In the present day, Hadoop and its individual ecosystem components are complex projects. As we saw earlier in this chapter, Hadoop has a number of different forks or code branches over a large number of releases. There are also a lot of different distributions of Hadoop. The distribution with the most activity and community involvement is the one that resides as part of Apache Software Foundation. This distribution is free and has a very large community behind it. The community contributions to the Apache Hadoop distribution shape the general direction taken by Hadoop. Support in the Apache Hadoop distribution is via online forums, where questions are addressed to the community and answered by its members.
Deployment and management of the Apache Hadoop distribution within an enterprise is tedious and nontrivial. Apache Hadoop is written in Java and optimized to run on Linux filesystems. This can lead to impedance mismatch between Hadoop and existing enterprise applications and infrastructures. Integration between the Hadoop ecosystem components is buggy and not straightforward.
To bridge these issues, a few companies came up with distribution models for Hadoop. There are three primary kinds of Hadoop distribution flavors. One flavor is to provide commercial or paid support and training for the Apache Hadoop distribution. Secondly, there are companies that provide a set of supporting tools for deployment and management of Apache Hadoop as an alternative flavor. These companies also provide robust integration layers between the different Hadoop ecosystem components. The third model is for companies to supplement Apache Hadoop with proprietary features and code. These features are paid enhancements, many of which solve certain use cases.
The parent of all these distributions is Apache Software Foundation's Hadoop sources. Users of these other distributions, particularly from companies following the third distribution model, might integrate proprietary code into Apache Hadoop. However, these distributions will always stay in touching distance with Apache Hadoop and follow its trends. Distributions are generally well tested and supported in a deep and timely manner, saving administration and management costs for an organization. The downside of using a distribution other than Apache Hadoop is vendor lock-in. The tools and proprietary features provided by one vendor might not be available in another distribution or be noncompatible with other third-party tools, bringing in a cost of migration. The cost of migration is not limited to technology shifts alone. It also involves training, capacity planning, and rearchitecting costs for the organization.
There are a number of Hadoop distributions offered by companies since 2008. Distributions excel in some or the other attribute. Decisions on the right distribution for an enterprise or organization should be made on a case-by-case basis. There are different criteria to evaluate distributions. We will inspect a few important ones.
The ability of the Hadoop distribution running on a cluster to process data quickly is obviously a desired feature. Traditionally, this has been the cornerstone for all performance benchmarks. This particular performance measure is termed as "throughput". A wide range of analysis workloads that are being processed on Hadoop, coupled with the diversity of use cases supported by analytics, brings in "latency" as an important performance criterion as well. The ability of the cluster to ingest input data and emit output data at a quick rate becomes very important for low-latency analytics. This input-output cost forms an integral part of the data processing workflow.
Note
Latency is the time required to perform an action. It is measured in time units such as milliseconds, seconds, minutes, or hours.
Throughput is the number of actions that can be performed in unit time. It gives a sense of the amount of work done for every time unit.
Scaling up hardware is one way to achieve low latency independent of the Hadoop distribution. However, this approach will be expensive and saturate out quickly. Architecturally, low I/O latency can be achieved in different ways; one will be able to reduce the number of intermediate data-staging layers between the data source or the data sink and Hadoop cluster. Some distributions provide streaming writes into the Hadoop cluster in an attempt to reduce intermediate staging layers. Operators used for filtering, compressing, and lightweight data processing can be plugged into the streaming layer to preprocess the data before it flows into storage.
The Apache Hadoop distribution is written in Java, a language that runs in its own virtual machine. Though this increases application portability, it comes with overheads such as an extra layer of indirection during execution by means of byte-code interpretation and background garbage collection. It is not as fast as an application directly compiled for target hardware. Some vendors optimize their distributions for particular hardware, increasing job performance per node. Features such as compression and decompression can also be optimized for certain hardware types.
Over time, data outgrows the physical capacity of the compute and storage resources provisioned by an organization. This will require expansion of resources in both the compute and storage dimensions. Scaling can be done vertically or horizontally. Vertical scaling or scaling up is expensive and tightly bound to hardware advancements. Lack of elasticity is another downside with vertical scaling. Horizontal scaling or scaling out is a preferred mode of scaling compute and storage.
Ideally, scaling out should be limited to addition of more nodes and disks to the cluster network, with minimal configuration changes. However, distributions might impose different degrees of difficulty, both in terms of effort and cost on scaling a Hadoop cluster. Scaling out might mean heavy administrative and deployment costs, rewriting a lot of the application's code, or a combination of both. Scaling costs will depend on the existing architecture and how it complements and complies with the Hadoop distribution that is being evaluated.
Note
Vertical scaling or scaling up is the process of adding more resources to a single node in a system. For example, adding additional CPUs, memory, or storage to a single computer comes under this bucket of scaling. Vertical scaling increases capacity, but does not decrease system load.
Horizontal scaling or scaling out is the process of adding additional nodes to a system. For example, adding another computer to a distributed system by connecting it to the network comes under this category of scaling. Horizontal scaling decreases the load on a system as the new node takes a part of the load. The capacity of individual nodes does not increase.
Any distributed system is subject to partial failures. Failures can stem from hardware, software, or network issues, and have a smaller mean time when running on commodity hardware. Dealing with these failures without disrupting services or compromising data integrity is the primary goal of any highly available and consistent system.
