Related
I know that converting a suitable object (e.g., a linked list) to a string using the toString() method is an O(n) operation, where 'n' is the length of the linked list. However, if you wanted to then replace something in that that string using the replace(), method, is that also an o(k) method, where 'k' is the length of the string?
For example, for the line String str = path.toString().replace("[", "").replace("]", "").replace(",", "");, does this run through the length of the linked list 1 time, and then the length of the string an additional 3 times? If so, is there a more efficient way to do what that line of code does?
Yes, it would. replace has no idea that [ and ] are only found at the start and end. In fact, it's worse - you get another loop for copying the string over (the string has an underlying array and that needs to be cloned in its entirety to lop a character out of it).
If your intent is to replace every [ in the string, then, no, there is no faster way. However, if your actual intent is to simply not have the opening brace and closing brace, then either write your own loop to toString the contents. Something like:
LinkedList<Foo> foos = ...;
StringBuilder out = new StringBuilder();
for (Foo f : foos) out.append(out.length() == 0 ? "" : ", ").append(f);
return out.toString();
Or even:
String.join(", ", foos);
Or even:
foos.stream().collect(Collectors.joining(", "));
None of this is the same thing as .replace("[", "") - after all, if a [ symbol is part of the toString() of any Foo object, it would be stripped out as well with .replace("[", "") - though you probably didn't want that to happen.
Note that the way modern CPUs work, unless that list has over a few thousand elements in it, looping it 4 times is essentially free and takes no measurable time. The concept of O(n) 'kicks in' after a certain number of loops. On modern hardware, it tends to be a lot of loops before it matters. Often other concerns are much more important. As a simple example, linked list, in general? Horrible performance relative to something like ArrayList. Even in cases where O(k) wise it should be faster. It's due to the way linkedlists create extra objects and how these tend to be non-contiguous (not near each other in memory). Modern CPUs can't read memory at all. They can ask the memory controller to take one of the on-die cache pages and replace it with the contents of another memory page, which takes 500 to a 1000 cycles. The CPU will ask the memory controller to do that and then go to sleep for 1000 cycles. You can see how reducing the number of times it does this can have a rather marked effect on performance, and yet the O(k) business doesn't and cannot take it into account.
Do not worry about performance unless you have a real life scenario where the program appears to run slower than you think it should. Then, use a profiler to figure out which 1% of the code is eating 99% of the resources (because it's virtually always a 1% 'hot path' that is responsible) and then optimize just that 1%. It's pretty much impossible to predict what the 1% is going to be. So, don't bother trying to do so while writing code, it just leads you to writing harder to maintain, less flexible code - which ironically enough tends to lead to situations where adjusting the hot path is harder. Worrying about performance, in essence, slows down the code. Hence why it's very very important not to worry about that, and worry instead about code that is easy to read and easy to modify.
Specifically, my end goal is to store every comma separated word from the file in a List<String> and I was wondering which approach I should take.
Approach 1:
String fileContents = new Scanner(new File("filepath")).useDelimiter("\\Z").next();
List<String> list = Arrays.asList(fileContents.split("\\s*,\\s*"));
Approach 2:
Scanner s = new Scanner(new File("filepath")).useDelimiter(",");
List<String> list = new ArrayList<>();
while (s.hasNext()){
list.add(s.next());
}
s.close();
Approach #1 will read the entire file into memory. This has a couple of performance-related issues:
If the file is big that uses a lot of memory.
Because of the way that the character's need to be accumulated by the Scanner.next() call, the characters may need to be copied 2 or even 3 times.
There are other inefficiencies due to the fact that you are using a general pattern matching engine for a very specific purpose.
Approach #3 (which is Approach #1 with the File reading done better) addresses a lot of the efficiency issues, but you still hold the entire file contents in memory.
Approach #2 is best from memory usage perspective because you don't hold the entire file contents as a single string or buffer1. The performance is also likely to be best because (my intuition says) this approach avoids at least one copy of the characters.
However, if this really matters, you should benchmark the alternatives, bearing in mind 2 things:
"Premature optimization" is usually wasted effort. (Or to put it another, the chances are that the performance of this part of your code really doesn't matter. The performance bottleneck is likely somewhere else.)
There a lot of pitfalls for writing Java benchmarks that can lead to bogus performance measures and incorrect conclusions.
The other thing to note is that what you are trying to do (create a list of all "words" in order) does not scale. For a large enough input file, the application will run out of heap space. If you anticipate running this on input files larger than 100Mb or so, it may start to become a concern.
