open trip planner scalibility (or an alternative library) - public

I am playing around with open trip planner novadays. Actually I will use some parts of the library like base public transportation functions, without bike, roads, streets, etc... functionality. I will only provide bus/subway stops, times, and routes information to the library.
As far as I understood OTP uses a Graph.obj file which could built from a custom route and street data. The process loads all the Graph.obj data into memory when application gets started.
My concern is; if I have a huge route data, then I will probably need to create a huge Graph.obj file from data itself. Then the process will load all the data into memory, and this will eat all my memory.
Question; Does OTP scalible? Are there any way to provide source data from a database? Or something already implemented in a database like mySql, postgreSql, etc..? And what are the other open source alternatives which I can scale my application?

From the data point of view most of the open source routing library use the same approach: generation of a custom data-structure (e.g. file for OpenTripPlanner) and memory loading.
How big it is your map?
Anyway here are a couple of alternatives:
http://graphhopper.com (in Java, very simple and lightweight)
http://project-osrm.org (in C++, probably the fastest one)
Take a look also here:
http://wiki.openstreetmap.org/wiki/Routing/online_routers#comparison_matrix

Related

What is the optimal way of merge few lines or few words in the large file using NodeJS?

I would appreciate insight from anyone who can suggest the best or better solution in editing large files anyway ranges from 1MB to 200MB using nodejs.
Our process needs to merge lines to an existing file in the filesystem, we get the changed data in the following format which needs to be merged to filesystem file at the position defined in the changed details.
[{"range":{"startLineNumber":3,"startColumn":3,"endLineNumber":3,"endColumn":3},"rangeLength":0,"text":"\n","rangeOffset":4,"forceMoveMarkers":false},{"range":{"startLineNumber":4,"startColumn":1,"endLineNumber":4,"endColumn":1},"rangeLength":0,"text":"\n","rangeOffset":5,"forceMoveMarkers":false},{"range":{"startLineNumber":5,"startColumn":1,"endLineNumber":5,"endColumn":1},"rangeLength":0,"text":"\n","rangeOffset":6,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":1,"endLineNumber":6,"endColumn":1},"rangeLength":0,"text":"f","rangeOffset":7,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":2,"endLineNumber":6,"endColumn":2},"rangeLength":0,"text":"a","rangeOffset":8,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":3,"endLineNumber":6,"endColumn":3},"rangeLength":0,"text":"s","rangeOffset":9,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":4,"endLineNumber":6,"endColumn":4},"rangeLength":0,"text":"d","rangeOffset":10,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":5,"endLineNumber":6,"endColumn":5},"rangeLength":0,"text":"f","rangeOffset":11,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":6,"endLineNumber":6,"endColumn":6},"rangeLength":0,"text":"a","rangeOffset":12,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":7,"endLineNumber":6,"endColumn":7},"rangeLength":0,"text":"s","rangeOffset":13,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":8,"endLineNumber":6,"endColumn":8},"rangeLength":0,"text":"f","rangeOffset":14,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":9,"endLineNumber":6,"endColumn":9},"rangeLength":0,"text":"s","rangeOffset":15,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":10,"endLineNumber":6,"endColumn":10},"rangeLength":0,"text":"a","rangeOffset":16,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":11,"endLineNumber":6,"endColumn":11},"rangeLength":0,"text":"f","rangeOffset":17,"forceMoveMarkers":false},{"range":{"startLineNumber":6,"startColumn":12,"endLineNumber":6,"endColumn":12},"rangeLength":0,"text":"s","rangeOffset":18,"forceMoveMarkers":false}]
If we just open the full file and merge those details would work but it would break if we getting too many of those changed details very frequently that can cause out of memory issues as the file been opened many times which is also a very inefficient way.
There is a similar question aimed specifically at c# here. If we open the file in stream mode, is there similar example in nodejs?
I would appreciate insight from anyone who can suggest the best or better solution in editing large files anyway ranges from 1MB to 200MB using nodejs.
Our process needs to merge lines to an existing file in the filesystem, we get the changed data in the following format which needs to be merged to filesystem file at the position defined in the changed details.
General OS file systems do not directly support the concept of inserting info into a file. So, if you have a flat file and you want to insert data into it starting at a particular line number, you have to do the following steps:
Open the file and start reading from the beginning.
As you read data from the file, count lines until you reach the desired linenumber.
Then, if you're inserting new data, you need to read some more and buffer into memory the amount of data you intend to insert.
Then do a write to the file at the position of insertion of the data to insert.
Now using another buffer the size of the data you inserted, take turns reading another buffer, then writing out the previous buffer.
Continue until the end of the file is reach and all data is written back to the file (after the newly inserted data).
This has the effect of rewriting all the data after the insertion point back to the file so it will now correctly be in its new location in the file.
As you can tell, this is not efficient at all for large files as you have to read the entire file a buffer at a time and you have to write the insertion and everything after the insertion point.
In node.js, you can use features in the fs module to carry out all these steps, but you have to write the logic to connect them all together as there is no built-in feature to insert new data into a file while pushing the existing data after it.
There is a similar question aimed specifically at c# here. If we open the file in stream mode, is there similar example in nodejs?
The C# example you reference appears to just be appending new data onto the end of the file. That's trivial to do in pretty much any file system library. In node.js, you can do that with fs.appendFile() or you can open any file handle in append mode and then write to it.
To insert data into a file more efficiently, you would need to use a more efficient storage system than a single flat file for all the data. For example, if you stored the file in pieces in approximately 100 line blocks, then to insert data you'd only have to rewrite a portion of one block of data and then perhaps have some cleanup process that rebalances the block boundaries if a block gets way too big or too small.
For efficient line management, you would need to maintain an accurate index of how many lines each file piece contains and obviously what order the pieces should be in. This would allow you to insert data at a somewhat fixed cost no matter how big the entire file was as the most you would need to do is to rewrite one or two blocks of data, even if the entire content was hundreds of GB in size.
Note, you would essentially be building a new file system on top of the OS file system in order to give yourself more efficient inserts or deletions within the overall data. Obviously, the chunks of data could also be stored in a database too and managed there.
Note, if this project is really an editor, text editing a line-based structure is a very well studied problem and you could also study the architectures used in previous projects for further ideas. It's a bit beyond the scope of a typical answer here to study the pros and cons of various architectures. If your system is also a client/server editor where the change instructions are being sent from a client to a server, that also affects some of the desired tradeoffs in the design since you may desire differing tradeoffs in terms of the number of transactions or the amount of data to be sent between client and server.
If some other language uses an optimal way then I think it would be better to find that option as you saying nodejs might not have that option.
This doesn't really have anything to do with the language you choose. This is about how modern and typical operating systems store data in files.
In fs module there is a function named appendFile. It would let you append data in your file. Link.

