Previously, the default ports of multiple Hadoop services were in the Linux ephemeral port range This meant that at startup, services would sometimes fail to bind to the port due to a conflict with another application. A single DataNode manages multiple disks.
During normal write operation, disks will be filled up evenly. However, adding or replacing disks can lead to significant skew within a DataNode. This situation is handled by the new intra-DataNode balancing functionality, which is invoked via the hdfs diskbalancer CLI. A series of changes have been made to heap management for Hadoop daemons as well as MapReduce tasks. MAPREDUCE simplifies the configuration of map and reduce task heap sizes, so the desired heap size no longer needs to be specified in both the task configuration and as a Java option.
Existing configs that already specify both are not affected by this change. This is similar to the existing ViewFs and HDFS Federation functionality, except the mount table is managed on the server-side by the routing layer rather than on the client. This simplifies access to a federated cluster for existing HDFS clients.
The OrgQueue extension to the capacity scheduler provides a programmatic way to change configurations by providing a REST API that users can call to modify queue configurations. For instance, the cluster administrator could define resources like GPUs, software licenses, or locally-attached storage.
YARN tasks can then be scheduled based on the availability of these resources. It also requires to pass in additional arguments to hadoop distcp in order to enable V4, specifically, -Dmapreduce. Insight Insight - Your bridge to a thriving career. Thanks to Bastian Haase and Ashwin Kumar. Insight Follow. Insight - Your bridge to a thriving career. Written by Hoa Nguyen Follow. More From Medium. Sahitya Maruvada in Days of Linux. English Grammar For Software Developers. Joshua Saunders in The Startup.
Kshitij Wadhwa in Rockset. Luis Robaina in Better Programming. Links in Linux File System. The surprising subtleties of link checking. Richard D Jones. With regard to dependencies, adding a dependency is an incompatible change, whereas removing a dependency is a compatible change.
Users are therefore discouraged from adopting this practice. Any dependencies that are not exposed to clients either because they are shaded or only exist in non-client artifacts SHALL be considered Private and Unstable. Users and related projects often utilize the environment variables exported by Hadoop e. Removing or renaming environment variables can therefore impact end user applications.
Hadoop uses Maven for project management. Changes to the contents of generated artifacts can impact existing user applications. Client artifacts are the following:. To keep up with the latest advances in hardware, operating systems, JVMs, and other software, new Hadoop releases may include features that require newer hardware, operating systems releases, or JVM versions than previous Hadoop releases.
For a specific environment, upgrading Hadoop might require upgrading other dependent software components. For each type of compatibility this document will: describe the impact on downstream projects or end-users where applicable, call out the policy adopted by the Hadoop developers when incompatible changes are permitted. Target Audience This document is intended for consumption by the Hadoop developer community.
Structure This document is arranged in sections according to the various compatibility concerns. InterfaceAudience captures the intended audience. InterfaceStability describes what types of interface changes are permitted.
Possible values are Stable , Evolving , and Unstable. Deprecated notes that the package, class, or member variable or method could potentially be removed in the future and should not be used.
Use Cases Public - Stable API compatibility is required to ensure end-user programs and downstream projects continue to work without modification. Public - Evolving API compatibility is useful to make functionality available for consumption before it is fully baked.
Limited Private- Stable API compatibility is required to allow upgrade of individual components across minor releases. Private - Stable API compatibility is required for rolling upgrades. Private - Unstable API compatibility allows internal components to evolve rapidly without concern for downstream consumers, and is how most interfaces should be labeled.
Policy The compatibility policy SHALL be determined by the relevant package, class, or member variable or method annotations. Semantic compatibility Apache Hadoop strives to ensure that the behavior of APIs remains consistent across releases, though changes for correctness may result in changes in behavior.
Java Binary compatibility for end-user applications i. Native Dependencies Hadoop includes several native components, including compression, the container executor binary, and various native integrations. The communications can be categorized as follows: Client-Server: communication between Hadoop clients and servers e. Client-Server Admin : It is worth distinguishing a subset of the Client-Server protocols used solely by administrative commands e.
Server-Server: communication between servers e. Transports In addition to compatibility of the protocols themselves, maintaining cross-version communications requires that the transports supported also be stable.
Policy Hadoop wire protocols are defined in. The following changes to a. For example, a Hadoop 2. Client-Server compatibility MUST be maintained so as to allow users to upgrade the client before upgrading the server cluster. This allows deployment of client-side bug fixes ahead of full cluster upgrades. Note that new cluster features invoked by new client APIs or shell commands will not be usable.
YARN applications that attempt to use new APIs including new fields in data structures that have not yet been deployed to the cluster can expect link exceptions. Client-Server compatibility MUST be maintained so as to allow upgrading individual components without upgrading others. For example, upgrade HDFS from version 2. Server-Server compatibility MUST be maintained so as to allow mixed versions within an active cluster so the cluster may be upgraded without downtime in a rolling fashion.
Log Output The Hadoop daemons and CLIs produce log output via Log4j that is intended to aid administrators and developers in understanding and troubleshooting cluster behavior. Audit Log Output Several components have audit logging systems that record system information in a machine readable format.
User-level file formats Changes to formats that end users use to store their data can prevent them from accessing the data in later releases, and hence are important to be compatible. Improve this answer. OneCricketeer OneCricketeer k 16 16 gold badges silver badges bronze badges. Add a comment. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.
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