Finding Changes between 2 HUGE zone (text) files_问答_开发者

I have access to the .com zone files. A zone file is a text file with a list of domain names and their nameservers. It follows a format such as:

mydomain NS ns.mynameserver.com.
mydomain NS ns2.mynameserver.com.
anotherdomain NS nameservers.com.
notinalphadomain NS ns.example.com.
notinalphadomain NS ns1.example.com.
notinalphadomain NS ns2.example.com.

As you can see, there can be multiple lines for each domain (when there are multiple nameservers), and the file is NOT in alpha order. These files are about 7GB in size.

I'm trying to take the previ开发者_如何转开发ous file and the new file, and compare them to find:

What domains have been Added
What domains have been Removed
What domains have had nameservers changed

Since 7GB is too much to load the entire file into memory, Obviously I need to read in a stream. The method I've currently thought up as the best way to do it is to make several passes over both files. One pass for each letter of the alphabet, loading all the domains in the first pass that start with 'a' for example. Once I've got all the 'a' domains from the old and new file, I can do a pretty simple comparison in memory to find the changes.

The problem is, even reading char by char, and optimizing as much as I've been able to think of, each pass over the file takes about 200-300 seconds, with collecting all the domains for the current pass's letter. So, I figure in its current state I'm looking at about an hour to process the files, without even storing the changes in the database (which will take some more time). This is on a dual quad core xeon server, so throwing more horsepower at it isn't much of an option for me. This timing may not be a dealbreaker, but I'm hoping someone has some bright ideas for how to speed things up... Admittedly I have not tried async IO yet, that's my next step.

Thanks in advance for any ideas!

Preparing your data may help, both in terms of the best kind of code: the unwritten kind, and in terms of execution speed.

cat yesterday-com-zone | tr A-Z a-z | sort > prepared-yesterday
cat today-com-zone | tr A-Z a-z | sort > prepared-today

Now, your program does a very simple differences algorithm, and you might even be able to use diff:

diff prepared-today prepared-yesterday

Edit:

And an alternative solution that removes some extra processing, at the possible cost of diff execution time. This also assumes the use of GnuWin32 CoreUtils:

sort -f <today-com-zone >prepared-today
sort -f <yesterday-com-zone >prepared-yesterday
diff -i prepared-today prepared-yesterday

The output from that will be a list of additions, removals, and changes. Not necessarily 1 change record per zone (consider what happens when two domains alphabetically in order are removed). You might need to play with the options to diff to force it to not check for as many lines of context, to avoid great swaths of false-positive changes.

You may need to write your program after all to take the two sorted input files and just run them in lock-step, per-zone. When a new zone is found in TODAY file, that's a new zone. When a "new" zone is found in YESTERDAY file (but missing in today), that's a removal. When the "same" zone is found in both files, then compare the NS records. That's either no-change, or a change in nameservers.

The question has been already answered, but I'll provide a more detailed answer, with facts that are good for everyone to understand. I'll try to cover the existing solutions, and even how to distribute , with explanations of why things turned out as they did.

You have a 7 GB text file. Your disk lets us stream data at, let's be pessimistic, 20 MB/second. This can stream the whole thing in 350 seconds. That is under 6 minutes.

If we suppose that an average line is 70 characters, we have 100 million rows. If our disk spins at 6000 rpm, the average rotation takes 0.01 seconds, so grabbing a random piece of data off of disk can take anywhere from 0 to 0.01 seconds, and on average will take 0.005 seconds. This is called our seek time. If you know exactly where every record is, and seek to each line, it will take you 0.005 sec * 100,000,000 = 500,000 sec which is close to 6 days.

Lessons?

When working with data on disk you really want to avoid seeking. You want to stream data.
When possible, you don't want your data to be on disk.

Now the standard way to address this issue is to sort data. A standard mergesort works by taking a block, sorting it, taking another block, sorting it, and then merging them together to get a larger block. The merge operation streams data in, and writes a stream out, which is exactly the kind of access pattern that disks like. Now in theory with 100 million rows you'll need 27 passes with a mergesort. But in fact most of those passes easily fit in memory. Furthermore a clever implementation - which nsort seems to be - can compress intermediate data files to keep more passes in memory. This dataset should be highly structured and compressible, in which all of the intermediate data files should be able to fit in RAM. Therefore you entirely avoid disk except for reading and writing data.

This is the solution you wound up with.

OK, so that tells us how to solve this problem. What more can be said?

