![]() ![]() Client A Near Cache key1, val Data Invalidation Message: value for key1 is now invalid Client B cache.put(key1, SomethingNew) cache.get(key1) Near Cache (in-process) Node Node Node Node Node Node.Data Storage Process Primary Bar Primary Node X Backup Node X Backup Foo Redistribution Repartioning Node X I think Node X has died I think Node X has died Death detection is vote based – there is no central management Redistribution Repartioning Data Storage Process Primary Node X Backup Bar Data Storage Process Primary… Backup… Data Storage Process Primary… Backup… Consensus.Client Connection Proxy Data Storage Process Primary Backup Connection Proxy m圜ache.put( “Key”,”Value”) Data Storage Process Primary Backup Data Storage Process Primary Backup Data Storage Process Primary Backup.Node Node Node Node Client UDP cache.get( “foo”) Foo Well Known Hashing Algorithm.Node 6 Node 1 Node 2 Node 5 Node 4 Node 3 Node 2 Backup The more machines, the faster failover will be! Thus, should one fail, the backup copy will still be available. Data is held on at least two machines.Node Node Node Node Node Node Client Client Client Near Cache Database Async write behind Near Cache kept Coherent via Proactive update/expiry of data as it changes Queries run in parallel where possible (aggregations etc) Objects held in serialised form In-memory storage of data – no disk induced latencies (unless you want them).Clustered therefore fault tolerant and scalable Supports ACID, SQL etc No disk access DB DB DB DB DB DB Cache Node Cache Node Cache Node Cache Node Cache Node Cache Node It supports all the features of vanilla Oracle DB but has better scalability, fault tolerance and bandwidth. What is RAC? RAC is a clustered database which runs in parallel over several machines.All data is stored as key value pairs. ![]() ![]() Database Node Node Node Node Node Node Client Client Client Near Cache.The Post-It Note is a great example of Business Evolution – a product that starts its life in one role, but evolves into something else … otherwise known as Accidental Genius.Client Each machine is responsible for a subset of the records.Copy 6 Copy 1 Copy 2 Copy 5 Copy 4 Copy 3 Client Writes Data Controller All nodes must be locked before data can be written => The Distributed Locking Problem.Data on Disk Record 1 = foo Record 1 = foo Record 1 = bar Client Writes Data Lock Record 1.Data on Disk Record 1 = foo Record 1 = foo Record 1 = bar Client Writes Data Becomes out of date. ![]() What is wrong with this architecture? Copy 6 Copy 1 Copy 2 Copy 5 Copy 6 Copy 3 Copy 6 Copy 1 Copy 2 Copy 5 Copy 6 Copy 3
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