Since the inception of my software engineering career, decades ago, I have enjoyed creating resilient software systems and building efficient engineering teams. I found that one of the most important aspects affecting the success of any project is how close engineering teams align with user and community needs. This is one of the reasons it’s crucial for engineering to understand users.
During the past year and a half I’ve been deeply focused on distributed systems and advocacy around Apache Cassandra at DataStax. I chose to work at the company for their commitment to building extremely scalable and performant products. Another reason was my respect for people who work at the company, many of whom are active contributors and committers to Apache Cassandra, Spark and other important open-source projects. DataStax maintains deep experience in the area of distributed databases and I am happy to have been able to contribute to improving products and educational materials around Apache Cassandra. Having gotten to work with many engineering teams within DataStax I am excited about our future efforts! Continue reading “Career Update: Back to Engineering!”→
The next thing I wanted setup for my development workspace is a DataStax Enterprise Cluster. This will give me all of the Apache Cassandra database features plus a lot of additional features around search, OpsCenter, analytics, and more. I’ll elaborate on that in some future posts. For now, let’s get an image built we can use to add nodes to our cluster and setup some other elements.
1: DataStax Enterprise
The general installation instructions for the process I’m stepping through here in this article can be found in this documentation. To do this I started with a Packer template like the one I setup in the second part of this series. It looks, with the installation steps taken out, just like the code below.
The first part of this process the machine image needs Open JDK installed, which I opted for the required version of 1.8. For more information about the Open JDK check out this material:
The next thing I needed to do was to get everything setup so that I could use this Azure Image to build an actual Virtual Machine. Since this process however is built outside of the primary Terraform main build process, I need to import the various assets that are created for Packer image creation and the actual Packer images. By importing these asset resources into Terraform’s state I can then write configuration and code around them as if I’d created them within the main Terraform build process. This might sound a bit confusing, but check out this process and it might make more sense. If it is still confusing do let me know, ping me on Twitter @adron and I’ll elaborate or edit things so that they read better.
Verification Checklist
At this point there now exists a solidly installed and baked image available for use to create a Virtual Machine.
2: Terraform State & Terraform Import Resources
Ok, if you check out part 1 of this series I setup Azure CLI, Terraform, and the pertinent configuration and parts to build out infrastructure as code using HCL (Hashicorp Configuration Language) with a little bit of Bash as glue here and there. Then in Part 2 and Part 3 I setup Packer images and some Terraform resources like Kubernetes and such. All of that is great, but these two parts of the process are now in entirely two different unknown states. The two pieces are:
Packer Images
Terraform Infrastructure
The Terraform Infrastructure doesn’t know the Packer Images exist, but they are sitting there in a resource group in Azure. The way to make Terraform aware that these images exist is to import the various things that store the images. To import these resources into the Terraform state, before doing an apply, run the terraform import command.
In order to get all of the resources we need in which to operate and build images, the following import commands need issued. I wrote a script file to help me out with each of these, and used jq to make retrieval of the Packer created Azure Image ID’s a bit easier. That code looks like this:
Breaking down the jq commands above, the following actions are being taken. First, the Azure CLI command is issued, az image list which is then piped | into the jq command of jq 'map({name: "theimagenamehere", id})'. This command takes the results of the Azure CLI command and finds the name element with the appropriate image name, matches that and then gets the id along with it. That command is then piped into another command that returns just the value of the id jq -r '.id'. The -r is a switch that tells jq to just return the raw data, without enclosing double quotes.
I also needed to import the resource group all of these are in too, which following a similar jq command style of piping one command’s results into another, issued this command to get the Resource Group ID RG-IMPORT=$(az group show --name adronsimages | jq -r '.id'). With those three ID’s there is one more element needed to be able to import these into Terraform state.
The Terraform resources that these imported pieces of state will map to need declared, which means the Terraform HCL itself needs written out. For that, there are the two images that are needed and the Resource Group. I added the images in an images.tf files and the Resource Group goes in the resource_groups.tf file.
Now, issuing these Terraform commands will pull the current state of those resource into the state, which we can then issue further Terraform commands and applies from.
Running those commands, the results come back something like this.
