AWS VPC NAT Instance Failover and High Availability

Amazon Virtual Private Cloud (VPC) is a great way to setup an isolated portion of AWS and control the network topology. It is a great way to extend your data center and use AWS for burst requirements. With the latest VPC for Everyone announcement, what was earlier "Classic" and "VPC" in AWS will  soon be only VPC. That is, every deployment in AWS will be on a VPC even though one might not need all the additional features that VPC provides. One might eventually start looking at utilizing VPC features such as multiple Subnets, Network isolation, Network ACLs, etc.. Those who have already worked with VPC's understand the role of NAT Instance in a VPC.

When you create a VPC, you create them with multiple Subnets (Public and Private). Instances launched in the Public Subnet have direct internet connectivity to send and receive internet traffic through the internet gateway of the VPC. Typically, internet facing servers such as web servers are kept in the Public Subnet. A Private Subnet can be used to launch Instances that do not require direct access from the internet. Instances in a Private Subnet can access the Internet without exposing their private IP address by routing their traffic through a Network Address Translation (NAT) instance in the Public Subnet. AWS provides an AMI that can be launched as a NAT Instance. Following diagram is the representation of a standard VPC that gets provisioned through the AWS Management Console wizard.

Standard Private and Public Subnets in a VPC

The above architecture has

• A Public Subnet that has direct internet connectivity through the Internet Gateway. Web Instances can be placed within the Public Subnet
• The custom Route Table associated with Public Subnet will have the necessary routing information to route traffic to the Internet Gateway
• A NAT Instance is also provisioned in the Public Subnet
• A Private Subnet that has outbound internet connectivity through the NAT Instance in the Public Subnet
• The Main Route Table is by default associated with the Private Subnet. This will have necessary routing information to route internet traffic to the NAT Instance
• Instances in the Private Subnet will use the NAT Instance for outbound internet connectivity. For example, DB backups from standby that needs to be stored in S3. Background programs that make external web services calls

Of course, the above architecture has limited High Availability since all the Subnets are created within the same Availability Zone. We can avoid this by creating multiple Subnets in multiple Availability Zones.

Public and Private Subnets with multiple Availability Zones

• Additional Subnets (Public and Private) are created in one another Availability Zone
• Both Private Subnets are attached to the Main Routing Table
• Both Public Subnets are attached to the same Custom Routing Table
• Instances in the Private Subnet still continue to use the NAT Instance for outbound internet connectivity

Though we increased the High Availability by utilizing multiple Availability Zones, the NAT Instance is still a Single Point of Failure. NAT Instance is just another EC2 Instance that can become unavailable any time. The updated architecture below uses two NAT Instances to provide failover and High Availability for the NAT Instances

NAT Instance High Availability

• Each Subnet is associated with its own Route Table
• NAT1 is provisioned in Public Subnet 1
• NAT2 is provisioned in Public Subnet 2
• Private Subnet 1's Route Table (RT) has routing entry to NAT1 for internet traffic
• Private Subnet 2's Route Table (RT) has routing entry to NAT2 for internet traffic

NAT Instance HA Illustration

A script can be installed on both the NAT Instances to monitor each other and swap the routing table association if one of them fails. For example, if NAT1 detects that NAT2 is not responding to its ping requests, it can change the Route Table of Private Subnet 2 to NAT1 for internet traffic. Once NAT2 becomes operational again, a reverse swapping can happen. AWS has a pretty good documentation on this and a sample script for the swapping.

Apart from HA, the above architecture also provides better overall throughput, since during normal conditions, both NAT Instances can be used to drive the outbound internet requirements of the VPC. If there are workloads that requires a lot of outbound internet connectivity, having more than one NAT Instance would make sense. Of course, you are still limited with one NAT Instance per Subnet.

Log Analysis and Archive with Amazon S3 and Glacier - Full Summary

Logging is an essential component of any system and helps you understand what's really going on in the system. Just like how you build systems that can scale, perform performance tweaks, design caching architecture, logging is an area that requires that special care to effectively collect logs and make some meaning out of it.

In the Cloud, and more specifically in AWS, there are numerous options and considerations with respect to logging such as

• What are the different sources from where you can collect logs
• How do you collect logs from a dynamic infrastrucuture
• How effectively logs can be collected without affecting the performance of the system
• What are the different storage options available
• And most importantly how one can do it cost effectively

When I set to write on this, I understood that it is going to be a lengthy article with many areas being covered. And logging is an area whose importance is understood only when things go wrong. Otherwise it is pretty boring :) So I decided to split my thoughts in to multiple posts and had been writing about it for the past one month. So this post is a summary of all those different posts.

The Introduction - this is the introductory post setting the context of the different areas that we are going to cover as part of this multi-part post

Part I - in this part, we define the log structure and look at how to collect logs from Amazon CloudFront, the Content Distribution Network service from AWS

Part II - this post describes on how to use the local storage of the EC2 Instance for logging

Part III - part III discusses on how to collect from multiple instances that are dynamically provisioned, how to rotate the log files and store them in a centralized log storage

Part IV - In this final post, we look at what different storage options are available  for cost effective logging, how one can use Glacier, the archival service from AWS, the best practices that one needs to remember and a list of third party / commercial log management solutions available in the market

I hope this of some use to you and provides some insights on logging in AWS. I would definitely like to hear any comments and alternative approaches towards this.

Log archive & analysis with Amazon S3 and Glacier - Part IV

We now have the logs coming from CloudFront, Web/App and Search tier to the centralized log storage in Amazon S3. In this final post of this series, let's now see what are the options at storage level from cost point of view and what to do with mountains of logs.

Using Reduced Redundancy Storage
Amazon S3 has different storage class - Standard, Reduced Redundancy Storage (RRS) and Glacier. By default when we create store any Object in Amazon S3, it is stored under the Standard storage class. Under "Standard" storage class, all Objects have 99.999999999% durability and 99.99% availability of objects over a given year. With RRS, the Objects that are stored in S3 are replicated at fewer locations to give 99.99% durability and 99.99% availability of objects over a given year. RRS comes cheaper than Standard storage. If we are storing 1TB of log files under Standard storage, it would cost about $95/month (in US-Standard region). Under RRS the same 1TB of storage would be $76/month.

The RRS option cannot be enabled at bucket level but rather at individual Objects level. We can enable RRS for the logs folders that we created through the Object properties

Enable S3 Reduced Redundancy Storage

Log Analysis

We can now initiate Elastic Map Reduce jobs to process these log files and produce log analytics. Elastic Map Reduce takes a S3 bucket as the input source location. We can point the "log" bucket as the input source and supply a Map-Reduce implementation to EMR to crunch the logs.

Yearly Analysis / Multi Year Analysis

Certain requirements want an on-off analysis to be performed at the end of an year. For example, we may perform monthly or on-demand analysis of the log data regularly. And at the end of an year we may require an analysis against the entire year's data and compare it with previous years. For such cases, if we maintain multi-year log files in S3, the cost of storage might be very high. And previous year log files will be accessed only once in an year. For such reasons, we can archive the older log data to Amazon Glacier. Amazon Glacier provides low cost archival service for $0.01 per GB per month.

Archiving to Glacier
We will not be storing the log files forever. Typically any application will have a requirement to store log files for certain period of time beyond which they can be deleted. Let's say that we are interested in retaining only last 6 month's log files. And occasionally we might be doing one year or three years analysis. In such cases, we can use set Lifecycle policies in S3 to automatically archive to Glacier beyond a certain period of time. We can also instruct S3 to automatically deleted Objects beyond a certain period of time.

