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Starting today, you can use InfluxDB as a database engine in Amazon Timestream. This support makes it easy for you to run near real-time time-series applications using InfluxDB and open source APIs, including open source Telegraf agents that collect time-series observations.

Now you have two database engines to choose in Timestream: Timestream for LiveAnalytics and Timestream for InfluxDB.

You should use the Timestream for InfluxDB engine if your use cases require near real-time time-series queries or specific features in InfluxDB, such as using Flux queries. Another option is the existing Timestream for LiveAnalytics engine, which is suitable if you need to ingest more than tens of gigabytes of time-series data per minute and run SQL queries on petabytes of time-series data in seconds.

With InfluxDB support in Timestream, you can use a managed instance that is automatically configured for optimal performance and availability. Furthermore, you can increase resiliency by configuring multi-Availability Zone support for your InfluxDB databases.

Timestream for InfluxDB and Timestream for LiveAnalytics complement each other for low-latency and large-scale ingestion of time-series data.

Getting started with Timestream for InfluxDB
Let me show you how to get started.

First, I create an InfluxDB instance. I navigate to the Timestream console, go to InfluxDB databases in Timestream for InfluxDB and select Create Influx database.

On the next page, I specify the database credentials for the InfluxDB instance.

I also specify my instance class in Instance configuration and the storage type and volume to suit my needs.

In the next part, I can choose a multi-AZ deployment, which synchronously replicates data to a standby database in a different Availability Zone or just a single instance of InfluxDB. In the multi-AZ deployment, if a failure is detected, Timestream for InfluxDB will automatically fail over to the standby instance without data loss.

Then, I configure how to connect to my InfluxDB instance in Connectivity configuration. Here, I have the flexibility to define network type, virtual private cloud (VPC), subnets, and database port. I also have the flexibility to configure my InfluxDB instance to be publicly accessible by specifying public subnets and set the public access to Publicly Accessible, allowing Amazon Timestream will assign a public IP address to my InfluxDB instance. If you choose this option, make sure that you have proper security measures to protect your InfluxDB instances.

In this demo, I set my InfluxDB instance as Not publicly accessible, which also means I can only access it through the VPC and subnets I defined in this section.

Once I configure my database connectivity, I can define the database parameter group and the log delivery settings. In Parameter group, I can define specific configurable parameters that I want to use for my InfluxDB database. In the log delivery settings, I also can define which Amazon Simple Storage Service (Amazon S3) bucket I have to export the system logs. To learn more about the required AWS Identity and Access Management (IAM) policy for the Amazon S3 bucket, visit this page.

Once I’m happy with the configuration, I select Create Influx database.

Once my InfluxDB instance is created, I can see more information on the detail page.

With the InfluxDB instance created, I can also access the InfluxDB user interface (UI). If I configure my InfluxDB as publicly accessible, I can access the UI using the console by selecting InfluxDB UI. As shown on the setup, I configured my InfluxDB instance as not publicly accessible. In this case, I need to access the InfluxDB UI with SSH tunneling through an Amazon Elastic Compute Cloud (Amazon EC2) instance within the same VPC as my InfluxDB instance.

With the URL endpoint from the detail page, I navigate to the InfluxDB UI and use the username and password I configured in the creation process.

With access to the InfluxDB UI, I can now create a token to interact with my InfluxDB instance.

I can also use the Influx command line interface (CLI) to create a token. Before I can create the token, I create a configuration to interact with my InfluxDB instance. The following is the sample command to create a configuration:

influx config create --config-name demo  
    --host-url https://<TIMESTREAM for INFLUX DB ENDPOINT> 
   --org demo-org  
   --username-password [USERNAME] 
   --active

With the InfluxDB configuration created, I can now create an operator, all-access or read/write token. The following is an example for creating an all-access token to grant permissions to all resources in the organization that I defined:

influx auth create --org demo-org --all-access

With the required token for my use case, I can use various tools, such as the Influx CLI, Telegraf agent, and InfluxDB client libraries, to start ingesting data into my InfluxDB instance. Here, I’m using the Influx CLI to write sample home sensor data in the line protocol format, which you can also get from the InfluxDB documentation page.

