The Hortonworks Blog

Posts categorized by : HDFS

Securing any system requires you to implement layers of protection.  Access Control Lists (ACLs) are typically applied to data to restrict access to data to approved entities. Application of ACLs at every layer of access for data is critical to secure a system. The layers for hadoop are depicted in this diagram and in this post we will cover the lowest level of access… ACLs for HDFS.

This is part of the HDFS Developer Trail series.  …

LDAP provides a central source for maintaining users and groups within an enterprise. There are two ways to use LDAP groups within Hadoop. The first is to use OS level configuration to read LDAP groups. The second is to explicitly configure Hadoop to use LDAP-based group mapping.

Here is an overview of steps to configure Hadoop explicitly to use groups stored in LDAP.

  • Create Hadoop service accounts in LDAP
  • Shutdown HDFS NameNode & YARN ResourceManager
  • Modify core-site.xml to point to LDAP for group mapping
  • Re-start HDFS NameNode & YARN ResourceManager
  • Verify LDAP based group mapping

Prerequisites: Access to LDAP and the connection details are available.…

Hortonworks would like to congratulate Leslie Lamport on winning the 2013 Turing Award given by the Association of Computing Machinery. This award is essentially the equivalent of the Nobel Prize for computer science.  Among Lamport’s many and varied contributions to the field computer science are: TLA (Temporal Logic for Actions)LaTeX and PAXOS.

The latter of these, the PAXOS three phase consensus protocol, inspires the Zookeeper coordination service, and powers HBase and highly available HDFS.…

It gives me great pleasure to announce that the Apache Hadoop community has voted to release Apache Hadoop 2.3.0!

hadoop-2.3.0 is the first release for the year 2014, and brings a number of enhancements to the core platform, in particular to HDFS.

With this release, there are two significant enhancements to HDFS:

  • Support for Heterogeneous Storage Hierarchy in HDFS (HDFS-2832)
  • In-memory Cache for data resident in HDFS via Datanodes (HDFS-4949)

With support for heterogeneous storage classes in HDFS, we now can take advantage of different storage types on the same Hadoop clusters.…

This guest post from Simon Elliston Ball, Head of Big Data at Red Gate and all round top bloke. 

Hadoop is a great place to keep a lot of data. The data-lake, the data-hub and the data platform;  it’s all about the data. So how do you manage that data? How do you get data in? How do you get results out? How do you get at the logs buried somewhere deep in HDFS?…

Hadoop has traditionally been used for batch processing data at large scale. Batch processing applications care more about raw sequential throughput than low-latency and hence the existing HDFS model where all attached storages are assumed to be spinning disks has worked well.

There is an increasing interest in using Hadoop for interactive query processing e.g. via Hive. Another class of applications makes use of random IO patterns e.g. HBase. Either class of application benefits from lower latency storage media.…

The Hadoop Distributed File System is the reliable and scalable data core of the Hortonworks Data Platform. In HDP 2.0, YARN + HDFS combine to form the distributed operating system for your Data Platform, providing resource management and scalable data storage to the next generation of analytical applications.

Over the past six months, HDFS has introduced a slew of major features to HDFS covering Enterprise Multi-tenancy, Business Continuity Processing and Enterprise Integration:

  • Enabled automated failover with a hot standby and full stack resiliency for the NameNode master service
  • Added enterprise standard NFS read/write access to HDFS
  • Enabled point in time recovery with Snapshots in HDFS
  • Wire Encryption for HDFS Data Transfer Protocol

Looking forward, there are evolving patterns in Data Center infrastructure and Analytical applications that are driving the evolution of HDFS.…

The last couple of weeks have been a period of intense activity around the Apache projects that comprise the Hadoop ecosystem. While most of the headlines were accorded to Apache Hadoop 2 going GA, it would be remiss not to pay attention to the great progress being made in the Apache projects that complement Hadoop.

We have blogged about these over the course of the past week and the list below provides a quick summary of the phenomenal work contributed in the open by the folks driving these diverse and vital communities.…

I’m thrilled to note that the Apache Hadoop community has declared Apache Hadoop 2.x as Generally Available with the release of hadoop-2.2.0!

This represents the realization of a massive effort by the entire Apache Hadoop community which started nearly 4 years to date, and we’re sure you’ll agree it’s cause for a big celebration. Equally, it’s a great credit to the Apache Software Foundation which provides an environment where contributors from various places and organizations can collaborate to achieve a goal which is as significant as Apache Hadoop v2.…

As part of a modern data architecture, Hadoop needs to be a good citizen and trusted as part of the heart of the business. This means it must provide for all the platform services and features that are expected of an enterprise data platform.

The Hadoop Distributed File System is the rock at the core of HDP and provides reliable, scalable access to data for all analytical processing needs. With HDP 2.0, built into the platform itself, HDFS now has automated failover with a hot standby, with full stack resiliency.…

With HDP 1.3 and HDP 2.0 Beta, we introduced the ability to create snapshots to protect important enterprise data sets from user or application errors.

HDFS Snapshots are read-only point-in-time copies of the file system. Snapshots can be taken on a subtree of the file system or the entire file system and are:

  • Performant and Reliable: Snapshot creation is atomic and instantaneous, no matter the size or depth of the directory subtree
  • Scalable: Snapshots do not create extra copies of blocks on the file system.

Another week, another release…  Following the release of Apache Hadoop 2.0 beta last week, we are excited to release the beta of Hortonworks Data Platform 2.0, the first commercial release of the stable YARN API and protocols on which new applications can now be built.

For our customers this is a great opportunity to ensure the release meets expectations and provides a vehicle to voice feedback that will work to improve Hadoop and shape its roadmap. …

The next in our series of quick interviews with Apache Hadoop project committers at Hortonworks.

In this video, we talk with Sanjay Radia, Hortonworks co-founder and Apache Hadoop committer, about the initiation of HDFS, the cost benefits it brings to data storage and future directions for the project.

Learn more about HDFS here or at the Apache Hadoop project site.

Before I was a developer of Hadoop, I was a user of Hadoop.  I was responsible for operation and maintenance of multiple Hadoop clusters, so it’s very satisfying when I get the opportunity to implement features that make life easier for operations staff.

Have you ever wondered what’s happening during a namenode restart?  A new feature coming in HDP 2.0 will give operators greater visibility into this critical process.  This is a feature that would have been very useful to me in my prior role.…

Four years ago, Arun Murthy entered a JIRA ticket (MAPREDUCE -279) that outlined a re-architecture of the original MapReduce.  In the ticket, he outlined a set of capabilities that allowed processes to better share resources and an architecture that would allow Hadoop to extend beyond batch data processing.

It turned out that this ticket was prescient of true enterprise requirements for Hadoop. As enterprise adoption accelerated, it became even clearer that multiple processing models – moving beyond batch – was critical for Hadoop to broaden its applicability for mainstream usage in the modern enterprise architecture.…

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