|By Bob Gourley||
|November 27, 2012 07:05 AM EST||
On October 24, 2012 Cloudera announced the release of Cloudera Impala and the commercial support subscription service of Cloudera Enterprise Real Time Query (RTQ). During the Hadoop World/STRATA Conference in NYC, I was invited over to see a demonstration. Impala is a SQL based Real Time Query/Ad Hoc query engine built on top of HDFS or Hbase. As I watched the demonstration unfold, I wondered if one of the remaining technology gaps in the NOSQL arsenal had been closed. What gap you ask? Near Real Time Analytics on a NOSQL stack. Working with customers across the Cyber Security customer space, not only do they face the familiar BIGDATA horsemen of the apocalypse: Volume, Velocity and Variety but one more large challenge crept in: Time (V3T). The Near Real Time Analysis/Near Real Time Analytic capability that Cloudera Impala provides is essential in many high value use cases associated with Cyber Security: comparing current activity with observed historical norms, correlation of many disparate data sources/enrichment and automated threat detection algorithms.
When the demonstration concluded, the Cloudera representatives and I discussed the potential of performing an informal independent evaluation of Cloudera Impala against some of the common Real Time/Near Real Time use cases in Cyber Security. I agreed to step up and perform an independent evaluation as well as developing a demonstration platform for FedCyber 2012 (almost three weeks hence for inquiring minds). So let us set the field: a new BETA technology, NO prior exposure to the technology or documentation, a vendor making promises, addressing a large technology gap and three weeks to implement, seemed straight forward; no pressure.
The day after I returned from the STRATA Conference, I returned to my office and provisioned four Virtual Machines in order to build the Impala demonstration. As a committer/contributor for SherpaSurfing an open source Cyber Security solution, I have an abundance of data sets, enrichment sources, Hive data structures and services. Given the amount of time and the audience for FedCyber 2012, I decided to focus on some Intrusion Detection and Netflow related use cases for the demonstration. The data sets for the demonstration included base data sets: 20 million Netflow events, 8 million Intrusion Detection System events and enrichment: Geographic, Blacklist, Whitelist and Protocol related information. Each of the selected uses cases for this demonstration is critical to the Perform Near-Real Time Network Analysis domain in Cyber Security. The name for the demonstration system was decided to be the Impala Mission Demonstration Platform (IMDP). The IMDP was implemented based on vendor recommendations with no tuning or optimization.
The IMDP effort provided me with my first opportunity to work with Cloudera Manager. Although this post is focused on Cloudera Impala I would be remiss not to mention Cloudera Manager. I have worked with Hadoop since 1.0 and built more than a few clusters over the years. I used the installation and configuration guides provided with Cloudera Impala and followed the recommendations. One of the first recommendations was use of the Cloudera Manager. Using the Cloudera Manager (CDH 4.1), I was able to roll out a four node cluster in two hours. I was able to discover the hosts, manage services and provision them in accordance with the IMDP deployment plan. The deployment plan consisted of:
- node 1 – hbase, hdfs, impala, mapreduce
- node2 – hbase, hdfs, impala, mapreduce
- node3 – hbase(region server, master), hdfs(namenode), impala(impalad, statestore), mapreduce(job tracker, tasktracker) , hue, oozie and zookeeper
- node4 – Application Tier, Cloudera Manager
The Cloudera Manager saved at least two days of effort in deploying the cluster, the tight integration with the support portal, comprehensive help and one place to work with all properties of the entire cluster and view space consumption metrics; verdict on Cloudera Manager: Cloudera masterful, bold stroke, thumbs up.
Now that the cluster build-out completed; I shifted attention to deploying and configuring the Cloudera Impala service. Using Cloudera Manager, I deployed Impala on three nodes: three instances of Impalad and one impala state store, in a matter of minutes. I completed the deployment and configuration of the Hive MetaStore. Keeping in mind this is a BETA; the documentation was complete, but fragmented on deployment and configuration (HIVE MetaStore portion); verdict on impala deployment and configuration: solid for a BETA (needs an example hive-site.xml, configuration guide needs better flow).
