1清华云计算课件--分布式计算.ppt

Consistency: Data Types Data entered in databases have rigorous data types associated with them, and explicit ranges Does not protect against all errors (entering a date in the past is still a valid date, etc), but eliminates tedious programmer concerns * Consistency: Foreign Keys Database designers declare that fields are indices into the keys of another table Database ensures that target key exists before allowing value in source field * Isolation Using mutual-exclusion locks, we can prevent other processes from reading data we are in the process of writing When a database is prepared to commit a set of changes, it locks any records it is going to update before making the changes * Faulty Locking Locking alone does not ensure isolation! Changes to table A are visible before changes to table B – this is not an isolated transaction * Two-Phase Locking After a transaction has released any locks, it may not acquire any new locks Effect: The lock set owned by a transaction has a “growing” phase and a “shrinking” phase * Relationship to Distributed Comp At the heart of a TPS is usually a large database server Several distributed clients may connect to this server at points in time Database may be spread across multiple servers, but must still maintain ACID * Conclusions We’ve seen 3 layers that make up a distributed system Designing a large distributed system involves engineering tradeoffs at each of these levels Appreciating subtle concerns at each level requires diving past the abstractions, but abstractions are still useful in general * Discussion Distributed System Design * Questions and Answers !!! * * The “corrected” example * Thread 1: void foo() { sem.lock(); x++; y = x; sem.unlock(); } Thread 2: void bar() { sem.lock(); y++; x++; sem.unlock(); } Global var “Semaphore sem = new Semaphore();” guards access to x y Condition Variables A condition variable notifies threads that a particular condition has been met Inform another thread that a q