influx write 
  --bucket demo-bucket 
  --precision s "
home,room=Living Room temp=21.1,hum=35.9,co=0i 1641024000
home,room=Kitchen temp=21.0,hum=35.9,co=0i 1641024000
home,room=Living Room temp=21.4,hum=35.9,co=0i 1641027600
home,room=Kitchen temp=23.0,hum=36.2,co=0i 1641027600
home,room=Living Room temp=21.8,hum=36.0,co=0i 1641031200
home,room=Kitchen temp=22.7,hum=36.1,co=0i 1641031200
home,room=Living Room temp=22.2,hum=36.0,co=0i 1641034800
home,room=Kitchen temp=22.4,hum=36.0,co=0i 1641034800
home,room=Living Room temp=22.2,hum=35.9,co=0i 1641038400
home,room=Kitchen temp=22.5,hum=36.0,co=0i 1641038400
home,room=Living Room temp=22.4,hum=36.0,co=0i 1641042000
home,room=Kitchen temp=22.8,hum=36.5,co=1i 1641042000
home,room=Living Room temp=22.3,hum=36.1,co=0i 1641045600
home,room=Kitchen temp=22.8,hum=36.3,co=1i 1641045600
home,room=Living Room temp=22.3,hum=36.1,co=1i 1641049200
home,room=Kitchen temp=22.7,hum=36.2,co=3i 1641049200
home,room=Living Room temp=22.4,hum=36.0,co=4i 1641052800
home,room=Kitchen temp=22.4,hum=36.0,co=7i 1641052800
home,room=Living Room temp=22.6,hum=35.9,co=5i 1641056400
home,room=Kitchen temp=22.7,hum=36.0,co=9i 1641056400
home,room=Living Room temp=22.8,hum=36.2,co=9i 1641060000
home,room=Kitchen temp=23.3,hum=36.9,co=18i 1641060000
home,room=Living Room temp=22.5,hum=36.3,co=14i 1641063600
home,room=Kitchen temp=23.1,hum=36.6,co=22i 1641063600
home,room=Living Room temp=22.2,hum=36.4,co=17i 1641067200
home,room=Kitchen temp=22.7,hum=36.5,co=26i 1641067200
"

Finally, I can query the data using the InfluxDB UI. I navigate to the Data Explorer page in the InfluxDB UI, create a simple Flux script, and select Submit.

Timestream for InfluxDB makes it easier for you to develop applications using InfluxDB, while continuing to use your existing tools to interact with the database. With the multi-AZ configuration, you can increase the availability of your InfluxDB data without worrying about the underlying infrastructure.

AWS and InfluxDB partnership
Celebrating this launch, here’s what Paul Dix, Founder and Chief Technology Officer at InfluxData, said about this partnership:

“The future of open source is powered by the public cloud—reaching the broadest community through simple entry points and practical user experience. Amazon Timestream for InfluxDB delivers on that vision. Our partnership with AWS turns InfluxDB open source into a force multiplier for real-time insights on time-series data, making it easier than ever for developers to build and scale their time-series workloads on AWS.”

Things to know
Here are some additional information that you need to know:

Availability – Timestream for InfluxDB is now generally available in the following AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland, Stockholm).

Migration scenario – To migrate from a self-managed InfluxDB instance, you can simply restore a backup from an existing InfluxDB database into Timestream for InfluxDB. If you need to migrate from existing Timestream LiveAnalytics engine to Timestream for InfluxDB, you can leverage Amazon S3. Read more on how to do migration for various use cases on Migrating data from self-managed InfluxDB to Timestream for InfluxDB page.

Supported version – Timestream for InfluxDB currently supports the open source 2.7.5 version of InfluxDB

Pricing – To learn more about pricing, please visit Amazon Timestream pricing.

Demo – To see Timestream for InfluxDB in action, have a look at this demo created by my colleague, Derek:

Start building time-series applications and dashboards with millisecond response times using Timestream for InfluxDB. To learn more, visit Amazon Timestream for InfluxDB page.

Happy building!
— Donnie

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Today is AWS Pi Day! Join us live on Twitch, starting at 1 PM Pacific time.

On this day 18 years ago, a West Coast retail company launched an object storage service, introducing the world to Amazon Simple Storage Service (Amazon S3). We had no idea it would change the way businesses across the globe manage their data. Fast forward to 2024, every modern business is a data business. We’ve spent countless hours discussing how data can help you drive your digital transformation and how generative artificial intelligence (AI) can open up new, unexpected, and beneficial doors for your business. Our conversations have matured to include discussion around the role of your own data in creating differentiated generative AI applications.