The solution may be to convert your processing into something that is more "stream" based ... so that you don't need to have a list of all words in memory.
This is essentially the same problem as the problem with Approach #1.
1 - unless the file is small and fits into the buffer ... and then the whole question is largely moot.
If you read the entire file into memory when you don't actually need to you are:
wasting time: nothing is processed until you've read the entire file
wasting space
using a technique that won't scale to large files.
Doing this has nothing to recommend it.
Approach 1:
Limit of String's maximum size i.e. a String of max length Integer.MAX_VALUE only is possible or the largest possible array at runtime
Hence, Prefer Approach 2 if it is a very large fie
I have to process 450 unique strings about 500 million times. Each string has unique integer identifier. There are two options for me to use.
I can append the identifier with the string and on arrival of the
string I can split the string to get the identifier and use it.
I can store the 450 strings in HashMap<String, Integer> and on
arrival of the string, I can query HashMap to get the identifier.
Can someone suggest which option will be more efficient in terms of processing?
It all depends on the sizes of the strings, etc.
You can do all sorts of things.
You can use a binary search to get the index in a list, and at that index is the identifier.
You can hash just the first 2 characters, rather than the entire string, that would likely be faster than the binary search, assuming the strings have an OK distribution.
You can use the first character, or first two characters, if they're unique as a "perfect index" in to 255 or 65K large array that points to the identifier.
Also, if your identifier is numeric, it's better to pre-calculate that, rather than convert it on the fly all the time. Text -> Binary is actually rather expensive (Binary -> Text is worse). So it's probably nice to avoid that if possible.
But it behooves you work the problem. 1 million anything at 1ms each, is 20 minutes of processing. At 500m, every nano-second wasted adds up to 8+ minutes extra of processing. You may well not care, but just demonstrating that at these scales "every little bit helps".
So, don't take our words for it, test different things to find what gives you the best result for your work set, and then go with that. Also consider excessive object creation, and avoiding that. Normally, I don't give it a second thought. Object creation is fast, but a nano-second is a nano-second.
If you're working in Java, and you don't REALLY need Unicode (i.e. you're working with single characters of the 0-255 range), I wouldn't use strings at all. I'd work with raw bytes. String are based on Java characters, which are UTF-16. Java Readers convert UTF-8 in to UTF-16 every. single. time. 500 million times. Yup! Another few nano-seconds. 8 nano-seconds adds an hour to your processing.
So, again, look in all the corners.
Or, don't, write it easy, fire it up, run it over the weekend and be done with it.
If each String has a unique identifier then retrieval is O(1) only in case of hashmaps.
I wouldn't suggest the first method because you are splitting every string for 450*500m, unless your order is one string for 500m times then on to the next. As Will said, appending numeric to strings then retrieving might seem straight forward but is not recommended.
So if your data is static (just the 450 strings) put them in a Hashmap and experiment it. Good luck.
Use HashMap<Integer, String>. Splitting a string to get the identifier is an expensive operation because it involves creating new Strings.
I don't think anyone is going to be able to give you a convincing "right" answer, especially since you haven't provided all of the background / properties of the computation. (For example, the average length of the strings could make a lot of difference.)
So I think your best bet would be to write a benchmark ... using the actual strings that you are going to be processing.
I'd also look for a way to extract and test the "unique integer identifier" that doesn't entail splitting the string.
Splitting the string should work faster if you write your code well enough. In fact if you already have the int-id, I see no reason to send only the string and maintain a mapping.
Putting into HashMap would need hashing the incoming string every time. So you are basically comparing the performance of the hashing function vs the code you write to append (prepending might be a bit more tricky) on sending end and to parse on receiving end.
OTOH, only 450 strings aren't a big deal, and if you're into it, writing your own hashing algo/function would actually be the most elegant and performant.
I'm writing a routine that takes a string and formats it as quoted printable. And it's got to be as fast as possible. My first attempt copied characters from one stringbuffer to another encoding and line wrapping along the way. Then I thought it might be quicker to just modify the original stringbuffer rather than copy all that data which is mostly identical. Turns out the inserts are far worse than copying, the second version (with the stringbuffer inserts) was 8 times slower, which makes sense, as it must be moving a lot of memory.
What I was hoping for was some kind of gap buffer data structure so the inserts wouldn't involve physically moving all the characters in the rest of the stringbuffer.
So any suggestions about the fastest way to rip through a string inserting characters every once in a while?
Suggestions to use the standard mimeutils library are not helpful because I'm also dot escaping the string so it can be dumped out to an smtp server in one shot.
