Meet Tephra, An Open Source Transaction Engine

Jul 18 2014, 8:00 am

Gary Helmling is a software engineer at Continuuity as well as an Apache HBase committer and Project Management Committee (PMC) member. Prior to Continuuity, Gary led the development of Hadoop and HBase applications at Twitter, TrendMicro, and Meetup.

Our platform, Continuuity Reactor, uses several open source technologies in the Apache Hadoop™ ecosystem to enable any developer to build data applications. One of the major components of our platform is Apache HBase, a non-relational, massively scalable column-oriented database modeled after Google’s BigTable. We use HBase for a number of reasons, including the strong data consistency it provides. One of the limitations of HBase as a standalone system, however, is that data updates are consistent only within a single region, or a set of contiguous rows, because it is very difficult to coordinate updates across these regions in a way that maintains scalability.

As a result, one of the tradeoffs is that HBase maintains consistency for a single row or region of rows, but anything across regions or tables, cannot be updated atomically—i.e., where the entire transaction is committed as one—nor can you do an atomic update that spans multiple remote procedure calls (RPCs). While we value what HBase provides, we believe providing globally consistent transactions simplifies application development a great deal, allowing developers to focus more on the problems and use cases they care about rather than on implementing complex data access patterns.

This is why we built Tephra, a distributed, scalable transaction engine designed for HBase and Hadoop. Tephra can also be extended to integrate with other NoSQL systems like MongoDB and LevelDB as well as traditional relational databases and data warehouses. Tephra is a powerful data management tool that makes a wide range of use cases easier to solve, especially online and OLTP applications. It utilizes the key features of HBase to make transactional capabilities available without sacrificing overall performance.

Today we’re open sourcing Tephra for anyone to use because we believe that the broader developer community can benefit from it, and for anyone to contribute to because we have built Tephra with extensibility in mind.

How can developers use Tephra?

One common use case is secondary indexes. Developers typically create secondary indexes on HBase by writing updates to a second table with additional rows that reference the rows in the main table based on the index values. The problem is that there isn’t consistency in operations across the two tables, so they can get out of sync. Based on their actual data access patterns and what their application cares about, developers are forced to adopt more complicated application logic to manage the data and work around the inconsistencies. In contrast, Tephra simplifies this use case by allowing updates to both tables to be performed in a single globally consistent transaction.

Why are we open sourcing Tephra?

Many developers and companies are successfully using HBase, but there are still gaps in its accessibility to developers. Tephra takes the strong foundation that HBase has given us to build upon and enhances it by making it more developer-friendly and broadening the potential users and use cases of HBase. We are open sourcing the technology because we want to give back to the community and believe Tephra will be useful to a broad range of developers.

We also are excited to see how others will use, apply, and extend Tephra transactions to their own applications, infrastructures, and environments. We recognize that developers have specific needs, some of which we haven’t anticipated, and we look forward to Tephra growing as a project and community.

Learn more and get involved

Check out the release notes or our slideshare for more details about Tephra. And please help us make the project better by joining our user and developer mailing list and contributing and reporting any issues, bugs, or ideas.

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Behind the scenes: Hacking our way to success

Jul 7 2014, 8:47 pm

Sreevatsan Raman is a software engineer at Continuuity where he is building and architecting a platform fueling the next generation of Big Data applications. Prior to Continuuity, Sree designed and implemented big data infrastructure at Klout, Nominum, and Yahoo!

We just wrapped up our latest hackathon and it was a great reminder of the unique engineering culture we have at Continuuity. We have created a new application development platform, Continuuity Reactor, which is focused on allowing developers to quickly and easily build Big Data applications.

Building a platform that no one has created before is a big challenge. We break this huge effort into a continuous cadence of platform releases that are delivered to production frequently. Before every release we take a break from our daily efforts and hack on our platform for 48 hours where we stretch our imaginations and the platform capabilities we just built.

Every hackathon gives us an opportunity to dog-food our technology. We come together wearing our developer hats to build features and applications, incorporating our lessons learned into continually improving the developer experience, with the goal of making Hadoop more simple and accessible.

