have written the program, the format of storage and execution differs widely across brands
Ladder logic is one of the 5 programming languages for PLC, the others being FBD (Function block diagram), ST (Structured text, similar to the Pascal programming language), IL (Instruction list, similar to assembly language) and SFC (Sequential function chart). These are just various representations of the programming language, various flavours if you will. But usually, a given brand supports only one of these. In USA, LLDs are widely used, while in Europe, ILs are more popular.
Ladder, often call LD is one of several language styles defined in ISO 61131 automation programming standard. Others are SFC (sequential flow chart), FBD (functional block diagram), ST (structured text), and IL (instruction list). IL is similar to assembler and very few people use it. ST is a text based programming much like early versions of BASIC. It is not often used either. LD is designed to resemble relay contacts off an electrical control panel (which many PLC replaced). FBD looks more like a circuit diagram. SFC is basically a flow chart.
Some PLC support all, other only some, or even one. While LD is the most common, FBD and SFC are gain popularity.
Different brands do use slightly different programming languages. They are usually similar enough that once you understand one brand, you can work with any of them, but you cannot directly take code from one PLC and using on another brand.
The answers given so far are pretty on target. One thing I found that PLCs have a split personality when it comes to their langauges and setup. Their core design is to give the electrical guys a flexible means of setting up control logic for their overall design. PLCs are basically a bunch of input and a bunch of outputs and how they are connected is controlled by the software you load into the device.
One of the emphasis of the languages that are used for PLCs is that they are accessible to people coming from an electrical background. So the idioms and structures seem counter intuitive for a person used to high level languages or even assembly languages. Ladder Logic for example is very accessible for electrical folks.
However in recent years PLCs have been supporting a multitude of languages for maximum flexibility. However in my opinion the handful of PLCs I worked are very lacking in terms of being a programming environment. Simple things like assigning variable names to memory location are often not designed into the language being used. The ones that are easy to work are often not the most cost effective for the job.
Despite these handicaps they are excellent for simplifying complex electrical systems. If you are working with others on a project, you will find that your knowledge of programming will help the project solve thorny programs. I was able to take a 100 rung ladder logic program and rewrite it into a third of the rungs. Once I was able to learn the ladder logic language I was able implement various optimizations that reduced the complexity of the program.
One tip is that you will need to learn about latching. Sometimes you will need to store or hold some output and unless you have a latch it the result will disappear the next cycle. Once you understand the issue it become clear but at first it was a great source of frustration for me.
PLC programming should be viewed as implementation activity of PLC software engineering output, unless you are using PLC as purely part of alternative components to mechanical or electrical solutions.
With this as basis, PLC programming environment is typically IEC61131 driven, gauranteed cycle time, "pre-emptive" realtime, no need to handle realtime OS related issues, continuous code scanning, non-program-pointer, different concept from typical computer task spawning kind of multi-tasking. Code execution is naturally atomic, no need to use monitors between tasks.
Each of the languages has its closeness to how conceivable is your code to the logic model you want to implement.
Ladder has its basic concept on electrical power flow interlocking style. Code resolution within single network is either horizontal or vertical scanning (your can find resource on this topic from manufacturer or other sites). If your code has single scan resolution nature and is within one network, some unconceivable behavior can be due to scanning type (important to remember that ladder is only emulation of electrical circuit, it is still sequential in execution).
FBD or function block diagram was electronic signal flow but today can be data flow depending on type of PLC. FBD shows clearer execution sequence quite similar to horizontal scanning ladder in scanning sequence. Today, FBD is typically used as container for object function blocks, although dependency implementation and visual similarity to process model is dependent on PLC type.
Literal is very similar to BASIC, but syntax only; execution is still scan-through. Literal language is good for mathematical calculation. For high level implementation, methods or derivation of attributes within object can be easier using Literal. State machine programming using English-like state representation or constants makes program very readable.
Statement list looks similar to assembly mnemonics but again execution is still scan-through and not program pointer. It is strong in bit operation and parenthesis-styled discrete logics. It can be a very efficient language to use with proper structuring and commenting.
SFC or sequential flow chart is a complementary language for sequence implementation. SFC has inherent rules on action block activation, state transitions, parellel sequence activation and merging. However, complex exception branching or concurrent action management can make implementation complicated and flow chart difficult to read.
PLC system management on IO handling, communication, hot-standby is hardware configuration effort, and is product dependent. Generally, can be treated separately from software engineering. However, data related to PLC system management are of "located" (independent data addressing area) type, good data modeling approach in software engineering can help in manageability of system data.
The Online PLC Simulator may be useful.
You can use Structured Text (ST) which consists of a series of instructions which, as determined in high level languages, ("IF..THEN..ELSE") or in loops (WHILE..DO) can be executed.
I find it better than Ladder as it is close to standard programming language.
