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Tag: MongoDB

How To: Create An AWS Lambda Function To Backup/Snapshot Your EBS Volumes

Published / by tuxninja / Leave a Comment

AWS Lambda functions are a great way to run some code on a trigger/schedule without needing a whole server dedicated to it. They can be cost effective, but be careful depending on how long they run, and the number of executions per hour, they can be quite costly as well.

For my use case, I wanted to create snapshot backups of EBS volumes for a Mongo Database every day. I originally implemented this using only CloudWatch, which is a monitoring service, but because it’s focused on scheduling, AWS also uses it for other things that require scheduling/cron like features. Unfortunately, the CloudWatch implementation of snapshot backups was very limited. I could not ‘tag’ the backups, which was certainly something I needed for easy finding and cleanups later (past a retention period).

Anyway, there were a couple pitfalls I ran into when creating this function.

Pitfalls

  1. Make sure you security group allows you to communicate to the Internet for any AWS API’s you need to talk to.
  2. Make sure your time-out is set to 1 minute or greater depending on your use case. The default is seconds, and is likely not high enough.
  3. “The Lambda function execution role must have permissions to create, describe and delete ENIs. AWS Lambda provides a permissions policy, AWSLambdaVPCAccessExecutionRole, with permissions for the necessary EC2 actions (ec2:CreateNetworkInterface, ec2:DescribeNetworkInterfaces, and ec2:DeleteNetworkInterface) that you can use when creating a role”
    1. Personally, I did inline permissions and included the specific actions.
  4. Upload your zip file and make sure your handler section is configured with the exact file_name.method_in_your_code_for_the_handler
  5. Also this one is more of an FYI, Lambda Function have a maximum TTL of 5 minutes ( 300 seconds).

I think that was it, after that everything worked fine. To finish this short article off, screenshots and the code!

Screenshots

 

 

And finally the code…

Function Code

And here is an additional function to add for cleanup

The end, happy server-lessing (ha !)

 

MongoDB data loss avoided courtesy of AWS EBS & Snapshots

Published / by tuxninja / Leave a Comment

Cross Region MongoDB Across A Slow Network (Napster) Bad, AWS Snapshots (Metallica) Good!

I recently found myself in a bit of a pickle. My team and I had deployed a 3 node MongoDB cluster configured as two nodes in us-east-1 and one node in us-west-2 to maximize our availability while minimizing cost. Ultimately, there were two problems with this approach. The first is that for reasons mostly outside of our control the rest of our application stack above the database was deployed in us-east-1 drastically reducing any availability benefit the tertiary node in us-west-2 was buying us. Additionally, we were not aware at the time we made this choice, but our cluster/replication traffic was going across a VPN with very limited bandwidth that frequently suffered network partitions due to network maintenance and a lack of redundancy. We found our MongoDB cluster failing over frequently due to losing communication with it’s members and when it did our cluster had a difficult time recovering because replication couldn’t catch up across the VPN.

After restarting Mongo several times, including removing the data directory and starting over fresh, ultimately replication was going to take days to sync, and we could not afford to wait that long. We needed to restore the cluster health ASAP so we could move all nodes to us-east-1 mitigating our network issue with our VPN that was introducing so much pain.

Now the system I am referring to is production, it cannot lose data, and it cannot take downtime/a maintenance. Given these constraints I started googling ways to catch up your MongoDB, when it will not catch up on it’s own. I tried some things I found like rsync etc, before realizing it wasn’t any faster across that slow VPN link. Ultimately, I decided I was going to try a snapshot. Now the document I read warned me that a live snapshot may result in potentially inconsistent data, but again I had to try it given the constraints I mentioned before. I had few options. In the end as it turns out, it worked perfectly and in under an hour I had my entire cluster healthy. Using the AWS CLI utility, here is how I did it…

Step 1 take the snapshot of the healthy node

I actually took the snapshot in the GUI at first… so not shown here, but for the record to create a snapshot, go to your volume under Ec2 Volumes and click actions then create snapshot and save the snapshot ID. (Or alternatively do it with the CLI like I did for everything else).

Step 2, copy the snapshot from your source region to your destination region

Make sure you copy to your clipboard the snapshot ID returned…

Step 3, Create a new volume from the copied snapshot

Response:

Step 4, Attach the volume to the system

Oh No We Got An Error!