One of my favorite aspects of our hackathons is how the whole company comes together to build cool stuff and have fun. From our CEO to our engineering team to people in non-technical roles, everyone participates. Here are some thoughts and experiences about our company, culture, and hackathons from our awesome engineering interns:

Shu Das, University of Michigan

The unique aspect of Continuuity that I like is that everyone has a clear sense of his or her agenda and responsibilities, so we’re empowered to stay on top of our game. Not only do I have the resources I need and responsiveness from the rest of the team, but also the working environment at Continuuity is lively and enjoyable.

My first project was building an application on Reactor that visualizes data about the test cases we run on our code. This work gave me great insights into what our platform is, how to use it, and how our technology can be used for simplifying Hadoop. I really appreciate the fact that the feature I worked on is used daily, as a component of the development lifecycle, and not left off as a side project.

For the hackathon, I teamed up with Kenneth and Gourav (see below) to build a Reactor application that can be used to aggregate, correlate, and visualize data - for instance, metrics, logs, or any other events. It was amazing to see the application built in a very short amount of time using new core functionalities of the platform and dogfooding the new APIs, runtime, and documentation.

Gourav Khaneja, University of Illinois

The work here is interesting because the problems we’re solving are hard. One of my favorite aspects of Continuuity is the willingness of team members to help each other to work through challenges. For example, even during crunch time, every Continuuity member is willing to stop what he or she is doing to help out a fellow employee. I learn a lot from the team on a daily basis.

When I joined, I was tasked with optimizing resource allocation in YARN using Apache Twill. YARN has a large codebase and although my previous experience with a large code base was limited, I was able to come up to speed quickly with great mentorship from the team and contributed towards a major feature in Twill.

Kenneth Le, University of California, Berkeley

Interns are involved in relevant projects right away. While we receive guidance when needed, the focus of the internship program is more on empowering us to deliver and learning more via open communication about the various projects that other people are working on.

My first project was improving a developer tool that is used to deploy code to clusters. The existing tool took about 30 minutes to build and deploy the entire code base. The newer version, which I rewrote in Python, takes about 6 minutes, thus saving developers a lot of time in their development life-cycle.

Julien Guery, Ecole nationale supérieure des Télécommunications de Bretagne

This is an extremely technical company solving challenging problems. One of the first things I noticed is that the interns get to be part of the core engineering team and are involved in all aspects of the company.

In my first project I learned a lot about Apache Hive and the Reactor platform while working on a feature to bring ad-hoc quering capabilities in to our platform. I had great mentors who taught me how to test and debug and gave me insights into the architecture of the systems, and now I can dive right into new projects and teams without fear.

During the hackathon, I used our APIs to build a Python SDK. I wanted to showcase how Python developers can easily write big-data applications using our platform and my efforts during the hackathon demonstrated how this could be accomplished. The hack was well received and a updated version of this SDK will be made available in a future release.


Our team is working to solve a difficult problem – making Hadoop a platform upon which data applications can be built by all developers. Whether at our hackathons or at our weekly company-wide demos, we are constantly sharing and collaborating so everyone can understand the impact that they have and the context of how their contributions map to the overall vision and mission of the company.

If you’re interested in learning more about our culture and careers opportunities at Continuuity, check out http://continuuity.com/careers.

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Hadoop Summit: Where is the value? Where are the apps?

Jun 24 2014, 8:00 am

Jonathan Gray, Founder & CEO of Continuuity, is an entrepreneur and software engineer with a background in open source and data. Prior to Continuuity, he was at Facebook working on projects like Facebook Messages. At startup Streamy, Jonathan was an early adopter of Hadoop and HBase committer.

Coming out of Hadoop Summit, one thing is clear to me – while there has been significant growth and success of the ecosystem, it is still early days and Hadoop is still exceptionally hard to consume for most organizations. As a result of this persistent issue, there weren’t many major announcements, nothing exceptionally new or different released, and the buzz remained largely centered on YARN and Spark, both of which are several years old.

