I'm working on a Qt GUI for visualizing 'live' data which is received via a TCP/IP connection. The issue is that the data is arriving rather quickly (a few dozen MB per second) - it's coming in faster than I'm able to visualize it even though I don't do any fancy visualization - I just show the data in a QTableView object.
As if that's not enough, the GUI also allows pressing a 'Freeze' button which will suspend updating the GUI (but it will keep receiving data in the background). As soon as the Freeze option was disabled, the data which has been accumulated in the background should be visualized.
What I'm wondering is: since the data is coming in so quickly, I can't possibly hold all of it in the memory. The customer might even keep the GUI running over night, so gigabytes of data will accumulate. What's a good data storage system for writing this data to disk? It should have the following properties:
It shouldn't be too much work to use it on a desktop system
It should be fast at appending new data at the end. I never need to touch previously written data anymore, so writing into anywhere but the end is not needed.
It should be possible to randomly access records in the data. This is because scrolling around in my GUI will make it necessary to quickly display the N to N+20 (or whatever the height of my table is) entries in the data stream.
The data which is coming in can be separated into records, but unfortunately the records don't have a fixed size. I'd rather not impose a maximum size on them (at least not if it's possible to get good performance without doing so).
Maybe some SQL database, or something like CouchDB? It would be great if somebody could share his experience with such scenarios.
I think that sqlite might do the trick. It seems to be fast. Unfortunately, I have no data flow like yours, but it works well as a backend for a log recorder. I have a GUI where you can view the n, n+k logs.
You can also try SOCI as a C++ database access API, it seems to work fine with sqlite (I have not used it for now but plan to).
my2c
I would recommend a simple file based solution.
If you can use fixed size records: If the you get the data continuously with constant sample rate, random access to data is easy and very fast when you know the time stamp of first data point and the sample rate. If the sample rate varies, then write time stamp with each data point. Now random access requires binary search, but it is still fast enough.
If you have variable size records: Write the variable size data to one file and to other file write indexes (which are fixed size) to the data file. And if the sample rate varies, write time stamps too. Now you can do the random access fast using the index file.
If you are using Qt to implement this kind of solution, you need two sets of QFile and QDataStream instances, one for writing and one for reading.
And a note about performance: don't flush the file after every data point write. But remember to flush the file before doing any random access to it.
Related
I am currently working on a big dataset (approximately a billion data points) and I have decided to use C++ over R in particular for convenience in memory allocation.
However, there does not seem to exist an equivalent to R Studio for C++ in order to "store" the data set and avoid to have to read the data every time I run the program, which is extremely time consuming...
What kind of techniques do C++ users use for big data in order to read the data "once for all" ?
Thanks for your help!
If I understand what you are trying to achieve, i.e. load some data into memory once and use the same data (in memory) with multiple runs of your code, with possible modifications to that code, there is no such IDE, as IDE are not ment to store any data.
What you can do is first load your data into some in-memory database and write your c++ program to read data from that database instead of reading it directly from data-source in C++.
how avoid multiple reads of big data set.
What kind of techniques do C++ users use for big data in order to read
the data "once for all" ?
I do not know of any C++ tool with such capabilities, but I doubt that I have ever searched for one ... seems like something you might do. Keywords appear to be 'data frame' and 'statistical analysis' (and C++).
If you know the 'data set' format, and wish to process raw data no more than one time, you might consider using Posix shared memory.
I can imagine that (a) the 'extremely time consuming' effort could (read and) transform the 'raw' data, and write into a 'data set' (a file) suitable for future efforts (i.e. 'once and for all').
Then (b) future efforts can 'simply' "map" the created 'data set' (a file) into the program's memory space, all ready for use with no (or at least much reduced) time consuming effort.
Expanding the memory map of your program is about using 'Posix' access to shared memory. (Ubuntu 17.10 has it, I have 'gently' used it in C++) Terminology includes, shm_open, mmap, munmap, shm_unlink, and a few others.
From 'man mmap':
mmap() creates a new mapping in the virtual address space of the
calling process. The starting address for
the new mapping is specified in ...
how avoid multiple reads of big data set. What kind of techniques do
C++ users use for big data in order to read the data "once for all" ?
I recently retried my hand at measuring std::thread context switch duration (on my Ubuntu 17.10, 64 bit desktop). My app captured <30 million entries over 10 seconds of measurement time. I also experimented with longer measurement times, and with larger captures.
As part of debugging info capture, I decided to write intermediate results to a text file, for a review of what would be input to the analysis.
