Distributed data management challenges
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The document discusses several concepts related to distributed data management including ACID properties, monitor object pattern, and sagas. The monitor object pattern describes how synchronized methods are used to control access to shared resources. Sagas are long-running transactions that coordinate multiple services through message passing. There are two types of sagas: choreography-based where the sequence is embedded in the message flow, and orchestration-based where a central process coordinates the steps. Issues like lost updates and dirty reads can occur without transactional isolation, and various countermeasures are proposed to handle the lack of isolation in a saga.
Distributed data management challenges
- 2. Bond Trading
- 3. ACID (Atomicity, Consistency, Isolation, Durability) A • Each transaction is "all or nothing". C • Data should be valid according to all defined rules. I • Transactions do not affect each other. D • Committed data would not be lost, even after power failure.
- 4. Monitor object pattern Monitor object: This component exposes the methods that are synchronized to the clients Synchronized methods: The thread-safe functions that are exported by the interface of the object are implemented by these methods Monitor conditions: This component along with the monitor lock decides whether the synchronized method should resume its processing or suspend it
- 6. Sagas
- 7. Sagas
- 8. Sagas
- 9. Sagas Order Service— Create an Order in an APPROVAL_PENDING state. Consumer Service— Verify that the consumer can place an order. Kitchen Service— Validate order details and create a Ticket in the CREATE_PENDING state. Accounting Service— Authorize consumer’s credit card, which fails. Kitchen Service— Change the state of the Ticket to CREATE_REJECTED. Order Service— Change the state of the Order to REJECTED.
- 12. The lack of isolation Lost updates— One saga overwrites without reading changes made by another saga. Dirty reads— A transaction or a saga reads the updates made by a saga that has not yet completed those updates. Fuzzy/nonrepeatable reads— Two different steps of a saga read the same data and get different results because another saga has made updates.
- 13. Countermeasures for handling the lack of isolation Semantic lock— An application-level lock. Commutative updates— Design update operations to be executable in any order. Pessimistic view— Reorder the steps of a saga to minimize business risk. Reread value— Prevent dirty writes by rereading data to verify that it’s unchanged before overwriting it. Version file— Record the updates to a record so that they can be reordered. By value— Use each request’s business risk to dynamically select the concurrency mechanism.
- 14. Order service