Click here to close now.


Java IoT Authors: Elizabeth White, Pat Romanski, Liz McMillan, Anders Wallgren, Betty Zakheim

Related Topics: @BigDataExpo, Java IoT, IoT User Interface, @CloudExpo, Apache, SDN Journal

@BigDataExpo: Article

So What? – Monitoring Hadoop Beyond Ganglia

Don’t just run Hadoop jobs at scale, run them efficiently and at scale

Over the last couple of months I have been talking to more and more customers who are either bringing their Hadoop clusters into production or have already done so and are now getting serious about operations. This leads to some interesting discussions about how to monitor Hadoop properly and one thing pops up quite often: Do they need anything beyond Ganglia? If yes, what should they do beyond it?

The Basics
As in every other system, monitoring in a Hadoop environment starts with the basics: System Metrics - CPU, Disk, Memory you know the drill. Of special importance in a Hadoop system is a well-balanced cluster; you don't want to have some nodes being much more (or less) utilized then others. Besides CPU and memory utilization, Disk utilization and of course I/O throughput is of high importance. After all the most likely bottleneck in a Big Data system is I/O - either with ingress (network and disk), moving data around (e.g., MapReduce shuffle on the network) and straightforward read/write to disk.

The problem in a Hadoop system is of course its size. Nothing new for us, some of our customers monitor well beyond 1000+ JVMs with CompuwareAPM. The "advantage" in a Hadoop system is its relative conformity - every node looks pretty much like the other. This is what Ganglia leverages.

Cluster Monitoring with Ganglia
What Ganglia is very good at is providing an overview over how a cluster is utilized. The load chart is particularly interesting:

This chart shows the CPU load on a 1000 Server cluster that has roughly 15.000 CPUs

It tells us the number of available cores in the system and the number of running processes (in theory a core can never handle more than one process at a time) and the 1-min load average. If the system is getting fully utilized the 1-min load average would approach the total number of CPUs. Another view on this is the well-known CPU utilization chart:

CPU Utilization over the last day. While the utilization stays well below 10% we see a lot of I/O wait spikes.

While the load chart gives a good overall impression of usage, the utilization tells us the story of how the CPUs are used. While typical CPU charts show a single server, Ganglia specializes in showing whole clusters (the picture shows CPU usage of a 1000 machine cluster). In the case of the depicted chart we see that the CPUs are experiencing a lot of I/O wait spikes, which points toward heavy disk I/O. Basically it seems the disk I/O is the reason that we cannot utilize our CPU better at these times. But in general our cluster is well underutilized in terms of CPU.

Trends are also easy to understand, as can be seen in this memory chart over a year.

Memory capacity and usage over a year

All this looks pretty good, so what is missing? The "so what" and "why" is what is missing. If my memory demand is growing, I have no way of knowing why it is growing. If the CPU chart tells me that I spend a lot of time waiting, it does not tell what to do, or why that is so? These questions are beyond the scope of Ganglia.

What about Hadoop specifics?
Ganglia also has a Hadoop plugin, which basically gives you access to all the usual Hadoop metrics (unfortunately a comprehensive list of Hadoop metrics is really hard to find, appreciate if somebody commented the link). There is a good explanation on what is interesting on Edward Caproli's page: JoinTheGrid. Basically you can use those metrics to monitor the capacity and usage trends of HDFS and the NameNodes and also how many jobs, mappers and reducers are running.

Capacity of the DataNodes over time

Capacity of the Name Nodes over time

The DataNode Operations give me an impression of I/O pressure on the Hadoop cluster

All these charts can of course be easily built in any modern monitoring or APM solution like CompuwareAPM, but Ganglia gives you a simple starting point; and it's Free as in Beer.

What's missing again is the so what? If my jobs are running a lot longer than yesterday, what should I do? Why do they run longer? A Hadoop expert might dig into 10 different charts around I/O and Network, spilling, look at log files among other things and try an educated guess as to what might be the problem. But we aren't all experts, neither do we have the time to dig into all of these metrics and log files all the time.

This is the reason that we and our customers are moving beyond Ganglia - to solve the "Why" and "So What" within time constraints.

Beyond the Basics #1 - Understanding Cluster Utilization
A use case that we get from customers is that they want to know which users or which pools (in case of the fair scheduler) are responsible for how much of the cluster utilization. LinkedIn just released White Elephant, a tool that parses MapReduce logs and builds some nice dashboards and shows you which of your users occupy how much of your cluster. This is of course based on log file analysis and thus okay for analysis but not for monitoring. With proper tools in place we can do the same thing in near real time.

