i want to konw this data struct will use how much memory in Erlang VM?
[{"3GPP-UTRAN-FDD", [{"utran-cell-id-3gpp","CID1000"}, "1996-12-19t16%3a39%3a57-08%3a00", "1996-12-19t15%3a39%3a27%2e20-08%3a00"]}]
In my application, every process will store this data in self's loop data, and the numbert of this proces will be 120000.
The result which i test:
don't store this data, the memory will be:
memory[kB]: proc 1922806, atom 2138, bin 24890, code 72757, ets 459321
store this data, the momory will be:
memory[kB]: proc 1684032, atom 2138, bin 24102, code 72757, ets 459080
So the big difference is the memoery used by proc: (1922806 - 1684032) / 1024 = 233M.
After research, i find an insterting thing:
L = [{"3GPP-UTRAN-FDD", [{"utran-cell-id-3gpp","CID1000"}, "1996-12-19t16%3a39%3a57-08%3a00", "1996-12-19t15%3a39%3a27%2e20-08%3a00"]}].
B = list_to_binary(io_lib:format("~p", L)).
erts_debug:size(B). % The output is 6
The memory just use 6 words after using binary? How to explain this?
There are two useful functions for measuring the size of an Erlang term: erts_debug:size/1 and erts_debug:flat_size/1. Both of these functions return the size of the term in words.
erts_debug:flat_size/1 gives you the total size of a message without term-sharing optimization. This is guaranteed to be the size of the term if it is copied to a new heap, as with message passing and ets tables.
erts_debug:size/1 gives you the size of the term as it is in the current process' heap, allowing for memory usage optimization by sharing repeated terms.
Here is a demonstration of the differences:
1> MyTerm = {atom, <<"binary">>, 1}.
{atom,<<"binary">>,1}
2> MyList = [ MyTerm || _ <- lists:seq(1, 100) ].
[{atom,<<"binary">>,1}|...]
3> erts_debug:size(MyList).
210
4> erts_debug:flat_size(MyList).
1200
As you can see, there is a significant difference in the sizes due to term sharing.
As for your specific term, I used the Erlang shell (R16B03) and measured the term with flat_size. According to this, the memory usage of your term is: 226 words (1808B, 1.77KB).
This is a lot of memory to use for what appears to be a simple term, but that is outside of the scope of this question.
the size of the whole binary is 135 bytes when you do it list_to_binary(io_lib:format("~p", L))., if you are on a 64 bit system it represents 4.375 words so 6 words should be the correct size, but you have lost the direct access to the internal structure.
Strange but can be understood:
19> erts_debug:flat_size(list_to_binary([random:uniform(26) + $a - 1 || _ <- lists:seq(1,1000)])).
6
20> erts_debug:flat_size(list_to_binary([random:uniform(26) + $a - 1 || _ <- lists:seq(1,10000)])).
6
21> size(list_to_binary([random:uniform(26) + $a - 1 || _ <- lists:seq(1,10000)])).
10000
22> (list_to_binary([random:uniform(26) + $a - 1 || _ <- lists:seq(1,10000)])).
<<"myeyrltgyfnytajecrgtonkdcxlnaoqcsswdnepnmdxfrwnnlbzdaxknqarfyiwewlugrtgjgklblpdkvgpecglxmfrertdfanzukfolpphqvkkwrpmb"...>>
23> erts_debug:display({list_to_binary([random:uniform(26) + $a - 1 || _ <- lists:seq(1,10000)])}).
{<<10000 bytes>>}
"{<<10000 bytes>>}\n"
24>
This means that the erts_debug:flat_size return the size of the variable (which is roughly a type information, a pointer to the data and its size), but not the size of the binary data itself. The binary data is stored elsewhere and can be shared by different variables.
Related
Can someone explain why s is a string with 4096 chars
iex(9)> s = String.duplicate("x", 4096)
... lots of "x"
iex(10)> String.length(s)
4096
but its memory size are a few 6 words?
iex(11)> :erts_debug.size(s)
6 # WHAT?!
