Do Be Dense! Dell C8000 unit merges best of bladed and rackable servers

“Double wide” is not a term I’ve commonly applied to servers, but that’s one of the cool things about this new class of servers that Dell, my employer, started shipping today.

My team has been itching for the chance to start cloud and big data reference architectures using this super dense and flexible chassis. You’ll see it included in our next Apache Hadoop release and we’ve already got customers who are making it the foundation of their deployments (Texas Adv Computing Center case study).

If you’re tracking the latest big data & cloud hardware then the Dell PowerEdge C8000 is worth some investigation.

Basically, the Dell C8000 is a chassis that holds a flexible configuration of compute or storage sleds. It’s not a blade frame because the sleds minimize shared infrastructure. In our experience, cloud customers like the dedicated i/o and independence of sleds (as per the Bootstrapping clouds white paper). Those attributes are especially well suited for Hadoop and OpenStack because they support a “flat edges” and scale out design. While i/o independence is valued, we also want shared power infrastructure and density for efficiency reasons. Using a chassis design seems to capture the best of both worlds.

The novelty for the Dell PowerEdge C8000 is that the chassis are scary flexible. You are not locked into a pre-loaded server mix.

There are a plethora of sled choices so that you can mix choices for power, compute density and spindle counts. That includes double-wide sleds positively brimming with drives and expanded GPU processers. Drive density is important for big data configurations that are disk i/o hungry; however, our experience is the customer deployments are highly varied based on the planned workload. There are also significant big data trends towards compute, network, and balanced hardware configurations. Using the C8000 as a foundation is powerful because it can cater to all of these use-case mixes.

That reminds me! Mike Pittaro (our team’s Hadoop lead architect) did an excellent Deploy Hadoop using Crowbar video.

Interested in more opinions about the C8000? Check out Barton George & David Meyer.

Crowbar Celebrates 1st Anniversary

Nearly a year ago at OSCON 2011, my team at Dell opened sourced “Crowbar, an OpenStack installer.” That first Github commit was a much more limited project than Crowbar today: there was no separation into barclamps, no distinct network configuration, one operating system option and the default passwords were all “openstack.” We simply did not know if our effort would create any interest.

The response to Crowbar has been exciting and humbling. I most appreciate those who looked at Crowbar and saw more than a bare metal installer. They are the ones who recognized that we are trying to solve a bigger problem: it has been too difficult to cope with change in IT operations.

During this year, we have made many changes. Many have been driven by customer, user and partner feedback while others support Dell product delivery needs. Happily, these inputs are well aligned in intent if not always in timing.

  • Introduction of barclamps as modular components
  • Expansion into multiple applications (most notably OpenStack and Apache Hadoop)
  • Multi-Operating System
  • Working in the open (with public commits)
  • Collaborative License Agreements

Dell‘s understanding of open source and open development has made a similar transformation. Crowbar was originally Apache 2 open sourced because we imagined it becoming part of the OpenStack project. While that ambition has faded, the practical benefits of open collaboration have proven to be substantial.

The results from this first year are compelling:

  • For OpenStack Diablo, coordination with the Rackspace Cloud Builder team enabled Crowbar to include the Keystone and Dashboard projects into Dell’s solution
  • For OpenStack Essex, the community focused work we did for the March Essex Hackday are directly linked to our ability to deliver Dell’s OpenStack-Powered Essex solution over two months earlier than originally planned.
  • For Apache Hadoop distributions for 3.x and 4.x with implementation of Cloudera Manager and eco system components.
  • We’ve amassed hundreds of mail subscribers and Github followers
  • Support for multiple releases of RHEL, Centos & Ubuntu including Ubuntu 12.04 while it was still in beta.
  • SuSE does their own port of Crowbar to SuSE with important advances in Crowbar’s install model (from ISO to package).

We stand on the edge of many exciting transformations for Crowbar’s second year. Based on the amount of change from this year, I’m hesitant to make long term predictions. Yet, just within next few months there are significant plans based on Crowbar 2.0 refactor. We have line of site to changes that expand our tool choices, improve networking, add operating systems and become more even production ops capable.

