what changes when your input is giga/terabyte sized?

Question

I just took my first baby step today into real scientific computing today when I was shown a data set where the smallest file is 48000 fields by 1600 rows (haplotypes for several people, for chromosome 22). And this is considered tiny.

I write Python, so I've spent the last few hours reading about HDF5, and Numpy, and PyTable, but I still feel like I'm not really grokking what a terabyte-sized data set actually means for me as a programmer.

For example, someone pointed out that with larger data sets, it becomes impossible to read the whole thing into memory, not because the machine has insufficient RAM, but because the architecture has insufficient address space! It blew my mind.

What other assumptions have I been relying in the classroom that just don't work with input this big? What kinds of things do I need to start doing or thinking about differently? (This doesn't have to be Python specific.)

Westfalen

With the now quite common 64-bit architecture, computers can address that much memory: 64-bits means that your can address about 2**32 ~ 4 billion times what 32-bit computers can address. This is enough for your data. – Ashling

Answer 1

I'm currently engaged in high-performance computing in a small corner of the oil industry and regularly work with datasets of the orders of magnitude you are concerned about. Here are some points to consider:

1. Databases don't have a lot of traction in this domain. Almost all our data is kept in files, some of those files are based on tape file formats designed in the 70s. I think that part of the reason for the non-use of databases is historic; 10, even 5, years ago I think that Oracle and its kin just weren't up to the task of managing single datasets of O(TB) let alone a database of 1000s of such datasets.

   Another reason is a conceptual mismatch between the normalisation rules for effective database analysis and design and the nature of scientific data sets.

   I think (though I'm not sure) that the performance reason(s) are much less persuasive today. And the concept-mismatch reason is probably also less pressing now that most of the major databases available can cope with spatial data sets which are generally a much closer conceptual fit to other scientific datasets. I have seen an increasing use of databases for storing meta-data, with some sort of reference, then, to the file(s) containing the sensor data.

   However, I'd still be looking at, in fact am looking at, HDF5. It has a couple of attractions for me (a) it's just another file format so I don't have to install a DBMS and wrestle with its complexities, and (b) with the right hardware I can read/write an HDF5 file in parallel. (Yes, I know that I can read and write databases in parallel too).

2. Which takes me to the second point: when dealing with very large datasets you really need to be thinking of using parallel computation. I work mostly in Fortran, one of its strengths is its array syntax which fits very well onto a lot of scientific computing; another is the good support for parallelisation available. I believe that Python has all sorts of parallelisation support too so it's probably not a bad choice for you.

   Sure you can add parallelism on to sequential systems, but it's much better to start out designing for parallelism. To take just one example: the best sequential algorithm for a problem is very often not the best candidate for parallelisation. You might be better off using a different algorithm, one which scales better on multiple processors. Which leads neatly to the next point.

3. I think also that you may have to come to terms with surrendering any attachments you have (if you have them) to lots of clever algorithms and data structures which work well when all your data is resident in memory. Very often trying to adapt them to the situation where you can't get the data into memory all at once, is much harder (and less performant) than brute-force and regarding the entire file as one large array.

4. Performance starts to matter in a serious way, both the execution performance of programs, and developer performance. It's not that a 1TB dataset requires 10 times as much code as a 1GB dataset so you have to work faster, it's that some of the ideas that you will need to implement will be crazily complex, and probably have to be written by domain specialists, ie the scientists you are working with. Here the domain specialists write in Matlab.

But this is going on too long, I'd better get back to work

Upali

+1: not sure about python having good support for parallelisation though --- GIL can be a pain! – Stair

@Autopopulated: well, I'm a Fortran programmer but you have to be nice to Python hereabouts or you get things thrown at you :-) But I wouldn't touch it with a bargepole for serious HPC, it's way toooo sloowwwww. – Upali

Answer 2

In a nutshell, the main differences IMO:

1. You should know beforehand what your likely bottleneck will be (I/O or CPU) and focus on the best algorithm and infrastructure to address this. I/O quite frequently is the bottleneck.
2. Choice and fine-tuning of an algorithm often dominates any other choice made.
3. Even modest changes to algorithms and access patterns can impact performance by orders of magnitude. You will be micro-optimizing a lot. The "best" solution will be system-dependent.
4. Talk to your colleagues and other scientists to profit from their experiences with these data sets. A lot of tricks cannot be found in textbooks.
5. Pre-computing and storing can be extremely successful.