Ah ok, simple fix, we created the volume in a different AZ than the node we were attaching to.

(delete the old volume) Then…

Step 5, create a new volume from the snapshot, but this time specify the same AZ (us-east-1b instead of us-east-1c) as the node we wish to attach it to

Step 6, try attaching the new volume (cross your fingers)

Response:

Sweet it worked…Now it’s time to do some work on the node we attached this volume to.

Step 7, check if the new attachment is visible to the system

Yup sure, is, we can see our device ‘xvdc’ is a 300G disk that has no mount point. We can also see ‘xvdb’ which is our original mongo data mount, mounted under /mnt.

Step 8, create mount point and mount the new device

Step 9, shutdown Mongo if it’s running

Step 10, copy the snapshot data, to the existing MongoDB data directory

Step 11, fix permissions for the copied data

NOTE: Do not forget this step or you will get errors starting the MongoDB service

Step 12, start Mongo back up

Step 13, Check Mongo Cluster Status

For contrast, here is what it looked like before, pay close attention to node/member 10.5.0.149

Now that our DB is verified healthy it’s time to cleanup.

Step 14, clean our now unnecessary waste ( and thank the gods)

Umount & Delete

Detach Volume

Delete Volume & Snapshots

 

When I ran into this issue and googled around a bit, I really didn’t find anyone with a detailed account of how they got out of it. Thus I was inspired by the opportunity to help others in the future and the result is this post. I hope it finds someone, someday, facing a similar scenario and graciously lifts them out of the depths! Godspeed, happy clouding.

How To: Launch A Jump Host In AWS Using Terraform

Published / by tuxninja / Leave a Comment

I have been a Hashicorp fan boy for a couple of years now. I am impressed, and happy with pretty much everything they have done from Vagrant to Consul and more. In short they make the DevOps world a better place. That being said this article is about the aptly named Terraform product. Here is how Hashicorp describes Terraform in their own words…

“Terraform enables you to safely and predictably create, change, and improve production infrastructure. It is an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.”

Interestingly, enough it doesn’t point out, but in a way implies it by omitting anything about providers that Terraform is multi-cloud (or cloud agnostic). Terraform works with AWS, GCP, Azure and Openstack. In this article we will be covering how to use Terraform with AWS.

Step 1, download Terraform, I am not going to cover that part 😉
https://www.terraform.io/downloads.html

Step 2, Configuration…

Configuration

Hashicorp uses their own configuration language for Terraform, it is fully JSON compatible, which is nice.. The details are covered here https://github.com/hashicorp/hcl.

After downloading and installing Terraform, its time to start generating the configs.

AWS IAM Keys

AWS keys are required to do anything with Terraform. You can read about how to generate an access key / secret key for a user here : http://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html#Using_CreateAccessKey

Terraform Configuration Files Overview

When you execute the terraform commands, you should be within a directory containing terraform configuration files. Those files ending in a ‘.tf’ extension will be loaded by Terraform in alphabetical order.

Before we jump into our configuration files for our Jump box, it may be helpful to review a quick primer on the syntax here https://www.terraform.io/docs/configuration/syntax.html

& some more advanced features such as lookup here https://www.terraform.io/docs/configuration/interpolation.html

Most users of Terraform choose to make their configs modular resulting in multiple .tf files with names like main.tf, variables.tf and data.tf… This is not required…you can choose to put everything in one big Terraform file, but I caution you modularity/compartmentalization is always a better approach than one monolithic file. Let’s take a look at our main.tf

Main.tf

Typically if you only see one terraform config file, it is called main.tf, most commonly there will be at least one other file called variables.tf used specifically for providing values for variables used in other TF files such as main.tf. Let’s take a look at our main.tf file section by section.

Provider

The provider keyword is used to identify the platform (cloud) you will be talking to, whether it is AWS, or another cloud. In our case it is AWS, and define three configuration items, an access key, secret key, and a region all of which we are inserting variables for, which will later be looked up / translated into real values in variables.tf

Resource aws_instance

This section defines a resource, which is an “aws_instance” that we are calling “jump_box”. We define all configuration requirements for that instance. Substituting variable names where necessary, and in some cases we just hard code the value. Notice we are attaching two security groups to the instance to allow for ICMP & SSH. We are also tagging our instance, which is critical an AWS environment so your administrators/teammates have some idea about the machine that is spun up and what it is used for.

