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This section is a summary of best practices for working with Amazon DynamoDB. Use this as a reference to quickly find recommendations for maximizing performance and minimizing throughput costs.
Amazon DynamoDB tables are distributed across multiple partitions. For best results, design your tables and applications so that read and write activity is spread evenly across all of the items in your tables, and avoid I/O "hot spots" that can degrade performance.
Amazon DynamoDB items are limited in size (see Limits in Amazon DynamoDB). However, there is no limit on the number of items in a table. Rather than storing large data attribute values in an item, consider one or more of these application design alternatives.
Sudden, unexpected read activity can quickly consume the provisioned read capacity for a table. In addition, such activity can be inefficient if it is not evenly spread across table partitions.
Local secondary indexes let you define alternate range keys on a table. You can then issue Query requests against those range keys, in addition to the table's hash key. Before using local secondary indexes, you should be aware of the inherent tradeoffs in terms of provisioned throughput costs, storage costs, and query efficiency.