⢠In 1970, a new style of data management appeared courtesy of Software AGFlat tables appeared, with multiple indicesData retrieval and buffering was managed via the data engine itselfNo more physical address pointers for the programmer to handleâFifth Generationâ languages commonly usedThese data structures are identical to relational database structures in a number of waysMultiple B-tree indices means flexible SEARCH capability Data stored in nth NF relations (denormalized for performance, eg aggregates in MU/PE)BUTâJoinâ still done by the programmer (given this record, go get that child recordâ¦) becauseâ¦Data retrieval was still a record-by-record request to the engineForeign Key relationships not constrained by the engineNo engine assistance on âbest means of traversalâLimited to 16M records/tableSince the data management overhead was minimal (relative to today), this was (and continues to be) an extremely fast engine.Entity-Attribute-Value model (EAV) is used interchangeably with name for name value pair.
BOM)IMS moved multi-file transaction management from the TP monitor to the data engineMany new mechanisms for organizing and traversing data were introduced: not just VSAM, but HDAM, HIDAM, HISAMâ¦All were fairly complex to useBecause the Programmer was the navigator through pointers from record to recordMeaning, all traversal, logical âJOINSâ etc., were explicitly done in code (usually cobol)Functionality was limited by the data structures.Obviously, some queries were much more expensive & code intensive than others In 1968, IMS (from IBM) arrived, bundling data management into a separate engine.Specifically, at the time, to manage hierarchical data (e.g.CICS) allowed many users âsimultaneousâ accessUser Interface was restricted to 24x80 character screensCapabilities somewhat equivalent to Internet Explorer⦠The monitor used logging & 2PC to synchronize across files & guarantee stateThus logging (then as now) was a major limiting factor The first data management facilitiesFlat sequential filesLater improvement: (single-key) indexingOne-user-at-a-time required no synchronizationTP monitors (e.g.Big iron (IBM, Burroughs, Univac) single-mainframe hardwarethe ONLY choice.Minimalmemory(1-16MB) meant marginal data retentionSo everything needed to be written to disk.1 MIP meant efficiency was paramount.S/W commonly built in Assembler.Compare to my phone:32 GB plus âexternal storageâ in the form of SD cards/cloud/etc.40 MIP processor in here â just for the A/D conversionSO: How did anyone do data management?.Hey, this is what it was really like.These were some of my co-workersâ¦.How did data management evolve to produce our current âuniversal databaseâ (RDBMS) world?AND How did that world influence current NoSQL development efforts?Letâs cover a little historySo youâll understand how we got to the current stateTo bring to light what data processing was like before âRelationalâ became the normTo explain that, in some ways, weâve sorta been there before.So letâs go back to when it all got started.Survey â where weâve been and where weâre headed.RDBMS still the âgo-toâ solution in most cases._id:ObjectId("4c4ba5c0672c685e5e8aabf3"),Äata Model rigid changeable rigid/flexible Requests and no data loss event has occurred to date.â Responses (without timing out) for 99.9995% of its âIn particular, applications have received successful Allows quick random read/write of massive.âA Bigtable is a sparse, distributed, persistent Extended and asymmetric network partitions.All the RDBMS overhead without the benefitsÄ®very 2 days we create as much informationĪs we did from the dawn of civilization to 2003.â5th NFâ to circumvent rigid data structures.Not always TPC-A accounting/inventory apps.non-RDBMS vendors forced to support SQL.â Acceptable performance on âmodernâ hardware 1970: Codd introduces Relational Calculus.â Navigation slightly easier but still in code ![]() ![]() FK references managed, followed in code.â Multiple compressed B-tree Indexes per table â Normalized data with optional MUâs/PEâs â âProgrammer as navigatorâ thru physical pointersĪllen Atkins Byggles Bygum Chen Eggers Finlay Myers Rex 1265Īustin 1625 Benson 1938 Bindle 1493 Byars 1266 1267 12973 â How Data Management brought us to today
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