Securing Kafka-To-Spark

Mark Grover explains how to secure communications between Apache Kafka and Apache Spark:

However, to read data from secure Kafka in distributed fashion, we need Hadoop-style delegation tokens in Kafka (KAFKA-1696), support for which doesn’t exist at the time of this writing (Spring 2017).

We considered various ways to solve this problem but ultimately decided that the recommended solution to read data securely from Kafka (at least until Kafka delegation tokens support is introduced) would be for the Spark application to distribute the user’s keytab so it’s accessible to the executors. The executors will then use the user’s keytab shared with them, to authenticate with the Kerberos Key Distribution Center (KDC) and read from Kafka brokers. YARN distributed cache is used for shipping and sharing the keytab to the driver and executors, from the client (that is, the gateway node). The figure below shows an overview of the current solution.

This turns out to be a bit more difficult than I would have anticipated.

Related Posts

Auto ML With SQL Server 2019 Big Data Clusters

Marco Inchiosa has a model scenario for using Big Data Clusters to scale out a machine learning problem: H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and pysparkling.ml.H2OAutoML. Both APIs use the same underlying algorithm implementations, […]

Read More

Erasure Coding In Hadoop

Guy Shilo explains erasure coding, a new feature in Hadoop 3: The benefits are, of course, space-saving, and for large files also improved performance (blocks striped across datanodes can be read in parallel, and less blocks are written because there is no x3 replication). The larger the file the more notable is the performance gain. […]

Read More

Categories