It's no secret that application logs can go well beyond the limits of naive log viewers, and the desired viewer functionality (say, filtering the log based on a condition, or highlighting particular message types, or splitting it into sublogs based on a field value, or merging several logs based on a time axis, or bookmarking etc.) is beyond the abilities of large-file text viewers.
I wonder:
Whether decent specialized applications exist (I haven't found any)
What functionality might one expect from such an application? (I'm asking because my student is writing such an application, and the functionality above has already been implemented to a certain extent of usability)
I've been using Log Expert lately.
alt text http://www.log-expert.de/images/stories/logexpertshowcard.gif
I can take a while to load large files, but it will in fact load them. I couldn't find the file size limit (if there is any) on the site, but just to test it, I loaded a 300mb log file, so it can at least go that high.
Windows Commander has a built-in program called Lister which works very quickly for any file size. I've used it with GBs worth of log files without a problem.
http://www.ghisler.com/lister/
A slightly more powerful tool I sometimes use it Universal Viewer from http://www.uvviewsoft.com/.
Related
Is it possible to speed up mapping with OSM by removing features and details (minor roads, bus stops, etc) -- or is that somewhat irrelevant to the tile download and rendering process.
aka, Are the SVG details added/removed on client or server side.
Further how are those 'church: invisible' type of instructions set
TIA
Perhaps that is a general mapping question; given that an engine (possibly) operates in much the same way at least when it comes to tiles and SVG details. And I simply don't know that process.
It is not quite clear about which specific process your are talking.
The tiles you see on http://openstreetmap.org are PNG, not SVG. The share menu allows to export the current view to SVG and some other formats. This resulting SVG is created server-side. Of course using less detail would speed up the SVG creation process But this process involves several other operations like querying the data base which won't benefit much from reduced details (ignoring the time to transfer the data between the database and the SVG creation process).
The PNG rendering will also depend somehow on the amount of detail but likewise there are a lot more operations necessary for rendering a single tile. I don't expect a large speedup by removing a few features.
Also note that there are several different renderers available and each will behave differently. And there is also the possibility of creating vector tiles which move some of the tile creation load from the server-side to the client-side. Here the amount of detail will slightly influence the server-side and significantly more the client-side, especially low-end systems.
Still I have no idea what these things have to do with mapping - the process of editing maps and adding/updating information.
I read some website development materials on the Web and every time a person is asking for the organization of a website's js, css, html and php files, people suggest single js for the whole website. And the argument is the speed.
I clearly understand the fewer request there is, the faster the page is responded. But I never understand the single js argument. Suppose you have 10 webpages and each webpage needs a js function to manipulate the dom objects on it. Putting 10 functions in a single js and let that js execute on every single webpage, 9 out of 10 functions are doing useless work. There is CPU time wasting on searching for non-existing dom objects.
I know that CPU time on individual client machine is very trivial comparing to bandwidth on single server machine. I am not saying that you should have many js files on a single webpage. But I don't see anything go wrong if every webpage refers to 1 to 3 js files and those js files are cached in client machine. There are many good ways to do caching. For example, you can use expire date or you can include version number in your js file name. Comparing to mess the functionality in a big js file for all needs of many webpages of a website, I far more prefer split js code into smaller files.
Any criticism/agreement on my argument? Am I wrong? Thank you for your suggestion.
A function does 0 work unless called. So 9 empty functions are 0 work, just a little exact space.
A client only has to make 1 request to download 1 big JS file, then it is cached on every other page load. Less work than making a small request on every single page.
I'll give you the answer I always give: it depends.
Combining everything into one file has many great benefits, including:
less network traffic - you might be retrieving one file, but you're sending/receiving multiple packets and each transaction has a series of SYN, SYN-ACK, and ACK messages sent across TCP. A large majority of the transfer time is establishing the session and there is a lot of overhead in the packet headers.
one location/manageability - although you may only have a few files, it's easy for functions (and class objects) to grow between versions. When you do the multiple file approach sometimes functions from one file call functions/objects from another file (ex. ajax in one file, then arithmetic functions in another - your arithmetic functions might grow to need to call the ajax and have a certain variable type returned). What ends up happening is that your set of files needs to be seen as one version, rather than each file being it's own version. Things get hairy down the road if you don't have good management in place and it's easy to fall out of line with Javascript files, which are always changing. Having one file makes it easy to manage the version between each of your pages across your (1 to many) websites.