Verification Checklist
At this point there now exists a solidly installed and baked image available for use to create a Virtual Machine.
Now there is also state in Terraform, that understands where and what these resources are.
Summary, for now.
This post is shorter than I’d like it to be. But it was taking to long for the next steps to get written up – but fear not they’re on the way! In the coming post I’ll cover more of other resource elements we’ll need to import, what is next for getting a virtual machine built off of the image that is now available, some Terraform HCL refactoring, and most importantly putting together the actual DataStax Enterprise / Apache Cassandra Clusters! So stay tuned, subscribe to the blog, and of course follow me on the Twitters @Adron.
Have a basic understanding of how to use Terraform and what it does. This is covered pretty well in the Hashicorp Docs here (single page read <5 minutes) and if you have a LinkedIn Learning account check out my Terraform course “Learning Terraform“.
Beyond that some basic CLI/terminal knowledge, understand where environment variables (as I detail here, here, and here for some starters) are, and miscellaneous knowledge. You’ll also need knowledge and user experience with Git. Most of these things I’ll detail explicitly but otherwise I’ll either link to or provide context for additional information throughout the article.
1: Terraform
Download
You’ll need to first install Terraform and make it available for use on your machine. To do this navigate over to the HashicorpTerraformTerraform site and to the download section. As of this time 0.12.6 is available, and for the foreseeable future this version or versions coming will be just fine.
Install
You’ll need to unzip this somewhere in a directory that you’ve got the path mapped for execution. In my case I’ve setup a directly I call “Apps” and put all of my CLI apps in that directory. Then add it to my path environment variable and then terraform becomes available to me from any terminal wherever I need it. My path variable export on Linux and Mac look like this.
[code]export PATH=$PATH:/home/adron/Apps[/code]
Now you can verify that the Terraform CLI is available by typing terraform in any terminal and you should get a read out of the available Terraform commands.
Terraform is installed and executable from the terminal in whichever folder on the system.
2: Azure CLI
For this tutorial, there are several ways for Terraform to authenticate to Azure, I’ll be using the Azure CLI authentication method as detailed in this tutorial from Hashicorp. There are also some important notes about the Azure CLI. The Azure CLI method in conjunction with the AzureRM Terraform Provider is used to build out resources using infrastructure as code paradigms, because of this it is important also to insure we have the right versions of everything to work together.
The Caveats
For the AzureRM, which will be downloaded automatically when we setup the repository and initialize it with the terraform init command, we’ll want to make sure we have version 1.20 or greater. Previous versions of the AzureRM Provider used a method of authorizing that reset credentials after an hour. A clear issue.
Terraform also only support authenticating using the az CLI and it must be avilable in the path of the system, same as the way terraform is available via the path. In other words, if both terraform and az can be executed from anywhere in the terminal we’re all set. Using the older methods of Powershell Cmdlets or azure CLI methods aren’t supported anymore.
Authenticating via the Azure CLI is only supported when using a “User Account” and not via Service Principal (ex: az login --service-principal). This works perfectly since these environments I’m building are specifically for my development needs. If you’d like to use this example as a more production focused example, then using something like Service Principals or another systems level verification, authentication, and authorization model should be used. For other examples check out authentication via a Service Principal and Client Secret, Service Principal and Client Certificate, or Managed Service Identity.
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It’ll bring up a browser that’ll give you a standard Microsoft auth login for your Azure Account.
When that completes successfully a response is returned in the terminal as shown.
Pieces of this information will be needed later on so I always copy this to a text file for easy access. I usually put this file in a folder I call “DELETE THIS CUZ SECURITY” so that I remember to delete it shortly thereafter so it doesn’t fall into the hands of evil!
For all the other operating systems and places that the Azure CLI can be installed, check out the docs here.
Once logged in a list of accounts can be retrieved too. Run az account list to get the list of accounts available. If you only have one account (re: subscriptions) then you’ll just see exactly what was displayed when you logged in. However if you have other peripheral information, those accounts will be shown here.
