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  2. Memory (storage engine) - Wikipedia

    en.wikipedia.org/wiki/MEMORY_(storage_engine)

    MEMORY is a storage engine for MySQL and MariaDB relational database management systems, developed by Oracle and MariaDB. Before the version 4.1 of MySQL it was called Heap. The SHOW ENGINES command describes MEMORY as: Hash based, stored in memory, useful for temporary tables. MEMORY writes table data in-memory.

  3. Checksum - Wikipedia

    en.wikipedia.org/wiki/Checksum

    This is especially true of cryptographic hash functions, which may be used to detect many data corruption errors and verify overall data integrity; if the computed checksum for the current data input matches the stored value of a previously computed checksum, there is a very high probability the data has not been accidentally altered or corrupted.

  4. Key–value database - Wikipedia

    en.wikipedia.org/wiki/Key–value_database

    A tabular data card proposed for Babbage's Analytical Engine showing a key–value pair, in this instance a number and its base-ten logarithm. A key–value database, or key–value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, and a data structure more commonly known today as a dictionary or hash table.

  5. List of hash functions - Wikipedia

    en.wikipedia.org/wiki/List_of_hash_functions

    hash HAS-160: 160 bits hash HAVAL: 128 to 256 bits hash JH: 224 to 512 bits hash LSH [19] 256 to 512 bits wide-pipe Merkle–Damgård construction: MD2: 128 bits hash MD4: 128 bits hash MD5: 128 bits Merkle–Damgård construction: MD6: up to 512 bits Merkle tree NLFSR (it is also a keyed hash function) RadioGatún: arbitrary ideal mangling ...

  6. Database engine - Wikipedia

    en.wikipedia.org/wiki/Database_engine

    A database engine (or storage engine) is the underlying software component that a database management system (DBMS) uses to create, read, update and delete (CRUD) data from a database.

  7. Comparison of relational database management systems - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_relational...

    Note (4): Used for InMemory ColumnStore index, temporary hash index for hash join, Non/Cluster & fill factor. Note (5): InnoDB automatically generates adaptive hash index [125] entries as needed. Note (6): Can be implemented using Function-based Indexes in Oracle 8i and higher, but the function needs to be used in the sql for the index to be used.

  8. Fletcher's checksum - Wikipedia

    en.wikipedia.org/wiki/Fletcher's_checksum

    The first weakness of the simple checksum is that it is insensitive to the order of the blocks (bytes) in the data word (message). If the order is changed, the checksum value will be the same and the change will not be detected. The second weakness is that the universe of checksum values is small, being equal to the chosen modulus.

  9. Hash table - Wikipedia

    en.wikipedia.org/wiki/Hash_table

    In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [3] [4] [5] Hashing is an example of a space-time tradeoff.