Other topics to consider:
dormant code - you might think that the uncalled functions are potentially reducing performance by taking up space in memory and you'd be right, however this performance is so so so so minuscule, that it doesn't matter. Functions are indexed in memory and while the index table may increase, it's super trivial when dealing with small projects, especially given the hardware today.
memory leaks - this is probably the largest reason why you wouldn't want to combine all the code, however this is such a small issue given the amount of memory in systems today and the better garbage collection browsers have. Also, this is something that you, as a programmer, have the ability to control. Quality code leads to less problems like this.
Why it depends?
While it's easy to say throw all your code into one file, that would be wrong. It depends on how large your code is, how many functions, who maintains it, etc. Surely you wouldn't pack your locally written functions into the JQuery package and you may have different programmers that maintain different blocks of code - it depends on your setup.
It also depends on size. Some programmers embed the encoded images as ASCII in their files to reduce the number of files sent. These can bloat files. Surely you don't want to package everything into 1 50MB file. Especially if there are core functions that are needed for the page to load.
So to bring my response to a close, we'd need more information about your setup because it depends. Surely 3 files is acceptable regardless of size, combining where you would see fit. It probably wouldn't really hurt network traffic, but 50 files is more unreasonable. I use the hand rule (no more than 5), but surely you'll see a benefit combining those 5 1KB files into 1 5KB file.
Two reasons that I can think of:
Less network latency. Each .js requires another request/response to the server it's downloaded from.
More bytes on the wire and more memory. If it's a single file you can strip out unnecessary characters and minify the whole thing.
The Javascript should be designed so that the extra functions don't execute at all unless they're needed.
For example, you can define a set of functions in your script but only call them in (very short) inline <script> blocks in the pages themselves.
My line of thought is that you have less requests. When you make request in the header of the page it stalls the output of the rest of the page. The user agent cannot render the rest of the page until the javascript files have been obtained. Also javascript files download sycronously, they queue up instead of pull at once (at least that is the theory).
Most dependency injection frameworks support initializing the components being injected with various parameters, typically read from an XML configuration file.
This seems like a very convenient place to store settings (like a server name, or a file path that the component will need). But is that the right way to go, or would it make more sense to have a seperate configuration file for settings ? And does it matter, in your opinion, whether the user should be able to change the settings via UI ?
IMHO, it's a matter of common sense. Usually, I keep everything in the same config file, but in different sections. If you use too many config files, it will be harder for someone new in the team to understand your code, for example. But if the file is getting too big, I think it's ok separating them.
About the UI settings, I think developers should make most of the choices, but open some space for configuration. I don't have the link right now, but there's an interesting story of a store which used to sell jam. They would let the customers taste the jams with toast, which highly increased sales (for customers would know the quality of what they were buying). In the beggining, they didn't have many jam options (just 3), and it was a success. When there were too many options (like 20), they noticed customers got a little confused with so many options.
Another reason for reducing configurability is to reduce development time. If you read the book Getting real (I recommend this one), they say a lot of time to build less. In chapter 10, they say:
* Less software is easier to manage.
* Less software reduces your codebase and that means
* Less maintenance busywork (and a happier staff).
* Less software lowers your cost of change so you can adapt quickly. You can change your mind without having to change boatloads of code. Less software results in fewer bugs.
* Less software means less support.
So, IMHO try to find out which settings users would like to change most, and make these configurable. Also, if your system is already at production, and keeps growing slowly, I think you could open more space for configuration as time goes by. Gmail, for example, didn't have many configuration options, but as users got used to the tool, they started creating options for the users, like changing themes and other features of google labs. This way, the learning curve "doesn't hurt" users :).
Hope I helped.
I would probably store the settings in a separate file, to make it more clear what settings might need modification in different environments. This also makes it easier for people not familiar with your dependency injection framework (e.g., system administrators, end users) to modify the settings.
If this is a very small application, and the settings will only be changed by developers, I might keep the settings in the DI configuration.
The next sentence caught my eye in Wget's manual
wget --spider --force-html -i bookmarks.html
This feature needs much more work for Wget to get close to the functionality of real web spiders.
