From bdc06cfcd23e9f2ae2c8dc392c67ed22e763b2d2 Mon Sep 17 00:00:00 2001
From: jr-marquez <jr.marquez.rompani@gmail.com>
Date: Thu, 22 Apr 2021 00:07:20 +0200
Subject: [PATCH] yeskaf

---
 labs/mqtt_demo.adoc | 43 +++++++++++++++++++++----------------------
 1 file changed, 21 insertions(+), 22 deletions(-)

diff --git a/labs/mqtt_demo.adoc b/labs/mqtt_demo.adoc
index 57fa879..8fccb31 100644
--- a/labs/mqtt_demo.adoc
+++ b/labs/mqtt_demo.adoc
@@ -11,8 +11,8 @@ cd /home/ec2-user/ksqldbWorkshop-main/docker
 
 === Data
 
-* Option 1 : load sample data from local
-+
+load sample data from local
+
 [source,bash]
 ----
 kafkacat -b localhost:9092 -t data_mqtt -K: -P -T -l ./data/dummy_data.kcat
@@ -221,12 +221,6 @@ We will need to create an index pattern in kibana
 
 * Go to the Kibana dashboard - <your lab ip>:5601
 
-
-
-image::images/mqtt_kafka_07a.png[]
-
-But who is `rmoff`, and does he mind us having access to all this information about him?
-
 Check out the source data in MySQL:
 
 [source,sql]
@@ -253,6 +247,7 @@ SELECT USERID, EMAIL, SHARE_LOCATION_OPTIN FROM USERS;
 | hugh   | hugh@example.com |                    0 |
 +--------+------------------+----------------------+ 
 ----
+exit mysql
 
 Ingest the data into ksqlDB
 
@@ -291,17 +286,18 @@ Declare the KSQL table on the topic populated from the database:
 [source,sql]
 ----
 
-CREATE STREAM USERS_STREAM_CDC WITH (KAFKA_TOPIC='workshop.demo.USERS-cdc', VALUE_FORMAT='AVRO');
-create stream USERS_STREAM with (partitions=1) as select after->USERID as USERID, 
-       after->EMAIL as EMAIL, 
-       after->SHARE_LOCATION_OPTIN as SHARE_LOCATION_OPTIN, 
-       after->PRIVACY_LOCATION_LAT as PRIVACY_LOCATION_LAT, 
-       after->PRIVACY_LOCATION_LON as PRIVACY_LOCATION_LON, 
-       after->PRIVACY_ZONE_KM as PRIVACY_ZONE_KM,
-       after->CREATE_TS as CREATE_TS,
-       after->UPDATE_TS as UPDATE_TS 
+CREATE STREAM USERS_STREAM_CDC WITH (KAFKA_TOPIC='mysql2-workshop.demo.USERS', VALUE_FORMAT='AVRO');
+
+create stream USERS_STREAM with (partitions=1) as select USERID, 
+       EMAIL,
+       SHARE_LOCATION_OPTIN, 
+       PRIVACY_LOCATION_LAT, 
+       PRIVACY_LOCATION_LON, 
+       PRIVACY_ZONE_KM,
+       CREATE_TS,
+       UPDATE_TS 
 from USERS_STREAM_CDC 
-partition by after->USERID; 
+partition by USERID; 
 
 
 CREATE TABLE USERS (
@@ -324,7 +320,7 @@ Examine the data:
 ----
 
 SELECT TIMESTAMPTOSTRING(R.ROWTIME, 'MMM-dd HH:mm:ss','Europe/London') AS TS,
-       R.WHO, 
+       R.WHOSIMPLE, 
        U.EMAIL, 
        U.SHARE_LOCATION_OPTIN, 
        R.LAT,
@@ -338,6 +334,7 @@ Open a new ssh terminal and run datagen
 
 [source,bash]
 ----
+cd /home/ec2-user/ksqldbWorkshop-main/docker
 ./run_datagen.sh
 ----
 
@@ -404,21 +401,23 @@ We can apply this logic in the SQL as part of the streaming application:
 
 [source,sql]
 ----
+SET 'auto.offset.reset' = 'earliest';
+
 CREATE STREAM PHONE_LOCATION_OPTIN AS
   SELECT WHOSIMPLE,
          EVENT_TIME_EPOCH_MS_TS,
          CASE 
             WHEN U.SHARE_LOCATION_OPTIN = 1 THEN 
               CASE 
-                WHEN GEO_DISTANCE (LAT,LON,PRIVACY_LOCATION_LAT,PRIVACY_LOCATION_LON,'KM') > PRIVACY_ZONE_KM 
+                WHEN GEO_DISTANCE (LAT,LON,U.PRIVACY_LOCATION_LAT,U.PRIVACY_LOCATION_LON,'KM') > U.PRIVACY_ZONE_KM 
                   THEN LOCATION 
                 ELSE '<Private>'
               END
             WHEN U.SHARE_LOCATION_OPTIN = 0 THEN '<Opted out>'
             ELSE '<No user record>' 
          END AS LOCATION, 
-         GEO_DISTANCE (LAT,LON,PRIVACY_LOCATION_LAT,PRIVACY_LOCATION_LON,'KM') AS DISTANCE_KM_FROM_PRIVACY_ZONE,
-         PRIVACY_ZONE_KM AS PRIVACY_ZONE_THRESHOLD_KM,
+         GEO_DISTANCE (LAT,LON,U.PRIVACY_LOCATION_LAT,U.PRIVACY_LOCATION_LON,'KM') AS DISTANCE_KM_FROM_PRIVACY_ZONE,
+         U.PRIVACY_ZONE_KM AS PRIVACY_ZONE_THRESHOLD_KM,
          BATTERY_PCT,
          BATTERY_STATUS,
          U.EMAIL AS EMAIL