• Click on the bucket properties and navigate to the "Lifecycle" tab
• Click on "Add Rule" to create a new "Lifecycle Rule"
• Specify that the rule needs to apply for the entire bucket and create a "Transition" and "Expiration" rule
• Create a "Transition" rule specifying "180" days. This will automatically move files from the S3 bucket to Glacier after 180 days
• Create an "Expiration" rule specifying "1095" days. This will delete the log files automatically from S3 or Glacier after 3 years

Lifecycle Rules to Archive to Glacier and Delete Log Files

With that the log files will get automatically archived to Glacier after 6 months (from creation) and will be deleted after 3 years. Once the log files are archived to Glacier, the storage class of these log files (objects) in S3 changes to Glacier indicating that they are being stored in Glacier.

S3 Storage Class for archived files

Restoring from Glacier

For our year end analysis, we will need the archived data in Glacier back in Amazon S3 so that we can run Elastic Map Reduce jobs against them to produce our year-end / multi-year analytics information.  We can do this through the AWS Management Console by

• Right clicking the particular object (log file) whose storage class is Glacier (meaning it is archived) and "Initiate Restore"
• Specify, how long we require the Object in S3 for us to perform the analysis and complete the request

Once this request is initiated, it normally takes around 3-4 hours for AWS to restore the object from Glacier to S3. The "Object Restoration" process can be done only at an individual object level. We will normally have large number of log files at the end of an year, and doing this way is not practically possible.

Restoring from Glacier programmatically

Restoring from Glacier is essentially a S3 operation and not a Glacier operation as it seems to be. We need to use the S3 API to initiate restoration

AmazonS3 s3Client = new AmazonS3Client(new BasicAWSCredentials("aws-access-key", "aws-secret-access-key"));
ObjectListing listing = s3Client.listObjects(new ListObjectsRequest()
             .withBucketName("my-global-logs")
             .withPrefix("web-logs/")); 

The first step is to list all the keys of the Objects that we want to restore. To do this, use the S3 ListObjects API call to list all the Objects. Few pointers while using this API

• Specify the bucket name that we want to list. Also include a prefix if we are interested in restoring only a specific directory within that bucket. For example, if we are interested only in performing analysis against the web-logs and not others, we can specify as indicated above
• Since a bucket can contain 1000's of Objects, S3's API does pagination when sending the response. Hence use the "isTruncated" method in the response "ObjectListing" to check if there are more Objects. If so, initiate further API calls to list till the end
• Since we are listing the entire bucket, the call will result in keys for the directory also. Something like the following. Hence check for the key containing a file instead of a directory and keep adding such keys to a list (like performing a simple 'contains(".log")' check)

          web-logs/2012/

          web-logs/2012/12/

          web-logs/2012/12/10/

          web-logs/2012/12/10/i-7a3flcd3/

          web-logs/2012/12/10/i-7a3flcd3/tomcat.log

          web-logs/2012/12/10/i-9d9dedf2/

          web-logs/2012/12/10/i-9d9dedf2/01/

          web-logs/2012/12/10/i-9d9dedf2/02/

          web-logs/2012/12/10/i-9d9dedf2/03/

Once we have entire list of Object Keys to restore, the next step is to initate the restore process for all the Objects

RestoreObjectRequest requestRestore = new RestoreObjectRequest("my-global-logs", "<object-key>", <restoration-period>);
s3Client.restoreObject(requestRestore);

Once the above request is initiated for all the Objects, Amazon Glacier takes about 3-5 hours to restore the Objects and make it available in Amazon S3. We can then run Elastic Map Reduce jobs with all the required data.

Things to remember / consider

• Archiving and Restoring are S3 operations and hence are part of S3 API
• If you have data stored in Glacier that weren't archived from S3, then to restore them, you should use the Glacier API to initiate downloads. See the steps outlined in AWS documentation for downloading an archive
• Restored objects by default are stored under "Reduced Redundancy Storage"
• If you have millions of Objects in S3 that has to be transitioned to Glacier, be aware of the cost of restore requests. Eric Hammond has put across a very detailed analysis here
• Glacier is designed for Archival Storage. Meaning, you do not access the data frequently and can wait for accessing the data. Any download request from Glacier, will take 3-5 hours before it is available. Hence carefully choose the archival policy. If you plan to retrieve the log data frequently, Glacier will not be right choice and will prove to be very expensive (since it is not designed for frequent retrieval)

Log Management Solutions

There are plenty for log management solutions that are available as a service and can be plugged in to existing applications and cloud environment.

• Splunk is a widely used log management and monitoring solution. Splunk can be setup on a server and can be easily configured to start collecting data from web servers. A SaaS version is also available where the service is completely managed by Splunk
• Loggly is another cloud based log management solution that is available as a service
• There are also open source solutions available such as LogStash that can be customized for our needs

That brings to the end of this series on what I wanted to cover as part of log analysis using S3 and Glacier. Logging is an essential component in any system and in the era of Cloud Computing, a good log management solution will prove handy. Once the problems of scale and performance gets sorted out with the help of Cloud Computing, the immediate next need of any system would be to have an effective way to look at the system and analyse at scale. A log management solution will definitely prove handy.

Log archive & analysis with Amazon S3 and Glacier - Part III

A recap from the previous posts:

• We had seen how to configure CloudFront to enable access logs. CloudFront will directly push all access logs to a configured S3 bucket
• We saw what the considerations are for logging at the web tier - such as local log storage, dynamic environment. We configured the EC2 Instances to use the Ephemeral Storage for storing the log files locally

In this post, we will see how to push logs from the local storage to the central log storage - Amazon S3 and what the considerations are.

Amazon S3 folder structure

The logs are going to be generated throughout the day and hence we need to have a proper folder structure to store them in S3. Logs will be particularly useful to perform analysis such as a production issue or to find a usage pattern such as feature adoption by users/month. Hence it will make sense to store them by year/month/day/hour_of_the_day structure.

Multiple Instances
The web tier will be automatically scaled and we will have multiple Instances always running for High Availability and Scalability. So, even if we have logs stored hourly basis, we will be having log files with similar names from multiple Instances. Hence the folder structure needs to factor multiple Instances as well. The resultant folder structure in S3 will look something like this

Amazon S3 Log Folder Structure

Note in the above picture (as encircled) that we are storing "Instance" wise logs for every hour.

Log Rotation
Every logging framework will have an option to rotate the log files on size, date, etc...We will be periodically pushing the log files to Amazon S3 and hence it might make sense to say, rotate the log file every hour and push it to S3. But the downside to that is, we cannot anticipate the traffic to the web tier and that's the reason we have the web tier scaling automatically on demand. If there is a sudden surge in the traffic which may result in large log files generated, it will start filling up the file system eventually making the Instance unavailable. Hence it is better to rotate the log files by size.

Linux-logrotate
You can use the default logrotate available in Linux systems to rotate the log files on size. Logrotate can be configured to call a post script after the rotation to enable us push the newly rotated files to S3. A sample logrotate implementation will look like this:

Note: If you are using logrotate, make sure your logging framework isn't configured to rotate

/var/log/applogs/httpd/web {
        missingok
        rotate 52
        size 50M
        copytruncate
        notifempty
        create 644 root root
        sharedscripts
        postrotate
                /usr/local/admintools/compress-and-upload.sh web &> /var/log/custom/web_logrotate.log
        endscript
}

The above set of commands rotate the "httpd" log files whenever the size reaches 50M. It also calls a "postrotate" script to compress the rotated file and upload it to S3.

