Have you tried out AWS Amplify and AWS Amplify Console yet? In my opinion, they provide one of the fastest ways to get a new web application from idea to prototype on AWS. So what are they? AWS Amplify is an opinionated framework for building modern applications, with a toolchain for easily adding services like authentication (via Amazon Cognito) or storage (via Amazon Simple Storage Service (S3)) or GraphQL APIs, all via a command-line interface. AWS Amplify Console makes continuous deployment and hosting for your modern web apps easy. It supports hosting the frontend and backend assets for single page app (SPA) frameworks including React, Angular, Vue.js, Ionic, and Ember. It also supports static site generators like Gatsby, Eleventy, Hugo, VuePress, and Jekyll.
With today’s launch, hosting options available from the AWS Amplify CLI now include Amplify Console in addition to S3 and Amazon CloudFront. By using Amplify Console, you can take advantage of features like continuous deployment, instant cache invalidation, custom redirects, and simple configuration of custom domains.
Initializing an Amplify App
Let’s take a look at a quick example. We’ll be deploying a static site demo of Amazon Transcribe. I’ve already got the AWS Command Line Interface (CLI) installed, as well as the AWS Amplify CLI. I’ve forked and then cloned the sample code to my local machine. In the following gif, you can see the initialization process for an AWS Amplify app. (I sped things up a little for the gif. It might take a few seconds for your app to be created.)
Now that I’ve got my app initialized, I can add additional services. Let’s add some hosting via AWS Amplify Console. After choosing Amplify Console for hosting, I can pick manual deployment or continuous deployment using a git-based workflow.
Continuous Deployment
First, I’m going to set up continuous deployment so that changes to our git repo will trigger a build and deploy.
The workflow for configuring continuous deployment requires a quick browser session. First, I select our git provider. The forked repo is on GitHub, so I need to authorize Amplify Console to use my GitHub account.
Once a provider is authorized, I choose the repo and branch to watch for changes.
AWS Amplify Console auto-detected the correct build settings, based on the contents of package.json.
Once I’ve confirmed the settings, the initial build and deploy will start. Then any changes to the selected git branch will result in additional builds and deploys. Now I need to finish the workflow in the CLI, and I the need the ARN of the new Amplify Console app for that. In the browser, under App Settings and then General, I copy the ARN and then paste it into my terminal, and check the status.
A quick check of the URL in my browser confirms that the app has been successfully deployed.
Manual Deploys
Manual deploys with Amplify Console also provide a bunch of useful features. The CLI can now manage front-end environments, making it easy to add a test or dev environment. It’s also easy to add URL redirects and rewrites, or add a username/password via HTTP Basic Auth.
Configuring manual deploys is straightforward. Just set your environment name. When it’s time to deploy, run amplify publishand the build scripts defined during the initialization of the project will be run. The generated artifact will then be uploaded automatically.
With manual deployments, you can set up multiple frontend environments (e.g. dev and prod) directly from the CLI. To create a new dev environment, run amplify env add (name it dev) and amplify publish. This will create a second frontend environment in Amplify Console. To view all your frontend and backend environments, run amplify console from the CLI to open your Amplify Console app.
Ever since using AWS Amplify Console for the first time a few weeks ago, it has become my go-to way to deploy applications, especially static sites. I’m excited to see the simplicity of hosting with AWS Amplify Console extended to the Amplify CLI, and I hope you are too. Happy building!
Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.
Amazon SageMaker Chainer Estimator
Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.
Luckily, using Chainer with SageMaker is just as easy as using a TensorFlow or MXNet estimator. In fact, it might even be a bit easier since it’s likely you can take your existing scripts and use them to train on SageMaker with very few modifications. With TensorFlow or MXNet users have to implement a train function with a particular signature. With Chainer your scripts can be a little bit more portable as you can simply read from a few environment variables like SM_MODEL_DIR, SM_NUM_GPUS, and others. We can wrap our existing script in a if __name__ == '__main__': guard and invoke it locally or on sagemaker.
import argparse
import os
if __name__ =='__main__':
parser = argparse.ArgumentParser()
# hyperparameters sent by the client are passed as command-line arguments to the script.
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--learning-rate', type=float, default=0.05)
# Data, model, and output directories
parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
args, _ = parser.parse_known_args()
# ... load from args.train and args.test, train a model, write model to args.model_dir.
Then, we can run that script locally or use the SageMaker Python SDK to launch it on some GPU instances in SageMaker. The hyperparameters will get passed in to the script as CLI commands and the environment variables above will be autopopulated. When we call fit the input channels we pass will be populated in the SM_CHANNEL_* environment variables.
from sagemaker.chainer.estimator import Chainer
# Create my estimator
chainer_estimator = Chainer(
entry_point='example.py',
train_instance_count=1,
train_instance_type='ml.p3.2xlarge',
hyperparameters={'epochs': 10, 'batch-size': 64}
)
# Train my estimator
chainer_estimator.fit({'train': train_input, 'test': test_input})
# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = chainer_estimator.deploy(
instance_type="ml.m4.xlarge",
initial_instance_count=1
)
Now, instead of bringing your own docker container for training and hosting with Chainer, you can just maintain your script. You can see the full sagemaker-chainer-containers on github. One of my favorite features of the new container is built-in chainermn for easy multi-node distribution of your chainer training jobs.
There’s a lot more documentation and information available in both the README and the example notebooks.
AWS GreenGrass ML with Chainer
AWS GreenGrass ML now includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson, TX2, and Raspberry Pi. So, now GreenGrass ML provides pre-built packages for TensorFlow, Apache MXNet, and Chainer! You can train your models on SageMaker then easily deploy it to any GreenGrass-enabled device using GreenGrass ML.
JAWS UG
I want to give a quick shout out to all of our wonderful and inspirational friends in the JAWS UG who attended the AWS Summit in Tokyo today. I’ve very much enjoyed seeing your pictures of the summit. Thanks for making Japan an amazing place for AWS developers! I can’t wait to visit again and meet with all of you.
SHB is a small invitational gathering of people studying various aspects of the human side of security, organized each year by Alessandro Acquisti, Ross Anderson, and myself. The 50 or so people in the room include psychologists, economists, computer security researchers, sociologists, political scientists, neuroscientists, designers, lawyers, philosophers, anthropologists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary.
The goal is to maximize discussion and interaction. We do that by putting everyone on panels, and limiting talks to 7-10 minutes. The rest of the time is left to open discussion. Four hour-and-a-half panels per day over two days equals eight panels; six people per panel means that 48 people get to speak. We also have lunches, dinners, and receptions — all designed so people from different disciplines talk to each other.
I invariably find this to be the most intellectually stimulating conference of my year. It influences my thinking in many different, and sometimes surprising, ways.
Here are my posts on the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, and tenth SHB workshops. Follow those links to find summaries, papers, and occasionally audio recordings of the various workshops.
This post courtesy of Jeff Levine Solutions Architect for Amazon Web Services
Amazon Linux 2 is the next generation of Amazon Linux, a Linux server operating system from Amazon Web Services (AWS). Amazon Linux 2 offers a high-performance Linux environment suitable for organizations of all sizes. It supports applications ranging from small websites to enterprise-class, mission-critical platforms.
Amazon Linux 2 includes support for the LAMP (Linux/Apache/MariaDB/PHP) stack, one of the most popular platforms for deploying websites. To secure the transmission of data-in-transit to such websites and prevent eavesdropping, organizations commonly leverage Secure Sockets Layer/Transport Layer Security (SSL/TLS) services which leverage certificates to provide encryption. The LAMP stack provided by Amazon Linux 2 includes a self-signed SSL/TLS certificate. Such certificates may be fine for internal usage but are not acceptable when attestation by a certificate authority is required.
In this post, I discuss how to extend the capabilities of Amazon Linux 2 by installing Let’s Encrypt, a certificate authority provided by the Internet Security Research Group. Let’s Encrypt offers basic SSL/TLS certificates for DNS hosts at no charge that you can use to add encryption-in-transit to a single web server. For commercial or multi-server configurations, you should consider AWS Certificate Manager and Elastic Load Balancing.
Let’s Encrypt also requires the certbot package, which you install from EPEL, the Extra Packaged for Enterprise Linux collection. Although EPEL is not included with Amazon Linux 2, I show how you can install it from the Fedora Project.
Walkthrough
At a high level, you perform the following tasks for this walkthrough:
Provision a VPC, Amazon Linux 2 instance, and LAMP stack.
Install and enable the EPEL repository.
Install and configure Let’s Encrypt.
Validate the installation.
Clean up.
Prerequisites and costs
To follow along with this walkthrough, you need the following:
Accept all other default values including with regard to storage.
Create a new security group and accept the default rule that allows TCP port 22 (SSH) from everywhere (0.0.0.0/0 in IPv4). For the purposes of this walkthrough, permitting access from all IP addresses is reasonable. In a production environment, you may restrict access to different addresses.
Allocate and associate an Elastic IP address to the server when it enters the running state.
Respond “Y” to all requests for approval to install the software.
Step 3: Install and configure Let’s Encrypt
If you are no longer connected to the Amazon Linux 2 instance, connect to it at the Elastic IP address that you just created.
Install certbot, the Let’s Encrypt client to be used to obtain an SSL/TLS certificate and install it into Apache.
sudo yum install python2-certbot-apache.noarch
Respond “Y” to all requests for approval to install the software. If you see a message appear about SELinux, you can safely ignore it. This is a known issue with the latest version of certbot.
Create a DNS “A record” that maps a host name to the Elastic IP address. For this post, assume that the name of the host is lamp.example.com. If you are hosting your DNS in Amazon Route 53, do this by creating the appropriate record set.