At this point all configuration and deployment was completed, attention turned to building data structures and loading data. I took the Data Definition Language (DDL) scripts or data structures for ten data sources and enrichment; ported them over to Hive and tested them in less than four hours. It is worthy of mention that the data sources for this demonstration are large flat tables: netflow and intrusion detection system. Cloudera Impala uses HIVE as an Extract Transform Load (ETL) engine, using Hive I defined all of the data structures in source files which were sourced using hive shell: created a database (Sherpa). Hive was then used to load data into the tables that were just created. Creating data structures in Hive was simple as usual and loading data sets was quick (20 million netflow events in 57 seconds). Logging into impala-shell, issued a refresh of the MetaStore and I was working with data. I performed verification of the data load, all data loaded and no issues were revealed. One area of potential improvement would be more comprehensive messages on load failure. Defining the data structures and loading data using Hive was nothing new; verdict: really good; easy to use, easy to load, but need to improve failed load messages.
Finally, we moved on to the most interesting stage which is using Cloudera Impala in a series of Real Time Query (RTQ) scenarios that are common across the Cyber Security customer space. The real world scenarios selected come from the perform netflow analysis set of use case(s). In each of these scenarios, the exact same queries were executed on the same cluster using Hive and then Impala against the same data structures (database and tables). In the Hive approach, we traverse the batch processing stack and with Impala we traverse the Real Time Query (RTQ) stack performing a series of analytics. In the first use case, I ran a five tuple (sip, sport, dip, dport, protocol) summary covering bytes per packet, summing bytes and packets for a 20 million event set resulted in: identical result sets, Hive 82 seconds – Impala 6 seconds. In the second use case, I performed a summary of destination ports where the source port is 80 which resulted in: identical result sets, Hive 57 seconds, Impala 5 seconds. In the third use case, I performed correlation between netflow and intrusion detection systems, correlating netflow with intrusion detection events for several hours which resulted in: identical result sets, Hive 40 seconds, Impala sub-second. Finally, for FedCyber 2012, I developed a java based situational awareness dashboard which connected to Cloudera Impala via ODBC and executed analytics performing: correlation of blacklists, Intrusion Detection, Netflow, statistical cubes for ten hours with a refresh of every five seconds without failure or issue. The ODBC implementation easily provided the ability to export data to desktop tools (using ODBC) and common BI tools as advertised. Developing and Using Cloudera Impala verdict: This is as advertised; easy to use, easy to implement on, very fast, very flexible and more than capable of running real time analytics. The Impala shell is limited but much of the demonstration work was done using result sets so it was not an impediment.
In summation, I have worked for over a decade across the vast BIGDATA technology space covering Legacy Relational Database, Data Warehouse, and NOSQL; Cloudera Impala proved more than capable of running near real time analytics and providing mission relevance to customers with a Near Real Time (NRT) requirement. Based on my initial review Cloudera Impala appears to be a bold step in closing the gap of near real time analytics on a NOSQL stack. I did encounter some minor problems, but the few problems and limitations that were encountered in this demonstration were documented and published in the known issues document so they will not be shared; none were show stoppers.
The notes, details and all of the lessons learned, data structures and the configuration guide from the demonstration are being published out on Github under SherpaSurfing in the coming days. These documents cover everything in detail and will enable developers to replicate the demonstration platform and get a jump start on Cloudera Impala. Finally, I would like to thank two contributors: Hanh Le, Robert Webb and Six3 Systems for helping me pull this off.
For it to be SOA – let alone SOA done right – we need to pin down just what "SOA done wrong" might be. First-generation SOA with Web Services and ESBs, perhaps? But then there's second-generation, REST-based SOA. More lightweight and cloud-friendly, but many REST-based SOA practices predate the microservices wave. Today, microservices and containers go hand in hand – only the details of "container-oriented architecture" are largely on the drawing board – and are not likely to look much like S...