The code spent only about 2.3 seconds to save this info to the capture text file. My original software would then proceed with analysis.
But this delay to get on with testing the analysis results (> 12 sec = 10 + 2.3) quickly became tedious.
I found the analysis effort otherwise challenging, and recognized I might save time by capturing intermediate data, and thus avoiding most (but not all) of the data measurement and capture effort. So the debug capture to intermediate file became a convenient split to the overall effort.
Part 2 of the split app reads the <30 million byte intermediate file in somewhat less 0.5 seconds, very much reducing the analysis development cycle (edit-compile-link-run-evaluate), which was was (usually) no longer burdened with the 12+ second measure and data gen.
While 28 M Bytes is not BIG data, I valued the time savings for my analysis code development effort.
FYI - My intermediate file contained a single letter for each 'thread entry into the critical section event'. With 10 threads, the letters were 'A', 'B', ... 'J'. (reminds me of dna encoding)
For each thread, my analysis supported splitting counts per thread. Where vxWorks would 'balance' the threads blocked at a semaphore, Linux does NOT ... which was new to me.
Each thread ran a different number of times through the single critical section, but each thread got about 10% of the opportunities.
Technique: simple encoded text file with captured information ready to be analyzed.
Note: I was expecting to test piping the output of app part 1 into app part 2. Still could, I guess. WIP.
I have to give some background first. I want to implement an optimized storage engine for OSM planet data (50GB+). The purpose of this engine is to enable map area extractions as fast as possible - while also remaining the ability for minutely updates. The design I've chosen for several reasons (not mentioning all of them here) is to use one data cell per grid. E.g. think of a all cells on a map being distinct files or databases: http://3.bp.blogspot.com/_CntRFtGsdQo/TTU5UMlLkTI/AAAAAAAAARk/_hW8n33t4Ok/s1600/utmworld.gif
(Jut to get the idea though, this is not the actual cell grid I'll be using)
I have never used leveldb before, but settled on it for it's bulk insert and update performance. However, I'd like to know about the "performance characteristics" when opening many very small and very large leveldb databases. very small meaning just a few kB, very large meaning a few hundred MB
I expect that I have to open / close somewhere between 10-100 dbs per minute. I'd rule out leveldb if it needs significant initialization time.
An answer to this question could be either concrete figures, or insight to what leveldb does during initialization and how it relates to data / index size.
PS. I'll also do my own measurements of course. But as with all tests, I may draw wrong conclusions from my sample data.
My current approach:
I have one domain class - Application
Each application in my system is stored in "applications" bucket under APPLICATION_KEY key
Apart from application metadata stored in this bucket, each application has its own bucket called "time_metrics/APPLICATION_KEY" where I store time series in a way:
KEY - timestamp / VALUE - some attributes
My concern is efficiency of queries made over specific time window for given application. Currently to get time series from some specific time window and eventually make some reductions I have to make map/reduce over whole "time_metric/APPLICATION_KEY" bucket, which what I have found is not the recommended use case for Riak Map/Reduce.
My question: what would be the best db structure for this kind of a system and how efficiently query it.
Adding onto #macintux's answer.
Basho has had a few customers that have used riak for time series metrics.
Boundary has a nice tech talk about how they use Riak with their network monitoring software. They rollup data into different chunks of time (1m, 5m, 15m) for analysis.
They also have a series of blog posts about lessons learned while implementing this system.
Kivra also has a good slide deck about how they use timeseries data with riak.
You could roll up your data into some sort of arbitrary time length, then read the range you need by issuing regular K/V gets, and then reconstruct the larger picture / reduce in your application.
If you have spare computing power and you know in advance what keys you need, you certainly can use Riak's MapReduce, but often retrieving the keys and running your processing on the client will be as fast (and won't strain your cluster).
Some general ideas:
Roll up your data into larger blocks
If you're concerned about losing data if your client crashes while buffering it, you can always store the data as it arrives
Similar idea: store the data as it arrives, then retrieve it and roll it up at certain intervals
You can automatically expire data once you're confident it is being reliably stored in larger blocks, using either the Bitcask or Memory backends
Memory backend is quite useful (RAM permitting) for any data that only needs to be stored for a limited period of time
Related: don't be afraid to store multiple copies of your data to make reading/reporting easier later
Multiple chunks of time (5- and 15-minute blocks, for example)
Multiple report formats
Having said all that, if you're doing straight key/value requests (it's ideal to always be able to compute the keys you need, rather than doing indexing or searching), Riak can support very heavy traffic loads, so I wouldn't recommend spending too much time creating alternative storage mechanisms unless you know you're going to face latency problems.
