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Clear up division of labour

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Yuriy Dupyn 2024-01-28 22:51:27 +01:00
parent 53c5d3f3f7
commit c4de02c1e6
1 changed files with 12 additions and 14 deletions
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26
DIVISION_OF_LABOUR.txt
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@ -1,21 +1,19 @@
Communication with client (mainly processing client's input) Communication with client (mainly processing client's input)
* understand and implement the postgres protocol * understand and implement the postgres protocol
* setup of concurrent connections * setup of concurrent connections
* when client sends a query, we send it to the parser "component" * when client sends a query, we send it to the parser
Parsing can be started now Parsing can be started now
basically a function basically a function
String -> Result<Operation, ParsingError> String -> Result<RawOperationSyntax, ParsingError>
Validation of operation Validation of operation
When parsing gives you an operation, it need not make sense (e.g. table doesn't exist) When parsing gives you a raw syntax of an operation, it need not make sense (e.g. table doesn't exist)
So we need to validate the operation. So we need to validate it.
The output of the validation is The output of the validation is
* relevant table * a proper operation validated against the database schema
* an "augmented" operation (e.g. operation with type operation)
* reject when e.g. table doesn't exist, condition refers to non-existent column etc * reject when e.g. table doesn't exist, condition refers to non-existent column etc
* This phaze doesn't need access to rows nor indices, just table meta-data. * This phaze doesn't need access to runtime data (e.g. rows or indices), just the table schemas.
==========Locking of tables should happen here======== ==========Locking of tables should happen here========
@ -30,9 +28,9 @@ Responding to the client
==========Lock on the table should be dropped here=========== ==========Lock on the table should be dropped here===========
Serialization/Desearilization to disk Serialization/Desearilization to disk
* There are two approaches, one is incremental (and very hard) There are two approaches
the other is: when the server is shut down. * one is incremental (and very hard), a storage engine where you have a huge database on disk
encoding something like a BTree, and then you have a small in-memory view (cache) on what's on the disk.
And you try to constantly keep these synced.
* the other is: when the server is shut down, just serialize the in-memory database state onto disk
in e.g. JSON. Trivial to implement.