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Utility for browsing and simple manipulation of Avro-based files

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This project provides a command-line utility for browsing and simple manipulation of Avro-based files.

Apache Avro is a serialization format invented to be a language-independent way of communication between Hadoop data processing tasks. Hadoop tasks produce output that, on an abstract level, can be regarded as a list of objects of the same type. In practice, when using Avro format, this list is represented in the file system as a directory containing many Avro-formatted files where each file has the same schema. We call this directory the Avro data store.

avroknife allows for browsing and simple manipulation of Avro data stores. It was inspired by Avro library’s own tool called avro-tools which is distributed with that library as a *.jar file. Apart from differences in particular functionalities, the main philosophical difference between these two is that avroknife operates on whole Avro data store while avro-tools operates on individual Avro files.


  • Accesses Avro data stores placed in the local file system as well as in the Hadoop Distributed File System (HDFS).
    • Note that in order to access HDFS, you need to have pydoop Python package installed.
  • Provides the following execution modes (run avroknife -h for details):
    • prints out schema of data store,
    • dumps data store as JSON,
    • dumps selected records from data store as a new data store,
    • dumps a field from selected records to file system or to stdout,
    • prints number of records inside a data store.
  • Allows for simple selection of the records to be accessed based on combination of the following constraints:
    • index range of the records,
    • limit set on number of returned records,
    • value of a field.

Usage examples

Let’s assume that we have an Avro data store available as /user/$USER/example_data_store directory in HDFS:

$ hadoop fs -ls example_data_store
Found 4 items
-rw-r--r--   1 mafju supergroup        408 2014-09-18 11:36 /user/mafju/example_data_store/part-m-00000.avro
-rw-r--r--   1 mafju supergroup        449 2014-09-18 11:36 /user/mafju/example_data_store/part-m-00001.avro
-rw-r--r--   1 mafju supergroup        364 2014-09-18 11:36 /user/mafju/example_data_store/part-m-00002.avro
-rw-r--r--   1 mafju supergroup        429 2014-09-18 11:36 /user/mafju/example_data_store/part-m-00003.avro

First, let’s check the schema of the data store

$ avroknife getschema example_data_store
    "namespace": "",
    "type": "record",
    "name": "User",
    "fields": [
            "type": "int",
            "name": "position"
            "type": "string",
            "name": "name"
            "type": [
            "name": "favorite_number"
            "type": [
            "name": "favorite_color"
            "type": [
            "name": "secret"

Then, let’s list all its records:

$ avroknife tojson example_data_store
{"position": 0, "name": "Alyssa", "favorite_number": 256, "favorite_color": null, "secret": null}
{"position": 1, "name": "Ben", "favorite_number": 4, "favorite_color": "red", "secret": null}
{"position": 2, "name": "Alyssa2", "favorite_number": 512, "favorite_color": null, "secret": null}
{"position": 3, "name": "Ben2", "favorite_number": 8, "favorite_color": "blue", "secret": "MDk4NzY1NDMyMQ=="}
{"position": 4, "name": "Ben3", "favorite_number": 2, "favorite_color": "green", "secret": "MTIzNDVhYmNk"}
{"position": 5, "name": "Alyssa3", "favorite_number": 16, "favorite_color": null, "secret": null}
{"position": 6, "name": "Mallet", "favorite_number": null, "favorite_color": "blue", "secret": "YXNkZmdm"}
{"position": 7, "name": "Mikel", "favorite_number": null, "favorite_color": "", "secret": null}

Now, let’s select the records where the favorite_color attribute is equal blue and the index of the record is 5 of larger:

$ avroknife tojson --select favorite_color="blue" --index 5- example_data_store
{"position": 6, "name": "Mallet", "favorite_number": null, "favorite_color": "blue", "secret": "YXNkZmdm"}

Next, let’s extract value of the name attribute for all records where the favorite_color attribute is equal blue:

$ avroknife extract --value_field name --select favorite_color="blue" example_data_store

Note that if the data store was placed in the local file system, you would have to prefix its path with local:, e.g.

$ avroknife tojson local:example_data_store

That’s it. Run avroknife -h to find out more about other modes and options of avroknife.


The project is available in the PyPI repository, so in oder to install it, you need to do

sudo pip install avroknife

If you want to access HDFS, pydoop Python library needs to be installed in the system. You can follow the description on Pydoop’s documentation page in order to proceed with its installation. On Ubuntu 14.04, installing Pydoop boils down to the following steps:

  • Install Hadoop. If you want to install it on a single node in a so-called pseudo-distributed mode, I recommend to use the Cloudera Hadoop distribution. This can be done by following Cloudera’s step-by-step guide. Apart from the hadoop-0.20-conf-pseudo package from the Cloudera repository that is mentioned in the guide, you also have to install hadoop-client package.
  • Make sure that Java JDK is installed correctly. This can be done by executing the following steps.
    • Make sure that Java JDK is installed. This can be done by installing openjdk-7-jdk package, i.e., sudo apt-get install openjdk-7-jdk.
    • Make sure that the JAVA_HOME environment variable is set properly. This can be done by adding line export JAVA_HOME="/usr/lib/jvm/default-java" in /etc/profile.d/ file.
  • Install the following Ubuntu packages: python-dev, libssl-dev, i.e., sudo apt-get install python-dev libssl-dev.
  • Install the Pydoop package through pip, i.e., sudo -i pip install pydoop.


On my system (Ubuntu 14.04) with my installation of Hadoop (CDH 4.7.0), the following message was printed on stderr every time that I accessed HDFS:

SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See for further details

It turned out that among the jars loaded by the pydoop library, the slf4j jar was missing (the symbolic link to it was broken). In order to amend this problem I

  • removed the broken symbolic link with sudo rm /usr/lib/hadoop/client/slf4j-log4j12.jar
  • created a correct symbolic link with sudo ln -s /usr/share/java/slf4j-log4j12.jar /usr/lib/hadoop/client/slf4j-log4j12.jar (you need to have the libslf4j-java package installed in order to have the target jar file present).


The initial version of avroknife was created in March 2013. The script has been used by the developers of the Information Inference Service in the OpenAIREplus project.


The code is licensed under Apache License, Version 2.0

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