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Commonly Used TCP/IP Ports in Streaming

Cloudera CDF and HDF Ports

NiFi and Friends
FLaNK Extended Stack

Note:

All of these ports can be changed by administrators or in version updates. Also if you are running Apache Knox like in Cloudera Data Platform Public Cloud, these ports may be changed or hidden. This is just based on a version of CDF I am running and defaults in. This does not include standard Cloudera ports for Cloudera Manager, Hadoop, Atlas, Ranger and other necessary and fun services.

Cloudera Flow Management (CFM Powered by Apache NiFi)

Cloudera NiFi HTTP: 8080 or 9090
Cloudera NiFi HTTPS: 8443 or 9443
Cloudera NiFi RIP Socket: 10443 or 50999
Cloudera NiFi Node Protocol: 11443
Cloudera NiFi Load Balancing: 6342
Cloudera NiFi Registry: 18080
Cloudera NiFi Registry SSL: 18433
Cloudera NiFi Certificate Authority: 10443

Cloudera Edge Flow Management (CEM Powered by Apache NiFi - MiNiFi)

Cloudera EFM HTTP: 10080
Cloudera EFM CoAP: 8989

Cloudera Stream Processing (CSP Powered by Apache Kafka)

Cloudera Kafka: 9092
Cloudera Kafka SSL: 9093
Cloudera Kafka Connect: 38083
Cloudera Kafka Connect SSL: 38085
Cloudera Kafka Jetty Metrics: 38084
Cloudera Kafka JMX: 9393
Cloudera Kafka MirrorMaker JMX: 9394
Cloudera Kafka HTTP Metric: 24042
Cloudera Schema Registry Registry: 7788
Cloudera Schema Registry Admin: 7789
Cloudera Schema Registry SSL: 7790
Cloudera Schema Registry Admin SSL: 7791
Cloudera Schema Registry Database (Postgresql): 5432
Cloudera SRM: 6669
Cloudera RPC: 8081
Cloudera SRM Rest: 6670
Cloudera SRM Rest SSL: 6671
Cloudera SMM Rest / UI: 9991
Cloudera SMM Manager: 8585
Cloudera SMM Manager SSL: 8587
Cloudera SMM Manager Admin: 8586
Cloudera SMM Manager Admin SSL: 8588
Cloudera SMM Service Monitor: 9997
Cloudera SMM Kafka Connect: 38083
Cloudera SMM Database (Postgresql): 5432

Cloudera Streaming Analytics (CSA Powered by Apache Flink)

Cloudera Flink Dashboard: 8082

References

FLaNK: Low Code Streaming: Populating Kafka Topics with FlinkSQL Joins in Real-Time

FLaNK: Low Code Streaming: Populating Kafka Topics with FlinkSQL Joins in Real-Time

Then I can create my 3 tables. Two are the source ones to join and the third is the destination for my insert.

INSERT INTO global_sensor_events

SELECT

scada.uuid,

scada.systemtime ,

scada.temperaturef ,

scada.pressure ,

scada.humidity ,

scada.lux ,

scada.proximity ,

scada.oxidising ,

scada.reducing ,

scada.nh3 ,

scada.gasko,

energy.`current`,

energy.voltage ,

energy.`power` ,

energy.`total`,

energy.fanstatus

FROM energy,

scada

WHERE

scada.systemtime = energy.systemtime;

Examples

https://github.com/tspannhw/meetup-sensors/blob/master/flink-sql/

Assets / Scripts / DDL / SQL

https://github.com/tspannhw/FlinkSQLDemo

Flink Guide to SQL Joins

https://www.youtube.com/watch?v=5AuBlVRKQuo

Slides

https://www.slideshare.net/bunkertor/time-series-analysis-dataflow

Article on Joins

https://www.datainmotion.dev/2020/05/flink-sql-preview.html

Resources

Time Series Analysis - Dataflow

In a first, we joined together for the forces of NYC, New Jersey and Philly to power this meetup. A huge thanks to John Kuchmek, Amol Thacker and Paul Vidal for promoting and cross running a sweet meetup. John was an amazing meetup lead and made sure we kept moving. A giant thanks to Cloudera marketing for helping with logistics and some awesome giveaways! Hopefully next year's we can do a Cinco De Mayo Taco Feast! Bill Brooks and Robert Hryniewicz were great help! And thanks for Cloudera for providing CDP Public Cloud on AWS and CDP-DC on OpenStack for demos, development and general data fun. And thanks for the initial meetup suggestion and speaker to Bethann Noble and her awesome machine learning people.