After the “A record” has propagated, browse to lamp.example.com. The Apache test page should appear. If the page does not appear, use a tool such as nslookup on your workstation to confirm that the DNS record has been properly configured.
You are now ready to install Let’s Encrypt. Let’s Encrypt does the following:
Confirms that you have control over the DNS domain being used, by having you create a DNS TXT record using the value that it provides.
Obtains an SSL/TLS certificate.
Modifies the Apache-related scripts to use the SSL/TLS certificate and redirects users browsing the site in HTTP mode to HTTPS mode.
Use the following command to install certbot:
sudo certbot -i apache -a manual \
--preferred-challenges dns -d lamp.example.com
The options have the following meanings:
-i apache Use the Apache installer.
-a manual Authenticate domain ownership manually.
--preferred-challenges dns Use DNS TXT records for authentication challenge.
-d lamp.example.com Specify the domain for the SSL/TLS certificate.
You are prompted for the following information: E-mail address for renewals? Enter an email address for certificate renewals. Accept the terms of services? Respond as appropriate. Send your e-mail address to the EFF? Respond as appropriate. Log your current IP address? Respond as appropriate.
You are prompted to deploy a DNS TXT record with the name “_acme-challenge.lamp.example.com” with the supplied value, as shown below.
After you enter the record, wait until the TXT record propagates. To look up the TXT record to confirm the deployment, use the nslookup command in a separate command window, as shown below. Remember to use the set ty=txt command before entering the TXT record. You are prompted to select a virtual host. There is only one, so choose 1. The final prompt asks whether to redirect HTTP traffic to HTTPS. To perform the redirection, choose 2. That completes the configuration of Let’s Encrypt.
Browse to the http:// lamp.example.com site. You are redirected to the SSL/TLS page https://lamp.example.com.
To look at the encryption information, use the appropriate actions within your browser. For example, in Firefox, you can open the padlock and traverse the menus. In the encryption technical details, you can see from the “Connection Encrypted” line that traffic to the website is now encrypted using TLS 1.2.
Security note: As of the time of publication, this website also supports TLS 1.0. I recommend that you disable this protocol because of some known vulnerabilities associated with it. To do this:
Edit the file /etc/letsencrypt/options-ssl-apache.conf.
Look for the line beginning with SSLProtocol and change it to the following:
SSLProtocol all -SSLv2 -SSLv3 -TLSv1
Save the file. After you make changes to this file, Let’s Encrypt no longer automatically updates it. Periodically check your log files for recommended updates to this file.
Restart the httpd server with the following command:
sudo service httpd restart
Step 5: Cleanup
Use the following steps to avoid incurring any further costs.
Terminate the Amazon Linux 2 instance that you created.
Release the Elastic IP address that you allocated.
Revert any DNS changes that you made, including the A and TXT records.
Conclusion
Amazon Linux 2 is an excellent option for hosting websites through the LAMP stack provided by the Amazon-Linux-Extras feature. You can then enhance the security of the Apache web server by installing EPEL and Let’s Encrypt. Let’s Encrypt provisions an SSL/TLS certificate, optionally installs it for you on the Apache server, and enables data-in-transit encryption. You can get started with Amazon Linux 2 in just a few clicks.
Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.
Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.
DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.
To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.
The solution that I describe provides the following benefits:
Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
Automatically updates your model to get real-time predictions
Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
Makes it easier for developers of all skill levels to use Amazon SageMaker
All code and data set in this post are available in this .zip file.
Solution architecture
The following diagram shows the overall architecture of the solution.
The steps that data follows through the architecture are as follows:
Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
Amazon SageMaker renews the model artifact and update the endpoint.
The converted CSV is available for ad hoc queries with Amazon Athena.
Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.
Building the auto-updating model
This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.
Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.
For this solution, the banking.csv should be imported into a DynamoDB table.
Export a DynamoDB table
To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.
One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.
For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.
Add the script to an existing pipeline
After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:
Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
For Actions, choose Edit.
In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.
Paste the following command into the new step after the data upload step:
The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.
The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.
Automation script: Convert JSON data to CSV with Hive
We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.
When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.
Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.
The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.
After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.
Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.
Automation script: Renew the Amazon SageMaker model
After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3. For renewing model artifact, you must create a new training job. Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.
In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.
#!/bin/bash
## Define variable
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>"
# Select containers image based on region.
case "$REGION" in
"us-west-2" )
IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
;;
"us-east-1" )
IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest"
;;
"us-east-2" )
IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest"
;;
"eu-west-1" )
IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest"
;;
*)
echo "Invalid Region Name"
exit 1 ;
esac
# Start training job and creating model artifact
TRAINING_JOB_NAME=TRAIN-${DTTIME}
S3OUTPUT="s3://<your bucket name>/model/"
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION} --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE} --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None" }]' --output-data-config S3OutputPath=${S3OUTPUT} --resource-config InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier
# Wait until job completed
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}
# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION} --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME} --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT} --execution-role-arn ${ROLE}
# create a new endpoint configuration
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME} --production-variants VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge
# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
aws sagemaker create-endpoint --endpoint-name ServiceEndpoint --endpoint-config-name ${CONFIGNAME} --region ${REGION}
else
aws sagemaker update-endpoint --endpoint-name ServiceEndpoint --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi
Grant permission
Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.
After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.
Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.
Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
Train the Amazon SageMaker model with the new data source.
When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.
Running ad hoc queries using Amazon Athena
Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.
With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog.
Creating an Amazon Athena table and running it
Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.
=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
age int,
job string,
marital string ,
education string,
default string,
housing string,
loan string,
contact string,
month string,
day_of_week string,
duration int,
campaign int,
pdays int ,
previous int ,
poutcome string,
emp_var_rate double,
cons_price_idx double,
cons_conf_idx double,
euribor3m double,
nr_employed double,
y int
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n'
LOCATION 's3://<your bucket name>/<datasource path>/';
The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.
=== Sample Query ===
SELECT corr(age,y) AS correlation_age_and_target,
corr(duration,y) AS correlation_duration_and_target,
corr(campaign,y) AS correlation_campaign_and_target,
corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y ,
CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact
FROM datasource
) datasource ;
Conclusion
In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.
You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.
Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”
This post is written by Eric Han – Vice President of Product Management Portworx and Asif Khan – Solutions Architect
Data is the soul of an application. As containers make it easier to package and deploy applications faster, testing plays an even more important role in the reliable delivery of software. Given that all applications have data, development teams want a way to reliably control, move, and test using real application data or, at times, obfuscated data.
For many teams, moving application data through a CI/CD pipeline, while honoring compliance and maintaining separation of concerns, has been a manual task that doesn’t scale. At best, it is limited to a few applications, and is not portable across environments. The goal should be to make running and testing stateful containers (think databases and message buses where operations are tracked) as easy as with stateless (such as with web front ends where they are often not).
Why is state important in testing scenarios? One reason is that many bugs manifest only when code is tested against real data. For example, we might simply want to test a database schema upgrade but a small synthetic dataset does not exercise the critical, finer corner cases in complex business logic. If we want true end-to-end testing, we need to be able to easily manage our data or state.
In this blog post, we define a CI/CD pipeline reference architecture that can automate data movement between applications. We also provide the steps to follow to configure the CI/CD pipeline.
Stateful Pipelines: Need for Portable Volumes
As part of continuous integration, testing, and deployment, a team may need to reproduce a bug found in production against a staging setup. Here, the hosting environment is comprised of a cluster with Kubernetes as the scheduler and Portworx for persistent volumes. The testing workflow is then automated by AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild.
Portworx offers Kubernetes storage that can be used to make persistent volumes portable between AWS environments and pipelines. The addition of Portworx to the AWS Developer Tools continuous deployment for Kubernetes reference architecture adds persistent storage and storage orchestration to a Kubernetes cluster. The example uses MongoDB as the demonstration of a stateful application. In practice, the workflow applies to any containerized application such as Cassandra, MySQL, Kafka, and Elasticsearch.
Using the reference architecture, a developer calls CodePipeline to trigger a snapshot of the running production MongoDB database. Portworx then creates a block-based, writable snapshot of the MongoDB volume. Meanwhile, the production MongoDB database continues serving end users and is uninterrupted.
Without the Portworx integrations, a manual process would require an application-level backup of the database instance that is outside of the CI/CD process. For larger databases, this could take hours and impact production. The use of block-based snapshots follows best practices for resilient and non-disruptive backups.
As part of the workflow, CodePipeline deploys a new MongoDB instance for staging onto the Kubernetes cluster and mounts the second Portworx volume that has the data from production. CodePipeline triggers the snapshot of a Portworx volume through an AWS Lambda function, as shown here
AWS Developer Tools with Kubernetes: Integrated Workflow with Portworx
In the following workflow, a developer is testing changes to a containerized application that calls on MongoDB. The tests are performed against a staging instance of MongoDB. The same workflow applies if changes were on the server side. The original production deployment is scheduled as a Kubernetes deployment object and uses Portworx as the storage for the persistent volume.
The continuous deployment pipeline runs as follows:
Developers integrate bug fix changes into a main development branch that gets merged into a CodeCommit master branch.
Amazon CloudWatch triggers the pipeline when code is merged into a master branch of an AWS CodeCommit repository.
AWS CodePipeline sends the new revision to AWS CodeBuild, which builds a Docker container image with the build ID.
AWS CodeBuild pushes the new Docker container image tagged with the build ID to an Amazon ECR registry.