Upload to S3
The next step is to upload the rotated log file to S3.

• We need a mechanism to access the S3 API from the shell to upload the files. S3cmd is a command line tool that is widely used and recommended for accessing all S3 APIs through the command line. We need to setup the CLI in the Instance
• We are rotating by size but we will be uploading to a folder structure that maintains log files by the hour
• We will also be uploading from multiple Instances and hence we need to fetch the Instance Id to store in the corresponding folder. Within EC2, there is an easy way to get Instance meta data. If we "wget" "http://169.254.169.254/latest/meta-data/" it will provide the Instance meta-data such as InstanceId, public DNS, etc.. For example if we "wget" "http://169.254.169.254/latest/meta-data/instance-id" we will get the current Instance Id

The following set of commands will compress the rotated file and upload them into the corresponding S3 bucket

# Perform Rotated Log File Compression
tar -czPf /var/log/httpd/"$1".1.gz /var/log/httpd/"$1".1

# Fetch the instance id from the instance
EC2_INSTANCE_ID="`wget -q -O - http://169.254.169.254/latest/meta-data/instance-id`"
if [ -z $EC2_INSTANCE_ID ]; then
echo "Error: Couldn't fetch Instance ID .. Exiting .."
exit;
else
        # Upload log file to Amazon S3 bucket
        /usr/bin/s3cmd -c /.s3cfg put /var/log/httpd/"$1".1.gz s3://$BUCKET_NAME/$(date +%Y)/$(date +%m)/$(date +%d)/$EC2_INSTANCE_ID/"$1"/$(hostname -f)-$(date +%H%M%S)-"$1".gz
fi
# Removing Rotated Compressed Log File
rm -f /var/log/httpd/"$1".1.gz

Now that the files are automatically getting rotated, compressed and uploaded to S3 there is one last thing to be taken care of.

Run While Shutdown

Since the web tier will automatically scale depending upon the load, Instances can be pulled off (terminated) when load decreases. During such scenarios, we might be still left with some log files (maximum of 50MB) that didn't get rotated and uploaded. During shutdown, we can have a small script, that will forcefully call the logrotate to rotate the final set of files, compress and upload.

stop() {
echo -n "Force rotation of log files and upload to s3 intitiated"
/usr/sbin/logrotate -f /etc/logrotate.d/web
exit 0
}

Use IAM

We need to provide Access Key and Secret Access Key to the S3cmd utility for S3 API access. Do NOT provide the AWS account's Access Key and Secret Access Key. Create an IAM user who has access to only the specific S3 bucket where we are uploading the files and use the IAM user's Access Key and Secret Access Key. A sample policy allowing access for the IAM user to the S3 log bucket would be

{
  "Statement": [
    {
      "Sid": "Stmt1355302111002",
      "Action": [
        "s3:*"
      ],
      "Effect": "Allow",
      "Resource": [
        "arn:aws:s3:::"
      ]
    }
  ]
}

Note:

• The above policy allows the IAM user to perform all actions on the S3 bucket. The user will not have permission to access any other buckets or services
• If you want to restrict further, instead of allowing all actions on the S3 bucket, we can allow only PutObject (s3:PutObject) for uploading the files
• Through the above approach, you will be storing the IAM credentials on the EC2 Instance itself. An alternative approach is to use IAM Roles so that the EC2 Instance will obtain the API credentials at runtime

With that we have the web tier log files automatically getting rotated, compressed and uploaded to Amazon S3 and stored in a central location. We have access to log information by the year/month/day/hour and Instance-wise.

Log archive & analysis with Amazon S3 and Glacier - Part II

In the previous post, we saw how to configure logging in AWS CloudFront and start collecting access logs. Let's now move on to the next tier in the architecture - web/app. The following are the key considerations for  logging in web/app layer:

• Local Log Storage - choosing the local storage for logging. Using a storage option that is sufficient, cost-effective and meets the performance requirement for logging
• Central Log Storage - how do we centrally store log files for log analysis in future
• Dynamic Infrastructure - how do we collect logs from multiple servers that are provisioned on demand

Local Log Storage

Except very few cases, EBS-backed Instances are the most sought after Instance type. They launch quickly and easier to build Images out of them. But they come with couple of limitations from logging perspective

• Limited storage - EBS-backed AMIs that are provided by AWS or third-party providers come with limited storage. For example, a typical RHEL AMI comes with around 6GB of EBS attached as the root partition. Similarly Windows AMI come with 30GB EBS attached as C:\
• Growing EBS - log files tend to grow faster. And it becomes difficult to grow the root EBS (or an additional EBS) as the log file sizes grow
• Performance - any I/O operation on an EBS Volume is over the network. And it tends to be slower than local disk writes. Specifically for logging, it is always better to remove the I/O bottleneck. Otherwise lot of system resources could be spent towards logging

Every EC2 Instance comes with ephemeral storage. These are local storage directly attached to the host on which the Instance is running. Ephemeral storage do not persist between stop-start cycles of an Instance (EBS-backed) but they are available when the Instance is running and persist during reboots. There are couple of advantages of Ephemeral storage:

• They are locally attached on the physical host on which the Instance runs and hence have better I/O throughput when compared to EBS Volumes
• They come in pretty good size - for example a m1.large Instance comes with 850GB of ephemeral storage
• And it comes free of cost - you aren't charged per GB or for any I/O operations on the ephemeral storage unlinke EBS

This makes ephemeral storage the ideal candidate for storing log files. For an EBS-backed Instance, the ephemeral storage is not mounted and readily available. Hence one needs to follow the following steps to start using the ephemeral storage for storing log files

• The logging framework usually comes with a configuration file to configure logging parameters. The log file path needs to be configured to point to the ephemeral storage mount directory that we create below
• All application related files (such as binaries, configuration files, web/app server) will be installed on the root EBS. Before the final AMI is created, the ephemeral storage needs to be setup and configured
• Run fdisk to list all the storage devices that are mounted

fdisk -l

• Created a directory such as "/applogs". This is the directory where the ephemeral storage will be mounted

mkdir /var/log/applogs

• Mount the storage device in this directory using the "mount" command

mount /dev/xvdj /var/log/applogs

• Add "fstab" entries so that the ephemeral storage is mounted in the same directory after stop/start or when new Instances are launched out of this AMI

/dev/xvdj  /var/log/applogs xfs defaults,noatime,nobarrier,allocsize=64k,logbufs=8 0 2
/dev/xvdj /var/log/applogs    ext3    defaults        0   0

The last step is essential especially from AutoScaling point of view. When AutoScaling launches new Instances, the ephemeral storage needs to be automatically mounted in the directory so that the application can start logging. Now, we can go ahead create the final AMI and launch Instances from them. The new Instances will have the ephemeral storage automatically mounted in the "/var/log/applogs" directory and applications can start storing the log files in them.

Log archive & analysis with Amazon S3 and Glacier - Part I

Since this would be a multi part article, here's an outline in terms of how the different parts will be arranged

1. First we will set the context in terms of taking a web application and identifying the areas of log generation for analysis
2. Next we will define the overall log storage structure since we have logs being generated from different sources
3. We will then look at each tier and look at how logs can be collected, uploaded to a centralized storage, what are the considerations
4. Finally, we will look at other factors such as cost implications, alternate storage options, how to utilize the logs for analysis

Let's take an e-commerce web application which has the following tiers

• Content Distribution Network - a CDN to serve the static assets of the website. AWS CloudFront
• Web/App Server running Apache / Nginx / Tomcat on Amazon EC2
• Search Server running Solr on Amazon EC2
• Database - Amazon RDS

The first three areas are the major source of log information. Your CDN provider will provide access logs in a standard format with information such as the edge location serving the request, the client IP address, the referrer, the user agent, etc...The web servers and search servers will write access logs, error logs and application logs (custom logging by your application).

