Readme.md

rdbms->ksql->grafana demo

I am using this as a playground and demo for a presentation on how to get data from a relational database into kafka, transform with KSQL and finally chart this in a real-time dashboard using grafana. It is heavily based on an existing ksql clickstream demo by confluent.

These are my notes if you want to replay the same thing.

prepare and run docker

Pull and run the docker image and start the container

docker pull brost/stream-etl:ksql
-- forwarding the kafka ports so i can connect from my laptop,too
docker run -p 2181:2181 -p 9092:9092 -p 9093:9093 -p 33000:3000 -ti brost/stream-etl:ksql bash

review the bits that are already running

Docker already starts most of the daemons, mysql and background scripts. Have a look around.

-- there is already a shell script running that inserts random orders
less db-inserts.sh

-- let's see what's happening in mysql
mysql code

mysql> describe orders;
mysql> select * from orders limit 42;
mysql> insert into orders (product, price, user_id) values ('lumpy', 100, 42);

Check for existing topics, there should be one called localhost.code.orders created by Debezium

-- show that all our topics are there
kafka-topics --zookeeper localhost:2181 --list

And we can run a console consumer to dump the contents of the debezium topic

kafka-avro-console-consumer --bootstrap-server=localhost:9092 --topic=localhost.code.orders

start ksql, look around and create the ksql objects

-- start ksql-cli and initialize the clickstream topics
ksql 

-- note the topic localhost.code.orders from connect-debezium

list topics;

-- now set up out orders table step by step
-- if the stream is already in avro, we do not even have to specify columns

create stream orders_raw with (kafka_topic = 'localhost.code.orders', value_format = 'AVRO', timestamp='ordertime');


-- but we need to cast at least the price to bigint
create stream orders as select id, product, cast(price as bigint) price, user_id, ordertime from orders_raw;


-- select product, count(*), sum(price) from orders window tumbling (size 15 seconds) group by product;

-- now the aggregating table
create table orders_per_min as select product, sum(price) amount from orders window hopping (size 60 seconds, advance by 15 seconds) group by product;

-- and enrich that with the event timestamp
CREATE TABLE orders_per_min_ts WITH (value_format='JSON') as select rowTime as event_ts, * from orders_per_min;

list streams;
list tables;
list topics;

load kafka topics into elastic

Now we load things into elastic and prepare them for grafana. I have messed um something in my dashboard jason and it only loads if I load the original one first.

-- now load the streams into elastic and grafana
 cd /usr/share/doc/ksql-clickstream-demo/

./orders-to-grafana.sh

./clickstream-analysis-dashboard.sh
cp dashboard.json clickstream-analysis-dashboard.json
./clickstream-analysis-dashboard.sh

ready to view the results in your browser

open http://localhost:33000/dashboard/db/real-time-order-volume-by-product and log in using admin/admin.

Now you can enter new rows into the orders table and watch the dashboard update.

-- now play with some mysql inserts
insert into orders (product, price, user_id, ordertime) values ('lumpy', 500, 42, now());

-- here is a late order - which window does this order count against?
insert into orders (product, price, user_id, ordertime) values ('lumpy', 300, 42, date_sub(now(), interval 1 minute));

random notes. here be dragons

To build the container after changes to the Dockerfile:

docker build -t brost/stream-etl:ksql .

CREATE STREAM USER_CLICKSTREAM_ORDER AS SELECT userid, u.username, ip, u.city, request, status, bytes, o.product, o.price FROM clickstream c LEFT JOIN web_users u ON c.userid = u.user_id LEFT JOIN orders o on c.userid = o.user_id

This seems to be broken create stream orders_partby as select cast(id as integer) as id, product, cast(price as integer) as price, cast(user_id as integer) as user_id from orders_flat partition by id;

--create table spending_per_min as select user_id, sum(price) amount from orders window tumbling (size 2 minutes) group by user_id ;

--create table user_tally as select user_id, sum(price) amount from orders group by user_id;

No SUM aggregate function with Schema{INT32} argument type exists!