Kubernetes downloads the new container (for the database client) from Amazon ECR and deploys the application (as a pod) and staging MongoDB instance (as a deployment object).
AWS CodePipeline, through a Lambda function, calls Portworx to snapshot the production MongoDB and deploy a staging instance of MongoDB• Portworx provides a snapshot of the production instance as the persistent storage of the staging MongoDB • The MongoDB instance mounts the snapshot.
At this point, the staging setup mimics a production environment. Teams can run integration and full end-to-end tests, using partner tooling, without impacting production workloads. The full pipeline is shown here.
Summary
This reference architecture showcases how development teams can easily move data between production and staging for the purposes of testing. Instead of taking application-specific manual steps, all operations in this CodePipeline architecture are automated and tracked as part of the CI/CD process.
This integrated experience is part of making stateful containers as easy as stateless. With AWS CodePipeline for CI/CD process, developers can easily deploy stateful containers onto a Kubernetes cluster with Portworx storage and automate data movement within their process.
The reference architecture and code are available on GitHub:
We launched AWS Support a full decade ago, with Gold and Silver plans focused on Amazon EC2, Amazon S3, and Amazon SQS. Starting from that initial offering, backed by a small team in Seattle, AWS Support now encompasses thousands of people working from more than 60 locations.
A Quick Look Back Over the years, that offering has matured and evolved in order to meet the needs of an increasingly diverse base of AWS customers. We aim to support you at every step of your cloud adoption journey, from your initial experiments to the time you deploy mission-critical workloads and applications.
We have worked hard to make our support model helpful and proactive. We do our best to provide you with the tools, alerts, and knowledge that will help you to build systems that are secure, robust, and dependable. Here are some of our most recent efforts toward that goal:
Trusted Advisor S3 Bucket Policy Check – AWS Trusted Advisor provides you with five categories of checks and makes recommendations that are designed to improve security and performance. Earlier this year we announced that the S3 Bucket Permissions Check is now free, and available to all AWS users. If you are signed up for the Business or Professional level of AWS Support, you can also monitor this check (and many others) using Amazon CloudWatch Events. You can use this to monitor and secure your buckets without human intervention.
Personal Health Dashboard – This tool provides you with alerts and guidance when AWS is experiencing events that may affect you. You get a personalized view into the performance and availability of the AWS services that underlie your AWS resources. It also generates Amazon CloudWatch Events so that you can initiate automated failover and remediation if necessary.
Well Architected / Cloud Ops Review – We’ve learned a lot about how to architect AWS-powered systems over the years and we want to share everything we know with you! The AWS Well-Architected Framework provide proven, detailed guidance in critical areas including operational excellence, security, reliability, performance efficiency, and cost optimization. You can read the materials online and you can also sign up for the online training course. If you are signed up for Enterprise support, you can also benefit from our Cloud Ops review.
Infrastructure Event Management – If you are launching a new app, kicking off a big migration, or hosting a large-scale event similar to Prime Day, we are ready with guidance and real-time support. Our Infrastructure Event Management team will help you to assess the readiness of your environment and work with you to identify and mitigate risks ahead of time.
To learn more about how AWS customers have used AWS support to realize all of the benefits that I noted above, watch these videos (and find more on the Customer Testmonials page):
The Amazon retail site makes heavy use of AWS. You can read my post, Prime Day 2017 – Powered by AWS, to learn more about the process of preparing to sustain a record-setting amount of traffic and to accept a like number of orders.
Come and Join Us The AWS Support Team is in continuous hiring mode and we have openings all over the world! Here are a couple of highlights:
After the outstanding success of their AIY Projects Voice and Vision Kits, Google has announced the release of upgraded kits, complete with Raspberry Pi Zero WH, Camera Module, and preloaded SD card.
Google’s AIY Projects Kits
Google launched the AIY Projects Voice Kit last year, first as a cover gift with The MagPi magazine and later as a standalone product.
Makers needed to provide their own Raspberry Pi for the original kit. The new kits include everything you need, from Pi to SD card.
Within a DIY cardboard box, makers were able to assemble their own voice-activated AI assistant akin to the Amazon Alexa, Apple’s Siri, and Google’s own Google Home Assistant. The Voice Kit was an instant hit that spurred no end of maker videos and tutorials, including our own free tutorial for controlling a robot using voice commands.
Later in the year, the team followed up the success of the Voice Kit with the AIY Projects Vision Kit — the same cardboard box hosting a camera perfect for some pretty nifty image recognition projects.
For more on the AIY Voice Kit, here’s our release video hosted by the rather delightful Rob Zwetsloot.
Check out the exclusive Google AIY Projects Kit that comes free with The MagPi 57! Grab yourself a copy in stores or online now: http://magpi.cc/2pI6IiQ This first AIY Projects kit taps into the Google Assistant SDK and Cloud Speech API using the AIY Projects Voice HAT (Hardware Accessory on Top) board, stereo microphone, and speaker (included free with the magazine).
AIY Projects 2
So what’s new with version 2 of the AIY Projects Voice Kit? The kit now includes the recently released Raspberry Pi Zero WH, our Zero W with added pre-soldered header pins for instant digital making accessibility. Purchasers of the kits will also get a micro SD card with preloaded OS to help them get started without having to set the card up themselves.
Everything you need to build your own Raspberry Pi-powered Google voice assistant
“Everything you need to get started is right there in the box,” explains Billy Rutledge, Google’s Director of AIY Projects. “We knew from our research that even though makers are interested in AI, many felt that adding it to their projects was too difficult or required expensive hardware.”
Google is also hard at work producing AIY Projects companion apps for Android, iOS, and Chrome. The Android app is available now to coincide with the launch of the upgraded kits, with the other two due for release soon. The app supports wireless setup of the AIY Kit, though avid coders will still be able to hack theirs to better suit their projects.
Google has also updated the AIY Projects website with an AIY Models section highlighting a range of neural network projects for the kits.
Get your kit
The updated Voice and Vision Kits were announced last night, and in the US they are available now from Target. UK-based makers should be able to get their hands on them this summer — keep an eye on our social channels for updates and links.
Good article about how difficult it is to insure an organization against Internet attacks, and how expensive the insurance is.
Companies like retailers, banks, and healthcare providers began seeking out cyberinsurance in the early 2000s, when states first passed data breach notification laws. But even with 20 years’ worth of experience and claims data in cyberinsurance, underwriters still struggle with how to model and quantify a unique type of risk.
“Typically in insurance we use the past as prediction for the future, and in cyber that’s very difficult to do because no two incidents are alike,” said Lori Bailey, global head of cyberrisk for the Zurich Insurance Group. Twenty years ago, policies dealt primarily with data breaches and third-party liability coverage, like the costs associated with breach class-action lawsuits or settlements. But more recent policies tend to accommodate first-party liability coverage, including costs like online extortion payments, renting temporary facilities during an attack, and lost business due to systems failures, cloud or web hosting provider outages, or even IT configuration errors.
There are challenges to creating these new insurance products. There are two basic models for insurance. There’s the fire model, where individual houses catch on fire at a fairly steady rate, and the insurance industry can calculate premiums based on that rate. And there’s the flood model, where an infrequent large-scale event affects large numbers of people — but again at a fairly steady rate. Internet+ insurance is complicated because it follows neither of those models but instead has aspects of both: individuals are hacked at a steady (albeit increasing) rate, while class breaks and massive data breaches affect lots of people at once. Also, the constantly changing technology landscape makes it difficult to gather and analyze the historical data necessary to calculate premiums.
Last week Backblaze made the exciting announcement that through partnerships with Packet and ServerCentral, cloud computing is available to Backblaze B2 Cloud Storage customers.
Those of you familiar with cloud computing will understand the significance of this news. We are now offering the least expensive cloud storage + cloud computing available anywhere. You no longer have to submit to the lock-in tactics and exorbitant prices charged by the other big players in the cloud services biz.
We understand that some of our cloud backup and storage customers might be unfamiliar with cloud computing. Backblaze made its name in cloud backup and object storage, and that’s what our customers know us for. In response to customers requests, we’ve directly connected our B2 cloud object storage with cloud compute providers. This adds the ability to use and run programs on data once it’s in the B2 cloud, opening up a world of new uses for B2. Just some of the possibilities include media transcoding and rendering, web hosting, application development and testing, business analytics, disaster recovery, on-demand computing capacity (cloud bursting), AI, and mobile and IoT applications.
The world has been moving to a multi-cloud / hybrid cloud world, and customers are looking for more choices than those offered by the existing cloud players. Our B2 compute partnerships build on our mission to offer cloud storage that’s astonishingly easy and low-cost. They enable our customers to move into a more flexible and affordable cloud services ecosystem that provides a greater variety of choices and costs far less. We believe we are helping to fulfill the promise of the internet by allowing customers to choose the best-of-breed services from the best vendors.
If You’re Not Familiar with Cloud Computing, Here’s a Quick Overview
Cloud computing is another component of cloud services, like object storage, that replicates in the cloud a basic function of a computer system. Think of services that operate in a cloud as an infinitely scalable version of what happens on your desktop computer. In your desktop computer you have computing/processing (CPU), fast storage (like an SSD), data storage (like your disk drive), and memory (RAM). Their counterparts in the cloud are computing (CPU), block storage (fast storage), object storage (data storage), and processing memory (RAM).
CPU, RAM, fast internal storage, and a hard drive are the basic building blocks of a computer
They also are the basic building blocks of cloud computing
Some customers require only some of these services, such as cloud storage. B2 as a standalone service has proven to be an outstanding solution for those customers interested in backing up or archiving data. There are many customers that would like additional capabilities, such as performing operations on that data once it’s in the cloud. They need object storage combined with computing.
