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‎docs/modules/demos/pages/nifi-kafka-druid-earthquake-data.adoc‎

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@@ -72,7 +72,6 @@ $ stackablectl stacklet list
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│ kafka ┆ kafka ┆ default ┆ broker-default-0-listener-broker-kafka-tls 172.19.0.4:32321 ┆ Available, Reconciling, Running │
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│ ┆ ┆ ┆ broker-default-0-listener-broker-metrics 172.19.0.4:30556 ┆ │
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│ ┆ ┆ ┆ broker-default-bootstrap-kafka-tls 172.19.0.4:31352 ┆ │
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│ ┆ ┆ ┆ broker-default-bootstrap-metrics 172.19.0.4:30241 ┆ │
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├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
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│ nifi ┆ nifi ┆ default ┆ node-https https://172.19.0.2:32348 ┆ Available, Reconciling, Running │
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├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
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You can see the started ProcessGroup consisting of three processors.
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The first one - `InvokeHTTP`, fetches the CSV file from the Internet and puts it into the queue of the next processor.
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The second processor - `SplitRecords`, takes the single FlowFile (NiFi Record) which contains all CSV records and splits it into chunks of 2000 records, which are then separately put into the queue of the next processor.
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The third one - `PublishKafkaRecord`, parses the CSV chunk, converts it to JSON records and writes them out into Kafka.
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The second processor - `SplitRecord`, takes the single FlowFile (NiFi Record) which contains all CSV records and splits it into chunks of 2000 records, which are then separately put into the queue of the next processor.
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The third one - `PublishKafka`, parses the CSV chunk, converts it to JSON records and writes them out into Kafka.
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Double-click on the `InvokeHTTP` processor to show the processor details.
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image::nifi-kafka-druid-earthquake-data/druid_4.png[]
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You can see the data source's segments by clicking on `segments` under `Availability` for the `earthquake` data source.
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In this case, the `earthquake` data source is partitioned by the year of the earthquakes, resulting in 73 segments.
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You can see the data source's segments by clicking on `segments` under `Availability` for the `earthquakes` data source.
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In this case, the `earthquakes` data source is partitioned by the year of the earthquakes, resulting in 73 segments.
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image::nifi-kafka-druid-earthquake-data/druid_5.png[]
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‎docs/modules/demos/pages/nifi-kafka-druid-water-level-data.adoc‎

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@@ -77,7 +77,6 @@ $ stackablectl stacklet list
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│ kafka ┆ kafka ┆ default ┆ broker-default-0-listener-broker-kafka-tls 172.19.0.3:31041 ┆ Available, Reconciling, Running │
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│ ┆ ┆ ┆ broker-default-0-listener-broker-metrics 172.19.0.3:31503 ┆ │
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│ ┆ ┆ ┆ broker-default-bootstrap-kafka-tls 172.19.0.3:30793 ┆ │
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│ ┆ ┆ ┆ broker-default-bootstrap-metrics 172.19.0.3:32540 ┆ │
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├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
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│ nifi ┆ nifi ┆ default ┆ node-https https://172.19.0.6:32038 ┆ Available, Reconciling, Running │
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├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
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[source,console]
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----
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$ kubectl k exec kafka-broker-default-0 -c kafka -- \
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$ kubectl exec kafka-broker-default-0 -c kafka -- \
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/stackable/kafka/bin/kafka-topics.sh \
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--describe \
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--bootstrap-server kafka-broker-default-headless.default.svc.cluster.local:9093 \
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process is called data normalization. The downside is that when analyzing the data, you need to combine the records from
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multiple tables in Druid (`stations` and `measurements`).
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If you are interested in how many records have been produced to the Kafka topic so far, use the following command. It
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will print the last record produced to the topic partition, formatted with the pattern specified in the `-f` parameter.
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The given pattern will print some metadata of the record.
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If you are interested in how many records have been produced to the Kafka topic so far, use the following command.
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It prints the current offset for each partition, which tells you how many records have been produced to it.
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[source,console]
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measurements:7:1369651
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Multiplying `1,324,098` records by `8` partitions, we end up with ~ 10,592,784 records.
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Summing all partition offsets gives the total number of records produced. In this example, that is roughly 10.9 million measurement records.
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To inspect the last produced records, use the following command. Here, we consume the last three records from partition
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`0` of the `measurements` topic.
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--offset latest \
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--partition 0 \
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--max-messages 3
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-...
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...
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{"timestamp":"2025-10-21T11:00:00+02:00","value":369.54,"station_uuid":"5cdc6555-87d7-4fcd-834d-cbbe24c9d08b"}
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{"timestamp":"2025-10-21T11:15:00+02:00","value":369.54,"station_uuid":"5cdc6555-87d7-4fcd-834d-cbbe24c9d08b"}
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{"timestamp":"2025-10-21T11:00:00+02:00","value":8.0,"station_uuid":"7deedc21-2878-40cc-ab47-f6da0d9002f1"}
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image::nifi-kafka-druid-water-level-data/superset_6.png[]
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// Comment out the next section as long as the mapbox api key is not active.
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// === View the Station Distribution on the World Map
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=== View the Station Distribution on the World Map
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// To look at the stations' geographical distribution, you have to click on the tab `Charts` at the top again. Afterwards,
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// click on the chart `Stations distribution`.
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To look at the stations' geographical distribution, you have to click on the tab `Charts` at the top again. Afterwards,
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click on the chart `Stations distribution`.
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// image::nifi-kafka-druid-water-level-data/superset_7.png[]
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image::nifi-kafka-druid-water-level-data/superset_7.png[]
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// The stations are, of course, placed alongside waterways. They are coloured by the waters they measure, so all stations
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// alongside a body of water have the same colour. You can move and zoom the map with your mouse to interactively explore
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// the map. You can, e.g. have a detailed look at the water https://en.wikipedia.org/wiki/Rhine[Rhein].
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The stations are, of course, placed alongside waterways. They are coloured by the waters they measure, so all stations
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alongside a body of water have the same colour. You can move and zoom the map with your mouse to interactively explore
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the map. You can, e.g. have a detailed look at the water https://en.wikipedia.org/wiki/Rhine[Rhein].
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// image::nifi-kafka-druid-water-level-data/superset_8.png[]
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image::nifi-kafka-druid-water-level-data/superset_8.png[]
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=== Execute arbitrary SQL statements
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