At the end, your gap data structure would have to be transformed into a String, which would need assembling all the chunks in a single array by appending them to a StringBuilder.
So using a StringBuilder directly will be faster. I don't think you'll find a faster technique than that. Make sure to initialize the StringBuilder with a large enough size to avoid copies of the whole buffer once the capacity is exhausted.
So taking the advice of some of the other answers here I've been writing many versions of this function, seeing what goes quickest and for future reference if anybody can gain from what I found:
1) The slowest: stringbuffer.append() but we knew that.
2) Almost twice as fast: stringbuilder.append(). locks are very expensive it seems.
3) another 20% faster is.... copying from one char[] to another.
4) and finally, coming in three times faster than even that... a JNI call to the exact same code compiled in C that copies from one char array to another.
You may consider #4 cheating, but cheaters win. It is by far the fastest way to go.
There is a risk of the GetCharArrayElements call causing the java char array to be copied so it can be handed to the C program, but I can't tell if that's happening, and it's still wicked fast compared to any java implementation.
I think a good balance between speed and coding grace would be using Matcher.appendReplacement. Formulate a regex that will catch all insertion points. In a loop you use find, analyze Matcher.group() to see what exactly has matched, and use your program logic to decide what to give to appendReplacement.
In any case, it is important not to copy the text over char by char. You must copy in the largest chunks possible.
The Matcher API is quite unfortunately bound to the StringBuffer, but, as you find, that only steels the final 5% from you.
I have a general question on your opinion about my "technique".
There are 2 textfiles (file_1 and file_2) that need to be compared to each other. Both are very huge (3-4 gigabytes, from 30,000,000 to 45,000,000 lines each).
My idea is to read several lines (as many as possible) of file_1 to the memory, then compare those to all lines of file_2. If there's a match, the lines from both files that match shall be written to a new file. Then go on with the next 1000 lines of file_1 and also compare those to all lines of file_2 until I went through file_1 completely.
But this sounds actually really, really time consuming and complicated to me.
Can you think of any other method to compare those two files?
How long do you think the comparison could take?
For my program, time does not matter that much. I have no experience in working with such huge files, therefore I have no idea how long this might take. It shouldn't take more than a day though. ;-) But I am afraid my technique could take forever...
Antoher question that just came to my mind: how many lines would you read into the memory? As many as possible? Is there a way to determine the number of possible lines before actually trying it?
I want to read as many as possible (because I think that's faster) but I've ran out of memory quite often.
Thanks in advance.
EDIT
I think I have to explain my problem a bit more.
The purpose is not to see if the two files in general are identical (they are not).
There are some lines in each file that share the same "characteristic".
Here's an example:
file_1 looks somewhat like this:
mat1 1000 2000 TEXT //this means the range is from 1000 - 2000
mat1 2040 2050 TEXT
mat3 10000 10010 TEXT
mat2 20 500 TEXT
file_2looks like this:
mat3 10009 TEXT
mat3 200 TEXT
mat1 999 TEXT
TEXT refers to characters and digits that are of no interest for me, mat can go from mat1 - mat50 and are in no order; also there can be 1000x mat2 (but the numbers in the next column are different). I need to find the fitting lines in a way that: matX is the same in both compared lines an the number mentioned in file_2 fits into the range mentioned in file_1.
So in my example I would find one match: line 3 of file_1and line 1 of file_2 (because both are mat3 and 10009 is between 10000 and 10010).
I hope this makes it clear to you!
So my question is: how would you search for the matching lines?
Yes, I use Java as my programming language.
EDIT
I now divided the huge files first so that I have no problems with being out of memory. I also think it is faster to compare (many) smaller files to each other than those two huge files. After that I can compare them the way I mentioned above. It may not be the perfect way, but I am still learning ;-)
Nonentheless all your approaches were very helpful to me, thank you for your replies!
I think, your way is rather reasonable.
I can imagine different strategies -- for example, you can sort both files before compare (where is efficient implementation of filesort, and unix sort utility can sort several Gbs files in minutes), and, while sorted, you can compare files sequentally, reading line by line.
But this is rather complex way to go -- you need to run external program (sort), or write comparable efficient implementation of filesort in java by yourself -- which is by itself not an easy task. So, for the sake of simplicity, I think you way of chunked read is very promising;
As for how to find reasonable block -- first of all, it may not be correct what "the more -- the better" -- I think, time of all work will grow asymptotically, to some constant line. So, may be you'll be close to that line faster then you think -- you need benchmark for this.
