Amazon Web Services offers cloud computing services on which you can run Flink.
Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly setup a Hadoop cluster. This is the recommended way to run Flink on AWS as it takes care of setting up everything.
Flink is a supported application on Amazon EMR. Amazon’s documentation describes configuring Flink, creating and monitoring a cluster, and working with jobs.
Amazon EMR services are regularly updated to new releases but a version of Flink which is not available can be manually installed in a stock EMR cluster.
Create EMR Cluster
The EMR documentation contains examples showing how to start an EMR cluster. You can follow that guide and install any EMR release. You don’t need to install the All Applications part of the EMR release, but can stick to Core Hadoop.
Note Access to S3 buckets requires configuration of IAM roles when creating an EMR cluster.
Install Flink on EMR Cluster
After creating your cluster, you can connect to the master node and install Flink:
HADOOP_CONF_DIR=/etc/hadoop/conf ./bin/flink run -m yarn-cluster -yn 1 examples/streaming/WordCount.jar
Amazon Simple Storage Service (Amazon S3) provides cloud object storage for a variety of use cases. You can use S3 with Flink for reading and writing data as well in conjunction with the streaming state backends or even as a YARN object storage.
You can use S3 objects like regular files by specifying paths in the following format:
s3://<your-bucket>/<endpoint>
The endpoint can either be a single file or a directory, for example:
// Read from S3 bucket
env.readTextFile("s3://<bucket>/<endpoint>");
// Write to S3 bucket
stream.writeAsText("s3://<bucket>/<endpoint>");
// Use S3 as FsStatebackend
env.setStateBackend(new FsStateBackend("s3://<your-bucket>/<endpoint>"));
Note that these examples are not exhaustive and you can use S3 in other places as well, including your high availability setup or the RocksDBStateBackend; everywhere that Flink expects a FileSystem URI.
For most use cases, you may use one of our shaded flink-s3-fs-hadoop
and flink-s3-fs-presto
S3
filesystem wrappers which are fairly easy to set up. For some cases, however, e.g. for using S3 as
YARN’s resource storage dir, it may be necessary to set up a specific Hadoop S3 FileSystem
implementation. Both ways are described below.
To use either flink-s3-fs-hadoop
or flink-s3-fs-presto
, copy the respective JAR file from the
opt
directory to the lib
directory of your Flink distribution before starting Flink, e.g.
cp ./opt/flink-s3-fs-presto-1.6.1.jar ./lib/
Both flink-s3-fs-hadoop
and flink-s3-fs-presto
register default FileSystem wrappers for URIs with the s3://
scheme, flink-s3-fs-hadoop
also registers for s3a://
.
After setting up the S3 FileSystem wrapper, you need to make sure that Flink is allowed to access your S3 buckets.
The recommended way of setting up credentials on AWS is via Identity and Access Management (IAM). You can use IAM features to securely give Flink instances the credentials that they need in order to access S3 buckets. Details about how to do this are beyond the scope of this documentation. Please refer to the AWS user guide. What you are looking for are IAM Roles.
If you set this up correctly, you can manage access to S3 within AWS and don’t need to distribute any access keys to Flink.
Access to S3 can be granted via your access and secret key pair. Please note that this is discouraged since the introduction of IAM roles.
You need to configure both s3.access-key
and s3.secret-key
in Flink’s flink-conf.yaml
:
s3.access-key: your-access-key
s3.secret-key: your-secret-key
This setup is a bit more complex and we recommend using our shaded Hadoop/Presto file systems
instead (see above) unless required otherwise, e.g. for using S3 as YARN’s resource storage dir
via the fs.defaultFS
configuration property in Hadoop’s core-site.xml
.
Interaction with S3 happens via one of Hadoop’s S3 FileSystem clients:
S3AFileSystem
(recommended for Hadoop 2.7 and later): file system for reading and writing regular files using Amazon’s SDK internally. No maximum file size and works with IAM roles.NativeS3FileSystem
(for Hadoop 2.6 and earlier): file system for reading and writing regular files. Maximum object size is 5GB and does not work with IAM roles.S3AFileSystem
(Recommended)This is the recommended S3 FileSystem implementation to use. It uses Amazon’s SDK internally and works with IAM roles (see Configure Access Credentials).
