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DynamoDB Applied Design Patterns: A Practical Guide by Prabhakaran Kuppusamy



Dynamodb Applied Design Patterns Prabhakaran Kuppusamy




If you are looking for a practical guide on how to design, develop, and optimize applications using Amazon's NoSQL database service, Dynamodb, then you might want to check out Dynamodb Applied Design Patterns by Prabhakaran Kuppusamy. This book is a comprehensive resource that covers various aspects of Dynamodb, from basic concepts and operations to advanced techniques and best practices. In this article, we will give you an overview of what this book has to offer and why you should read it.




Dynamodb Applied Design Patterns Prabhakaran Kuppusamy


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What is Dynamodb and why use it?




Dynamodb is a fully managed, scalable, and serverless NoSQL database service that provides fast and consistent performance for any scale of data. It supports key-value and document data models, allowing you to store and query diverse types of data with ease. It also offers flexible schema, automatic scaling, encryption at rest, point-in-time recovery, on-demand backup, global tables, streams, triggers, transactions, and many other features that make it a powerful and reliable solution for various use cases.


Some of the benefits of using Dynamodb are:


  • It eliminates the need for provisioning, patching, or managing servers, reducing operational overhead and complexity.



  • It handles high throughput and low latency requests with minimal response time variation, ensuring a smooth user experience.



  • It scales automatically based on your workload demand, without affecting performance or availability.



  • It charges only for the resources you consume, not for the capacity you provision, saving you money.



  • It integrates seamlessly with other AWS services, such as Lambda, S3, Kinesis, Cognito, IAM, CloudFormation, CloudWatch, etc., enabling you to build end-to-end applications with ease.



What are design patterns and why use them?




Design patterns are reusable solutions to common problems that arise in software development. They are not specific to any programming language or framework, but rather describe general principles and guidelines that can be applied to different situations. They help you to write clean, maintainable, and efficient code that follows best practices and avoids common pitfalls.


Some of the benefits of using design patterns are:


  • They improve the quality and readability of your code, making it easier to understand, debug, and modify.



  • They enhance the reusability and extensibility of your code, allowing you to reuse existing solutions and adapt them to new requirements.



  • They facilitate communication and collaboration among developers, as they provide a common vocabulary and a shared understanding of the problem and the solution.



How to apply design patterns to Dynamodb?




Applying design patterns to Dynamodb is not a trivial task, as it requires a good understanding of the features and limitations of the service, as well as the trade-offs involved in different design choices. Unlike relational databases, Dynamodb does not support joins, complex queries, or schema enforcement, which means that you have to design your data model and access patterns carefully to achieve optimal performance and functionality.


The book Dynamodb Applied Design Patterns provides a systematic approach and practical examples on how to apply design patterns to Dynamodb, covering three main categories: data modeling patterns, performance optimization patterns, and reliability and scalability patterns. In the following sections, we will briefly introduce each category and some of the patterns included in the book.


Data modeling patterns




Data modeling patterns are design patterns that help you to organize and access your data in Dynamodb. They help you to define your table structure, primary keys, indexes, attributes, and relationships among your data entities. They also help you to optimize your queries and avoid scanning or filtering large amounts of data.


Some of the data modeling patterns covered in the book are:


Single-table pattern




The single-table pattern is a design pattern that uses a single table to store and query different types of data. It leverages the composite primary key feature of Dynamodb, which allows you to combine a partition key and a sort key to create a unique identifier for each item. By using different values for the partition key and the sort key, you can create multiple logical tables within a single physical table.


An example of how to use the single-table pattern is shown below:


PK SK Attributes --- --- --- USER#john USER#john name: John, email: john@example.com USER#john ORDER#2021-01-01 date: 2021-01-01, amount: 100 USER#john ORDER#2021-01-02 date: 2021-01-02, amount: 200 USER#mary USER#mary name: Mary, email: mary@example.com USER#mary ORDER#2021-01-03 date: 2021-01-03, amount: 300 In this example, we use the prefix USER# for the partition key and the sort key of user items, and the prefix ORDER# for the sort key of order items. This way, we can store both user and order data in the same table, and query them efficiently using different key conditions. For example, we can get all the orders for a user by querying with the partition key USER#john and the sort key begins_with(ORDER#). We can also get all the users by scanning with the filter expression begins_with(PK, USER#).


Adjacency list pattern




The adjacency list pattern is a design pattern that uses an adjacency list to model hierarchical or graph data in Dynamodb. An adjacency list is a data structure that represents a graph as a collection of nodes and edges, where each node has a list of its adjacent nodes. In Dynamodb, you can use a composite primary key to store both nodes and edges in the same table.


An example of how to use the adjacency list pattern is shown below:


PK SK Attributes --- --- --- NODE#A NODE#A name: A NODE#A EDGE#B weight: 10 NODE#A EDGE#C weight: 20 NODE#B NODE#B name: B NODE#B EDGE#C weight: 30 NODE#C NODE#C name: C the prefix NODE# for the partition key and the sort key of node items, and the prefix EDGE# for the sort key of edge items. This way, we can store both nodes and edges in the same table, and query them efficiently using different key conditions. For example, we can get all the adjacent nodes for a node by querying with the partition key NODE#A and the sort key begins_with(EDGE#). We can also get all the nodes by scanning with the filter expression begins_with(PK, NODE#).


