Any concept.
clear in 60 seconds.

Sharding splits databases into smaller parts to improve performance and scalability.

Primary shards partition data, while replica shards duplicate it for redundancy.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Database per Service ensures each microservice owns its data, reducing dependencies.

Choosing between a shared database and a database per service affects system design significantly.

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

Database replication enhances data availability and read performance but has challenges like replication lag.

Database replication helps keep data available and allows systems to handle more read requests.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Consistent hashing helps distribute data across servers with minimal movement.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Distributed caching spreads data across multiple servers for better performance and reliability.

Distributed locks ensure only one server performs a task, avoiding duplication.

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

The choice between SQL and NoSQL databases depends on your application's specific needs.

Failover ensures systems remain operational by managing leader changes during failures.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

Load shedding helps systems reject excess requests to maintain performance during high demand.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Choosing the right distributed lock depends on your system's needs and existing tools.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Database indexing speeds up data retrieval by allowing quick lookups.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

Replicas keep your system available, while snapshots allow for data recovery.

Kafka uses replication and in-sync replicas to ensure data is always available.

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

B-Trees help databases find data quickly without scanning every row.

Stateless servers are easier to scale because they don't store user session data locally.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

API pagination helps manage large datasets by breaking them into smaller parts.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

Cache stampede occurs when many requests hit the database due to a cache miss.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Redis is a fast in-memory data store used for caching and low-latency applications.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.