You need to point Flink to a valid Hadoop configuration, which contains the following properties in core-site.xml
:
<configuration>
<property>
<name>fs.s3.impl</name>
<value>org.apache.hadoop.fs.s3a.S3AFileSystem</value>
</property>
<!-- Comma separated list of local directories used to buffer
large results prior to transmitting them to S3. -->
<property>
<name>fs.s3a.buffer.dir</name>
<value>/tmp</value>
</property>
</configuration>
This registers S3AFileSystem
as the default FileSystem for URIs with the s3a://
scheme.
NativeS3FileSystem
This file system is limited to files up to 5GB in size and it does not work with IAM roles (see Configure Access Credentials), meaning that you have to manually configure your AWS credentials in the Hadoop config file.
You need to point Flink to a valid Hadoop configuration, which contains the following property in core-site.xml
:
<property>
<name>fs.s3.impl</name>
<value>org.apache.hadoop.fs.s3native.NativeS3FileSystem</value>
</property>
This registers NativeS3FileSystem
as the default FileSystem for URIs with the s3://
scheme.
You can specify the Hadoop configuration in various ways pointing Flink to the path of the Hadoop configuration directory, for example
HADOOP_CONF_DIR
, orfs.hdfs.hadoopconf
configuration option in flink-conf.yaml
:fs.hdfs.hadoopconf: /path/to/etc/hadoop
This registers /path/to/etc/hadoop
as Hadoop’s configuration directory with Flink. Flink will look for the core-site.xml
and hdfs-site.xml
files in the specified directory.
After setting up the S3 FileSystem, you need to make sure that Flink is allowed to access your S3 buckets.
When using S3AFileSystem
, the recommended way of setting up credentials on AWS is via Identity and Access Management (IAM). You can use IAM features to securely give Flink instances the credentials that they need in order to access S3 buckets. Details about how to do this are beyond the scope of this documentation. Please refer to the AWS user guide. What you are looking for are IAM Roles.
If you set this up correctly, you can manage access to S3 within AWS and don’t need to distribute any access keys to Flink.
Note that this only works with S3AFileSystem
and not NativeS3FileSystem
.
S3AFileSystem
(Discouraged)Access to S3 can be granted via your access and secret key pair. Please note that this is discouraged since the introduction of IAM roles.
For S3AFileSystem
you need to configure both fs.s3a.access.key
and fs.s3a.secret.key
in Hadoop’s core-site.xml
:
<property>
<name>fs.s3a.access.key</name>
<value></value>
</property>
<property>
<name>fs.s3a.secret.key</name>
<value></value>
</property>
NativeS3FileSystem
(Discouraged)Access to S3 can be granted via your access and secret key pair. But this is discouraged and you should use S3AFileSystem
with the required IAM roles.
For NativeS3FileSystem
you need to configure both fs.s3.awsAccessKeyId
and fs.s3.awsSecretAccessKey
in Hadoop’s core-site.xml
:
<property>
<name>fs.s3.awsAccessKeyId</name>
<value></value>
</property>
<property>
<name>fs.s3.awsSecretAccessKey</name>
<value></value>
</property>
Hadoop’s S3 FileSystem clients are packaged in the hadoop-aws
artifact (Hadoop version 2.6 and later). This JAR and all its dependencies need to be added to Flink’s classpath, i.e. the class path of both Job and TaskManagers. Depending on which FileSystem implementation and which Flink and Hadoop version you use, you need to provide different dependencies (see below).
There are multiple ways of adding JARs to Flink’s class path, the easiest being simply to drop the JARs in Flink’s lib
folder. You need to copy the hadoop-aws
JAR with all its dependencies. You can also export the directory containing these JARs as part of the HADOOP_CLASSPATH
environment variable on all machines.