Quite a bit. Let's analyze what happened with the database suggestions. The standard database has a table and some indexes. An index is just a structured data set that tells you where your data is in your table. So you walk the index (potentially doing multiple seeks, though in practice all but the last tend to be in RAM), which then tells you where your data is in the table, which you then have to seek to again to get the data. So grabbing a piece of data out of a large table potentially means 2 disk seeks. Furthermore writing a piece of data to a table means writing the data to the table, and updating the index. Which means writing in several places. That means more disk seeks.

As I explained at the beginning, disk seeks are bad. You don't want to do this. It is a disaster.

But, you ask, don't database people know this stuff? Well of course they do. They design databases to do what users ask them to do, and they don't control users. But they also design them to do the right thing when they can figure out what that is. If you're working with a decent database (eg Oracle or PostgreSQL, but not MySQL), the database will have a pretty good idea when it is going to be worse to use an index than it is to do a mergesort, and will choose to do the right thing. But it can only do that if it has all of the context, which is why it is so important to push work into the database rather than coding up a simple loop.

Furthermore the database is good about not writing all over the place until it needs to. In particular the database writes to something called a WAL log (write access log - yeah, I know that the second log is redundant) and updates data in memory. When it gets around to it it writes changes in memory to disk. This batches up writes and causes it to need to seek less. However there is a limit to how much can be batched. Thus maintaining indexes is an inherently expensive operation. That is why standard advice for large data loads in databases is to drop all indexes, load the table, then recreate indexes.

But all this said, databases have limits. If you know the right way to solve a problem inside of a database, then I guarantee that using that solution without the overhead of the database is always going to be faster. The trick is that very few developers have the necessary knowledge to figure out the right solution. And even for those who do, it is much easier to have the database figure out how to do it reasonably well than it is to code up the perfect solution from scratch.

And the final bit. What if we have a cluster of machines available? The standard solution for that case (popularized by Google, which uses this heavily internally) is called MapReduce. What it is based on is the observation that merge sort, which is good for disk, is also really good for distributing work across multiple machines. Thus we really, really want to push work to a sort.

The trick that is used to do this is to do the work in 3 basic stages:

Take large body of data and emit a stream of key/value facts.
Sort facts, partition them them into key/values, and send off for further processing.
Have a reducer that takes a key/values set and does something with them.

If need be the reducer can send the data into another MapReduce, and you can string along any set of these operations.

From the point of view of a user, the nice thing about this paradigm is that all you have to do is write a simple mapper (takes a piece of data - eg a line, and emits 0 or more key/value pairs) and a reducer (takes a key/values set, does something with it) and the gory details can be pushed off to your MapReduce framework. You don't have to be aware of the fact that it is using a sort under the hood. And it can even take care of such things as what to do if one of your worker machines dies in the middle of your job. If you're interested in playing with this, http://hadoop.apache.org/mapreduce/ is a widely available framework that will work with many other languages. (Yes, it is written in Java, but it doesn't care what language the mapper and reducer are written in.)

In your case your mapper could start with a piece of data in the form (filename, block_start), open that file, start at that block, and emit for each line a key/value pair of the form domain: (filename, registrar). The reducer would then get for a single domain the 1 or 2 files it came from with full details. It then only emits the facts of interest. Adds are that it is in the new but not the old. Drops are that it is in the old but not the new. Registrar changes are that it is in both but the registrar changed.

Assuming that your file is readily available in compressed form (so it can easily be copied to multiple clients) this can let you process your dataset much more quickly than any single machine could do it.

This is very similar to a Google interview question that goes something like "say you have a list on one-million 32-bit integers that you want to print in ascending order, and the machine you are working on only has 2 MB of RAM, how would you approach the problem?".

The answer (or rather, one valid answer) is to break the list up into manageable chunks, sort each chunk, and then apply a merge operation to generate the final sorted list.

So I wonder if a similar approach could work here. As in, starting with the first list, read as much data as you can efficiently work with in memory at once. Sort it, and then write the sorted chunk out to disk. Repeat this until you have processed the entire file, and then merge the chunks to construct a single sorted dataset (this step is optional...you could just do the final comparison using all the sorted chunks from file 1 and all the sorted chunks from file 2).

Repeat the above steps for the second file, and then open your two sorted datasets and read through them one line at a time. If the lines match then advance both to the next line. Otherwise record the difference in your result-set (or output file) and then advance whichever file has the lexicographically "smaller" value to the next line, and repeat.

Not sure how fast it would be, but it's almost certainly faster than doing 26 passes through each file (you've got 1 pass to build the chunks, 1 pass to merge the chunks, and 1 pass to compare the sorted datasets).

That, or use a database.

You should read each file once and save them into a database. Then you can perform whatever analysis you need using database queries. Databases are designed to quickly handle and process large amounts of data like this.

It will still be fairly slow to read all of the data into the database the first time, but you won't have to read the files more than once.