While we saw reports of early adopting companies seeing real value created with Hadoop, the focus was more technical this year than I anticipated—from the keynotes to the breakout sessions to the show floor, this year’s summit seemed more about the endless variety of different technologies than use cases and actual return on investment realized. A brief overview of a few other trends we observed is below:

Hadoop is not quite enterprise ready…yet

Hadoop Summit generated significant discussion about whether Hadoop is truly ready for real, production enterprise use. Of particular concern is security and related issues of privacy and data policies needed for companies, especially those dealing with customer or financial information. Recent acquisitions of Hadoop security upstarts by the major Hadoop distributions indicate that this will continue to be an important area of focus in the near term.

Hadoop vs. The EDW: To Replace or To Augment

Another hot topic is whether Hadoop is a replacement for the traditional EDW or if it is only to augment and offload certain workloads. In years past, this has been much more of a debate; however this year it seems clear that most have accepted a symbiotic relationship for the time being. While I do expect this to change, it is evident today that there is a significant gap in the capabilities of the Hadoop stack compared to proprietary EDW technologies.

Hadoop is becoming more fragmented

This year it became apparent that the Hadoop ecosystem is splintering into multiple and often competing projects. Competing vendors are establishing parallel but increasingly separate stacks while differentiated vendors are marketing overlapped messages. There has been an explosion in the variety of ways to work with Hadoop and in the number of companies trying to make Hadoop consumable, and it’s becoming even more confusing to choose which path is best to follow. This is true not only for business leaders who are making decisions about Big Data projects in their company but even for knowledgeable developers.

Hadoop (still) needs to be simplified

This mass confusion in the market is undercutting companies’ ability to achieve value and realize what they want from their Big Data initiatives. A lot of attention is still being paid to the infrastructure rather than the applications, so although the disruptive value of Big Data should be at the forefront, it remains elusive for most.

The Big Data Application revolution is still forthcoming. It is still early days, Hadoop is still very difficult, and very few people understand how to work with it. That’s why we are building a platform that focuses on making Hadoop easier for developers, allowing anyone to build applications (today in Java) without worrying about the low-level infrastructure. Rather than grapple with myriad technology options, they are free to focus on what matters – turning their brilliant ideas for data into apps that solve real problems. This is where Hadoop can produce desired outcomes – in data applications that quickly provide measurable value.

Adding Jet Fuel to the Fire

Not to be left out of the new choices in the Hadoop menagerie, in case you missed it, we announced a project in collaboration with AT&T Labs: a distributed framework for real-time data processing and analytics applications, codenamed jetStream. Available in open source in Q3 2014, you can find more information about this effort in our recent blog post and at jetStream.io.

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Continuuity & AT&T Labs to Open Source Real-Time Data Processing Framework

Jun 3 2014, 10:32 am

Nitin Motgi is Co-founder of Continuuity, where he leads engineering. Prior to Continuuity, Nitin was at Yahoo! working on a large-scale content optimization system externally known as C.O.R.E. He previously held senior engineering roles at Altera and FedEx.

Today we announced an exciting collaborative effort with AT&T Labs that will facilitate the integration of Continuuity BigFlow, our distributed framework for building durable high-throughput real-time data processing applications, with AT&T’s streaming analytics tool, an extremely fast, low-latency streaming analytics database originally built out of the necessity for managing its network at scale. The outcome of this joint endeavor is to make a new project, codenamed jetStream, available to the market as an Apache-licensed open source project with general availability in the third quarter of 2014.

Why are we combining our technologies?

We decided to bring together the complementary functionality of BigFlow and AT&T’s streaming analytics tool to create a unified real-time framework that combines in-memory stream processing with model-based event processing including direct integration for a variety of existing data systems like Apache HBase™ and HDFS. By combining AT&T’s low-latency and declarative language support with BigFlow’s durable, high-throughput computing capabilities and procedural language support, jetStream provides developers with a new way to take in and store vast quantities of data, build massively scalable applications, and update your applications in real-time as new data is ingested.

