How do I return interval of rows from 100mil rows *.gz file?
Let's say I need 5 mil rows starting from 15mil up to 20mil?
is this the best performing option?
zcat myfile.gz|head -20000000|tail -500
real 0m43.106s
user 0m43.154s
sys 0m9.259s
That's a perfectly reasonable option; since you don't know how long a line will be, you basically have to decompress and iterate the lines to figure out where the line separators are. All three tools are fairly heavily optimized, so I/O and decompression time will likely dominate regardless.
In theory, rolling your own solution that combines all three tools in a single executable might save a little (by reducing the costs of IPC a bit), but the savings would likely be negligible.
Related
I'm looking for an efficient way to compare if two files are uniq in Node.js.
All I need to know is if files are equal or not. Simple true / false as output will be enough.
Building checksums on a files is a bit slow. Meanwhile Linux diff command is quite fast for comparing even large files. So i just curious if there any equivalent or module in Node.js of efficient Linux diff command.
As suggested in comments - we can try to use stream-equal module for that.
I just tried to compare same 1.3 GB files with both stream-equal and diff
See timeframes below:
9.352s - stream-equal
0.008s - diff
Looks like diff is insanely fast.
One way I'm thinking of speeding things up on large files is via reading first 10 bytes and last 10 bytes of the same size large files and compare by that. If the first and the last bytes are equal then there is quite a big chance that files are identical.
But I'm not pretty sure for now what is the correct way to implement this.
I'm downloading daily 600MB netcdf-4 files that have this structure:
netcdf myfile {
dimensions:
time_counter = 18 ;
depth = 50 ;
latitude = 361 ;
longitude = 601 ;
variables:
salinity
temp, etc
I'm looking for a better way to convert the time_counter dimension from a fixed size (18) to an unlimited dimension.
I found a way of doing it with the netcdf commands and sed. Like this:
ncdump myfile.nc | sed -e "s#^.time_counter = 18 ;#time_counter = UNLIMITED ; // (currently 18)#" | ncgen -o myfileunlimited.nc
which worked for me for small files, but when dumping a 600 MB netcdf files, takes to much memory and time.
Somebody knows another method for accomplishing this?
Your answers are very insightful. I'm not really looking a way to improve this ncdump-sed-ncgen method, I know that dumping a netcdf file that is 600MB uses almost 5 times more space in a text file (CDL representation). To then modify some header text and generate the netcdf file again, doesn't feels very efficient.
I read the latest NCO commands documentation, and found a option specific to ncks "--mk_rec_dmn". Ncks mainly extracts and writes or appends data to a new netcdf file, then this seems the better approach, extract all the data of myfile.nc and write it with a new record dimension (unlimited dimension) which the "--mk_rec_dmn" does, then replace the old file.
ncks --mk_rec_dmn time_counter myfile.nc -o myfileunlimited.nc ; mv myfileunlimited.nc myfile.nc
To do the opposite operation (record dimension to fixed-size) would be.
ncks --fix_rec_dmn time_counter myfile.nc -o myfilefixedsize.nc ; mv myfilefixedsize.nc myfile.nc
The shell pipeline can only be marginally improved by making the sed step only modify the beginning of the file and pass everything else through, but the expression you have is very cheap to process and will not make a dent in the time spent.
The core problem is likely that you're spending a lot of time in ncdump formatting the file information into textual data, and in ncgen parsing textual data into a NetCDF file format again.
As the route through dump+gen is about as slow as it is shown, that leaves using NetCDF functionality to do the conversion of your data files.
If you're lucky, there may be tools that operate directly on your data files to do changes or conversions. If not, you may have to write them yourself with the NetCDF libraries.
If you're extremely unlucky, NetCDF-4 files are HDF5 files with some extra metadata. In particular, the length of the dimensions is stored in the _netcdf_dim_info dataset in group _netCDF (or so the documentation tells me).
It may be possible to modify the information there to turn the current length of the time_counter dimension into the value for UNLIMITED (which is the number 0), but if you do this, you really need to verify the integrity of the resulting file, as the documentation neatly puts it:
"Note that modifying these files with HDF5 will almost certainly make them unreadable to netCDF-4."
