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Posted to commits@flink.apache.org by rm...@apache.org on 2017/02/14 14:05:22 UTC
flink git commit: [FLINK-5690][docs] Add note on shading to best
practices guide
Repository: flink
Updated Branches:
refs/heads/master 8efacf588 -> d32281444
[FLINK-5690][docs] Add note on shading to best practices guide
This closes #3300
Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/d3228144
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/d3228144
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/d3228144
Branch: refs/heads/master
Commit: d322814447e9ce437eb1adffef24699f26803038
Parents: 8efacf5
Author: Robert Metzger <rm...@apache.org>
Authored: Mon Feb 13 20:50:23 2017 +0100
Committer: Robert Metzger <rm...@apache.org>
Committed: Tue Feb 14 15:05:16 2017 +0100
----------------------------------------------------------------------
docs/dev/best_practices.md | 314 +++++++++++++++++++++++++
docs/monitoring/best_practices.md | 312 ------------------------
docs/monitoring/debugging_classloading.md | 14 ++
3 files changed, 328 insertions(+), 312 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/flink/blob/d3228144/docs/dev/best_practices.md
----------------------------------------------------------------------
diff --git a/docs/dev/best_practices.md b/docs/dev/best_practices.md
new file mode 100644
index 0000000..b2111c4
--- /dev/null
+++ b/docs/dev/best_practices.md
@@ -0,0 +1,314 @@
+---
+title: "Best Practices"
+nav-parent_id: dev
+nav-pos: 90
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+This page contains a collection of best practices for Flink programmers on how to solve frequently encountered problems.
+
+
+* This will be replaced by the TOC
+{:toc}
+
+## Parsing command line arguments and passing them around in your Flink application
+
+
+Almost all Flink applications, both batch and streaming rely on external configuration parameters.
+For example for specifying input and output sources (like paths or addresses), also system parameters (parallelism, runtime configuration) and application specific parameters (often used within the user functions).
+
+Since version 0.9 we are providing a simple utility called `ParameterTool` to provide at least some basic tooling for solving these problems.
+
+Please note that you don't have to use the `ParameterTool` explained here. Other frameworks such as [Commons CLI](https://commons.apache.org/proper/commons-cli/),
+[argparse4j](http://argparse4j.sourceforge.net/) and others work well with Flink as well.
+
+
+### Getting your configuration values into the `ParameterTool`
+
+The `ParameterTool` provides a set of predefined static methods for reading the configuration. The tool is internally expecting a `Map<String, String>`, so its very easy to integrate it with your own configuration style.
+
+
+#### From `.properties` files
+
+The following method will read a [Properties](https://docs.oracle.com/javase/tutorial/essential/environment/properties.html) file and provide the key/value pairs:
+{% highlight java %}
+String propertiesFile = "/home/sam/flink/myjob.properties";
+ParameterTool parameter = ParameterTool.fromPropertiesFile(propertiesFile);
+{% endhighlight %}
+
+
+#### From the command line arguments
+
+This allows getting arguments like `--input hdfs:///mydata --elements 42` from the command line.
+{% highlight java %}
+public static void main(String[] args) {
+ ParameterTool parameter = ParameterTool.fromArgs(args);
+ // .. regular code ..
+{% endhighlight %}
+
+
+#### From system properties
+
+When starting a JVM, you can pass system properties to it: `-Dinput=hdfs:///mydata`. You can also initialize the `ParameterTool` from these system properties:
+
+{% highlight java %}
+ParameterTool parameter = ParameterTool.fromSystemProperties();
+{% endhighlight %}
+
+
+### Using the parameters in your Flink program
+
+Now that we've got the parameters from somewhere (see above) we can use them in various ways.
+
+**Directly from the `ParameterTool`**
+
+The `ParameterTool` itself has methods for accessing the values.
+{% highlight java %}
+ParameterTool parameters = // ...
+parameter.getRequired("input");
+parameter.get("output", "myDefaultValue");
+parameter.getLong("expectedCount", -1L);
+parameter.getNumberOfParameters()
+// .. there are more methods available.
+{% endhighlight %}
+
+You can use the return values of these methods directly in the main() method (=the client submitting the application).