I find the following lines of code relevant for the spider option in wget.
src/ftp.c
780: /* If we're in spider mode, don't really retrieve anything. The
784: if (opt.spider)
889: if (!(cmd & (DO_LIST | DO_RETR)) || (opt.spider && !(cmd & DO_LIST)))
1227: if (!opt.spider)
1239: if (!opt.spider)
1268: else if (!opt.spider)
1827: if (opt.htmlify && !opt.spider)
src/http.c
64:#include "spider.h"
2405: /* Skip preliminary HEAD request if we're not in spider mode AND
2407: if (!opt.spider
2428: if (opt.spider && !got_head)
2456: /* Default document type is empty. However, if spider mode is
2570: * spider mode. */
2571: else if (opt.spider)
2661: if (opt.spider)
src/res.c
543: int saved_sp_val = opt.spider;
548: opt.spider = false;
551: opt.spider = saved_sp_val;
src/spider.c
1:/* Keep track of visited URLs in spider mode.
37:#include "spider.h"
49:spider_cleanup (void)
src/spider.h
1:/* Declarations for spider.c
src/recur.c
52:#include "spider.h"
279: if (opt.spider)
366: || opt.spider /* opt.recursive is implicitely true */
370: (otherwise unneeded because of --spider or rejected by -R)
375: (opt.spider ? "--spider" :
378: (opt.delete_after || opt.spider
440: if (opt.spider)
src/options.h
62: bool spider; /* Is Wget in spider mode? */
src/init.c
238: { "spider", &opt.spider, cmd_boolean },
src/main.c
56:#include "spider.h"
238: { "spider", 0, OPT_BOOLEAN, "spider", -1 },
435: --spider don't download anything.\n"),
1045: if (opt.recursive && opt.spider)
I would like to see the differences in code, not abstractly. I love code examples.
How do web spiders differ from Wget's spider in code?
A real spider is a lot of work
Writing a spider for the whole WWW is quite a task --- you have to take care about many "little details" such as:
Each spider computer should receive data from a few thousand servers in parallel in order to make efficient use of the connection bandwidth. (asynchronous socket i/o).
You need several computers that spider in parallel in order to cover the vast amount of information on the WWW (clustering; partitioning the work)
You need to be polite to the spidered web sites:
Respect the robots.txt files.
Don't fetch a lot of information too quickly: this overloads the servers.
Don't fetch files that you really don't need (e.g. iso disk images; tgz packages for software download...).
You have to deal with cookies/session ids: Many sites attach unique session ids to URLs to identify client sessions. Each time you arrive at the site, you get a new session id and a new virtual world of pages (with the same content). Because of such problems, early search engines ignored dynamic content. Modern search engines have learned what the problems are and how to deal with them.
You have to detect and ignore troublesome data: connections that provide a seemingly infinite amount of data or connections that are too slow to finish.
Besides following links, you may want to parse sitemaps to get URLs of pages.
You may want to evaluate which information is important for you and changes frequently to be refreshed more frequently than other pages. Note: A spider for the whole WWW receives a lot of data --- you pay for that bandwidth. You may want to use HTTP HEAD requests to guess whether a page has changed or not.
Besides receiving, you want to process the information and store it. Google builds indices that list for each word the pages that contain it. You may need separate storage computers and an infrastructure to connect them. Traditional relational data bases don't keep up with the data volume and performance requirements of storing/indexing the whole WWW.
This is a lot of work. But if your target is more modest than reading the whole WWW, you may skip some of the parts. If you just want to download a copy of a wiki etc. you get down to the specs of wget.
Note: If you don't believe that it's so much work, you may want to read up on how Google re-invented most of the computing wheels (on top of the basic Linux kernel) to build good spiders. Even if you cut a lot of corners, it's a lot of work.
Let me add a few more technical remarks on three points
Parallel connections / asynchronous socket communication
You could run several spider programs in parallel processes or threads. But you need about 5000-10000 parallel connections in order to make good use of your network connection. And this amount of parallel processes/threads produces too much overhead.
A better solution is asynchronous input/output: process about 1000 parallel connections in one single thread by opening the sockets in non-blocking mode and use epoll or select to process just those connections that have received data. Since Linux kernel 2.4, Linux has excellent support for scalability (I also recommend that you study memory-mapped files) continuously improved in later versions.
Note: Using asynchronous i/o helps much more than using a "fast language": It's better to write an epoll-driven process for 1000 connections written in Perl than to run 1000 processes written in C. If you do it right, you can saturate a 100Mb connection with processes written in perl.
