Apache Hadoop Compatibility


This purpose of this document is to distill down the Hadoop Compatibility Guidelines into the information relevant for a system administrator.

Target Audience

The target audience is administrators who are responsible for maintaining Apache Hadoop clusters and who must plan for and execute cluster upgrades.

Hadoop Releases

The Hadoop development community periodically produces new Hadoop releases to introduce new functionality and fix existing issues. Realeses fall into three categories:

  • Major: a major release will typically include significant new functionality and generally represents the largest upgrade compatibility risk. A major release increments the first number of the release version, e.g. going from 2.8.2 to 3.0.0.
  • Minor: a minor release will typically include some new functionality as well as fixes for some notable issues. A minor release should not pose much upgrade risk in most cases. A minor release increments the middle number of release version, e.g. going from 2.8.2 to 2.9.0.
  • Maintenance: a maintenance release should not include any new functionality. The purpose of a maintenance release is to resolve a set of issues that are deemed by the developer community to be significant enough to be worth pushing a new release to address them. Maintenance releases should pose very little upgrade risk. A maintenance release increments the final number in the release version, e.g. going from 2.8.2 to 2.8.3.

Platform Dependencies

The set of native components on which Hadoop depends is considered part of the Hadoop ABI. The Hadoop development community endeavors to maintain ABI compatibility to the fullest extent possible. Between minor releases the minimum supported version numbers for Hadoop’s native dependencies will not be increased unless necessary, such as for security or licensing issues. When such changes occur, the Hadoop developer community to try to keep the same major version and only update the minor version.

Hadoop depends on the Java virtual machine. The minimum supported version of the JVM will not change between major releases of Hadoop. In the event that the current minimum supported JVM version becomes unsupported between major releases, the minimum supported JVM version may be changed in a minor release.


Hadoop has dependencies on some transport level technologies, such as SSL. The minimum supported version of these dependencies will not be increased unless necessary, such as for security or licensing issues. When such changes occur, the Hadoop developer community to try to keep the same major version and only update the minor version.

Service port numbers for Hadoop will remain the same within a major version, though may be changed in a major release.

Hadoop’s internal wire protocols will be maintained as backward and forward compatible across minor releases within the same major version, both between clients and servers and between servers, with the intent of enabling rolling upgrades. Forward and backward compatibility of wire protocols across major releases may be possible and may allow for rolling upgrades under certain conditions, but no guarantees are made.

Scripting and Automation


The Hadoop REST APIs provide an easy mechanism for collecting information about the state of the Hadoop system. To support REST clients, the Hadoop REST APIs are versioned and will not change incompatibly within a version. Both the endpoint itself along with the list of supported parameters and the output from the endpoint are prohibited from changing incompatibly within a REST endpoint version. Note, however, that introducing new fields and other additive changes are considered compatible changes, so any consumer of the REST API should be flexible enough to ignore unknown fields.

The REST API version is a single number and has no relationship with the Hadoop version number. The version number is encoded in the endpoint URL prefixed with a ‘v’, for example ‘v1’. A new REST endpoint version may only be introduced with a minor or major release. A REST endpoint version may only be removed after being labeled as deprecated for a full major release.

Parsing Hadoop Output

Hadoop produces a variety of outputs that could conceivably parsed by automated tools. When consuming output from Hadoop, please consider the following:

  • Hadoop log output is not expected to change with a maintenance release unless it resolves a correctness issue. While log output can be consumed by software directly, it is intended primarily for a human reader.
  • Hadoop produces audit logs for a variety of operations. The audit logs are intended to be machine readable, though the addition of new records and fields are considered to be compatible changes. Any consumer of the audit logs should allow for unexpected records and fields. The audit log format may not change incompatibly between major releases.
  • Metrics data produced by Hadoop is mostly intended for automated consumption. The metrics format may not change in an incompatible way between major releases, but new records and fields can be compatibly added at any time. Consumers of the metrics data should allow for unknown records and fields.


Hadoop’s set of CLIs provide the ability to manage various aspects of the system as well as discover information about the system’s state. Between major releases, no CLI tool options will be removed or change semantically. The exception to that rule is CLI tools and tool options that are explicitly labeled as experimental and subject to change. The output from CLI tools will likewise remain the same within a major version number unless otherwise documented.

Note that any change to CLI tool output is considered an incompatible change, so between major versions, the CLI output will not change. Note that the CLI tool output is distinct from the log output produced by the CLI tools. Log output is not intended for automated consumption and may change at any time.

Web UI

The web UIs that are exposed by Hadoop are for human consumption only. Scraping the UIs for data is not a supported use. No effort is made to ensure any kind of compatibility between the data displayed in any of the web UIs across releases.

Hadoop State Data

Hadoop’s internal system state is private and should not be modified directly. The following policies govern the upgrade characteristics of the various internal state stores:

  • The internal MapReduce state data will remain compatible across minor releases within the same major version to facilitate rolling upgrades while MapReduce workloads execute.
  • HDFS maintains metadata about the data stored in HDFS in a private, internal format that is versioned. In the event of an incompatible change, the store’s version number will be incremented. When upgrading an existing cluster, the metadata store will automatically be upgraded if possible. After the metadata store has been upgraded, it is always possible to reverse the upgrade process.
  • The AWS S3A guard keeps a private, internal metadata store that is versioned. Incompatible changes will cause the version number to be incremented. If an upgrade requires reformatting the store, it will be indicated in the release notes.
  • The YARN resource manager keeps a private, internal state store of application and scheduler information that is versioned. Incompatible changes will cause the version number to be incremented. If an upgrade requires reformatting the store, it will be indicated in the release notes.
  • The YARN node manager keeps a private, internal state store of application information that is versioned. Incompatible changes will cause the version number to be incremented. If an upgrade requires reformatting the store, it will be indicated in the release notes.
  • The YARN federation service keeps a private, internal state store of application and cluster information that is versioned. Incompatible changes will cause the version number to be incremented. If an upgrade requires reformatting the store, it will be indicated in the release notes.

Hadoop Configurations

Hadoop uses two primary forms of configuration files: XML configuration files and logging configuration files.

XML Configuration Files

The XML configuration files contain a set of properties as name-value pairs. The names and meanings of the properties are defined by Hadoop and are guaranteed to be stable across minor releases. A property can only be removed in a major release and only if it has been marked as deprecated for at least a full major release. Most properties have a default value that will be used if the property is not explicitly set in the XML configuration files. The default property values will not be changed during a maintenance releas. For details about the properties supported by the various Hadoop components, see the component documentation.

Downstream projects and users can add their own properties into the XML configuration files for use by their tools and applications. While Hadoop makes no formal restrictions about defining new properties, a new property that conflicts with a property defined by Hadoop can lead to unexpected and undesirable results. Users are encouraged to avoid using custom configuration property names that conflict with the namespace of Hadoop-defined properties and thus should avoid using any prefixes used by Hadoop, e.g. hadoop, io, ipc, fs, net, file, ftp, kfs, ha, file, dfs, mapred, mapreduce, and yarn.

Logging Configuration Files

The log output produced by Hadoop daemons and CLIs is governed by a set of configuration files. These files control the minimum level of log message that will be output by the various components of Hadoop, as well as where and how those messages are stored. Between minor releases no changes will be made to the log configuration that reduce, eliminate, or redirect the log messages.

Other Configuration Files

Hadoop makes use of a number of other types of configuration files in a variety of formats, such as the JSON resource profiles configuration or the XML fair scheduler configuration. No incompatible changes will be introduced to the configuration file formats within a minor release. Even between minor releases incompatible configuration file format changes will be avoided if possible.

Hadoop Distribution

Configuration Files

The location and general structure of the Hadoop configuration files, job history information (as consumed by the job history server), and logs files generated by Hadoop will be maintained across maintenance releases.

JARs, etc.

The contents of the Hadoop distribution, e.g. JAR files, are subject to change at any time and should not be treated as reliable, except for the client artifacts. Client artifacts and their contents will remain compatible within a major release. It is the goal of the Hadoop development community to allow application code to continue to function unchanged across minor releases and, whenever possible, across major releases. The current list of client artifacts is as follows:

  • hadoop-client
  • hadoop-client-api
  • hadoop-client-minicluster
  • hadoop-client-runtime
  • hadoop-hdfs-client
  • hadoop-hdfs-native-client
  • hadoop-mapreduce-client-app
  • hadoop-mapreduce-client-common
  • hadoop-mapreduce-client-core
  • hadoop-mapreduce-client-jobclient
  • hadoop-mapreduce-client-nativetask
  • hadoop-yarn-client

Environment Variables

Some Hadoop components receive information through environment variables. For example, the HADOOP_OPTS environment variable is interpreted by most Hadoop processes as a string of additional JVM arguments to be used when starting a new JVM. Between minor releases the way Hadoop interprets environment variables will not change in an incompatible way. In other words, the same value placed into the same variable should produce the same result for all Hadoop releases within the same major version.

Library Dependencies

Hadoop relies on a large number of third-party libraries for its operation. As much as possible the Hadoop developer community works to hide these dependencies from downstream developers. Nonetheless Hadoop does expose some of its dependencies, especially prior to Hadoop 3. No new dependency will be exposed by Hadoop via the client artifacts between major releases.

A common downstream anti-pattern is to use the output of hadoop classpath to set the downstream application’s classpath or add all third-party JARs included with Hadoop to the downstream application’s classpath. This practice creates a tight coupling between the downstream application and Hadoop’s third-party dependencies, which leads to a fragile application that is hard to maintain as Hadoop’s dependencies change. This practice is strongly discouraged.

Hadoop also includes several native components, including compression, the container executor binary, and various native integrations. These native components introduce a set of native dependencies for Hadoop. The set of native dependencies can change in a minor release, but the Hadoop developer community will try to limit any dependency version changes to minor version changes as much as possible.

Hardware and OS Dependencies

Hadoop is currently supported by the Hadoop developer community on Linux and Windows running on x86 and AMD processors. These OSes and processors are likely to remain supported for the foreseeable future. In the event that support plans change, the OS or processor to be dropped will be documented as deprecated for at least a full minor release, but ideally a full major release, before actually being dropped. Hadoop may function on other OSes and processor architectures, but the community may not be able to provide assistance in the event of issues.

There are no guarantees on how the minimum resources required by Hadoop daemons will change between releases, even maintenance releases. Nonetheless, the Hadoop developer community will try to avoid increasing the requirements within a minor release.

Any file systems supported Hadoop, such as through the FileSystem API, will in most cases continue to be supported throughout a major release. The only case where support for a file system can be dropped within a major version is if a clean migration path to an alternate client implementation is provided.


For question about developing applications and projects against Apache Hadoop, please contact the user mailing list.