That’s quite a busy year!

Crowbar deploying Dell | Cloudera 4 | Apache Hadoop

Hopefully you wrote “Cloudera 3.7” in pencil on your to-do list because the Dell Crowbar team has moved to CHD4 & Cloudera Enterprise 4.0. This aligns with the Cloudera GA announcement on Tuesday 6/5 and continues our drive keep Crowbar deployments both fresh and spicy.

With the GA drop, the Crowbar Cloudera Barclamps are effectively at release candidate state (ISO). The Cloudera Barclamps include a freemium version of Cloudera Enterprise 4 that supports up to 50 nodes.

I’m excited about this release because it addresses concerns around fault tolerance, multi-tenant and upgrade.

These tools are solving real world problems ranging from data archival, ad hoc analysis and click stream analysis. We’ve invested a lot of Crowbar development effort in making it fast and easy to build a Hadoop cluster. Now, Cloudera makes it even easier to manage and maintain.

We need an OpenStack Reference Deployment (My objectives for Deploy Day)

I’m overwhelmed and humbled by the enthusiasm my team at Dell is seeing for the OpenStack Essex Deploy day on 5/31 (or 6/1 for Asia). What started as a day for our engineers to hack on Essex Cookbooks with a few fellow Crowbarians has morphed into an international OpenStack event spanning Europe, Americas & Asia.

If you want to read more about the event, check out my event logistics post (link pending).

I do not apologize for my promotion of the Dell-lead open source Crowbar as the deployment tool for the OpenStack Essex Deploy. For a community to focus on improving deployment tooling, there must be a stable reference infrastructure. Crowbar provides a fast and repeatable multi-node environment with scriptable networking and packaging.

I believe that OpenStack benefits from a repeatable multi-node reference deployment. I’ll go further and state that this requires DevOps tooling to ensure consistency both within and between deployments.

DevStack makes trunk development more canonical between different developers. I hope that Crowbar will help provide a similar experience for operators so that we can truly share deployment experience and troubleshooting. I think it’s already realistic for Crowbar deployments to a repeatable enough deployment that they provide a reference for defect documentation and reproduction.

Said more plainly, it’s a good thing if a lot of us use OpenStack in the same way so that we can help each out.

My team’s choice to accelerate releasing the Crowbar barclamps for OpenStack Essex makes perfect sense if you accept our rationale for creating a community baseline deployment.

Crowbar is Dell-lead, not Dell specific.

One of the reasons that Crowbar is open source and we do our work in the open (yes, you can see our daily development in github) is make it safe for everyone to invest in a shared deployment strategy. We encourage and welcome community participation.

PS: I believe the same is true for any large scale software project. Watch out for similar activity around Apache Hadoop as part of our collaboration with Cloudera!

Crowbar v1.3 Release adds Cloudera Hadoop & RHEL/Centos 6.2. Preps v1.4 for Essex & Ubuntu 12.04

I’m very pleased to post we’ve cut the 1.3 Crowbar release!

1.3 Release Highlights (branch “elefante”)

  • Introduction of Cloudera 3.7 Hadoop Barclamp
  • New Operating System versions: Ubuntu 11.10, RHEL 6.2, Centos 6.2
  • Upgrade the Sledgehammer image to Centos 6.2
  • Alias & Group Feature (Alias is linked into DNS & Chef Search)
  • Barclamp import from UI
  • Pre-populate Node names & descriptions
  • Export of logs & database snapshot from UI
  • For the Dell Additions: Support for 12g Hardware Models (720xd & 720) via WSMAN

1.4 Previews already in the tree (“essex-hack” branch)

We’ve been working in advance for the 1.4 Release on the Essex-Hack branch.