A distribution that treats reliability seriously provides high availability of its components out of the box. Eliminating Single Point of Failures (SPOF) ensures availability. The means of eliminating SPOFs is to increase the redundancy of components. For a long time, Apache Hadoop had a single NameNode. Any failure to the NameNode's hardware meant the entire cluster becoming unusable. Now, there is the concept of a secondary NameNode and hot standbys that can be used to restore the name node in the event of NameNode failure.
Distributions that reduce manual tasks for cluster administrators are more reliable. Human intervention is directly correlated to higher error rates. An example of this is handling failovers. Failovers are critical periods for systems as they operate with lower degrees of redundancy. Any error during these periods can be disastrous for the application. Also, automated failover handling means the system can recover and run in a short amount of time. Lower the recovery time from failure better is the availability of the system.
The integrity of data needs to be maintained during normal operations and when failures are encountered. Data checksums for error detection and possible recovery, data replication, data mirroring, and snapshots are some ways to ensure data safety. Replication follows the redundancy theme to ensure data availability. Rack-aware smart placement of data and handling under or over replication are parameters to watch out for. Mirroring helps recovery from site failures by asynchronous replication across the Internet. Snapshotting is a desirable feature in any distribution; not only do they aid disaster recovery but also facilitate offline access to data. Data analytics involves experimentation and evaluation of rich data. Snapshots can be a way to facilitate this to a data scientist without disrupting production.
Deploying and managing the Apache Hadoop open source distribution requires internal understanding of the source code and configuration. This is not a widely available skill in IT administration. Also, administrators in enterprises are caretakers of a wide range of systems, Hadoop being one of them.
Versions of Hadoop and its ecosystem components that are supported by a distribution might need to be evaluated for suitability. Newer versions of Hadoop support paradigms other than MapReduce within clusters. Depending on the plans of the enterprise, newer versions can increase the efficiency of enterprise-provisioned hardware.
Capabilities of Hadoop management tools are key differentiators when choosing an appropriate distribution for an enterprise. Management tools need to provide centralized cluster administration, resource management, configuration management, and user management. Job scheduling, automatic software upgrades, user quotas, and centralized troubleshooting are other desirable features.
Monitoring cluster health is also a key feature in the manageability function. Dashboards for visualization of cluster health and integration points for other tools are good features to have in distribution. Ease of data access is another parameter that needs to be evaluated; for example, support for POSIX filesystems on Hadoop will make browsing and accessing data convenient for engineers and scientists within any enterprise. On the flip side, this makes mutability of data possible, which can prove to be risky in certain situations.
Evaluation of options for data security of a distribution is extremely important as well. Data security entails authentication of a Hadoop user and authorization to datasets and data confidentiality. Every organization or enterprise might have its authentication systems such as Kerberos or LDAP already in place. Hadoop distribution, with capabilities to integrate with existing authentication systems, is a big plus in terms of lower costs and higher compliance. Fine-grained authorization might help control access to datasets and jobs at different levels. When data is moving in and out of an organization, encryption of the bits travelling over the wire becomes important to protect against data snooping.
Distributions offer integration with development and debugging tools. Developers and scientists in an enterprise will already be using a set of tools. The more overlap between the toolset used by the organization and distribution, the better it is. The advantage of overlap not only comes in the form of licensing costs, but also in a lesser need for training and orientation. It might also increase productivity within the organization as people are already accustomed to certain tools.
There are a number of distributions of Hadoop. A comprehensive list can be found at http://wiki.apache.org/hadoop/Distributions%20and%20Commercial%20Support. We will be examining four of them:
Cloudera was formed in March 2009 with a primary objective of providing Apache Hadoop software, support, services, and training for enterprise-class deployment of Hadoop and its ecosystem components. The software suite is branded as Cloudera Distribution of Hadoop (CDH). The company being one of the Apache Software Foundation sponsors, pushes most enhancements it makes during support and servicing of Hadoop deployments upstream back into Apache Hadoop.
CDH is in its fifth major version right now and is considered a mature Hadoop distribution. The paid version of CDH comes with a proprietary management software, Cloudera Manager.
The Yahoo Hadoop team spurned off to form Hortonworks in June, 2011, a company with objectives similar to Cloudera. Their distribution is branded as Hortonworks Data Platform (HDP). The HDP suite's Hadoop and other software are completely free, with paid support and training. Hortonworks also pushes enhancements upstream, back to Apache Hadoop.
HDP is in its second major version currently and is considered the rising star in Hadoop distributions. It comes with a free and open source management software called Ambari.
MapR was founded in 2009 with a mission to bring enterprise-grade Hadoop. The Hadoop distribution they provide has significant proprietary code when compared to Apache Hadoop. There are a handful of components where they guarantee compatibility with existing Apache Hadoop projects. Key proprietary code for the MapR distribution is the replacement of HDFS with a POSIX-compatible NFS. Another key feature is the capability of taking snapshots.
MapR comes with its own management console. The different grades of the product are named as M3, M5, and M7. M5 is a standard commercial distribution from the company, M3 is a free version without high availability, and M7 is a paid version with a rewritten HBase API.
Greenplum is a marquee parallel data store from EMC. EMC integrated Greenplum within Hadoop, giving way to an advanced Hadoop distribution called Pivotal HD. This move alleviated the need to import and export data between stores such as Greenplum and HDFS, bringing down both costs and latency.
The HAWQ technology provided by Pivotal HD allows efficient and low-latency query execution on data stored in HDFS. The HAWQ technology has been found to give 100 times more improvement on certain MapReduce workloads when compared to Apache Hadoop. HAWQ also provides SQL processing in Hadoop, increasing its popularity among users who are familiar with SQL.