File Systems #
Apache Flink uses file systems to consume and persistently store data, both for the results of applications and for fault tolerance and recovery. These are some of most of the popular file systems, including local, hadoop-compatible, Amazon S3, MapR FS, Aliyun OSS and Azure Blob Storage.
The file system used for a particular file is determined by its URI scheme.
file:///home/user/text.txt refers to a file in the local file system, while
hdfs://namenode:50010/data/user/text.txt is a file in a specific HDFS cluster.
File system instances are instantiated once per process and then cached/pooled, to avoid configuration overhead per stream creation and to enforce certain constraints, such as connection/stream limits.
Local File System #
Flink has built-in support for the file system of the local machine, including any NFS or SAN drives mounted into that local file system. It can be used by default without additional configuration. Local files are referenced with the file:// URI scheme.
Pluggable File Systems #
The Apache Flink project supports the following file systems:
Amazon S3 object storage is supported by two alternative implementations:
flink-s3-fs-hadoop. Both implementations are self-contained with no dependency footprint.
MapR FS file system adapter is already supported in the main Flink distribution under the maprfs:// URI scheme. You must provide the MapR libraries in the classpath (for example in
Aliyun Object Storage Service is supported by
flink-oss-fs-hadoopand registered under the oss:// URI scheme. The implementation is based on the Hadoop Project but is self-contained with no dependency footprint.
Azure Data Lake Store Gen2 is supported by
flink-azure-fs-hadoopand registered under the abfs(s):// URI schemes. The implementation is based on the Hadoop Project but is self-contained with no dependency footprint.
Azure Blob Storage is supported by
flink-azure-fs-hadoopand registered under the wasb(s):// URI schemes. The implementation is based on the Hadoop Project but is self-contained with no dependency footprint.
Except MapR FS, you can and should use any of them as plugins.
To use a pluggable file systems, copy the corresponding JAR file from the
opt directory to a directory under
of your Flink distribution before starting Flink, e.g.
mkdir ./plugins/s3-fs-hadoop cp ./opt/flink-s3-fs-hadoop-1.14.0.jar ./plugins/s3-fs-hadoop/
Attention The plugin mechanism for file systems was introduced in Flink version
support dedicated Java class loaders per plugin and to move away from the class shading mechanism.
You can still use the provided file systems (or your own implementations) via the old mechanism by copying the corresponding
JAR file into
lib directory. However, since 1.10, s3 plugins must be loaded through the plugin mechanism; the old
way no longer works as these plugins are not shaded anymore (or more specifically the classes are not relocated since 1.10).
It’s encouraged to use the plugins-based loading mechanism for file systems that support it. Loading file systems components from the
directory will not supported in future Flink versions.
Adding a new pluggable File System implementation #
File systems are represented via the
org.apache.flink.core.fs.FileSystem class, which captures the ways to access and modify files and objects in that file system.
To add a new file system:
- Add the File System implementation, which is a subclass of
- Add a factory that instantiates that file system and declares the scheme under which the FileSystem is registered. This must be a subclass of
- Add a service entry. Create a file
META-INF/services/org.apache.flink.core.fs.FileSystemFactorywhich contains the class name of your file system factory class (see the Java Service Loader docs for more details).
During plugins discovery, the file system factory class will be loaded by a dedicated Java class loader to avoid class conflicts with other plugins and Flink components. The same class loader should be used during file system instantiation and the file system operation calls.
In practice, it means you should avoid using
Thread.currentThread().getContextClassLoader()class loader in your implementation.
Hadoop File System (HDFS) and its other implementations #
For all schemes where Flink cannot find a directly supported file system, it falls back to Hadoop.
All Hadoop file systems are automatically available when
flink-runtime and the Hadoop libraries are on the classpath.
This way, Flink seamlessly supports all of Hadoop file systems implementing the
and all Hadoop-compatible file systems (HCFS).
- HDFS (tested)
- Alluxio (tested, see configuration specifics below)
- XtreemFS (tested)
- FTP via Hftp (not tested)
- HAR (not tested)
The Hadoop configuration has to have an entry for the required file system implementation in the
See example for Alluxio.
We recommend using Flink’s built-in file systems unless required otherwise. Using a Hadoop File System directly may be required,
for example, when using that file system for YARN’s resource storage, via the
fs.defaultFS configuration property in Hadoop’s
For Alluxio support add the following entry into the
<property> <name>fs.alluxio.impl</name> <value>alluxio.hadoop.FileSystem</value> </property>