Yet data modeling with DynamoDB is tricky for those used to the relational databases that have dominated for the past few decades. There are a number of quirks around data modeling with DynamoDB, but the biggest one is the recommendation from AWS to use a single table for all of your records.
In this post, we’ll do a deep dive on the concepts behind single-table design. You’ll learn:
Using Z-order indexing, you can efficiently run range queries on any combination of fields in your schema. Although Amazon DynamoDB doesn’t natively support Z-order indexing, you can implement the functionality entirely from the client side. A single Z-order index can outperform and even replace entire collections of secondary indexes, saving you money and improving your throughput.
In a previous AWS Database Blog post, I introduced Z-order indexing, a way in which you can sort your data to efficiently query an Amazon DynamoDB table by using range bounds on multiple attributes. In this post, we explore the process of creating a schema for your index. We look at how to decide which attributes to include in your schema, how your index’s schema impacts query efficiency, and how to work with a variety of data types.
This post builds on concepts that are described in Part 1, so I recommend taking some time to review it before diving in.
Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision.
You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3, Amazon SQS, AWS Lambda, AWS Step Functions, and Amazon SageMaker.
To simulate a production scenario, the model is trained using an example dataset containing images of an open-source printed circuit board, with defects and without. An accompanying AWS Serverless Application Repository application deploys the Step Functions workflow for handling image classification and notifications.
A curated list of guides, development tools, and resources for Amazon Quantum Ledger Database (QLDB). This list includes both community created content as well as content created by AWS.
Announcing the API Gateway HTTP API
We talk to customers every day that use API Gateway for critical production applications. This includes everything ranging from simple HTTP proxies to full-blown API management with request transformation, authentication, and validation. API Gateway supports REST APIs and WebSocket APIs, but customers have told us they want more features, lower latency, and lower cost.
Customers have explained their need for the core features of API Gateway at a lower price along with an easier developer experience. Based on this feedback, we are excited to announce the availability of HTTP APIs (Preview).
HTTP APIs is a new flavor of API Gateway. It focuses on delivering enhanced features, improved performance, and an easier developer experience for customers building with API Gateway. Today, we’re launching the first phase, and we will continue to enhance HTTP APIs over the next few months.
We are introducing a new pricing model for HTTP APIs that better represents customer usage patterns. Staying true to our serverless principles, you will only pay for the requests you receive. With existing REST APIs, you pay $3.50/million requests plus data transferred out.
With HTTP APIs, we have reduced request pricing to $1.00/million requests for the first 300 million requests, and $0.90/million for all requests after that. Most customers will see an average cost saving up to 70%, when compared to API Gateway REST APIs. In addition, you will see significant performance improvements in the API Gateway service overhead.
An interactive deep learning book with code, math, and discussions, based on the NumPy interface.
Recently we’ve needed to run queries on our Cloudfront web logs, for example, to determine the number of Googlebot hits to certain page types. We configured Cloudfront to write logs to an S3 bucket, and we set up an AWS Athena table to query those logs.
(By the way, if you haven’t used Athena before, I think it’s nothing short of amazing — the ability to use SQL to query fields from a bunch of gzipped text files sitting in S3? That’s incredible.)
But over time our queries became really slow. For every query, Athena had to scan the entire log history, reading through all the log files in our S3 bucket. This churned through a lot of data (about 120 GB) and made the queries slow and pretty expensive — taking over 65 seconds to run and costing about $0.70 per query.
So we did some searching and came across this AWS blog post, which describes how to set up Athena partitioning to vastly speed up date-based queries. We decided to use AWS Lambda to partition our logs, along with the Serverless framework to easily deploy the Lambda code.