+For example you could set the parallelism of a operator like this:
+
+{% highlight java %}
+ParameterTool parameters = ParameterTool.fromArgs(args);
+int parallelism = parameters.get("mapParallelism", 2);
+DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).setParallelism(parallelism);
+{% endhighlight %}
+
+Since the `ParameterTool` is serializable, you can pass it to the functions itself:
+
+{% highlight java %}
+ParameterTool parameters = ParameterTool.fromArgs(args);
+DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer(parameters));
+{% endhighlight %}
+
+and then use them inside the function for getting values from the command line.
+
+
+#### Passing it as a `Configuration` object to single functions
+
+The example below shows how to pass the parameters as a `Configuration` object to a user defined function.
+
+{% highlight java %}
+ParameterTool parameters = ParameterTool.fromArgs(args);
+DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).withParameters(parameters.getConfiguration())
+{% endhighlight %}
+
+In the `Tokenizer`, the object is now accessible in the `open(Configuration conf)` method:
+
+{% highlight java %}
+public static final class Tokenizer extends RichFlatMapFunction<String, Tuple2<String, Integer>> {
+ @Override
+ public void open(Configuration parameters) throws Exception {
+ parameters.getInteger("myInt", -1);
+ // .. do
+{% endhighlight %}
+
+
+#### Register the parameters globally
+
+Parameters registered as a global job parameter at the `ExecutionConfig` allow you to access the configuration values from the JobManager web interface and all functions defined by the user.
+
+**Register the parameters globally**
+
+{% highlight java %}
+ParameterTool parameters = ParameterTool.fromArgs(args);
+
+// set up the execution environment
+final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
+env.getConfig().setGlobalJobParameters(parameters);
+{% endhighlight %}
+
+Access them in any rich user function:
+
+{% highlight java %}
+public static final class Tokenizer extends RichFlatMapFunction<String, Tuple2<String, Integer>> {
+
+ @Override
+ public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
+ ParameterTool parameters = (ParameterTool) getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
+ parameters.getRequired("input");
+ // .. do more ..
+{% endhighlight %}
+
+
+## Naming large TupleX types
+
+It is recommended to use POJOs (Plain old Java objects) instead of `TupleX` for data types with many fields.
+Also, POJOs can be used to give large `Tuple`-types a name.
+
+**Example**
+
+Instead of using:
+
+
+~~~java
+Tuple11<String, String, ..., String> var = new ...;
+~~~
+
+
+It is much easier to create a custom type extending from the large Tuple type.
+
+~~~java
+CustomType var = new ...;
+
+public static class CustomType extends Tuple11<String, String, ..., String> {
+ // constructor matching super
+}
+~~~
+
+## Using Logback instead of Log4j
+
+**Note: This tutorial is applicable starting from Flink 0.10**
+
+Apache Flink is using [slf4j](http://www.slf4j.org/) as the logging abstraction in the code. Users are advised to use sfl4j as well in their user functions.
+
+Sfl4j is a compile-time logging interface that can use different logging implementations at runtime, such as [log4j](http://logging.apache.org/log4j/2.x/) or [Logback](http://logback.qos.ch/).
+
+Flink is depending on Log4j by default. This page describes how to use Flink with Logback. Users reported that they were also able to set up centralized logging with Graylog using this tutorial.
+
+To get a logger instance in the code, use the following code:
+
+
+{% highlight java %}
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class MyClass implements MapFunction {
+ private static final Logger LOG = LoggerFactory.getLogger(MyClass.class);
+ // ...
+{% endhighlight %}
+
+
+### Use Logback when running Flink out of the IDE / from a Java application
+
+
+In all cases were classes are executed with a classpath created by a dependency manager such as Maven, Flink will pull log4j into the classpath.
+
+Therefore, you will need to exclude log4j from Flink's dependencies. The following description will assume a Maven project created from a [Flink quickstart](../quickstart/java_api_quickstart.html).
