Developing Applications With Apache Kudu

Apache Kudu provides C++ and Java client APIs, as well as reference examples to illustrate their use. A Python API is included, but it is currently considered experimental, unstable, and is subject to change at any time.

Viewing the API Documentation

C++ API Documentation

The documentation for the C++ client APIs is included in the header files in /usr/include/kudu/ if you installed Kudu using packages or subdirectories of src/kudu/client/ if you built Kudu from source. If you installed Kudu using parcels, no headers are included in your installation. and you will need to build Kudu from source in order to have access to the headers and shared libraries.

The following command is a naive approach to finding relevant header files. Use of any APIs other than the client APIs is unsupported.
$ find /usr/include/kudu -type f -name *.h

Java API Documentation

View the Java API documentation online. Alternatively, after building the Java client, Java API documentation is available in java/kudu-client/target/apidocs/index.html.

Building the Java Client

  • JDK 7
  • Apache Maven 3.x
  • protoc 2.6 or newer installed in your path, or built from the thirdparty/ directory. Run the following commands to build protoc from the third-party dependencies:
    thirdparty/ protobuf

To build the Java client, clone the Kudu Git repository, change to the java directory, and issue the following command:

$ mvn install -DskipTests

For more information about building the Java API, as well as Eclipse integration, see java/

Kudu Example Applications

Several example applications are provided in the kudu-examples Github repository. Each example includes a README that shows how to compile and run it. These examples illustrate correct usage of the Kudu APIs, as well as how to set up a virtual machine to run Kudu. The following list includes a few of the examples that are available today.
A simple Java application which connects to a Kudu instance, creates a table, writes data to it, then drops the table.
A simple Java application which listens on a TCP socket for time series data corresponding to the Collectl wire protocol. The commonly-available collectl tool can be used to send example data to the server.

A Java application that generates random insert load.


An example program that shows how to use the Kudu Python API to load data into a new / existing Kudu table generated by an external program, dstat in this case.


An experimental plugin for using graphite-web with Kudu as a backend.

Scripts to download and run a VirtualBox virtual machine with Kudu already installed. For more information see the Kudu Quickstart documentation.

These examples should serve as helpful starting points for your own Kudu applications and integrations.

Maven Artifacts

The following Maven <dependency> element is valid for the Apache Kudu GA release:


Convenience binary artifacts for the Java client and various Java integrations (e.g. Spark, Flume) are also now available via the ASF Maven repository and the Central Maven repository.

Kudu Python Client

The Kudu Python client provides a Python friendly interface to the C++ client API. The sample below demonstrates the use of part of the Python client.

import kudu
from kudu.client import Partitioning
from datetime import datetime

# Connect to Kudu master server
client = kudu.connect(host='kudu.master', port=7051)

# Define a schema for a new table
builder = kudu.schema_builder()
builder.add_column('ts_val', type_=kudu.unixtime_micros, nullable=False, compression='lz4')
schema =

# Define partitioning schema
partitioning = Partitioning().add_hash_partitions(column_names=['key'], num_buckets=3)

# Create new table
client.create_table('python-example', schema, partitioning)

# Open a table
table = client.table('python-example')

# Create a new session so that we can apply write operations
session = client.new_session()

# Insert a row
op = table.new_insert({'key': 1, 'ts_val': datetime.utcnow()})

# Upsert a row
op = table.new_upsert({'key': 2, 'ts_val': "2016-01-01T00:00:00.000000"})

# Updating a row
op = table.new_update({'key': 1, 'ts_val': ("2017-01-01", "%Y-%m-%d")})

# Delete a row
op = table.new_delete({'key': 2})

# Flush write operations, if failures occur, capture print them.
except kudu.KuduBadStatus as e:

# Create a scanner and add a predicate
scanner = table.scanner()
scanner.add_predicate(table['ts_val'] == datetime(2017, 1, 1))

# Open Scanner and read all tuples
# Note: This doesn't scale for large scans
result =

Example Apache Impala Commands With Kudu

See Using Apache Impala with Kudu for guidance on installing and using Impala with Kudu, including several impala-shell examples.

