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<h1>7 Tables and databases</h1>
<h3><a name="id67596">7.1
Ets, Dets and Mnesia</a></h3>
<p>Every example using Ets has a corresponding example in
Mnesia. In general all Ets examples also apply to Dets tables.</p>
<h4>Select/Match operations</h4>
<p>Select/Match operations on Ets and Mnesia tables can become
very expensive operations. They usually need to scan the complete
table. You should try to structure your
data so that you minimize the need for select/match
operations. However, if you really need a select/match operation,
it will still be more efficient than using <span class="code">tab2list</span>.
Examples of this and also of ways to avoid select/match will be provided in
some of the following sections. The functions
<span class="code">ets:select/2</span> and <span class="code">mnesia:select/3</span> should be preferred over
<span class="code">ets:match/2</span>,<span class="code">ets:match_object/2</span>, and <span class="code">mnesia:match_object/3</span>.</p>
<div class="note">
<div class="label">Note</div>
<div class="content"><p>
<p>There are exceptions when the complete table is not
scanned, for instance if part of the key is bound when searching an
<span class="code">ordered_set</span> 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 will, of course, be
no point in doing a select/match, unless you have a bag table and
you are only interested in a sub-set of the elements with
the specific key.</p>
</p></div>
</div>
<p>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:</p>
<div class="example"><pre>
#person{age = 42, _ = '_'}. </pre></div>
<h4>Deleting an element</h4>
<p>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.</p>
<p><strong>DO</strong></p>
<div class="example"><pre>
...
ets:delete(Tab, Key),
...</pre></div>
<p><strong>DO NOT</strong></p>
<div class="example"><pre>
...
case ets:lookup(Tab, Key) of
[] ->
ok;
[_|_] ->
ets:delete(Tab, Key)
end,
...</pre></div>
<h4>Data fetching</h4>
<p>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 <span class="code">print_person/1</span> that uses the internal functions
<span class="code">print_name/1</span>, <span class="code">print_age/1</span>, <span class="code">print_occupation/1</span>.</p>
<div class="note">
<div class="label">Note</div>
<div class="content"><p>
<p>If the functions <span class="code">print_name/1</span> and so on, had been interface
functions the matter comes in to a whole new light, as you
do not want the user of the interface to know about the
internal data representation. </p>
</p></div>
</div>
<p><strong>DO</strong></p>
<div class="example"><pre>
%%% 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]).</pre></div>
<p><strong>DO NOT</strong></p>
<div class="example"><pre>
%%% 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 functionss
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]).</pre></div>
<h4>Non-persistent data storage </h4>
<p>For non-persistent database storage, prefer Ets tables over
Mnesia local_content tables. Even the Mnesia <span class="code">dirty_write</span>
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
<span class="code">dirty_write</span>. Thus, Ets writes will always be faster than
Mnesia writes.</p>
<h4>tab2list</h4>
<p>Assume we have an Ets-table, which uses <span class="code">idno</span> as key,
and contains:</p>
<div class="example"><pre>
[#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"}]</pre></div>
<p>If we <strong>must</strong> return all data stored in the Ets-table we
can use <span class="code">ets:tab2list/1</span>. However, usually we are only
interested in a subset of the information in which case
<span class="code">ets:tab2list/1</span> is expensive. If we only want to extract
one field from each record, e.g., the age of every person, we
should use:</p>
<p><strong>DO</strong></p>
<div class="example"><pre>
...
ets:select(Tab,[{ #person{idno='_',
name='_',
age='$1',
occupation = '_'},
[],
['$1']}]),
...</pre></div>
<p><strong>DO NOT</strong></p>
<div class="example"><pre>
...
TabList = ets:tab2list(Tab),
lists:map(fun(X) -> X#person.age end, TabList),
...</pre></div>
<p>If we are only interested in the age of all persons named
Bryan, we should:</p>
<p><strong>DO</strong></p>
<div class="example"><pre>
...
ets:select(Tab,[{ #person{idno='_',
name="Bryan",
age='$1',
occupation = '_'},
[],
['$1']}]),
...</pre></div>
<p><strong>DO NOT</strong></p>
<div class="example"><pre>
...
TabList = ets:tab2list(Tab),
lists:foldl(fun(X, Acc) -> case X#person.name of
"Bryan" ->
[X#person.age|Acc];
_ ->
Acc
end
end, [], TabList),
...</pre></div>
<p><strong>REALLY DO NOT</strong></p>
<div class="example"><pre>
...
