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Showing of 51What is data partitioning and why is it important in database design?
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Data partitioning is the process of dividing a large database into smaller, more manageable pieces called partitions or segments. Each partition contains a subset of the total data and can be stored on the same or different physical storage devices.
Importance:
- Improved Performance: Queries can run faster by accessing only relevant partitions
- Scalability: Enables horizontal scaling across multiple servers
- Manageability: Easier maintenance, backup, and recovery operations
- Parallel Processing: Multiple partitions can be processed simultaneously
- Storage Optimization: Different partitions can use different storage types based on access patterns
Example: A sales database partitioned by year, where 2023 data is in one partition and 2024 data is in another, allowing faster queries when filtering by date ranges.
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What's the difference between partitioning and sharding?
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Partitioning typically refers to dividing data within a single database instance, where all partitions are managed by the same database server.
Sharding is a specific type of horizontal partitioning where data is distributed across multiple independent database instances (shards), often on different physical servers.
Key Differences:
- Scope: Partitioning is usually within one database; sharding spans multiple databases
- Complexity: Sharding requires more complex application logic for routing queries
- Scalability: Sharding provides better horizontal scalability
- Management: Partitioning is easier to manage; sharding requires coordination between multiple systems
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Explain the three main types of data partitioning.
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1. Horizontal Partitioning (Sharding)
- Divides rows of a table across multiple partitions
- Each partition contains the same columns but different rows
- Example: User table split by user ID ranges (1-1000, 1001-2000, etc.)
2. Vertical Partitioning
- Divides columns of a table across multiple partitions
- Each partition contains different columns but all rows
- Example: User table split into UserBasicInfo(id, name, email) and UserProfile(id, bio, preferences)
3. Functional Partitioning
- Divides data based on feature or service boundaries
- Different tables/schemas for different business functions
- Example: Separate databases for user management, order processing, and inventory
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What is replication in distributed systems?
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Replication is the process of maintaining multiple copies of data across different nodes or servers in a distributed system. The primary goals are to improve availability, fault tolerance, and performance by ensuring that if one node fails, the data remains accessible from other nodes.
Key benefits include:
- High Availability: System remains operational even if some nodes fail
- Fault Tolerance: Data is preserved even during hardware failures
- Performance: Reduced latency by serving data from geographically closer replicas
- Load Distribution: Multiple nodes can handle read requests simultaneously
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What's the difference between replication and sharding?
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Replication involves copying the same data to multiple nodes, while sharding involves splitting different portions of data across multiple nodes.
Replication: Each node contains a complete copy of the dataset
- Node A: {user1, user2, user3}
- Node B: {user1, user2, user3}
Sharding: Each node contains a subset of the data
- Node A: {user1, user3}
- Node B: {user2}
Replication focuses on availability and fault tolerance, while sharding focuses on horizontal scaling and performance for large datasets.
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Explain the master-slave replication model.
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Master-slave replication involves one primary node (master) that handles all write operations, while one or more secondary nodes (slaves) receive copies of the data and typically handle read operations.
How it works:
- All writes go to the master node
- Master propagates changes to slave nodes
- Reads can be served from either master or slaves
- If master fails, one slave can be promoted to master
Advantages:
- Simple to implement and understand
- Consistent writes (single point of truth)
- Read scalability through multiple slaves
Disadvantages:
- Single point of failure for writes
- Write bottleneck at master
- Potential data loss during failover
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What is eventual consistency?
What is log shipping and how does it differ from streaming replication?
What are the main advantages and disadvantages of database sharding?
What is a shard key and why is choosing the right shard key crucial?
Explain range-based partitioning with its pros and cons.
What is hash-based partitioning and when would you use it?
Describe directory-based partitioning and its use cases.
Explain the concept of hot spots in sharding and how to avoid them.
What are the different approaches to implement sharding at the application level?
How does sharding impact database indexing strategies?
What are the key metrics to monitor in a sharded system?
What is the CAP theorem and how does it apply to sharded systems?
What are the database-specific sharding implementations you should know about?
What are some real-world examples of sharding implementations and their trade-offs?
What is synchronous vs asynchronous replication?
Describe master-master (multi-master) replication.
What is logical vs physical replication?
Explain the CAP theorem and its implications for replication.
What are the different levels of consistency in distributed systems?
How does replication impact read and write performance?
What is read-after-write consistency and how do you implement it?
How do you monitor replication lag?
How does MySQL replication work internally?
What are the differences between statement-based and row-based replication?
How does PostgreSQL streaming replication work?
What is the difference between active-active and active-passive replication?
What is consistent hashing and how does it solve resharding problems?
How do you handle cross-shard queries and what are the challenges?
How do you handle transactions across multiple shards?
Explain the concept of resharding and its challenges.
How do you maintain data consistency in a sharded environment?
Explain eventual consistency and how to implement it in sharded systems.
How do you design a sharded system for geographic distribution?
How do you handle schema evolution in a sharded environment?
What is federated sharding and when would you use it?
How do you implement automatic rebalancing in a sharded system?
How do you handle write conflicts in multi-master replication?
What are vector clocks and how are they used in replication?
What is the split-brain problem and how do you prevent it?
Explain the concept of write quorum and read quorum.
What are Conflict-free Replicated Data Types (CRDTs)?
How do you implement cross-datacenter replication?
How do you handle schema changes in a replicated environment?
What are the considerations for handling large object replication?
How do you implement multi-region replication with disaster recovery?
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Sharding & Replication, in short videos.
Sharding & Replication cheatsheet
- What is Data Partitioning?01
- Why Data Partitioning?02
- Key Concepts03
- Partitioning Strategies04
- Consistent Hashing05
- Architecture Patterns06
- Trade-offs & Challenges07
- Real-World Examples08
- Interview Tips09
- Quick Decision Framework10
- Performance Considerations11
- Common Pitfalls12
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