+
+Change your projects `pom.xml` file like this:
+
+{% highlight xml %}
+<dependencies>
+ <!-- Add the two required logback dependencies -->
+ <dependency>
+ <groupId>ch.qos.logback</groupId>
+ <artifactId>logback-core</artifactId>
+ <version>1.1.3</version>
+ </dependency>
+ <dependency>
+ <groupId>ch.qos.logback</groupId>
+ <artifactId>logback-classic</artifactId>
+ <version>1.1.3</version>
+ </dependency>
+
+ <!-- Add the log4j -> sfl4j (-> logback) bridge into the classpath
+ Hadoop is logging to log4j! -->
+ <dependency>
+ <groupId>org.slf4j</groupId>
+ <artifactId>log4j-over-slf4j</artifactId>
+ <version>1.7.7</version>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-java</artifactId>
+ <version>{{ site.version }}</version>
+ <exclusions>
+ <exclusion>
+ <groupId>log4j</groupId>
+ <artifactId>*</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>org.slf4j</groupId>
+ <artifactId>slf4j-log4j12</artifactId>
+ </exclusion>
+ </exclusions>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-streaming-java{{ site.scala_version_suffix }}</artifactId>
+ <version>{{ site.version }}</version>
+ <exclusions>
+ <exclusion>
+ <groupId>log4j</groupId>
+ <artifactId>*</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>org.slf4j</groupId>
+ <artifactId>slf4j-log4j12</artifactId>
+ </exclusion>
+ </exclusions>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-clients{{ site.scala_version_suffix }}</artifactId>
+ <version>{{ site.version }}</version>
+ <exclusions>
+ <exclusion>
+ <groupId>log4j</groupId>
+ <artifactId>*</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>org.slf4j</groupId>
+ <artifactId>slf4j-log4j12</artifactId>
+ </exclusion>
+ </exclusions>
+ </dependency>
+</dependencies>
+{% endhighlight %}
+
+The following changes were done in the `<dependencies>` section:
+
+ * Exclude all `log4j` dependencies from all Flink dependencies: This causes Maven to ignore Flink's transitive dependencies to log4j.
+ * Exclude the `slf4j-log4j12` artifact from Flink's dependencies: Since we are going to use the slf4j to logback binding, we have to remove the slf4j to log4j binding.
+ * Add the Logback dependencies: `logback-core` and `logback-classic`
+ * Add dependencies for `log4j-over-slf4j`. `log4j-over-slf4j` is a tool which allows legacy applications which are directly using the Log4j APIs to use the Slf4j interface. Flink depends on Hadoop which is directly using Log4j for logging. Therefore, we need to redirect all logger calls from Log4j to Slf4j which is in turn logging to Logback.
+
+Please note that you need to manually add the exclusions to all new Flink dependencies you are adding to the pom file.
+
+You may also need to check if other dependencies (non Flink) are pulling in log4j bindings. You can analyze the dependencies of your project with `mvn dependency:tree`.
+
+
+
+### Use Logback when running Flink on a cluster
+
+This tutorial is applicable when running Flink on YARN or as a standalone cluster.
+
+In order to use Logback instead of Log4j with Flink, you need to remove the `log4j-1.2.xx.jar` and `sfl4j-log4j12-xxx.jar` from the `lib/` directory.
+
+Next, you need to put the following jar files into the `lib/` folder:
+
+ * `logback-classic.jar`
+ * `logback-core.jar`
+ * `log4j-over-slf4j.jar`: This bridge needs to be present in the classpath for redirecting logging calls from Hadoop (which is using Log4j) to Slf4j.
+
+Note that you need to explicitly set the `lib/` directory when using a per job YARN cluster.
+
+The command to submit Flink on YARN with a custom logger is: `./bin/flink run -yt $FLINK_HOME/lib <... remaining arguments ...>`
+
+
http://git-wip-us.apache.org/repos/asf/flink/blob/d3228144/docs/monitoring/best_practices.md
----------------------------------------------------------------------
diff --git a/docs/monitoring/best_practices.md b/docs/monitoring/best_practices.md
deleted file mode 100644
index 0bd362e..0000000
--- a/docs/monitoring/best_practices.md
+++ /dev/null
@@ -1,312 +0,0 @@
----
-title: "Best Practices"
-nav-parent_id: dev
-nav-pos: 90
----
-<!--
-Licensed to the Apache Software Foundation (ASF) under one
-or more contributor license agreements. See the NOTICE file
-distributed with this work for additional information
-regarding copyright ownership. The ASF licenses this file
-to you under the Apache License, Version 2.0 (the
-"License"); you may not use this file except in compliance
-with the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an
-"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, either express or implied. See the License for the
-specific language governing permissions and limitations
-under the License.