https://www.meetup.com/futureofdata-newyork/ - https://www.meetup.com/futureofdata-princeton/ - https://www.meetup.com/futureofdata-philadelphia/

https://www.meetup.com/pro/futureofdata/

To quote, John Kuchmek:

The Internet of Things (IoT) is growing in popularity but it isn’t new. Connected devices have existed in manufacturing and utilities with Supervisory Control and Data Acquisition (SCADA) systems. Time series data has been looked at for sometime in these industries as well as the stock market. Time series analysis can bring valuable insight to businesses and individuals with smart homes. There are many parts and components to be able to collect data at the edge, store in a central location for initial analysis, model build, train and eventually deploy. Time series forecasting is one of the more challenging problems to solve in data science. Important factors in time series analysis and forecasting are seasonality, stationary nature of data and autocorrelation of target variables. We show you a platform, built on open source technology, that has this potential. Sensor data will be collected at the edge, off a Raspberry Pi, using Cloudera’s Edge Flow Manager (powered by MiNiFi). The data will then be pushed to a cluster containing Cloudera Flow Manager (powered by NiFi) so it can be manipulated, routed, and then be stored in Kudu on Cloudera’s Data Platform. Initial inspection can be done in Hue using Impala. The time series data will be analyzed with potential forecasting using an ARIMA model in CML (Cloudera Machine Learning). Time series analysis and forecasting can be applied to but not limited to stock market analysis, forecasting electricity loads, inventory studies, weather conditions, census analysis and sales forecasting.

The main portion of our meetup was an amazing talk by Data Scientist - Victor Dibia.

Analyzing Time Series Data with an ARIMA model

His talk comes right after mine and is about an hour of in-depth Data Science with many hard questions answered. Also a cool demo. Thanks again Victor.

We also had some really great attendees who asked some tough question. My favorite question was by a Flink expert who joined from the West Coast who asked for a FLaNK sticker.

Time Series Analysis - Dataflow

For my small part I did a demo of ingesting data from MiNiFi to NiFi to CML and Kafka. Flink reads from two Kafka topics, joins them and inserts into a third Kafka topic. We call the ML model for classification as part of our ingest flow. This is an example of my FLaNK Stack.

MiNiFi sends the data it reads from sensors and a camera and sends them to a local NiFi gateway. That NiFi gateway sends a stream to my CDP hosted CFM NiFi cluster for processing. This cluster splits the data based on which set of sensors (energy or scada) and then publishes to Kafka topics and populates Kudu tables with an UPSERT.

We have great options for monitoring, querying and analyzing our data with the tools from CDP and CDP-DC. These include Cloudera DAS, Apache Hue, Cloudera SMM for Kafka, Flink SQL console, Flink Dashboard, CML Notebooks, Jupyter Notebooks from CML and Apache Zeppelin.

As a separate way to investigate Kafka, I have created a Hive external table in beeline and connected that to a Kafka topic. I can know query the current state of that topic.

Video Walkthrough of FlinkSQL Application (and awesome Machine Learning Talk on Time Series)

Slides From Talk

https://www.slideshare.net/bunkertor/time-series-analysis-dataflow

Related Articles

Demo Repositories

Up Next: More Flink

https://www.meetup.com/futureofdata-princeton/events/269933905/

Flink SQL Preview

FLaNK: Flink SQL Preview

From our Web Flink Dashboard, we can see how our insert is doing and view the joins and records passing quickly through our tiny cluster.

See: https://github.com/tspannhw/meetup-sensors/blob/master/flink-sql/test2.ddl

As part of the May 7th, 2020 Virtual Meetup, I was doing some work with Flink SQL to show for a quick demo as the introduction to the meetup and I found out how easy it was to do some cool stuff. This was inspired by my Streaming Hero, Abdelkrim, who wrote this amazing article on Flink SQL use cases: https://towardsdatascience.com/event-driven-supply-chain-for-crisis-with-flinksql-be80cb3ad4f9

As part of our time series meetup, I have a few streams of data coming from one device from a MiNiFi Java agent to NiFi for some transformation, routing and processing and then sent to Apache Flink for final processing. I decided to join Kafka topics with Flink SQL.