As a side note, if this process is important to your group, it may be worth looking into what hardware could do the task faster. On my Bulldozer system, the process of converting a 78 megabyte file takes 20 seconds, using around 500 MB memory for ncgen working set (1 GB virtual) and 12 MB memory for ncdump working set (111 MB virtual), each task taking up the better part of a core.
Any decent disk should read/sink your files in 10 seconds or so, memory doesn't matter as long as you don't swap, so CPU is probably your primary concern if you take the dump+gen route.
If concurrent memory use is a big concern, you can trade some bytes for space by saving the intermediary result from sed onto disk, which will likely take up to 1.5 gigabytes or so.
You can use the xarray python package's xr.to_netdf() method, then optimise memory usage via using Dask.
You just need to pass names of the dimensions to make unlimited to the unlimited_dims argument and use the chunks to split the data. For instance:
import xarray as xr
ds = xr.open_dataset('myfile.nc', chunks={'time_counter': 18})
ds.to_netcdf('myfileunlimited.nc', unlimited_dims={'time_counter':True})
There is a nice summary of combining Dask and xarray linked here.
I have following two files disconnect.txt and answered.txt:
disconnect.txt
2011-07-08 00:59:06,363 [socketProcessor] DEBUG ProbeEventDetectorIS41Impl:459 - AnalyzedInfo had ActCode = Disconnected from: 40397400012 to:40397400032
2011-07-08 00:59:06,363 [socketProcessor] DEBUG ProbeEventDetectorIS41Impl:459 - AnalyzedInfo had ActCode = Disconnected from: 4035350012 to:40677400032
answered.txt
2011-07-08 00:59:40,706 [socketProcessor] DEBUG ProbeEventDetectorIS41Impl:404 - Normal Call Answered, billingid=2301986 from: 40397643433 to:403###34**
2011-07-08 00:59:40,706 [socketProcessor] DEBUG ProbeEventDetectorIS41Impl:404 - Normal Call Answered, billingid=2301986 from: 3455334459 to:1222
2011-07-08 00:59:48,893 [socketProcessor] DEBUG ProbeEventDetectorIS41Impl:404 - Normal Call Answered, billingid=2220158 from: 4035350012 to:40677400032
I would like to create a join on these files based on the from: and to: fields and the output should be matching field from answered.txt. For example, in the above two files, the output would be:
2011-07-08 00:59:48,893 [socketProcessor] DEBUG ProbeEventDetectorIS41Impl:404 - Normal Call Answered, billingid=2220158 from: 4035350012 to:40677400032
I'm currently doing it by comparing each line in file 1 with each line in file 2, but want to know if an efficient way exists (these files will be in tens of gigabytes).
Thank you
Sounds like you have hundreds of millions of lines?
Unless the files are sorted in such a way that you can expect the order of the from: and to: to at least vaguely correlate, this is a job for a database.
If the files are large the quadratic algorithm will take a lifetime.
Here is a Ruby script that uses just a single hash table lookup per line in answered.txt:
def key s
s.split('from:')[1].split('to:').map(&:strip).join('.')
end
h = {}
open 'disconnect.txt', 'r' do |f|
while s = f.gets
h[key(s)] = true
end
end
open 'answered.txt', 'r' do |f|
while a = f.gets
puts a if h[key(a)]
end
end
Like ysth says, it all depends on the number of lines in disconnect.txt. If that's a really big1 number, then you will probably not be able to fit all the keys in memory and you will need a database.
1. The number of lines in disconnect.txt multiplied by (roughly) 64 should be less than the amount of memory in your machine.
First, sort the files on the from/to timestamps if they are not already sorted that way. (Yes, I know the from/to appear to be stored as epoch seconds, but that's still a timestamp.)
Then take the sorted files and compare the first lines of each.
If the timestamps are the same, you have a match. Hooray! Advance a line in one or both files (depending on your rules for duplicate timestamps in each) and compare again.
If not, grab the next line in whichever file has the earlier timestamp and compare again.
This is the fastest way to compare two (or more) sorted files and it guarantees that no line will be read from disk more than once.