I had a little of PLC programming on University. It seemed to me, to be a one level lower than assembly, but device we were using wasn't the newest one.
I belive you need to have a PLC driver, but I would first look for simulators and read more about it before buying.
Allen-Bradley has a free dos based software PLC, specifically for training. You can probably find it if you go to their site, or Google it. It's used to teach PLC programming in schools.
For a beginner trying to learn ladder logic, the best way is to attend free online training at http://plcs.net
PLC is the term used for the devices that use ladder logic. The devices that are programmed in more typical programming languages are generally called microcontrollers. However, there are some of us that on occasion lump them all under the PLC name. :-) Not sure how much ladder logic varies, but microcontroller code can vary significantly.
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It seems that there has been a recent rising interest in STM (software transactional memory) frameworks and language extensions. Clojure in particular has an excellent implementation which uses MVCC (multi-version concurrency control) rather than a rolling commit log. GHC Haskell also has an extremely elegant STM monad which also allows transaction composition. Finally, so as to toot my own horn just a bit, I've recently implemented an STM framework for Scala which statically enforces reference restrictions.
All of these are interesting experiments, but they seem to be confined to that sphere alone (experimentation). So my question is: have any of you seen or used STM in the real world? If so, why? What sort of benefits did it bring? What about performance? (there seems to be a great deal of conflicting information on this point) Would you use STM again or would you prefer to use some other concurrency abstraction like actors?
I participated in the hobbyist development of the BitTorrent client in Haskell (named conjure). It uses STM quite heavily to coordinate different threads (1 per peer + 1 for storage management + 1 for overall management).
Benefits: less locks, readable code.
Speed was not an issue, at least not due to STM usage.
Hope this helps
We use it pretty routinely for high concurrency apps at Galois (in Haskell). It works, its used widely in the Haskell world, and it doesn't deadlock (though of course you can have too much contention). Sometimes we rewrite things to use MVars, if we've got the design right -- as they're faster.
Just use it. It's no big deal. As far as I'm concerned, STM in Haskell is "solved". There's no further work to do. So we use it.
The article "Software Transactional Memory: Why is it Only a Research Toy?" (Călin Caşcaval et al., Communications of the ACM, Nov. 2008), fails to look at the Haskell implementation, which is a really big omission. The problem for STM, as the article points out, is that implementations must chose between either making all variable accesses transactional unless the compiler can prove them safe (which kills performance) or letting the programmer indicate which ones are to be transactional (which kills simplicity and reliability). However the Haskell implementation uses the purity of Haskell to avoid the need to make most variable uses transactional, while the type system provides a simple model together with effective enforcement for the transactional mutation operations. Thus a Haskell program can use STM for those variables that are truly shared between threads whilst guaranteeing that non-transactional memory use is kept safe.
We, factis research GmbH, are using Haskell STM with GHC in production. Our server receives a stream of messages about new and modified "objects" from a clincal "data server", it transforms this event stream on the fly (by generating new objects, modifying objects, aggregating things, etc) and calculates which of these new objects should be synchronized to connected iPads. It also receives form inputs from iPads which are processed, merged with the "main stream" and also synchronized to the other iPads. We're using STM for all channels and mutable data structures that need to be shared between threads. Threads are very lightweight in Haskell so we can have lots of them without impacting performance (at the moment 5 per iPad connection). Building a large application is always a challenge and there were many lessons to be learned but we never had any problems with STM. It always worked as you'd naively expect. We had to do some serious performance tuning but STM was never a problem. (80% of the time we were trying to reduce short-lived allocations and overall memory usage.)
STM is one area where Haskell and the GHC runtime really shines. It's not just an experiment and not for toy programs only.
We're building a different component of our clincal system in Scala and have been using Actors so far, but we're really missing STM. If anybody has experience of what it's like to use one of the Scala STM implementations in production I'd love to hear from you. :-)
We have implemented our entire system (in-memory database and runtime) on top of our own STM implementation in C. Prior to this, we had some log and lock based mechanism to deal with concurrency, but this was a pain to maintain. We are very happy with STM since we can treat every operation the same way. Almost all locks could be removed. We use STM now for almost anything at any size, we even have a memory manager implement on top.
The performance is fine but to speed things up we now developed a custom operating system in collaboration with ETH Zurich. The system natively supports transactional memory.
But there are some challenges caused by STM as well. Especially with larger transactions and hotspots that cause unnecessary transaction conflicts. If for example two transactions put an item into a linked list, an unnecessary conflict will occur that could have been avoided using a lock free data structure.
I'm currently using Akka in some PGAS systems research. Akka is a Scala library for developing scalable concurrent systems using Actors, STM, and built-in fault tolerance capabilities modeled after Erlang's "Let It Fail/Crash/Crater/ROFL" philosophy. Akka's STM implementation is supposedly built around a Scala port of Clojure's STM implementation. An overview of Akka's STM module can be found here.