Oct. 8, 2015 12:00 PM EDT Reads: 468
Despite all the talk about public cloud services and DevOps, you would think the move to cloud for enterprises is clear and simple. But in a survey of almost 1,600 IT decision makers across the USA and Europe, the state of the cloud in enterprise today is still fraught with considerable frustration. The business case for apps in the real world cloud is hybrid, bimodal, multi-platform, and difficult. Download this report commissioned by NTT Communications to see the insightful findings – registra...
Oct. 8, 2015 12:00 PM EDT Reads: 238
Manufacturing has widely adopted standardized and automated processes to create designs, build them, and maintain them through their life cycle. However, many modern manufacturing systems go beyond mechanized workflows to introduce empowered workers, flexible collaboration, and rapid iteration. Such behaviors also characterize open source software development and are at the heart of DevOps culture, processes, and tooling.
Oct. 8, 2015 12:00 PM EDT Reads: 1,047
Any Ops team trying to support a company in today’s cloud-connected world knows that a new way of thinking is required – one just as dramatic than the shift from Ops to DevOps. The diversity of modern operations requires teams to focus their impact on breadth vs. depth. In his session at DevOps Summit, Adam Serediuk, Director of Operations at xMatters, Inc., will discuss the strategic requirements of evolving from Ops to DevOps, and why modern Operations has begun leveraging the “NoOps” approa...
Oct. 8, 2015 12:00 PM EDT
Between the compelling mockups and specs produced by analysts, and resulting applications built by developers, there exists a gulf where projects fail, costs spiral, and applications disappoint. Methodologies like Agile attempt to address this with intensified communication, with partial success but many limitations. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, will present a revolutionary model enabled by new technologies. Learn how busine...
Oct. 8, 2015 11:45 AM EDT Reads: 205
DevOps has often been described in terms of CAMS: Culture, Automation, Measuring, Sharing. While we’ve seen a lot of focus on the “A” and even on the “M”, there are very few examples of why the “C" is equally important in the DevOps equation. In her session at @DevOps Summit, Lori MacVittie, of F5 Networks, will explore HTTP/1 and HTTP/2 along with Microservices to illustrate why a collaborative culture between Dev, Ops, and the Network is critical to ensuring success.
Oct. 8, 2015 11:45 AM EDT
The APN DevOps Competency highlights APN Partners who demonstrate deep capabilities delivering continuous integration, continuous delivery, and configuration management. They help customers transform their business to be more efficient and agile by leveraging the AWS platform and DevOps principles.
Oct. 8, 2015 11:30 AM EDT Reads: 189
Containers are changing the security landscape for software development and deployment. As with any security solutions, security approaches that work for developers, operations personnel and security professionals is a requirement. In his session at @DevOpsSummit, Kevin Gilpin, CTO and Co-Founder of Conjur, will discuss various security considerations for container-based infrastructure and related DevOps workflows.
Oct. 8, 2015 11:15 AM EDT Reads: 156
Overgrown applications have given way to modular applications, driven by the need to break larger problems into smaller problems. Similarly large monolithic development processes have been forced to be broken into smaller agile development cycles. Looking at trends in software development, microservices architectures meet the same demands. Additional benefits of microservices architectures are compartmentalization and a limited impact of service failure versus a complete software malfunction....
Oct. 8, 2015 11:00 AM EDT Reads: 115
With containerization using Docker, the orchestration of containers using Kubernetes, the self-service model for provisioning your projects and applications and the workflows we built in OpenShift is the best in class Platform as a Service that enables introducing DevOps into your organization with ease. In his session at DevOps Summit, Veer Muchandi, PaaS evangelist with RedHat, will provide a deep dive overview of OpenShift v3 and demonstrate how it helps with DevOps.
Oct. 8, 2015 10:45 AM EDT Reads: 612
The last decade was about virtual machines, but the next one is about containers. Containers enable a service to run on any host at any time. Traditional tools are starting to show cracks because they were not designed for this level of application portability. Now is the time to look at new ways to deploy and manage applications at scale. In his session at @DevOpsSummit, Brian “Redbeard” Harrington, a principal architect at CoreOS, will examine how CoreOS helps teams run in production. Attende...
