Transactions are important when designing fault-tolerant, distributed systems. A Mnesia transaction is a mechanism by which a series of database operations can be executed as one functional block. The functional block that is run as a transaction is called a Functional Object (Fun), and this code can read, write, and delete Mnesia records. The Fun is evaluated as a transaction that either commits or terminates. If a transaction succeeds in executing the Fun, it replicates the action on all nodes involved, or terminates if an error occurs.

The following example shows a transaction that raises the salary of certain employee numbers:

The function raise/2 contains a Fun made up of four code lines. This Fun is called by the statement mnesia:transaction(F) and returns a value.

The Mnesia transaction system facilitates the construction of reliable, distributed systems by providing the following important properties:

Different transaction managers employ different strategies to satisfy the isolation property. Mnesia uses the standard technique of two phase locking. That is, locks are set on records before they are read or written. Mnesia uses the following lock types:

Mnesia employs a strategy whereby functions, such as mnesia:read/1 acquire the necessary locks dynamically as the transactions execute. Mnesia automatically sets and releases the locks and the programmer does not need to code these operations.

Deadlocks can occur when concurrent processes set and release locks on the same records. Mnesia employs a "wait-die" strategy to resolve these situations. If Mnesia suspects that a deadlock can occur when a transaction tries to set a lock, the transaction is forced to release all its locks and sleep for a while. The Fun in the transaction is evaluated once more.

It is therefore important that the code inside the Fun given to mnesia:transaction/1 is pure. Some strange results can occur if, for example, messages are sent by the transaction Fun. The following example illustrates this situation:

This transaction can write the text "Trying to write ... " 1000 times to the terminal. However, Mnesia guarantees that each transaction will eventually run. As a result, Mnesia is not only deadlock free, but also livelock free.

The Mnesia programmer cannot prioritize one particular transaction to execute before other transactions that are waiting to execute. As a result, the Mnesia DBMS transaction system is not suitable for hard real-time applications. However, Mnesia contains other features that have real-time properties.

Mnesia dynamically sets and releases locks as transactions execute. It is therefore dangerous to execute code with transaction side-effects. In particular, a receive statement inside a transaction can lead to a situation where the transaction hangs and never returns, which in turn can cause locks not to release. This situation can bring the whole system to a standstill, as other transactions that execute in other processes, or on other nodes, are forced to wait for the defective transaction.

If a transaction terminates abnormally, Mnesia automatically releases the locks held by the transaction.

Up to now, examples of a number of functions that can be used inside a transaction have been shown. The following list shows the simplest Mnesia functions that work with transactions. Notice that these functions must be embedded in a transaction. If no enclosing transaction (or other enclosing Mnesia activity) exists, they all fail.

In many applications, the overhead of processing a transaction can result in a loss of performance. Dirty operation are short cuts that bypass much of the processing and increase the speed of the transaction.

Dirty operation are often useful, for example, in a datagram routing application where Mnesia stores the routing table, and it is time consuming to start a whole transaction every time a packet is received. Mnesia has therefore functions that manipulate tables without using transactions. This alternative to processing is known as a dirty operation. However, notice the trade-off in avoiding the overhead of transaction processing:

The major advantage of dirty operations is that they execute much faster than equivalent operations that are processed as functional objects within a transaction.

Dirty operations are written to disc if they are performed on a table of type disc_copies or type disc_only_copies. Mnesia also ensures that all replicas of a table are updated if a dirty write operation is performed on a table.

A dirty operation ensures a certain level of consistency. For example, dirty operations cannot return garbled records. Hence, each individual read or write operation is performed in an atomic manner.

All dirty functions execute a call to exit({aborted, Reason}) on failure. Even if the following functions are executed inside a transaction no locks are acquired. The following functions are available:

In Mnesia, all records in a table must have the same name. All the records must be instances of the same record type. The record name, however, does not necessarily have to be the same as the table name, although this is the case in most of the examples in this User's Guide. If a table is created without property record_name, the following code ensures that all records in the tables have the same name as the table:

However, if the table is created with an explicit record name as argument, as shown in the following example, subscriber records can be stored in both of the tables regardless of the table names:

To access such tables, simplified access functions (as described earlier) cannot be used. For example, writing a subscriber record into a table requires the function mnesia:write/3 instead of the simplified functions mnesia:write/1 and mnesia:s_write/1:

The following simple code illustrates the relationship between the simplified access functions used in most of the examples and their more flexible counterparts:

As previously described, a Functional Object (Fun) performing table access operations, as listed here, can be passed on as arguments to the function mnesia:transaction/1,2,3:

These functions are performed in a transaction context involving mechanisms, such as locking, logging, replication, checkpoints, subscriptions, and commit protocols. However, the same function can also be evaluated in other activity contexts.

