Sharding in DynamoDB refers to partitioning a table's data across multiple partitions. Each partition is a subset of the table's data and is stored on a different server. Sharding increases the performance and capacity of a table by spreading the data across multiple servers.
Sharding can affect the performance of DynamoDB reads in a few ways:
- Increased throughput: Sharding a table can increase the number of requests a table can handle simultaneously, improving the overall performance of read operations.
- Improved data locality: When a table is sharded, the data is spread across multiple servers. This can improve the performance of read operations by reducing the amount of data that needs to be retrieved from a single server.
- Data partitioning: Each partition is stored on a separate server when the data is partitioned. This allows for better scalability and performance, as the read and write requests can be spread across multiple servers, reducing the load on a single server.
- Increased latency: When a table is sharded, retrieving data from a specific partition may take longer. DynamoDB needs to determine which partition the data is stored in and retrieve it from that specific partition. This can increase the latency of read operations.
Overall, sharding can improve the performance and capacity of a table. Still, it also adds complexity to your DynamoDB architecture, so it's important to weigh the benefits against the added complexity when deciding whether or not to shard your table.
Other Common DynamoDB FAQ (with Answers)
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- Can I add another region to global DynamoDB?
- What is DynamoDB used for?
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- How to access DynamoDB from EC2?
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- How to divert the traffic from S3 to DynamoDB?
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