With the just announced compute partnerships, Backblaze is able to offer computing services to anyone using B2. A direct connection between Backblaze’s and our partners’ data centers means that our customers can process data stored in B2 with high speed, low latency, and zero data transfer costs.
Cloud service providers package up CPU, storage, and memory into services that you can rent on an hourly basis
You can scale up and down and add or remove services as you need them
How Does Computing + B2 Work?
Those wanting to use B2 with computing will need to sign up for accounts with Backblaze and either Packet or ServerCentral. Packet customers need only select “SJC1” as their region and then get started. The process is also simple for ServerCentral customers — they just need to register with a ServerCentral account rep.
The direct connection between B2 and our compute partners means customers will experience very low latency (less than 10ms) between services. Even better, all data transfers between B2 and the compute partner are free. When combined with Backblaze B2, customers can obtain cloud computing services for as little as 50% of the cost of Amazon’s Elastic Compute Cloud (EC2).
Opening Up the Cloud “Walled Garden”
Traditionally, cloud vendors charge fees for customers to move data outside the “walled garden” of that particular vendor. These fees reach upwards of $0.12 per gigabyte (GB) for data egress. This large fee for customers accessing their own data restricts users from using a multi-cloud approach and taking advantage of less expensive or better performing options. With free transfers between B2 and Packet or ServerCentral, customers now have a predictable, scalable solution for computing and data storage while avoiding vendor lock in. Dropbox made waves when they saved $75 million by migrating off of AWS. Adding computing to B2 helps anyone interested in moving some or all of their computing off of AWS and thereby cutting their AWS bill by 50% or more.
What are the Advantages of Cloud Storage + Computing?
Using computing and storage in the cloud provide a number of advantages over using in-house resources.
You don’t have to purchase the actual hardware, software licenses, and provide space and IT resources for the systems.
Cloud computing is available with just a few minutes notice and you only pay for whatever period of time you need. You avoid having additional hardware on your balance sheet.
Resources are in the cloud and can provide online services to customers, mobile users, and partners located anywhere in the world.
You can isolate the work on these systems from your normal production environment, making them ideal for testing and trying out new applications and development projects.
Computing resources scale when you need them to, providing temporary or ongoing extra resources for expected or unexpected demand.
They can provide redundant and failover services when and if your primary systems are unavailable for whatever reason.
Where Can I Learn More?
We encourage B2 customers to explore the options available at our partner sites, Packet and ServerCentral. They are happy to help customers understand what services are available and how to get started.
We are excited to see what you build! And please tell us in the comments what you are doing or have planned with B2 + computing.
Amazon SageMaker continues to iterate quickly and release new features on behalf of customers. Starting today, SageMaker adds support for many new instance types, local testing with the SDK, and Apache MXNet 1.1.0 and Tensorflow 1.6.0. Let’s take a quick look at each of these updates.
New Instance Types
Amazon SageMaker customers now have additional options for right-sizing their workloads for notebooks, training, and hosting. Notebook instances now support almost all T2, M4, P2, and P3 instance types with the exception of t2.micro, t2.small, and m4.large instances. Model training now supports nearly all M4, M5, C4, C5, P2, and P3 instances with the exception of m4.large, c4.large, and c5.large instances. Finally, model hosting now supports nearly all T2, M4, M5, C4, C5, P2, and P3 instances with the exception of m4.large instances. Many customers can take advantage of the newest P3, C5, and M5 instances to get the best price/performance for their workloads. Customers also take advantage of the burstable compute model on T2 instances for endpoints or notebooks that are used less frequently.
Open Sourced Containers, Local Mode, and TensorFlow 1.6.0 and MXNet 1.1.0
Today Amazon SageMaker has open sourced the MXNet and Tensorflow deep learning containers that power the MXNet and Tensorflow estimators in the SageMaker SDK. The ability to write Python scripts that conform to simple interface is still one of my favorite SageMaker features and now those containers can be additionally customized to include any additional libraries. You can download these containers locally to iterate and experiment which can accelerate your debugging cycle. When you’re ready go from local testing to production training and hosting you just change one line of code.
These containers launch with support for Tensorflow 1.6.0 and MXNet 1.1.0 as well. Tensorflow has a number of new 1.6.0 features including support for CUDA 9.0, cuDNN 7, and AVX instructions which allows for significant speedups in many training applications. MXNet 1.1.0 adds a number of new features including a Text API mxnet.text with support for text processing, indexing, glossaries, and more. Two of the really cool pre-trained embeddings included are GloVe and fastText. <
Available Now All of the features mentioned above are available today. As always please let us know on Twitter or in the comments below if you have any questions or if you’re building something interesting. Now, if you’ll excuse me I’m going to go experiment with some of those new MXNet APIs!
You can always view and manage your Amazon GuardDuty findings on the Findings page in the GuardDuty console or by using GuardDuty APIs with the AWS CLI or SDK. But there’s a quicker and easier way, you can use Amazon Alexa as a conversational interface to review your GuardDuty findings. With Alexa, you can build natural voice experiences and create a more intuitive way of interacting GuardDuty.
In this post, I show you how to deploy a sample custom Alexa skill and use an Alexa-enabled device, such as Amazon Echo, to get information about GuardDuty findings across your AWS accounts and regions. The information provided by this sample skill gives you a broad overview of GuardDuty finding statistics, severities, and descriptions. When you hear something interesting, you can log in to the GuardDuty console or another analysis tool to investigate the findings data.
Note: Although not covered here, you can also deploy this sample skill using Alexa for Business, which you can use to make skills available to your shared devices and enrolled users without having to publish them to the Alexa skills store.
Prerequisites
To complete the steps in this post, make sure you have:
A basic understanding of Alexa Custom Skills, which is helpful for deploying the sample skill described here. If you’re not already familiar with Alexa custom skill concepts and terminology, you might want to review the following documentation resources.
An AWS account with GuardDuty enabled in one or more AWS regions.
Deploy the Lambda function by using the CloudFormation Template.
Create the custom skill in the Alexa developer console.
Test the skill using an Alexa-enabled device.
Deploy the Lambda function with the CloudFormation Template
For this next step, make sure you deploy the template within the AWS account you want to monitor.
To deploy the Lambda function in the N. Virginia region (see the note below), you can use the CloudFormation template provided by clicking the following link: load the supplied template. In the CloudFormation console, on the Select Template page, select Next.
Note: The following AWS regions support hosting custom Alexa skills: US East (N. Virginia), Asia Pacific (Tokyo), EU (Ireland), West (Oregon). If you want to deploy in a region other than N. Virginia, you will first need to upload the custom skill’s Lambda deployment package (zip file with code) to an S3 bucket in the selected region.
After you load the template, provide the following input parameters:
Input parameter
Input parameter description
FLASHREGIONS
Comma separated list of region Ids with NO spaces to include in flash briefing stats. At least one region is required. Make sure GuardDuty is enabled in regions declared.
MAXRESP
Max number of findings to return in a response.
ArtifactsBucket
S3 Bucket where Lambda deployment package resides. Leave the default for N. Virginia.
ArtifactsPrefix
Path in S3 bucket where Lambda deployment package resides. Leave the default for N. Virginia.
On the Specify Details page, enter the input parameters (see above), and then select Next.
On the Options page, accept the default values, and then select Next.
On the Review page, confirm the details, and then select Create. The stack will be created in approximately 2 minutes.
Create the custom skill in the Alexa developer console
In the second part of this solution implementation, you will create the skill in the Amazon Developer Console.
Sign in to the Alexa area of the Amazon Developer Console, select Your Alexa Consoles in the top right, and then select Skills.
Select Create Skill.
For the name, enter Ask Amazon GuardDuty, and then select Next.
In the Choose a model to add to your skill page, select Custom, and then select Create skill.
Select the JSON Editor and paste the contents of the alexa_ask_guardduty_skill.json file into the code editor, and overwrite the existing content. This file contains the intent schema which defines the set of intents the service can accept and process.
Select Save Model, select Build Model, and then wait for the build to complete.
When the model build is complete, on the left side, select Endpoint.
In the Endpoint page, in the Service Endpoint Type section, select AWS Lambda ARN (Amazon Resource Name).
In the Default Region field, copy and paste the value from the CloudFormation Stack Outputs key named AlexaAskGDSkillArn. Leave the default values for other options, and then select Save Endpoints.
Because you’re not publishing this skill, you don’t need to complete the Launch section of the configuration. The skill will remain in the “Development” status and will only be available for Alexa devices linked to the Amazon developer account used to create the skill. Anyone with physical access to the linked Alexa-enabled device can use the custom skill. As a best practice, I recommend that you delete the Lambda trigger created by the CloudFormation template and add a new one with Skill ID verification enabled.
Test the skill using an Alexa-enabled device
Now that you’ve deployed the sample solution, the next step is to test the skill. Make sure you’re using an Alexa-enabled device linked to the Amazon developer account used to create the skill. Before testing, if there are no current GuardDuty findings available, you can generate sample findings in the console. When you generate sample findings, GuardDuty populates your current findings list with one sample finding for each supported finding type.
You can test using the following voice commands:
“Alexa, Open GuardDuty” — Opens the skill and provides a welcome response. You can also use “Alexa, Ask GuardDuty”.
“Get flash briefing” — Provides global and regional counts for low, medium, and high severity findings. The regions declared in the FLASHREGIONS parameter are included. You can also use “Ask GuardDuty to get flash briefing” to bypass the welcome message. You can learn more about GuardDuty severity levels in the documentation.