Log Analysis Architecture

In AWS, Amazon S3 becomes the natural choice for centralized log storage. Since S3 comes with unlimited storage and is internally replicated for redundancy, it will be the right choice for storing the log files generated by the CDN provider, web servers and search servers. Per above architecture, all of the above tiers will be configured/setup to push their respective logs to Amazon S3. We will evaluate each layer independently and look at how to setup logging and the different considerations associated.

S3 Log Storage Structure

Since we have logs coming in front different sources, it is better to create a bucket structure to organize them. Let's say we have the following S3 bucket structure

S3 Log Storage Bucket Structure

• my-global-logs: Bucket containing all the logs
• cf-logs: Folder under the bucket for storing CloudFront logs
• web-logs: Folder under the bucket for storing Web Server logs
• solr-logs: Folder under the bucket for storing Solr Server logs

AWS CloudFront

AWS CloudFront is the Content Distribution Network service from AWS. With a growing list of 37 edge locations, it serves as a vital component in e-commerce applications hosted in AWS for serving static content. By using CloudFront, one can deliver static assets and streaming videos to users from the nearest edge location and thereby reducing latency, round trips and also off loading such delivery from the web servers.

Enable CloudFront Access Logging

You can configure CloudFront to log all access information during the "Create Distribution" step. You "Enable Logging" and specify the bucket to which CloudFront should push the logs.

Configure CloudFront for Access Logging

• Specify the bucket that we created above in the "Bucket for Logs" option. This field will accept only a bucket in your account and not any sub-folders in the bucket
• Since we have a folder called "cf-logs" under the bucket to store the logs, mention the name of that folder in the "Log Prefix" option
• CloudFront will start pushing access logs to this location every hour. The logs will be in W3C extended format. The logs will be compressed by AWS since the original size could be significantly large for websites that attract massive traffic

Once this is setup CloudFront will periodically start pushing access logs to this folder.

CloudFront Logs

In the next post, we will see how to configure the web tier to push logs to S3 and what are the different considerations.

Log archive & analysis with Amazon S3 and Glacier - Introduction

Logging is an essential part of any system. It let's you understand what's going on in your system especially serving as a vital source for debugging. Primarily many systems uses logging to let developers debug issues in the production environment. But there are systems where logging becomes the essential component to understand the following

• User Behavior - understanding user behavior patterns such as which areas of the system is being used by the user
• Feature Adoption - evaluate new feature adoption by tracking how a new feature is being used by the users. Do they vanish after a particular step in a particular flow? Are people from a specific geography use this during a specific time of the day?
• Click through analysis - let's say you are placing relevant ads across different pages in your websites. You would like to know how many users clicked them, the demographic analysis and such
• System performance
• Any abnormal behavior in certain areas in the system - a particular step in a workflow resulting in error/exception conditions
• Analyzing performance of different areas in the system - such as finding out if a particular screen takes more time to load because of a longer query getting executed. Should we optimize the database? Should we probably introduce a caching layer?

Any architect would enforce logging as a core component in the technical architecture. While logging is definitely required, many a times, inefficient logging such as too much logging, using inappropriate log levels might lead to the following

• Under performance of the system - the system could be spending more resources in logging than actively serving requests
• Huge log files - generally log files grow very fast, especially when inappropriate log levels are used such as "debug" levels for all log statements
• Inadequate data - if the log contains only debug information by the developer there will not be much of an analysis that can be performed

On the other hand, the infrastructure architecture also needs to support for efficient logging and analysis

• Local Storage - how do you efficiently store the log files on the local server without running out of disk space; especially when log files tend to grow
• Central Log Storage - how do you centrally store log files so that it can be used later for analysis
• Dynamic Server Environment - how do you make sure you collect & store all the log files in a dynamic server environment where servers will be provisioned and de-provisioned on demand depending upon load
• Multi source - handling log files from different sources - like your web servers, search servers, Content Distribution Network logs, etc...
• Cost effective - when your application grows, so does your log files. How do you store the log files in the most cost effective manner without burning a lot of cash

In this multi-post article let's take up a case of a typical e-commerce web application with the above characteristics and setup a best practice architecture for logging, analysis and archiving in AWS. We will see how different AWS services can be used effectively to store and process the logs from different sources in a cost effective and efficient manner.

Another AWS outage. And how do you overcome it?

Disclaimer I: I don't work at AWS :) but seriously think that building your own cloud (as some people call it) or setting up a private cloud doesn't really address anything apart from posting a comment in article about an outage.

Disclaimer II: Though not every one admits, some people who have not been affected during outages have been lucky. They are lucky that their Instances and EBS Volumes weren't in the affected AZ. Though people spend time on building sophisticated architecture, it is only during such outages, the architecture is put to real test. Just like how AWS themselves uncover new failure areas, the architecture will also open up new failure points during outages. And you need to work on improving them.

We just had another outage in AWS. And this time too there were a good number of popular websites that were affected by the outage - Reddit, Foursquare and Heroku are few from the list. AWS has published a detailed analysis of what caused the outage. The previous outage was on June 29th which was even more widespread taking down Netflix and others. AWS did provide a detailed analysis on what went wrong and how they are taking actions to prevent such incidents in future.

The aftermath and reactions of such outages can be classified in to two - from people who understand Cloud and people who don't. No one likes outages. But people who understand Cloud, they clearly see outages as an opportunity to build better systems. Better architectures. And there are people who would start blaming it on the Cloud. Saying things like "this is the reality behind Cloud", or even say it is better to own than rent. Public Cloud versus Private Cloud is a huge debate. And if you have been in the Cloud market you will even hear more types of Cloud - like Hybrid Cloud.

Why Cloud?

Be it public cloud, private cloud or hybrid cloud. Be it Open Stack or Cloud Stack. One should definitely know why they need a Cloud strategy. Are you getting into Cloud for

• Scalability
• High Availability
• Cost
• Getaway from infrastructure management
• Everyone is getting in to Cloud
• My resume needs to show I know Cloud

Be honest in your answer to this evaluation because the benefits vary depending upon where you fit in to the above classification. Of course, every one is concerned about cost and would like to keep costs low. But there are other invisible costs that doesn't stand out normally. The cost of slow performing website, the cost of a lost customer are not seen directly as against infrastructure cost. And designing scalable systems within a specific cost is an even bigger challenge.

Hardware fails
It has to be understood that hardware fails all the time. The Cloud Service Provider doesn't setup some magic data center where hardware doesn't fail. And if some one advises you towards private cloud because of such outages in public cloud, be wary that such hardware failures will happen in private cloud setup as well. In contrast, because of the scale at which the public cloud service provider is operating, there is massive automation in place. There are systems what will detect failures well ahead of time and act on its own. There are disk's that reach their lifetime and they get replaced automatically. Those disks will go through a purge cycle to be destroyed completely as per the policy of service provider. Servers get replaced, security systems get updated and network gets upgraded. All these are happening continuously and transparently. Probably as an every day routine in all the data centers. So such things need to happen if you are going to setup a private cloud

Efficiency
Amazon.com is successful because they mastered retail and are operating it at cut throat efficiency. They have successfully replicated the same model into AWS. At any given point of time, AWS tries to operate their data centers at maximum efficiency. That's where they make the most of the buck. By continuously operating at that efficiency and because of the economies of scale, they are also able to pass on the benefits as cost reduction to end customers (without even any significant competition). Whether a private cloud setup or another service provider that would operate at that efficiency and continue to automate is something that one has to evaluate.

