Since distributed graph processing involves the analyzing large scale graphs distributed across multiple machines. In this approach the large graph data is partitioned into small graphs and all those small graphs are processed on different machines.
My question is that how does Apache Age ensure consistency and reliability in this scenario.
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Answers
There are many methods that can be used to ensure reliability and consistency, some of them are as follows:
The first mechanism helps to make sure that data is always present even if your machine fails.
The second method gives functionality that helps the system to continue working on the alternate machines, if the concerned machine fails.
For the consistency, AGE make sure that every transaction is committed rolled back.
As Apache age ( a graph extension of PostgreSQL) means that it inherits the PostgreSql features for the data consistency and reliability.
It adds additional mechanisms to ensure the consistency and the reliability in graph processing.
It provides us Fault tolerant by allowing multiple replicas of same data that stored on different machines.
It provides us Distributed lock manager and master-slave node architecture.
Apache AGE uses various distributed systems concepts such as
Consistent hashing: This partitions the graph data across multiple machines, which ensures that if a machine fails or joins the cluster, only a small portion of the graph needs to be re-partitioned.
Replication
Distributed transactions: This ensures that multiple machines can work together to perform a transaction that involves multiple subgraphs. Distributed transactions ensure that all operations in a transaction are either committed or aborted together, ensuring consistency.