For the next set of commands, you can specify the region, use region names such as <Virginia>, <Oregon>, <Ireland>, and so on:
“Get statistics for region” — Provides regional counts for low, medium, and high severity findings.
“Get findings for region” — Returns finding information for the requested region. The number of findings returned is configured in the MAXRESP parameter.
“Get <high/medium/low> severity findings for region” – Returns finding information with the minimum severity requested as high, medium, or low. The number of findings returned is configured in the MAXRESP parameter.
“Help” — Provides information about the skill and supported utterances. Also provides current configuration for FLASHREGIONS and MAXRESP.
You can use this sample solution to get GuardDuty statistics and findings through the Alexa conversational interface. You’ll be able to identify findings that require further investigation quickly. This solution’s code is available on GitHub.
Unless you’ve been AFK for the last two days, you’ll no doubt be aware of the release of the brand-spanking-new Raspberry Pi 3 Model B+. With faster connectivity, more computing power, Power over Ethernet (PoE) pins, and the same $35 price point, the new board has been a hit across all our social media accounts! So while we wind down from launch week, let’s all pull up a chair, make yet another cup of coffee, and look through some of our favourite reactions from the last 48 hours.
Twitter
Our Twitter mentions were refreshing at hyperspeed on Wednesday, as you all began to hear the news and spread the word about the newest member to the Raspberry Pi family.
This sort of attention to detail work is exactly what I love about being involved with @Raspberry_Pi. We’re squeezing the last drops of performance out of the 40nm process node, and perfecting Pi 3 in the same way that the original B+ perfected Pi 1.” https://t.co/hEj7JZOGeZ
And I think we counted about 150 uses of this GIF on Twitter alone:
Is something going on with the @Raspberry_Pi today? You’d never guess from my YouTube subscriptions page… 😀
A few members of our community were lucky enough to get their hands on a 3B+ early, and sat eagerly by the YouTube publish button, waiting to release their impressions of our new board to the world. Others, with no new Pi in hand yet, posted reaction vids to the launch, discussing their plans for the upgraded Pi and comparing statistics against its predecessors.
Happy Pi Day World! There is a new Raspberry Pi 3, the B+! In this video I will review the new Pi 3 B+ and do some speed tests. Let me know in the comments if you are getting one and what you are planning on making with it!
It’s Pi day! Sorry, wondrous Mathematical constant, this day is no longer about you. The Raspberry Pi foundation just released a new version of the Raspberry Pi called the Rapsberry Pi B+.
If you have a YouTube or Vimeo channel, or if you create videos for other social media channels, and have published your impressions of the new Raspberry Pi, be sure to share a link with us so we can see what you think!
Instagram
We shared a few photos and videos on Instagram, and over 30000 of you checked out our Instagram Story on the day.
5,609 Likes, 103 Comments – Raspberry Pi (@raspberrypifoundation) on Instagram: “Some glamour shots of the latest member of the #RaspberryPi family – the Raspberry Pi 3 Model B+ ….”
As hot off the press (out of the oven? out of the solder bath?) Pi 3B+ boards start to make their way to eager makers’ homes, they are all broadcasting their excitement, and we love seeing what they plan to get up to with it.
8 Likes, 1 Comments – Mat (@notenoughtech) on Instagram: “The new #raspberrypi 3B+ suits the industrial setting. Check out my website for #RPI3B Vs RPI3BPlus…”
16 Likes, 3 Comments – Rob Edlin (@niddocks) on Instagram: “The new Raspberry Pi 3 Model B+ is here and will be used for our Python staging server for our APIs…”
In the news
Eben made an appearance on ITV Anglia on Wednesday, talking live on Facebook about the new Raspberry Pi.
As the latest version of the Raspberry Pi computer is launched in Cambridge, Dr Eben Upton talks about the inspiration of Professor Stephen Hawking and his legacy to science. Add your questions in…
He was also fortunate enough to spend the morning with some Sixth Form students from the local area.
On a day where science is making the headlines, lovely to see the scientists of the future in our office – getting tips from fab @Raspberry_Pi founder @EbenUpton #scientists #RaspberryPi #PiDay2018 @sirissac6thform
Principal Hardware Engineer Roger Thornton will also make a live appearance online this week: he is co-hosting Hack Chat later today. And of course, you can see more of Roger and Eben in the video where they discuss the new 3B+.
Raspberry Pi 3 Model B+ is now on sale now for $35.
It’s been a supremely busy week here at Pi Towers and across the globe in the offices of our Approved Resellers, and seeing your wonderful comments and sharing in your excitement has made it all worth it. Please keep it up, and be sure to share the arrival of your 3B+ as well as the projects into which you’ll be integrating them.
If you’d like to order a Raspberry Pi 3 Model B+, you can do so via our product page. And if you have any questions at all regarding the 3B+, the conversation is still taking place in the comments of Wednesday’s launch post, so head on over.
A couple of weekends ago, we celebrated our sixth birthday by coordinating more than 100 simultaneous Raspberry Jam events around the world. The Big Birthday Weekend was a huge success: our fantastic community organised Jams in 40 countries, covering six continents!
We sent the Jams special birthday kits to help them celebrate in style, and a video message featuring a thank you from Philip and Eben:
To celebrate the Raspberry Pi’s sixth birthday, we coordinated Raspberry Jams all over the world to take place over the Raspberry Jam Big Birthday Weekend, 3-4 March 2018. A massive thank you to everyone who ran an event and attended.
The Raspberry Jam photo booth
I put together code for a Pi-powered photo booth which overlaid the Big Birthday Weekend logo onto photos and (optionally) tweeted them. We included an arcade button in the Jam kits so they could build one — and it seemed to be quite popular. Some Jams put great effort into housing their photo booth:
If you want to try out the photo booth software yourself, find the code on GitHub.
The great Raspberry Jam bake-off
Traditionally, in the UK, people have a cake on their birthday. And we had a few! We saw (and tasted) a great selection of Pi-themed cakes and other baked goods throughout the weekend:
Raspberry Jams everywhere
We always say that every Jam is different, but there’s a common and recognisable theme amongst them. It was great to see so many different venues around the world filling up with like-minded Pi enthusiasts, Raspberry Jam–branded banners, and Raspberry Pi balloons!
Thank you so much to all the attendees of the Ikana Jam in Krakow past Saturday! We shared fun experiences, some of them… also painful 😉 A big thank you to @Raspberry_Pi for these global celebrations! And a big thank you to @hubraum for their hospitality! #PiParty #rjam
We also had a super successful set of wearables workshops using @adafruit Circuit Playground Express boards and conductive thread at today’s @Raspberry_Pi Jam! Very popular! #PiParty
Learning how to scare the zombies in case of an apocalypse- it worked on our young learners #PiParty @worksopcollege @Raspberry_Pi https://t.co/pntEm57TJl
Being one of the two places in Kenya where the #PiParty took place, it was an amazing time spending the day with this team and getting to learn and have fun. @TaitaTavetaUni and @Raspberry_Pi thank you for your support. @TTUTechlady @mictecttu ch
The Philly & Pi #PiParty event with @Bresslergroup and @TechGirlzorg was awesome! The Scratch and Pi workshop was amazing! It was overall a great day of fun and tech!!! Thank you everyone who came out!
Thanks everyone who came out to the @Raspberry_Pi Big Birthday Jam! Special thanks to @PBFerrell @estefanniegg @pcsforme @pandafulmanda @colnels @bquentin3 couldn’t’ve put on this amazing community event without you guys!
Así terminamos el #Raspberry Jam Big Birthday Weekend #Bogota 2018 #PiParty de #RaspberryJamBogota 2018 @Raspberry_Pi Nos vemos el 7 de marzo en #ArduinoDayBogota 2018 y #RaspberryJamBogota 2018
Happy 6th birthday, @Raspberry_Pi! Greetings all the way from CEBU,PH! #PiParty #IoTCebu Thanks @CebuXGeeks X Ramos for these awesome pics. #Fablab #UPCebu
ラズパイ、6才のお誕生日会スタート in Tokyo PCNブースで、いろいろ展示とhttps://t.co/L6E7KgyNHFとIchigoJamつないだ、こどもIoTハッカソンmini体験やってます at 東京蒲田駅近 https://t.co/yHEuqXHvqe #piparty #pipartytokyo #rjam #opendataday
Personally, I managed to get to three Jams over the weekend: two run by the same people who put on the first two Jams to ever take place, and also one brand-new one! The Preston Raspberry Jam team, who usually run their event on a Monday evening, wanted to do something extra special for the birthday, so they came up with the idea of putting on a Raspberry Jam Sandwich — on the Friday and Monday around the weekend! This meant I was able to visit them on Friday, then attend the Manchester Raspberry Jam on Saturday, and finally drop by the new Jam at Worksop College on my way home on Sunday.
I’m at my first Raspberry Jam #PiParty event of the big birthday weekend! @PrestonRJam has been running for nearly 6 years and is a great place to start the celebrations!
Thanks to everyone who came to our Jam and everyone who helped out. @phoenixtogether thanks for amazing cake & hosting. Ademir you’re so cool. It was awesome to meet Craig Morley from @Raspberry_Pi too. #PiParty
Great #PiParty today at the @cotswoldjam with bloody delicious cake and lots of raspberry goodness. Great to see @ClareSutcliffe @martinohanlon playing on my new pi powered arcade build:-)
It’s @Raspberry_Pi 6th birthday and we’re celebrating by taking part in @amsterjam__! Happy Birthday Raspberry Pi, we’re so happy to be a part of the family! #PiParty
For more Jammy birthday goodness, check out the PiParty hashtag on Twitter!
