Erlang B for 1 to 60 channels, Erl. C. A (Erl). 1. 1,5. 2. 2,5. 3. 3,5. 4. 5. 5. 6. 7. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0,5. 0,6. 0, 0, 0, 0, This is a pair of routines for computing Erlang B and C probabilities used in queueing theory and telecommunications. The routines use a numerically stable . Tables can be replicated on different Erlang nodes in various ways. The Mnesia [[email protected],[email protected],[email protected]] ([email protected])3> Tab = dictionary. dictionary ([email protected])4> .

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The previous sections describe how to get started with Mnesia and how to build a Mnesia database. This section describes the more advanced features available when building a distributed, fault-tolerant Mnesia database. The following topics are included:. Data retrieval and matching can be performed efficiently if the key for the tsbelle is known. Conversely, if the key is unknown, all records in a table must be searched.

The larger the table, tabele more time consuming it becomes.

Working with Query Results

To remedy this problem, Mnesia indexing capabilities are used to improve data retrieval and matching of records. These functions create or delete a erlan index on a field defined by AttributeName. The function that adds an index on element salary can be expressed as mnesia: The indexing capabilities of Mnesia are used with the following three functions, which retrieve and match records based on index entries in the database:. These functions are further described and exemplified in Pattern Matching.

Mnesia is a distributed, fault-tolerant DBMS. Tables can be replicated on different Erlang nodes in various ways.

The Mnesia programmer does not need to state where the different tables reside, only the tablle of the different tables need to be specified in the program code. This is known as “location transparency” and is an important concept. A program works regardless of the data location. It makes no difference whether the data resides on the local node or on a remote node.

It has previously been shown that each table has a number of system attributes, such as index and type. Table attributes are specified when the table is created. For example, the following function creates a table with two RAM replicas:.

Tables can also have the following properties, where each attribute has a list of Erlang nodes as its value:. The value of the node list is a list of Erlang nodes, and a RAM replica of the table resides on each node in the list. Notice that no disc operations are performed when a program executes write operations to these replicas. However, if permanent RAM replicas are required, the following alternatives are available:.

Tabelpe addition, table properties can be set and changed. For details, see Define a Schema. There are basically two reasons for using more than one table replica: Notice that table replication provides a solution to both of these system requirements. If there are two active table replicas, all information is still available if one replica fails.

Erlang — Miscellaneous Mnesia Features

tabellee This can be an important property in many applications. Furthermore, if a table replica exists at two specific nodes, applications that execute at either of these nodes can read data from the table without accessing the network.


Network operations are considerably slower and consume more resources than local operations. It can be advantageous to create table replicas for a distributed application that reads data often, but writes data seldom, to achieve fast read operations on the local node. The major disadvantage with replication erlqng the increased time to write data.

Erlang B and C probabilities – File Exchange – MATLAB Central

If a table has two replicas, every write operation must access both table replicas. Since one of these write operations must be a network operation, it is considerably more expensive to perform a write operation to a replicated table than to a non-replicated table.

A concept of table fragmentation has been introduced to cope with large tables. The idea is to split a table into several manageable fragments. Each fragment is implemented as a first class Mnesia table and can be replicated, have indexes, and so on, as any other table.

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To be able to access a record in a fragmented table, Mnesia must determine to which fragment the actual record belongs. It is recommended to read the documentation about the function mnesia: Second, the name of the table fragment is determined from the hash value.

Finally the actual table access is performed by the same functions as for non-fragmented tables. When the key is not known beforehand, all fragments are searched for matching records. The following code illustrates how a Mnesia table is converted to be a fragmented table and how more fragments are added later:.

The fragmentation properties are a list of tagged tuples with arity 2. By default the list is empty, but when it is non-empty it triggers Mnesia to regard the table as fragmented. The fragmentation properties are as follows:. At table creation Mnesia tries to distribute the replicas of each fragment evenly over all the nodes in the node pool.

Hopefully all nodes end up with the same number of replicas. This property can explicitly be set at table creation. Mnesia ensures that the number of fragments in this table and in the foreign table are always the same. When fragments are added or deleted, Mnesia automatically propagates the operation to all fragmented tables that have a foreign key referring to this table.

Instead of using the record key to determine which fragment to access, the value of field Attr is used. This feature makes it possible to colocate records automatically in different tables to the same node. However, if the foreign key is set to something else, it causes the default values of the other fragmentation properties to be the same values as the actual fragmentation properties of the foreign table.

Enables definition of an alternative hashing scheme. Enables a table-specific parameterization of a generic hash module. Argument Change is to have one of the following values:. Activates the fragmentation properties of an existing table. Deactivates the fragmentation properties of a table. The number of fragments must be 1. No other table can refer to this table in its foreign key. Adds a fragment to a fragmented table. All records in one of the old fragments are rehashed and about half of them are moved to the new last fragment.


All other fragmented tables, which refer to this table in their foreign key, automatically get a new fragment. Also, their records are dynamically rehashed in the same manner as for the main table. Argument NodesOrDist can either be a list of nodes or the result from the function mnesia: Argument NodesOrDist is assumed to be a sorted list with the best nodes to host new replicas first in the list.

The NodesOrDist list must at least contain one element for each replica that needs to be allocated. Deletes a fragment from a fragmented table. All records in the last fragment are moved to one of the other fragments. All other fragmented tables, which refer to this table in their foreign key, automatically lose their last fragment. The new node pool affects the list returned from the function mnesia: There must however not exist any other fragmented tables that refer to this table in their foreign key.

If the function mnesia: The actual values are dynamically derived from the first fragment. The first fragment serves as a protype.

When the actual values need to be computed for example, when adding new fragments they are determined by counting the number of each replica for each storage type. This means that when the functions mnesia: Count is the total number of replicas that this fragmented table hosts on each Node. There are several algorithms for distributing records in a fragmented table evenly over a pool of nodes.

No one is best, it depends on the application needs. The following examples of situations need some attention:.

Use the function mnesia: Replicated tables have the same content on all nodes where they are replicated. However, it is sometimes advantageous to have tables, but different content on different nodes.

Furthermore, when the table is initialized at startup, the table is only initialized locally, and the table content is not copied from another node. Mnesia can be run on nodes that do not have a disc. This is especially troublesome for the schema table, as Mnesia needs the schema to initialize itself.

The schema table can, as other tables, reside on one or more nodes. At startup, Mnesia uses its schema to determine with which nodes it is to try to establish contact. If any other node is started already, the starting node merges its table definitions with the table definitions brought from the other nodes. This also applies to the definition of the schema table itself. Default is [] empty list. Without this configuration parameter set, Mnesia starts as a single node system.

Also, the function mnesia: The parameter can be one of the following atoms:. The schema is assumed to be located in the Mnesia directory.