Skip to main content

Tez - The race for secure low latency Hadoop picks up more speed



We had been expecting this… honestly, yes. Hortonworks, a Yahoo spin-off for Hadoop, has made the headlines again. So while Marissa Mayer was busy revealing a new version of Yahoo.com home page in a purple Facebook avatar, the erudite guys at Hortonworks revealed the open source Apache Hadoop immediate roadmap.

Hortonwoks, the Palo Alto based company revealed its 3 pronged strategy to stay ahead of the sharp competition in the Hadoop ecosystem. The key drivers of its strategy include:
(1)   Accelerate enterprise adoption of Hadoop
(2)  Providing a low latency based SQL type query interface for Hadoop
(3)  Making Hadoop cluster more secure

With these in mind, the company revealed 3 components of its strategy:
(1)   Stinger initiative
(2)  Tez framework
(3)  Knox Gateway

Stinger seems to be a strategic move to keep Hive as the central interface for Hadoop querying. Just this week, hadoopsphere.com published a post citing HCatalog could become partof Hive project. With Stinger, the company plans to make use of community driven contributions to add more SQL like querying clause to HQL (Hive Query language). Also, it claims to have achieved 90% reduction in Hive query result time. Another significant addition is the introduction of ORCFile in direct competition to Trevni and to tide over RC File format limitations. Tez framework described in next paragraph also constitutes part of Stinger initiative. Future additions include Buffer Caching, Vector Querying engine and Query Planner.



Tez is one of the most exciting revelations of the day. It is a “general-purpose, highly customizable framework that creates simplifies data-processing tasks across both small scale (low-latency) and large-scale (high throughput) workloads in Hadoop.”
(Tez in Hindi language means fast speed) 
Tez apparently throws a challenge to Cloudera Impala. Currently, proposed as an Apache incubator project with seed work already done, the project already has 22 committers which tells us something about the exciting race here. Tez aims to optimize the latency by running the query in a single job rather than multiple MapReduce jobs. Further it aims to leverage YARN to share data processing primitives across Apache Pig, Apache Hive, Cascading and others.


Knox is the other significant project which has been proposed for Apache incubation. Since security is one of the key focus areas, it “provides a single point of authentication and access for Apache Hadoop services in a cluster”. Earlier, hadoopsphere.com had proposed comprehensive security architecture forApache Hadoop cluster which could be implemented with custom built utilities or custom off the shelf tools. Knox fills in the vital authentication layer of the security architecture instead of just relying on Kerberos. However, it still needs to do a bit of work on cloud integration and web interface for the Hadoop cluster.

Overall, Hortonworks has shown its commitment to open source once again and driving upon the fact that community based contributions can be innovative, exciting and ‘tez’ (fast).




ps: Arun Murthy/folks at Hortonworks, please excuse the discretion of using Arun’s image in Speed movie look-alike poster. And of course, kudos to many more heroes in the team.

All other images in this post taken from Hortonworks blog

Comments

Popular posts from this blog

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Pricing models for Hadoop products

A look at the various pricing models adopted by the vendors in the Hadoop ecosystem. While the pricing models are evolving in this rapid and dynamic market, listed below are some of the major variations utilized by companies in the sphere.
1) Per Node:Among the most common model, the node based pricing mechanism utilizes customized rules for determining pricing per node. This may be as straight forward as pricing per name node and data node or could have complex variants of pricing based on number of core processors utilized by the nodes in the cluster or per user license in case of applications.
2) Per TB:The data based pricing mechanism charges customer for license cost per TB of data. This model usually accounts non replicated data for computation of cost.
3) Subscription Support cost only:In this model, the vendor prefers to give away software for free but charges the customer for subscription support on a specified number of nodes. The support timings and level of support further …

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs.
To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground.
1)      Many Eyes: Many Eyes is a data visualization experiment by IBM Researchandthe IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Map, Tag/Word cloud and ge…