AWS Blog

Earlier this year I told you about Amazon DynamoDB Accelerator (DAX), a fully-managed caching service that sits in front of (logically speaking) your Amazon DynamoDB tables. DAX returns cached responses in microseconds, making it a great fit for eventually-consistent read-intensive workloads. DAX supports the DynamoDB API, and is seamless and easy to use. As a managed service, you simply create your DAX cluster and use it as the target for your existing reads and writes. You don’t have to worry about patching, cluster maintenance, replication, or fault management.

Now Generally Available
Today I am pleased to announce that DAX is now generally available. We have expanded DAX into additional AWS Regions and used the preview time to fine-tune performance and availability:

Now in Five Regions – DAX is now available in the US East (Northern Virginia), EU (Ireland), US West (Oregon), Asia Pacific (Tokyo), and US West (Northern California) Regions.

In Production – Our preview customers are reporting that they are using DAX in production, that they loved how easy it was to add DAX to their application, and have told us that their apps are now running 10x faster.

Getting Started with DAX
As I outlined in my earlier post, it is easy to use DAX to accelerate your existing DynamoDB applications. You simply create a DAX cluster in the desired region, update your application to reference the DAX SDK for Java (the calls are the same; this is a drop-in replacement), and configure the SDK to use the endpoint to your cluster. As a read-through/write-through cache, DAX seamlessly handles all of the DynamoDB read/write APIs.

We are working on SDK support for other languages, and I will share additional information as it becomes available.

DAX Pricing
You pay for each node in the cluster (see the DynamoDB Pricing page for more information) on a per-hour basis, with prices starting at $0.269 per hour in the US East (Northern Virginia) and US West (Oregon) regions. With DAX, each of the nodes in your cluster serves as a read target and as a failover target for high availability. The DAX SDK is cluster aware and will issue round-robin requests to all nodes in the cluster so that you get to make full use of the cluster’s cache resources.

Because DAX can easily handle sudden spikes in read traffic, you may be able to reduce the amount of provisioned throughput for your tables, resulting in an overall cost savings while still returning results in microseconds.

— Jeff;

AWS WAF (Web Application Firewall) helps to protect your application from many different types of application-layer attacks that involve requests that are malicious or malformed. As I showed you when I first wrote about this service (New – AWS WAF), you can define rules that match cross-site scripting, IP address, SQL injection, size, or content constraints:

When incoming requests match rules, actions are invoked. Actions can either allow, block, or simply count matches.

The existing rule model is powerful and gives you the ability to detect and respond to many different types of attacks. It does not, however, allow you to respond to attacks that simply consist of a large number of otherwise valid requests from a particular IP address. These requests might be a web-layer DDoS attack, a brute-force login attempt, or even a partner integration gone awry.

New Rate-Based Rules
Today we are adding Rate-based Rules to WAF, giving you control of when IP addresses are added to and removed from a blacklist, along with the flexibility to handle exceptions and special cases:

Blacklisting IP Addresses – You can blacklist IP addresses that make requests at a rate that exceeds a configured threshold rate.

IP Address Tracking– You can see which IP addresses are currently blacklisted.

IP Address Removal – IP addresses that have been blacklisted are automatically removed when they no longer make requests at a rate above the configured threshold.

IP Address Exemption – You can exempt certain IP addresses from blacklisting by using an IP address whitelist inside of the a rate-based rule. For example, you might want to allow trusted partners to access your site at a higher rate.

Monitoring & Alarming – You can watch and alarm on CloudWatch metrics that are published for each rule.

You can combine new Rate-based Rules with WAF Conditions to implement sophisticated rate-limiting strategies. For example, you could use a Rate-based Rule and a WAF Condition that matches your login pages. This would allow you to impose a modest threshold on your login pages (to avoid brute-force password attacks) and allow a more generous one on your marketing or system status pages.

Thresholds are defined in terms of the number of incoming requests from a single IP address within a 5 minute period. Once this threshold is breached, additional requests from the IP address are blocked until the request rate falls below the threshold.

