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Ets, Dets, and Mnesia
Every example using Ets has a corresponding example in Mnesia. In general, all Ets examples also apply to Dets tables.
Select/Match Operations
Select/match operations on Ets and Mnesia tables can become very expensive
operations. They usually need to scan the complete table. Try to structure the
data to minimize the need for select/match operations. However, if you require a
select/match operation, it is still more efficient than using tab2list
.
Examples of this and of how to avoid select/match are provided in the following
sections. The functions ets:select/2
and mnesia:select/3
are to be preferred
over ets:match/2
, ets:match_object/2
, and mnesia:match_object/3
.
In some circumstances, the select/match operations do not need to scan the
complete table. For example, if part of the key is bound when searching an
ordered_set
table, or if it is a Mnesia table and there is a secondary index
on the field that is selected/matched. If the key is fully bound, there is no
point in doing a select/match, unless you have a bag table and are only
interested in a subset of the elements with the specific key.
When creating a record to be used in a select/match operation, you want most of
the fields to have the value _
. The easiest and fastest way to do that is as
follows:
#person{age = 42, _ = '_'}.
Deleting an Element
The delete
operation is considered successful if the element was not present
in the table. Hence all attempts to check that the element is present in the
Ets/Mnesia table before deletion are unnecessary. Here follows an example for
Ets tables:
DO
ets:delete(Tab, Key),
DO NOT
case ets:lookup(Tab, Key) of
[] ->
ok;
[_|_] ->
ets:delete(Tab, Key)
end,
Fetching Data
Do not fetch data that you already have.
Consider that you have a module that handles the abstract data type Person
.
You export the interface function print_person/1
, which uses the internal
functions print_name/1
, print_age/1
, and print_occupation/1
.
Note
If the function
print_name/1
, and so on, had been interface functions, the situation would have been different, as you do not want the user of the interface to know about the internal data representation.
DO
%%% Interface function
print_person(PersonId) ->
%% Look up the person in the named table person,
case ets:lookup(person, PersonId) of
[Person] ->
print_name(Person),
print_age(Person),
print_occupation(Person);
[] ->
io:format("No person with ID = ~p~n", [PersonID])
end.
%%% Internal functions
print_name(Person) ->
io:format("No person ~p~n", [Person#person.name]).
print_age(Person) ->
io:format("No person ~p~n", [Person#person.age]).
print_occupation(Person) ->
io:format("No person ~p~n", [Person#person.occupation]).
DO NOT
%%% Interface function
print_person(PersonId) ->
%% Look up the person in the named table person,
case ets:lookup(person, PersonId) of
[Person] ->
print_name(PersonID),
print_age(PersonID),
print_occupation(PersonID);
[] ->
io:format("No person with ID = ~p~n", [PersonID])
end.
%%% Internal functions
print_name(PersonID) ->
[Person] = ets:lookup(person, PersonId),
io:format("No person ~p~n", [Person#person.name]).
print_age(PersonID) ->
[Person] = ets:lookup(person, PersonId),
io:format("No person ~p~n", [Person#person.age]).
print_occupation(PersonID) ->
[Person] = ets:lookup(person, PersonId),
io:format("No person ~p~n", [Person#person.occupation]).
Non-Persistent Database Storage
For non-persistent database storage, prefer Ets tables over Mnesia
local_content
tables. Even the Mnesia dirty_write
operations carry a fixed
overhead compared to Ets writes. Mnesia must check if the table is replicated or
has indices, this involves at least one Ets lookup for each dirty_write
. Thus,
Ets writes is always faster than Mnesia writes.
