Storm components for interacting with HDFS file systems
The following example will write pipe(“|”)-delimited files to the HDFS path hdfs://localhost:54310/foo. After every 1,000 tuples it will sync filesystem, making that data visible to other HDFS clients. It will rotate files when they reach 5 megabytes in size.
// use "|" instead of "," for field delimiter
RecordFormat format = new DelimitedRecordFormat()
.withFieldDelimiter("|");
// sync the filesystem after every 1k tuples
SyncPolicy syncPolicy = new CountSyncPolicy(1000);
// rotate files when they reach 5MB
FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, Units.MB);
FileNameFormat fileNameFormat = new DefaultFileNameFormat()
.withPath("/foo/");
HdfsBolt bolt = new HdfsBolt()
.withFsUrl("hdfs://localhost:54310")
.withFileNameFormat(fileNameFormat)
.withRecordFormat(format)
.withRotationPolicy(rotationPolicy)
.withSyncPolicy(syncPolicy);
When packaging your topology, it’s important that you use the maven-shade-plugin as opposed to the maven-assembly-plugin.
The shade plugin provides facilities for merging JAR manifest entries, which the hadoop client leverages for URL scheme resolution.
If you experience errors such as the following:
java.lang.RuntimeException: Error preparing HdfsBolt: No FileSystem for scheme: hdfs
it’s an indication that your topology jar file isn’t packaged properly.
If you are using maven to create your topology jar, you should use the following maven-shade-plugin configuration to
create your topology jar:
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>1.4</version>
<configuration>
<createDependencyReducedPom>true</createDependencyReducedPom>
</configuration>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer
implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
<transformer
implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass></mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
By default, storm-hdfs uses the following Hadoop dependencies:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.2.0</version>
<exclusions>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.2.0</version>
<exclusions>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
</exclusion>
</exclusions>
</dependency>
If you are using a different version of Hadoop, you should exclude the Hadoop libraries from the storm-hdfs dependency and add the dependencies for your preferred version in your pom.
Hadoop client version incompatibilites can manifest as errors like:
com.google.protobuf.InvalidProtocolBufferException: Protocol message contained an invalid tag (zero)
Record format can be controlled by providing an implementation of the org.apache.storm.hdfs.format.RecordFormat
interface:
public interface RecordFormat extends Serializable {
byte[] format(Tuple tuple);
}
The provided org.apache.storm.hdfs.format.DelimitedRecordFormat is capable of producing formats such as CSV and
tab-delimited files.
File naming can be controlled by providing an implementation of the org.apache.storm.hdfs.format.FileNameFormat
interface:
public interface FileNameFormat extends Serializable {
void prepare(Map conf, TopologyContext topologyContext);
String getName(long rotation, long timeStamp);
String getPath();
}
The provided org.apache.storm.hdfs.format.DefaultFileNameFormat will create file names with the following format:
{prefix}{componentId}-{taskId}-{rotationNum}-{timestamp}{extension}
For example:
MyBolt-5-7-1390579837830.txt
By default, prefix is empty and extenstion is “.txt”.
Sync policies allow you to control when buffered data is flushed to the underlying filesystem (thus making it available
to clients reading the data) by implementing the org.apache.storm.hdfs.sync.SyncPolicy interface:
public interface SyncPolicy extends Serializable {
boolean mark(Tuple tuple, long offset);
void reset();
}
The HdfsBolt will call the mark() method for every tuple it processes. Returning true will trigger the HdfsBolt
to perform a sync/flush, after which it will call the reset() method.
The org.apache.storm.hdfs.sync.CountSyncPolicy class simply triggers a sync after the specified number of tuples have
been processed.
Similar to sync policies, file rotation policies allow you to control when data files are rotated by providing a
org.apache.storm.hdfs.rotation.FileRotation interface:
public interface FileRotationPolicy extends Serializable {
boolean mark(Tuple tuple, long offset);
void reset();
}
The org.apache.storm.hdfs.rotation.FileSizeRotationPolicy implementation allows you to trigger file rotation when
data files reach a specific file size:
FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, Units.MB);
Both the HDFS bolt and Trident State implementation allow you to register any number of RotationActions.