--->
-
-This page contains a collection of best practices for Flink programmers on how to solve frequently encountered problems.
-
-
-* This will be replaced by the TOC
-{:toc}
-
-## Parsing command line arguments and passing them around in your Flink application
-
-
-Almost all Flink applications, both batch and streaming rely on external configuration parameters.
-For example for specifying input and output sources (like paths or addresses), also system parameters (parallelism, runtime configuration) and application specific parameters (often used within the user functions).
-
-Since version 0.9 we are providing a simple utility called `ParameterTool` to provide at least some basic tooling for solving these problems.
-
-Please note that you don't have to use the `ParameterTool` explained here. Other frameworks such as [Commons CLI](https://commons.apache.org/proper/commons-cli/),
-[argparse4j](http://argparse4j.sourceforge.net/) and others work well with Flink as well.
-
-
-### Getting your configuration values into the `ParameterTool`
-
-The `ParameterTool` provides a set of predefined static methods for reading the configuration. The tool is internally expecting a `Map<String, String>`, so its very easy to integrate it with your own configuration style.
-
-
-#### From `.properties` files
-
-The following method will read a [Properties](https://docs.oracle.com/javase/tutorial/essential/environment/properties.html) file and provide the key/value pairs:
-{% highlight java %}
-String propertiesFile = "/home/sam/flink/myjob.properties";
-ParameterTool parameter = ParameterTool.fromPropertiesFile(propertiesFile);
-{% endhighlight %}
-
-
-#### From the command line arguments
-
-This allows getting arguments like `--input hdfs:///mydata --elements 42` from the command line.
-{% highlight java %}
-public static void main(String[] args) {
- ParameterTool parameter = ParameterTool.fromArgs(args);
- // .. regular code ..
-{% endhighlight %}
-
-
-#### From system properties
-
-When starting a JVM, you can pass system properties to it: `-Dinput=hdfs:///mydata`. You can also initialize the `ParameterTool` from these system properties:
-
-{% highlight java %}
-ParameterTool parameter = ParameterTool.fromSystemProperties();
-{% endhighlight %}
-
-
-### Using the parameters in your Flink program
-
-Now that we've got the parameters from somewhere (see above) we can use them in various ways.
-
-**Directly from the `ParameterTool`**
-
-The `ParameterTool` itself has methods for accessing the values.
-{% highlight java %}
-ParameterTool parameters = // ...
-parameter.getRequired("input");
-parameter.get("output", "myDefaultValue");
-parameter.getLong("expectedCount", -1L);
-parameter.getNumberOfParameters()
-// .. there are more methods available.
-{% endhighlight %}
-
-You can use the return values of these methods directly in the main() method (=the client submitting the application).
-For example you could set the parallelism of a operator like this:
-
-{% highlight java %}
-ParameterTool parameters = ParameterTool.fromArgs(args);
-int parallelism = parameters.get("mapParallelism", 2);
-DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).setParallelism(parallelism);
-{% endhighlight %}
-
-Since the `ParameterTool` is serializable, you can pass it to the functions itself:
-
-{% highlight java %}
-ParameterTool parameters = ParameterTool.fromArgs(args);
-DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer(parameters));
-{% endhighlight %}
-
-and then use them inside the function for getting values from the command line.
-
-
-#### Passing it as a `Configuration` object to single functions
-
-The example below shows how to pass the parameters as a `Configuration` object to a user defined function.
-
-{% highlight java %}
-ParameterTool parameters = ParameterTool.fromArgs(args);
-DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).withParameters(parameters.getConfiguration())
-{% endhighlight %}
-
-In the `Tokenizer`, the object is now accessible in the `open(Configuration conf)` method:
-
-{% highlight java %}
-public static final class Tokenizer extends RichFlatMapFunction<String, Tuple2<String, Integer>> {
- @Override
- public void open(Configuration parameters) throws Exception {
- parameters.getInteger("myInt", -1);
- // .. do
-{% endhighlight %}
-
-
-#### Register the parameters globally
-
-Parameters registered as a global job parameter at the `ExecutionConfig` allow you to access the configuration values from the JobManager web interface and all functions defined by the user.