Oct. 8, 2015 10:45 AM EDT Reads: 1,206
IT data is typically silo'd by the various tools in place. Unifying all the log, metric and event data in one analytics platform stops finger pointing and provides the end-to-end correlation. Logs, metrics and custom event data can be joined to tell the holistic story of your software and operations. For example, users can correlate code deploys to system performance to application error codes.
Oct. 8, 2015 10:30 AM EDT Reads: 156
Containers are revolutionizing the way we deploy and maintain our infrastructures, but monitoring and troubleshooting in a containerized environment can still be painful and impractical. Understanding even basic resource usage is difficult - let alone tracking network connections or malicious activity. In his session at DevOps Summit, Gianluca Borello, Sr. Software Engineer at Sysdig, will cover the current state of the art for container monitoring and visibility, including pros / cons and li...
Oct. 8, 2015 10:30 AM EDT Reads: 132
As the world moves towards more DevOps and microservices, application deployment to the cloud ought to become a lot simpler. The microservices architecture, which is the basis of many new age distributed systems such as OpenStack, NetFlix and so on, is at the heart of Cloud Foundry - a complete developer-oriented Platform as a Service (PaaS) that is IaaS agnostic and supports vCloud, OpenStack and AWS. In his session at 17th Cloud Expo, Raghavan "Rags" Srinivas, an Architect/Developer Evangeli...
Oct. 8, 2015 10:15 AM EDT Reads: 148
In their session at DevOps Summit, Asaf Yigal, co-founder and the VP of Product at Logz.io, and Tomer Levy, co-founder and CEO of Logz.io, will explore the entire process that they have undergone – through research, benchmarking, implementation, optimization, and customer success – in developing a processing engine that can handle petabytes of data. They will also discuss the requirements of such an engine in terms of scalability, resilience, security, and availability along with how the archi...
Oct. 8, 2015 10:00 AM EDT Reads: 349
In a report titled “Forecast Analysis: Enterprise Application Software, Worldwide, 2Q15 Update,” Gartner analysts highlighted the increasing trend of application modernization among enterprises. According to a recent survey, 45% of respondents stated that modernization of installed on-premises core enterprise applications is one of the top five priorities. Gartner also predicted that by 2020, 75% of
Oct. 8, 2015 09:00 AM EDT Reads: 299
DevOps Summit at Cloud Expo 2014 Silicon Valley was a terrific event for us. The Qubell booth was crowded on all three days. We ran demos every 30 minutes with folks lining up to get a seat and usually standing around. It was great to meet and talk to over 500 people! My keynote was well received and so was Stan's joint presentation with RingCentral on Devops for BigData. I also participated in two Power Panels – ‘Women in Technology’ and ‘Why DevOps Is Even More Important than You Think,’ both ...
Oct. 8, 2015 08:00 AM EDT Reads: 8,648
The web app is agile. The REST API is agile. The testing and planning are agile. But alas, data infrastructures certainly are not. Once an application matures, changing the shape or indexing scheme of data often forces at best a top down planning exercise and at worst includes schema changes that force downtime. The time has come for a new approach that fundamentally advances the agility of distributed data infrastructures. Come learn about a new solution to the problems faced by software organ...
Oct. 8, 2015 08:00 AM EDT Reads: 764
What Is Emergent About Emergent Architecture? By @TheEbizWizard | @DevOpsSummit #DevOps #BigData #API
All we need to do is have our teams self-organize, and behold! Emergent design and/or architecture springs up out of the nothingness! If only it were that easy, right? I follow in the footsteps of so many people who have long wondered at the meanings of such simple words, as though they were dogma from on high. Emerge? Self-organizing? Profound, to be sure. But what do we really make of this sentence?
Oct. 8, 2015 08:00 AM EDT Reads: 389
There once was a time when testers operated on their own, in isolation. They’d huddle as a group around the harsh glow of dozens of CRT monitors, clicking through GUIs and recording results. Anxiously, they’d wait for the developers in the other room to fix the bugs they found, yet they’d frequently leave the office disappointed as issues were filed away as non-critical. These teams would rarely interact, save for those scarce moments when a coder would wander in needing to reproduce a particula...
Oct. 8, 2015 05:00 AM EDT Reads: 289