I have the following problem to solve. I have to build a graph viewer to view a massive data set.
We have some files in a particular format that has millions of records representing the result of an experiment. Each record represents a sample point on a large graph plot. The biggest file I have seen has 43.7 Million records.
An average file contains 10 Million records. Each record is small (76 Bytes + optional 12 Bytes each). The complete data cannot be loaded in to the main memory as it is too large. I have build a new file format that compresses the data to 48 bytes per record and organises the data in to chunks that are associated to each other. I want to "view" the data by displaying the records in a 2D/3D plot. As the data is very dense, I would like to progressively increase the level of detail by loading more data and removing data that is not shown in the view from the main memory.
I would also like to access group of associated records in real time and pre-load similar records in order to keep the loading time to bare minimum. This will give the user a smooth control to view the data instead of an experience similar to viewing a video on YouTube with a very slow internet connection. the user cannot randomly and has to use the controls to navigate and I would like to use this info to load the relevant records into the main memory.
The data has to be loaded progressively from the disc based on what is currently in the main memory. Records in the main memory that are not required in the current context can be removed and if required re loaded.
How to I access data from a disc at high speeds based on some hash number
How do I manage main memory if the data to be viewed in the current context is too large. If your answer is level of detail, then how do I build it for a large data set and should this data be part of the file ?
I have been working on this for the last two weeks and I seem to get stuck due to IO speed.
I am working in native C++ and I cannot use work under GPL. If you need any more info, let me know.
Ram
Under most modern file systems (Linux, Unixes, Windows) you can map a file into memory.
Which means you can access the content of the file as if it was entirely in memory (eg you can use data[i++], strchr(data,..), etc) and it's the operating system that does the mapping between used memory and file. When you want to read some data that is not already in memory, the o/s will fetch it from the file.
You should read this question's answer: Mmap() an entire large file
I think you are looking for organization similar to what's used to store level geometry in games, just that you maybe (depending on how your program works and what data you need to show) need just one dimension. See Quadtree and similar methods (bottom of that article).
I'm going to write a program that plots data from a sensor connected to the computer. The sensor value is going to be plotted as a function of the time (sensor value on the y-axis, time on the x-axis). I want to be able to add new values to the plot in real time. What would be best to do this with in C++?
Edit: And by the way, the program will be running on a Linux machine
Are you particularly concerned about the C++ aspect? I've done 10Hz or so rate data without breaking a sweat by putting gnuplot into a read/plot/refresh loop or with LiveGraph with no issues.
Write a function that can plot a std::deque in a way you like, then .push_back() values from the sensor onto the queue as they come available, and .pop_front() values from the queue if it becomes too long for nice plotting.
The exact nature of your plotting function depends on your platform, needs, sense of esthetics, etc.
You can use ring buffers. In such buffer you have read position and write position. This way one thread can write to buffer and other read and plot a graph. For efficiency you usually end up writing your own framework.
Size of such buffer can be estimated using eg.: data delivery speed from sensor (40KHz?), size of one probe and time span you would like to keep for plotting purposes.
It also depends whether you would like to store such data uncompressed, store rendered plot - all for further offline analysis. In non-RTOS environment your "real-time" depends on processing speed: how fast you can retrieve/store/process and plot data. Usually it is near-real time efficiency.
You might want to check out RRDtool to see whether it meets your requirements.
RRDtool is a high performance data logging and graphing system for time series data.
I did a similar thing for a device that had a permeability sensor attached via RS232.
package bytes received from sensor into packets
use a collection (mainly a list) to store them
prevent the collection to go over a fixed size by trashing least recent values before new ones arrive
find a suitable graphics library to draw with (maybe SDL if you wanna keep it easy and cross-platform), but this choice depends on what kind of graph you need (ncurses may be enough)
last but not least: since you are using a sensor I suppose your approach will be multi-threaded so think about it and use a synchronized collection or a collection that allows adding values when other threads are retrieving them (so forgot iterators, maybe an array is enough)
Btw I think there are so many libraries, just search for them:
first
second
...
I assume that you will deploy this application on a RTOS. But, what will be the data rate and what are real-time requirements! Therefore, as written above, a simple solution may be more than enough. But, if you have hard-real time constraints everything changes drastically. A multi-threaded design with data pipes may solve your real-time problems.