The CPU Usage in the Hadoop Cluster on per User basis

In this example I wanted to monitor which user consumed how much of my Amazon EMR cluster. If we see a user or pool that occupies a lot of the cluster we can course also see which jobs are running and how much of the cluster they occupy.

The CPU Usage in the Hadoop Cluster on per Job basis

And this will also tell us if that job has always been there, and just uses a lot more resources now. This would be our cue to start analyzing what has changed.

Beyond the Basics #2 - Understanding why my jobs are slow(er)
If we want to understand why a job is slow we need to look at a high-level break down first.

In which phase of the map reduce do we spend the most time, or did we spend more time than yesterday? Understanding these timings in context with the respective job counters, like Map Input or Spilled Records, helps us understand why the phase took longer.

Overview of the time spent in different phases and the respective input/output counters

At this point we will already have a pretty good idea as to what happened. We either simply have more data to crunch (more input data) or a portion of the MapReduce job consumes more CPU (code change?) or we spill more records to disk (code change or Hadoop config change?). We might also detect an unbalanced cluster in the performance breakdown.

This job is executing nearly exclusively on a single node instead of distributing

In this case we want to check whether all the involved nodes processed the same amount of data

Here we see that there is a wide range from minimum, average to maximum on mapped input and output records. The data is not balanced

or if the difference can again be found in the code (different kinds of computations). If we are running against HBase we might of course have an issue with HBase performance or distribution.

At the beginning of the job only a single HBase region Server consumes CPU while all others remain idle

On the other hand, if a lot of mapping time is spent in the garbage collector then you should maybe invest in larger JVMs.

The Performance Breakdown of this particular job shows considerable time in GC suspension

If spilling data to disk is where we spend our time, we should take a closer look at that phase. It might turn out that all of our time is spent on disk wait.

If the Disk were the bottleneck we would see it on disk I/O here

Now if disk write is our bottleneck, then really the only thing that we can do is reduce the map output records. Adding a combiner will not reduce the disk write (it will actually increase it, read here). In other words combining only optimizes the shuffle phase, thus the amount of data sent over the network, but not spill time!!

And at the very detailed level we can look at single task executions and understand in detail what is really going on.

The detailed data about each Map, Reduce Task Atttempt as well as the spills and shuffles

Ganglia is a great tool for high-level monitoring of your Hadoop cluster utilization, but it is not enough. The fact that everybody is working on additional means to understand the Hadoop cluster (Hortonworks with Ambari, Cloudera with their Manager, LinkedIn with White Elephant, the Star Fish project...) shows that there is a lot more needed beyond simple monitoring. Even those more advanced monitoring tools are not always answering the "why" though, which is what we really need to do. This is where the Performance Management discipline can add a lot of value and really help you get the best out of your Hadoop cluster. In other words don't just run Hadoop jobs at scale, run them efficiently and at scale!

More Stories By Michael Kopp

Michael Kopp has over 12 years of experience as an architect and developer in the Enterprise Java space. Before coming to CompuwareAPM dynaTrace he was the Chief Architect at GoldenSource, a major player in the EDM space. In 2009 he joined dynaTrace as a technology strategist in the center of excellence. He specializes application performance management in large scale production environments with special focus on virtualized and cloud environments. His current focus is how to effectively leverage BigData Solutions and how these technologies impact and change the application landscape.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.

