Tuning Kafka And Spark Data Pipelines

Larry Murdock explains the tuning options available to Kafka and Spark Streams:

Kafka is not the Ferrari of messaging middleware, rather it is the salt flats rocket car. It is fast, but don’t expect to find an AUX jack for your iPhone. Everything is stripped down for speed.

Compared to other messaging middleware, the core is simpler and handles fewer features. It is a transaction log and its job is to take the message you sent asynchronously and write it to disk as soon as possible, returning an acknowledgement once it is committed via an optional callback. You can force a degree of synchronicity by chaining a get to the send call, but that is kind of cheating Kafka’s intention. It does not send it on to a receiver. It only does pub-sub. It does not handle back pressure for you.

I like this as a high-level overview of the different options available.  Definitely gets a More Research Is Required tag, but this post helps you figure out where to go next.

Related Posts

Hortonworks Data Platform 3.0 Released

Saumitra Buragohain, et al, announce the newest version of the Hortonworks Data Platform: Highlighted Apache Hive features include: Workload management for LLAP:  You can assign resource pools within LLAP pool and allocate resources on a per user or per group basis. This enables support for large multi-tenant deployments. ACID v2 and ACID on by default:  We are […]

Read More

Replicating Data In HDFS Between Clusters

Murali Ramasami and Niru Anisetti have an article showing how to use the Hortonworks Data Lifecycle Manager to set up replication between two Hadoop clusters: Data Lifecycle Manager (DLM) delivers on the promise of location-agnostic, secure replication by encapsulating and copying data seamlessly across physical private storage and public cloud environments. This empowers businesses to […]

Read More

Categories

March 2017
MTWTFSS
« Feb Apr »
 12345
6789101112
13141516171819
20212223242526
2728293031