When designing scalable distributed web architecture, some key features to consider are:
– Make service scale
– Make deployment consistent
– Understand all layers
– Monitor everything
– Plan for failure
– Break things in a controlled manner
The design of a system should specifically address at least the following:
During operations keep track of:
– Capacity management and planning
– Traffic routing
– Multiple clusters
– Stage releases
During implementation consider some of the following strategies:
– Data partitions
* Collapsing requests
– Load balancers
Keep in mind that distributed systems are subject to the CAP theorem.
CAP theorem states that a shared-data system can have at most two of the three following:
– Partition Tolerance
Consistency is “requests of the distributed shared memory to act as if they were executing on a single node, responding to operations one at a time”.
Availability is “every request received by a non failing node in the system must result in a response” .
Partition tolerance means that when the network is partitioned, it will continue to deliver the same results as when the network is whole.
Consistency is of different types:
* Two phase commits
* Sync replication
* Memory access in multi-core
* Relational Databases
Consistency has trade off with latency and operational simplicity.
CAP Availability is different than high availability (HA). HA refers to the whole system, CAP refers to non-failing nodes.
CAP expects unbounded response time, HA has realistic response time.
PACELC (pass-elk) is another theorem that talks about distributed systems.
It tries to figure out:
– If there is a partition, how does the system trade-off availability and consistency
– If there is no partition, how does the system trade-off latency and consistency
For additional information refer to: