new semester here we go
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@@ -19,4 +19,4 @@ If nothing comes up we commit with no issue.
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If something does come up we abort and restart the transaction with a new snapshot, _this time with the new stuff_.
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This time around we should be ok to commit.
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The overhead comes in hard if we have to be correcting transaction but, if we don't find ourselveds do that too much then it beats graphs and trees since there's barely anything to maintain.
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The overhead comes in hard if we have to be correcting transaction but, if we don't find ourselves doing that too much then it beats graphs and trees since there's barely anything to maintain.
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@@ -2,7 +2,7 @@
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## Functional Dependancy
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If we have an attribute a that could produce `b,c,d` reliably everytime then we would only need to keep track of `a` instead of keeping track of all the repeats because the dependants depend on `a`.
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If we have an attribute `a` that could produce `b,c,d` reliably everytime then we would only need to keep track of `a` instead of keeping track of all the repeats because the dependants depend on `a`.
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Example:
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```
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37
cst363/lec/lec26.md
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37
cst363/lec/lec26.md
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# lec26
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## Some types of Database Structures
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1. Key-Values
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Just like a standard map, we provide a `key` and get a `value`.
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Maks things easy for usage but recall that our hash function is really delicate.
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Because we're using a map or a set type of container.
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2. Document
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Welcome to json/xml world.
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Now we just look for documents(json/xml) instead of looking for some data with a predefined structure.
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3. Column Family
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Variant of key/value but this time we store things in columns instead of rows.
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Advantage here is that we can quickly search through columns for analysis type things.
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4. Graph
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Data is a _graph_(wow).
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We still have some key-value system to find a node in the graph but we can create edges between values to create relationships.
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## NoSQL
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### In favor of usage
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* Data is not uniform
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* Dataset is massive
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### Against
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* You need consistency
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*
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@@ -0,0 +1,3 @@
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# Introduction
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