-
-**Register the parameters globally**
-
-{% highlight java %}
-ParameterTool parameters = ParameterTool.fromArgs(args);
-
-// set up the execution environment
-final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
-env.getConfig().setGlobalJobParameters(parameters);
-{% endhighlight %}
-
-Access them in any rich user function:
-
-{% highlight java %}
-public static final class Tokenizer extends RichFlatMapFunction<String, Tuple2<String, Integer>> {
-
- @Override
- public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
- ParameterTool parameters = (ParameterTool) getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
- parameters.getRequired("input");
- // .. do more ..
-{% endhighlight %}
-
-
-## Naming large TupleX types
-
-It is recommended to use POJOs (Plain old Java objects) instead of `TupleX` for data types with many fields.
-Also, POJOs can be used to give large `Tuple`-types a name.
-
-**Example**
-
-Instead of using:
-
-
-~~~java
-Tuple11<String, String, ..., String> var = new ...;
-~~~
-
-
-It is much easier to create a custom type extending from the large Tuple type.
-
-~~~java
-CustomType var = new ...;
-
-public static class CustomType extends Tuple11<String, String, ..., String> {
- // constructor matching super
-}
-~~~
-
-## Using Logback instead of Log4j
-
-**Note: This tutorial is applicable starting from Flink 0.10**
-
-Apache Flink is using [slf4j](http://www.slf4j.org/) as the logging abstraction in the code. Users are advised to use sfl4j as well in their user functions.
-
-Sfl4j is a compile-time logging interface that can use different logging implementations at runtime, such as [log4j](http://logging.apache.org/log4j/2.x/) or [Logback](http://logback.qos.ch/).
-
-Flink is depending on Log4j by default. This page describes how to use Flink with Logback. Users reported that they were also able to set up centralized logging with Graylog using this tutorial.
-
-To get a logger instance in the code, use the following code:
-
-
-{% highlight java %}
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-public class MyClass implements MapFunction {
- private static final Logger LOG = LoggerFactory.getLogger(MyClass.class);
- // ...
-{% endhighlight %}
-
-
-### Use Logback when running Flink out of the IDE / from a Java application
-
-
-In all cases were classes are executed with a classpath created by a dependency manager such as Maven, Flink will pull log4j into the classpath.
-
-Therefore, you will need to exclude log4j from Flink's dependencies. The following description will assume a Maven project created from a [Flink quickstart](../quickstart/java_api_quickstart.html).
-
-Change your projects `pom.xml` file like this:
-
-{% highlight xml %}
-<dependencies>
- <!-- Add the two required logback dependencies -->
- <dependency>
- <groupId>ch.qos.logback</groupId>
- <artifactId>logback-core</artifactId>
- <version>1.1.3</version>
- </dependency>
- <dependency>
- <groupId>ch.qos.logback</groupId>
- <artifactId>logback-classic</artifactId>
- <version>1.1.3</version>
- </dependency>
-
- <!-- Add the log4j -> sfl4j (-> logback) bridge into the classpath
- Hadoop is logging to log4j! -->
- <dependency>
- <groupId>org.slf4j</groupId>
- <artifactId>log4j-over-slf4j</artifactId>
- <version>1.7.7</version>
- </dependency>
-
- <dependency>
- <groupId>org.apache.flink</groupId>
- <artifactId>flink-java</artifactId>
- <version>{{ site.version }}</version>
- <exclusions>
- <exclusion>
- <groupId>log4j</groupId>
- <artifactId>*</artifactId>
- </exclusion>
- <exclusion>
- <groupId>org.slf4j</groupId>
- <artifactId>slf4j-log4j12</artifactId>
- </exclusion>
- </exclusions>
- </dependency>
- <dependency>
- <groupId>org.apache.flink</groupId>
- <artifactId>flink-streaming-java{{ site.scala_version_suffix }}</artifactId>
- <version>{{ site.version }}</version>
- <exclusions>
- <exclusion>
- <groupId>log4j</groupId>
- <artifactId>*</artifactId>
- </exclusion>
- <exclusion>
- <groupId>org.slf4j</groupId>
- <artifactId>slf4j-log4j12</artifactId>
- </exclusion>
- </exclusions>
- </dependency>
- <dependency>
- <groupId>org.apache.flink</groupId>
- <artifactId>flink-clients{{ site.scala_version_suffix }}</artifactId>
- <version>{{ site.version }}</version>
- <exclusions>
- <exclusion>
- <groupId>log4j</groupId>
- <artifactId>*</artifactId>
- </exclusion>
- <exclusion>
- <groupId>org.slf4j</groupId>
- <artifactId>slf4j-log4j12</artifactId>
- </exclusion>
- </exclusions>
- </dependency>
-</dependencies>
-{% endhighlight %}
-
-The following changes were done in the `<dependencies>` section:
-
- * Exclude all `log4j` dependencies from all Flink dependencies: This causes Maven to ignore Flink's transitive dependencies to log4j.