Next -- you may read lines to buffer like this:
final List<String> lines = new ArrayList<>();
try{
final List<String> block = new ArrayList<>(BLOCK_SIZE);
for(int i=0;i<BLOCK_SIZE;i++){
final String line = ...;//read line from file
block.add(line);
}
lines.addAll(block);
}catch(OutOfMemory ooe){
//break
}
So you read as many lines, as you can -- leaving last BLOCK_SIZE of free memory. BLOCK_SIZE should be big enouth to the rest of you program to run without OOM
In an ideal world, you would be able to read in every line of file_2 into memory (probably using a fast lookup object like a HashSet, depending on your needs), then read in each line from file_1 one at a time and compare it to your data structure holding the lines from file_2.
As you have said you run out of memory however, I think a divide-and-conquer type strategy would be best. You could use the same method as I mentioned above, but read in a half (or a third, a quarter... depending on how much memory you can use) of the lines from file_2 and store them, then compare all of the lines in file_1. Then read in the next half/third/quarter/whatever into memory (replacing the old lines) and go through file_1 again. It means you have to go through file_1 more, but you have to work with your memory constraints.
EDIT: In response to the added detail in your question, I would change my answer in part. Instead of reading in all of file_2 (or in chunks) and reading in file_1 a line at a time, reverse that, as file_1 holds the data to check against.
Also, with regards searching the matching lines. I think the best way would be to do some processing on file_1. Create a HashMap<List<Range>> that maps a String ("mat1" - "mat50") to a list of Ranges (just a wrapper for a startOfRange int and an endOfRange int) and populate it with the data from file_1. Then write a function like (ignoring error checking)
boolean isInRange(String material, int value)
{
List<Range> ranges = hashMapName.get(material);
for (Range range : ranges)
{
if (value >= range.getStart() && value <= range.getEnd())
{
return true;
}
}
return false;
}
and call it for each (parsed) line of file_2.
Now that you've given us more specifics, the approach I would take relies upon pre-partitioning, and optionally, sorting before searching for matches.
This should eliminate a substantial amount of comparisons that wouldn't otherwise match anyway in the naive, brute-force approach. For the sake of argument, lets peg both files at 40 million lines each.
Partitioning: Read through file_1 and send all lines starting with mat1 to file_1_mat1, and so on. Do the same for file_2. This is trivial with a little grep, or should you wish to do it programmatically in Java it's a beginner's exercise.
That's one pass through two files for a total of 80million lines read, yielding two sets of 50 files of 800,000 lines each on average.
Sorting: For each partition, sort according to the numeric value in the second column only (the lower bound from file_1 and the actual number from file_2). Even if 800,000 lines can't fit into memory I suppose we can adapt 2-way external merge sort and perform this faster (fewer overall reads) than a sort of the entire unpartitioned space.
Comparison: Now you just have to iterate once through both pairs of file_1_mat1 and file_2_mat1, without need to keep anything in memory, outputting matches to your output file. Repeat for the rest of the partitions in turn. No need for a final 'merge' step (unless you're processing partitions in parallel).
Even without the sorting stage the naive comparison you're already doing should work faster across 50 pairs of files with 800,000 lines each rather than with two files with 40 million lines each.
there is a tradeoff: if you read a big chunk of the file, you save the disc seek time, but you may have read information you will not need, since the change was encountered on the first lines.
You should probably run some experiments [benchmarks], with varying chunk size, to find out what is the optimal chunk to read, in the average case.
No sure how good an answer this would be - but have a look at this page: http://c2.com/cgi/wiki?DiffAlgorithm - it summarises a few diff algorithms. Hunt-McIlroy algorithm is probably the better implementation. From that page there's also a link to a java implementation of the GNU diff. However, I think an implementation in C/C++ and compiled into native code will be much faster. If you're stuck with java, you may want to consider JNI.
Indeed, that could take a while. You have to make 1,200.000,000 line comparisions.
There are several possibilities to speed that up by an order of magnitute:
One would be to sort file2 and do kind of a binary search on file level.
Another approach: compute a checksum of each line, and search that. Depending on average line length, the file in question would be much smaller and you really can do a binary search if you store the checksums in a fixed format (i.e. a long)
The number of lines you read at once from file_1 does not matter, however. This is micro-optimization in the face of great complexity.
If you want a simple approach: you can hash both of the files and compare the hash. But it's probably faster (especially if the files differ) to use your approach. About the memory consumption: just make sure you use enough memory, using no buffer for this kind a thing is a bad idea..