Because Amazon S3 stores more than 350 trillion objects and exabytes of data for virtually any use case and averages over 100 million requests per second, it may be the starting point of your generative AI journey. But no matter how much data you have or where you have it stored, what counts the most is its quality. Higher quality data improves the accuracy and reliability of model response. In a recent survey of chief data officers (CDOs), almost half (46 percent) of CDOs view data quality as one of their top challenges to implementing generative AI.

This year, with AWS Pi Day, we’ll spend Amazon S3’s birthday looking at how AWS Storage, from data lakes to high performance storage, has transformed data strategy to becom the starting point for your generative AI projects.

This live online event starts at 1 PM PT today (March 14, 2024), right after the conclusion of AWS Innovate: Generative AI + Data edition. It will be live on the AWS OnAir channel on Twitch and will feature 4 hours of fresh educational content from AWS experts. Not only will you learn how to use your data and existing data architecture to build and audit your customized generative AI applications, but you’ll also learn about the latest AWS storage innovations. As usual, the show will be packed with hands-on demos, letting you see how you can get started using these technologies right away.

Data for generative AI
Data is growing at an incredible rate, powered by consumer activity, business analytics, IoT sensors, call center records, geospatial data, media content, and other drivers. That data growth is driving a flywheel for generative AI. Foundation models (FMs) are trained on massive datasets, often from sources like Common Crawl, which is an open repository of data that contains petabytes of web page data from the internet. Organizations use smaller private datasets for additional customization of FM responses. These customized models will, in turn, drive more generative AI applications, which create even more data for the data flywheel through customer interactions.

There are three data initiatives you can start today regardless of your industry, use case, or geography.

First, use your existing data to differentiate your AI systems. Most organizations sit on a lot of data. You can use this data to customize and personalize foundation models to suit them to your specific needs. Some personalization techniques require structured data, and some do not. Some others require labeled data or raw data. Amazon Bedrock and Amazon SageMaker offer you multiple solutions to fine-tune or pre-train a wide choice of existing foundation models. You can also choose to deploy Amazon Q, your business expert, for your customers or collaborators and point it to one or more of the 43 data sources it supports out of the box.

But you don’t want to create a new data infrastructure to help you grow your AI usage. Generative AI consumes your organization’s data just like existing applications.

Second, you want to make your existing data architecture and data pipelines work with generative AI and continue to follow your existing rules for data access, compliance, and governance. Our customers have deployed more than 1,000,000 data lakes on AWS. Your data lakes, Amazon S3, and your existing databases are great starting points for building your generative AI applications. To help support Retrieval-Augmented Generation (RAG), we added support for vector storage and retrieval in multiple database systems. Amazon OpenSearch Service might be a logical starting point. But you can also use pgvector with Amazon Aurora for PostgreSQL and Amazon Relational Database Service (Amazon RDS) for PostgreSQL. We also recently announced vector storage and retrieval for Amazon MemoryDB for Redis, Amazon Neptune, and Amazon DocumentDB (with MongoDB compatibility).

You can also reuse or extend data pipelines that are already in place today. Many of you use AWS streaming technologies such as Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Managed Service for Apache Flink, and Amazon Kinesis to do real-time data preparation in traditional machine learning (ML) and AI. You can extend these workflows to capture changes to your data and make them available to large language models (LLMs) in near real-time by updating the vector databases, make these changes available in the knowledge base with MSK’s native streaming ingestion to Amazon OpenSearch Service, or update your fine-tuning datasets with integrated data streaming in Amazon S3 through Amazon Kinesis Data Firehose.

When talking about LLM training, speed matters. Your data pipeline must be able to feed data to the many nodes in your training cluster. To meet their performance requirements, our customers who have their data lake on Amazon S3 either use an object storage class like Amazon S3 Express One Zone, or a file storage service like Amazon FSx for Lustre. FSx for Lustre provides deep integration and enables you to accelerate object data processing through a familiar, high performance file interface.

The good news is that if your data infrastructure is built using AWS services, you are already most of the way towards extending your data for generative AI.

Third, you must become your own best auditor. Every data organization needs to prepare for the regulations, compliance, and content moderation that will come for generative AI. You should know what datasets are used in training and customization, as well as how the model made decisions. In a rapidly moving space like generative AI, you need to anticipate the future. You should do it now and do it in a way that is fully automated while you scale your AI system.