With that as a background, here are some thoughts on keeping your website highly available.

Availability Zones
I read your mind. "Yeah. I know. You gonna tell me to use multiple Availability Zones right?". Yes, almost the first thing that you will notice in any article which talks about overcoming outages is utilizing multiple Availability Zones (AZs). So is there something new that I am gonna talk about? Yes.

• AZs are data centers and they are isolated. Some AZ's might have multiple data centers supporting them. In the sense that they have their own independent power, diesel generators, cooling and outbound internet connectivity. And they are not isolated across coast or cities. They are just near by data centers having low latency network between them 
• AZs have low latency between them. But there is a latency. And it might be considerable for you depending upon the number of concurrent requests that your application is expected to handle. So if you have the application servers distributed across AZ-1 and AZ-2 and database only in AZ-1, the AZ-2 application will experience a latency when talking to the DB

So make use of multiple AZ's. If you are planning for High Availability at web layer you will have to have a minimum of two EC2 Instances. Launch them across two different AZ's. Each region in AWS has at least two AZ's and any new regions that will be launched by AWS will have at least two AZ's. If you look at the latest outage, it was specific to a particular AZ. So infrastructure spanning across multiple AZ's weren't completely down. But then why Reddit went down? I will talk about that a little later.

Elastic Load Balancing

Elastic Load Balancing (ELB) is the load balancing service from AWS. When you look at ELB, there are two things that you should pay attention to. One - ELB servers. The EC2 Instances that AWS will be using to run the ELB service. Two - the servers behind the ELB which are your web/app servers.

• ELB, being a service from AWS has HA, scalability and fault tolerance built into it. The Instances that AWS will be provisioning to support the ELB service will span across multiple AZ's and will automatically scale on its own. All of this are abstracted to us and it is an obvious guess that AWS will utilize multiple AZ's by themselves

Configuring ELB's to distribute traffic across multiple AZ's

• For your web/app Instances that sit behind ELB, you have an option to specify the AZ's that the ELB has to support while creating it. For example, when you create an ELB in US-East-1 region, you can mention "us-east-1a" and "us-east-1b" to be supported by the ELB. ELB will now distribute the incoming traffic between the AZ's. Now why is this important? Because, if you specify multiple AZ's for ELB, it results in two things
• The Instances that ELB creates for load balancing will created in all the AZ's that you mention. This means there is a AZ level HA for load balancing Instances itself
• If there is an AZ level failure, then the ELB service will route the traffic to the load balancing Instances in the other AZ thereby continuing the service operation
• So why ELB service was interrupted during the outage? It appears that there was a software bug in the traffic diversion software which resulted in traffic still being routed to the load balancing Instances in the affected AZ itself. Looks like AWS is fixing that bug pretty soon

AutoScaling

AutoScaling will automatically scale up/down your Instances according to the pre-configured triggers (based on CPU, Network, etc...). AutoScaling works in conjunction with ELB where it also gets signals from ELB. AutoScaling can be configured with multiple AZ's as follows

as-create-auto-scaling-group AutoScalingGroupName  --availability-zones  value[,value...] --launch-configuration  value  --max-size  value  --min-size  value [--cooldown  value ] [--load-balancers  value[,value...] ] [General Options]

Once configured, when AutoScaling launches/terminates Instances it will distribute across the configured AZ's. So if the scale up condition is "2" (launching two Instances), and AutoScaling has been configured with "us-east-1a" and "us-east-1b" then AutoScaling will launch one Instance in each AZ.

Go HA with 3 not 2

We know that we have to use multiple AZs. And regions like US-East has 5 AZs. How many do you use in ELB and AutoScaling? It is always better to use three AZ's at a minimum. If you are using only two AZ's and if one of the AZ is down, then the other AZ is receiving traffic twice its normal since ELB will redirect all the traffic to the other AZ. Your AutoScaling is configured to scale up by 1 and now an AZ is down as well. So by the time your AutoScaling reacts you are probably flooded with loads of traffic. And,

• You aren't alone - you are not the only one affected. There are 100s of other customers whose 1000's of Instances are affected. And they are all going to spin up Instances in an alternate AZ. Now AWS might run out of Instances in the alternate AZ's because they are already operating at 20% reduced capacity (in case of US-East)
• API error rates - normally with any such outage, the APIs start throwing up errors. You provision the infrastructure through API's or through AWS Management Console which is a UI wrapper around APIs. During an outage, AWS definitely doesn't want the other AZ's to be flooded with requests which may cause a spiral effect and make the other AZ's also reach critical infrastructure thresholds. And AWS API's themselves will be running in multi-AZ mode and probably are affected because of non-availabilty of an AZ. In such cases, I am sure there is a lot of throttling that AWS will employ at the API level to protect the API from customer requests causing a spiral effect in other AZ's
• Other AWS services - there are other services like ELB, Route53, SQS, SES, etc...which are services that will have HA built on to the them. For example, the ELB service itself to support its load balancing service not going down during an outage, might provision it's own ELB Instances in an alternate AZ's

So in effect, though multi AZ setup is the recommended setup in AWS, it is always better to use more than two AZ's (if a region has). In case of US-East, you can, say, utilize three AZ's in your AutoScaling configuration and ELB settings. The probability of two AZ's going down at the same time is less and there is a better distribution of traffic even if one AZ is down. Of course, it can be argued that we are just avoiding an eventuality. Yes, but during an outage the primary goal of anyone would be to sustain operations (even at reduced capacity) during the outage and return to normalcy at the earliest.

Understand AWS Services that are multi-AZ
The following services from AWS are inherently multi-AZ and are highly available. During an outage, these services will be still available and they would have internally failed over to another AZ.

• Route53 - DNS
• Elastic Load Balancing - Load Balancer
• CloudFront - Content Distribution Network
• Amazon S3 - Storage Service
• DynamoDB - NoSQL Database Service
• SQS - Simple Queue Service
• SES - Simple Email Service

RDS Multi-AZ supports multiple AZ's but you unlike the other services listed above, the application will notice the RDS failover. During an AZ outage, when RDS internally triggers a failover to the standby instance in another AZ, the application will lose connectivity to the RDS endpoint. Depending upon the volume of data and transactions that were happening at the time of failure, the failover time could be around 3-5 minutes.

But even Multi AZ RDS Instances were affected
Yes, and that again seems to be because of bugs in the automatic failover software. RDS relies on EBS for database storage and this outage started with problems in EBS Volumes (which was a result of improper internal DNS propagation). If the EBS of primary RDS Instance is stuck it would result in an automatic failover to the standby immediately. The failover didn't happen immediately because the software didn't anticipate the type of EBS I/O issue that happened during this outage. But for some of the RDS Instances, looks like the primary lost connectivity to the standby and the primary was still accepting transactions. This should have immediately triggered a failover. Because the standby is not receiving updates and can result in data inconsistency. But again, there seems to be a software bug which prevented such a failover. This has to be addressed immediately because RDS is a service where AWS has developed synchronous replication to the standby which isn't available in native MySQL. And that needs to definitely work perfectly all the time.