GSI overloading pattern




The GSI overloading pattern is a design pattern that uses a global secondary index (GSI) to create multiple access patterns for the same data in Dynamodb. A GSI is an alternate view of your table that has a different primary key and can project some or all of your table attributes. By using different values for the GSI primary key and projecting different attributes, you can create different views of your data that support different queries.


An example of how to use the GSI overloading pattern is shown below:


PK SK GSI1PK GSI1SK Attributes --- --- --- --- --- USER#john USER#john EMAIL#john@example.com USER#john name: John USER#john ORDER#2021-01-01 ORDER#2021-01-01 100 date: 2021-01-01, amount: 100 USER#john ORDER#2021-01-02 ORDER#2021-01-02 200 date: 2021-01-02, amount: 200 USER#mary USER#mary EMAIL#mary@example.com USER#mary name: Mary USER#mary ORDER#2021-01-03 ORDER#2021-01-03 300 date: 2021-01-03, amount: 300 In this example, we use a GSI with a partition key GSI1PK and a sort key GSI1SK to create two different views of our data. The first view projects the email attribute of user items and uses it as the GSI partition key. This way, we can query users by their email address using the GSI. The second view projects the date and amount attributes of order items and uses them as the GSI partition key and sort key respectively. This way, we can query orders by their date or amount using the GSI.


Performance optimization patterns




Performance optimization patterns are design patterns that help you to improve the speed and efficiency of your Dynamodb applications. They help you to reduce latency, cost, and resource consumption by using various techniques such as caching, compression, batching, parallelization, etc.


Some of the performance optimization patterns covered in the book are:


Caching pattern




The caching pattern is a design pattern that uses a caching layer to reduce latency and cost of accessing data in Dynamodb. A caching layer is a component that stores frequently or recently accessed data in memory or on disk, so that subsequent requests can be served faster and cheaper than querying Dynamodb directly. You can use various caching strategies such as write-through, write-behind, read-through, read-aside, etc., depending on your application requirements.


An example of how to use the caching pattern is shown below:


```python import boto3 import redis dynamodb = boto3.resource('dynamodb') table = dynamodb.Table('Users') cache = redis.Redis(host='localhost', port=6379) def get_user_by_id(user_id): # Try to get user from cache user = cache.get(user_id) if user: # Return user from cache return user else: # Get user from Dynamodb user = table.get_item(Key='user_id': user_id)['Item'] # Store user in cache cache.set(user_id, user) # Return user from Dynamodb return user ``` In this example, we use Redis as a caching layer for our Users table in Dynamodb. We define a function get_user_by_id that tries to get a user from the cache first. If the user is found in the cache, we return it immediately. If not, we query Dynamodb for the user and store it in the cache before returning it. This way, we can reduce the number of calls to Dynamodb and improve the response time of our application.


DAX pattern




The DAX pattern is a design pattern that uses DynamoDB Accelerator (DAX) to boost read performance of Dynamodb applications. DAX is a fully managed, in-memory cache for Dynamodb that delivers up to 10 times faster response times than Dynamodb alone. It is compatible with Dynamodb API, so you can use it with minimal code changes. It also supports write-through and read-through caching strategies, as well as automatic scaling and encryption.


An example of how to use the DAX pattern is shown below:


```python import boto3 import amazondax # Create a normal Dynamodb client dynamodb = boto3.client('dynamodb') # Create a DAX client dax = amazondax.AmazonDaxClient(endpoint_url='dax-cluster-url') # Use the DAX client instead of the Dynamodb client for read operations user = dax.get_item(TableName='Users', Key='user_id': 'john') ``` In this example, we use DAX as a caching layer for our Users table in Dynamodb. We create a DAX client using the amazondax library and the endpoint URL of our DAX cluster. We use the DAX client instead of the Dynamodb client for read operations, such as get_item. This way, we can leverage the in-memory cache of DAX and get faster response times than Dynamodb alone.


Partitioning pattern




The partitioning pattern is a design pattern that uses partitioning strategies to distribute data evenly and avoid hotspots in Dynamodb. A hotspot is a situation where a large number of requests are directed to a single partition or node, causing high latency, throttling, or unavailability. You can use various partitioning strategies such as random, range, hash, composite, etc., depending on your data distribution and access patterns.


An example of how to use the partitioning pattern is shown below:


PK SK Attributes --- --- --- USER#john#2021-01 ORDER#2021-01-01 date: 2021-01-01, amount: 100 USER#john#2021-01 ORDER#2021-01-02 date: 2021-01-02, amount: 200 USER#john#2021-02 ORDER#2021-02-01 date: 2021-02-01, amount: 300 USER#mary#2021-01 ORDER#2021-01-03 date: 2021-01-03, amount: 400 USER#mary#2021-02 ORDER#2021-02-02 date: 2021-02-02, amount: 500 In this example, we use a composite partitioning strategy for our Orders table in Dynamodb. We combine the user ID and the month of the order as the partition key, and use the order ID as the sort key. This way, we can split the orders of each user into different partitions based on the month, and avoid hotspots caused by frequent or large orders from a single user.