Bandwidth and I/O

Initially, bandwidth and I/O often is the bottleneck. To give you a perspective: at the theoretical limit for SATA 3, it takes about 30 minutes to read 1 TB. If you need random access, read several times or write, you want to do this in memory most of the time or need something substantially faster (e.g. iSCSI with InfiniBand). Your system should ideally be able to do parallel I/O to get as close as possible to the theoretical limit of whichever interface you are using. For example, simply accessing different files in parallel in different processes, or HDF5 on top of MPI-2 I/O is pretty common. Ideally, you also do computation and I/O in parallel so that one of the two is "for free".

Clusters

Depending on your case, either I/O or CPU might than be the bottleneck. No matter which one it is, huge performance increases can be achieved with clusters if you can effectively distribute your tasks (example MapReduce). This might require totally different algorithms than the typical textbook examples. Spending development time here is often the best time spent.

Algorithms

In choosing between algorithms, big O of an algorithm is very important, but algorithms with similar big O can be dramatically different in performance depending on locality. The less local an algorithm is (i.e. the more cache misses and main memory misses), the worse the performance will be - access to storage is usually an order of magnitude slower than main memory. Classical examples for improvements would be tiling for matrix multiplications or loop interchange.

Computer, Language, Specialized Tools

If your bottleneck is I/O, this means that algorithms for large data sets can benefit from more main memory (e.g. 64 bit) or programming languages / data structures with less memory consumption (e.g., in Python __slots__ might be useful), because more memory might mean less I/O per CPU time. BTW, systems with TBs of main memory are not unheard of (e.g. HP Superdomes).

Similarly, if your bottleneck is the CPU, faster machines, languages and compilers that allow you to use special features of an architecture (e.g. SIMD like SSE) might increase performance by an order of magnitude.

The way you find and access data, and store meta information can be very important for performance. You will often use flat files or domain-specific non-standard packages to store data (e.g. not a relational db directly) that enable you to access data more efficiently. For example, kdb+ is a specialized database for large time series, and ROOT uses a TTree object to access data efficiently. The pyTables you mention would be another example.

Marseilles

Answer 3

While some languages have naturally lower memory overhead in their types than others, that really doesn't matter for data this size - you're not holding your entire data set in memory regardless of the language you're using, so the "expense" of Python is irrelevant here. As you pointed out, there simply isn't enough address space to even reference all this data, let alone hold onto it.

What this normally means is either a) storing your data in a database, or b) adding resources in the form of additional computers, thus adding to your available address space and memory. Realistically you're going to end up doing both of these things. One key thing to keep in mind when using a database is that a database isn't just a place to put your data while you're not using it - you can do WORK in the database, and you should try to do so. The database technology you use has a large impact on the kind of work you can do, but an SQL database, for example, is well suited to do a lot of set math and do it efficiently (of course, this means that schema design becomes a very important part of your overall architecture). Don't just suck data out and manipulate it only in memory - try to leverage the computational query capabilities of your database to do as much work as possible before you ever put the data in memory in your process.

Heliozoan

Answer 4

The main assumptions are about the amount of cpu/cache/ram/storage/bandwidth you can have in a single machine at an acceptable price. There are lots of answers here at stackoverflow still based on the old assumptions of a 32 bit machine with 4G ram and about a terabyte of storage and 1Gb network. With 16GB DDR-3 ram modules at 220 Eur, 512 GB ram, 48 core machines can be build at reasonable prices. The switch from hard disks to SSD is another important change.

Embarkment