If there is more than one subscription, one needs selected and set. To do that execute the following command by passing the subscription id. That’s the second value in that list of values above. Yes, it is kind of odd that they use account and subscription interchangeably in this situation, and that subscription id isn’t exactly obvious if this data is identified as an account and not a subscription, but we’ll give Microsoft a pass for now. Suffice it to say, account id and subscription id in this data is the stand alone id field in the aforementioned data.
az account set --subscription="id" where id is subscription id, or as shown in az account list the id there, whatever one wants to call it.
Configuring Terraform Azure CLI Auth
To do this we will go ahead and setup the initial repository and files. What I’ve done specifically for this is to navigate to Github to the new repository path https://github.com/new. Then I selected the following options:
Repository Name: terraform-todo-list
Description: This is the infrastructure project that I’ll be using to “turn on” and “turn off” my development environment every day.
Public Repo
I checked Initialize this repository with a README and then added the .gitignore file with the Terraform template, and the Apache License 2.0.
This repository is now available at https://github.com/Adron/terraformer-todo-list. All of the steps and details outlined in this blog entry will be available in this repository plus any of my ongoing work on bastion servers, clusters, kubernetes, or other related items specific to my development needs.
With the repository cloned locally via git clone git@github.com:Adron/terraformer-todo-list.git there needs to be a main.tf file created. Once created I’ve added the azurerm provider block provider "azurerm" { version = "=1.27.0" } into the file. This enables Terraform to be executed from this repository directory with terraform init. Running this will pull down the azurerm provider dependency. If everything succeeds you’ll get a response from the command.
However if it fails, routinely I’ve ended up out of sync with Terraform version vs. provider version. As mentioned above we definitely need 1.20.0 or greater for the examples in this post. However, I’m also running Terraform at version 0.12.6 which requires at least 1.27.0 of the azurerm or better. If you see an error like this, it’s usually informative and you’ll just need to change the version number so the version of Terraform you’re using will pull down the right version.
Next I run terraform plan and everything should respond with no change to infrastructure requested response.
At this point I want to verify authentication against my Azure account with my Terraform CLI, to do this there are two additional fields that need to be added to the provider: subscription_id and tenant_id. The configuration will look similar to this, except with the subscription id and tenant id from the az account list data that was retrieved earlier when setting up and finding the the Azure account details from the Azure CLI.
Run terraform plan again to see the authentication results, which will look just like the terraform plan results above. With this done there’s just one more thing to do so that we have a good work space in which to work with Terraform against Azure. I always, at this point of any project with Terraform and Azure, setup a Resource Group.
Verification Checklist
Terraform is installed and executable from the terminal in whichever folder on the system.
Azure CLI is installed and executable from the terminal in whichever folder on the system.
The Azure CLI has been used to login to the Azure account and the subscription/account set for use as the default subscription/account for the Azure CLI commands.
A repository has been setup on Github (here) that has a main.tf file that I used to create a single Azure Resource Group in which to do future work within.
3: Azure Resource Group
Just for clarity, a few details about the resource group. A Resource Group in Azure is a grouping that should share the same lifecycle, which is exactly what I’m aiming to do with all of these resources on a day to day basis for development. Every day I intend to start these resources in this Resource Group and then shut them all down at the end of the day.
There are other specifics about what exactly a Resource Group is, but I’ll leave the documentation to be read to elaborate further, for my mission today I just want to have a Resource Group available for further Terraform work. In Terraform the way I go about creating a Resource Group is by adding the following to my main.tf file.
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Run terraform plan to see the changes. Then run terraform apply to make the changes, which will need a confirmation of yes.
Once I’m done with that I go ahead and issue a terraform destroy command, giving it a yes confirmation when asked, to destroy and wrap up this work for now.
Verification Checklist
Terraform is installed and executable from the terminal in whichever folder on the system.
Azure CLI is installed and executable from the terminal in whichever folder on the system.
The Azure CLI has been used to login to the Azure account and the subscription/account set for use as the default subscription/account for the Azure CLI commands.
A repository has been setup on Github (here) that has a main.tf file that I used to create a single Azure Resource Group in which to do future work within.
I ran terraform destroy to clean up for this set of work.
