Running High performance Nifi Cluster on Kubernetes

Apache NiFi is a powerful tool for data integration and processing that allows users to automate data flows across various systems. While running NiFi on-premises provides greater control and security over data, it also poses several challenges.

Key challenges of running NiFi on-premises

1. Hardware requirements: Running NiFi on-premises requires dedicated hardware with sufficient computing power and memory to handle the data processing requirements. This can be a significant investment, particularly if you need to scale up or down based on demand.
2. Maintenance and upgrades: Running NiFi on-premises requires ongoing maintenance and upgrades to keep the system running smoothly. This includes software updates, security patches, and hardware upgrades as needed. This can be time-consuming and expensive, particularly if you need to hire additional staff to manage the system.
3. Security: When running NiFi on-premises, you are responsible for securing the system and protecting your data from cyber threats. This includes configuring firewalls, monitoring traffic, and implementing access controls. Ensuring data security and privacy can be challenging, especially if you are dealing with sensitive information.
4. Scalability: Scaling NiFi on-premises can be difficult as it requires adding additional hardware and resources, which can be time-consuming and expensive. As your data processing needs grow, you may need to invest in new hardware or reconfigure your existing infrastructure.
5. Data integration challenges: Running NiFi on-premises can present challenges when it comes to integrating with other systems, particularly cloud-based platforms. This can create data silos and limit the ability to leverage data insights across the organization.

Considerations while running on Azure Cloud

Azure Virtual Machines: ?

It is recommended to use F-series compute optimized virtual machines to run this cluster. F-series VMs feature a higher CPU-to-memory ratio. They are equipped with 2 GB RAM and 16 GB of local solid-state drive (SSD) per CPU core and are optimized for compute-intensive workloads. These VMs are specifically designed for batch processing, web servers, analytics applications.?

Considerations while running on Kubernetes Cluster

Node Isolation ?

NiFi being a very compute and disk heavy application and requires lot of resources while doing the data processing, hence NiFi pods running in the k8s cluster should be given either a dedicated node (without sharing with other applications) or given higher priority than other pods.?

This will ensure that when bulk of data processing are needed NiFi has enough capacity to process without competing for resources with other applications.?

Storage affinity ?

The Nifi pod should be assigned the persistent volume from the same host where the pod is running with highest grade of service to ensure NiFi gets high I/O throughput while accessing the flow files.??

Segregating Streaming and Batch Processing into separate NiFi instances?

The nature of batch processes are bulk and heavy batch mode which causes sudden spike into single NiFi cluster which is hosting multiple flows and hence causing performance bottleneck (I/O, Network, CPU). By segregating each process with their own instances (preferably with dedicated nodes) we will be able to distribute the sporadic load of different processes without causing any performance bottleneck.?

General Guidelines for setting up a High performance Nifi cluster

1. Determine the Cluster Architecture: The first step in setting up a high-performance NiFi cluster is to determine the architecture based on your business requirements. A cluster can have one or more nodes, and each node can have a primary and secondary component. The primary component handles the data flow, while the secondary component acts as a backup in case the primary component fails.
2. Configure NiFi Properties: The next step is to configure the NiFi properties on each node in the cluster. These properties include the nifi.properties, authorizers.xml, flow.xml.gz, state-management.xml, and the bootstrap.conf files. Ensure that the properties are configured correctly, and the cluster configuration is consistent across all nodes.
3. Configure ZooKeeper: Apache ZooKeeper is a distributed coordination service that helps manage cluster configuration and coordination. It is a critical component of a NiFi cluster, and it must be configured correctly for the cluster to function correctly. Configure the ZooKeeper ensemble and the ZooKeeper properties file.
4. Configure Load Balancing: Load balancing ensures that the data flow is evenly distributed across the nodes in the cluster. There are several load balancing techniques that you can use, such as round-robin, sticky sessions, and IP hashing. Configure the load balancer to ensure optimal performance and reliability.
5. Configure NiFi Cluster Nodes: Configure the NiFi cluster nodes to ensure that they are correctly set up and ready to process data. Ensure that the nodes are configured to use the correct ports, the correct JVM settings, and that they have access to the correct directories and files.
6. Configure Security: Configure security to ensure that the data flowing through the cluster is secure. Implement encryption, access control, and other security measures to protect the data.
7. Test the Cluster: Test the cluster to ensure that it is working correctly. Test the data flow, failover, and load balancing to ensure that the cluster is functioning optimally.