From the original answer:
The down side of this approach is that you will have to implement the HTTP specification yourself in an asynchronous form (I am not aware of a re-usable library that does this). It's much easier to do this with the simpler HTTP/1.0 protocol than the modern HTTP/1.1 protocol. You probably would not benefit from the advantages of HTTP/1.1 for normal browsers anyhow, so this may be a good place to save some development costs.
Edit five years later:
Today, there is a lot of free/open source technology available to help you with this work. I personally like the asynchronous http implementation of node.js --- it saves you all the work mentioned in the above original paragraph. Of course, today there are also a lot of modules readily available for the other components that you need in your spider. Note, however, that the quality of third-party modules may vary considerably. You have to check out whatever you use. [Aging info:] Recently, I wrote a spider using node.js and I found the reliability of npm modules for HTML processing for link and data extraction insufficient. For this job, I "outsourced" this processing to a process written in another programming language. But things are changing quickly and by the time you read this comment, this problem may already a thing of the past...
Partitioning the work over several servers
One computer can't keep up with spidering the whole WWW. You need to distribute your work over several servers and exchange information between them. I suggest to assign certain "ranges of domain names" to each server: keep a central data base of domain names with a reference to a spider computer.
Extract URLs from received web pages in batches: sort them according to their domain names; remove duplicates and send them to the responsible spider computer. On that computer, keep an index of URLs that already are fetched and fetch the remaining URLs.
If you keep a queue of URLs waiting to be fetched on each spider computer, you will have no performance bottlenecks. But it's quite a lot of programming to implement this.
Read the standards
I mentioned several standards (HTTP/1.x, Robots.txt, Cookies). Take your time to read them and implement them. If you just follow examples of sites that you know, you will make mistakes (forget parts of the standard that are not relevant to your samples) and cause trouble for those sites that use these additional features.
It's a pain to read the HTTP/1.1 standard document. But all the little details got added to it because somebody really needs that little detail and now uses it.
I am not sure exactly what the original author of the comment was referring to, but I can guess that wget is slow as a spider, since it appears to only use a single thread of execution (at least by what you have shown).
"Real" spiders such as heritrix use a lot of parallelism and tricks to optimize their crawling speed, while simultaneously being nice to the website they are crawling. This typically means limiting hits to one site at a rate of 1 per second (or so), and crawling multiple websites at the same time.
Again this is all just a guess based on what I know of spiders in general, and what you posted here.
Unfortunately, many of the more well-known 'real' web spiders are closed-source, and indeed closed-binary. However there are a number of basic techniques wget is missing:
Parallelism; you're never going to be able to keep up with the entire web without retrieving multiple pages at a time
Prioritization; some pages are more important to spider than others
Rate limiting; you'll be banned quickly if you keep pulling down pages as quickly as you can
Saving to something other than a local filesystem; the Web is big enough that it's not going to fit in a single directory tree
Rechecking pages periodically without restarting the entire process; in practice, with a real spider you'll want to recheck 'important' pages for updates frequently, while less interesting pages can go for months.
There are also various other inputs that can be used such as sitemaps and the like. Point is, wget isn't designed to spider the entire web, and it's not really a thing that can be captured in a small code sample, as it's a problem of the whole overall technique being used, rather than any single small subroutine being wrong for the task.
I'm not going to go into details of how to spider the internet, I think that wget comment is regarding to spidering one website which is still a serious challenge.
As a spider you need to figure out when to stop, not go into recursive crawls just because the URL changed like date=1/1/1900 to 1/2/1900 and so
Even bigger challenge to sort out URL Rewrite (I have no clue what so ever how google or any other handles this). It's pretty big challenge to crawl enough but not too much. And how one can automatically recognise URL Rewrite with some random parameters and random changes in the content?
You need to parse Flash / Javascript at least up to some level
You need to consider some crazy HTTP issues like base tag. Even parsing the HTML is not easy, considering most of the websites are not XHTML and browsers are so flexible in the syntax.
I don't know how much of these implemented or considered in wget but you might want to take a look at httrack to understand the challenges of this task.
I'd love to give you some code examples but this is big tasks and a decent spider will be about 5000 loc without 3rd party libraries.
