Modern Scalable Databases

To perform joins between shards.

To route client requests to the appropriate shard.

To store backup configurations for shards.

To hash keys for partitioning data.

A method of encrypting sensitive data.

A backup server in a replica set.

A horizontal partition of a database.

A tool for monitoring database traffic.

It eliminates the need for backups.

It increases both scalability and redundancy.

It simplifies complex SQL queries.

It reduces the need for a router.

A set of configuration servers.

A collection of shards distributed across regions.

A primary server and its associated secondary servers for redundancy.

A group of shards that handle write-heavy traffic.

Traffic is rerouted to the config server.

Secondary servers automatically elect a new primary.

The router assigns new shards dynamically.

The primary server is rebooted automatically.

Data is always immediately consistent across all nodes.

Data might take time to propagate across nodes, leading to temporary inconsistencies.

Data must be manually synchronized across nodes.

Consistency is not guaranteed due to system failures.

High traffic on specific shards causes uneven load distribution.

Celebrities’ data is stored differently for security reasons.

All shards are overloaded with high read and write requests.

Certain shards fail to replicate data efficiently.

Redistributing data when adding or removing shards, which requires fault-tolerant methods.

Creating backups for new shards added to the system.

Replicating data across all shards during traffic spikes.

Distributing write-heavy traffic across existing shards.

It minimizes storage space and simplifies updates.

It allows faster reads with fewer database hits.

It duplicates data for high performance.

It eliminates the need for joins between tables.

It reduces redundancy and improves update efficiency..

It allows for single-query retrieval of all necessary data.

It enforces strict schema rules.

It is easier to scale horizontally.

Using as many joins as possible for efficiency.

Designing around simple key-value lookups to avoid joins.

Avoiding hash functions for data distribution.

Relying heavily on a single primary server for consistency.

PostgreSQL and MySQL.

MongoDB, Cassandra, and DynamoDB.

Oracle and SQLite.

Redis and HBase and SQLite

When updates are frequent and space efficiency is important.

When scaling horizontally is a top priority.

When high traffic requires fewer database hits.

When you need to perform complex joins efficiently.