Using Rate-Based Rules
Here’s how you would define a Rate-based Rule that protects the /login portion of your site. Start by defining a WAF condition that matches the desired string in the URI of the page:

Then use this condition to define a Rate-based Rule (the rate limit is expressed in terms of requests within a 5 minute interval, but the blacklisting goes in to effect as soon as the limit is breached):

With the condition and the rule in place, create a Web ACL (ProtectLoginACL) to bring it all together and to attach it to the AWS resource (a CloudFront distribution in this case):

Then attach the rule (ProtectLogin) to the Web ACL:

The resource is now protected in accord with the rule and the web ACL. You can monitor the associated CloudWatch metrics (ProtectLogin and ProtectLoginACL in this case). You could even create CloudWatch Alarms and use them to fire Lambda functions when a protection threshold is breached. The code could examine the offending IP address and make a complex, business-driven decision, perhaps adding a whitelisting rule that gives an extra-generous allowance to a trusted partner or to a user with a special payment plan.

Available Now
The new, Rate-based Rules are available now and you can start using them today! Rate-based rules are priced the same as Regular rules; see the WAF Pricing page for more info.

— Jeff;

Last year we launched new AWS Regions in Canada, India, Korea, the UK (London), and the United States (Ohio), and announced that new regions are coming to France (Paris), China (Ningxia), and Sweden (Stockholm).

Coming to Hong Kong in 2018
Today, I am happy to be able to tell you that we are planning to open up an AWS Region in Hong Kong, in 2018. Hong Kong is a leading international financial center, well known for its service oriented economy. It is rated highly on innovation and for ease of doing business. As an evangelist, I get to visit many great cities in the world, and was lucky to have spent some time in Hong Kong back in 2014 and met a number of awesome customers there. Many of these customers have given us feedback that they wanted a local AWS Region.

This will be the eighth AWS Region in Asia Pacific joining six other Regions there — Singapore, Tokyo, Sydney, Beijing, Seoul, and Mumbai, and an additional Region in China (Ningxia) expected to launch in the coming months. Together, these Regions will provide our customers with a total of 19 Availability Zones (AZs) and allow them to architect highly fault tolerant applications.

Today, our infrastructure comprises 43 Availability Zones across 16 geographic regions worldwide, with another three AWS Regions (and eight Availability Zones) in France, China, and Sweden coming online throughout 2017 and 2018, (see the AWS Global Infrastructure page for more info).

We are looking forward to serving new and existing customers in Hong Kong and working with partners across Asia-Pacific. Of course, the new region will also be open to existing AWS customers who would like to process and store data in Hong Kong. Public sector organizations such as government agencies, educational institutions, and nonprofits in Hong Kong will be able to use this region to store sensitive data locally (the AWS in the Public Sector page has plenty of success stories drawn from our worldwide customer base).

If you are a customer or a partner and have specific questions about this Region, you can contact our Hong Kong team.

Help Wanted
If you are interested in learning more about AWS positions in Hong Kong, please visit the Amazon Jobs site and set the location to Hong Kong.

— Jeff;

AWS Marketplace lets AWS customers find and use products and services offered by members of the AWS Partner Network (APN). Some marketplace offerings are billed on an hourly basis, many with a cost-saving annual option designed to line up with the procurement cycles of our enterprise customers. Other offerings are available in SaaS (Software as a Service) form and are billed based on consumption units specified by the seller. The SaaS model (described in New – SaaS subscriptions on AWS Marketplace) give sellers the flexibility to bill for actual usage: number of active hosts, number of requests, GB of log files processed, and so forth.

Recently we extended the SaaS model with the addition of SaaS contracts, which my colleague Brad Lyman introduced in his post, Announcing SaaS Contracts, a Feature to Simplify SaaS Procurement on AWS Marketplace. The contracts give our customers the opportunity save money by setting up monthly subscriptions that can be expanded to cover a one, two, or three year contract term, with automatic, configurable renewals. Sellers can provide services that require up-front payment or that offer discounts in exchange for a usage commitment.

