The goal of this chapter is to help you understand the basics of memory management in R, moving from individual objects to functions to larger blocks of code. Memory profiling with lineprof shows you how to use the lineprof package to understand how memory is allocated and released in larger code blocks.
Modification in place introduces you to the address and refs functions so that you can understand when R modifies in place and when R modifies a copy.
Understanding when objects are copied is very important for writing efficient R code. Most of the information in this chapter was gleaned from a close reading of the documentation particularly?
Memory and? The rest I figured out by reading the C source code, performing small experiments, and asking questions on R-devel. Any mistakes are entirely mine. This function tells you how many bytes of memory an object occupies:. This function is better than the built-in object.
The code below computes and plots the memory usage of integer vectors ranging in length from 0 to 50 elements. You might expect that the size of an empty vector would be zero and that memory usage would grow proportionately with length.
Neither of those things are true! Every length 0 vector occupies 40 bytes of memory:. Object metadata 4 bytes. These metadata store the base type e.
This doubly-linked list makes it easy for internal R code to loop through every object in memory. The length of the vector 4 bytes. But in R 3. Read R-internals to see how support for long vectors was added without having to change the size of this field. This is basically never used, except when the object is the hash table used for an environment.
In that case, the true length represents the allocated space, and the length represents the space currently used. The data?? An empty vector has 0 bytes of data. Numeric vectors occupy 8 bytes for every element, integer vectors 4, and complex vectors This explains the intercept on the graph. But why does the memory size grow irregularly? To understand why, you need to know a little bit about how R requests memory from the operating system. Requesting memory with malloc is a relatively expensive operation.
Having to request memory every time a small vector is created would slow R down considerably. Instead, R asks for a big block of memory and then manages that block itself.
Moreover, each memory allocation will eventually have to be deallocated and in R this is done automatically by the garbage collector, which runs in the background and recovers any blocks of memory that are allocated but no longer in use.
Garbage collection takes time and therefore slows down the overall processing in R even further. The profmem function uses the utils::Rprofmem function for logging memory allocation events to a temporary file. The logged events are parsed and returned as an in-memory R object in a format that is convenient to work with. All memory allocations that are done via the native allocVector3 part of R's native API are logged, which means that nearly all memory allocations are logged.
Any objects allocated this way are automatically deallocated by R's garbage collector at some point. Garbage collection events are not logged by profmem. Such objects are not handled by the R garbage collector. In addition to utils::Rprofmem , R also provides utils::Rprof memory.
Despite the close similarity of their names, they use completely different approaches for profiling the memory usage. Note that parts of the garbage collection process goes through the full reserved pointer protection stack and hence becomes slower when the size is increased.
Currently the maximum value accepted is Memory-limits for the design limitations. Details R has a variable-sized workspace. See Also An Introduction to R for more command-line options. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more.
Determining memory usage of objects? Ask Question. Asked 12 years, 2 months ago. Active 27 days ago. Viewed 83k times. Improve this question. Josh Reich Josh Reich 6, 5 5 gold badges 26 26 silver badges 26 26 bronze badges. I do not understand why this question was closed.
This question asks about determining memory usage which is clearly distinct from the linked "duplicate" which is about managing memory.
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