# 8 Tables and Databases

###
8.1 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),
    ...

REALLY DO NOT

    ...
    TabList = ets:tab2list(Tab),
    BryanList = lists:filter(fun(X) -> X#person.name == "Bryan" end,
                             TabList),
    lists:map(fun(X) -> X#person.age end, BryanList),
    ...

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 as ets:select/2 appear in key order even if the key is not included in the result.

###
8.2 Ets-Specific

####
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(logN). 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 never uses ets:match/2 but instead uses the ets:lookup/2 call. The lists:map/2 call is only used to traverse the idnos 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.

###
8.3 Mnesia-Specific

####
Secondary Index

If you frequently do a lookup on a field that is not the key of the table, you lose performance using "mnesia:select/match_object" as this function traverses the whole table. You can create a secondary index instead and use "mnesia:index_read" to get faster access, however this requires 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 dirty 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}),
    ...