In case you didn’t see the news, I wanted to share the announcement that HCatalog 0.4.0 is now available.

For those of you that are new to the project, HCatalog provides a metadata and table management system that simplifies data sharing between Apache Hadoop and other enterprise data systems. You can learn more about the project on the Apache project site.

The highlights of the 0.4.0 release include:

- Full support for reading from and writing to Hive.
- Support for deeply nested maps, arrays, and structs.
- Switch from StorageDrivers to SerDes. HCatalog no longer supports its own StorageDriver classes for data (de)serialization. Instead it uses Hive’s SerDe classes.
- Addition of JSonSerDe to support reading and writing JSON data.
- The HCatalog binary distribution no longer includes Apache Hive. We now require that Hive first be installed.
- The HCatalog source distribution no longer includes Apache Hive source. It now pulls the required jars via Maven.

The details of the release can be found here.

~ Alan Gates

Since joining Hortonworks at the beginning of the year, a question I’ve heard over and over again is “What is Apache Hadoop and what is it used for?”

There’s clearly a lot of hype [and confusion] in this emerging Big Data market, and it feels as if each new technology, as well as existing technologies, are pushing the meme of “all your data are belong to us”. It is great to see the wave of innovation occurring across the landscape of SQL, NoSQL, NewSQL, EDW, MPP DBMS, Data Marts, and Apache Hadoop (to name just a few), but enterprises and the market in general can use a healthy dose of clarity on just how to use and interconnect these various technologies in ways that benefit the business.

In my post entitled 7 Key Drivers for the Big Data Market, I asserted that the Big Data movement is not only about the classic world of transactions, but it factors in the new(er) worlds of interactions and observations. This new world brings with it a wide range of multi-structured data sources that are forcing a new way of looking at things.

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I attended the Goldman Sachs Cloud Conference and participated on a panel focused on “Data: The New Competitive Advantage”. The panel covered a wide range of questions, but kicked off covering two basic questions:

“What is Big Data?” and “What are the drivers behind the Big Data market?”

While most definitions of Big Data focus on the new forms of unstructured data flowing through businesses with new levels of “volume, velocity, variety, and complexity”, I tend to answer the question using a simple equation:

Big Data = Transactions + Interactions + Observations

The following graphic illustrates what I mean:

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We just added a video to the Hortonworks Executive Video library that features Alan Gates, Hortonworks co-founder and Apache PMC member. In this video, Alan discusses HCatalog, one of the most compelling projects in the Apache Hadoop ecosystem.

HCatalog is a metadata and table management system that provides a consistent data model and schema for users of tools such as MapReduce, Hive and Pig. When you consider that there are often users accessing Hadoop clusters using different tools that independently don’t agree on schema, data types, how and where data is stored, etc., then you can understand the value of having a tool such as HCatalog.

In this video, Alan does a good job of not only explaining the role of HCatalog, but also laying out the future direction of the project. He talks about improving the integration with HBase, improving information lifecycle management and expanding the HCatalog data model to address the challenges of unstructured data.

The third installment of the Hortonworks executive video series features Arun C. Murthy, co-founder of Hortonworks and VP of Apache Hadoop for the Apache Software Foundation. In this video, Arun shares his view of the power of Apache Hadoop and provides some insight into the future direction of MapReduce, including the ability to support alternate programming paradigms.

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Another important milestone for Apache Pig was reached this week with the release of Pig 0.10. The purpose of this blog is to summarize the new features in Pig 0.10.

Boolean Data Type

Pig 0.10 introduces boolean data type as a first-class Pig data type. Users can use the keyword “boolean” anywhere where a data type is expected, such as load-as clause, type cast clause, etc.

Here are some sample use cases:

a = load ‘input’ as (a0:boolean, a1:tuple(a10:boolean, a11:int), a2);

b = foreach a generate a0, a1, (boolean)a2;

c = group b by a2; — group by a boolean field

When loading boolean data using PigStorage, Pig expects the text “true” (ignore case) for a true value, and “false” (ignore case) for a false value; while other values map to null. When storing boolean data using PigStorage, true value will emit text “true” and false value will emit text “false”.
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This blog covers our on-going work on Snapshots in Apache Hadoop HDFS. In this blog, I will cover the motivations for the work, a high level design and some of the design choices we made. Having seen snapshots in use with various filesystems, I believe that adding snapshots to Apache Hadoop will be hugely valuable to the Hadoop community. With luck this work will be available to Hadoop users in late 2012 or 2013.

A snapshot is a point-in-time image of the entire filesystem or a subtree of a filesystem. Some of the scenarios where snapshots are very useful:

1. Protection against user errors:  Admin sets up a process to take read-only (RO) snapshots periodically in a rolling manner so that there are always x number of RO snapshots on HDFS. If a user accidentally deletes a file, the file can be restored from the latest RO snapshot that contains the file.
2. Backup: Admin wants backup the entire file system, a subtree in the file system or just a file. Depending on the requirements, admin takes a read-only (henceforth referred to as RO) snapshot and uses this snapshot as the starting point of a full backup. Incremental backups are then taken by doing a diff between two snapshots.
3. Experimental/Test setups:  A user wants to test an application against the main dataset. Normally, without doing a full copy of the dataset, this is a very risky proposition because the test setup can corrupt/overwrite production data. Admin creates a read-write (henceforth referred to as RW) snapshot of the production dataset and assigns the RW snapshot to the user to be used for experiment. Changes done to the RW snapshot will not be reflected on the production dataset.
4. Disaster Recovery:  RO Snapshots can be used to create a consistent point in time image for replication and this can be copied over to remote site for Disaster Recovery.