And why s2 is a much shorter string than s
iex(13)> s2 = "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20"
"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20"
iex(14)> String.length(s)
50
but its size has more 3 words than s?
iex(15)> :erts_debug.size(s2)
9 # WHAT!?
And why does the size of these strings does not match their lengths?
Thanks
First clue why this is showing that values can be found in this question. Quoting size/1 docs:
%% size(Term)
%% Returns the size of Term in actual heap words. Shared subterms are
%% counted once. Example: If A = [a,b], B =[A,A] then size(B) returns 8,
%% while flat_size(B) returns 12.
Second clue can be found in Erlang documentation about bitstrings implementation.
So in the first case the string is too big to fit on heap alone, so it uses refc binaries which are stored on stack and on heap there is only pointer to given binary.
In second case string is shorter than 64 bytes and it uses heap binaries which is just array of bytes stored directly in on the heap, so that gives us 8 bytes per word (64-bit) * 9 = 72 and when we check documentation about exact memory overhead in VM we see that Erlang uses 3..6 words per binary + data, where data can be shared.
I know that lookup time is constant for ETS tables. But I also heard that the table is kept outside of the process and when retrieving data, it needs to be moved to the process heap. So, this is expensive. But then, how to explain this:
18> {Time, [[{ok, Binary}]]} = timer:tc(ets, match, [utilo, {a, '$1'}]).
{0,
[[{ok,<<255,216,255,225,63,254,69,120,105,102,0,0,73,
73,42,0,8,0,0,0,10,0,14,...>>}]]}
19> size(Binary).
1759017
1.7 MB binary takes 0 time to be retrieved from the table!?
EDIT: After I saw Odobenus Rosmarus's answer, I decided to convert the binary to list. Here is the result:
1> {ok, B} = file:read_file("IMG_2171.JPG").
{ok,<<255,216,255,225,63,254,69,120,105,102,0,0,73,73,42,
0,8,0,0,0,10,0,14,1,2,0,32,...>>}
2> size(B).
1986392
3> L = binary_to_list(B).
[255,216,255,225,63,254,69,120,105,102,0,0,73,73,42,0,8,0,0,
0,10,0,14,1,2,0,32,0,0|...]
4> length(L).
1986392
5> ets:insert(utilo, {a, L}).
true
6> timer:tc(ets, match, [utilo, {a, '$1'}]).
{106000,
[[[255,216,255,225,63,254,69,120,105,102,0,0,73,73,42,0,8,0,
0,0,10,0,14,1,2|...]]]}
Now it takes 106000 microseconds to retrieve 1986392 long list from the table which is pretty fast, isn't it? Lists are 2 words per element. Thus the data is 4x1.7MB.
EDIT 2: I started a thread on erlang-question (http://groups.google.com/group/erlang-programming/browse_thread/thread/5581a8b5b27d4fe1) and it turns out that 0.1 second is pretty much the time it takes to do memcpy() (move the data to the process's heap). On the other hand Odobenus Rosmarus's answer explains why retrieving binary takes 0 time.
binaries itself (that longer than 64 bits) are stored in the special heap, outside of process heap.
So, retrieval of binary from the ets table moves to process heap just 'Procbin' part of binary. (roughly it's pointer to start of binary in the binaries memory and size).
I have a binary M such that 34= will always be present and the rest may vary between any number of digits but will always be an integer.
M = [<<"34=21">>]
When I run this command I get an answer like
hd([X || <<"34=", X/binary >> <- M])
Answer -> <<"21">>
How can I get this to be an integer with the most care taken to make it as efficient as possible?
[<<"34=",X/binary>>] = M,
list_to_integer(binary_to_list(X)).
That yields the integer 21
As of R16B, the BIF binary_to_integer/1 can be used:
OTP-10300
Added four new bifs, erlang:binary_to_integer/1,2,
erlang:integer_to_binary/1, erlang:binary_to_float/1 and
erlang:float_to_binary/1,2. These bifs work similarly to how
their list counterparts work, except they operate on
binaries. In most cases converting from and to binaries is
faster than converting from and to lists.
These bifs are auto-imported into erlang source files and can
therefore be used without the erlang prefix.