If your files aren't appropriately sorted, then the initial sorting operation may be somewhat expensive on files in the "tens of gigabytes each" size range, but:
You can split the files into arbitrarily-sized chunks (ideally small enough for each chunk to fit into memory), sort each chunk independently, and then generalize the above algorithm from two files to as many as are necessary.
Even if you don't do that and you deal with the disk thrashing involved with sorting files larger than the available memory, sorting and then doing a single pass over each file will still be a lot faster than any solution involving a cartesian join.
Or you could just use a database as mentioned in previous answers. The above method will be more efficient in most, if not all, cases, but a database-based solution would be easier to write and would also provide a lot of flexibility for analyzing your data in other ways without needing to do a complete scan through each file every time you need to access anything in it.
I have a script that creates file system in a file on a linux machine. I see that to create the file system, it uses 'dd' with bs=x option, reads from /dev/zero and writes to a file. I think usually specifying ibs/obs/bs is useful to read from real hardware devices as one has specific block size constraints. In this case however, as it is reading from virtual device and writing to a file, I don't see any point behind using 'bs=x bytes' option. Is my understanding wrong here?
(Just in case if it helps, this file system is later on used to boot a qemu vm)
To understand block sizes, you have to be familiar with tape drives. If you're not interested in tape drives - for example, you don't think you're ever going to use one - then you can go back to sleep now.
Remember the tape drives from films in the 60s, 70s, maybe even 80s? The ones where the reel went spinning around, and so on? Not your Exabyte or even QIC - quarter-inch cartridge - tapes; your good old fashioned reel-to-reel half-inch tape drives? On those, block size mattered.
The data on a tape was written in blocks. Each block was separated from the next by an inter-record gap.
----+-------+-----+-------+-----+----
... | block | IRG | block | IRG | ...
----+-------+-----+-------+-----+----
Depending on the tape drive hardware and software, there were a variety of problems that could happen. For example, if the tape was written with a block size of 5120 bytes and you read the tape with a block size of 512 bytes, then the tape drive might read the first block, return you 512 bytes of it, and then discard the remaining data; the next read would start on the next block. Conversely, if the tape was written with a block size of 512 bytes and you requested blocks of 5120 bytes, you would get short reads; each read would return just 512 bytes, and if your software wasn't paying attention, you'd be reading garbage. There was also the issue that the tape drive had to get up to speed to read the block, and then slow down. The ASCII art suggests that the IRG was smaller than the data blocks; that was not necessarily the case. And it took time to read one block, overshoot the IRG, rewind backwards to get to the next block, and start forwards again. And if the tape drive didn't have the memory to buffer data - the cheaper ones did not - then you could seriously affect your tape drive performance.
War story: work prepared on newer machine with a slightly more modern tape drive. I wrote a tape using tar without a sensible block size (so it defaulted to 512 bytes). It was a large bit of software - must have been, oh, less than 100 MB in total (a long time ago, in other words). The tape wrote nicely because the machine was modern enough, and it took just a few seconds to do so. But, I had to get the material off the tape on a machine with an older tape drive, one that did not have any on-board buffer. So, it read the material, 512 bytes at a time, and the reel rocked forward, reading one block, and then rocked back all but maybe half an inch, and then read forwards to get to the next block, and then rocked back, and ... well, you could see it doing this, and since it took appreciable portions of a second to read each 512 byte block, the total time taken was horrendous. My flight was due to leave...and I needed to get that data across too. (It was long enough ago, and in a land far enough away, that last minute changes to flights weren't much of an option either.) To cut a long story short, it did get read - but if I'd used a sensible block size (such as 5120 bytes instead of the default of 512), I would have been done much, much quicker and with much less danger of missing the plane (but I did actually catch the plane, with maybe 20 minutes to spare, despite a taxi ride across Paris in the rush hour).
With more modern tape drives, there was enough memory on the drive to do buffering and getting a tape drive to stream - write continuously without reversing - was feasible. It used to be that I'd use a block size like 256 KB to get QIC tapes to stream. I've not done much with tape drives recently - let's see, not this millennium and not much for a few years before that, either; certainly not much since CD and DVD became the software distribution mechanisms of choice (when electronic download wasn't used).