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We just released the second video in the Hortonworks Executive Series. This one features Matt Foley, Test and Release Engineering Manager for Hortonworks.

In this video, Matt provides an overview of Hortonworks Data Platform (HDP), including a summary of the Apache Hadoop components included in the distribution and the testing processes involved in the release process. Matt also provides an overview of Apache Ambari, an open source project that is adding monitoring and management capabilities to Apache Hadoop.

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I’m pleased to announce the first in a series of videos featuring Hortonworks founders and executives sharing their thoughts on how Apache Hadoop is being extended to become the next generation enterprise data platform. Over the coming weeks and months, you will be hearing from folks such as Matt Foley, Arun Murthy, Sanjay Radia and Alan Gates, just to name a few.

The first video features Shaun Connolly, Hortonworks VP of Corporate Strategy, talking about the Hortonworks vision for Apache Hadoop. In this video, Shaun does a nice job of outlining our vision that Apache Hadoop will process or touch half of the world’s data by 2015. How is Hortonworks helping to make this happen? Click on the video image below to find out.

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We reached a significant milestone in HDFS: the Namenode HA branch was merged into the trunk. With this merge, HDFS trunk now supports HOT failover.

Significant enhancements were completed to make HOT Failover work:

• Configuration changes for HA
• Notion of active and standby states were added to the Namenode
• Client-side redirection
• Standby processing journal from Active
• Dual block reports to Active and Standby

We have extensively tested HOT manual failover in our labs over the last few months. The HDFS team is now working on completing automatic failover. Please see HDFS-1623 for more details.

~Jitendra Pandey

A very short while ago, Vinod blogged about some of the significant improvements in Hadoop.Next (a.k.a hadoop-0.23.1).

To recap, the Hortonworks and Yahoo! teams have done a huge amount of work to test, validate and benchmark Hadoop.Next, the next generation of Apache Hadoop that includes HDFS Federation, NextGen MapReduce (a.k.a. YARN) and many other significant features and performance improvements.

Today, I’m very excited to announce that the Apache Hadoop community voted to release hadoop-0.23.1 and it’s now available for all to use!

Please head over to the Apache Hadoop Releases page to download and play with it. Happy Hadoop-ing!

Of course, many thanks to everyone in the community who contributed!

~Arun

Hadoop RPC is the primary communication mechanism between the nodes in an Apache Hadoop cluster. Maintaining wire compatibility, as new features are added to Apache Hadoop, has been a significant challenge with the current RPC architecture. In this blog, I highlight the architectural improvement in Hadoop RPC and how it enables wire compatibility and rolling upgrades.

Challenges for Wire Compatibility

Earlier Hadoop RPC used Writable serialization that made it difficult to evolve the protocols while maintaining wire compatibility. Hadoop RPC also did not distinguish between data types that are exchanged over the wire, and the types used on the client side or the server. This made it more complicated to maintain compatibility as new features that required changes to the common data types were added.

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For those of you new to Apache ZooKeeper, it is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. To learn more about ZooKeeper, please visit the Apache ZooKeeper homepage.

As part of stabilizing Apache ZooKeeper 3.4 branch, ZooKeeper 3.4.3 has just been released. It is a bug fix release on the 3.4 branch and fixes 17 issues out of which 1 is very critical and can cause data inconsistency (ZOOKEEPER-1367). This particular issue can lead to data inconsistencies in your ZooKeeper servers. If you are currently using any of the 3.4.* releases, please make sure you upgrade to 3.4.3.

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In our previous blogs and webinars we have discussed the significant improvements and architectural changes coming to Apache Hadoop .Next (0.23). To recap, the major ones are:

• Federation for Scaling HDFS – HDFS has undergone a transformation to separate Namespace management from the Block (storage) management to allow for significant scaling of the filesystem. In previous architectures, they were intertwined in the NameNode.
• NextGen MapReduce (aka YARN) – MapReduce has undergone a complete overhaul in hadoop-0.23, including a fundamental change to split up the major functionalities of the JobTracker, resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs. Thus, Hadoop becomes a general purpose data-processing platform that can support MapReduce as well as other application execution frameworks such as MPI, Graph processing, Iterative processing etc.

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Today we announced our plans to release a public preview of the Hortonworks Data Platform (HDP) version 2. HDP v2 will leverage Apache Hadoop 0.23, which is the first major update to Hadoop in more than three years. Among other advancements, HDP v2 will include the NextGen MapReduce architecture, HDFS NameNode HA and HDFS Federation. It will also include the most up-to-date stable components including HCatalog, HBase, Hive and Pig; all fully integrated and tested at scale.

In order to avoid confusion, let me explain the two versions of HDP:

• HDP v1 is based upon Apache Hadoop 1.0 (which comes from the 0.20.205 branch). It the most stable, production-ready version of Hadoop that is currently found in many large enterprise deployments. HDP v1 is currently available as a private technology preview. A public technology preview will be made available later this quarter.
• HDP v2 is based upon Apache Hadoop 0.23, which includes the next generation advancements mentioned above. It’s an important step forward in terms of scalability, performance, high availability and data integrity. A technology preview will also be made publicly available in the second half of 2012.

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