Why whenever I look information on how to use the SDRAM of my DE1-SOC on internet, it takes me to use NIOS-II?

I'm doing a simple project of taking 100 numbers from an external memory (one by one), doing a simple arithmetic to that number (like adding 1) and returning it to another memory.
I successfully did that project "representing" a memory in verilog code, however I want now to synthesize my design but using the SDRAM of the board. The way I load data to the SDRAM or what I do with the resulting data outputted again to the SDRAM is irrelevant for my homework.
But I just can't understand what to do, all the information in internet takes me to the utilization of NIOS-II. Considering I have to load data to the SDRAM to make it able to serve me, and other reasons, maybe, is that NIOS-II is the most recommended way to do this? Can be done with out it, and would it be more practical?
this might not be the place to have your homework done. Additionally your question is very unclear. Let's try anyway:
I successfully did that project "representing" a memory in verilog code
I assume that you mean that you downloaded a model corresponding to the memory you have on your board.
taking 100 numbers from an external memory
I wonder how you do that. Did you load some initialization file or did you write the numbers first? In case of the first: this will not be synthesized and you might read random data, you should refer to the datasheet of your memory for this. If you expect specific values, you will need to write them to memory during some initialization procedure.
Of course you will need the correct constraints for your device. So I'd suggest that you take the NIOSII example, get it up and running and get rid of the NIOSII in a next step. At least you will be sure that the interfacing between controller and sdram is correct. Then read the datasheet of the controller. Probably you have a readstrobe, write strobe, data in, data out port, some configuration, perhaps a burstlength. If you need help with that you'll need to come up with a more specific question