Your data architecture uses different AWS services for auditing, such as AWS CloudTrail, Amazon DataZone, Amazon CloudWatch, and OpenSearch to govern and monitor data usage. This can be easily extended to your AI systems. If you are using AWS managed services for generative AI, you have the capabilities for data transparency built in. We launched our generative AI capabilities with CloudTrail support because we know how critical it is for enterprise customers to have an audit trail for their AI systems. Any time you create a data source in Amazon Q, it’s logged in CloudTrail. You can also use a CloudTrail event to list the API calls made by Amazon CodeWhisperer. Amazon Bedrock has over 80 CloudTrail events that you can use to audit how you use foundation models.

During the last AWS re:Invent conference, we also introduced Guardrails for Amazon Bedrock. It allows you to specify topics to avoid, and Bedrock will only provide users with approved responses to questions that fall in those restricted categories

New capabilities just launched
Pi Day is also the occasion to celebrate innovation in AWS storage and data services. Here is a selection of the new capabilities that we’ve just announced:

The Amazon S3 Connector for PyTorch now supports saving PyTorch Lightning model checkpoints directly to Amazon S3. Model checkpointing typically requires pausing training jobs, so the time needed to save a checkpoint directly impacts end-to-end model training times. PyTorch Lightning is an open source framework that provides a high-level interface for training and checkpointing with PyTorch. Read the What’s New post for more details about this new integration.

Amazon S3 on Outposts authentication caching – By securely caching authentication and authorization data for Amazon S3 locally on the Outposts rack, this new capability removes round trips to the parent AWS Region for every request, eliminating the latency variability introduced by network round trips. You can learn more about Amazon S3 on Outposts authentication caching on the What’s New post and on this new post we published on the AWS Storage blog channel.

Mountpoint for Amazon S3 Container Storage Interface (CSI) driver is available for Bottlerocket – Bottlerocket is a free and open source Linux-based operating system meant for hosting containers. Built on Mountpoint for Amazon S3, the CSI driver presents an S3 bucket as a volume accessible by containers in Amazon Elastic Kubernetes Service (Amazon EKS) and self-managed Kubernetes clusters. It allows applications to access S3 objects through a file system interface, achieving high aggregate throughput without changing any application code. The What’s New post has more details about the CSI driver for Bottlerocket.

Amazon Elastic File System (Amazon EFS) increases per file system throughput by 2x – We have increased the elastic throughput limit up to 20 GB/s for read operations and 5 GB/s for writes. It means you can now use EFS for even more throughput-intensive workloads, such as machine learning, genomics, and data analytics applications. You can find more information about this increased throughput on EFS on the What’s New post.

There are also other important changes that we enabled earlier this month.

Amazon S3 Express One Zone storage class integrates with Amazon SageMaker – It allows you to accelerate SageMaker model training with faster load times for training data, checkpoints, and model outputs. You can find more information about this new integration on the What’s New post.

Amazon FSx for NetApp ONTAP increased the maximum throughput capacity per file system by 2x (from 36 GB/s to 72 GB/s), letting you use ONTAP’s data management features for an even broader set of performance-intensive workloads. You can find more information about Amazon FSx for NetApp ONTAP on the What’s New post.

What to expect during the live stream
We will address some of these new capabilities during the 4-hour live show today. My colleague Darko will host a number of AWS experts for hands-on demonstrations so you can discover how to put your data to work for your generative AI projects. Here is the schedule of the day. All times are expressed in Pacific Time (PT) time zone (GMT-8):

• Extend your existing data architecture to generative AI (1 PM – 2 PM).
  If you run analytics on top of AWS data lakes, you’re most of your way there to your data strategy for generative AI.
• Accelerate the data path to compute for generative AI (2 PM – 3 PM).
  Speed matters for compute data path for model training and inference. Check out the different ways we make it happen.
• Customize with RAG and fine-tuning (3 PM – 4 PM).
  Discover the latest techniques to customize base foundation models.
• Be your own best auditor for GenAI (4 PM – 5 PM).
  Use existing AWS services to help meet your compliance objectives.

Join us today on the AWS Pi Day live stream.

I hope I’ll meet you there!

— seb

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Last week, Anthropic announced their Claude 3 foundation model family. The family includes three models: Claude 3 Haiku, the fastest and most compact model for near-instant responsiveness; Claude 3 Sonnet, the ideal balanced model between skills and speed; and Claude 3 Opus, the most intelligent offering for top-level performance on highly complex tasks. AWS also announced the general availability of Claude 3 Sonnet in Amazon Bedrock.

