Consistent Hashing easy
Consistent Hashing enables which scaling pattern?
- A Vertical Scaling, where additional RAM and CPU resources are added to a single high-performance database instance.
- B Linear Scaling (Horizontal Partitioning), where commodity servers can be added to the cluster to gracefully increase capacity.
- C Data Compression Scaling, where the system focuses on reducing the physical footprint of data rather than adding nodes.
- D Recursive Scaling, where the cluster creates an internal hierarchy of nested rings to handle extremely massive datasets.
Answer: B. Linear Scaling (Horizontal Partitioning), where commodity servers can be added to the cluster to gracefully increase capacity.
Consistent Hashing is the fundamental building block of 'Scale Out' architectures. Because it allows adding nodes with minimal data reshuffling, systems like Cassandra and DynamoDB can scale linearly to thousands of machines, providing nearly infinite storage and throughput capacity using standard hardware.
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- In a 'Smart Client' architecture (e.g., modern Cassandra drivers), how does the client find the right node?
- When a new node (Node X) joins the ring, where does it acquire its initial data from?
- Google's Maglev hashing algorithm (used in their Network Load Balancers) differs from standard Consistent Hashing rings. How does it generate the lookup table?
- Virtual Nodes improve balance but increase metadata size (Memory). How does 'Multi-Probe Consistent Hashing' achieve good balance without VNodes?
- In the Chord DHT protocol (a P2P consistent hashing implementation), naive lookups are O(N). How does Chord optimize this to O(log N)?
- Which of these systems is a famous user of Consistent Hashing?