Reliability and scalability patterns




Reliability and scalability patterns are design patterns that help you to ensure availability and durability of your Dynamodb applications. They help you to protect your data from accidental or malicious deletion, recover from failures or disasters, process data changes in real time, and synchronize data across regions or accounts.


Some of the reliability and scalability patterns covered in the book are:


Backup and restore pattern




The backup and restore pattern is a design pattern that uses backup and restore features to protect data from accidental or malicious deletion in Dynamodb. You can use two types of backups in Dynamodb: on-demand backups and point-in-time recovery (PITR). On-demand backups allow you to create full backups of your table at any time, and restore them to a new table later. PITR allows you to enable continuous backups of your table, and restore it to any point in time within the last 35 days.


An example of how to use the backup and restore pattern is shown below:


```python import boto3 dynamodb = boto3.client('dynamodb') # Create an on-demand backup of a table backup = dynamodb.create_backup( TableName='Users', BackupName='UsersBackup' ) # Restore a table from an on-demand backup restore = dynamodb.restore_table_from_backup( TargetTableName='UsersRestored', BackupArn=backup['BackupDetails']['BackupArn'] ) a table dynamodb.update_continuous_backups( TableName='Users', PointInTimeRecoverySpecification= 'PointInTimeRecoveryEnabled': True ) # Restore a table from a PITR backup restore = dynamodb.restore_table_to_point_in_time( SourceTableName='Users', TargetTableName='UsersRestored', RestoreDateTime=datetime(2021, 1, 1, 12, 0, 0) ) ``` In this example, we use both on-demand backups and PITR for our Users table in Dynamodb. We create an on-demand backup of the table and restore it to a new table later. We also enable PITR for the table and restore it to a specific point in time later.


Streams and triggers pattern




The streams and triggers pattern is a design pattern that uses streams and triggers to process data changes in real time in Dynamodb. A stream is a feature that captures a time-ordered sequence of item-level changes in your table, such as inserts, updates, and deletes. A trigger is a feature that invokes a Lambda function to perform custom actions based on the stream records. You can use streams and triggers to implement various use cases such as auditing, notifications, analytics, replication, etc.


An example of how to use the streams and triggers pattern is shown below:


```python import boto3 import json dynamodb = boto3.resource('dynamodb') lambda = boto3.client('lambda') # Create a table with a stream enabled table = dynamodb.create_table( TableName='Users', KeySchema=[ 'AttributeName': 'user_id', 'KeyType': 'HASH' ], AttributeDefinitions=[ 'AttributeName': 'user_id', 'AttributeType': 'S' ], ProvisionedThroughput= 'ReadCapacityUnits': 5, 'WriteCapacityUnits': 5 , StreamSpecification= 'StreamEnabled': True, 'StreamViewType': 'NEW_AND_OLD_IMAGES' ) # Create a Lambda function that sends an email notification when a user is created or updated lambda.create_function( FunctionName='SendEmailNotification', Runtime='python3.8', Role='arn:aws:iam::123456789012:role/lambda-role', Handler='lambda_function.lambda_handler', Code= 'ZipFile': b""" import boto3 import json ses = boto3.client('ses') def lambda_handler(event, context): for record in event['Records']: # Get the user data from the stream record user_id = record['dynamodb']['Keys']['user_id']['S'] new_image = record['dynamodb']['NewImage'] old_image = record['dynamodb']['OldImage'] # Check if the user is created or updated if record['eventName'] == 'INSERT': # User is created subject = f'Welcome new_image["name"]["S"]!' body = f'Thank you for joining our service. Your user ID is user_id.' elif record['eventName'] == 'MODIFY': # User is updated subject = f'Your profile has been updated, new_image["name"]["S"].' body = f'Your user ID is user_id. Here are the changes:\n' for key, value in new_image.items(): if key not in old_image or value != old_image[key]: body += f'- key: value["S"]\n' # Send an email notification to the user ses.send_email( Source='admin@example.com', Destination= 'ToAddresses': [ new_image['email']['S'] ] , Message= 'Subject': 'Data': subject , 'Body': 'Text': 'Data': body ) """ ) # Create a trigger that invokes the Lambda function when the table stream records are available lambda.create_event_source_mapping( EventSourceArn=table.latest_stream_arn, FunctionName='SendEmailNotification', BatchSize=100, StartingPosition='LATEST' ) ``` In this example, we use streams and triggers to send email notifications to users when they are created or updated in our Users table in Dynamodb. We create a table with a stream enabled that captures both new and old images of the items. We create a Lambda function that sends an email notification to the user's email address based on the stream record data. We create a trigger that invokes the Lambda function when the table stream records are available.


Replication pattern