The sensor data: https://www.datainmotion.dev/2020/04/predicting-sensor-readings-with-time.html

Let's create Flink Tables:

This table will be used to insert the joined events from both source Kafka topics.

CREATE TABLE global_sensor_events (

uuid STRING,

systemtime STRING ,

temperaturef STRING ,

pressure DOUBLE,

humidity DOUBLE,

lux DOUBLE,

proximity int,

oxidising DOUBLE ,

reducing DOUBLE,

nh3 DOUBLE ,

gasko STRING,

`current` INT,

voltage INT ,

`power` INT,

`total` INT,

fanstatus STRING

) WITH (

'connector.type' = 'kafka',

'connector.version' = 'universal',

'connector.topic' = 'global_sensor_events',

'connector.startup-mode' = 'earliest-offset',

'connector.properties.bootstrap.servers' = 'tspann-princeton0-cluster-0.general.fuse.l42.cloudera.com:9092',

'connector.properties.group.id' = 'flink-sql-global-sensor_join',

'format.type' = 'json'

);

This table will hold Kafka topic messages from our energy reader.

CREATE TABLE energy (

uuid STRING,

systemtime STRING,

`current` INT,

voltage INT,

`power` INT,

`total` INT,

swver STRING,

hwver STRING,

type STRING,

model STRING,

mac STRING,

deviceId STRING,

hwId STRING,

fwId STRING,

oemId STRING,

alias STRING,

devname STRING,

iconhash STRING,

relaystate INT,

ontime INT,

activemode STRING,

feature STRING,

updating INT,

rssi INT,

ledoff INT,

latitude INT,

longitude INT,

`day` INT,

`index` INT,

zonestr STRING,

tzstr STRING,

dstoffset INT,

host STRING,

currentconsumption INT,

devicetime STRING,

ledon STRING,

fanstatus STRING,

`end` STRING,

te STRING,

cpu INT,

memory INT,

diskusage STRING

) WITH (

'connector.type' = 'kafka',

'connector.version' = 'universal',

'connector.topic' = 'energy',

'connector.startup-mode' = 'earliest-offset',

'connector.properties.bootstrap.servers' = 'tspann-princeton0-cluster-0.general.fuse.l42.cloudera.com:9092',

'connector.properties.group.id' = 'flink-sql-energy-consumer',

'format.type' = 'json'

);

The scada table holds events from our sensors.

CREATE TABLE scada (

uuid STRING,

systemtime STRING,

amplitude100 DOUBLE,

amplitude500 DOUBLE,

amplitude1000 DOUBLE,

lownoise DOUBLE,

midnoise DOUBLE,

highnoise DOUBLE,

amps DOUBLE,

ipaddress STRING,

host STRING,

host_name STRING,

macaddress STRING,

endtime STRING,

runtime STRING,

starttime STRING,

cpu DOUBLE,

cpu_temp STRING,

diskusage STRING,

memory DOUBLE,

id STRING,

temperature STRING,

adjtemp STRING,

adjtempf STRING,

temperaturef STRING,

pressure DOUBLE,

humidity DOUBLE,

lux DOUBLE,

proximity INT,

oxidising DOUBLE,

reducing DOUBLE,

nh3 DOUBLE,

gasko STRING

) WITH (

'connector.type' = 'kafka',

'connector.version' = 'universal',

'connector.topic' = 'scada',

'connector.startup-mode' = 'earliest-offset',

'connector.properties.bootstrap.servers' = 'tspann-princeton0-cluster-0.general.fuse.l42.cloudera.com:9092',

'connector.properties.group.id' = 'flink-sql-scada-consumer',

'format.type' = 'json'

);

This is the magic part:

INSERT INTO global_sensor_events

SELECT

scada.uuid,

scada.systemtime ,

scada.temperaturef ,

scada.pressure ,

scada.humidity ,

scada.lux ,

scada.proximity ,

scada.oxidising ,

scada.reducing ,

scada.nh3 ,

scada.gasko,

energy.`current`,

energy.voltage ,

energy.`power` ,

energy.`total`,

energy.fanstatus

FROM energy,

scada

WHERE

scada.systemtime = energy.systemtime;

So we join two Kafka topics and use some of their fields to populate a third Kafka topic that we defined above.

With Cloudera, it is so easy to monitor our streaming Kafka events with SMM.

For context, this is where the data comes from: