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Apache Hadoop Downstream Developer's Guide
==========================================
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Purpose
-------
The point of this document is to provide downstream developers with a clear
reference for what to expect when building applications against the Hadoop
source base. This document is primarily a distillation of the
[Hadoop Compatibility Guidelines](./Compatibility.html) and hence focuses on
what the compatibility guarantees are for the various Hadoop interfaces
across releases.
### Target Audience
The target audience for this document is any developer working on a project
or application that builds or depends on Apache Hadoop, whether the
dependency is on the source code itself, a build artifact, or interacting
with a running system.
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.
Consuming Hadoop APIs
---------------------
When writing software that calls methods or uses classes that belong to Apache
Hadoop, developers should adhere to the following guidelines. Failure to adhere
to the guidelines may result in problems transitioning from one Hadoop release
to another.
### Privacy
Packages, classes, and methods may be annotated with an audience annotation.
The three privacy levels are:
[Public](./InterfaceClassification.html#Public),
[Limited-Private](./InterfaceClassification.html#Limited-Private), and
[Private](./InterfaceClassification.html#Private). Downstream developers should
only use packages, classes, methods, and fields that are marked as
[Public](./InterfaceClassification.html#Public). Packages, classes, and methods
that are not marked as [Public](./InterfaceClassification.html#Public) are
considered internal to Hadoop and are intended only for consumption by other
components of Hadoop.
If an element has an annotation that conflicts with it's containing element's
annotation, then the most restrictive annotation takes precedence. For example,
If a [Private](./InterfaceClassification.html#Private) method is contained
in a [Public](./InterfaceClassification.html#Public) class, then the method
should be treated as [Private](./InterfaceClassification.html#Private). If a
[Public](./InterfaceClassification.html#Public) method is contained in a
[Private](./InterfaceClassification.html#Private) class, the method should be
treated as [Private](./InterfaceClassification.html#Private).
If a method has no privacy annotation, then it inherits its privacy from its
class. If a class has no privacy, it inherits its privacy from its package.
If a package has no privacy, it should be assumed to be
class. Dynamically adjusting to stream capabilities can make an applcation
more robust in the face of changing implementations and environments.
Consuming Hadoop REST APIs
--------------------------
The Hadoop REST APIs are a primary interface for a variety of downstream
and internal applications and services. 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.
Consuming Hadoop Output
-----------------------
Hadoop produces a variety of outputs that could conceivably be consumed by
application clients or downstream libraries. 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 must 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.
Consuming Hadoop Data
---------------------
Binary file formats used by Hadoop to store data, such as sequence files, HAR
files, etc, are guaranteed to remain compatible between minor releases. In
addition, in cases where changes are made between major releases,
both backward and forward compatibility must be maintained. Note that only the
sequence file format is guaranteed not to change incompatibly, not the
serialized classes that are contained therein.
In addition to the data produced by operations, Hadoop maintains its state
information in a variety of data stores in various formats, such as the HDFS
metadata store, the YARN resource manager state store, and the YARN federation
state store. All Hadoop internal data stores are considered internal and
[Private](./InterfaceClassification.html#Private) to Hadoop. Downstream
developers should not attempt to consume data from the Hadoop state store as
the data and/or data format may change unpredictably.
Automating Operations with the Hadoop CLI
-----------------------------------------
The set of tools that make up the Hadoop command-line interface are intended
both for consumption by end users and by downstream developers who are
creating tools that execute the CLI tools and parse the output. For this reason
the Hadoop CLI tools are treated like an interface and held stable between
major releases. Between major releases, no CLI tool options will be removed or
change semantically. The output from CLI tools will likewise remain the same
within a major version number. Note that any change to CLI tool output is
considered an incompatible change, so between major version, 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.
Consuming the Hadoop 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 case. No effort is made to ensure any
kind of compatibility between the data displayed in any of the web UIs
across releases.
Working with 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 release. For details
about the properties supported by the various Hadoop components,
see the component documentation.
Downstream developers 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.
Using and Consuming Hadoop Artifacts
------------------------------------
### Source and Configuration Files
As a downstream developer or consumer of Hadoop, it's possible to access all
elements of the Hadoop platform, including source code, configuration files,
build artifacts, etc. While the open nature of the platform allows it,
developers should not create dependencies on these internal details of Hadoop
as they may change at any time. The Hadoop development community will attempt,
however, to keep the existing structure stable within a major version.
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.
### Build Artifacts
The build artifacts produced by the Hadoop build process, 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. Some common libraries, such as Guava, could cause
significant compatibility issues between Hadoop and downstream applications if
those dependencies were exposed downstream. 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 depends on the Java virtual machine for its operation, which can
impact downstream applications. To minimize disruption, 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 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.
Questions
---------
For question about developing applications and projects against Apache Hadoop,
please contact the developer mailing list for the relevant component(s):