Depending on which file system you use, please add the following dependencies. You can find these as part of the Hadoop binaries in hadoop-2.7/share/hadoop/tools/lib
:
S3AFileSystem
:
hadoop-aws-2.7.3.jar
aws-java-sdk-s3-1.11.183.jar
and its dependencies:
aws-java-sdk-core-1.11.183.jar
aws-java-sdk-kms-1.11.183.jar
jackson-annotations-2.6.7.jar
jackson-core-2.6.7.jar
jackson-databind-2.6.7.jar
joda-time-2.8.1.jar
httpcore-4.4.4.jar
httpclient-4.5.3.jar
NativeS3FileSystem
:
hadoop-aws-2.7.3.jar
guava-11.0.2.jar
Note that hadoop-common
is available as part of Flink, but Guava is shaded by Flink.
Depending on which file system you use, please add the following dependencies. You can find these as part of the Hadoop binaries in hadoop-2.6/share/hadoop/tools/lib
:
S3AFileSystem
:
hadoop-aws-2.6.4.jar
aws-java-sdk-1.7.4.jar
and its dependencies:
jackson-annotations-2.1.1.jar
jackson-core-2.1.1.jar
jackson-databind-2.1.1.jar
joda-time-2.2.jar
httpcore-4.2.5.jar
httpclient-4.2.5.jar
NativeS3FileSystem
:
hadoop-aws-2.6.4.jar
guava-11.0.2.jar
Note that hadoop-common
is available as part of Flink, but Guava is shaded by Flink.
These Hadoop versions only have support for NativeS3FileSystem
. This comes pre-packaged with Flink for Hadoop 2 as part of hadoop-common
. You don’t need to add anything to the classpath.
The following sections lists common issues when working with Flink on AWS.
If your job submission fails with an Exception message noting that No file system found with scheme s3
this means that no FileSystem has been configured for S3. Please check out the configuration sections for our shaded Hadoop/Presto or generic Hadoop file systems for details on how to configure this properly.
org.apache.flink.client.program.ProgramInvocationException: The program execution failed:
Failed to submit job cd927567a81b62d7da4c18eaa91c3c39 (WordCount Example) [...]
Caused by: org.apache.flink.runtime.JobException: Creating the input splits caused an error:
No file system found with scheme s3, referenced in file URI 's3://<bucket>/<endpoint>'. [...]
Caused by: java.io.IOException: No file system found with scheme s3,
referenced in file URI 's3://<bucket>/<endpoint>'.
at o.a.f.core.fs.FileSystem.get(FileSystem.java:296)
at o.a.f.core.fs.Path.getFileSystem(Path.java:311)
at o.a.f.api.common.io.FileInputFormat.createInputSplits(FileInputFormat.java:450)
at o.a.f.api.common.io.FileInputFormat.createInputSplits(FileInputFormat.java:57)
at o.a.f.runtime.executiongraph.ExecutionJobVertex.<init>(ExecutionJobVertex.java:156)
If you see your job failing with an Exception noting that the AWS Access Key ID and Secret Access Key must be specified as the username or password
, your access credentials have not been set up properly. Please refer to the access credential section for our shaded Hadoop/Presto or generic Hadoop file systems for details on how to configure this.
org.apache.flink.client.program.ProgramInvocationException: The program execution failed:
Failed to submit job cd927567a81b62d7da4c18eaa91c3c39 (WordCount Example) [...]
Caused by: java.io.IOException: The given file URI (s3://<bucket>/<endpoint>) points to the
HDFS NameNode at <bucket>, but the File System could not be initialized with that address:
AWS Access Key ID and Secret Access Key must be specified as the username or password
(respectively) of a s3n URL, or by setting the fs.s3n.awsAccessKeyId
or fs.s3n.awsSecretAccessKey properties (respectively) [...]
