Skip to main content

Sizing Your Apache NiFi Cluster For Production Workloads

Sizing Your Apache NiFi Cluster For Production Workloads

Cloudera Flow Management provides an enterprise edition of support Apache NiFi managed by Cloudera Manager.    The official documentation provides a great guide for sizing your cluster.

https://docs.cloudera.com/cfm/2.0.1/nifi-sizing/topics/cfm-nifi-sizing.html

If the use case fits, NiFi Stateless Engine may fit and perform better utilizing no disk.

Check out that heap usage and utilization, you may need to increase.    24-32 Gigabytes of RAM is a nice sweet spot for most instances.



Check out how your nodes, threads and queues are doing.   If queue is not processing fast or thread count is high, you may need more cores, RAM or nodes.



When you are managing your cluster in Cloudera Manager, make sure you increase the default JVM memory for Apache NiFi.  512MB is not going to cut it for anything but single user development.



Do this correctly and process a billion events!!!   https://blog.cloudera.com/benchmarking-nifi-performance-and-scalability/.  - Notice the hardware and performance sections of that article


General tips:

Make sure you use SSD for Provenance and other repositories.  Faster disk, happier user. https://docs.cloudera.com/cfm/2.0.1/nifi-sizing/topics/cfm-sizing-disk-configuration.html

Monitor your flows to see how much resources you need:  https://www.datainmotion.dev/2020/07/report-on-this-apache-nifi-1114-monitor.html.


Use Records, if it's semistructured GrokReader can help.   https://www.nifi.rocks/record-path-cheat-sheet/  If it's CSV, JSON, XML, Parquet, Logs then use Readers and writers.   They are much faster, easier and cleaner.



Minimize use of CPU or Memory intensive processors (or make a not of them during sizing):   https://docs.cloudera.com/cfm/2.0.1/nifi-sizing/topics/cfm-sizing-resource-intensive-processors.html

There are a few decisions to make on repositories, talk to your Cloudera friends.    https://docs.cloudera.com/HDPDocuments/HDF3/HDF-3.5.1/nifi-configuration-best-practices/content/configuration-best-practices.html



Popular posts from this blog

Ingesting Drone Data From DJII Ryze Tello Drones Part 1 - Setup and Practice

Ingesting Drone Data From DJII Ryze Tello Drones Part 1 - Setup and Practice In Part 1, we will setup our drone, our communication environment, capture the data and do initial analysis. We will eventually grab live video stream for object detection, real-time flight control and real-time data ingest of photos, videos and sensor readings. We will have Apache NiFi react to live situations facing the drone and have it issue flight commands via UDP. In this initial section, we will control the drone with Python which can be triggered by NiFi. Apache NiFi will ingest log data that is stored as CSV files on a NiFi node connected to the drone's WiFi. This will eventually move to a dedicated embedded device running MiniFi. This is a small personal drone with less than 13 minutes of flight time per battery. This is not a commercial drone, but gives you an idea of the what you can do with drones. Drone Live Communications for Sensor Readings and Drone Control You must connect t

Advanced XML Processing with Apache NiFi 1.9.1

Advanced XML Processing with Apache NiFi 1.9.1 With the latest version of Apache NiFi, you can now directly convert XML to JSON or Apache AVRO, CSV or any other format supported by RecordWriters.   This is a great advancement.  To make it even easier, you don't even need to know the schema before hand.   There is a built-in option to Infer Schema. The results of an RSS (XML) feed converted to JSON and displayed in a slack channel. Besides just RSS feeds, we can grab regular XML data including XML data that is wrapped in a Zip file (or even in a Zipfile in an email, SFTP server or Google Docs). Get the Hourly Weather Observation for the United States Decompress That Zip  Unpack That Zip into Files One ZIP becomes many XML files of data. An example XML record from a NOAA weather station. Converted to JSON Automagically Let's Read Those Records With A Query and Convert the results to JSON Records

Simple Change Data Capture (CDC) with SQL Selects via Apache NiFi (FLaNK)

 Simple Change Data Capture (CDC) with SQL Selects via Apache NiFi (FLaNK) Sometimes you need real CDC and you have access to transaction change logs and you use a tool like QLIK REPLICATE or GoldenGate to pump out records to Kafka and then Flink SQL or NiFi can read them and process them. Other times you need something easier for just some basic changes and inserts to some tables you are interested in receiving new data as events.   Apache NiFi can do this easily for you with QueryDatabaseTableRecord, you don't need to know anything but the database connection information, table name and what field may change.  NiFi will query, watch state and give you new records.   Nothing is hardcoded, parameterize those values and you have a generic Any RDBMS to Any Other Store data pipeline.   We are reading as records which means each FlowFile in NiFi can have thousands of records that we know all the fields, types and schema related information for.   This can be ones that NiFi infers the s