There are many different data formats that fall under the umbrella of "numerical data" (and they need not be entirely numerical in nature). The main distinction between numerical data and generic data is that the desired Mathematica form of the data will be represented by reals, integers, rationals, or complex numbers, rather than strings or symbols.
Another common form of numerical data is time series data, which has a time stamp associated with measurements of some kind (temperature, speed, pressure, etc). Here is a sample of financial time series data that is included with Mathematica.
|Import||user-friendly, supports many formats|
|ReadList||fast, flexible control of types, supports streams|
|BinaryReadList||better performance than the two previous functions|
|Get(DumpSave)||most efficient loading operation, preserves packed arrays|
|Import||high memory overhead, limited performance|
|ReadList||not as user-friendly, works best with plain-text formats|
|BinaryReadList||generally the least user-friendly, can involve a lot of data processing|
|Get(DumpSave)||files are not transferable, need to be loaded by another mechanism first|
For small files, and when working with new data for the first time, it is often easiest to use Import to load data. Import offers the most user-friendly interface for loading data, and in these cases the extra overhead generally is not a major concern. However, when deploying an application or working with particularly large datasets, working with a function like ReadList or BinaryReadList can greatly enhance the performance of your data loading.
As a general rule, ReadList will outperform a comparable Import operation in terms of speed and memory overhead. This is due in part to the type restriction used as a second argument in ReadList, which allows Mathematica to skip the various parsing operations used in Import (which make sure that entries that should be strings are interpreted as strings, and reals and integers are used appropriately for numerical values). This generally leads to Import being more user-friendly, but at the cost of requiring additional time and memory to do the same operation as ReadList.
ReadList is simple to use in cases where a large data file has a single type (such as rows and columns of reals or integers) but can be more work when multiple types are used (such as when strings and reals are intermixed, or where a data file with real values has strings in the first row of each column).
For small datasets, it generally is more efficient to use Import for time series data, but in cases where a large amount of information is being read, the processing overhead associated with Import may make it worth the extra effort so that your data can be handled using less memory, and more quickly.
Not all Import operations are handled by Mathematica directly, but instead rely on external converter binaries to handle the import operation. In many cases, these binaries have their own limitations on file size or may have a certain amount of overhead associated with them.
XLS files (Microsoft Excel documents), for example, use an XLS converter to process the XLS documents and read them into Mathematica. This process requires additional memory, since not only Mathematica but the converter also needs a copy of the file in memory to process, and then must pass the data over MathLink to Mathematica. It is not uncommon to see very large amounts of memory overhead associated with importing these file formats.
|Comma-Separated Values||.csv||plaintext format that separates records with commas and new lines|
|Tab-Separated Values||.tsv||plaintext format that separates records with tabs and new lines|
|Table||.dat||plaintext format that separates records with tabs and new lines|
|Plain Text||.txt||plaintext files can store data using arbitrary record separators|
|Binary||*||binary data format; can have various file extensions, or none|
Accurate measures of load time and memory overhead are important when optimizing a data-loading operation. Mathematica has a number of functions available for both of these tasks, which can help you determine the best way to load and process your data.
There are two functions in Mathematica that measure the time taken to perform a kernel operation (note that these values do not include the amount of time taken to render in the front end): Timing and AbsoluteTiming.
The biggest difference between Timing and AbsoluteTiming is that Timing measures calculation time in the kernel, whereas AbsoluteTiming measures elapsed time in the kernel. This distinction is easiest to see in the following example.
Timing measures the amount of time spent computing in the kernel, and does not count time waiting.
When reading data in, it can be useful to look at the results of Timing, but in most cases elapsed time (and therefore AbsoluteTiming) is a more valuable measure of speed, since the actual time taken is more likely to impact the application. The following examples illustrate the differences in Timing and AbsoluteTiming on an example dataset.
Another way of measuring timing is to manually create time stamps before and after the data loading operation, which you can see gives the same value as AbsoluteTiming (which actually has higher resolution than DateList in this case).
When measuring timing, it is often a good idea to put a time limit on a specific operation. TimeConstrained can be wrapped around the target function, and a time limit (in seconds) is specified.
It is important to note, especially when working with large data files, that Mathematica keeps a history of previous evaluations, which is managed by the environment variable $HistoryLength.