Caused by: java.lang.IllegalArgumentException: AWS Access Key ID and Secret Access Key must
be specified as the username or password (respectively) of a s3 URL, or by setting
the fs.s3n.awsAccessKeyId or fs.s3n.awsSecretAccessKey properties (respectively) [...]
at o.a.h.fs.s3.S3Credentials.initialize(S3Credentials.java:70)
at o.a.h.fs.s3native.Jets3tNativeFileSystemStore.initialize(Jets3tNativeFileSystemStore.java:80)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at o.a.h.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at o.a.h.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at o.a.h.fs.s3native.$Proxy6.initialize(Unknown Source)
at o.a.h.fs.s3native.NativeS3FileSystem.initialize(NativeS3FileSystem.java:330)
at o.a.f.runtime.fs.hdfs.HadoopFileSystem.initialize(HadoopFileSystem.java:321)
If you see this Exception, the S3 FileSystem is not part of the class path of Flink. Please refer to S3 FileSystem dependency section for details on how to configure this properly.
Caused by: java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3native.NativeS3FileSystem not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2186)
at org.apache.flink.runtime.fs.hdfs.HadoopFileSystem.getHadoopWrapperClassNameForFileSystem(HadoopFileSystem.java:460)
at org.apache.flink.core.fs.FileSystem.getHadoopWrapperClassNameForFileSystem(FileSystem.java:352)
at org.apache.flink.core.fs.FileSystem.get(FileSystem.java:280)
at org.apache.flink.core.fs.Path.getFileSystem(Path.java:311)
at org.apache.flink.api.common.io.FileInputFormat.createInputSplits(FileInputFormat.java:450)
at org.apache.flink.api.common.io.FileInputFormat.createInputSplits(FileInputFormat.java:57)
at org.apache.flink.runtime.executiongraph.ExecutionJobVertex.<init>(ExecutionJobVertex.java:156)
... 25 more
Caused by: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3native.NativeS3FileSystem not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2154)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2178)
... 32 more
Caused by: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3native.NativeS3FileSystem not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2060)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2152)
... 33 more
400: Bad Request
If you have configured everything properly, but get a Bad Request
Exception and your S3 bucket is located in region eu-central-1
, you might be running an S3 client, which does not support Amazon’s signature version 4.
[...]
Caused by: java.io.IOException: s3://<bucket-in-eu-central-1>/<endpoint> : 400 : Bad Request [...]
Caused by: org.jets3t.service.impl.rest.HttpException [...]
or
com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 400, AWS Service: Amazon S3, AWS Request ID: [...], AWS Error Code: null, AWS Error Message: Bad Request, S3 Extended Request ID: [...]
This should not apply to our shaded Hadoop/Presto S3 file systems but can occur for Hadoop-provided
S3 file systems. In particular, all Hadoop versions up to 2.7.2 running NativeS3FileSystem
(which
depend on JetS3t 0.9.0
instead of a version >= 0.9.4)
are affected but users also reported this happening with the S3AFileSystem
.
Except for changing the bucket region, you may also be able to solve this by
requesting signature version 4 for request authentication,
e.g. by adding this to Flink’s JVM options in flink-conf.yaml
(see
configuration):
env.java.opts: -Dcom.amazonaws.services.s3.enableV4
This Exception is usually caused by skipping the local buffer directory configuration fs.s3a.buffer.dir
for the S3AFileSystem
. Please refer to the S3AFileSystem configuration section to see how to configure the S3AFileSystem
properly.
[...]
Caused by: java.lang.NullPointerException at
o.a.h.fs.LocalDirAllocator$AllocatorPerContext.confChanged(LocalDirAllocator.java:268) at
o.a.h.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:344) at
o.a.h.fs.LocalDirAllocator$AllocatorPerContext.createTmpFileForWrite(LocalDirAllocator.java:416) at
o.a.h.fs.LocalDirAllocator.createTmpFileForWrite(LocalDirAllocator.java:198) at
o.a.h.fs.s3a.S3AOutputStream.<init>(S3AOutputStream.java:87) at
o.a.h.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:410) at
o.a.h.fs.FileSystem.create(FileSystem.java:907) at
o.a.h.fs.FileSystem.create(FileSystem.java:888) at
o.a.h.fs.FileSystem.create(FileSystem.java:785) at
o.a.f.runtime.fs.hdfs.HadoopFileSystem.create(HadoopFileSystem.java:404) at
o.a.f.runtime.fs.hdfs.HadoopFileSystem.create(HadoopFileSystem.java:48) at
... 25 more