  • Ubuntu 12.04 Support
  • OpenStack Essex Packages (from Ubuntu)

Cloudera Manager Barclamp posted! (part of updated Dell | Cloudera Apache Hadoop Solution)

My team at Dell has been driving to transparency and openness around Crowbar plus our OpenStack and Hadoop powered solutions.  Specifically, our work for our coming release is maintained in the open on the Dell CloudEdge Github site.  You can see (and participate in!) our development and validation work in advance of our official release.

I’m pleased to note that our Cloudera Manager barclamp has been posted to Github!

This barclamp supersedes  the Hadoop barclamp in the next release of the Dell | Cloudera Apache Hadoop solution.  You can built it in Crowbar using the “cloudera-os-build”  branch for Crowbar.  Do not fear!  The Hadoop barclamp still exists (hadoop-os-build branch).

Both the new and original Hadoop barclamp use the Cloudera Hadoop distribution (aka CDH); however, the new barclamp is able to leverage Cloudera‘s latest management capabilities.  For the Dell solution, Cloudera Manager has always been part of the offering.  The primary difference is that we are improving the level of integration.  I promise to post more about the features of the solution as we get closer to release.

Analyze This! Big Data | Apache Hadoop | Dell | Cloudera | Crowbar

This article about Target using buying patterns to expose a teen was pregnant before she told her parents puts big data analysis into everyday terms better than the following 555 words (of course, I recommend that you read both).

Recently, I had the pleasure of being one of our team presenting Dell’s BIG DATA story at an internal conference. From the questions and buzz, it’s clear that the big data is big news this year. My team is at the center of that storm because we are responsible for the Dell | Cloudera Apache™ Hadoop™ solution. The solution is significant because we’ve integrated many pieces necessary to build and sustain a Hadoop cluster: that includes Dell servers, the Cloudera Hadoop distribution, the Crowbar framework and Services to make it useful.

Big Data Analytics spins data straws into information gold.

Before I jump into technical details, it’s worth stating the big data analytics value proposition. The problem is that we are awash in a tsunami of data: we’ve grown beyond the neat rows and columns of application databases, data today include source like website click logs and emails to call records and cash register receipts to including social media tweets and posts. While much of the data is unstructured noise, there is also incredibility valuable information.  (video of my Hadoop “escalator pitch”)

Value is not just hidden inside the bulk data; it lies in correlations between sets of the data.

The big data analytics value proposition is to provide a system to hold a lot of loosely structured information (thus “big data”) and then sift and correlate the information (thus “analytics”). The result is a technology that helps us make data driven decisions. In many applications, the analysis is fed directly back into applications so they can alter behavior in near real-time. For example, an online retail store could offer you purple bunny slippers as you browse for crowbars in the hardware section knowing that you’re reading this post. That is the type of correlations on disparate data that I’m talking about.

This is really two problems: storing a lot of data and then computing over it.

Hadoop, the leading open source big data analytics project, is a suite of applications that implement and extend two core capabilities: a distributed file system (HDFS) and the map-reduce (M-R) algorithm. My point is not to define Hadoop (others have done better and here); instead, I want to highlight that it’s a combination big data analysis is a merger of storage and compute. When learning about any big data analysis solution, you cannot decouple how the data is stored from how the data is analyzed – storage and compute are fundamentally linked.

For that reason, the architecture of a Hadoop cluster is different than either a traditional database or compute cluster. The IO and the resiliency patterns are different. Since Hadoop is a distributed system, hardware redundancy is less important and eliminating IO bottlenecks is paramount. For this reason, our Hadoop clusters use a lot of local, non-RAID drives with a target of delivering a 1:1 CPU core to spindle ratio (ratios are tuned based on planned loads).

Imagine that you are looking for correlations in web click data. To do that analysis, Hadoop need to spend a lot of time cracking open log files, sifting for specific data and then reporting back its results. That process involves thousands of jobs each doing disk IO, CPU & RAM workload and then network transfer; consequently, contention between network and disk demands reduces performance.

Wow… that’s a lot of description and just scratching the surface of Big Data Analytics. I’ll going to have to add the technical details about the Dell solution architecture (Hardware) and software components (Cloudera & Crowbar) in another post.