The following activity access contexts are currently supported:

By passing the same "fun" as argument to the function mnesia:sync_transaction(Fun [, Args]) it is performed in synced transaction context. Synced transactions wait until all active replicas has committed the transaction (to disc) before returning from the mnesia:sync_transaction call. Using sync_transaction is useful in the following cases:

By passing the same "fun" as argument to the function mnesia:async_dirty(Fun [, Args]), it is performed in dirty context. The function calls are mapped to the corresponding dirty functions. This still involves logging, replication, and subscriptions but no locking, local transaction storage, or commit protocols are involved. Checkpoint retainers are updated but updated "dirty". Thus, they are updated asynchronously. The functions wait for the operation to be performed on one node but not the others. If the table resides locally, no waiting occurs.

By passing the same "fun" as an argument to the function mnesia:sync_dirty(Fun [, Args]), it is performed in almost the same context as the function mnesia:async_dirty/1,2. The difference is that the operations are performed synchronously. The caller waits for the updates to be performed on all active replicas. Using mnesia:sync_dirty/1,2 is useful in the following cases:

To check if your code is executed within a transaction, use the function mnesia:is_transaction/0. It returns true when called inside a transaction context, otherwise false.

Mnesia tables with storage type RAM_copies and disc_copies are implemented internally as ets tables. Applications can access the these tables directly. This is only recommended if all options have been weighed and the possible outcomes are understood. By passing the earlier mentioned "fun" to the function mnesia:ets(Fun [, Args]), it is performed but in a raw context. The operations are performed directly on the local ets tables, assuming that the local storage type is RAM_copies and that the table is not replicated on other nodes.

Subscriptions are not triggered and no checkpoints are updated, but this operation is blindingly fast. Disc resident tables are not to be updated with the ets function, as the disc is not updated.

The Fun can also be passed as an argument to the function mnesia:activity/2,3,4, which enables use of customized activity access callback modules. It can either be obtained directly by stating the module name as argument, or implicitly by use of configuration parameter access_module. A customized callback module can be used for several purposes, such as providing triggers, integrity constraints, runtime statistics, or virtual tables.

The callback module does not have to access real Mnesia tables, it is free to do whatever it wants as long as the callback interface is fulfilled.

Appendix B, Activity Access Callback Interface provides the source code, mnesia_frag.erl, for one alternative implementation. The context-sensitive function mnesia:table_info/2 can be used to provide virtual information about a table. One use of this is to perform QLC queries within an activity context with a customized callback module. By providing table information about table indexes and other QLC requirements, QLC can be used as a generic query language to access virtual tables.

QLC queries can be performed in all these activity contexts (transaction, sync_transaction, async_dirty, sync_dirty, and ets). The ets activity only works if the table has no indexes.

Transactions can be nested in an arbitrary fashion. A child transaction must run in the same process as its parent. When a child transaction terminates, the caller of the child transaction gets return value {aborted, Reason} and any work performed by the child is erased. If a child transaction commits, the records written by the child are propagated to the parent.

No locks are released when child transactions terminate. Locks created by a sequence of nested transactions are kept until the topmost transaction terminates. Furthermore, any update performed by a nested transaction is only propagated in such a manner so that the parent of the nested transaction sees the updates. No final commitment is done until the top-level transaction terminates. So, although a nested transaction returns {atomic, Val}, if the enclosing parent transaction terminates, the entire nested operation terminates.

The ability to have nested transaction with identical semantics as top-level transaction makes it easier to write library functions that manipulate Mnesia tables.

Consider a function that adds a subscriber to a telephony system:

      add_subscriber(S) ->
          mnesia:transaction(fun() ->
              case mnesia:read( ..........

This function needs to be called as a transaction. Assume that you wish to write a function that both calls the function add_subscriber/1 and is in itself protected by the context of a transaction. By calling add_subscriber/1 from within another transaction, a nested transaction is created.

Also, different activity access contexts can be mixed while nesting. However, the dirty ones (async_dirty, sync_dirty, and ets) inherit the transaction semantics if they are called inside a transaction and thus grab locks and use two or three phase commit.

Example:

      add_subscriber(S) ->
          mnesia:transaction(fun() ->
             %% Transaction context 
             mnesia:read({some_tab, some_data}),
             mnesia:sync_dirty(fun() ->
                 %% Still in a transaction context.
                 case mnesia:read( ..) ..end), end).
      add_subscriber2(S) ->
          mnesia:sync_dirty(fun() ->
             %% In dirty context 
             mnesia:read({some_tab, some_data}),
             mnesia:transaction(fun() ->
                 %% In a transaction context.
                 case mnesia:read( ..) ..end), end).

When the function mnesia:read/3 cannot be used, Mnesia provides the programmer with several functions for matching records against a pattern. The most useful ones are the following:

These functions match a Pattern against all records in table Tab. In a mnesia:select call, Pattern is a part of MatchSpecification described in the following. It is not necessarily performed as an exhaustive search of the entire table. By using indexes and bound values in the key of the pattern, the actual work done by the function can be condensed into a few hash lookups. Using ordered_set tables can reduce the search space if the keys are partially bound.

