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.

• https://min-api.cryptocompare.com/data/price?fsym=ETH&tsyms=BTC,USD,EUR

• https://data.cdc.gov/api/views/cjae-szjv/rows.json?accessType=DOWNLOAD

• https://api.weather.gov/stations/KTTN/observations/current * https://www1.nyc.gov/assets/tlc/downloads/csv/data_reports_monthly_indicators.csv

• https://docs.coincap.io/?version=latest

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.

One Minute NiFi Tip: Calcite SQL Notes

NiFi Quick Tip on SQL

You sometimes have to cast, as fields aren't what you think they are.   I have some temperatures that are stored as string, yeah I know let's yell at who did that.   Maybe it was some lazy developer (Me?~??~?~?!!!).    Let's just cast to a type that makes sense for math and comparisons.   CAST is my friend.

SELECT * 

FROM FLOWFILE

WHERE CAST(temperaturef as FLOAT) > 60

Apache NiFi (and lots of other awesome projects) use Apache Calcite for queries.   So if you need some SQL help, always look here:   https://calcite.apache.org/docs/reference.html

You can also include variables in your QueryRecord queries.

SELECT * 

FROM FLOWFILE

WHERE CAST(temperaturef as FLOAT) >= (CAST(${predictedTemperature} as FLOAT) - 5)

There are wildcard characters that you may need to watch.

Underscore has special meaning.  Also there often column names that are reserved words.   I got a lot of columns coming from IoT often with names like timestamp, start, end and other ones used by SQL.   Just put a `start` around it.

Watch those wildcards.

select * from flowfile where internal = false
                    and name not like '@_@_%' ESCAPE '@'

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

• https://github.com/cloudera/flink-tutorials
• https://docs.cloudera.com/csa/1.1.0/job-lifecycle/topics/csa-run-job.html
• https://github.com/asdaraujo/edge2ai-workshop/tree/master/flink
• https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/sqlClient.html
• https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/table/sqlClient.html 

Available Now: CFM 1.1.0

Cloudera Flow Management 1.1.0

Now with all the cool new features of Parameters, Predictive Monitoring, Rules Engine, Encrypted Repositories and SQL Reporting Tasks.

https://docs.cloudera.com/cfm/1.1.0/release-notes/topics/cfm-whats-new.html

Download it today:   https://www.cloudera.com/downloads/cdf/cfm.html

Features are highlighted here:   https://www.slideshare.net/bunkertor/introduction-to-apache-nifi-1114

https://www.datainmotion.dev/2020/03/using-nifi-cli-to-restore-nifi-flows.html

https://www.datainmotion.dev/2020/02/new-and-improved-its-nifi.html

Apache NiFi 1.11.4 Release Notes.