Since Brad has already covered the seller side of this powerful and flexible new model, I would like to show you what it is like to purchase a SaaS contract. Let’s say that I want to use Splunk Cloud. I simply search for it as usual:

I click on Splunk Cloud and see that it is available in SaaS Contract form:

I can also see and review the pricing options, noting that pricing varies by location, index volume, and subscription duration:

I click on Continue. Since I do not have a contract with Splunk for this software, I’ll be redirected to the vendor’s site to create one as part of the process. I choose my location, index volume, and contract duration, and opt for automatic renewal, and then click on Create Contract:

This sets up my subscription, and I need only set up my account with Splunk:

I click on Set Up Your Account and I am ready to move forward by setting up my custom URL on the Splunk site:

This feature is available now and you can start using it today.

— Jeff;

Amazon WorkSpaces allows you to access a virtual desktop in the cloud from the web and from a wide variety of desktop and mobile devices. This flexibility makes WorkSpaces ideal for environments where users have the ability to use their existing devices (often known as BYOD, or Bring Your Own Device). In these environments, organizations sometimes need the ability to manage the devices which can access WorkSpaces. For example, they may have to regulate access based on the client device operating system, version, or patch level in order to help meet compliance or security policy requirements.

Managed Device Authentication
Today we are launching device authentication for WorkSpaces. You can now use digital certificates to manage client access from Apple OSX and Microsoft Windows. You can also choose to allow or block access from iOS, Android, Chrome OS, web, and zero client devices. You can implement policies to control which device types you want to allow and which ones you want to block, with control all the way down to the patch level. Access policies are set for each WorkSpaces directory. After you have set the policies, requests to connect to WorkSpaces from a client device are assessed and either blocked or allowed. In order to make use of this feature, you will need to distribute certificates to your client devices using Microsoft System Center Configuration Manager or a mobile device management (MDM) tool.

Here’s how you set your access control options from the WorkSpaces Console:

Here’s what happens if a client is not authorized to connect:

Available Today
This feature is now available in all Regions where WorkSpaces is available.

— Jeff;

Every product planning session at AWS revolves around customers. We do our best to listen and to learn, and to use what we hear to build the roadmaps for future development. Approximately 90% of the items on the roadmap originate with customer requests and are designed to meet specific needs and requirements that they share with us.

I strongly believe that this customer-driven innovation has helped us to secure the top-right corner of the Leaders quadrant in Gartner’s Magic Quadrant for Cloud Infrastructure as a Service (IaaS) for the 7th consecutive year, earning highest placement for ability to execute and furthest for completeness of vision:

To learn more, read the full report. It contains a lot of detail and is a great summary of the features and factors that our customers examine when choosing a cloud provider.

— Jeff;

Box is a cloud-based file sharing and content management system, with an API that recently became available in AWS Marketplace (Box Platform – Cloud Content Management APIs). With an array of features for collaboration and an emphasis on security, Box has found a home in many enterprises (see their success stories page for a list).

The Box API allows developers to build content experiences into web and mobile apps. Today I would like to tell you about some AWS Lambda blueprints and templates that will help you to build AWS applications that use this API to simplify user authentication and to add metadata to newly uploaded content. The templates are based on the Box Node Lambda Sample and should be a robust starting point for your own development.

Let’s take a look at the blueprints and then review some handy blog posts written by our friends at Box.

Box Blueprints for Lambda
The blueprints show you how to call the Box APIS and to connect a Box webhook to a Lambda function via Amazon API Gateway. To find them, simply open up the Lambda Console and search for box:

The first blueprint uses security credentials stored in the BOX_CONFIG environment variable. You can set the variable from within the Lambda Console:

The code in this blueprint retrieves and logs the Box User object for the user identified by the credentials.

The second blueprint implements a Box webhook that sits behind an API Gateway endpoint. It accepts requests, validates them, and logs them to Amazon CloudWatch:

Handy Blog Posts
The developer relations team at Box has written some blog posts that show you how to use Box in conjunction with several AWS services:

Manage User Authentication with Box Platform using Amazon Cognito – This post shows you how to use Amazon Cognito to power a login page for your app users. Cognito will handle authentication and user pool management and the code outlined in the blog post will create an App User in Box the first time the user logs in. The code is available as box-node-cognito-lambdas-sample on GitHub.