The Jam makers!
A lot of preparation went into each Jam, and we really appreciate all the hard work the Jam makers put in to making these events happen, on the Big Birthday Weekend and all year round. Thanks also to all the teams that sent us a group photo:
Lots of the Jams that took place were brand-new events, so we hope to see them continue throughout 2018 and beyond, growing the Raspberry Pi community around the world and giving more people, particularly youths, the opportunity to learn digital making skills.
So many wonderful people in the @Raspberry_Pi community. Thanks to everyone at #PottonPiAndPints for a great afternoon and for everything you do to help young people learn digital making. #PiParty
Special thanks to ModMyPi for shipping the special Raspberry Jam kits all over the world!
Don’t forget to check out our Jam page to find an event near you! This is also where you can find free resources to help you get a new Jam started, and download free starter projects made especially for Jam activities. These projects are available in English, Français, Français Canadien, Nederlands, Deutsch, Italiano, and 日本語. If you’d like to help us translate more content into these and other languages, please get in touch!
PS Some of the UK Jams were postponed due to heavy snowfall, so you may find there’s a belated sixth-birthday Jam coming up where you live!
I am a regular and frequent user of Amazon Chime, as are most of my colleagues. In addition to dozens of ongoing point-to-point sessions with individual colleagues, I participate in one chat room for the AWS Blog team and another for the AWS Evangelists. I attend several meetings per day, and use Amazon Chime’s conferencing feature to set up my own meetings once or twice a month. While I don’t actually see the monthly bill, I am on Amazon Chime’s Pro plan, at a per-user rate of $15 per month. This is, based on feedback from our customers, a good value for users who regularly host meetings, but a bit too high for those who use the other features and/or spend more time attending meetings than hosting them.
Usage-Based Pricing To meet the needs of these users and to make Amazon Chime even more economical we are introducing a new, usage-based pricing model that goes in to effect on April 1, 2018.
During the initial, 30-day free trial, users have access to all Amazon Chime features at no charge. After the trial period ends, the users can chat with each other and attend meetings, both at no charge. On days that they host meetings, a $3 per day charge will be made, up to a maximum of $15 per month. Based on historical usage patterns, this will result in an overall price reduction for virtually all Amazon Chime customers.
In order to use the scheduling and hosting features after the end of the trial period, users must be connected to an AWS account and the administrator must enable the Amazon Pro Features features for the account. Thanks to the usage-based model, administrators will no longer have to purchase licenses for individual users in their organization. Instead, they can use Active Directory settings and policy management within the Amazon Chime Console to set permissions as desired.
As part of this change we are also eliminating the Plus plan and adding the screen sharing and corporate directory features to the Basic plan.
This change goes in to effect on April 1, 2018! To learn more, take a look at the Amazon Chime Pricing page.
(Даже вестниците вече писаха за това, крайно време е да довърша тоя post)
Та, има “нова” директива на ЕК, за унифициране на законите за авторските права из европейския съюз.
(нова – на 10на години е, ама чак сега се опитва да влезе, и има някакви бегли шансове да се вкара по времето на нашето председателство)
В нея има количество глупости, но частта, с която имам занимание е чл. 13, който в общи линии казва “internet/hosting доставчиците са отговорни за съдържанието, което се качва при тях и трябва да го филтрират”. Не знам за вас, но за мен цялото нещо е твърде много deja vu, в последните 20на години все някой се опитва да пробута нещо подобно, в някакъв вид. Това без значение, че няма как да се направи работеща система, без значение, че всъщност е инфраструктура за цензура (но по-мощна от тази, която има в закона за хазарта) и че в крайна сметка няма да доведе до подобрение за когото и да е, освен може би хората, които продават “филтриращи” системи.
Един от хубавите доводи за това, че такава система няма как да стане идва от wikipedia – има две правителства, които са им поискали да филтрират съдържание, Иран и Китай. Не знам за колко са добре технологично иранците, но ми се струва, че китайците ако можеха, вече щяха да са направили работеща такава система и нямаше да занимават wikipedia. Нещата, които аз съм виждал, са неефективни и с достатъчно грешки (и false positives, и false negatives), че да са неизползваеми за среден размер сайт, понеже така и така някой трябва да преглежда проблемите.
(Вероятно не съм единствения, дето поради идиотщини на youtube постоянно има отворени няколко copyright dispute-а. В един момент човек решава да си ползва собствен streaming и да не го занимават с такива неща…)
Та, има една инициатива по темата това да не влезе, article13.bg, към които съм се включил. Подкрепя ги фондация “Отворени проекти”, initLab и всякакви други добри хора. Вижте, подкрепете, споменете на всички, дето ще ги засегне (всички хостинги, например…).
Kumar Venkateswar, Product Manager on the AWS ML Platforms Team, shares details on the announcement of Auto Scaling with Amazon SageMaker.
With Amazon SageMaker, thousands of customers have been able to easily build, train and deploy their machine learning (ML) models. Today, we’re making it even easier to manage production ML models, with Auto Scaling for Amazon SageMaker. Instead of having to manually manage the number of instances to match the scale that you need for your inferences, you can now have SageMaker automatically scale the number of instances based on an AWS Auto Scaling Policy.
SageMaker has made managing the ML process easier for many customers. We’ve seen customers take advantage of managed Jupyter notebooks and managed distributed training. We’ve seen customers deploying their models to SageMaker hosting for inferences, as they integrate machine learning with their applications. SageMaker makes this easy – you don’t have to think about patching the operating system (OS) or frameworks on your inference hosts, and you don’t have to configure inference hosts across Availability Zones. You just deploy your models to SageMaker, and it handles the rest.
Until now, you have needed to specify the number and type of instances per endpoint (or production variant) to provide the scale that you need for your inferences. If your inference volume changes, you can change the number and/or type of instances that back each endpoint to accommodate that change, without incurring any downtime. In addition to making it easy to change provisioning, customers have asked us how we can make managing capacity for SageMaker even easier.
With Auto Scaling for Amazon SageMaker, in the SageMaker console, the AWS Auto Scaling API, and the AWS SDK, this becomes much easier. Now, instead of having to closely monitor inference volume, and change the endpoint configuration in response, customers can configure a scaling policy to be used by AWS Auto Scaling. Auto Scaling adjusts the number of instances up or down in response to actual workloads, determined by using Amazon CloudWatch metrics and target values defined in the policy. In this way, customers can automatically adjust their inference capacity to maintain predictable performance at a low cost. You simply specify the target inference throughput per instance and provide upper and lower bounds for the number of instances for each production variant. SageMaker will then monitor throughput per instance using Amazon CloudWatch alarms, and then it will adjust provisioned capacity up or down as needed.
After you configure the endpoint with Auto Scaling, SageMaker will continue to monitor your deployed models to automatically adjust the instance count. SageMaker will keep throughput within desired levels, in response to changes in application traffic. This makes it easier to manage models in production, and it can help reduce the cost of deployed models, as you no longer have to provision sufficient capacity in order to manage your peak load. Instead, you configure the limits to accommodate your minimum expected traffic and the maximum peak, and Amazon SageMaker will work within those limits to minimize cost.
How do you get started? Open the SageMaker console. For existing endpoints, you first access the endpoint to modify the settings.
Then, scroll to the Endpoint runtime settings section, select the variant, and choose Configure auto scaling.
First, configure the minimum and maximum number of instances.
Next, choose the throughput per instance at which you want to add an additional instance, given previous load testing.
You can optionally set cool down periods for scaling in or out, to avoid oscillation during periods of wide fluctuation in workload. If not, SageMaker will assume default values.
And that’s it! You now have an endpoint that will automatically scale with increasing inferences.
You pay for the capacity used at regular SageMaker pay-as-you-go pricing, so you no longer have to pay for unused capacity during relative idle periods!
Auto Scaling in Amazon SageMaker is available today in the US East (N. Virginia & Ohio), EU (Ireland), and U.S. West (Oregon) AWS regions. To learn more, see the Amazon SageMaker Auto Scaling documentation.
Kumar Venkateswar is a Product Manager in the AWS ML Platforms team, which includes Amazon SageMaker, Amazon Machine Learning, and the AWS Deep Learning AMIs. When not working, Kumar plays the violin and Magic: The Gathering.
This post was contributed by Christoph Kassen, AWS Solutions Architect
With the emergence of microservices architectures, the number of services that are part of a web application has increased a lot. It’s not unusual anymore to build and operate hundreds of separate microservices, all as part of the same application.
Think of a typical e-commerce application that displays products, recommends related items, provides search and faceting capabilities, and maintains a shopping cart. Behind the scenes, many more services are involved, such as clickstream tracking, ad display, targeting, and logging. When handling a single user request, many of these microservices are involved in responding. Understanding, analyzing, and debugging the landscape is becoming complex.
AWS X-Ray provides application-tracing functionality, giving deep insights into all microservices deployed. With X-Ray, every request can be traced as it flows through the involved microservices. This provides your DevOps teams the insights they need to understand how your services interact with their peers and enables them to analyze and debug issues much faster.
With microservices architectures, every service should be self-contained and use the technologies best suited for the problem domain. Depending on how the service is built, it is deployed and hosted differently.
One of the most popular choices for packaging and deploying microservices at the moment is containers. The application and its dependencies are clearly defined, the container can be built on CI infrastructure, and the deployment is simplified greatly. Container schedulers, such as Kubernetes and Amazon Elastic Container Service (Amazon ECS), greatly simplify deploying and running containers at scale.