+ Some of them already explained by #yaakov-belch so I'm not going to type them again
A friend of mine brought up this questiont he other day, he's recently bought a garmin heart rate moniter device which keeps track of his heart rate and allows him to upload his heart rate stats for a day to his computer.
The only problem is there are no linux drivers for the garmin USB device, he's managed to interpret some of the data, such as the model number and his user details and has identified that there are some binary datatables essentially which we assume represent a series of recordings of his heart rate and the time the recording was taken.
Where does one start when reverse engineering data when you know nothing about the structure?
I had the same problem and initially found this project at Google Code that aims to complete a cross-platform version of tools for the Garmin devices ... see: http://code.google.com/p/garmintools/. There's a link on the front page of that project to the protocols you need, which Garmin was thoughtful enough to release publically.
And here's a direct link to the Garmin I/O specification: http://www.garmin.com/support/pdf/IOSDK.zip
I'd start looking at the data in a hexadecimal editor, hopefully a good one which knows the most common encodings (ASCII, Unicode, etc.) and then try to make sense of it out of the data you know it has stored.
As another poster mentioned, reverse engineering can be hairy, not in practice but in legality.
That being said, you may be able to find everything related to your root question at hand by checking out this project and its' code...and they do handle the runner's heart rate/GPS combo data as well
http://www.gpsbabel.org/
I'd suggest you start with checking the legality of reverse engineering in your country of origin. Most countries have very strict laws about what is allowed and what isn't regarding reverse engineering devices and code.
I would start by seeing what data is being sent by the device, then consider how such data could be represented and packed.
I would first capture many samples, and see if any pattern presents itself, since heart beat is something which is regular and that would suggest it is measurement related to the heart itself. I would also look for bit fields which are monotonically increasing, as that would suggest some sort of time stamp.
Having formed a hypothesis for what is where, I would write a program to test it and graph the results and see if it makes sense. If it does but not quite, then closer inspection would probably reveal you need some scaling factors here or there. It is also entirely possible I need to process the data first before it looks anything like what their program is showing, i.e. might need to integrate the data points. If I get garbage, then it is back to the drawing board :-)
I would also check the manufacturer's website, or maybe run strings on their binaries. Finding someone who works in the field of biomedical engineering would also be on my list, as they would probably know what protocols are typically used, if any. I would also look for these protocols and see if any could be applied to the data I am seeing.
I'd start by creating a hex dump of the data. Figure it's probably blocked in some power-of-two-sized chunks. Start looking for repeating patterns. Think about what kind of data they're probably sending. Either they're recording each heart beat individually, or they're recording whatever the sensor is sending at fixed intervals. If it's individual beats, then there's going to be a time delta (since the last beat), a duration, and a max or avg strength of some sort. If it's fixed intervals, then it'll probably be a simple vector of readings. There'll probably be a preamble of some sort, with a start timestamp and the sampling rate. You can try decoding the timestamp yourself, or you might try simply feeding it to ctime() and see if they're using standard absolute time format.
Keep in mind that lots of cheap A/D converters only produce 12-bit outputs, so your readings are unlikely to be larger than 16 bits (and the high-order 4 bits may be used for flags). I'd recommend resetting the device so that it's "blank", dumping and storing the contents, then take a set of readings, record the results (whatever the device normally reports), then dump the contents again and try to correlate the recorded results with whatever data appeared after the "blank" dump.
Unsure if this is what you're looking for but Garmin has created an API that runs with your browser. It seems OSX is supported, as well as Windows browsers... I would try it from Google Chromium to see if it can be used instead of this reverse engineering...
http://developer.garmin.com/web-device/garmin-communicator-plugin/
API Features
Auto-detection of devices connected to a computer Access to device
product information like product name and software version Read
tracks, routes and waypoints from supported recreational, fitness and
navigation devices Write tracks, routes and waypoints to supported
recreational, fitness and navigation devices Read fitness data from
supported fitness devices Geo-code address and save to a device as a
waypoint or favorite Read and write Garmin XML files (GPX and TCX) as
well as binary files. Support for most Garmin devices (USB, USB
mass-storage, most serial devices) Support for Internet Explorer,
Firefox and Chrome on Microsoft Windows. Support for Safari, Firefox
and Chrome on Mac OS X.
Can you synthesize a heart beat using something like a computer speaker? (I have no idea how such devices actually work). Watch how the binary results change based on different inputs.
Ripping apart the device and checking out what's inside would probably help too.