DynamoDB Applied Design Patterns.

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DynamoDB provides fast and predictable performance with seamless scalability. If you are a developer, you can use DynamoDB to create a database table that can store and retrieve any amount of data, and serve any level of request traffic. As a database administrator, you can create and scale up or down your request capacity for your DynamoDB table without downtime or performance degradation. Designed as a complete solutions guide for AWS DynamoDB, this book is a fully managed proprietary NoSQL database service pattern. The book begins with a description of the concepts of data modeling including tables, items, attributes, primary keys, indexes, and design patterns. You will learn how to access DynamoDB in the management console, command line, and the Eclipse plugin. You will also gain insights into DynamoDB Local and CLI commands. By the end of the book, you will have all that it takes to efficiently use DynamoDB to its utmost capabilities.

Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Modeling with DynamoDB; Data model; Efficient use of primary keys; Basics of indexes; Default or primary indexes; Secondary indexes; DynamoDB data types; Summary; Chapter 2: DynamoDB Interfaces; The management console; Managing the DynamoDB table; Managing DynamoDB table items; The Eclipse plugin; The command-line interface; Summary; Chapter 3: Tools and Libraries of AWS DynamoDB; Creating your first SDK project; Java SDK operations; DynamoDB Local; Summary.

Chapter 4: Working with Secondary IndexesSecondary indexes; Projection; Local secondary index; Global secondary index; Item sharding; Ideal item writing; Best practices with secondary indexes; Distributing the load by choosing the correct key; Making use of the sparse index; Using the global secondary index for quicker retrieval; Creating a read replica; Using indexes sparingly; Choosing projections carefully; Optimizing frequent queries to avoid fetches; Watching for expanding item collections; Summary; Chapter 5: Query and Scan Operations in DynamoDB; Querying tables; Consistency.

Exclusive start keyScanning tables; Parallel scanning; Summary; Chapter 6: Working with the DynamoDB API; Data format; HTTP requests; Request header; Request body; Response header; Error handling; Client-side error with status code 400 -- retry not needed; AccessDeniedException; ConditionalCheckFailedException; IncompleteSignatureException; LimitExceededException; MissingAuthenticationTokenException; ResourceInUseException; ResourceNotFoundException; ValidationException; Client-side error with status code 400 -- retry possible; ProvisionedThroughputExceededException.

ItemCollectionSizeLimitExceededExceptionThrottlingException; UnrecognizedClientException; Client-side error with status code 413; Server-side error with status code 500; InternalFailure and InternalServerError; ServiceUnavailableException; Error retry and exponential back off; Operations in DynamoDB; CreateTable; PutItem; UpdateItem; GetItem; Query; Scan; DeleteItem; DescribeTable; UpdateTable; DeleteTable; ListTables; BatchGetItem; BatchWriteItem; Summary; Chapter 7: Distributed Locking with DynamoDB; Distributed locking; Solutions available to solve distributed writes; Java high-level API.

Optimistic lockingImportance of distributed locking; Summary; Chapter 8: DynamoDB with Redshift, Data Pipeline, and MapReduce; Loading data from DynamoDB into Redshift; Importing and exporting data between DynamoDB and S3; Creating Data Pipeline; Exporting, importing, querying, and joining tables using AWS MapReduce; Exporting data from DynamoDB to S3; Exporting the DynamoDB table to HDFS; Importing data to DynamoDB; Querying data in DynamoDB; Joining two DynamoDB tables; Summary; Chapter 9: DynamoDB -- Best Practices; DynamoDB use cases; Schema-less-ISH; Batch applications.

English.

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