Running X-Ray on Kubernetes
Kubernetes is an open-source container management platform that automates deployment, scaling, and management of containerized applications.
This post shows you how to run X-Ray on top of Kubernetes to provide application tracing capabilities to services hosted on a Kubernetes cluster. Additionally, X-Ray also works for applications hosted on Amazon ECS, AWS Elastic Beanstalk, Amazon EC2, and even when building services with AWS Lambda functions. This flexibility helps you pick the technology you need while still being able to trace requests through all of the services running within your AWS environment.
The complete code, including a simple Node.js based demo application is available in the corresponding aws-xray-kubernetes GitHub repository, so you can quickly get started with X-Ray.
The sample application within the repository consists of two simple microservices, Service-A and Service-B. The following architecture diagram shows how each service is deployed with two Pods on the Kubernetes cluster:
Requests are sent to the Service-A from clients.
Service-A then contacts Service-B.
The requests are serviced by Service-B.
Service-B adds a random delay to each request to show different response times in X-Ray.
To test out the sample applications on your own Kubernetes cluster use the Dockerfiles provided in the GitHub repository, build the two containers, push them to a container registry and apply the yaml configuration with kubectl to your Kubernetes cluster.
Prerequisites
If you currently do not have a cluster running within your AWS environment, take a look at Amazon Elastic Container Service for Kubernetes (Amazon EKS), or use the instructions from the Manage Kubernetes Clusters on AWS Using Kops blog post to spin up a self-managed Kubernetes cluster.
Security Setup for X-Ray
The nodes in the Kubernetes cluster hosting web application Pods need IAM permissions so that the Pods hosting the X-Ray daemon can send traces to the X-Ray service backend.
The easiest way is to set up a new IAM policy allowing all worker nodes within your Kubernetes cluster to write data to X-Ray. In the IAM console or AWS CLI, create a new policy like the following:
Adjust the AWS account ID within the resource. Give the policy a descriptive name, such as k8s-nodes-XrayWriteAccess.
Next, attach the policy to the instance profile for the Kubernetes worker nodes. Therefore, select the IAM role assigned to your worker instances (check the EC2 console if you are unsure) and attach the IAM policy created earlier to it. You can attach the IAM permissions directly from the command line with the following command:
aws iam attach-role-policy --role-name k8s-nodes --policy-arn arn:aws:iam::000000000000:policy/k8s-nodes-XrayWriteAccess
Build the X-Ray daemon Docker image
The X-Ray daemon is available as a single, statically compiled binary that can be downloaded directly from the AWS website.
The first step is to create a Docker container hosting the X-Ray daemon binary and exposing port 2000 via UDP. The daemon is either configured via command line parameters or a configuration file. The most important option is to set the listen port to the correct IP address so that tracing requests from application Pods can be accepted.
To build your own Docker image containing the X-Ray daemon, use the Dockerfile shown below.
# Use Amazon Linux Version 1
FROM amazonlinux:1
# Download latest 2.x release of X-Ray daemon
RUN yum install -y unzip && \
cd /tmp/ && \
curl https://s3.dualstack.us-east-2.amazonaws.com/aws-xray-assets.us-east-2/xray-daemon/aws-xray-daemon-linux-2.x.zip > aws-xray-daemon-linux-2.x.zip && \
unzip aws-xray-daemon-linux-2.x.zip && \
cp xray /usr/bin/xray && \
rm aws-xray-daemon-linux-2.x.zip && \
rm cfg.yaml
# Expose port 2000 on udp
EXPOSE 2000/udp
ENTRYPOINT ["/usr/bin/xray"]
# No cmd line parameters, use default configuration
CMD ['']
This container image is based on Amazon Linux which results in a small container image. To build and tag the container image run docker build -t xray:latest.
Create an Amazon ECR repository
Create a repository in Amazon Elastic Container Registry (Amazon ECR) to hold your X-Ray Docker image. Your Kubernetes cluster uses this repository to pull the image from upon deployment of the X-Ray Pods.
Use the following CLI command to create your repository or alternatively the AWS Management Console:
Make sure that you configure the kubectl tool properly for your cluster to be able to deploy the X-Ray Pod onto your Kubernetes cluster. After the X-Ray Pods are deployed to your Kubernetes cluster, applications can send tracing information to the X-Ray daemon on their host. The biggest advantage is that you do not need to provide X-Ray as a sidecar container alongside your application. This simplifies the configuration and deployment of your applications and overall saves resources on your cluster.
To deploy the X-Ray daemon as Pods onto your Kubernetes cluster, run the following from the cloned GitHub repository:
kubectl apply -f xray-k8s-daemonset.yaml
This deploys and maintains an X-Ray Pod on each worker node, which is accepting tracing data from your microservices and routing it to one of the X-Ray pods. When deploying the container using a DaemonSet, the X-Ray port is also exposed directly on the host. This way, clients can connect directly to the daemon on your node. This avoids unnecessary network traffic going across your cluster.
Connecting to the X-Ray daemon
To integrate application tracing with your applications, use the X-Ray SDK for one of the supported programming languages:
Java
Node.js
.NET (Framework and Core)
Go
Python
The SDKs provide classes and methods for generating and sending trace data to the X-Ray daemon. Trace data includes information about incoming HTTP requests served by the application, and calls that the application makes to downstream services using the AWS SDK or HTTP clients.
By default, the X-Ray SDK expects the daemon to be available on 127.0.0.1:2000. This needs to be changed in this setup, as the daemon is not part of each Pod but hosted within its own Pod.
The deployed X-Ray DaemonSet exposes all Pods via the Kubernetes service discovery, so applications can use this endpoint to discover the X-Ray daemon. If you deployed to the default namespace, the endpoint is:
xray-service.default
Applications now need to set the daemon address either with the AWS_XRAY_DAEMON_ADDRESS environment variable (preferred) or directly within the SDK setup code:
To set up the environment variable, include the following information in your Kubernetes application deployment description YAML. That exposes the X-Ray service address via the environment variable, where it is picked up automatically by the SDK.
Sending tracing information from your application is straightforward with the X-Ray SDKs. The example code below serves as a starting point to instrument your application with traces. Take a look at the two sample applications in the GitHub repository to see how to send traces from Service A to Service B. The diagram below visualizes the flow of requests between the services.
Because your application is running within containers, enable both the EC2Plugin and ECSPlugin, which gives you information about the Kubernetes node hosting the Pod as well as the container name. Despite the name ECSPlugin, this plugin gives you additional information about your container when running your application on Kubernetes.
var app = express();
//...
var AWSXRay = require('aws-xray-sdk');
AWSXRay.config([XRay.plugins.EC2Plugin, XRay.plugins.ECSPlugin]);
app.use(AWSXRay.express.openSegment('defaultName')); //required at the start of your routes
app.get('/', function (req, res) {
res.render('index');
});
app.use(AWSXRay.express.closeSegment()); //required at the end of your routes / first in error handling routes
For more information about all options and possibilities to instrument your application code, see the X-Ray documentation page for the corresponding SDK information.
The picture below shows the resulting service map that provides insights into the flow of requests through the microservice landscape. You can drill down here into individual traces and see which path each request has taken.
From the service map, you can drill down into individual requests and see where they originated from and how much time was spent in each service processing the request.
You can also view details about every individual segment of the trace by clicking on it. This displays more details.
On the Resources tab, you will see the Kubernetes Pod picked up by the ECSPlugin, which handled the request, as well as the instance that Pod was running on.
Summary
In this post, I shared how to deploy and run X-Ray on an existing Kubernetes cluster. Using tracing gives you deep insights into your applications to ease analysis and spot potential problems early. With X-Ray, you get these insights for all your applications running on AWS, no matter if they are hosted on Amazon ECS, AWS Lambda, or a Kubernetes cluster.
Should we host in the cloud or on our own servers? This question was
at the center of Dmytro Dyachuk’s talk, given
during KubeCon +
CloudNativeCon last November. While many services
simply launch in the cloud without the organizations behind them
considering other options, large
content-hosting services have actually
moved back to their own data centers: Dropbox
migrated in 2016
and Instagram
in 2014. Because such transitions can be expensive
and risky, understanding the economics of hosting is a critical part
of launching a new service. Actual hosting costs are often
misunderstood, or secret, so it is sometimes difficult to get the
numbers right. In this article, we’ll use Dyachuk’s talk to try to
answer the “million dollar question”: “buy or rent?”
This is part one of a series. The second part will be posted later this week. Use the Join button above to receive notification of future posts in this series.
Though most of us have never set foot inside of a data center, as citizens of a data-driven world we nonetheless depend on the services that data centers provide almost as much as we depend on a reliable water supply, the electrical grid, and the highway system. Every time we send a tweet, post to Facebook, check our bank balance or credit score, watch a YouTube video, or back up a computer to the cloud we are interacting with a data center.
In this series, The Challenges of Opening a Data Center, we’ll talk in general terms about the factors that an organization needs to consider when opening a data center and the challenges that must be met in the process. Many of the factors to consider will be similar for opening a private data center or seeking space in a public data center, but we’ll assume for the sake of this discussion that our needs are more modest than requiring a data center dedicated solely to our own use (i.e. we’re not Google, Facebook, or China Telecom).
Data center technology and management are changing rapidly, with new approaches to design and operation appearing every year. This means we won’t be able to cover everything happening in the world of data centers in our series, however, we hope our brief overview proves useful.
What is a Data Center?
A data center is the structure that houses a large group of networked computer servers typically used by businesses, governments, and organizations for the remote storage, processing, or distribution of large amounts of data.
While many organizations will have computing services in the same location as their offices that support their day-to-day operations, a data center is a structure dedicated to 24/7 large-scale data processing and handling.
