Apache HBase is an open source software project that provides users with the ability to do real-time random read/write access to their data in Apache Hadoop. This means that when you want to use Hadoop for real-time data processing, HBase is the project you are looking for. The HBase developer community includes contributors from many organizations such as StumbleUpon, Facebook, Salesforce.com, TrendMicro, eBay, Explorys, Huawei and Cloudera. In fact, the HBaseCon Program Committee, constructors of the HBaseCon 2012 agenda, are all committers and PMC members of the Apache HBase project.
Presentations in the HBaseCon 2012 Development track will explain how and why HBase is built the way it is and will also cover HBase schema design and HDFS, the file system on which HBase is most commonly deployed. Some of the presentations for this track include the following below.
Development Track Presentations
The strength of an open source project resides entirely in its developer community; a strong democratic culture of participation and hacking makes for a better piece of software. The key requirement is having developers who are not only willing to contribute, but also knowledgeable about the project’s internal structure and architecture. This session will introduce developers to the core internal architectural concepts of HBase, not just “what” it does from the outside, but “how” it works internally, and “why” it does things a certain way. We’ll walk through key sections of code and discuss key concepts like the MVCC implementation and memstore organization. The goal is to convert serious “HBase Users” into HBase Developer Users,” and give voice to some of the deep knowledge locked in the committers’ heads.
OpenTSDB was built on the belief that, through HBase, a new breed of monitoring systems could be created, one that can store and serve billions of data points forever without the need for destructive downsampling, one that could scale to millions of metrics, and where plotting real-time graphs is easy and fast. In this presentation we’ll review some of the key points of OpenTSDB’s design, some of the mistakes that were made, how they were or will be addressed, and what were some of the lessons learned while writing and running OpenTSDB as well as asynchbase, the asynchronous high-performance thread-safe client for HBase. Specific topics discussed will be around the schema, how it impacts performance and allows concurrent writes without need for coordination in a distributed cluster of OpenTSDB instances.
Most developers are familiar with the topic of “database design.” In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
Apache HDFS, the file system on which HBase is most commonly deployed, was originally designed for high-latency high-throughput batch analytic systems like MapReduce. Over the past two to three years, the rising popularity of HBase has driven many enhancements in HDFS to improve its suitability for real-time systems, including durability support for write-ahead logs, high availability, and improved low-latency performance. This talk will give a brief history of some of the enhancements from Hadoop 0.20.2 through 0.23.0, discuss some of the most exciting work currently under way, and explore some of the future enhancements we expect to develop in the coming years. We will include both high-level overviews of the new features as well as practical tips and benchmark results from real deployments.
For Map/Reduce programmers used to HDFS, the mutability of HBase tables poses new challenges: Data can change over the duration of a job, multiple jobs can write concurrently, writes are effective immediately, and it is not trivial to clean up partial writes. Revision Manager introduces atomic commits and point-in-time consistent snapshots over a table, guaranteeing repeatable reads and protection from partial writes. Revision Manager is optimized for a relatively small number of concurrent write jobs, which is typical within Hadoop clusters. This session will discuss the implementation of Revision Manager using ZooKeeper and coprocessors, and paying extra care to ensure security in multi-tenant clusters. Revision Manager is available as part of the HBase storage handler in HCatalog, but can easily be used stand-alone with little coding effort.
HBase application developers face a number of challenges: schema management is performed at the application level, decoupled components of a system can break one another in unexpected ways, less-technical users cannot easily access data, and evolving data collection and analysis needs are difficult to plan for. In this talk, we describe a schema management methodology based on Apache Avro that enables users and applications to share data in HBase in a scalable, evolvable fashion. By adopting these practices, engineers independently using the same data have guarantees on how their applications interact. As data collection needs change, applications are resilient to drift in the underlying data representation. This methodology results in a data dictionary that allows less-technical users to understand what data is available to them for analysis and inspect data using general-purpose tools (for example, export it via Sqoop to an RDBMS). And because of Avro’s cross-language capabilities, HBase’s power can reach new domains, like web apps built in Ruby.