But the block size really did matter in the old days. And dd provided good support for it. You could even transfer data from a tape drive that was written with, say, 4 KB block to another that you wanted to write with, say, 16 KB blocks, by specifying the ibs (input block size) separately from the obs (output block size). Darned useful!
Also, the count parameter is in terms of the (input) block size. It was useful to say 'dd bs=1024 count=1024 if=/dev/zero of=/my/file/of/zeroes' to copy 1 MB of zeroes around. Or to copy 1 MB of a file.
The importance of dd is vastly diminished; it was an essential part of the armoury for anybody who worked with tape drives a decade or more ago.
The block size is the number of bytes that are read and written at a time. Presumably there is a count= option, and that is specified in units of the block size. If there is a skip= or seek= option, those will also be in block size units. However if you are reading and writing a regular file, and there are no disk errors, then the block size doesn't really matter as long as you can scale those parameters accordingly and they are still integers. However certain sizes may be more efficient than others.
For reading from /dev/zero, it doesn't matter. ibs/obs/bs specify how many bytes will be read at a time. It's helpful to choose a number based on the way bytes are read/written in the operating system. For instance, Linux usually reads from a hard drive in 4096 byte chunks. If you have at least some idea about how the underlying hardware reads/writes, it might be a good idea to specify ibs/obs/bs. By the way, if you specify bs, it will override whatever you specify for ibs and obs.
In addition to the great answer by Jonathan Leffler, keep in mind that the bs= option isn't always a substitute for using both ibs= and obs=, in particular for the old [ugly] days of tape drives.
The bs= option reserves the right for dd to write the data as soon as it's read. This can cause you to no longer have identically sized blocks on the output. Here is GNU's take on this, but the behavior dates back as far as I can remember (80's):
(bs=) Set both input and output block sizes to bytes. This makes dd read and write bytes per block, overriding any ‘ibs’ and ‘obs’ settings. In addition, if no data-transforming conv option is specified, input is copied to the output as soon as it’s read, even if it is smaller than the block size.
For instance, back in the QIC days on an old Sun system, if you did this:
tar cvf /dev/rst0c /bla
It would work, but cause an enormous amount of back and forth thrashing while the drive wrote a small block, and tried to backup and read to reposition itself properly for the next write.
If you swapped this with:
tar cvf - /bla | dd ibs=16K obs=16K of=/dev/rst0c
You'd get the QIC drive writing much larger chunks and not thrashing quite so much.
However, if you made the mistake of this:
tar cvf - /bla | dd bs=16K of=/dev/rst0c
You'd run the risk of having precisely the same thrashing you had before depending upon how much data was available at the time of each read.
Specifying both ibs= and obs= precludes this from happening.
I have some code which will generate an infinite number of lines in output. So, I can't store those values in a single output file.
Instead, I split the output file into more files. I am splitting the file according to the index numbers. Now my doubt is I don't know how many numbers my file will be having. So is it possible to split the file into different output without giving index? For example:
first 100,000 lines in m.txt
from 100,001 to next 200,000 in n.txt
If you don't need to be able to find a particular line based on the file name, you can split the output based on the file size. Write lines to m1.txt until the next line will make it >1MB; then move to the next file - m2.txt.
split(1) appears to be exactly the tool for your job.
Generate files with a running index. Start with opening e.g. m_000001.txt. Write a fixed nuber of lines to that file. Close file. Open next file, e.g. m_000002.txt, and continue.
Making sure that you don't overflow the disk is an housekeepting task to be done separately. Here one can think of backups, compression, file rotation and so on.
You may want to use logrotate for this purpose. It has a lot of options: check out the man page.
Here's the introduction of the man page:
"logrotate is designed to ease administration of systems that generate
large numbers of log files. It allows automatic rotation, compression,
removal, and mailing of log files. Each log file may be handled daily,
weekly, monthly, or when it grows too large."
4 ways to split while writing:
A) Fixed no of characters (Size)
B) Fixed no of lines
C) Fixed Interval of time before writing
D) Fixed Counter of a function before calling a write
Based on those splitings, You can name the output file.