tab2list
Assuming an Ets table that uses idno
as key and contains the following:
[#person{idno = 1, name = "Adam", age = 31, occupation = "mailman"},
#person{idno = 2, name = "Bryan", age = 31, occupation = "cashier"},
#person{idno = 3, name = "Bryan", age = 35, occupation = "banker"},
#person{idno = 4, name = "Carl", age = 25, occupation = "mailman"}]
If you must return all data stored in the Ets table, you can use
ets:tab2list/1
. However, usually you are only interested in a subset of the
information in which case ets:tab2list/1
is expensive. If you only want to
extract one field from each record, for example, the age of every person, then:
DO
ets:select(Tab, [{#person{idno='_',
name='_',
age='$1',
occupation = '_'},
[],
['$1']}]),
DO NOT
TabList = ets:tab2list(Tab),
lists:map(fun(X) -> X#person.age end, TabList),
If you are only interested in the age of all persons named "Bryan", then:
DO
ets:select(Tab, [{#person{idno='_',
name="Bryan",
age='$1',
occupation = '_'},
[],
['$1']}])
DO NOT
TabList = ets:tab2list(Tab),
lists:foldl(fun(X, Acc) -> case X#person.name of
"Bryan" ->
[X#person.age|Acc];
_ ->
Acc
end
end, [], TabList)
If you need all information stored in the Ets table about persons named "Bryan", then:
DO
ets:select(Tab, [{#person{idno='_',
name="Bryan",
age='_',
occupation = '_'}, [], ['$_']}]),
DO NOT
TabList = ets:tab2list(Tab),
lists:filter(fun(X) -> X#person.name == "Bryan" end, TabList),
ordered_set
Tables
If the data in the table is to be accessed so that the order of the keys in the
table is significant, the table type ordered_set
can be used instead of the
more usual set
table type. An ordered_set
is always traversed in Erlang term
order regarding the key field so that the return values from functions such as
select
, match_object
, and foldl
are ordered by the key values. Traversing
an ordered_set
with the first
and next
operations also returns the keys
ordered.
Note
An
ordered_set
only guarantees that objects are processed in key order. Results from functions such asets:select/2
appear in key order even if the key is not included in the result.
ETS
Using Keys of Ets Table
An Ets table is a single-key table (either a hash table or a tree ordered by the
key) and is to be used as one. In other words, use the key to look up things
whenever possible. A lookup by a known key in a set
Ets table is constant and
for an ordered_set
Ets table it is O(log N). A key lookup is always preferable
to a call where the whole table has to be scanned. In the previous examples, the
field idno
is the key of the table and all lookups where only the name is
known result in a complete scan of the (possibly large) table for a matching
result.
A simple solution would be to use the name
field as the key instead of the
idno
field, but that would cause problems if the names were not unique. A more
general solution would be to create a second table with name
as key and idno
as data, that is, to index (invert) the table regarding the name
field.
Clearly, the second table would have to be kept consistent with the master
table. Mnesia can do this for you, but a home-brew index table can be very
efficient compared to the overhead involved in using Mnesia.
An index table for the table in the previous examples would have to be a bag (as keys would appear more than once) and can have the following contents:
[#index_entry{name="Adam", idno=1},
#index_entry{name="Bryan", idno=2},
#index_entry{name="Bryan", idno=3},
#index_entry{name="Carl", idno=4}]
Given this index table, a lookup of the age
fields for all persons named
"Bryan" can be done as follows:
MatchingIDs = ets:lookup(IndexTable,"Bryan"),
lists:map(fun(#index_entry{idno = ID}) ->
[#person{age = Age}] = ets:lookup(PersonTable, ID),
Age
end,
MatchingIDs),
Notice that this code does not use ets:match/2
, but instead uses the
ets:lookup/2
call. The lists:map/2
call is only used to traverse the idno
s
matching the name "Bryan" in the table; thus the number of lookups in the master
table is minimized.
Keeping an index table introduces some overhead when inserting records in the table. The number of operations gained from the table must therefore be compared against the number of operations inserting objects in the table. However, notice that the gain is significant when the key can be used to lookup elements.
Mnesia
Secondary Index
If you frequently do lookups on a field that is not the key of the table, you
lose performance using mnesia:select() or
mnesia:match_object()
as these function traverse
the whole table. Instead, you can create a secondary index and use
mnesia:index_read/3
to get faster access at the expense of using more
memory.
Example:
-record(person, {idno, name, age, occupation}).
...
{atomic, ok} =
mnesia:create_table(person, [{index,[#person.age]},
{attributes,
record_info(fields, person)}]),
{atomic, ok} = mnesia:add_table_index(person, age),
...
PersonsAge42 =
mnesia:dirty_index_read(person, 42, #person.age),
Transactions
Using transactions is a way to guarantee that the distributed Mnesia database remains consistent, even when many different processes update it in parallel. However, if you have real-time requirements it is recommended to use dirtry operations instead of transactions. When using dirty operations, you lose the consistency guarantee; this is usually solved by only letting one process update the table. Other processes must send update requests to that process.
Example:
...
%% Using transaction
Fun = fun() ->
[mnesia:read({Table, Key}),
mnesia:read({Table2, Key2})]
end,
{atomic, [Result1, Result2]} = mnesia:transaction(Fun),
...
%% Same thing using dirty operations
...
Result1 = mnesia:dirty_read({Table, Key}),
Result2 = mnesia:dirty_read({Table2, Key2}),