Add Deep Learning-based Image Recognition to your Box App with Amazon Rekognition – This post shows you how to build an image tagging application that is powered by Amazon Rekognition. Users take and upload photos, which are automatically labeled with metadata that that is stored in Amazon DynamoDB. The code is activated by a webhook when a file is uploaded. You can find the code in the box-node-rekognition-webhook on GitHub.

Thanks to our friends at Box for taking the time to create these helpful developer resources!

— Jeff;

Amazon DynamoDB has more than one hundred thousand customers, spanning a wide range of industries and use cases. These customers depend on DynamoDB’s consistent performance at any scale and presence in 16 geographic regions around the world. A recent trend we’ve been observing is customers using DynamoDB to power their serverless applications. This is a good match: with DynamoDB, you don’t have to think about things like provisioning servers, performing OS and database software patching, or configuring replication across availability zones to ensure high availability – you can simply create tables and start adding data, and let DynamoDB handle the rest.

DynamoDB provides a provisioned capacity model that lets you set the amount of read and write capacity required by your applications. While this frees you from thinking about servers and enables you to change provisioning for your table with a simple API call or button click in the AWS Management Console, customers have asked us how we can make managing capacity for DynamoDB even easier.

Today we are introducing Auto Scaling for DynamoDB to help automate capacity management for your tables and global secondary indexes. You simply specify the desired target utilization and provide upper and lower bounds for read and write capacity. DynamoDB will then monitor throughput consumption using Amazon CloudWatch alarms and then will adjust provisioned capacity up or down as needed. Auto Scaling will be on by default for all new tables and indexes, and you can also configure it for existing ones.

Even if you’re not around, DynamoDB Auto Scaling will be monitoring your tables and indexes to automatically adjust throughput in response to changes in application traffic. This can make it easier to administer your DynamoDB data, help you maximize availability for your applications, and help you reduce your DynamoDB costs.

Let’s see how it works…

Using Auto Scaling
The DynamoDB Console now proposes a comfortable set of default parameters when you create a new table. You can accept them as-is or you can uncheck Use default settings and enter your own parameters:

Here’s how you enter your own parameters:

Target utilization is expressed in terms of the ratio of consumed capacity to provisioned capacity. The parameters above would allow for sufficient headroom to allow consumed capacity to double due to a burst in read or write requests (read Capacity Unit Calculations to learn more about the relationship between DynamoDB read and write operations and provisioned capacity). Changes in provisioned capacity take place in the background.

Auto Scaling in Action
In order to see this important new feature in action, I followed the directions in the Getting Started Guide. I launched a fresh EC2 instance, installed (sudo pip install boto3) and configured (aws configure) the AWS SDK for Python. Then I used the code in the Python and DynamoDB section to create and populate a table with some data, and manually configured the table for 5 units each of read and write capacity.

I took a quick break in order to have clean, straight lines for the CloudWatch metrics so that I could show the effect of Auto Scaling. Here’s what the metrics look like before I started to apply a load:

I modified the code in Step 3 to continually issue queries for random years in the range of 1920 to 2007, ran a single copy of the code, and checked the read metrics a minute or two later:

The consumed capacity is higher than the provisioned capacity, resulting in a large number of throttled reads. Time for Auto Scaling!

I returned to the console and clicked on the Capacity tab for my table. Then I clicked on Read capacity, accepted the default values, and clicked on Save:

DynamoDB created a new IAM role (DynamoDBAutoscaleRole) and a pair of CloudWatch alarms to manage the Auto Scaling of read capacity:

DynamoDB Auto Scaling will manage the thresholds for the alarms, moving them up and down as part of the scaling process. The first alarm was triggered and the table state changed to Updating while additional read capacity was provisioned:

The change was visible in the read metrics within minutes:

I started a couple of additional copies of my modified query script and watched as additional capacity was provisioned, as indicated by the red line:

I killed all of the scripts and turned my attention to other things while waiting for the scale-down alarm to trigger. Here’s what I saw when I came back:

The next morning I checked my Scaling activities and saw that the alarm had triggered several more times overnight:

This was also visible in the metrics:

Until now, you would prepare for this situation by setting your read capacity well about your expected usage, and pay for the excess capacity (the space between the blue line and the red line). Or, you might set it too low, forget to monitor it, and run out of capacity when traffic picked up. With Auto Scaling you can get the best of both worlds: an automatic response when an increase in demand suggests that more capacity is needed, and another automated response when the capacity is no longer needed.