- * Exclude the `slf4j-log4j12` artifact from Flink's dependencies: Since we are going to use the slf4j to logback binding, we have to remove the slf4j to log4j binding.
- * Add the Logback dependencies: `logback-core` and `logback-classic`
- * Add dependencies for `log4j-over-slf4j`. `log4j-over-slf4j` is a tool which allows legacy applications which are directly using the Log4j APIs to use the Slf4j interface. Flink depends on Hadoop which is directly using Log4j for logging. Therefore, we need to redirect all logger calls from Log4j to Slf4j which is in turn logging to Logback.
-
-Please note that you need to manually add the exclusions to all new Flink dependencies you are adding to the pom file.
-
-You may also need to check if other dependencies (non Flink) are pulling in log4j bindings. You can analyze the dependencies of your project with `mvn dependency:tree`.
-
-
-
-### Use Logback when running Flink on a cluster
-
-This tutorial is applicable when running Flink on YARN or as a standalone cluster.
-
-In order to use Logback instead of Log4j with Flink, you need to remove the `log4j-1.2.xx.jar` and `sfl4j-log4j12-xxx.jar` from the `lib/` directory.
-
-Next, you need to put the following jar files into the `lib/` folder:
-
- * `logback-classic.jar`
- * `logback-core.jar`
- * `log4j-over-slf4j.jar`: This bridge needs to be present in the classpath for redirecting logging calls from Hadoop (which is using Log4j) to Slf4j.
-
-Note that you need to explicitly set the `lib/` directory when using a per job YARN cluster.
-
-The command to submit Flink on YARN with a custom logger is: `./bin/flink run -yt $FLINK_HOME/lib <... remaining arguments ...>`
http://git-wip-us.apache.org/repos/asf/flink/blob/d3228144/docs/monitoring/debugging_classloading.md
----------------------------------------------------------------------
diff --git a/docs/monitoring/debugging_classloading.md b/docs/monitoring/debugging_classloading.md
index d50d60d..85ee9bb 100644
--- a/docs/monitoring/debugging_classloading.md
+++ b/docs/monitoring/debugging_classloading.md
@@ -126,3 +126,17 @@ Common causes for class leaks and suggested fixes:
- *Interners*: Avoid caching objects in special structures that live beyond the lifetime of the functions/sources/sinks. Examples are Guava's
interners, or Avro's class/object caches in the serializers.
+
+## Resolving Dependency Conflicts with Flink using the maven-shade-plugin.
+
+Apache Flink loads many classes by default into its classpath. If a user uses a different version of a library that Flink is using, often `IllegalAccessExceptions` or `NoSuchMethodError` are the result.
+
+Through Hadoop, Flink for example depends on the `aws-sdk` library or on `protobuf-java`. If your user code is using these libraries and you run into issues we recommend relocating the dependency in your user code jar.
+
+Apache Maven offers the [maven-shade-plugin](https://maven.apache.org/plugins/maven-shade-plugin/), which allows one to change the package of a class *after* compiling it (so the code you are writing is not affected by the shading). For example if you have the `com.amazonaws` packages from the aws sdk in your user code jar, the shade plugin would relocate them into the `org.myorg.shaded.com.amazonaws` package, so that your code is calling your aws sdk version.
+
+This documentation page explains [relocating classes using the shade plugin](https://maven.apache.org/plugins/maven-shade-plugin/examples/class-relocation.html).
+
+
+Note that some of Flink's dependencies, such as `guava` are shaded away by the maintainers of Flink, so users usually don't have to worry about it.
+