How can you maintain data consistency when scaling a database horizontally?

Atomicity: Atomicity ensures that each transaction is treated as a single, indivisible unit of work. Consistency: Consistency ensures that the database remains in a valid state before and after each transaction. This means that the database constraints, such as integrity rules and relationships, are always enforced. Isolation: Isolation ensures that the execution of one transaction is isolated from the effects of other concurrent transactions. Each transaction appears to execute in isolation, as if it were the only transaction running on the system. Durability: Durability ensures that the effects of a committed transaction are permanent and persistent, even in the event of system failures or crashes.

ACID properties are essential for maintaining the integrity & reliability of transactions in a db system, ensuring tht data remains consistent, isolated, & durable even in the face of failures or concurrent operations. Atomicity: ensures that a transaction is treated as a single, indivisible unit of work. Consistency: ensures that a transaction brings the database from one valid state to another valid state even if it is slower in response. Isolation: ensures that the execution of one transaction is isolated from the execution of other transactions. Consider this same as idempotent property in API. Durability: guarantees that once a transaction is committed, its effects are permanent.

BASE (Basically Available, Soft state, Eventually consistent) principles offer an alternative to ACID, prioritizing availability and partition tolerance over strict consistency. While ACID ensures strong consistency, BASE allows for eventual consistency, beneficial in highly distributed systems where latency and partition tolerance are crucial. BASE systems trade immediate consistency for scalability and fault tolerance. In a recent project, we adopted a BASE approach to horizontally scale our database, achieving higher availability and performance under heavy loads. However, it required careful application logic design to manage eventual consistency and resolve conflicts effectively.

BASE consistency is an alternative approach to data consistency in distributed systems, which stands for Basically Available, Soft state, and eventually consistent. In simple terms, BASE provides a more relaxed approach to consistency compared to the strict ACID model. It acknowledges that achieving immediate consistency in a distributed system can be challenging and may come at the cost of availability. BASE embraces the idea that consistency will eventually be reached over time as the system reconciles the differences between its distributed components.

Basically available means that the system remains operational and responsive to requests, even in the face of failures or partitions. Unlike the strict availability requirement of ACID systems, where the system might become unavailable during certain operations (e.g., during a transaction commit), BASE systems prioritize availability by allowing operations to proceed even if consistency is temporarily compromised. This property ensures that users can continue to interact with the system and access data, even under adverse conditions such as network failures or hardware faults.

Consistency models in distributed systems span from strong consistency to eventual consistency, dictating how data consistency is managed. Strong consistency guarantees uniform data visibility across all nodes simultaneously, whereas eventual consistency permits temporary disparities that are later rectified. Selecting the appropriate consistency model hinges on application needs, weighing consistency assurances against scalability and performance considerations.

Consistency models describe how changes to data are seen by different parts of a distributed system. Some common consistency models explained in simple language: Strong Consistency: It's like friends checking their watches and agreeing on the time after some delay, even if they started with slightly different times. Eventual Consistency: It's like friends checking their watches and agreeing on the time after some delay, even if they started with slightly different times. Causal Consistency: Causal consistency ensures that if one event happened before another, all nodes in the system will agree on the order of those events. Session and Sequential consistency are also useful models mainly used in stateful applications.

When the data grows, instead of scaling up a single server (vertical scaling), horizontal scaling adds more servers, each handling a subset of the data. Data partitioning in horizontal database scaling is about breaking down a large dataset into smaller, more manageable pieces and distributing them across multiple servers. This approach enhances system performance, scalability, fault tolerance, and allows for more efficient resource utilization in handling growing volumes of data and user requests. By distributing data across multiple servers, the system can handle a larger volume of transactions & queries in parallel. Each server is responsible for a specific partition, enabling concurrent processing and improving overall performance.

Data partitioning divides a dataset into smaller subsets (shards) across multiple nodes, enhancing scalability and performance. Strategies like range-based, hash-based, or key-based partitioning evenly distribute data while minimizing inter-shard communication. In a social media platform project, we utilized hash-based partitioning to distribute user data across database nodes. Through hashing user IDs, we maintained related user data on the same shard, reducing cross-shard queries and enhancing scalability without compromising performance.

Data replication entails maintaining data copies across multiple nodes to bolster fault tolerance, reliability, and read scalability. Mechanisms like master-slave replication, multi-master replication, or quorum-based replication ensure update propagation to replica nodes for consistency maintenance. Replication additionally facilitates load balancing and failover capabilities, guaranteeing uninterrupted availability during node failures.

In synchronous replication, all nodes must replicate the data before acknowledging the write operation to the client. This ensures strong consistency across all replicas, as all replicas are updated synchronously with the primary copy. Synchronous replication guarantees that all replicas have the same up-to-date data, minimizing the risk of data loss or inconsistency. However, synchronous replication can introduce latency and performance overhead, as the client must wait for all replicas to acknowledge the write operation before proceeding. Additionally, the system's availability may be impacted if one or more replica nodes are slow or unavailable, as the write operation cannot proceed until all replicas are updated.