Depending on how you define the term, there are anywhere from a half million data centers in the world to many millions. While it’s possible to say that an organization’s on-site servers and data storage can be called a data center, in this discussion we are using the term data center to refer to facilities that are expressly dedicated to housing computer systems and associated components, such as telecommunications and storage systems. The facility might be a private center, which is owned or leased by one tenant only, or a shared data center that offers what are called “colocation services,” and rents space, services, and equipment to multiple tenants in the center.
A large, modern data center operates around the clock, placing a priority on providing secure and uninterrrupted service, and generally includes redundant or backup power systems or supplies, redundant data communication connections, environmental controls, fire suppression systems, and numerous security devices. Such a center is an industrial-scale operation often using as much electricity as a small town.
Types of Data Centers
There are a number of ways to classify data centers according to how they will be used, whether they are owned or used by one or multiple organizations, whether and how they fit into a topology of other data centers; which technologies and management approaches they use for computing, storage, cooling, power, and operations; and increasingly visible these days: how green they are.
Data centers can be loosely classified into three types according to who owns them and who uses them.
Exclusive Data Centers are facilities wholly built, maintained, operated and managed by the business for the optimal operation of its IT equipment. Some of these centers are well-known companies such as Facebook, Google, or Microsoft, while others are less public-facing big telecoms, insurance companies, or other service providers.
Managed Hosting Providers are data centers managed by a third party on behalf of a business. The business does not own data center or space within it. Rather, the business rents IT equipment and infrastructure it needs instead of investing in the outright purchase of what it needs.
Colocation Data Centers are usually large facilities built to accommodate multiple businesses within the center. The business rents its own space within the data center and subsequently fills the space with its IT equipment, or possibly uses equipment provided by the data center operator.
Backblaze, for example, doesn’t own its own data centers but colocates in data centers owned by others. As Backblaze’s storage needs grow, Backblaze increases the space it uses within a given data center and/or expands to other data centers in the same or different geographic areas.
Availability is Key
When designing or selecting a data center, an organization needs to decide what level of availability is required for its services. The type of business or service it provides likely will dictate this. Any organization that provides real-time and/or critical data services will need the highest level of availability and redundancy, as well as the ability to rapidly failover (transfer operation to another center) when and if required. Some organizations require multiple data centers not just to handle the computer or storage capacity they use, but to provide alternate locations for operation if something should happen temporarily or permanently to one or more of their centers.
Organizations operating data centers that can’t afford any downtime at all will typically operate data centers that have a mirrored site that can take over if something happens to the first site, or they operate a second site in parallel to the first one. These data center topologies are called Active/Passive, and Active/Active, respectively. Should disaster or an outage occur, disaster mode would dictate immediately moving all of the primary data center’s processing to the second data center.
While some data center topologies are spread throughout a single country or continent, others extend around the world. Practically, data transmission speeds put a cap on centers that can be operated in parallel with the appearance of simultaneous operation. Linking two data centers located apart from each other — say no more than 60 miles to limit data latency issues — together with dark fiber (leased fiber optic cable) could enable both data centers to be operated as if they were in the same location, reducing staffing requirements yet providing immediate failover to the secondary data center if needed.
This redundancy of facilities and ensured availability is of paramount importance to those needing uninterrupted data center services.
LEED Certification
Leadership in Energy and Environmental Design (LEED) is a rating system devised by the United States Green Building Council (USGBC) for the design, construction, and operation of green buildings. Facilities can achieve ratings of certified, silver, gold, or platinum based on criteria within six categories: sustainable sites, water efficiency, energy and atmosphere, materials and resources, indoor environmental quality, and innovation and design.
Green certification has become increasingly important in data center design and operation as data centers require great amounts of electricity and often cooling water to operate. Green technologies can reduce costs for data center operation, as well as make the arrival of data centers more amenable to environmentally-conscious communities.
The ACT, Inc. data center in Iowa City, Iowa was the first data center in the U.S. to receive LEED-Platinum certification, the highest level available.
ACT Data Center exterior
ACT Data Center interior
Factors to Consider When Selecting a Data Center
There are numerous factors to consider when deciding to build or to occupy space in a data center. Aspects such as proximity to available power grids, telecommunications infrastructure, networking services, transportation lines, and emergency services can affect costs, risk, security and other factors that need to be taken into consideration.
The size of the data center will be dictated by the business requirements of the owner or tenant. A data center can occupy one room of a building, one or more floors, or an entire building. Most of the equipment is often in the form of servers mounted in 19 inch rack cabinets, which are usually placed in single rows forming corridors (so-called aisles) between them. This allows staff access to the front and rear of each cabinet. Servers differ greatly in size from 1U servers (i.e. one “U” or “RU” rack unit measuring 44.50 millimeters or 1.75 inches), to Backblaze’s Storage Pod design that fits a 4U chassis, to large freestanding storage silos that occupy many square feet of floor space.
Location
Location will be one of the biggest factors to consider when selecting a data center and encompasses many other factors that should be taken into account, such as geological risks, neighboring uses, and even local flight paths. Access to suitable available power at a suitable price point is often the most critical factor and the longest lead time item, followed by broadband service availability.
With more and more data centers available providing varied levels of service and cost, the choices increase each year. Data center brokers can be employed to find a data center, just as one might use a broker for home or other commercial real estate.
Websites listing available colocation space, such as upstack.io, or entire data centers for sale or lease, are widely used. A common practice is for a customer to publish its data center requirements, and the vendors compete to provide the most attractive bid in a reverse auction.
Business and Customer Proximity
The center’s closeness to a business or organization may or may not be a factor in the site selection. The organization might wish to be close enough to manage the center or supervise the on-site staff from a nearby business location. The location of customers might be a factor, especially if data transmission speeds and latency are important, or the business or customers have regulatory, political, tax, or other considerations that dictate areas suitable or not suitable for the storage and processing of data.
Climate
Local climate is a major factor in data center design because the climatic conditions dictate what cooling technologies should be deployed. In turn this impacts uptime and the costs associated with cooling, which can total as much as 50% or more of a center’s power costs. The topology and the cost of managing a data center in a warm, humid climate will vary greatly from managing one in a cool, dry climate. Nevertheless, data centers are located in both extremely cold regions and extremely hot ones, with innovative approaches used in both extremes to maintain desired temperatures within the center.
Geographic Stability and Extreme Weather Events
A major obvious factor in locating a data center is the stability of the actual site as regards weather, seismic activity, and the likelihood of weather events such as hurricanes, as well as fire or flooding.
Backblaze’s Sacramento data center describes its location as one of the most stable geographic locations in California, outside fault zones and floodplains.
Sometimes the location of the center comes first and the facility is hardened to withstand anticipated threats, such as Equinix’s NAP of the Americas data center in Miami, one of the largest single-building data centers on the planet (six stories and 750,000 square feet), which is built 32 feet above sea level and designed to withstand category 5 hurricane winds.
Equinix “NAP of the Americas” Data Center in Miami
Most data centers don’t have the extreme protection or history of the Bahnhof data center, which is located inside the ultra-secure former nuclear bunker Pionen, in Stockholm, Sweden. It is buried 100 feet below ground inside the White Mountains and secured behind 15.7 in. thick metal doors. It prides itself on its self-described “Bond villain” ambiance.
Bahnhof Data Center under White Mountain in Stockholm
Usually, the data center owner or tenant will want to take into account the balance between cost and risk in the selection of a location. The Ideal quadrant below is obviously favored when making this compromise.
Risk mitigation also plays a strong role in pricing. The extent to which providers must implement special building techniques and operating technologies to protect the facility will affect price. When selecting a data center, organizations must make note of the data center’s certification level on the basis of regulatory requirements in the industry. These certifications can ensure that an organization is meeting necessary compliance requirements.
Power
Electrical power usually represents the largest cost in a data center. The cost a service provider pays for power will be affected by the source of the power, the regulatory environment, the facility size and the rate concessions, if any, offered by the utility. At higher level tiers, battery, generator, and redundant power grids are a required part of the picture.
Fault tolerance and power redundancy are absolutely necessary to maintain uninterrupted data center operation. Parallel redundancy is a safeguard to ensure that an uninterruptible power supply (UPS) system is in place to provide electrical power if necessary. The UPS system can be based on batteries, saved kinetic energy, or some type of generator using diesel or another fuel. The center will operate on the UPS system with another UPS system acting as a backup power generator. If a power outage occurs, the additional UPS system power generator is available.
Many data centers require the use of independent power grids, with service provided by different utility companies or services, to prevent against loss of electrical service no matter what the cause. Some data centers have intentionally located themselves near national borders so that they can obtain redundant power from not just separate grids, but from separate geopolitical sources.
Higher redundancy levels required by a company will of invariably lead to higher prices. If one requires high availability backed by a service-level agreement (SLA), one can expect to pay more than another company with less demanding redundancy requirements.
Stay Tuned for Part 2 of The Challenges of Opening a Data Center
That’s it for part 1 of this post. In subsequent posts, we’ll take a look at some other factors to consider when moving into a data center such as network bandwidth, cooling, and security. We’ll take a look at what is involved in moving into a new data center (including stories from Backblaze’s experiences). We’ll also investigate what it takes to keep a data center running, and some of the new technologies and trends affecting data center design and use. You can discover all posts on our blog tagged with “Data Center” by following the link https://www.backblaze.com/blog/tag/data-center/.
The second part of this series on The Challenges of Opening a Data Center will be posted later this week. Use the Join button above to receive notification of future posts in this series.
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