Persistant storage values handling in Linux

I have a QSPI flash on my embedded board.
I have a driver + process "Q" to handle reading and writing into.
I want to store variables like SW revisions, IP, operation time, etc.
I would like to ask for suggestions how to handle the different access rights to write and read values from user space and other processes.
I was thinking to have file for each variable. Than I can assign access rights for those files and process Q can change the value in file if value has been changed. So process Q will only write into and other processes or users can only read.
But I don't know about writing. I was thinking about using message queue or zeroMQ and build the software around it but i am not sure if it is not overkill. But I am not sure how to manage access rights anyway.
What would be the best approach? I would really appreciate if you could propose even totally different approach.
Thanks!
This question will probably be downvoted / flagged due to the "Please suggest an X" nature.
That said, if a file per variable is what you're after, you might want to look at implementing a FUSE file system that wraps your SPI driver/utility "Q" (or build it into "Q" if you get to compile/control source to "Q"). I'm doing this to store settings in an EEPROM on a current work project and its turned out nicely. So I have, for example, a file, that when read, retrieves 6 bytes from EEPROM (or a cached copy) provides a MAC address in std hex/colon-separated notation.
The biggest advantage here, is that it becomes trivial to access all your configuration / settings data from shell scripts (e.g. your init process) or other scripting languages.
Another neat feature of doing it this way is that you can use inotify (which comes "free", no extra code in the fusefs) to create applications that efficiently detect when settings are changed.
A disadvantage of this approach is that it's non-trivial to do atomic transactions on multiple settings and still maintain normal file semantics.

store some data in the struct inode

Hello I am a newbie to kernel programming. I am writing a small kernel module
that is based on wrapfs template to implement a backup mechanism. This is
purely for learning basis.
I am extending wrapfs so that when a write call is made wrapfs transparently
makes a copy of that file in a separate directory and then write is performed
on the file. But I don't want that I create a copy for every write call.
A naive approach could be I check for existence of file in that directory. But
I think for each call checking this could be a severe penalty.
I could also check for first write call and then store a value for that
specific file using private_data attribute. But that would not be stored on
disk. So I would need to check that again.
I was also thinking of making use of modification time. I could save a
modification time. If the older modification time is before that time then only
a copy is created otherwise I won't do anything. I tried to use inode.i_mtime
for this but it was the modified time even before write was called, also
applications can modify that time.
So I was thinking of storing some value in inode on disk that indicates its
backup has been created or not. Is that possible? Any other suggestions or
approaches are welcome.
You are essentially saying you want to do a Copy-On-Write virtual filesystem layer.
IMO, some of these have been done, and it would be easier to implement these in userland (using libfuse and the fuse module, e.g.). That way, you can be king of your castle and add your metadata in any which way you feel is appriate:
just add (hidden) metadata files to each directory
use extended POSIX attributes (setfattr and friends)
heck, you could even use a sqlite database
If you really insist on doing these things in-kernel, you'll have a lot more work since accessing the metadata from kernel mode is goind to take a lot more effort (you'd most likely want to emulate your own database using memory mapped files so as to minimize the amount of 'userland (style)' work required and to make it relatively easy to get atomicity and reliability right1.
1
On How Everybody Gets File IO Wrong: see also here
You can use atime instead of mtime. In that case setting S_NOATIME flag on the inode prevents it from updating (see touch_atime() function at the inode.c). The only thing you'll need is to mount your filesystem with noatime option.

multithreading and reading from one file (perl)

Hej sharp minds!
I need your expert guidance in making some choices.
Situation is like this:
1. I have approx. 500 flat files containing from 100 to 50000 records that have to be processed.
2. Each record in the files mentioned above has to be replaced using value from the separate huge file (2-15Gb) containing 100-200 million entries.
So I thought to make the processing using multicores - one file per thread/fork.
Is that a good idea? Since each thread needs to read from same huge file? It's a bit of a problem loading it into memory do to the size? Using file::tie is an option, but is that working with threads/forks?
Need your advise how to proceed.
Thanks
Yes, of course, using multiple cores for multi-threaded application is a good idea, because that's what those cores are for. Though it sounds like your problem involves heavy I/O, so, it might be that you will not use that much of CPU anyway.
Also since you are only going to read that big file, tie should work perfectly. I haven't heard of problems with that. But if you are going to search that big file for each record in your smaller files, then I guess it would take you a long time despite of the number of threads you use. If data from big file can be indexed based on some key, then I would advice to put it in some NoSQL databse and access it from your program. That would probably speed up your task even more than using multiple threads/cores.

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