Kudu Integration with Spark

Kudu integrates with Spark through the Data Source API as of version 1.0.0. Include the kudu-spark dependency using the --packages option:

Spark 1.x - Use the kudu-spark_2.10 artifact if you are using Spark 1.x with Scala 2.10:

spark-shell --packages org.apache.kudu:kudu-spark_2.10:1.1.0

Spark 2.x - Use the kudu-spark2_2.11 artifact if you are using Spark 2.x with Scala 2.11:

spark2-shell --packages org.apache.kudu:kudu-spark2_2.11:1.4.0

Then import kudu-spark and create a dataframe as demonstrated in the following sample code. In the following example, replace <kudu.master> with the actual hostname of the host running a Kudu master service, and <kudu_table> with the name of a pre-existing table in Kudu.

import org.apache.kudu.spark.kudu._

// Read a table from Kudu 
val df ="kudu.master" -> "<kudu.master>:7051","kudu.table" -> "<kudu_table>")).kudu

// Query <kudu_table> using the Spark API..."id").filter("id" >= 5).show()

// ...or register a temporary table and use SQL
val filteredDF = sqlContext.sql("select id from <kudu_table> where id >= 5").show()

// Use KuduContext to create, delete, or write to Kudu tables
val kuduContext = new KuduContext("<kudu.master>:7051", sqlContext.sparkContext)

// Create a new Kudu table from a dataframe schema
// NB: No rows from the dataframe are inserted into the table
kuduContext.createTable("test_table", df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))

// Insert data
kuduContext.insertRows(df, "test_table")

// Delete data
kuduContext.deleteRows(filteredDF, "test_table")

// Upsert data
kuduContext.upsertRows(df, "test_table")

// Update data
val alteredDF ="id", $"count" + 1)
kuduContext.updateRows(filteredRows, "test_table"

// Data can also be inserted into the Kudu table using the data source, though the methods on KuduContext are preferred
// NB: The default is to upsert rows; to perform standard inserts instead, set operation = insert in the options map
// NB: Only mode Append is supported
df.write.options(Map("kudu.master"-> "<kudu.master>:7051", "kudu.table"-> "test_table")).mode("append").kudu

// Check for the existence of a Kudu table

// Delete a Kudu table

Using Spark with a Secure Kudu cluster

The Kudu-Spark integration is able to operate on secure Kudu clusters which have authentication and encryption enabled, but the submitter of the Spark job must provide the proper credentials. For Spark jobs using the default 'client' deploy mode, the submitting user must have an active Kerberos ticket granted through kinit. For Spark jobs using the 'cluster' deploy mode, a Kerberos principal name and keytab location must be provided through the --principal and --keytab arguments to spark2-submit.

Spark Integration Known Issues and Limitations

  • Spark 2.2 (and higher) requires Java 8 at runtime even though Kudu Spark 2.x integration is Java 7 compatible. Spark 2.2 is the default dependency version as of Kudu 1.5.0.

  • Kudu tables with a name containing upper case or non-ASCII characters must be assigned an alternate name when registered as a temporary table.

  • Kudu tables with a column name containing upper case or non-ASCII characters must not be used with SparkSQL. Columns can be renamed in Kudu to work around this issue.

  • <> and ORpredicates are not pushed to Kudu, and instead will be evaluated by the Spark task. Only LIKE predicates with a suffix wildcard are pushed to Kudu. This means LIKE "FOO%" will be pushed, but LIKE "FOO%BAR" won't.

  • Kudu does not support all the types supported by Spark SQL. For example, Date, Decimal, and complex types are not supported on Kudu.

  • Kudu tables can only be registered as temporary tables in SparkSQL.

  • Kudu tables cannot be queried using HiveContext.

Integration with MapReduce, YARN, and Other Frameworks

Kudu was designed to integrate with MapReduce, YARN, Spark, and other frameworks in the Hadoop ecosystem. See and for examples which you can model your own integrations on.