HashiCorp Packer automates the creation of machine images, coming out of the box with support to build images for Amazon EC2, CloudStack, DigitalOcean, Docker, Google Compute Engine, Microsoft Azure, QEMU, VirtualBox, and VMware.
N/A
HashiCorp Terraform
Score 8.7 out of 10
N/A
Terraform from HashiCorp is a cloud infrastructure automation tool that enables users to create, change, and improve production infrastructure, and it allows infrastructure to be expressed as code. It codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. It is available Open Source, and via Cloud and Self-Hosted editions.
There are lot of tools in market which does the job for Image creation but all of them are not complete Machine/Image as a code. All other alternatives can create Image partially.
We have used Vagrant to develop our application in a virtual box environment and prepare it to be packed with Packer. The image created from these two tools will be deployed by Terraform.
We are using Consul for service discovery and as a job locking so we don't have two jobs or …
Features
HashiCorp Packer
HashiCorp Terraform
Configuration Management
Comparison of Configuration Management features of Product A and Product B
We use packer to generate new machine images for multiple platforms on every change to our Configuration Management tools like Chef/Puppet/Ansible It's act single tool for Image building for Multi-provider like AWS/Azure/GCP Helps to achieve Dev/Prod Parity Packer itself doesn't have a state like Terraform. You can't do packer output AMI ID. If you have a scenario where you want to maintain the state for images it would be tough to manage via Packer.
Anything that needs to be repeated en masse. Terraform is great at taking a template and have it be repeated across your estate. You can dynamically change the assets they're generating depending on certain variables. Which means though templated assets will all be similar, they're allowed to have unique properties about them. For example flattening JSON into tabular data and ensuring the flattening code is unique to the file's schema.
The language itself is a bit unusual and this makes it hard for new users to get onboarded into the codebase. While it's improving with later releases, basic concepts like "map an array of options into a set of configurations" or "apply this logic if a variable is specified" are possible but unnecessarily cumbersome.
The 'Terraform Plan' operation could be substantially more sophisticated. There are many situations where a Terraform file could never work but successfully passes the 'plan' phase only to fail during the 'apply' phase.
Environment migrations could be smoother. Renaming/refactoring files is a challenge because of the need to use 'Terraform mv' commands, etc.
I love Terraform and I think it has done some great things for people that are working to automate their provisioning processes and also for those that are in the process of moving to the cloud or managing cloud resources. There are some quirks to HCL that take a little bit of getting used to and give picking up Terraform a little bit of a learning curve, thus the rating
Terraform's performance is quite amazing when it comes to deployment of resources in AWS. Of course, the deployment times depend on various parameters like the number of resources to deploy and different regions to deploy. Terraform cannot control that. The only minor drawback probably shows up when a terraform job is terminated mid way. Then in many cases, time-consuming manual cleanup is required.
I have yet to have an opportunity to reach out directly to HashiCorp for support on Terraform. However, I have spent a great deal of time considering their documentation as I use the tool. This opinion is based solely on that. I find the Terraform documentation to have great breadth but lacking in depth in many areas. I appreciate that all of the tool's resources have an entry in the docs but often the examples are lacking. Often, the examples provided are very basic and prompt additional exploration. Also, the links in the documentation often link back to the same page where one might expect to be linked to a different source with additional information.
There are lot of tools in market which does the job for Image creation but all of them are not complete Machine/Image as a code. All other alternatives can create Image partially. Main reason for selecting Packer are Packer is lightweight, portable, and command-line driven Packer helps keep development, staging, and production as similar as possible. Packer automates the creation of any type of machine image Multi-provider portability is the feature to die for
Terraform is the solid leader in the space. It allows you to do more then just provisioning within a pre-existing servers. It is more extensible and has more providers available than it competitors. It is also open source and more adopted by the community then some of the other solutions that are available in the market place.
we are able to deploy our infrastructure in a couple of ours in an automated and repeatable way, before this could take weeks if the work was done manually and was a lot of error prone.
having the state file, you can see a diff of what things have changed manually out side of Terraform which is a huge plus
if state file gets corrupted, it is very hard to debug or restore it without an impact or spending hours ..
writing big scale code can be very challenging and hard to be efficient so it's usable by the whole team