What RotationActions do is provide a hook to allow you to perform some action right after a file is rotated. For
example, moving a file to a different location or renaming it.
public interface RotationAction extends Serializable {
void execute(FileSystem fileSystem, Path filePath) throws IOException;
}
Storm-HDFS includes a simple action that will move a file after rotation:
public class MoveFileAction implements RotationAction {
private static final Logger LOG = LoggerFactory.getLogger(MoveFileAction.class);
private String destination;
public MoveFileAction withDestination(String destDir){
destination = destDir;
return this;
}
@Override
public void execute(FileSystem fileSystem, Path filePath) throws IOException {
Path destPath = new Path(destination, filePath.getName());
LOG.info("Moving file {} to {}", filePath, destPath);
boolean success = fileSystem.rename(filePath, destPath);
return;
}
}
If you are using Trident and sequence files you can do something like this:
HdfsState.Options seqOpts = new HdfsState.SequenceFileOptions()
.withFileNameFormat(fileNameFormat)
.withSequenceFormat(new DefaultSequenceFormat("key", "data"))
.withRotationPolicy(rotationPolicy)
.withFsUrl("hdfs://localhost:54310")
.addRotationAction(new MoveFileAction().withDestination("/dest2/"));
The org.apache.storm.hdfs.bolt.SequenceFileBolt class allows you to write storm data to HDFS sequence files:
// sync the filesystem after every 1k tuples
SyncPolicy syncPolicy = new CountSyncPolicy(1000);
// rotate files when they reach 5MB
FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, Units.MB);
FileNameFormat fileNameFormat = new DefaultFileNameFormat()
.withExtension(".seq")
.withPath("/data/");
// create sequence format instance.
DefaultSequenceFormat format = new DefaultSequenceFormat("timestamp", "sentence");
SequenceFileBolt bolt = new SequenceFileBolt()
.withFsUrl("hdfs://localhost:54310")
.withFileNameFormat(fileNameFormat)
.withSequenceFormat(format)
.withRotationPolicy(rotationPolicy)
.withSyncPolicy(syncPolicy)
.withCompressionType(SequenceFile.CompressionType.RECORD)
.withCompressionCodec("deflate");
The SequenceFileBolt requires that you provide a org.apache.storm.hdfs.bolt.format.SequenceFormat that maps tuples to
key/value pairs:
public interface SequenceFormat extends Serializable {
Class keyClass();
Class valueClass();
Writable key(Tuple tuple);
Writable value(Tuple tuple);
}
storm-hdfs also includes a Trident state implementation for writing data to HDFS, with an API that closely mirrors
that of the bolts.
Fields hdfsFields = new Fields("field1", "field2");
FileNameFormat fileNameFormat = new DefaultFileNameFormat()
.withPath("/trident")
.withPrefix("trident")
.withExtension(".txt");
RecordFormat recordFormat = new DelimitedRecordFormat()
.withFields(hdfsFields);
FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, FileSizeRotationPolicy.Units.MB);
HdfsState.Options options = new HdfsState.HdfsFileOptions()
.withFileNameFormat(fileNameFormat)
.withRecordFormat(recordFormat)
.withRotationPolicy(rotationPolicy)
.withFsUrl("hdfs://localhost:54310");
StateFactory factory = new HdfsStateFactory().withOptions(options);
TridentState state = stream
.partitionPersist(factory, hdfsFields, new HdfsUpdater(), new Fields());
To use the sequence file State implementation, use the HdfsState.SequenceFileOptions:
HdfsState.Options seqOpts = new HdfsState.SequenceFileOptions()
.withFileNameFormat(fileNameFormat)
.withSequenceFormat(new DefaultSequenceFormat("key", "data"))
.withRotationPolicy(rotationPolicy)
.withFsUrl("hdfs://localhost:54310")
.addRotationAction(new MoveFileAction().toDestination("/dest2/"));
##Working with Secure HDFS If your topology is going to interact with secure HDFS, your bolts/states needs to be authenticated by NameNode. We currently have 2 options to support this:
Your administrator can configure nimbus to automatically get delegation tokens on behalf of the topology submitter user. The nimbus should be started with following configurations:
nimbus.autocredential.plugins.classes : ["org.apache.storm.hdfs.security.AutoHDFS"]
nimbus.credential.renewers.classes : ["org.apache.storm.hdfs.security.AutoHDFS"]
hdfs.keytab.file: "/path/to/keytab/on/nimbus" (This is the keytab of hdfs super user that can impersonate other users.)