Btw, the this resource type is provided by an AWS module found here https://www.terraform.io/docs/providers/aws/r/instance.html

You have to download the module using terraform get (which can be done once you write some config files and type terraform get 🙂 ).

Also, note the usage of ‘user_data’ here to update the machines packages at boot time. This is an AWS feature that is exposed through the AWS module in terraform.

Resource aws_security_group

Next we define a new security group (vs attaching an existing one in the above section). We are creating this new security group for other VM’s in the environment to attach later, such that it can be used to allow SSH from the Jump host to the VM’s in the environment.

Also notice under cidr_blocks we define a single IP address a /32 of our jump host…but more important is to notice how we determine that jump hosts IP address. Using .private_ip to access the attribute of the “jump_box” aws_instance we are creating/just created in AWS. That is pretty cool.

Resource aws_route53_record

The last entry in our main.tf creates a DNS entry for our jump host in Route53. Again notice we are specifying a name of jump. has a prefix to an entry, but the remainder of the FQDN is figured out by the lookup command.  The lookup command is used to lookup values inside of a map. In this case the map is defined in our variables.tf that we will review next.

Variables.tf

I will attempt to match the section structure I used above for main.tf when explaining the variables in variables.tf though it is not really in as clear of a layout using sections.

Provider variables

When terraform is run it compiles all .tf files, and replaces any key that equals a variable, with the value it finds listed in the variables.tf file (in our case) with the variable keyword as a prefix. Notice that the first two variables are empty, they have no value defined. Why ? Terraform supports taking input at runtime, by leaving these values blank, Terraform will prompt us for the values. Region is pretty straight forward, default is the value returned and description in this case is really an unused value except as a comment.

I would like to demonstrate the behavior of Terraform as described above, when the variables are left empty

At this phase you would enter your AWS key info, and terraform would ‘plan’ out your deployment. Meaning it would run through your configs, and print to your screen it’s plan, but not actually change any state in AWS. That is the difference between Terraform plan & Terraform apply.

Resource aws_instance variables

Here we define the values of our AMI, SSH Key, Instance prefix for the name, Tags, security groups, and subnets. Again this should be pretty straight forward, no magic here, just the use of string variables & maps where necessary.

It’s important to note the variables above are also used in other sections as needed, such as the aws_security_group section in main.tf …

Resource aws_route53_record variables

Here we define the ID & Name that are used in the ‘lookup’ functionality from our main.tf Route53 section above.

It’s important to note Terraform or TF files do not care when or where things are loaded. All files are loaded and variables require no specific order consistent with any other part of the configuration. All that is required is that for each variable you try to insert a value for, it has a value listed via the variable keyword in a TF file somewhere.

Output.tf

Again, I want to remind folks you can put these terraform syntax in one file if you wanted to, but I choose to split things up for readability and simplicity. So we have an output.tf file specifically for the output command, there is only one command, which lists the results of our terraform configurations upon success.

Ok so let’s run this and see how it looks…First a reminder, to test your config you can run Terraform plan first..It will tell you the changes its going to make…example

If everything looks good & is green, you are ready to apply.

Congratulations, you now have a jump box in AWS using Terraform. Remember to attach the required security group to each machine you want to grant access to, and start locking down your jump box / bastion and VM’s.

Outro

Remember if you take the above config and try to run it, swapping out only the variables it will error something about a required module. Downloading the required modules is as simple as typing ‘terraform get‘ , which I believe the error message even tells you 🙂

So again this was a brief intro to Terraform it does a lot & is extremely powerful. One of the thing I did when setting up a Mongo cluster using Terraform, was to take advantage of a map to change node count per region. So if you wanted to deploy a different number of instances in different regions, your config might look something like…

main.tf

variables.tf

It also supports split if you want to multi-value a string variable.

Another couple things before I forget, Terraform apply, doesn’t just set up new infrastructure, it also can be used to modify existing infrastructure, which is a very powerful feature. So if you have something deployed and want to make a change, terraform apply is your friend.

And finally, when you are done with the infrastructure you spun up or it’s time to bring her down… ‘terraform destroy’

I hope this article helps.

Happy Terraforming 😉