TabList = ets:tab2list(Tab),
BryanList = lists:filter(fun(X) -> X#person.name == "Bryan" end,
TabList),
lists:map(fun(X) -> X#person.age end, BryanList),
...</pre></div>
<p>If we need all information stored in the Ets table about
persons named Bryan we should:</p>
<p><strong>DO</strong></p>
<div class="example"><pre>
...
ets:select(Tab, [{#person{idno='_',
name="Bryan",
age='_',
occupation = '_'}, [], ['$_']}]),
...</pre></div>
<p><strong>DO NOT</strong></p>
<div class="example"><pre>
...
TabList = ets:tab2list(Tab),
lists:filter(fun(X) -> X#person.name == "Bryan" end, TabList),
...</pre></div>
<h4>Ordered_set tables</h4>
<p>If the data in the table should be accessed so that the order
of the keys in the table is significant, the table type
<span class="code">ordered_set</span> could be used instead of the more usual
<span class="code">set</span> table type. An <span class="code">ordered_set</span> is always
traversed in Erlang term order with regard to the key field
so that return values from functions such as <span class="code">select</span>,
<span class="code">match_object</span>, and <span class="code">foldl</span> are ordered by the key
values. Traversing an <span class="code">ordered_set</span> with the <span class="code">first</span> and
<span class="code">next</span> operations also returns the keys ordered.</p>
<div class="note">
<div class="label">Note</div>
<div class="content"><p>
<p>An <span class="code">ordered_set</span> only guarantees that
objects are processed in <strong>key</strong> order. Results from functions as
<span class="code">ets:select/2</span> appear in the <strong>key</strong> order even if
the key is not included in the result.</p>
</p></div>
</div>
<h3><a name="id67983">7.2
Ets specific</a></h3>
<h4>Utilizing the keys of the Ets table</h4>
<p>An Ets table is a single key table (either a hash table or a
tree ordered by the key) and should 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 a
ordered_set Ets table it is O(logN). A key lookup is always
preferable to a call where the whole table has to be
scanned. In the examples above, the field <span class="code">idno</span> is the
key of the table and all lookups where only the name is known
will result in a complete scan of the (possibly large) table
for a matching result.</p>
<p>A simple solution would be to use the <span class="code">name</span> field as
the key instead of the <span class="code">idno</span> field, but that would cause
problems if the names were not unique. A more general solution
would be to create a second table with <span class="code">name</span> as key and
<span class="code">idno</span> as data, i.e. to index (invert) the table with regards
to the <span class="code">name</span> field. The second table would of course have to be
kept consistent with the master table. Mnesia could do this
for you, but a home brew index table could be very efficient
compared to the overhead involved in using Mnesia.</p>
<p>An index table for the table in the previous examples would
have to be a bag (as keys would appear more than once) and could
have the following contents:</p>
<div class="example"><pre>
[#index_entry{name="Adam", idno=1},
#index_entry{name="Bryan", idno=2},
#index_entry{name="Bryan", idno=3},
#index_entry{name="Carl", idno=4}]</pre></div>
<p>Given this index table a lookup of the <span class="code">age</span> fields for
all persons named "Bryan" could be done like this:</p>
<div class="example"><pre>
...
MatchingIDs = ets:lookup(IndexTable,"Bryan"),
lists:map(fun(#index_entry{idno = ID}) ->
[#person{age = Age}] = ets:lookup(PersonTable, ID),
Age
end,
MatchingIDs),
...</pre></div>
<p>Note that the code above never uses <span class="code">ets:match/2</span> but
instead utilizes the <span class="code">ets:lookup/2</span> call. The
<span class="code">lists:map/2</span> call is only used to traverse the <span class="code">idno</span>s
matching the name "Bryan" in the table; therefore the number of lookups
in the master table is minimized.</p>
<p>Keeping an index table introduces some overhead when
inserting records in the table, therefore the number of operations
gained from the table has to be weighted against the number of
operations inserting objects in the table. However, note that the gain when
the key can be used to lookup elements is significant.</p>
<h3><a name="id68087">7.3
Mnesia specific</a></h3>
<h4>Secondary index</h4>
<p>If you frequently do a lookup on a field that is not the
key of the table, you will lose performance using
"mnesia:select/match_object" as this function will traverse the
whole table. You may create a secondary index instead and
use "mnesia:index_read" to get faster access, however this
will require more memory. Example:</p>
<div class="example"><pre>
-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),
...</pre></div>
<h4>Transactions </h4>
<p>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 dirty
operations instead of transactions. When using the dirty
operations you lose the consistency guarantee, this is usually
solved by only letting one process update the table. Other
processes have to send update requests to that process.</p>
<div class="example"><pre>
...
% 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}),
...</pre></div>
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