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Serializable if there exist some serial order of exectuion of those same transaction that yields the same result as the actual execution actual exeuction might have parallel transaction 2 legal results -> only these 2 results are serializable concurrency control 1. pessimistic = use lock 2 phase locking (standard) 1. acquire lock before using record 2. hold until done why hold lock until transac..

petal = act as diskdrive that are sharable to Frangipani(F)[FileSystem] Frangipani acts like cache F sends read,write call to Petal to get data Challenges 1. cache coherence user1 creates file /A and store it only in it's local cache but user2,3 cannot see -> we want other user can see even the most recent write 2. Atomicity user 2 wants to create /B -> even user1,2 simultaneously modify somethi..

History EC2 = rent bunch of virutal machines in a single physical node + locally attached storage (hard drive for virtual machine guest) for webserver ,since it is state less , it is okay if ec2 instances go down client can just fire up new webserver but for db if the ec2 goes down there is no way to access that attached storage -> AWS provides S3 to store snapshot of DB but between snapshot are..

why focus on zookeepr 1. N * Servers -> N * Performance? 2. what is api for genral purpose coordinate service? ,raft is library zk works on top of ZAB which is similar to raft replica should never serves(write operation) client because it's data might not be consistent with other , leader can work fine with majority and some replica might not get updated write should be served with leader zookee..

we should avoid split brain 1. client needs to talk to both servers to get some progress (worst idea, worst than single server communication) 2. client can talk to single server to get some progress , but we might have some network issue , each server will hold wrong result and move on majority vote more than half of server should response to get some progress (leader election.. etc) always calc..

state transfer = primary send what ram is storing to backup (replica) replicated state machine = send operation from client not memory , primary and backups are executing operation same time primary fails -> cut-over == client knows it need to send request to backup even with replicated state machine when replica fails and we need to create new replica , we need to use state transfter and send f..

Why is it hard to build BIg Stoarge want performance -> do sharding -> faults(some storage might fail) -> we want tolerance -> so we use replication -> now there can be inconsistency -> we need lots of network talking to achieve consistency -> low performance trade off happens bad replication design want to keep this 2 table identical we haven't made any process that 2 servers are going to handl..

why use Threads I/O Concurrency = using mutiple thread to call remote procedure, each thread will call remote i/o and wait for answer Parallelism Convience = do something in background or periodically with out Threads event driven programming = has single thread and single loop (like nodejs) , cant have parallelism , hard to write sequential programming , Thread chanllenges threads share same me..