Moving to real-time data applications

When you look at the wealth of data being generated and processed, and the opportunity within that data, giving more organizations the ability to make informed, real-time decisions with data is critical. We believe that the next commercial opportunity in big data is moving beyond ad-hoc, batch analysis to a real-time model where applications serve relevant data continuously to business users and consumers.

Open sourcing jetStream and making it available within Continuuity Reactor will enable enterprises and developers to create a wide range of big data analytics and streaming applications that address a broad set of business use cases. Examples of these include network intrusion detection and network analytics, real-time analysis for spam filtering, social media market analysis, location analytics, and real-time recommendation engines that match relevant content to the right users at the right time.

New developer features

By using jetStream, developers will be able to do the following:

  • Direct integration of real-time data ingestion and processing applications with Hadoop and HBase and utilization of YARN for deployment and resource management

  • Framework-level correctness, fault tolerance guarantees, and application logic scalability that reduces friction, errors, and bugs during development

  • A transaction engine that provides delivery, isolation and consistency guarantees that enable exactly-once processing semantics

  • Scalability without increased operational cost of building and maintaining applications

  • Develop pipelines that combine in-memory continuous query semantics with persistent, procedural event processing with simple Java APIs

For more information, please visit jetStream.io.

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HBaseCon: Moving Beyond the Core to Address Availability & Usability

May 19 2014, 12:58 pm

Jonathan Gray, CEO & Co-founder of Continuuity, is an entrepreneur and software engineer with a background in open source and data. Prior to Continuuity, he was at Facebook working on projects like Facebook Messages. At startup Streamy, Jonathan was an early adopter of Hadoop and HBase committer.

We just wrapped HBaseCon 2014, the annual event for Apache HBase™ contributors, developers, and users. As in years past, this is one of the most technical conferences that we attend, and it’s really focused on the core community of developers who are doing something meaningful with the enabling technology. What makes HBaseCon so compelling is that it’s not theoretical but rather all about overcoming real technical challenges and actual business use cases. And this year, we noticed a couple of key trends that are shaping the future of HBase.

Overall, we noticed that the HBase discussion has moved up a level, and this is a good thing. We’re no longer talking about the core architecture of HBase, which is pretty much set at this point. So people aren’t talking about doing the architecture better, but instead it’s all about building above what’s already there. Last year was very focused on improvements to the core platform, such as detecting server failure more quickly and recovering, and describing new use cases launching on HBase. But, in the year since, HBase has further stabilized into a mature platform and the new use cases are now established production systems. Now the conversation is around building above HBase and around it for higher availability and usability.

There was a lot of good discussion of increasing availability from an HBase standpoint. In the Facebook keynote on HydraBase, they discussed using a consensus protocol for HBase reads and writes in order to tolerate individual server failures without sacrificing availability or strong consistency. Similarly, Hortonworks and others shared work they’ve been doing on timeline consistent read replicas. For example, if a single server goes down you can still read data consistently up to a given point in time—the most updated snapshot of the data. Google’s Bigtable team also touched on availability by addressing their approach to the long tail of latency.

Multiple approaches to availability are happening, but they ultimately lead to the same goals of trying to reduce the big latency outliers and getting to 5-9s (i.e., 99.999%) reliability. In addition to early adopters like Facebook, Cloudera, and Hortonworks, we’re also encouraged to see a lot of other real users step up and take an active role in the community—we’ve seen this particularly in contributions from Salesforce, Xiaomi, and Bloomberg.

All of these companies are using HBase at very large scale, contributing to its development to continue to move it forward, and then sharing their successes with others. For us at Continuuity, HBase usability is what we’re driving at, and we’ll remain very focused on improving usability so that more developers can build their own HBase and Hadoop applications. This is where HBase is going, and we’re excited to be a part of this community and contribute to its success.

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Introducing Continuuity Loom: Modern Cluster Management

Mar 20 2014, 11:28 am

Jonathan Gray, CEO & Co-founder of Continuuity, is an entrepreneur and software engineer with a background in open source and data. Prior to Continuuity, he was at Facebook working on projects like Facebook Messages. At startup Streamy, Jonathan was an early adopter of Hadoop and HBase committer.