So that would look like:
[<<"34=",X/binary>>] = M,
binary_to_integer(X).
A string representation of a number can be converted by N-48. For multi-digit numbers you can fold over the binary, multiplying by the power of the position of the digit:
-spec to_int(binary()) -> integer().
to_int(Bin) when is_binary(Bin) ->
to_int(Bin, {size(Bin), 0}).
to_int(_, {0, Acc}) ->
erlang:trunc(Acc);
to_int(<<N/integer, Tail/binary>>, {Pos, Acc}) when N >= 48, N =< 57 ->
to_int(Tail, {Pos-1, Acc + ((N-48) * math:pow(10, Pos-1))}).
The performance of this is around 100 times slower than using the list_to_integer(binary_to_list(X)) option.
As suggested in answers to a previous question, I tried using Erlang proplists to implement a prefix trie.
The code seems to work decently well... But, for some reason, it doesn't play well with the interactive shell. When I try to run it, the shell hangs:
> Trie = trie:from_dict(). % Creates a trie from a dictionary
% ... the trie is printed ...
% Then nothing happens
I see the new trie printed to the screen (ie, the call to trie:from_dict() has returned), then the shell just hangs. No new > prompt comes up and ^g doesn't do anything (but ^c will eventually kill it off).
With a very small dictionary (the first 50 lines of /usr/share/dict/words), the hang only lasts a second or two (and the trie is built almost instantly)... But it seems to grow exponentially with the size of the dictionary (100 words takes 5 or 10 seconds, I haven't had the patience to try larger wordlists). Also, as the shell is hanging, I notice that the beam.smp process starts eating up a lot of memory (somewhere between 1 and 2 gigs).
So, is there anything obvious that could be causing this shell hang and incredible memory usage?
Some various comments:
I have a hunch that the garbage collector is at fault, but I don't know how to profile or create an experiment to test that.
I've tried profiling with eprof and nothing obvious showed up.
Here is my "add string to trie" function:
add([], Trie) ->
[ stop | Trie ];
add([Ch|Rest], Trie) ->
SubTrie = proplists:get_value(Ch, Trie, []),
NewSubTrie = add(Rest, SubTrie),
NewTrie = [ { Ch, NewSubTrie } | Trie ],
% Arbitrarily decide to compress key/value list once it gets
% more than 60 pairs.
if length(NewTrie) > 60 ->
proplists:compact(NewTrie);
true ->
NewTrie
end.
The problem is (amongst others ? -- see my comment) that you are always adding a new {Ch, NewSubTrie} tuple to your proplist Tries, no matter if Ch already existed, or not.
Instead of
NewTrie = [ { Ch, NewSubTrie } | Trie ]
you need something like:
NewTrie = lists:keystore(Ch, 1, Trie, {Ch, NewSubTrie})
You're not really building a trie here. Your end result is effectively a randomly ordered proplist of proplists that requires full scans at each level when walking the list. Tries are typically implied ordering based on position in the array (or list).
Here's an implementation that uses tuples as the storage mechanism. Calling set only rebuilds the root and direct path tuples.
(note: would probably have to make the pair a triple (adding size) make delete work with any efficiency)
I believe erlang tuples are really just arrays (thought I read that somewhere), so lookup should be super fast, and modify is probably straight forward. Maybe this is faster with the array module, but I haven't really played with it much to know.
this version also stores an arbitrary value, so you can do things like:
1> c(trie).
{ok,trie}
2> trie:get("ab",trie:set("aa",bar,trie:new("ab",foo))).
foo
3> trie:get("abc",trie:set("aa",bar,trie:new("ab",foo))).
undefined
4>
code (entire module): note2: assumes lower case non empty string keys
-module(trie).
-compile(export_all).
-define(NEW,{ %% 26 pairs, to avoid cost of calculating a new level at runtime
{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},
{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},
{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},
{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},
{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},
{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},{undefined,nodepth},
{undefined,nodepth},{undefined,nodepth}
}
).
