These functions are experimental and somewhat advanced. By experimental we mean their names might change and perhaps the syntax, argument names and types. So if you write a lot of code using them, you have been warned! They should work and be stable, though, so please report problems with them. alloc.col is just an alias to setalloccol. We recommend to use setalloccol (though alloc.col will continue to be supported) because the set* prefix in setalloccol makes it clear that its input argument is modified in-place.
truelength(x)
setalloccol(DT,
n = getOption("datatable.alloccol"), # default: 1024L
verbose = getOption("datatable.verbose")) # default: FALSE
alloc.col(DT,
n = getOption("datatable.alloccol"), # default: 1024L
verbose = getOption("datatable.verbose")) # default: FALSEAny type of vector, including data.table which is a list vector of column pointers.
A data.table.
The number of spare column pointer slots to ensure are available. If DT is a 1,000 column data.table with 24 spare slots remaining, n=1024L means grow the 24 spare slots to be 1024. truelength(DT) will then be 2024 in this example.
Output status and information.
truelength(x) returns the length of the vector allocated in memory. length(x) of those items are in use. Currently, it is just the list vector of column pointers that is over-allocated (i.e. truelength(DT)), not the column vectors themselves, which would in future allow fast row insert(). For tables loaded from disk however, truelength is 0 in R 2.14.0+ (and random in R <= 2.13.2), which is perhaps unexpected. data.table detects this state and over-allocates the loaded data.table when the next column addition occurs. All other operations on data.table (such as fast grouping and joins) do not need truelength.
setalloccol reallocates DT by reference. This may be useful for efficiency if you know you are about to going to add a lot of columns in a loop. It also returns the new DT, for convenience in compound queries.
When adding columns by reference using :=, we could simply create a new column list vector (one longer) and memcpy over the old vector, with no copy of the column vectors themselves. That requires negligible use of space and time, and is what v1.7.2 did. However, that copy of the list vector of column pointers only (but not the columns themselves), a shallow copy, resulted in inconsistent behaviour in some circumstances. So, as from v1.7.3 data.table over allocates the list vector of column pointers so that columns can be added fully by reference, consistently.
When the allocated column pointer slots are used up, to add a new column data.table must reallocate that vector. If two or more variables are bound to the same data.table this shallow copy may or may not be desirable, but we don't think this will be a problem very often (more discussion may be required on data.table issue tracker). Setting options(datatable.verbose=TRUE) includes messages if and when a shallow copy is taken. To avoid shallow copies there are several options: use copy to make a deep copy first, use setalloccol to reallocate in advance, or, change the default allocation rule (perhaps in your .Rprofile); e.g., options(datatable.alloccol=10000L).
Please note : over allocation of the column pointer vector is not for efficiency per se; it is so that := can add columns by reference without a shallow copy.
# NOT RUN {
DT = data.table(a=1:3,b=4:6)
length(DT) # 2 column pointer slots used
truelength(DT) # 1026 column pointer slots allocated
setalloccol(DT, 2048)
length(DT) # 2 used
truelength(DT) # 2050 allocated, 2048 free
DT[,c:=7L] # add new column by assigning to spare slot
truelength(DT)-length(DT) # 2047 slots spare
# }
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