Our flagship platform, Continuuity Reactor, utilizes several technologies within the Apache HadoopTM ecosystem, so as we started building it, our developers needed an easy way to test Reactor on real distributed systems. They needed a fast, self-service way to provision clusters, both on our own hardware and in public clouds, because testing against real clusters is such a critical and frequent component of our development cycle. Our vision was to enable all Continuuity developers to be able to quickly create a cluster with the push of a button.

We started off with simple scripts. But scripts had multiple issues - they required a lot of setup, there was no central management keeping track of available clusters, and the scripts had to be changed whenever we moved from one cloud to another, or one software deployment to another. Next, we started looking at different SCM (software configuration management) technologies and picked Chef. Combining scripts and Chef recipes, we’re able to get to one-button push, but it became extremely complex for two reasons - there were an increasing number of customizations needed for each new type of cluster and a significant amount of setup was needed on every developer’s box.

When we launched Reactor, we wanted to enable developers to deploy our single-node Sandbox Reactor to a public cloud directly from the website. This forced us to think harder about designing a complete production system while still providing an easy and flexible solution for our developers. We wrote a combination of scripts and Chef recipes, designed a system that can templatize the Hadoop cluster, added capabilities to make it extendable to other services and providers, and made it very developer and ops friendly - that’s when Loom was born.

Today, Loom is used to provision Sandbox Reactors on the Continuuity Cloud and as an internal DevOps tool to test new and incremental features on an ongoing basis. We’ve been developing and using Loom for almost a year and we’re noticing the benefits: our developer and DevOps productivity has doubled, and our development and test cycles have become faster.

We built Loom for ourselves. But today, we’re open sourcing it for anyone to use because we believe that the broader developer community can benefit from it.

What is Continuuity Loom?

Continuuity Loom is cluster management software that provisions, manages, and scales clusters on public clouds and private clouds. Clusters created with Loom utilize templates of any hardware and software stack, from simple standalone LAMP-stack servers and traditional application servers like JBoss to full Apache Hadoop clusters comprised of thousands of nodes. Clusters can be deployed across many cloud providers (Rackspace, Joyent, etc.) and virtualization platforms (like OpenStack) while utilizing common SCM tools (Chef, scripts, etc.).

Loom from Your Laptop

Developers can use Loom to provision and decommission a cluster directly from their laptop. Loom creates a very simple user API that allows the developer to choose the type of cluster, how many nodes, and which cloud. Any developer can go from zero to Hadoop, from their laptop to any cloud, in minutes.

Worry-Free Provisioning

Loom simplifies the installation and configuration of any software stack, including Hadoop, and ensures that all installations are verified before a cluster is made available. Developers can create custom cluster types ranging from single VM application servers to large-scale, distributed, heterogeneous clusters.

Download Loom for Free

As a developer, building Big Data applications using Continuuity Reactor is just one part of the equation. The other part involves provisioning and managing your Hadoop cluster, and deploying your applications to the Hadoop cluster. With Loom, we’re empowering DevOps in a truly self-service way. Download and run Loom on a Mac laptop (currently experimental), and start or spin up Hadoop clusters and other software stacks across any private or public cloud.

Open Sourcing Loom

Loom is a relatively new project but highly extensible in the providers and services that can be supported. Open sourcing Loom allows others to add support for new software stacks, currently unsupported recipes/scripts, and new infrastructure/provisioning systems. Help us make Loom better by contributing here and please report any issues, bugs, or ideas.

Loom for the Enterprise

In many companies, developers must submit requests to individuals in other parts of the organization to obtain a Hadoop cluster or to run distributed software on a group of machines. This process can be incredibly slow and burdensome for both the developer and IT, decreasing developer productivity and increasing IT overhead. With Loom, the IT department can codify reference architectures and maintain a centrally controlled catalog of cluster templates that can be provisioned directly by developers. IT administrators then use a centralized dashboard to monitor and manage multiple clusters concurrently across multiple clouds.

Download Loom to start managing and provisioning your own clusters today, or contact sales if you need enterprise support for Loom.

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