The pattern provided to the functions must be a valid record, and the first element of the provided tuple must be the record_name of the table. The special element '_' matches any data structure in Erlang (also known as an Erlang term). The special elements ']]> behave as Erlang variables, that is, they match anything, bind the first occurrence, and match the coming occurrences of that variable against the bound value.

Use function mnesia:table_info(Tab, wild_pattern) to obtain a basic pattern, which matches all records in a table, or use the default value in record creation. Do not make the pattern hard-coded, as this makes the code more vulnerable to future changes of the record definition.

Example:

For the employee table, the wild pattern looks as follows:

To constrain the match, it is needed to replace some of the '_' elements. The code for matching out all female employees looks as follows:

The match function can also be used to check the equality of different attributes. For example, to find all employees with an employee number equal to their room number:

The function mnesia:match_object/3 lacks some important features that mnesia:select/3 have. For example, mnesia:match_object/3 can only return the matching records, and it cannot express constraints other than equality. To find the names of the male employees on the second floor:

The function select can be used to add more constraints and create output that cannot be done with mnesia:match_object/3.

The second argument to select is a MatchSpecification. A MatchSpecification is a list of MatchFunctions, where each MatchFunction consists of a tuple containing {MatchHead, MatchCondition, MatchBody}:

For details about the match specifications, see "Match Specifications in Erlang" in ERTS User's Guide. For more information, see the ets and dets manual pages in STDLIB.

The functions select/4 and select/1 are used to get a limited number of results, where Continuation gets the next chunk of results. Mnesia uses NObjects as a recommendation only. Thus, more or less results than specified with NObjects can be returned in the result list, even the empty list can be returned even if there are more results to collect.

If the key attribute is bound in a pattern, the match operation is efficient. However, if the key attribute in a pattern is given as '_' or '$1', the whole employee table must be searched for records that match. Hence if the table is large, this can become a time-consuming operation, but it can be remedied with indexes (see Indexing) if the function mnesia:match_object is used.

QLC queries can also be used to search Mnesia tables. By using the function mnesia:table/[1|2] as the generator inside a QLC query, you let the query operate on a Mnesia table. Mnesia-specific options to mnesia:table/2 are {lock, Lock}, {n_objects,Integer}, and {traverse, SelMethod}:

If no options are specified, a read lock is acquired, 100 results are returned in each chunk, and select is used to traverse the table, that is:

The function mnesia:all_keys(Tab) returns all keys in a table.

Mnesia provides the following functions that iterate over all the records in a table:

These functions iterate over the Mnesia table Tab and apply the function Fun to each record. Fun takes two arguments, the first is a record from the table, and the second is the accumulator. Fun returns a new accumulator.

The first time Fun is applied, Acc0 is the second argument. The next time Fun is called, the return value from the previous call is used as the second argument. The term the last call to Fun returns is the return value of the function mnesia:foldl/3 or mnesia:foldr/3.

The difference between these functions is the order the table is accessed for ordered_set tables. For other table types the functions are equivalent.

LockType specifies what type of lock that is to be acquired for the iteration, default is read. If records are written or deleted during the iteration, a write lock is to be acquired.

These functions can be used to find records in a table when it is impossible to write constraints for the function mnesia:match_object/3, or when you want to perform some action on certain records.

For example, finding all the employees who have a salary less than 10 can look as follows:

To raise the salary to 10 for everyone with a salary less than 10 and return the sum of all raises:

Many nice things can be done with the iterator functions but take some caution about performance and memory use for large tables.

Call these iteration functions on nodes that contain a replica of the table. Each call to the function Fun access the table and if the table resides on another node it generates much unnecessary network traffic.

Mnesia also provides some functions that make it possible for the user to iterate over the table. The order of the iteration is unspecified if the table is not of type ordered_set:

The order of first/last and next/prev is only valid for ordered_set tables, they are synonyms for other tables. When the end of the table is reached, the special key '$end_of_table' is returned.

If records are written and deleted during the traversal, use the function mnesia:foldl/3 or mnesia:foldr/3 with a write lock. Or the function mnesia:write_lock_table/1 when using first and next.

Writing or deleting in transaction context creates a local copy of each modified record. Thus, modifying each record in a large table uses much memory. Mnesia compensates for every written or deleted record during the iteration in a transaction context, which can reduce the performance. If possible, avoid writing or deleting records in the same transaction before iterating over the table.

In dirty context, that is, sync_dirty or async_dirty, the modified records are not stored in a local copy; instead, each record is updated separately. This generates much network traffic if the table has a replica on another node and has all the other drawbacks that dirty operations have. Especially for commands mnesia:first/1 and mnesia:next/2, the same drawbacks as described previously for mnesia:dirty_first/1 and mnesia:dirty_next/2 applies, that is, no writing to the table is to be done during iteration.