This means that even if a variable is cleared, there is still a copy of the data contained in that variable in the memory. When working with large datasets, it is generally a good idea to set the $HistoryLength to a small number or zero to prevent unwanted memory overhead.
When initially working with a large dataset, it can be easy to run evaluations that are taxing on system resources. When first prototyping your application, it can be helpful to wrap evaluations in MemoryConstrained to limit the amount of resources that are available to a particular evaluation.
ByteCount results can vary from those returned by MemoryInUse, as ByteCount counts each expression and subexpression as though it were unique; but in fact, subexpressions can be identical and therefore are shared.
The following example illustrates this.
These results vary because a 1,000,000-element expression (like ) takes 8,000,000 bytes, independent of what the elements take. (This is a 64-bit machine the author is using, where a pointer is 8 bytes, so an array of 1 million pointers is 8 million bytes.) All those pointers are pointing to the same expression, the String whose contents are "Sample long string...".
ByteCount uses a simple method for computing the size of an expression: it simply adds up the size of the components and the size of the containers.
The 8,000,000 bytes returned, plus 1 million copies of the String expression, results in an estimated byte count of 152 million.
In Mathematica Version 9 and higher, the only real restriction on how much data you can import is how much memory is available on your system, or more specifically, how much memory your operating system will allow any single process to utilize. That said, there are a number of factors that you should be aware of when loading data, and how they might limit your ability to load larger datasets.
It is important to note that memory is affected by not only what is held within an expression, but also by the dimensionality of that data. For example, the following two datasets were loaded using both ReadList and Import on the same machine, each with a fresh kernel, and have the same number of elements, but require very different amounts of memory to load and store the expressions.
You will notice that the packed expressions are all the same size (as are the Import and ReadList unpacked sizes for each data file), but there are large differences between the amount of memory required to load and store the expressions based on the dimensionality of the file (expressions that are long in just one dimension will generally require more memory).
The previous section outlines some of the general limits when loading data into Mathematica. In this section, some of the techniques that can be used to push those limits are discussed, and work is done with particularly large datasets.
BinaryReadList is one of the most memory- and time-efficient functions available to load data, running at what is essentially a 1-to-1 file-size-to-memory footprint.
As the previous section illustrates, BinaryReadList is generally going to run significantly faster than a comparable ReadList operation when working with data that is natively in a binary format. However, there may be some instances where your data is already in a plain text/ASCII format, in which case BinaryReadList can still be used.
The next step is to translate the list of bytes into ASCII characters, using FromCharacterCode (whose default format is $CharacterEncoding, in this case UTF-8) and then trim out the miscellaneous white spaces.
During development you may use Import on the file to get the data in, and let Mathematica handle the formatting and data interpretation.
While the RecordSeparators give the proper dimensionality for the data, the items are still both being loaded as strings. The addition of ToExpression on the second element of each list will return the desired results.
This function is more than 10 times as fast as Import, and uses a fraction of the memory required, because it is custom-tailored to this data format, rather than a generalized function like Import (import of plaintext files is essentially just a very robust, generalized ReadList function that handles a wide variety of data formats, with additional post-processing).
Large data files, especially those aggregated via scripts or logging systems, can sometimes take up so much room that they cannot be loaded into memory on the system all at once. However, this does not mean that the data is inaccessible to Mathematica, and there are a number of methods available for reading out portions of the data from very large files.
ReadList can be used directly on large files to read in subsets of the data, if that data is at the top of the file.
However, if that data is farther into the file, and it is not feasible to load in all the data before the desired information, OpenRead and Find can be used to locate a particular element within the data, for example, a date.
Some applications may require you to reload data every time it is used, or may require accessing a specific list of data files. In these cases, data loading can be heavily optimized by using Mathematica's DumpSave function.
Mathematica has two built-in methods for storing variables in data files, beyond the normal Write/Export functions. Save and DumpSave can be used to store variable values on a file system, allowing them to be reloaded in the future, using Get.
There are a few important distinctions between Save and DumpSave. The biggest difference in terms of data storage is that Save uses a plaintext format to store the information, while DumpSave uses a binary data format (which takes up less room, and can be read more quickly). DumpSave also preserves packed arrays, which improves both speed and memory overhead.
The main limitation of DumpSave is that its encoding system requires that the data be loaded on the same machine architecture on which it was encoded, so the files cannot be transferred between platforms.