1/7/2024 0 Comments Most universal database![]() This is also because databases run on the hard drive and key-value stores run in memory (reading data from memory is always faster than from a hard disk). The big advantage over databases is that key-value stores are ultra-fast in getting data. The requested will return the key plus the value of that key. The key can be anything: name, number, etc. ![]() The application can request any data by the key. Key value data stores just store two components: a key and a value. It’s not a real database but it can hold large amounts of data. Key value storeĪ key-value data store is somewhat different. No-SQL powers what managers mostly refer to as big data. No-SQL is mostly used in large-scale applications and when you need to hold large amounts of data. Then certain search mechanisms blaze through to the document to get the requested data and send it back. When you request data from a No-SQL, it will first look in which document(s) the data should be and point the request to it. Data is stored in documents, like the documents you read only waaaayyyyyy bigger in the amount of text. Most No-SQL databases are document based. It means it doesn’t work like described above and doesn’t use SQL (= the language in which you talk to a relational database). No-SQL stands for non-SQL or non-relational. Unlike people, computers are programmed to start counting from 0 instead of 1. Wait, why would counting chronologically to 7 take 8 reads? Not building correct indexing massively slows down a relational database. Generating such a B-tree structure is what developers refer to as indexing (and mostly complain about when not done properly). So without a B-tree getting the data record would be 50% SLOWER. It would take 8 reads to get to 7 if we just put all numbers chronologically (0,1,2,3,4….7) It would take 4 reads to get to 7 in a B-Tree. If you look back at the image, the number 7 is on depth level 4. Every time you progress in a B-tree you go through its depth. It’s all about the amount of reads required. The last read is the actual data of the record. But it didn’t read the actual data record yet. The search algorithm only got the number (key). Bingo, we’ve got 7! This would be the end of the search, right?Ħ. ![]() Between 4 and 7, the higher number is 7 and selects that. Between 1 and 6, the higher number is 6 and selects that. Between 3 and 10, the lower number is 3 and it selects that. It compares the number you’re searching for (7) with the current number selected (8) and select the next higher/lower number accordingly. Now here is how it knows which one to pick. The next you can choose between is 3 and 10. How does it go through the tree and find the number 7?ģ. Now let’s say the application needs to have the data which is held by the record with the number 7. The algorithm (= based on Big O notation) puts all those numbers in a tree like this:Ģ. Every record in a relational database is assigned a number (also called a key). This is a simplified explanation of how it really works:ġ.Behind the database is something called a B-tree structure. How can a database go through millions of data records and retrieve so much information in milliseconds? Here is a myth debunked: it’s not about hardware speed. ![]() Most applications are powered by a relational database in some shape or form. It’s also referred to as RDBMS (Relational Database Management System). The most fundamental database of all is the relational database. 3.1 MySQL / MSSQL / PostgreSQL (relational databases).
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