Data In Motion

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

Cloudera Edge Management 1.1.0 Release

Cloudera Edge Management 1.1.1 Release

See: https://docs.cloudera.com/cem/1.1.0/index.html

https://docs.cloudera.com/cem/1.1.0/using-cem/topics/cem-deployment-monitor.html

We can now integrate with Grafana and use Prometheus Metrics:

https://docs.cloudera.com/cem/1.1.0/using-cem/topics/cem-monitoring-grafana-dashboard.html

Let's Query Kafka with Hive

Let's Query Kafka with Hive

I can hop into beeline and build an external Hive table to access my Cloudera CDF Kafka cluster whether it is in the public cloud in CDP DataHub, on-premise in HDF or CDF or in CDP-DC.

I just have to set my KafkaStorageHandler, Kafka Topic Name and my bootstrap servers (usually port 9092). Now I can use that table to do ELT/ELT for populating Hive tables or populating Kafka topics from Hive tables. This is a nice and easy way to do data engineering on the quick and easy.

This is a good item to augment CDP Data Engineering with Spark, CDP DataHub with NiFi, CDP DataHub with Kafka and KafkaStreams and various SQOOP or Python utilities you may have in your environment.

For real-time continuous queries on Kafka with SQL, you can use Flink SQL. https://www.datainmotion.dev/2020/05/flank-low-code-streaming-populating.html

Example Table Create

CREATE EXTERNAL TABLE <tableName>

(`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)

STORED BY 'org.apache.hadoop.hive.kafka.KafkaStorageHandler'

TBLPROPERTIES

("kafka.topic" = "<TopicName>",

"kafka.bootstrap.servers"="<ServerName>:9092");

show tables;

describe extended kafka_table;

select *

from kafka_table;

I can browse my Kafka topics with Cloudera SMM to see what the data is and why I want to load or need to load.

For more information take a look at the documentation for Integrating Hive and Kafka at Cloudera below:

https://docs.cloudera.com/HDPDocuments/HDP3/HDP-3.1.5/integrating-hive/content/hive_kafka_query_table.html
https://docs.cloudera.com/runtime/7.0.3/integrating-hive-and-bi/topics/hive-kafka-integration.html
https://docs.cloudera.com/runtime/7.1.0/integrating-hive-and-bi/topics/hive-kafka-integration.html
https://docs.cloudera.com/runtime/7.1.0/integrating-hive-and-bi/topics/hive_ingest_kafka_data_into_hive.html

Cloudera Flow Management 101: Let's Build a Simple REST Ingest to Cloud Datawarehouse With LowCode. Powered by Apache NiFi

Use NiFi to call REST API, transform, route and store the data

Pick any REST API of your choice, but I have walked through this one to grab a number of weather stations reports. Weather or not we have good weather, we can query it anyway.

We are going to build a GenerateFlowFile to feed our REST calls.

[
{"url":"http://weather.gov/xml/current_obs/CWAV.xml"},
{"url":"http://weather.gov/xml/current_obs/KTTN.xml"},
{"url":"http://weather.gov/xml/current_obs/KEWR.xml"},
{"url":"http://weather.gov/xml/current_obs/KEWR.xml"},
{"url":"http://weather.gov/xml/current_obs/CWDK.xml"},
{"url":"http://weather.gov/xml/current_obs/CWDZ.xml"},
{"url":"http://weather.gov/xml/current_obs/CWFJ.xml"},
{"url":"http://weather.gov/xml/current_obs/PAEC.xml"},
{"url":"http://weather.gov/xml/current_obs/PAYA.xml"},
{"url":"http://weather.gov/xml/current_obs/PARY.xml"},
{"url":"http://weather.gov/xml/current_obs/K1R7.xml"},
{"url":"http://weather.gov/xml/current_obs/KFST.xml"},
{"url":"http://weather.gov/xml/current_obs/KSSF.xml"},
{"url":"http://weather.gov/xml/current_obs/KTFP.xml"},
{"url":"http://weather.gov/xml/current_obs/CYXY.xml"},
{"url":"http://weather.gov/xml/current_obs/KJFK.xml"},
{"url":"http://weather.gov/xml/current_obs/KISP.xml"},
{"url":"http://weather.gov/xml/current_obs/KLGA.xml"},
{"url":"http://weather.gov/xml/current_obs/KNYC.xml"},
{"url":"http://weather.gov/xml/current_obs/KJRB.xml"}
]

So we are using ${url} which will be one of these. Feel free to pick your favorite airports or locations near you. https://w1.weather.gov/xml/current_obs/index.xml

If you wish to choose your own data adventure, you can pick one of these others. You will have to build your own table if you wish to store it. They return CSV, JSON or XML, since we have record processors we don’t care. Just know which you pick.

Then we will use SplitJSON to split the JSON records into single rows.

Then use EvaluateJSONPath to extract the URL.

Now we are going to call those REST URLs with InvokeHTTP.

You will need to create a Standard SSL controller.

This is the default JDK JVM on Mac or some Centos 7. You may have a real password, if so you are awesome. If you don't know it, that's rough. You can build a new one with SSL.

For more cloud ingest fun, https://docs.cloudera.com/cdf-datahub/7.1.0/howto-data-ingest.html.

SSL Defaults (In CDP Datahub, one is built for you automagically, thanks Michael).