Perceived Availability
Different applications need different levels of availability. And it is easier to achieve 99.99% of availability. But pushing things forward, every extra nine comes with additional complexity in the architecture and cost. As application architects, it is also required to understand how the application end users perceive availability of a system. For example, let's consider a photo sharing website built on top of an open source CMS. If you break down the different areas in the architecture, you will be looking at the following

• A DNS service
• Content Distribution Network - serving static assets from the nearest edge location
• A front end web load balancer
• A page cache - to cache the HTML
• The web server - storing templates, etc...
• Object Cache - to cache the content, store user sessions
• Database - storing user data and other transactional data

AWS Services and High Availability

With such kind of integration of services that Amazon offers, you have certain portions of your website still available during an outage. For example, your users can still view photos (through CDN) but might not be able to upload new photos (at normal throughput) during outage. Users will still be able to see data from cache which is not up-to-date. Likewise, each area in the application needs to be looked at from a failure point of view and addressed accordingly.

Improve
As architects, one needs to continuously ask questions on failures in each area/tier in the architecture. What if I pull out that web server behind the ELB, will my users sessions be lost? What if that EBS volume becomes unresponsive? What if my RDS Instance is flooded with database connections? Such probing can lead in to small improvements in the architecture which can increase the overall site availability.

So why did Reddit and Heroku go down?
I am sure the engineers at Reddit and Heroku are well aware of all these and would have designed an architecture that scales and tries to keep the service up and running all the time. While, there is no information on what went really wrong, we all know that software systems are not perfect at all the time. No one designs systems that anticipates all probabilities of failure and has a fool proof mechanism to address them. Only when the system is put to use, pushed to boundaries and scales it opens up new flaws in the architecture that once we thought was perfect. I am sure Reddit is learning from areas in their architecture that went down during the outage and will make it better. Just like how Netflix wrote an article on what lessons they learnt from the previous outage.

AWS updates that makes architects happy

AWS keeps adding a lot of features to its existing services and also keeps launching new services. With the pace of innovation from AWS and the rate at which new services and updates are released, one may lose track of them pretty easily. As architects it is essential to know all of these all the time so that one proposes the right solution for a given requirement. Here are some of the features and services that has made the lives of architects easy and can help in simpler infrastructure design addressing specific needs. There is no order to this list - I am just listing them down. And here's a summary before we go detail in to each of them

High I/O EC2 Instance Type - This is a new family of Instance types from AWS in addition to the existing High Memory and High CPU Instance types. The first one in this family is hi1.4xlarge which comes with 60GB of memory and 8 virtual cores. The best part of this Instance type is the whopping 2 TB of SSB attached to them. These SSDs are the ephemeral disks that come as part of the Instance and are of course non-persistent. AWS promises around 120K random read IOPS and between 10K and 85K write IOPS. This Instance type is suitable for I/O intensive applications and running NoSQL databases. Here are couple of pointers about this Instance type

• SSD disks are ephemeral - you will lose data on Instance termination (of course if you stop and start the Instance)
• Data will be persistent between reboot like any other EC2 Instance
• You will see two 1TB volumes on the Instance. Can be striped to double the throughput
• If you are launching a Windows Instance, choose the "Microsoft Windows 2008 R2 64-bit for Cluster Instances" AMI
• Currently this Instance type is available only in US-East and EU-West regions
• And its priced at $3.10 an hour :)

I am sure AWS will be releasing lower Instance types (such as High I/O large) since not everyone will be looking to use an 4xlarge Instance. There are already some benchmarks that you can refer here and specifically netflix has performed a detailed benchmark of this Instance type.

Provisioned IOPS for EBS - EBS is great for storing persistent data and the ability to attach and detach it to/from any Instance. But from day zero the performance and throughput has been an issue with EBS. The IO throughput that one gets out of EBS is very poor and all the more it is not consistent. If the network load during that time is not heavy then one might see large throughput from EBS. If not the throughput will hover around 100 IOPS. Fast forward to now and with provisioned IOPS we can set the IOPS that we expect from EBS and AWS will guarantee the IOPS at all times. This is great news for hosting databases on EC2 (if you are not using RDS). Here are couple of pointers on this:

• Currently the max IOPS that can be set is 1000. AWS will soon increase this limit
• We can of course drive more throughput by attaching multiple EBS volumes and striping them
• Costs little extra than a normal EBS Volume - 0.125/GB against 0.10/GB. Of course the benefits will be far more than the cost difference. Guess everyone will eventually move to provisioned IOPS. Nice way to increase AWS revenue :)

I am assuming that provisioned IOPS will be extended to RDS as well in near future. Just like DynamoDB where one can specify the required throughput, AWS should let the user specify the throughput required from RDS. RDS already uses EBS behind the scenes for storing the data and hence this should definitely be in the pipeline. At least I wish so.

EBS-Optimized EC2 Instances - One thing with EBS that everyone understands is that it is network attached. Unlike local ephemeral storage which is directly attached to the EC2 Instance, there is a pipe which transfers all the data that will be written to EBS. So, even if we use provisioned IOPS to increase the throughput from an EBS, the network throughput can be significantly low that we may not utilize the increased EBS throughput; especially when the network is shared between many EC2 Instances. AWS has introduced EBS-Optimized EC2 Instances which have dedicated network throughput to the EBS that are attached to it. An EBS-Optimized m1.large Instance will have about 500 Mbits/s of guaranteed network throughput to EBS.

• Currently only m1.large, m1.xlarge and m2.4xlarge are available as EBS-Optimized Instances
• The guaranteed throughput is 500 Mbits/s for m1.large and 1000 Mbits/s for m1.xlarge and m2.4xlarge
• This should not be confused with the regular network throughput of the EC2 Instance - for example talking to another EC2 Instance. This guaranteed throughput is only for the EBS related traffic. All other traffic is through the other network pipe given to the EC2 Instance (which is not guaranteed and differs by Instance)
• Comes with additional hourly cost - Optimized m1.large will cost $0.345/hr against a normal on demand m1.large costing $0.320/hr
• Always use EBS-Optimized Instance when using Provisioned IOPS

Elastic Network Interface (ENI) - This feature is available for Instances launched within Virtual Private Cloud (VPC). When you launch Instances within VPC you normally specify the subnet to launch it in and also specify the private IP address of that EC2 Instance. If the Instance is in a public subnet, you will attach and Elastic IP so that you can access the Instance over the internet. With ENI, you no longer specify a subnet while launching the Instance. You create an ENI (which is attached to a subnet) and attach the ENI to the Instance. So what are the benefits of using ENI?

• You can attach multiple ENI (belonging to different subnets) to an EC2 Instance
• One ENI can be configured to have a public IP (Elastic IP) and private IP and the other ENI can be configured to have just a private IP
• With such a setup, you can allow traffic on port 80 through the first ENI and SSH traffic (port 22) through the other ENI
• This way you can further secure your VPN architecture by allowing SSH traffic only from your corporate datacenter

Image Courtesy - AWS

Multiple IP Addresses per Instance - with this feature release, AWS addressed the long waited requested for hosting multiple SSL websites on a given Instance. This feature is an extension of  the ENI feature, where each ENI is now allowed to have multiple public and private IPs. We can create an ENI and create multiple Elastic IPs and attach them to the ENI. The ENI will be then attached to the EC2 Instance (during launch or hot attach during running) and the Instance gets multiple IP addresses.

