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The terms data dictionary and data repository indicate a more general software utility than a catalogue. A catalogue is closely coupled with the DBMS software. It provides the information stored in it to the user and the DBA, but it is mainly accessed by the various software modules of the DBMS itself, such as DDL and DML compilers, the query optimiser, the transaction processor, report ...
Linear probing performs better due to better locality of reference, though as the table gets full, its performance degrades drastically. The most frequently used general-purpose implementation of an associative array is with a hash table : an array combined with a hash function that separates each key into a separate "bucket" of the array.
For example, one could define a dictionary having a string "toast" mapped to the integer 42 or vice versa. The keys in a dictionary must be of an immutable Python type, such as an integer or a string, because under the hood they are implemented via a hash function. This makes for much faster lookup times, but requires keys not change.
In a relational database, the schema defines the tables, fields, relationships, views, indexes, packages, procedures, functions, queues, triggers, types, sequences, materialized views, synonyms, database links, directories, XML schemas, and other elements. A database generally stores its schema in a data dictionary. Although a schema is defined ...
In computer science, a symbol table is a data structure used by a language translator such as a compiler or interpreter, where each identifier, symbol, constant, procedure and function in a program's source code is associated with information relating to its declaration or appearance in the source. In other words, the entries of a symbol table ...
In a dynamically typed language, where type can only be determined at runtime, many type errors can only be detected at runtime. For example, the Python code a + b is syntactically valid at the phrase level, but the correctness of the types of a and b can only be determined at runtime, as variables do not have types in Python, only values do.
The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.