Things to Know
DynamoDB Auto Scaling is designed to accommodate request rates that vary in a somewhat predictable, generally periodic fashion. If you need to accommodate unpredictable bursts of read activity, you should use Auto Scaling in combination with DAX (read Amazon DynamoDB Accelerator (DAX) – In-Memory Caching for Read-Intensive Workloads to learn more). Also, the AWS SDKs will detect throttled read and write requests and retry them after a suitable delay.

I mentioned the DynamoDBAutoscaleRole earlier. This role provides Auto Scaling with the privileges that it needs to have in order for it to be able to scale your tables and indexes up and down. To learn more about this role and the permissions that it uses, read Grant User Permissions for DynamoDB Auto Scaling.

Auto Scaling has complete CLI and API support, including the ability to enable and disable the Auto Scaling policies. If you have some predictable, time-bound spikes in traffic, you can programmatically disable an Auto Scaling policy, provision higher throughput for a set period of time, and then enable Auto Scaling again later.

As noted on the Limits in DynamoDB page, you can increase provisioned capacity as often as you would like and as high as you need (subject to per-account limits that we can increase on request). You can decrease capacity up to nine times per day for each table or global secondary index.

You pay for the capacity that you provision, at the regular DynamoDB prices. You can also purchase DynamoDB Reserved Capacity to further savings.

Available Now
This feature is available now in all regions and you can start using it today!

— Jeff;

We’re continuing to iterate on the AWS X-Ray service based on customer feedback and today we’re excited to release a set of tools to help you quickly dive deep on latencies in your applications. Visual Node and Edge latency distribution graphs are shown in a handy new “Service Details” side bar in your X-Ray Service Map.

The X-Ray service graph gives you a visual representation of services and their interactions over a period of time that you select. The nodes represent services and the edges between the nodes represent calls between the services. The nodes and edges each have a set of statistics associated with them. While the visualizations provided in the service map are useful for estimating the average latency in an application they don’t help you to dive deep on specific issues. Most of the time issues occur at statistical outliers. To alleviate this X-Ray computes histograms like the one above help you solve those 99th percentile bugs.

To see a Response Distribution for a Node just click on it in the service graph. You can also click on the edges between the nodes to see the Response Distribution from the viewpoint of the calling service.

The team had a few interesting problems to solve while building out this feature and I wanted to share a bit of that with you now! Given the large number of traces an app can produce it’s not a great idea (for your browser) to plot every single trace client side. Instead most plotting libraries, when dealing with many points, use approximations and bucketing to get a network and performance friendly histogram. If you’ve used monitoring software in the past you’ve probably seen as you zoom in on the data you get higher fidelity. The interesting thing about the latencies coming in from X-Ray is that they vary by several orders of magnitude.

If the latencies were distributed between strictly 0s and 1s you could easily just create 10 buckets of 100 milliseconds. If your apps are anything like mine there’s a lot of interesting stuff happening in the outliers, so it’s beneficial to have more fidelity at 1% and 99% than it is at 50%. The problem with fixed bucket sizes is that they’re not necessarily giving you an accurate summary of data. So X-Ray, for now, uses dynamic bucket sizing based on the t-digests algorithm by Ted Dunning and Otmar Ertl. One of the distinct advantages of this algorithm over other approximation algorithms is its accuracy and precision at extremes (where most errors typically are).

An additional advantage of X-Ray over other monitoring software is the ability to measure two perspectives of latency simultaneously. Developers almost always have some view into the server side latency from their application logs but with X-Ray you can examine latency from the view of each of the clients, services, and microservices that you’re interacting with. You can even dive deeper by adding additional restrictions and queries on your selection. You can identify the specific users and clients that are having issues at that 99th percentile.

This info has already been available in API calls to GetServiceGraph as ResponseTimeHistogram but now we’re exposing it in the console as well to make it easier for customers to consume. For more information check out the documentation here.

– Randall

The guest post below, written by Ananth Vaidyanathan (Senior Product Manager for EC2 Systems Manager) and Rich Urmston (Senior Director of Cloud Architecture at Pegasystems) shows you how to use EC2 Run Command to manage a large collection of EC2 instances without having to resort to SSH.

— Jeff;

Enterprises often have several managed environments and thousands of Amazon EC2 instances. It’s important to manage systems securely, without the headaches of Secure Shell (SSH). Run Command, part of Amazon EC2 Systems Manager, allows you to run remote commands on instances (or groups of instances using tags) in a controlled and auditable manner. It’s been a nice added productivity boost for Pega Cloud operations, which rely daily on Run Command services.

You can control Run Command access through standard IAM roles and policies, define documents to take input parameters, control the S3 bucket used to return command output. You can also share your documents with other AWS accounts, or with the public. All in all, Run Command provides a nice set of remote management features.

Better than SSH
Here’s why Run Command is a better option than SSH and why Pegasystems has adopted it as their primary remote management tool:

Run Command Takes Less Time –  Securely connecting to an instance requires a few steps e.g. jumpboxes to connect to or IP addresses to whitelist etc. With Run Command, cloud ops engineers can invoke commands directly from their laptop, and never have to find keys or even instance IDs. Instead, system security relies on AWS auth, IAM roles and policies.

Run Command Operations are Fully Audited – With SSH, there is no real control over what they can do, nor is there an audit trail. With Run Command, every invoked operation is audited in CloudTrail, including information on the invoking user, instances on which command was run, parameters, and operation status. You have full control and ability to restrict what functions engineers can perform on a system.

Run Command has no SSH keys to Manage – Run Command leverages standard AWS credentials, API keys, and IAM policies. Through integration with a corporate auth system, engineers can interact with systems based on their corporate credentials and identity.

Run Command can Manage Multiple Systems at the Same Time – Simple tasks such as looking at the status of a Linux service or retrieving a log file across a fleet of managed instances is cumbersome using SSH. Run Command allows you to specify a list of instances by IDs or tags, and invokes your command, in parallel, across the specified fleet. This provides great leverage when troubleshooting or managing more than the smallest Pega clusters.

Run Command Makes Automating Complex Tasks Easier – Standardizing operational tasks requires detailed procedure documents or scripts describing the exact commands. Managing or deploying these scripts across the fleet is cumbersome. Run Command documents provide an easy way to encapsulate complex functions, and handle document management and access controls. When combined with AWS Lambda, documents provide a powerful automation platform to handle any complex task.

Example – Restarting a Docker Container
Here is an example of a simple document used to restart a Docker container. It takes one parameter; the name of the Docker container to restart. It uses the AWS-RunShellScript method to invoke the command. The output is collected automatically by the service and returned to the caller. For an example of the latest document schema, see Creating Systems Manager Documents.

{
  "schemaVersion":"1.2",
  "description":"Restart the specified docker container.",
  "parameters":{
    "param":{
      "type":"String",
      "description":"(Required) name of the container to restart.",
      "maxChars":1024
    }
  },
  "runtimeConfig":{
    "aws:runShellScript":{
      "properties":[
        {
          "id":"0.aws:runShellScript",
          "runCommand":[
            "docker restart {{param}}"
          ]
        }
      ]
    }
  }
}

Putting Run Command into practice at Pegasystems
The Pegasystems provisioning system sits on AWS CloudFormation, which is used to deploy and update Pega Cloud resources. Layered on top of it is the Pega Provisioning Engine, a serverless, Lambda-based service that manages a library of CloudFormation templates and Ansible playbooks.

A Configuration Management Database (CMDB) tracks all the configurations details and history of every deployment and update, and lays out its data using a hierarchical directory naming convention. The following diagram shows how the various systems are integrated:

For cloud system management, Pega operations uses a command line version called cuttysh and a graphical version based on the Pega 7 platform, called the Pega Operations Portal. Both tools allow you to browse the CMDB of deployed environments, view configuration settings, and interact with deployed EC2 instances through Run Command.

CLI Walkthrough
Here is a CLI walkthrough for looking into a customer deployment and interacting with instances using Run Command.

