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Today's db optimizer builds a set of execution plans and chooses the best one based on "cost". Number of rows, memory access vs. disk access, and other things
by acscott 10y ago
Today's db optimizer builds a set of execution plans and chooses the best one based on "cost". Number of rows, memory access vs. disk access, and other things factor into this cost. Also, the higher the number of joins, the greater the number of plans. 7 joins would be 7! possible plans +. This has to happen fast, so db optimizer cannot generate every possible plan and pick the optimal one. There's a body of research to delve more into how it does this. The point is that the optimizer does real work, as fast as possible to generate the execution plan (they called this dynamic programming in one paper I read).
The LINQ interpreter on the client would have to retrieve the statistics from the server's tables and indexes of interest and perform the same operation as the optimizer (costing more network and some db cpu). Next it would have to send back the plan to the db executor in some way. This plan "marshalling" would cause more network traffic, requiring the executor to "unmarshall" the plan costing more CPU. This is less efficient than the current scheme.
Alternatively the LINQ language could be implemented on the database but with its own inherent difficulties, but in reverse.
Once a plan is cached and is reused, this inefficiency goes away to some degree. So there could be a possible mechanism for the server to send back to LINQ client a hash identifying the plan if it is cached and then have LINQ only send the hash with parameters to the DB on next execution. (You would have to see if this isn't covered by some patent of course!)
The constraint is the network connection. If the network connection between client and server were faster and bigger than the data bus on the computers, then it would change the equation significantly. But longer distances means slower communication all things being equal (lightspeed and all). So a network connection being better than the data bus would be inefficient and quickly remedied in a competitive marketplace.
- eeperson 10y agoHow is implementing LINQ on the database any different implementing SQL on the database?
- 6nf 10y agoLinq is compiled once. The database will continually monitor performance of the query and recompile the execution plan as needed. Linq runs on the client machine. The query planner runs on the database machine. Many clients will connect to the same database server, and they may not even know beforehand what exactly the RDBMS is capable of, what indexes are available, what hardware the database server is running on, etc. Therefore LINQ can't compile an optimal execution plan before sending the request off to the database server. In short - LINQ runs on the client machine, the execution plan happens on the database server. Think of it this way: Lets say you have a database of clients and contacts. Lots of systems in your company connects to this database to access this data. Each of those systems will submit SQL in the form of 'select clientname from clients where id = 123' or whatever. Now lets say the client list grows and the old execution plan is not optimal any more. Our smart RDBMS can just dynamically fix the execution plan and performance goes up for every system accessing the database. If the RDBMS instead received a rigid execution plan, EVERY client system will need to recalculate the execution plan. Also: lets say your client app connects to several different database servers. What's a good exection plan on one server it not going to be a good execution plan on another server, so LINQ would need to keep a list of database servers with table statistics, indexes, etc etc and continually monitor all of those for changes. It's massive duplication of work. It's much more efficient to have each database server look after its own execution plans.
- eeperson 10y agoSorry,I should have been clearer. The GP post mentioned 2 ways to implement a LINQ to execution plan transform. The first way would be to do it on the client. This is problematic because the client doesn't have enough information to generate an efficient execution plan. That makes sense to me. The second way would be to do it on the server. This is the part I don't understand. Why can't we do it that way? This is how it is done for SQL.