Apache NiFi Load Balancing via Load Balanced Connections

tspannhw profile image Timothy Spann Originally published at datainmotion.dev on ・4 min read

Modern Apache NiFi Load Balancing

In today's Apache NiFi, there is a new and improved means of load balancing data between nodes in your cluster. With the introduction of NiFi 1.8.0, connection load balancing has been added between every processor in any connection. You now have an easy to set option for automatically load balancing between your nodes. The legacy days of using Remote Process

Groups to distribute load between Apache NiFi nodes is over. For maximum flexibility,

performance and ease, please make sure you upgrade your existing flows to

use the built-in Connection Load Balancing.

If you are running newer Apache NiFI or Cloudera Flow Management (CFM), you have had a

better way of distributing processing between processors and servers. This is for

Apache NiFi 1.8.0 and higher including the newest version 1.9.2.

Note: Remote Process Groups are no longer necessary for load balancing!

Use actual load balanced connections instead!

Remote Process Groups should only be used for distributing to other clusters.

Apache NiFi Load Balancing

Since 2018, it's been an awesome feature:


We have a few options for Load Balancing Options, these strategies include

"Round Robin" that during failure conditions data will be rebalanced to

another node. This can rebalance thousands of flow files per second or

more depending on flow file size. This is done to give a node a chance to

reconnect and continue processing.

Data Distribution Strategies

Other option is to “Partition by Attribute” and “Single Node” which will

queue up data until that single node or partitioned node returns. You

cannot pick which Node in the cluster does that processing for portability

purposes. We need to be dynamic and elastic, so it just needs to be one

node. This allows for “like data” can be sent to the same node in cluster

which may be necessary for certain use cases. Using a custom

Attribute Name for this routing can be powerful as well as for Merges

in table loading use cases. We can also choose to not load balance at all.

Elastic Scaling for Apache NiFi

An important new feature that was added to NiFi is to allow nodes to be

decommissioned and disconnected from the cluster and all of their data

offloaded. This is important for Kubernetes and dynamic scaling for

elasticity. Elastic Scaling is important for workloads that differ during

the day or year like once an hour loads or weekly jobs. Scale up to

meet SLAs and deadlines, but scale down when possible to save cloud

spend! Now NiFi not only solves data problems but saves you cash


Apache NiFi Node Affinity

Remote Process Groups do not support node affinity. Node affinity is

supported in our Partition by Attribute strategy and has many uses.

Remote Process Groups

To replace the former big use case, we used Remote Process Groups.

We have a better solution, for a first connection like ListSFTP runs on

one node and the connections can then be "Round Robin".

Important Use Case

This load balancing feature of Apache NiFi shows the power of distributing a large dataset

or unstructured data capture at the edge or other datacenter, split and transfer, then use

attribute affinity to a node to reconstitute the data in a particular order.

So what happens is sometimes you have a large bulk data export from a system like a

relational database dump in one multiple terabyte file. We need one NiFi node to load

this file and then split it up into chunks, transfer it and send it to nodes to process. Sometimes

ordering of records will require we use an attribute to keep related chunks (say the same Table) together on one node.

We also see this with a large zip file containing many files of many types. Often there will

be hundreds of files of the multiple types and we may want to route to the same

node based on filename root. That way one NiFi node will be processing all the same file types or

table. This is now trivial to implement and easy for any NiFi user to examine and see what is

going on in this ETL process.


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tspannhw profile

Timothy Spann


I am a Principal Field Engineer for Data in Motion at Cloudera. I work with Apache NiFi, Apache Kafka, Apache Spark, Apache Flink, IoT, MXNet, DLJ.AI, Deep Learning, Machine Learning, Streaming...


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