-define(POS(Ch), Ch - $a + 1).
new(Key,V) -> set(Key,V,?NEW).
set([H],V,Trie) ->
Pos = ?POS(H),
{_,SubTrie} = element(Pos,Trie),
setelement(Pos,Trie,{V,SubTrie});
set([H|T],V,Trie) ->
Pos = ?POS(H),
{SubKey,SubTrie} = element(Pos,Trie),
case SubTrie of
nodepth -> setelement(Pos,Trie,{SubKey,set(T,V,?NEW)});
SubTrie -> setelement(Pos,Trie,{SubKey,set(T,V,SubTrie)})
end.
get([H],Trie) ->
{Val,_} = element(?POS(H),Trie),
Val;
get([H|T],Trie) ->
case element(?POS(H),Trie) of
{_,nodepth} -> undefined;
{_,SubTrie} -> get(T,SubTrie)
end.
I'd like to work out how much RAM is being used by each of my objects inside my current workspace. Is there an easy way to do this?
some time ago I stole this little nugget from here:
sort( sapply(ls(),function(x){object.size(get(x))}))
it has served me well
1. by object size
to get memory allocation on an object-by-object basis, call object.size() and pass in the object of interest:
object.size(My_Data_Frame)
(unless the argument passed in is a variable, it must be quoted, or else wrapped in a get call.)variable name, then omit the quotes,
you can loop through your namespace and get the size of all of the objects in it, like so:
for (itm in ls()) {
print(formatC(c(itm, object.size(get(itm))),
format="d",
big.mark=",",
width=30),
quote=F)
}
2. by object type
to get memory usage for your namespace, by object type, use memory.profile()
memory.profile()
NULL symbol pairlist closure environment promise language
1 9434 183964 4125 1359 6963 49425
special builtin char logical integer double complex
173 1562 20652 7383 13212 4137 1
(There's another function, memory.size() but i have heard and read that it only seems to work on Windows. It just returns a value in MB; so to get max memory used at any time in the session, use memory.size(max=T)).
You could try the lsos() function from this question:
R> a <- rnorm(100)
R> b <- LETTERS
R> lsos()
Type Size Rows Columns
b character 1496 26 NA
a numeric 840 100 NA
R>
This question was posted and got legitimate answers so much ago, but I want to let you know another useful tips to get the size of an object using a library called gdata and its ll() function.
library(gdata)
ll() # return a dataframe that consists of a variable name as rownames, and class and size (in KB) as columns
subset(ll(), KB > 1000) # list of object that have over 1000 KB
ll()[order(ll()$KB),] # sort by the size (ascending)
another (slightly prettier) option using dplyr
data.frame('object' = ls()) %>%
dplyr::mutate(size_unit = object %>%sapply(. %>% get() %>% object.size %>% format(., unit = 'auto')),
size = as.numeric(sapply(strsplit(size_unit, split = ' '), FUN = function(x) x[1])),
unit = factor(sapply(strsplit(size_unit, split = ' '), FUN = function(x) x[2]), levels = c('Gb', 'Mb', 'Kb', 'bytes'))) %>%
dplyr::arrange(unit, dplyr::desc(size)) %>%
dplyr::select(-size_unit)
A data.table function that separates memory and unit for easier sorting:
ls.obj <- {as.data.table(sapply(ls(),
function(x){format(object.size(get(x)),
nsmall=3,digits=3,unit="Mb")}),keep.rownames=TRUE)[,
c("mem","unit") := tstrsplit(V2, " ", fixed=TRUE)][,
setnames(.SD,"V1","obj")][,.(obj,mem=as.numeric(mem),unit)][order(-mem)]}
ls.obj
obj mem unit
1: obj1 848.283 Mb
2: obj2 37.705 Mb
...
Here's a tidyverse-based function to calculate the size of all objects in your environment:
weigh_environment <- function(env){
purrr::map_dfr(env, ~ tibble::tibble("object" = .) %>%
dplyr::mutate(size = object.size(get(.x)),
size = as.numeric(size),
megabytes = size / 1000000))
}
I've used the solution from this link
for (thing in ls()) { message(thing); print(object.size(get(thing)), units='auto') }
Works fine!