![]() Aggregated data result sets are further pushed to Amazon S3 for data sharing, other analytics services, and long-term storage. You can use Amazon Redshift to accommodate data from all database shards and construct a time-consistent, global dataset for data analytics functions. Data is pulled out of the OLTP environment into the OLAP environment based on a schedule.Note: If you choose an Aurora DB cluster to build a database shard, you can also achieve high availability by configuring a read replica with the primary instance. Each database shard is built for high availability using a standalone database deployed with the Multi-AZ feature. It consists of a group of databases built with Amazon RDS for high scalability to meet the growing demand for write throughput. The OLTP environment uses database sharding.Data storage is layered where the OLTP environment is separated from the OLAP environment to meet different business and ownership requirements.Data is entered into the system through web applications that are hosted on a group of Amazon EC2 instances with the Auto Scaling feature.In the context of the AWS Cloud computing environment, its position in the data flow path has several characteristics (illustrated in the following diagram). Let’s take a look at an example of a sharded database architecture that is built with Amazon RDS. You can use any one of these as the building block for a database shard in the sharded database architecture. Amazon RDS offers a set of database engines, including Amazon RDS for MySQL, MariaDB, PostgreSQL, Oracle, SQL Server, and Amazon Aurora. This makes working with a sharded database architecture a much easier task. With the advent of Amazon RDS, database setup and operations have been automated to a large extent. In an online transaction processing (OLTP) environment, where the high volume of writes or transactions can go beyond the capacity of a single database, and scalability is of concern, sharding is always worth pursuing. The inability to offer a consistent, global image of all data limits the sharded database architecture in playing an active role in the online analytic processing (OLAP) environment, where data analytic functions are usually performed on the whole dataset. It typically incurs a higher latency than its peer that runs on only one shard. The query to read or join data from multiple database shards must be specially engineered. However, the share-nothing model also introduces an unavoidable drawback of sharding: The data spreading out on different database shards is separated. Sharding has the potential to take advantage of as many database servers as it wants, provided that there is very little latency coming from a piece of data mapping and routing logic residing at the application tier. If one database shard has a hardware issue or goes through failover, no other shards are impacted because a single point of failure or slowdown is physically isolated. The complexities and overhead involved in doing so don’t exist. There is no need to manage communications and contentions among database members. The share-nothing model offers the sharded database architecture unique strengths in scalability and fault tolerance. However, they have no knowledge of each other, which is the key characteristic that differentiates sharding from other scale-out approaches such as database clustering or replication. All database shards usually have the same type of hardware, database engine, and data structure to generate a similar level of performance. Each server is referred to as a database shard. Sharding is a technique that splits data into smaller subsets and distributes them across a number of physically separated database servers. ![]() ![]() I also outline the challenges for resharding and highlight the push-button scale-up and scale-out solutions in Amazon RDS. I discuss considerations for schema design and monitoring metrics when deploying Amazon RDS as a database shard. In this post, I describe how to use Amazon RDS to implement a sharded database architecture to achieve high scalability, high availability, and fault tolerance for data storage. Amazon Relational Database Service (Amazon RDS) is a managed relational database service that provides great features to make sharding easy to use in the cloud. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases.
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