You are viewing documentation for an older version of Confluent Platform. For the latest, click here.



Deprecated since version 3.0.0: Camus in Confluent Platform is deprecated in Confluent Platform 3.0 and may be removed in a release after Confluent Platform 3.1. To export data from Kafka to HDFS and Hive, we recommend Kafka Connect with the Confluent HDFS connector as an alternative.

Camus is a simple MapReduce job developed by LinkedIn to load data from Kafka into HDFS. It is capable of incrementally copying data from Kafka into HDFS such that every run of the MapReduce job picks up where the previous run left off. At LinkedIn, Camus is used to load billions of messages per day from Kafka into HDFS. Confluent’s version of Camus integrates with Confluent’s Schema Registry which ensures data compatibility when loading to HDFS as schemas are evolved. You can find the design and architecture of Camus in the design section.

Key Features

  1. Automatic topic discovery: When a Camus job starts, it automatically fetches available topics from Zookeeper and offsets from Kafka and filters topics.
  2. Avro schema management: Camus integrates with Confluent’s Schema Registry to ensure compatibility as Avro schema evolves.
  3. Output partitioning: Camus automatically partitions the output based on the timestamp of each record.
  4. Fault tolerance: Camus saves previous Kafka ETL requests and topic partition offsets to HDFS to provide fault tolerance on Zookeeper and Kafka failures. It also uses temp work directory to ensure consistency between Kafka and HDFS.
  5. Customizability: Many components of Camus are customizable. Camus provides interfaces for customized implementations of message decoder, data writer, data partitioner and work allocator.
  6. Load balance: Camus evenly assigns data to MapReduce tasks based on the size of each topic partitions. Moreover, as Camus jobs use temp working directories, speculative execution can be effective for straggler migration.
  7. Low operation overhead: Camus offers configurations to balance contention between topics and to control the Camus job behavior in case of incompatible data. By default, Camus will not fail the MapReduce job in case of incompatible data.


These instructions assume you have already installed Confluent Platform and that you have access to a Hadoop Cluster. For installation and deployment of a single node Hadoop in pseudo-distributed mode, see this guide. Finally, you should have Kafka and the Schema Registry running .

The recommended way to run a Camus job is via a small wrapper script, bin/camus-run. It sets the environment variables and passes the arguments required to get all the jars deployed correctly and ensures the Camus jars are given priority, which ensures compatibility across a variety of Hadoop distributions.

# Assuming that you have hadoop on your PATH, HADOOP_CONF_DIR is properly configured, and that
# schema.registry.url points to the correct address.
$ bin/camus-run -D schema.registry.url=http://localhost:8081 -P etc/camus/

If you need more control over how the job is executed, see the Deployment section for more details about required configuration.

Once the Camus job is successfully completed, a couple of Avro files are created under the topic output directory in sub-directories for each topic and date partition. One example of full filename is /user/username/topics/testAvro/hourly/2015/02/16/15/testAvro. The filename is . separated format that embeds metadata as TopicName.BrokerId.PartitionId.NumberRecords.FinalOffset.UTC.

You may use Hive or other tools to perform offline analysis on the ingested Avro files.


See the installation instructions for the Confluent Platform. Before starting a Camus job you must have Hadoop, Kafka, and the Schema Registry running. The Confluent Platform quickstart explains how to start Kafka and the Schema Registry locally for testing. See this guide to setup a single Hadoop node in pseudo-distributed mode.


Camus can be run from the command line. You will need to set some configurations either by specifying a properties file on the classpath using -p (filename), or an external properties file using -P (path to local file system, or to hdfs), or from the command line using -D property=value. If the same property is set with multiple methods, the order of precedence is command-line properties, external properties file and classpath properties file. You can find a list of settings in configuration section .

The recommended deployment method is to use the camus-run script to initiate the MapReduce job:

$ bin/camus-run -D <property=value> \
   -P <path to external properties file> \
   -p <path to properties file from classpath>

If you need more control you can run the job yourself, but will have to configure some parameters and environment variables yourself. You may want to reuse the bin/camus-config script to generate the configs without running the job. That script should be sourced into your script and sets up four environment variables:

  1. HADOOP_CLASSPATH is updated to include the jars for Camus and its dependencies
  2. CAMUS_LIBJARS contains a comma separated list of the jars for Camus and its dependencies, suitable for use as the value of the -libjars option
  3. CAMUS_JAR is set to the primary Camus jar, which is the jar file you should pass as the first argument to hadoop jar
  4. HADOOP_USER_CLASSPATH_FIRST is set to true to ensure versions of libraries included with the Hadoop distribution do not conflict with versions required by Camus

If you do not use the camus-config script, you will need to configure these settings manually. If you install Camus via zip/tgz archive, you can find Camus’s jar files under share/java/camus/. If you install Camus via rpm or deb, the Camus’s jar files under /usr/share/java/camus/. After configuring the appropriate settings, as listed here, you can run the job with a command like this:

# Assuming:
# 1. hadoop is on your PATH
# 2. HADOOP_CLASSPATH includes all the Camus jars
# 4. CAMUS_LIBJARS contains the comma-separated list of all Camus jars
$ cd camus
$ hadoop jar confluent-camus-$VERSION.jar com.linkedin.camus.etl.kafka.CamusJob \
   -libjars $CAMUS_LIBJARS -D mapreduce.job.user.classpath.first=true \
   -D <property=value> \
   -P <path to external properties file> \
   -p <path to properties file from classpath>

For some Hadoop distributions, you may be able to remove some of these settings to simplify the command. Specifically, the HADOOP_USER_CLASSPATH_FIRST environment variable and the mapreduce.job.user.classpath.first setting are only required when jars included on the classpath by the Hadoop distribution are too old to satisfy Camus’s requirements.


To build a development version of Camus, you need to get the development version of the Schema Registry and its dependencies and install it into your local Maven repository. Once the dependencies are installed, you can build the Confluent version of Camus as follows:

$ git clone
$ cd camus
$ mvn clean package


  • Hadoop: Camus works with both MRv1 and YARN. We recommend CDH 5.3.x or HDP 2.2.x.
  • Kafka:
  • Schema Registry: Confluent Schema Registry 3.3.0



Because Camus is deprecated both in Confluent Platform and upstream, only critical bug fixes are accepted to these repositories.


The project is licensed under the Apache 2 license.