Welcome!

Microservices Expo Authors: Zakia Bouachraoui, Pat Romanski, Elizabeth White, Liz McMillan, Yeshim Deniz

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Containers Expo Blog, Release Management , Apache

@CloudExpo: Blog Post

Cloudera Impala – Closing the Near Real Time Gap Working with Big Data

Building data structures and loading data

By

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.

Read the original blog entry...

More Stories By Bob Gourley

Bob Gourley writes on enterprise IT. He is a founder of Crucial Point and publisher of CTOvision.com

Microservices Articles
When building large, cloud-based applications that operate at a high scale, it’s important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. “Fly two mistakes high” is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee A...
Lori MacVittie is a subject matter expert on emerging technology responsible for outbound evangelism across F5's entire product suite. MacVittie has extensive development and technical architecture experience in both high-tech and enterprise organizations, in addition to network and systems administration expertise. Prior to joining F5, MacVittie was an award-winning technology editor at Network Computing Magazine where she evaluated and tested application-focused technologies including app secu...
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure ...
Modern software design has fundamentally changed how we manage applications, causing many to turn to containers as the new virtual machine for resource management. As container adoption grows beyond stateless applications to stateful workloads, the need for persistent storage is foundational - something customers routinely cite as a top pain point. In his session at @DevOpsSummit at 21st Cloud Expo, Bill Borsari, Head of Systems Engineering at Datera, explored how organizations can reap the bene...
Using new techniques of information modeling, indexing, and processing, new cloud-based systems can support cloud-based workloads previously not possible for high-throughput insurance, banking, and case-based applications. In his session at 18th Cloud Expo, John Newton, CTO, Founder and Chairman of Alfresco, described how to scale cloud-based content management repositories to store, manage, and retrieve billions of documents and related information with fast and linear scalability. He addresse...
The now mainstream platform changes stemming from the first Internet boom brought many changes but didn’t really change the basic relationship between servers and the applications running on them. In fact, that was sort of the point. In his session at 18th Cloud Expo, Gordon Haff, senior cloud strategy marketing and evangelism manager at Red Hat, will discuss how today’s workloads require a new model and a new platform for development and execution. The platform must handle a wide range of rec...
SYS-CON Events announced today that DatacenterDynamics has been named “Media Sponsor” of SYS-CON's 18th International Cloud Expo, which will take place on June 7–9, 2016, at the Javits Center in New York City, NY. DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
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 ...
In his keynote at 19th Cloud Expo, Sheng Liang, co-founder and CEO of Rancher Labs, discussed the technological advances and new business opportunities created by the rapid adoption of containers. With the success of Amazon Web Services (AWS) and various open source technologies used to build private clouds, cloud computing has become an essential component of IT strategy. However, users continue to face challenges in implementing clouds, as older technologies evolve and newer ones like Docker c...