• The multiple public IP address and private IP addresses are attached to the ENI. And to the Instance when we attach the ENI to the Instance. The difference is that, if we terminate an Instance and relaunch a new one with the same ENI, all the mappings will remain and the new Instance will also get all the public IPs and their private IP mappings
• Available only within VPC currently
• Additional cost for each of the additional public IPs that we attach. No cost for the additional private IPs attached

ELB Security Group - When you use an Elastic Load Balancer (ELB), you will obviously place EC2 Instances behind it to accept incoming traffic. For quite a while, on the EC2 Instances Security Group, you will have to open the incoming port (to which ELB will be sending traffic) to all IPs (0.0.0.0/0). This was because of the fact that ELB will scale itself and AWS will not know at a given point of time how many ELB Instances will be running. But now, ELB's come with their own security groups. So on your EC2 Instances (that sit behind the ELB) Security Group, you will have to open up the port to only ELB's security group. This way you secure your EC2 Instances to accept traffic only from ELB.

Internal Load Balancer - You can launch ELB within VPC as an internal load balancer. By this fashion, the ELB's do not have a public IP addresses and will only have private IP addresses. Such a load balancer can be used for internal load balancing requirements between different tiers in the architecture. I wrote a separate article explaining how it can be used.

Custom Security Group of ELB - If you launch ELBs within VPC (either internal or public facing) you have an option of specifying a custom Security Group of your own. This way custom rules can be configured on the ELB's Security Group instead of using the ELB's default Security Group (which cannot be edited).

S3 Object Expiration

This feature is great for storing logs and scheduling them to be deleted after a definite period of time. When you have multiple web or application servers you will definitely consider pushing the logs generated on them to S3 for centralized storage. Even if you are running a minimal setup, you certainly do not want run out of space on your Instance. But you do not want to store the logs perpetually - one month old logs will be good enough for most of the applications. Instead of writing custom scripts that periodically deletes the logs stored in S3, use this feature so that Amazon will automatically delete them. Saves some cost.

Route 53 Alias record

Route53 is a scalable DNS service from AWS. With pay-as-you-go pricing, there are whole lot of benefits (in terms of flexibility) with Route53. It is definitely not sophisticated like other DNS service providers in terms of feature set. Be it latency based routing or weighted round robin, the service is updated with features. One feature that will be of interest is "Alias Record". Let's say you have a website called "example.com". And you are hosting the infrastructure in AWS. If your load balancer layer uses AWS Elastic Load Balancing, then you will be provided with a CNAME for the ELB. ELB internally may employ "n" number of servers to scale itself and hence it may have more than one IP. Hence AWS provides the CNAME for an ELB which may resolve to one or more IPs at any time. With that background,

• You want to create a DNS record for "www.example.com"
• You also need to create a DNS record for "example.com"; the naked domain or top level domain. Most of the users will use only the naked domain

By design, DNS does not support adding a CNAME entry for the naked domain - simply put you cannot point "http://example.com" to an ELB but you can point "http://www.example.com" to an ELB CNAME. The naked domain can be pointed to only an A-record. Having only A-records for the naked domain will introduce complexity in HA and Scalability for the load balancing layer. And if your load balancing layer employs ELB, there is no way you will know the IPs before hand. One way to address this constraint is to put a separate Apache server which can do the translation. But this will be a serious bottleneck in the architecture since most of the users will use only the naked domain and soon the infrastructure will crumble even though we have build HA and Scalability in all other layers. With the "Alias" feature

• First create a Route53 record set for "http://www.example.com" and point it to the ELB CNAME
• Create another record set for "example.com" and set the type as "Alias". Point the record value to the record set created above. Or directly point it to the ELB CNAME
• With the "Alias" option you will be able to point the record value to either an ELB CNAME or another record set that is available in Route53.

This way, you take care of both naked domain and "www" with both pointing to the load balancing layer in the AWS setup.

Of course there are a whole lot of features and updates that AWS keeps pushing across its services. The above are some of the features that helps architects while designing the infrastructure architecture. These have personally helped us build better architectures. Prior to these features, we always had to look for alternatives such has tuning, custom solutions or sacrifice on certain parameters to achieve others. I am sure there are many more such updates that we can uncover and will be coming from AWS in future. That will be for another post here.

Yet another post on High Availability

Looks like High Availability, Scalability are becoming the most abused words / jargons these days. And Cloud Computing has helped these words reaching the common man. I was really annoyed at an article on High Availability on CloudAve. The article was posted immediately after the latest AWS outage and talks about how companies like Netflix and Pinterest have all got it wrong and how to "program" for failover. Or is that failure? Here are the main items that the article talks about

1. Programming for failover / failure. Anyone who doesn't is a fool
2. Doing an S3 sync can help you avoid outages
3. Elastic Load Balancing can ensure regions are healthy

1. Agreed. Any large scale system should definitely be designed for failure. And I am sure the folks at Netflix have not ignored this fact. Netflix is at a scale where they are probably one of the largest customer of AWS and they indeed work very closely with AWS. And to imagine what their scale is, they made their own custom AWS management solution called Asgard  and open sourced it. They were not happy with the feature set of AWS management console and built their own. And the news also appeared in AWS blog. Now, I am at loss of words if Netflix hasn't thought about using multiple AZs, load balancers, etc...That is like a bare minimum for their scale.

2. Syncing between S3 buckets. Here we are talking about Syncing large amount of objects between S3 buckets located across continents. Over the Internet. Now, this looks all the more simple task to do in paper. Anyone who has thought about the actual implementation will fairly appreciate the complexity and cost involved in doing so. Of course that may outweigh the benefits of being available during an outage. But before we get to that there are many implementation bottlenecks to be addressed. You just cannot imagine putting a Single EC2 Instance (oh yeah we are talking about HA. lets make it one Instance in each AZ) and writing a custom script to perform the sync. Bandwidth availability from a single EC2, availability of that setup during an outage and inter region internet bandwidth charges should definitely be considered to go for such a design. And in large scale architecture, companies heavily rely on Content Distribution Network (CDN) such as Akamai / AWS CloudFront to deliver the media assets. If the content is stored in S3, it is most likely served through a CDN. In such case, even if the origin server is not available (in this case an outage at AWS), the content is always available at the edge location. Apart from the media assets, companies on the likes/scale of Netflix will heavily use other technologies like NoSQL (Mongo, Cassandra), Relational Databases (RDS) and Cache Clusters. And they are probably running 100s in each. Replicating them, building failover is a slow and complex process.

3. Elastic Load Balancing is a load balancing service from AWS. It can be used to bring HA in to your architecture - within a region. You cannot use ELB to ensure regions are healthy. ELB is a service that is restricted to a specific region and operates within it. To detect multiple region health status, one needs a solution at the DNS level. Such as going for a managed DNS solution which can perform health checks between multiple data centers (geo distributed) and failover. AWS Route53 can do that. Route53 Latency Based Routing (LBR) can detect multi region latency and route traffic accordingly.

Building a Highly Available solution is not about just utilizing multiple data centers or syncing data. It involves very deep understanding of the application and the infrastructure. And how they work together. Availability is perceived by the user. Hence it varies from application to application. For example, a photo sharing website might have three major tiers - photo viewer, photo upload, commenting. If the website is completely hosted on AWS, during at outage (depending upon which AWS services are affected), some of the tiers can still be perceived to be working. Photo viewer might continue to serve the pictures from a CDN while users might not be able to add new comments. Or, new comments might be working for the users who are posting but might be delayed for the ones who are viewing (achieved through queues and cache clusters). Architects can think of various ways to build such systems and that's how one increases the overall availability of the system. It is not an one time activity but a continuous process requiring patience, careful examination and planning. Guess such architectures will quietly work behind the scenes and let others abuse words like High Availability, Scalability for eternity.