Today, we are announcing the availability of Claude 3 Haiku on Amazon Bedrock. The Claude 3 Haiku foundation model is the fastest and most compact model of the Claude 3 family, designed for near-instant responsiveness and seamless generative artificial intelligence (AI) experiences that mimic human interactions. For example, it can read a data-dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds.

With Claude 3 Haiku’s availability on Amazon Bedrock, you can build near-instant responsive generative AI applications for enterprises that need quick and accurate targeted performance. Like Sonnet and Opus, Haiku has image-to-text vision capabilities, can understand multiple languages besides English, and boasts increased steerability in a 200k context window.

Claude 3 Haiku use cases
Claude 3 Haiku is smarter, faster, and more affordable than other models in its intelligence category. It answers simple queries and requests with unmatched speed. With its fast speed and increased steerability, you can create AI experiences that seamlessly imitate human interactions.

Here are some use cases for using Claude 3 Haiku:

• Customer interactions: quick and accurate support in live interactions, translations
• Content moderation: catch risky behavior or customer requests
• Cost-saving tasks: optimized logistics, inventory management, fast knowledge extraction from unstructured data

To learn more about Claude 3 Haiku’s features and capabilities, visit Anthropic’s Claude on Amazon Bedrock and Anthropic Claude models in the AWS documentation.

Claude 3 Haiku in action
If you are new to using Anthropic models, go to the Amazon Bedrock console and choose Model access on the bottom left pane. Request access separately for Claude 3 Haiku.

To test Claude 3 Haiku in the console, choose Text or Chat under Playgrounds in the left menu pane. Then choose Select model and select Anthropic as the category and Claude 3 Haiku as the model.

To test more Claude prompt examples, choose Load examples. You can view and run examples specific to Claude 3 Haiku, such as advanced Q&A with citations, crafting a design brief, and non-English content generation.

Using Compare mode, you can also compare the speed and intelligence between Claude 3 Haiku and the Claude 2.1 model using a sample prompt to generate personalized email responses to address customer questions.

By choosing View API request, you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. Here is a sample of the AWS CLI command:

aws bedrock-runtime invoke-model 
     --model-id anthropic.claude-3-haiku-20240307-v1:0 
     --body "{"messages":[{"role":"user","content":[{"type":"text","text":"Write the test case for uploading the image to Amazon S3 bucket\nCertainly! Here's an example of a test case for uploading an image to an Amazon S3 bucket using a testing framework like JUnit or TestNG for Java:\n\n...."}]}],"anthropic_version":"bedrock-2023-05-31","max_tokens":2000}" 
     --cli-binary-format raw-in-base64-out 
     --region us-east-1 
     invoke-model-output.txt

To make an API request with Claude 3, use the new Anthropic Claude Messages API format, which allows for more complex interactions such as image processing. If you use Anthropic Claude Text Completions API, you should upgrade from the Text Completions API.

Here is sample Python code to send a Message API request describing the image file:

def call_claude_haiku(base64_string):

    prompt_config = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 4096,
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": "image/png",
                            "data": base64_string,
                        },
                    },
                    {"type": "text", "text": "Provide a caption for this image"},
                ],
            }
        ],
    }

    body = json.dumps(prompt_config)

    modelId = "anthropic.claude-3-haiku-20240307-v1:0"
    accept = "application/json"
    contentType = "application/json"

    response = bedrock_runtime.invoke_model(
        body=body, modelId=modelId, accept=accept, contentType=contentType
    )
    response_body = json.loads(response.get("body").read())

    results = response_body.get("content")[0].get("text")
    return results

To learn more sample codes with Claude 3, see Get Started with Claude 3 on Amazon Bedrock, Diagrams to CDK/Terraform using Claude 3 on Amazon Bedrock, and Cricket Match Winner Prediction with Amazon Bedrock’s Anthropic Claude 3 Sonnet in the Community.aws.

Now available
Claude 3 Haiku is available now in the US West (Oregon) Region with more Regions coming soon; check the full Region list for future updates.

Claude 3 Haiku is the most cost-effective choice. For example, Claude 3 Haiku is cheaper, up to 68 percent of the price per 1,000 input/output tokens compared to Claude Instant, with higher levels of intelligence. To learn more, see Amazon Bedrock Pricing.

Give Claude 3 Haiku a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy