Learn When to Use Database Replica and Database Sharding for Systems Design

Most Microservices must scale databases, and the concepts of database replica and database sharding are vital to understanding when to use them for systems design and systems design interviews.

Database replica will replicate the database in different servers to keep the database more available and avoid losing data. It’s even possible to replicate servers in different countries for disaster recovery.

Database sharding will break the database into separate partitions and give faster data access. Those partition shardings can be distributed across different servers, improving performance, scalability, and availability.

Database Replica

Database replication is the process of creating and maintaining copies of a database on multiple servers, known as replicas. Replication is commonly used in distributed systems to improve data availability, enhance performance, and ensure data durability. It involves synchronizing data changes from a source database to one or more replica databases in a controlled and consistent manner.

Here’s an overview of how database replication works:

Replication Topology: A replication topology defines the relationship between the source database and replica databases. Different replication topologies exist, including master-slave, master-master, and multi-level replication.

Source Database: The source database is the primary database that holds the original and authoritative copy of the data. It is responsible for processing read and write operations and propagating data changes to the replicas.

Replica Databases: Replica databases are copies of the source database. They receive and apply data changes from the source database to keep their data in sync. Replica databases can be located on the same server as the source database or distributed across different servers for improved performance and availability.

Data Replication: Data replication involves capturing and transmitting data changes from the source database to the replicas. When a write operation (such as an insert, update, or delete) occurs on the source database, we record the change in a replication or transaction log.

Replication Process: The replication process reads the changes recorded in the replication log and applies them to the replica databases. Depending on the database system and replication technology, we do database replication through various mechanisms, such as log-based, statement-based, or trigger-based.

Consistency and Synchronization: Maintaining data consistency and synchronization is crucial in database replication. Depending on the replication method, we can use various techniques to ensure we apply data changes to the replicas in the same order and with the same consistency as the source database.

Types of Database Replica

There are several database replicas, each with its characteristics and purposes. Here are some common types:

Full Replication: The entire database is copied to multiple servers, creating identical copies of the original database. Any changes made to the original database are propagated to all replicas. This type of replication provides high availability and fault tolerance, as any replica can take over if the primary database fails.

Snapshot Replication: Snapshot replication involves taking periodic snapshots of the database and distributing them to replicas. The snapshots capture the state of the database at a specific point in time, and subsequent changes are not propagated to the replicas. This type of replication is useful when read-only access to a particular database version is required.

Transactional Replication: Transactional replication involves replicating individual database transactions from the primary database to the replicas. Each transaction is applied to the replicas in the same order it was executed on the primary database. This type of replication is commonly used in scenarios where real-time data synchronization is required.

Merge Replication: Merge replication allows multiple replicas to independently modify the database and then merge the changes into a consolidated version. This type of replication is useful in scenarios where multiple users need to work with their local copies of the database and periodically synchronize changes with the central database.

Peer-to-Peer Replication: Peer-to-peer replication involves multiple databases acting as publishers and subscribers. Each database can independently make changes, which are propagated to other databases in the network. This type of replication is beneficial for distributed systems where data needs to be shared and synchronized across multiple locations or nodes.

Log Shipping: Log shipping involves shipping transaction log backups from the primary database server to one or more standby servers. The standby servers restore the transaction logs and apply them to maintain an up-to-date copy of the database. This type of replication is often used for disaster recovery purposes.

These are just a few examples of the types of database replicas. The choice of replication method depends on factors such as the desired level of data consistency, availability requirements, network topology, and specific use cases.

When to Use Database Replica?

A database replica is a copy of a database created and maintained for various purposes, such as improved performance, high availability, and disaster recovery. Here are some scenarios where using a database replica is beneficial:

Load Balancing and Performance: If your application has a high volume of read operations, you can distribute the read workload across multiple replicas. The primary database server can focus on handling write operations by offloading read queries to replicas, improving overall performance.

High Availability: Database replicas can provide fault tolerance and ensure high data availability. If the primary database server fails, one replica can take over as the new primary server, minimizing downtime and ensuring continuous operation.

Disaster Recovery: Replication can be part of a disaster recovery strategy. By maintaining a replica in a separate geographic location or on a different server, you can quickly switch to the replica in case of a catastrophic event, ensuring data integrity and minimizing data loss.

Scaling: We can use replication to scale the database infrastructure horizontally. We can add more replicas to handle the increased demand as the workload increases. This approach allows you to scale read capacity independently from the primary database server.

Backup and Reporting: We can use replicas for performing backups and generating reports without impacting the performance of the primary database. We can offload resource-intensive tasks from the primary server by running backup jobs or generating reports on replicas.

It’s important to note that while database replication offers benefits, it also introduces considerations such as additional complexity, data consistency challenges, and potential latency between the primary and replica databases.

Therefore, it’s crucial to carefully design and configure database replication based on your specific requirements and workload characteristics.

Real World Use Cases for Database Replica

Here are some real-world use cases where using a database replica can be beneficial:

E-commerce Applications: In e-commerce applications, quick and reliable access to product information is essential. Using database replicas, you can distribute read traffic to handle a high volume of product searches, inventory lookups, and catalog browsing while ensuring the primary database remains available for write operations.

Content Delivery Networks (CDNs): CDNs serve static content to users from edge servers located in various geographical regions. To minimize latency and improve user experience, CDNs often replicate their databases across multiple locations. This allows the edge servers to retrieve content from nearby replicas, reducing the round-trip time for database queries.

Financial Systems: Financial institutions require high availability and data integrity. Organizations can use database replicas to ensure uninterrupted access to financial data and provide failover capabilities in case of system failures. Replicas can also be used for reporting and analysis, allowing financial analysts to generate insights without impacting the primary system.

Social Media Platforms: Social media platforms handle massive user-generated content and interactions. By utilizing database replicas, these platforms can distribute the read load across multiple replicas to provide fast access to user profiles, posts, comments, and other social content.

Analytics and Business Intelligence: Organizations often have separate transactional systems and analytics databases. Replicating data from the transactional database to an analytics database allows for efficient data analysis without impacting the operational system’s performance. This setup enables businesses to generate reports, perform complex queries, and gain insights from historical data.

Mobile Applications: Mobile apps frequently require real-time access to data, and network conditions can be unpredictable. Mobile applications can provide a responsive user experience with reduced latency by using local replicas on mobile devices or in close proximity to the users.

These are just a few examples, and the use of database replicas can be beneficial in various other scenarios depending on the application’s specific requirements and the organization’s needs.

Technologies for Database Replica

There are several technologies and approaches commonly used for implementing database replication:

Master-Slave Replication: This is one of the simplest forms of database replication. It involves a primary/master database server that accepts both read and write operations and one or more secondary/slave servers that replicate data from the primary server. Read operations can be distributed to the slave servers, offloading the read workload from the primary server.

Master-Master Replication: In this setup, multiple database servers act as both master and slave, allowing read and write operations on each server. Changes made on one server are replicated on other servers, ensuring data consistency. Master-master replication provides higher availability and load-balancing capabilities.

Asynchronous Replication: Asynchronous replication allows the primary database server to continue processing transactions without waiting for the replicas to acknowledge the changes. Replicas receive and apply changes from the primary server at their own pace, introducing a slight delay in data synchronization.

Synchronous Replication: In synchronous replication, the primary server waits for replicas to acknowledge the changes before confirming the transaction’s success. It ensures that data changes are immediately applied to replicas, providing stronger consistency guarantees but potentially impacting the primary server’s performance.

Log Shipping: Log shipping involves copying and applying transaction logs from the primary server to the replica servers. The logs are periodically shipped and applied to keep the replica databases up to date. We commonly use this approach in disaster recovery scenarios.

Database Clustering: Clustering involves multiple interconnected database servers operating as a single system. Clusters typically provide high availability, load balancing, and automatic failover capabilities. Various clustering technologies exist for different database systems, such as MySQL Cluster, Oracle RAC, or Microsoft SQL Server Always On Availability Groups.

It’s worth noting that different database management systems (DBMS) offer their native replication features, such as MySQL replication, PostgreSQL streaming replication, or SQL Server Always On Availability Groups. Additionally, third-party replication tools and solutions are available in the market, such as Oracle Data Guard, VMware vSphere Replication, or Amazon RDS Multi-AZ deployment for AWS. The choice of technology depends on the specific DBMS and requirements of the application or organization.

Database Sharding

Sharding is a database partitioning technique used to distribute and store data across multiple database servers, known as shards. It is primarily employed in large-scale, high-traffic systems to improve performance, scalability, and availability.

In a traditional database setup, we store in a single server. As the amount of data and the number of concurrent users increases, this can lead to performance bottlenecks and limit the system’s ability to handle the load. Sharding addresses this issue by horizontally partitioning the data into smaller subsets and distributing them across multiple servers.

Here’s a simplified explanation of how sharding works:

Data Partitioning: The database is divided into smaller logical partitions called shards. Each shard contains a subset of the data based on a defined partitioning strategy. For example, you might partition data based on a specific range of values (e.g., customer IDs) or use a hashing algorithm that evenly distributes the data.

Shard Distribution: We distribute the shards across multiple database servers. Each server is responsible for storing and managing one or more shards. The number of servers and shards can vary based on the application’s requirements.

Query Routing: A client application must determine which shard contains the relevant information when it wants to read or write data. Based on the partitioning strategy, a query router or load balancer sits between the client and the database servers and redirects the queries to the appropriate shard.

Data Consistency: Maintaining data consistency is a crucial aspect of sharding. There are different approaches to handle consistency, depending on the system requirements. A common practice is to store related data within the same shard. Furthermore, we can employ distributed transactions or eventual consistency mechanisms to synchronize data across shards.

Real World Use Cases for Database Sharding

Database sharding is suitable in various real-world situations where the data volume and workload requirements of a system exceed the capabilities of a single database server. Here are some specific real-life scenarios where sharding can be beneficial:

E-commerce Applications: E-commerce platforms often handle a large volume of data, including product catalogs, customer information, and transaction records. Sharding can distribute the data across multiple servers, ensuring high performance and scalability during peak shopping seasons.

Social Networking Platforms: Social media platforms generate enormous amounts of user-generated content, such as posts, images, and videos. Sharding can help handle the massive data load and user concurrency, enabling efficient storage, retrieval, and real-time interactions.

Gaming Applications: Online gaming applications often deal with high user concurrency, real-time interactions, and large datasets. We can use sharding to distribute player data, game states, and matchmaking information across multiple servers to ensure smooth gameplay and fast response times.

IoT Data Management: Internet of Things (IoT) applications generate vast amounts of data from connected devices. We can use sharding to handle the scale and velocity of incoming data, enabling efficient storage, analysis, and processing of IoT data.

Big Data Analytics: Sharding can be beneficial in distributed big data analytics systems. By sharding data based on specific criteria (e.g., time ranges or geographical regions), queries and analysis can be performed in parallel across multiple shards, improving query performance and enabling scalable data processing.

Content Management Systems: Content-heavy applications, such as news websites or multimedia platforms, may experience high traffic and require efficient storage and retrieval of content. Sharding can help distribute content across servers, enabling faster content delivery and reducing the load on individual servers.

SaaS Applications: Software-as-a-service (SaaS) providers often serve multiple customers with diverse data requirements. Sharding can isolate customer data, ensuring data privacy and providing scalability as the number of customers grows.

Evaluating your application’s specific requirements, anticipated data growth, and performance needs is essential before deciding to implement database sharding. Furthermore, it is crucial to consider data consistency, query complexity, and shard management during the planning and implementation.

Benefits of Sharding

Improved Performance: Sharding allows database operations to be distributed across multiple servers, enabling parallel processing and reducing the load on individual servers. Parallel processing can result in improved query response times and increased throughput.

Scalability: Sharding allows scaling a database system horizontally by adding more servers as the data or workload grows. Each shard can be hosted on a separate server, allowing the system to handle increased data volume and user concurrency.

High Availability: Sharding enhances fault tolerance and availability by distributing data across multiple servers. If one server or shard goes down, the remaining shards can continue to serve requests, reducing the impact of failures on the overall system.

Challenges of Sharding

Data Distribution Complexity: Sharding introduces database design and application logic complexity. It requires careful data partitioning and proper query routing to maintain data integrity and consistency.

Joining Data: Sharding complicates the joining of data from different shards because it distributes the data across multiple servers. Complex queries that involve various shards may need extra coordination and processing.

Shard Management: Managing the distribution, scaling, and rebalancing of shards as the system evolves can be complex. It requires monitoring the data distribution, adding or removing shards, and redistributing data without disrupting the system’s operation.

It’s important to note that sharding is only sometimes necessary or suitable for every application. We typically use sharding in scenarios where the data and workload exceed the capabilities of a single server.

Types of Database Sharding

Database sharding is a technique used to horizontally partition a database into smaller, more manageable pieces called shards. Each shard contains a subset of the data, allowing for improved scalability and performance. Here are some common types of database sharding:

Hash-based Sharding: In hash-based sharding, a hash function is applied to a shard key (such as a primary key or a specific field) to determine which shard the data should be assigned to. The hash function distributes the data evenly across the shards based on the key’s value. This approach ensures a relatively even distribution of data but can make it challenging to perform range-based queries efficiently.

Range-based Sharding: Range-based sharding involves dividing the data based on a predefined range of values. For example, a shard may contain data for a specific range of customer IDs or time periods. Range-based sharding allows for efficient range queries, but it requires careful planning to ensure an even distribution of data and to handle data growth and rebalancing.

List-based Sharding: List-based sharding involves explicitly assigning specific data values or criteria to different shards. For example, you could assign customers from a specific region to one shard and customers from another region to a different shard. This approach provides more control over shard distribution but requires manual configuration and maintenance.

Directory-based Sharding: Directory-based sharding maintains a separate directory or metadata layer that maps data to the appropriate shard. The directory contains information about the shard location for each data item based on predefined rules or configurations. This approach provides flexibility and ease of management but introduces an additional layer of complexity and potential performance overhead.

Database-specific Sharding: Some databases, such as MongoDB and Apache Cassandra, provide built-in sharding capabilities. These databases offer proprietary sharding mechanisms tailored to their specific architecture and features. For example, MongoDB uses a sharding method called range-based partitioning, while Cassandra uses a consistent hashing algorithm combined with virtual nodes.

It’s worth noting that the choice of sharding method depends on various factors, including the nature of the data, query patterns, scalability requirements, and the capabilities provided by the database management system being used. Additionally, some systems may employ a combination of sharding techniques to optimize performance and accommodate different types of data.

Technologies for Database Sharding

There are several technologies and approaches available for implementing database sharding. The choice of technology depends on factors such as the specific database system, the requirements of the application, and the scalability needs. Here are some commonly used technologies for implementing database sharding:

Database-specific Sharding: Some databases provide built-in sharding capabilities. For example, MongoDB has a built-in sharding feature called “MongoDB Sharding” that allows you to distribute data across multiple servers. Similarly, other NoSQL databases like Cassandra and Couchbase also provide native sharding capabilities.

Consistent Hashing: Consistent hashing distributes data across multiple decentralized shards. It allows for easy scalability and rebalancing of data. Tools like Apache Kafka and Redis Cluster use consistent hashing for sharding.

Virtual Sharding: Virtual sharding involves using a middleware layer or proxy between the application and the database servers. It abstracts the sharding logic from the application and handles the routing of queries to the appropriate shard. Tools like Vitess and ProxySQL provide virtual sharding capabilities for MySQL databases.

Shared-Nothing Architecture: In a shared-nothing architecture, each database server has its dedicated resources and does not share memory, disks, or processors with other servers. This approach simplifies sharding, as data can be partitioned and distributed across servers without complex coordination. Tools like Google Cloud Spanner and CockroachDB implement shared-nothing architectures with built-in sharding capabilities.

Distributed SQL Databases: Distributed SQL databases provide a distributed and horizontally scalable SQL database solution. They typically provide automatic sharding and replication capabilities while still offering SQL compatibility. Examples of distributed SQL databases include YugabyteDB, CockroachDB, and TiDB.

Cloud-based Database Sharding: Many cloud providers offer managed database services with built-in sharding capabilities. For example, Amazon RDS for MySQL and PostgreSQL, Azure SQL Database, and Google Cloud Spanner provide sharding features as part of their managed database offerings.

It’s important to note that the suitability of these technologies depends on various factors, including the specific requirements of your application, the database system, and the scalability needs. We must thoroughly evaluate and test different technologies before selecting the best fit for the use case.

Summary

Let’s recap the concepts of database replica and database sharding.

Database Replica:

• A copy of a database used for improved performance, high availability, and disaster recovery.
• Distributes read workload across replicas, allowing the primary server to focus on write operations.
• Provides fault tolerance and continuous operation in case of primary server failure.
• Supports disaster recovery by maintaining replicas in separate locations.
• Enables scaling of read capacity independently from the primary server.
• Can be used for backups and generating reports without impacting the primary server.

Database Sharding:

• Divides a database into smaller, independent shards to distribute data and workload.
• Improves performance by parallelizing data access and query execution.
• Allows horizontal scaling by adding more shards as the data and workload grows.
• Reduces contention and improves the overall throughput of the system.
• Requires careful planning and consideration to ensure data distribution and query routing.
• Can be used in scenarios with large data volumes, high write rates, and complex queries.
• Both database replica and sharding are techniques used to enhance database performance, scalability, and availability, but they address different aspects of database management. Database replica focuses on data redundancy, high availability, and workload distribution, while database sharding focuses on horizontal scalability and performance optimization by partitioning data.