Making your apps truely HA on AWS - lessons from the recent outage

During last week, AWS had an outage in one of their data centers. Unlike last year, this year's outage had lesser impact on only one Availability Zone in the US-East region of AWS. None of the customers for whom we had setup their infrastructure in AWS suffered any downtime during this outage. One of the customer panicked and reported Instances becoming unreachable only to find that the Instances in other AZs were running fine and the website continued to be available.  What was more interesting was, the next day, one of India's leading entertainment content provider got in touch with us and wanted us to evaluate their existing AWS infrastructure. While I was talking to the customer, I casually asked the prime reason for this evaluation. He mentioned that their website is completely hosted in AWS and the website went down during the outage. And he was not aware that the website was down until some one from their team accidentally found the website to be down.

Here's the update from AWS on what went wrong. The full information is available on AWS Service Health Dashboard page but might become difficult to access by next week as the date widget moves ahead

"At approximately 8:44PM PDT, there was a cable fault in the high voltage Utility power distribution system. Two Utility substations that feed the impacted Availability Zone went offline, causing the entire Availability Zone to fail over to generator power. All EC2 instances and EBS volumes successfully transferred to back-up generator power. At 8:53PM PDT, one of the generators overheated and powered off because of a defective cooling fan. At this point, the EC2 instances and EBS volumes supported by this generator failed over to their secondary back-up power (which is provided by a completely separate power distribution circuit complete with additional generator capacity). Unfortunately, one of the breakers on this particular back-up power distribution circuit was incorrectly configured to open at too low a power threshold and opened when the load transferred to this circuit. After this circuit breaker opened at 8:57PM PDT, the affected instances and volumes were left without primary, back-up, or secondary back-up power. Those customers with affected instances or volumes that were running in multi-Availability Zone configurations avoided meaningful disruption to their applications; however, those affected who were only running in this Availability Zone, had to wait until the power was restored to be fully functional."

Utilize Multiple Availability Zones
Or AZ's in short. AWS provides multiple Availability Zones in each region. Availability Zones are distinct locations that are engineered to be insulated from failures in other Availability Zones and provide inexpensive, low latency network connectivity to other Availability Zones in the same Region. Additionally in this FAQ answer, AWS clearly states that common points of failures like generators, cooling systems are not shared between AZs and each AZ is physically separate and independent. This recent outage was primarily because of a fault in the power distribution system and since AZs are isolated only one of the AZ (us-east-1c) in the US-East region got affected. Remaining AZ's were running smoothly without any interruption. Hence it is prudent to have the infrastructure distributed across multiple AZ's. Distribute your EC2 Instances across multiple AZ's and bring up your HA quotient.
Note: You need not worry about any performance issues because of Instances deployed across different AZs. All AZs have very low network latency between them and can be ignored for web applications.

Configure ELB to use Multi AZ
AWS Elastic Load Balancer (ELB) can be configured to work across multiple AZ's. This way you can launch EC2 Instances in multiple AZs and have them tied to a single ELB. In event of a failure of one AZ, you will still have Instances running out of the other AZs and ELB will failover to them and your website will continue to be operational. When you create an ELB through the AWS Management Console you will not have the option to specify the AZ distribution. However, when you add Instances running in multiple AZ's to the ELB, the ELB automatically gets configured to work with multi AZs.

If you are using the command line interface or the API's, while creating the ELB itself you can mention the AZ's that the ELB needs to handle:

elb-create-lb LoadBalancerName  --availability-zones  value[,value...]  --listener "protocol=value, lb-port=value, instance-port=value, [cert-id=value]" [ --listener "protocol=value, lb-port=value, instance-port=value, [cert-id=value]" ...]  [General Options]

Additionally you create an AutoScalingGroup and mention the same list of Availability Zones, you are automating the whole failover process.

as-create-auto-scaling-group AutoScalingGroupName  --availability-zones  value[,value...] --launch-configuration  value  --max-size  value  --min-size  value [--cooldown  value ] [--load-balancers  value[,value...] ] [General Options]

In this case, the moment the Instances in one AZ went down,

• AutoScaling would have tried to launch new Instances to replace the failed ones
• AWS API's would have thrown errors due to AZ unavailability
• AutoScaling would have launched the new Instances in another AZ

RDS Multi AZ
Utilize the Multi-AZ feature of RDS where RDS will run a standby database Instance in another AZ in addition to the primary database Instance. In the event of a primary failure, RDS will internally initiate a failover to the standby and the database will continue to serve the traffic. AWS has mentioned in the RDS status dashboard that during the outage none of their Multi-AZ deployments were affected (failover got triggered). Only the Single AZ RDS had to be recovered by AWS team.

"As described in the EC2 post, a number of EBS volumes in the affected Availability Zone were impacted, disabling many Single Availability Zone (Single-AZ) RDS instances. Multi-AZ RDS instances were able to immediately begin failing over to other healthy Availability Zones. We started the recovery process for the affected Single-AZ RDS instances after EBS APIs became available at approximately 10:40 PM PST. Upon completing storage volume checks, we began processing EBS recovery volumes and recovered most of the affected RDS instances by 5:00 AM PDT on June 15th."

There are couple of things to note here:

• Unlike ELB, RDS failover is not instant. In simple words, the front end application that connects to the RDS will see a down time when the failover happens. During the time RDS completely fails over to the standby, connectivity to the RDS endpoint will not be available. We have seen that the time taken is approximately 5-7 minutes for around 100GB of data. But with more data probably it might take little longer.
• When it comes to database the impact is different when compare to, say, web servers. With database one will worry more about the data consistency and data durability than just availability. So when AWS talks above about recovering the Single RDS, it is actually the recovery of the EBS behind the RDS. These Single RDS EBS became unavailable and since there was a power failure it potentially resulted in a write corruption. Hence customers were asked to restore to the nearest point in time snapshot. With Multi-AZ such data corruption issue will not arise since there will be an entire replica on another AZ and data is written on both concurrently on a commit.
• Of course, Multi-AZ comes with additional cost. Probably double. The cost of running a Single RDS large Instance in US-East is about $300/mo give or take. In case of a Multi-AZ setup, this cost will double and AWS would be charging around $600/mo. Availability, consistency and data durability versus cost is something individual application owners need to decide

Stateless Servers

Apart from the persistent store (database) of the application, it is recommended to make all other layers in the infrastructure stateless. Web and application servers specifically needs to be designed to be stateless. Most of the applications store the web session data on the web server memory itself. Similarly there are other assets like user uploaded files, server and application log files that typically reside on the web/app servers. All of these assets needs to move to a central repository. For example, session information can be stored on caching servers or databases and files can be periodically rotated to, say, Amazon S3. Once we design such an application, then it is easy to make modifications in the infrastructure to maintain HA and also to scale on demand. Spreading the infrastructure across multiple AZ's can definitely help you achieve HA. But the failover will not be transparent to the user if the session information is stored in memory. If 100s of users session state is maintained in Instances in AZ1 and that AZ becomes unavailable, the front end Load Balancer will immediately failover to the Instances running in AZ2. But users' session state will be unavailable in these AZ2 Instances and will probably be asked to login again.

With these simple principles coupled with utilizing AWS building blocks effectively one can definitely build an infrastructure that will be truely Highly Available. Of course, these are lessons that one learn as Cloud delivery model matures and as architects one has to keep fine tuning the architecture post live as well.

Note: All the above thoughts are to increase uptime during problems with a single AZ. Last year we saw a different problem in AWS where a configuration change coupled with the architecture of EBS an entire region (US-East) had a service disruption. Infrastructure architecture can be designed to withstand such huge disruption as well. However those come with very high costs of replicating data over the internet. And in most of the cases the cost of operating such an infrastructure outweighs the benefits.