And all those answers about hashes, checksums etc: those are not faster. You have to read the whole file in both cases. With hashes/checksums you even have to compute something...
What you can do is sort each individual file. e.g. the UNIX sort or similar in Java. You can read the sorted files one line at a time to perform a merge sort.
I have never worked with such huge files but this is my idea and should work.
You could look into hash. Using SHA-1 Hashing.
Import the following
import java.io.FileInputStream;
import java.security.MessageDigest;
Once your text file etc has been loaded have it loop through each line and at the end print out the hash. The example links below will go into more depth.
StringBuffer myBuffer = new StringBuffer("");
//For each line loop through
for (int i = 0; i < mdbytes.length; i++) {
myBuffer.append(Integer.toString((mdbytes[i] & 0xff) + 0x100, 16).substring(1));
}
System.out.println("Computed Hash = " + sb.toString());
SHA Code example focusing on Text File
SO Question about computing SHA in JAVA (Possibly helpful)
Another sample of hashing code.
Simple read each file seperatley, if the hash value for each file is the same at the end of the process then the two files are identical. If not then something is wrong.
Then if you get a different value you can do the super time consuming line by line check.
Overall, It seems that reading line by line by line by line etc would take forever. I would do this if you are trying to find each individual difference. But I think hashing would be quicker to see if they are the same.
SHA checksum
If you want to know exactly if the files are different or not then there isn't a better solution than yours -- comparing sequentially.
However you can make some heuristics that can tell you with some kind of probability if the files are identical.
1) Check file size; that's the easiest.
2) Take a random file position and compare block of bytes starting at this position in the two files.
3) Repeat step 2) to achieve the needed probability.
You should compute and test how many reads (and size of block) are useful for your program.
My solution would be to produce an index of one file first, then use that to do the comparison. This is similar to some of the other answers in that it uses hashing.
You mention that the number of lines is up to about 45 million. This means that you could (potentially) store an index which uses 16 bytes per entry (128 bits) and it would use about 45,000,000*16 = ~685MB of RAM, which isn't unreasonable on a modern system. There are overheads in using the solution I describe below, so you might still find you need to use other techniques such as memory mapped files or disk based tables to create the index. See Hypertable or HBase for an example of how to store the index in a fast disk-based hash table.
So, in full, the algorithm would be something like:
Create a hash map which maps Long to a List of Longs (HashMap<Long, List<Long>>)
Get the hash of each line in the first file (Object.hashCode should be sufficient)
Get the offset in the file of the line so you can find it again later
Add the offset to the list of lines with matching hashCodes in the hash map
Compare each line of the second file to the set of line offsets in the index
Keep any lines which have matching entries
EDIT:
In response to your edited question, this wouldn't really help in itself. You could just hash the first part of the line, but it would only create 50 different entries. You could then create another level in the data structure though, which would map the start of each range to the offset of the line it came from.
So something like index.get("mat32") would return a TreeMap of ranges. You could look for the range preceding the value you are looking for lowerEntry(). Together this would give you a pretty fast check to see if a given matX/number combination was in one of the ranges you are checking for.
try to avoid memory consuming and make it disc consuming.
i mean divide each file into loadable size parts and compare them, this may take some extra time but will keep you safe dealing with memory limits.
What about using source control like Mercurial? I don't know, maybe it isn't exactly what you want, but this is a tool that is designed to track changes between revisions. You can create a repository, commit the first file, then overwrite it with another one an commit the second one:
hg init some_repo
cd some_repo
cp ~/huge_file1.txt .
hg ci -Am "Committing first huge file."
cp ~/huge_file2.txt huge_file1.txt
hg ci -m "Committing second huge file."
From here you can get a diff, telling you what lines differ. If you could somehow use that diff to determine what lines were the same, you would be all set.
That's just an idea, someone correct me if I'm wrong.
I would try the following: for each file that you are comparing, create temporary files (i refer to it as partial file later) on disk representing each alphabetic letter and an additional file for all other characters. then read the whole file line by line. while doing so, insert the line into the relevant file that corresponds to the letter it starts with. since you have done that for both files, you can now limit the comparison for loading two smaller files at a time. a line starting with A for example can appear only in one partial file and there will not be a need to compare each partial file more than once. If the resulting files are still very large, you can apply the same methodology on the resulting partial files (letter specific files) that are being compared by creating files according to the second letter in them. the trade-of here would be usage of large disk space temporarily until the process is finished. in this process, approaches mentioned in other posts here can help in dealing with the partial files more efficiently.