hdfs.kerberos.principal: "superuser@EXAMPLE.com"
nimbus.credential.renewers.freq.secs : 82800 (23 hours, hdfs tokens needs to be renewed every 24 hours so this value should be less then 24 hours.)
topology.hdfs.uri:"hdfs://host:port" (This is an optional config, by default we will use value of "fs.defaultFS" property specified in hadoop's core-site.xml)
Your topology configuration should have:
topology.auto-credentials :["org.apache.storm.hdfs.common.security.AutoHDFS"]
If nimbus did not have the above configuration you need to add and then restart it. Ensure the hadoop configuration files (core-site.xml and hdfs-site.xml) and the storm-hdfs jar with all the dependencies is present in nimbus’s classpath.
As an alternative to adding the configuration files (core-site.xml and hdfs-site.xml) to the classpath, you could specify the configurations as a part of the topology configuration. E.g. in you custom storm.yaml (or -c option while submitting the topology),
hdfsCredentialsConfigKeys : ["cluster1", "cluster2"] (the hdfs clusters you want to fetch the tokens from)
"cluster1": {"config1": "value1", "config2": "value2", ... } (A map of config key-values specific to cluster1)
"cluster2": {"config1": "value1", "hdfs.keytab.file": "/path/to/keytab/for/cluster2/on/nimubs", "hdfs.kerberos.principal": "cluster2user@EXAMPLE.com"} (here along with other configs, we have custom keytab and principal for "cluster2" which will override the keytab/principal specified at topology level)
Instead of specifying key values you may also directly specify the resource files for e.g.,
"cluster1": {"resources": ["/path/to/core-site1.xml", "/path/to/hdfs-site1.xml"]}
"cluster2": {"resources": ["/path/to/core-site2.xml", "/path/to/hdfs-site2.xml"]}
Storm will download the tokens separately for each of the clusters and populate it into the subject and also renew the tokens periodically. This way it would be possible to run multiple bolts connecting to separate HDFS cluster within the same topology.
Nimbus will use the keytab and principal specified in the config to authenticate with Namenode. From then on for every topology submission, nimbus will impersonate the topology submitter user and acquire delegation tokens on behalf of the topology submitter user. If topology was started with topology.auto-credentials set to AutoHDFS, nimbus will push the delegation tokens to all the workers for your topology and the hdfs bolt/state will authenticate with namenode using these tokens.
As nimbus is impersonating topology submitter user, you need to ensure the user specified in hdfs.kerberos.principal has permissions to acquire tokens on behalf of other users. To achieve this you need to follow configuration directions listed on this link http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/Superusers.html
You can read about setting up secure HDFS here: http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SecureMode.html.
If you have distributed the keytab files for hdfs user on all potential worker hosts then you can use this method. You should specify a hdfs config key using the method HdfsBolt/State.withconfigKey(“somekey”) and the value map of this key should have following 2 properties:
hdfs.keytab.file: “/path/to/keytab/” hdfs.kerberos.principal: “user@EXAMPLE.com”
On worker hosts the bolt/trident-state code will use the keytab file with principal provided in the config to authenticate with Namenode. This method is little dangerous as you need to ensure all workers have the keytab file at the same location and you need to remember this as you bring up new hosts in the cluster.