Truststore filename: /usr/lib/jvm/java-openjdk/jre/lib/security/cacerts

Truststore password: changeit

Truststore type: JKS

TLS Protocol: TLS

StandardSSLContextService for Your GET ${url}

We can tweak these defaults.

Then we are going to run a query to convert these and route based on our queries.

Example query on the current NOAA weather observations to look for temperature in fareneheit below 60 degrees. You can make a query with any of the fields in the where cause. Give it a try!

You will need to set the Record Writer and Record Reader:

Record Reader: XML

Record Writer: JSON

SELECT * FROM FLOWFILE
WHERE temp_f <= 60

SELECT * FROM FLOWFILE

Now we are splitting into three concurrent paths. This shows the power of Apache NiFi. We will write to Kudu, HDFS and Kafka.

For the results of our cold path (temp_f ⇐60), we will write to a Kudu table.

Kudu Masters: edge2ai-1.dim.local:7051 Table Name: impala::default.weatherkudu Record Reader: Infer Json Tree Reader Kudu Operation Type: UPSERT

Before you run this, go to Hue and build the table.

CREATE TABLE weatherkudu
(`location` STRING,`observation_time` STRING, `credit` STRING, `credit_url` STRING, `image` STRING, `suggested_pickup` STRING, `suggested_pickup_period` BIGINT,
`station_id` STRING, `latitude` DOUBLE, `longitude` DOUBLE,  `observation_time_rfc822` STRING, `weather` STRING, `temperature_string` STRING,
`temp_f` DOUBLE, `temp_c` DOUBLE, `relative_humidity` BIGINT, `wind_string` STRING, `wind_dir` STRING, `wind_degrees` BIGINT, `wind_mph` DOUBLE, `wind_gust_mph` DOUBLE, `wind_kt` BIGINT,
`wind_gust_kt` BIGINT, `pressure_string` STRING, `pressure_mb` DOUBLE, `pressure_in` DOUBLE, `dewpoint_string` STRING, `dewpoint_f` DOUBLE, `dewpoint_c` DOUBLE, `windchill_string` STRING,
`windchill_f` BIGINT, `windchill_c` BIGINT, `visibility_mi` DOUBLE, `icon_url_base` STRING, `two_day_history_url` STRING, `icon_url_name` STRING, `ob_url` STRING, `disclaimer_url` STRING,
`copyright_url` STRING, `privacy_policy_url` STRING,
PRIMARY KEY (`location`, `observation_time`)
)
PARTITION BY HASH PARTITIONS 4
STORED AS KUDU
TBLPROPERTIES ('kudu.num_tablet_replicas' = '1');

Let it run and query it. Kudu table queried via Impala, try it in Hue.

The Second fork is to Kafka, this will be for the 'all' path.

Kafka Brokers: edge2ai-1.dim.local:9092 Topic: weather Reader & Writer: reuse the JSON ones

The Third and final fork is to HDFS (could be ontop of S3 or Blob Storage) as Apache ORC files. This will also autogenerate the DDL for an external Hive table as an attribute, check your provenance after running.

JSON in and out for record readers/writers, you can adjust the time and size of your batch or use defaults.

Hadoop Config: /etc/hadoop/conf/hdfs-site.xml,/etc/hadoop/conf/core-site.xml Record Reader: Infer Json Directory: /tmp/weather Table Name: weather

Before we run, build the /tmp/weather directory in HDFS and give it 777 permissions. We can do this with Apache Hue.

Once we run we can get the table DDL and location:

Go to Hue to create your table.

CREATE EXTERNAL TABLE IF NOT EXISTS `weather`
(`credit` STRING, `credit_url` STRING, `image` STRUCT<`url`:STRING, `title`:STRING, `link`:STRING>, `suggested_pickup` STRING, `suggested_pickup_period` BIGINT,
`location` STRING, `station_id` STRING, `latitude` DOUBLE, `longitude` DOUBLE, `observation_time` STRING, `observation_time_rfc822` STRING, `weather` STRING, `temperature_string` STRING,
`temp_f` DOUBLE, `temp_c` DOUBLE, `relative_humidity` BIGINT, `wind_string` STRING, `wind_dir` STRING, `wind_degrees` BIGINT, `wind_mph` DOUBLE, `wind_gust_mph` DOUBLE, `wind_kt` BIGINT,
`wind_gust_kt` BIGINT, `pressure_string` STRING, `pressure_mb` DOUBLE, `pressure_in` DOUBLE, `dewpoint_string` STRING, `dewpoint_f` DOUBLE, `dewpoint_c` DOUBLE, `windchill_string` STRING,
`windchill_f` BIGINT, `windchill_c` BIGINT, `visibility_mi` DOUBLE, `icon_url_base` STRING, `two_day_history_url` STRING, `icon_url_name` STRING, `ob_url` STRING, `disclaimer_url` STRING,
`copyright_url` STRING, `privacy_policy_url` STRING)
STORED AS ORC
LOCATION '/tmp/weather'

You can now use Apache Hue to query your tables and do some weather analytics. When we are upserting into Kudu we are ensuring no duplicate reports for a weather station and observation time.

select `location`, weather, temp_f, wind_string, dewpoint_string, latitude, longitude, observation_time
from weatherkudu
order by observation_time desc, station_id asc

select *
from weather

In Atlas, we can see the flow.