4: Using Environment Variables
There is one more thing before I want to commit this code to the repository. I need to get the subscription id and tenant id out of the main.tf file. One wouldn’t want to post their cloud access and identification information to a public repository, or ideally to any repository. The easy fix for this is to implement some interpolated variables to pull from environment variables. I can then set the environment variables via my startup script (such as .bash_profile or .bashrc or even the IDE I’m running the Terraform from like Intellij or Webstorm for example). In that script setting the variables would look something like this.
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Note that each variable is prepended with TF_VAR. This is the convention so that Terraform will look through and pick up all of the variables that it needs to work with. Once these variables are added to the startup script, run a source ~/.bashrc (linux) or source ~/.bash_profile (on Mac) to set those variables. For Windows check out this to set the environment variables. With that set there are a few more steps.
In the repository create a file named variables.tf and add the two variables variable "subscription_id" {} and variable "tenant_id" {}. Then in the main.tf file change the subscription_id and tenant_id fields to be assigned variables like subscription_id = var.subscription_id and tenant_id = var.tenant_id. Now run terraform plan and these results should display.
Now the terraform apply can be applied or terraform destroy to create or destroy the Resource Group. The last step now is to just commit this infrastructure code with the variables now removed from the main.tf file.
git add -A
git commit -m 'First executable resource.'
git rebaseto pull in all the remote default files and such and merge those with the local additions.
git push -u origin master then to push the changes and set the master local branch to track with the remote branch master.
Verification Checklist
Terraform is installed and executable from the terminal in whichever folder on the system.
Azure CLI is installed and executable from the terminal in whichever folder on the system.
The Azure CLI has been used to login to the Azure account and the subscription/account set for use as the default subscription/account for the Azure CLI commands.
A repository has been setup on Github (here) that has a main.tf file that I used to create a single Azure Resource Group in which to do future work within.
I ran terraform destroy to clean up for this set of work.
Private sensitive data has been moved from the main.tf file into environment variables so that it isn’t copied to the repository.
A variables.tf file has been added for the aforementioned variables that map to environment variables.
SITREP = Situation Report. It’s military speak. 💂🏻♂️
Apache Cassandra is one of the most popular databases in use today. It has many characteristics and distinctive architectural details. In this post I’ll provide a description and some details for a number of these features and characteristics, divided as such. Then, after that (i.e. toward the end, so skip there if you just want to the differences) I’m doing to summarize key differences with the latest release of the DataStax Enterprise 6 version of the database.
Cassandra Characteristics
Cassandra is a linearly scalable, highly available, fault tolerant, distributed database. That is, just to name a few of the most important characteristics. The Cassandra database is also cross-platform (runs on any operating systems), multi-cloud (runs on and across multiple clouds), and can survive regional data center outages or even in multi-cloud scenarios entire cloud provider outages!
Columnar Store, Column Based, or Column Family? What? Ok, so you might have read a number of things about what Cassandra actually is. Let’s break this down. First off, a columnar or column store or column oriented database guarantees data location for a single column in a node on disk. The column may span a bunch of or all of the rows that depend on where or how you specify partitions. However, this isn’t what the Cassandra Database uses. Cassandra is a column-family database.
A column-family storage architecture makes sure the data is stored based on locality of the data at the partition level, not the column level. Cassandra partitions group rows and columns split by a partition key, then clustered together by a specified clustering column or columns. To query Cassandra, because of this, you must know the partition key in order to avoid full data scans!
Cassandra has these partitions that guarantee to be on the same node and sort strings table (referred to most commonly as an SSTable *) in the same location within that file. Even though, depending on the compaction strategy, this can change things and the partition can be split across multiple files on a disk. So really, data locality isn’t guaranteed.
Column-family stores are great for high throughput writes and the ability to linearly scale horizontally (ya know, getting lots and lots of nodes in the cloud!). Reads using the partition key are extremely fast since this key points to exactly where the data resides. However, this often – at least last I know of – leads to a full scan of the data for any type of ad-hoc query.
A sort of historically trivial but important point is the column-family term comes from the storage engine originally used based on a key value store. The value was a set of column value tuples, which where often referenced as family, and later this family was abstracted into partitions, and then the storage engine was matched to that abstraction. Whew, ok, so that’s a lot of knowledge being coagulated into a solid eh! [scuse’ my odd artful language use if you visualized that!]
With all of this described, a that little history sprinkled in, when reading the description of Cassandra in the README.asc file of the actual Cassandra Github Repo things make just a little more sense. In the file it starts off with a description,
Apache Cassandra is a highly-scalable partitioned row store. Rows are organized into tables with a required primary key.
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster.
Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Now that I’ve covered the 101 level of what Cassandra is I’ll give a look at DataStax and their respective offering.
DataStax
DataStax Enterprise at first glance might be a bit confusing since immediate questions pop up like, “Doesn’t DataStax make Cassandra?”, “Isn’t DataStax just selling support for Cassandra?”, or “Eh, wha, who is DataStax and what does this have to do with Cassandra?”. Well, I’m gonna tell ya all about where we are today regarding all of these things fit.
Performance
DataStax provides a whole selection of amenities around a database, which is derived from the Cassandra Distributed Database System. The core product and these amenities are built into what we refer to as the “DataStax Enterprise 6“. Some of specific differences are that the database engine itself has been modified out of band and now delivers 2x the performance of the standard Cassandra implemented database engine. I was somewhat dubious when I joined but after the third party benchmarks where completed that showed the difference I grew more confident. My confidence in this speed increase grew as I’ve gotten to work with the latest version I can tell in more than a few situations that it’s faster.
Read Repair & NodeSync
If you already use Cassandra, read repair works a certain way and that still works just fine in DataStax Enterprise 6. But one also has the option of using NodeSync which can help eliminate scripting, manual intervention, and other repair operations.
Spark SQL Connectivity
There’s also an always on SQL Engine for automated uptime for apps using DataStax Enterprise Analytics. This provides a better level of analytics requests and end -user analytics. Sort of on this related note, DataStax Studio also has notebook support for Spark SQL now. Writing one’s Spark SQL gets a little easier with this option.
Multi-Cloud / Hybrid-Cloud
Another huge advantage of DataStax Enterprise is going multi-cloud or hybrid-cloud with DataStax Enterprise Cassandra. Between the Lifecycle Manager (LCM), OpsCenter, and related tooling getting up and running with a cluster across a varying range of data-centers wherever they may be is quick and easy.
Summary
I’ll be providing deeper dives into the particular technology, the specific differences, and more in the future. For now I’ll wrap up this post as I’ve got a few others coming distinctively related to distributed database systems themselves ranging from specific principles (like CAP Theorem) to operational (how to and best ways to manage) and development (patterns and practices of developing against) related topics.
Overall the solutions that DataStax offers are solid advantages if you’re stepping into any large scale data (big data or whatever one would call their plethora of data) needs. Over the coming months I’ve got a lot of material – from architectural research and guidance to tactical coding implementation work – that I’ll be blogging about and providing. I’m really looking forward to exploring these capabilities, being the developer advocate to DataStax for the community of users, and learning a thing or three million.
Over the last few weeks the I’ve been putting together multi-cloud conversations and material related to multi-cloud implementation, conversations, and operational situations that exist today. I took a quick look at some of my repos on Github and realized I’d put together a multi-cloud Node.js sample app some time ago and should update it. I’ll get to that, hopefully, but I also stumbled into some tweets and other material I wanted to collect a few of them together.
Our Killrvideo Reference Application Work @ DataStax which we’ll have more coverage of, blog entries, and related material coming out in the coming weeks about what we’ve been doing to make this reference application multi-cloud ready and then multi-cloud capable.
A well worded tweet… lot’s of talk per Google’s somewhat underlying push for GKE on prem. Which means more clouds, more zones, and more multi-cloud options.
MyPOV: @googlecloud announces GKE on prem. It's just another zone.
Here's to #multicloud and a consistent approach across all deployment types
— @ces R “Ray” Wang 王瑞光 #1A #RuleTheWorld #CES2021 (@rwang0) July 24, 2018
Distributed Data Show Conversations
Leave a comment, tweet at me (@adron), let me know your thoughts or what you’re working on re: multi-cloud. I’m curious to learn about and know more war stories.
Verification Checklist
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