@ThingsExpo Stories
Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
With all the incredible momentum behind the Internet of Things (IoT) industry, it is easy to forget that not a single CEO wakes up and wonders if “my IoT is broken.” What they wonder is if they are making the right decisions to do all they can to increase revenue, decrease costs, and improve customer experience – effectively the same challenges they have always had in growing their business. The exciting thing about the IoT industry is now these decisions can be better, faster, and smarter. Now all corporate assets – people, objects, and spaces – can share information about themselves and thei...
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists discussed what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
Growth hacking is common for startups to make unheard-of progress in building their business. Career Hacks can help Geek Girls and those who support them (yes, that's you too, Dad!) to excel in this typically male-dominated world. Get ready to learn the facts: Is there a bias against women in the tech / developer communities? Why are women 50% of the workforce, but hold only 24% of the STEM or IT positions? Some beginnings of what to do about it! In her Day 2 Keynote at 17th Cloud Expo, Sandy Carter, IBM General Manager Cloud Ecosystem and Developers, and a Social Business Evangelist, wil...
PubNub has announced the release of BLOCKS, a set of customizable microservices that give developers a simple way to add code and deploy features for realtime apps.PubNub BLOCKS executes business logic directly on the data streaming through PubNub’s network without splitting it off to an intermediary server controlled by the customer. This revolutionary approach streamlines app development, reduces endpoint-to-endpoint latency, and allows apps to better leverage the enormous scalability of PubNub’s Data Stream Network.
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true change and transformation possible.
Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem" in this scenario: microservice A (releases daily) depends on a couple of additions to backend B (re...
I recently attended and was a speaker at the 4th International Internet of @ThingsExpo at the Santa Clara Convention Center. I also had the opportunity to attend this event last year and I wrote a blog from that show talking about how the “Enterprise Impact of IoT” was a key theme of last year’s show. I was curious to see if the same theme would still resonate 365 days later and what, if any, changes I would see in the content presented.
Apps and devices shouldn't stop working when there's limited or no network connectivity. Learn how to bring data stored in a cloud database to the edge of the network (and back again) whenever an Internet connection is available. In his session at 17th Cloud Expo, Ben Perlmutter, a Sales Engineer with IBM Cloudant, demonstrated techniques for replicating cloud databases with devices in order to build offline-first mobile or Internet of Things (IoT) apps that can provide a better, faster user experience, both offline and online. The focus of this talk was on IBM Cloudant, Apache CouchDB, and ...
Container technology is shaping the future of DevOps and it’s also changing the way organizations think about application development. With the rise of mobile applications in the enterprise, businesses are abandoning year-long development cycles and embracing technologies that enable rapid development and continuous deployment of apps. In his session at DevOps Summit, Kurt Collins, Developer Evangelist at, examined how Docker has evolved into a highly effective tool for application delivery by allowing increasingly popular Mobile Backend-as-a-Service (mBaaS) platforms to quickly crea...
With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo 2016 in New York and Silicon Valley. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be! Internet of @ThingsExpo, taking place Nov 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 17th Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound cha...
Internet of @ThingsExpo, taking place June 7-9, 2016 at Javits Center, New York City and Nov 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with the 18th International @CloudExpo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world and ThingsExpo New York Call for Papers is now open.
The cloud. Like a comic book superhero, there seems to be no problem it can’t fix or cost it can’t slash. Yet making the transition is not always easy and production environments are still largely on premise. Taking some practical and sensible steps to reduce risk can also help provide a basis for a successful cloud transition. A plethora of surveys from the likes of IDG and Gartner show that more than 70 percent of enterprises have deployed at least one or more cloud application or workload. Yet a closer inspection at the data reveals less than half of these cloud projects involve production...
Cloud computing delivers on-demand resources that provide businesses with flexibility and cost-savings. The challenge in moving workloads to the cloud has been the cost and complexity of ensuring the initial and ongoing security and regulatory (PCI, HIPAA, FFIEC) compliance across private and public clouds. Manual security compliance is slow, prone to human error, and represents over 50% of the cost of managing cloud applications. Determining how to automate cloud security compliance is critical to maintaining positive ROI. Raxak Protect is an automated security compliance SaaS platform and ma...
In his keynote at @ThingsExpo, Chris Matthieu, Director of IoT Engineering at Citrix and co-founder and CTO of Octoblu, focused on building an IoT platform and company. He provided a behind-the-scenes look at Octoblu’s platform, business, and pivots along the way (including the Citrix acquisition of Octoblu).
Today air travel is a minefield of delays, hassles and customer disappointment. Airlines struggle to revitalize the experience. GE and M2Mi will demonstrate practical examples of how IoT solutions are helping airlines bring back personalization, reduce trip time and improve reliability. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Dr. Sarah Cooper, M2Mi’s VP Business Development and Engineering, explored the IoT cloud-based platform technologies driving this change including privacy controls, data transparency and integration of real time context with p...
There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content. Join @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 7-9, 2016 in New York City, for three days of intense 'Internet of Things' discussion and focus, including Big Data's indespensable role in IoT, Smart Grids and Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) IoT's use in Vertical Markets.
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data shows "less than 10 percent of IoT developers are making enough to support a reasonably sized team....
We all know that data growth is exploding and storage budgets are shrinking. Instead of showing you charts on about how much data there is, in his General Session at 17th Cloud Expo, Scott Cleland, Senior Director of Product Marketing at HGST, showed how to capture all of your data in one place. After you have your data under control, you can then analyze it in one place, saving time and resources.