Launching the cuttysh tool brings you to the root of the CMDB and a list of the provisioned customers:

% cuttysh
d CUSTA
d CUSTB
d CUSTC
d CUSTD

You interact with the CMDB using standard Linux shell commands, such as cd, ls, cat, and grep. Items prefixed with s are services that have viewable properties. Items prefixed with d are navigable subdirectories in the CMDB hierarchy.

In this example, change directories into customer CUSTB’s portion of the CMDB hierarchy, and then further into a provisioned Pega environment called env1, under the Dev network. The tool displays the artifacts that are provisioned for that environment. These entries map to provisioned CloudFormation templates.

> cd CUSTB
/ROOT/CUSTB/us-east-1 > cd DEV/env1

The ls –l command shows the version of the provisioned resources. These version numbers map back to source control–managed artifacts for the CloudFormation, Ansible, and other components that compose a version of the Pega Cloud.

/ROOT/CUSTB/us-east-1/DEV/env1 > ls -l
s 1.2.5 RDSDatabase 
s 1.2.5 PegaAppTier 
s 7.2.1 Pega7 

Now, use Run Command to interact with the deployed environments. To do this, use the attach command and specify the service with which to interact. In the following example, you attach to the Pega Web Tier. Using the information in the CMDB and instance tags, the CLI finds the corresponding EC2 instances and displays some basic information about them. This deployment has three instances.

/ROOT/CUSTB/us-east-1/DEV/env1 > attach PegaWebTier
 # ID         State  Public Ip    Private Ip  Launch Time
 0 i-0cf0e84 running 52.63.216.42 10.96.15.70 2017-01-16 
 1 i-0043c1d running 53.47.191.22 10.96.15.43 2017-01-16 
 2 i-09b879e running 55.93.118.27 10.96.15.19 2017-01-16 

From here, you can use the run command to invoke Run Command documents. In the following example, you run the docker-ps document against instance 0 (the first one on the list). EC2 executes the command and returns the output to the CLI, which in turn shows it.

/ROOT/CUSTB/us-east-1/DEV/env1 > run 0 docker-ps
. . 
CONTAINER ID IMAGE             CREATED      STATUS        NAMES
2f187cc38c1  pega-7.2         10 weeks ago  Up 8 weeks    pega-web

Using the same command and some of the other documents that have been defined, you can restart a Docker container or even pull back the contents of a file to your local system. When you get a file, Run Command also leaves a copy in an S3 bucket in case you want to pass the link along to a colleague.

/ROOT/CUSTB/us-east-1/DEV/env1 > run 0 docker-restart pega-web
..
pega-web

/ROOT/CUSTB/us-east-1/DEV/env1 > run 0 get-file /var/log/cfn-init-cmd.log
. . . . . 
get-file

Data has been copied locally to: /tmp/get-file/i-0563c9e/data
Data is also available in S3 at: s3://my-bucket/CUSTB/cuttysh/get-file/data

Now, leverage the Run Command ability to do more than one thing at a time. In the following example, you attach to a deployment with three running instances and want to see the uptime for each instance. Using the par (parallel) option for run, the CLI tells Run Command to execute the uptime document on all instances in parallel.

/ROOT/CUSTB/us-east-1/DEV/env1 > run par uptime
 …
Output for: i-006bdc991385c33
 20:39:12 up 15 days, 3:54, 0 users, load average: 0.42, 0.32, 0.30

Output for: i-09390dbff062618
 20:39:12 up 15 days, 3:54, 0 users, load average: 0.08, 0.19, 0.22

Output for: i-08367d0114c94f1
 20:39:12 up 15 days, 3:54, 0 users, load average: 0.36, 0.40, 0.40

Commands are complete.
/ROOT/PEGACLOUD/CUSTB/us-east-1/PROD/prod1 > 

Summary
Run Command improves productivity by giving you faster access to systems and the ability to run operations across a group of instances. Pega Cloud operations has integrated Run Command with other operational tools to provide a clean and secure method for managing systems. This greatly improves operational efficiency, and gives greater control over who can do what in managed deployments. The Pega continual improvement process regularly assesses why operators need access, and turns those operations into new Run Command documents to be added to the library. In fact, their long-term goal is to stop deploying cloud systems with SSH enabled.

If you have any questions or suggestions, please leave a comment for us!

— Ananth and Rich