Hi,
I've been doing some tests using serialize() to a raw vector:
df <- data.frame(runif(50e6,1,10))
ser <- serialize(df,NULL)
In this example the data frame and the serialized raw vector occupy ~400MB
each, for a total of ~800M. However the memory peak during serialize() is
~1.2GB:
$ cat /proc/15155/status |grep Vm
...
VmHWM: 1207792 kB
VmRSS: 817272 kB
We work with very large data frames and in many cases this is killing R with an
"out of memory" error.
This is the relevant code in R 3.1.3 in src/main/serialize.c:2494
InitMemOutPStream(&out, &mbs, type, version, hook, fun);
R_Serialize(object, &out);
val = CloseMemOutPStream(&out);
The serialized object is being stored in a buffer pointed by out.data. Then in
CloseMemOutPStream() R copies the whole buffer to a newly allocated SEXP object
(the raw vector that stores the final result):
PROTECT(val = allocVector(RAWSXP, mb->count));
memcpy(RAW(val), mb->buf, mb->count);
free_mem_buffer(mb);
UNPROTECT(1);
Before calling free_mem_buffer() the process is using ~1.2GB (the original data
frame + the serialization buffer + final serialized raw vector).
One possible solution would be to allocate a buffer for the final raw vector
and store the serialization result directly into that buffer. This would bring
the memory peak down from ~1.2GB to ~800MB.
Thanks,
-Jorge
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