Location Analytics - Real-Time Geofencing using Apache Kafka

2. Agenda 1. Introduction & Motivation 2. Implementing Geo Fencing with Kafka • Using Oracle Stream Analytics • Using ksqlDB • Using Kafka Streams • Using Tile38 3. Visualization using ArcadiaData 4. Summary

4. Introduction

5. Geofencing – What is it? Use of GPS or RFID technology to create a virtual geographic boundary, enabling software to trigger a response when an object/device enters or leaves a particular area Possible Events • ENTER • EXIT • OUTSIDE • lNSIDE Source: https://tile38.com

6. Apache Kafka – A Streaming Platform Source Connector Sink Connector trucking_ driver ksqlDB Engine Kafka Streams Kafka Broker ksqlDB

7. Apache Kafka Kafka Cluster Consumer 1 Consume 2r Broker 1 Broker 2 Broker 3 Zookeeper Ensemble ZK 1 ZK 2ZK 3 Schema Registry Service 1 Management Control Center Kafka Manager KAdmin Producer 1 Producer 2 kafkacat Data Retention: • Never • Time (TTL) or Size-based • Log-Compacted based Producer3Producer3 ConsumerConsumer 3

8. Apache Kafka – How does it scale? • horizontally scalable, guaranteed order

9. Streaming Analytics: ksqlDB or Kafka Streams Stream • unbounded sequence of structured data ("facts") • Facts in a stream are immutable Table • collected state of a stream • Latest value for each key in a stream • Facts in a table are mutable ksqlDB: Stream Processing with zero coding using SQL-like language (now supporting push and pull queries) Kafka Streams: Java library providing stream analytics capabilities trucking_ driver Kafka Broker ksqlDB Engine Kafka Streams ksqlDB REST Commands ksqlDB CLI push pull

10. Stream vs. Table Vehicle Position Stream id latitude longitude 1 52.3924 13.0514 id latitude longitude 1 52.3924 13.0514 4 38.4847 -90.23345 id latitude longitude 1 52.3924 13.0514 4 38.4847 -90.23345 1 52.39052 13.06455 id name geometry_wkt 10 St. Louis POLYGON ((13.297920227050781 52.56195151687443, …)) 11 Berlin POLYGON ((-90.23345947265625 38.484769753492536,…)) id name geometry_wkt 10 St. Louis POLYGON ((13.297920227050781 52.56195151687443, …)) id name geometry_wkt 10 St. Louis (US) POLYGON ((13.297920227050781 52.56195151687443, …)) 11 Berlin (GE) POLYGON ((-90.23345947265625 38.484769753492536,…)) GeoFence Table

11. First "naïve " idea when answering the CFP • geofence being an UDF (User Defined Function) • would need access to the geofences defined somewhere • what about updates? • a function should not cause any side-effects SELECT geofence(latitude, longitude) geo_event FROM geo_position_stream

12. Dash board High Level Overview of Test Case geofence Join Position & Geofences Vehicle Position vehicle position pos & geofences Geo fencing geofence status key=10 { "id" : "10", "latitude" : 38.35821, "longitude" : -90.15311} key=3 {"id":3,"name":"Berlin, Germany","geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))","last_update":1560607149015} Geofence Mgmt Vehicle Position Weather Service

13. Geo-Processing Well-known text (WKT) is a text markup language for representing vector geometry objects on a map GeoTools is a free software GIS toolkit for developing standards compliant solutions

14. Implementing Geo Fencing - using Oracle Stream Analytics

15. Oracle Stream Analytics (OSA) • Expressive Patterns Library, including Geo Processing • Abstracted visual façade to interrogate live real time streaming data • Integrate with Apache Kafka • Runs on top of Spark Cluster as a Spark Streaming Job

16. Implementing Geo Fencing - using ksqlDB

17. ksqlDB – Streams and Tables geofence Table vehicle position Stream KSQL Geofencing id latitude longitude 1 52.3924 13.0514 4 38.4847 -90.23345 3 .. .. id name geometry_wkt 10 St. Louis POLYGON ((13.297920227050781 52.56195151687443, …)) 11 Berlin POLYGON ((-90.23345947265625 38.484769753492536,…)) 12 xxxxx POLYGON ((…))

18. ksqlDB – Streams and Tables geofence Table vehicle position Stream CREATE STREAM vehicle_position_s (id VARCHAR, latitude DOUBLE, longitude DOUBLE) WITH (KAFKA_TOPIC='vehicle_position', VALUE_FORMAT='DELIMITED'); CREATE TABLE geo_fence_t (id BIGINT, name VARCHAR, geometry_wkt VARCHAR) WITH (KAFKA_TOPIC='geo_fence', VALUE_FORMAT='JSON', KEY = 'id'); KSQL Geofencing

19. How to determine "inside" or "outside" geofence? Only one standard UDF for geo processing in KSQL: GEO_DISTANCE Implement custom UDF using functionality from GeoTools Java library public String geo_fence(final double latitude, final double longitude, final String geometryWKT){ .. } public List<String> geo_fence_bulk(final double latitude , final double longitude, List<String> idGeometryListWKT) { .. } ksql> SELECT geo_fence(latitude, longitude, 'POLYGON ((13.297920227050781 52.56195151687443, 13.2440185546875 52.530216577830124, ...))') FROM test_geo_udf_s; 52.4497 | 13.3096 | OUTSIDE 52.4556 | 13.3178 | INSIDE

20. Custom UDF to determine if Point is inside a geometry @Udf(description = "determines if a lat/long is inside or outside the geometry passed as the 3rd parameter as WKT encoded ...") public String geo_fence(@UdfParameter(value="latitude") final double latitude, @UdfParameter(value="longitude") ) final double longitude, @UdfParameter(value="geometryWKT") ) final String geometryWKT) { String status = ""; GeometryFactory geometryFactory = JTSFactoryFinder.getGeometryFactory(); WKTReader reader = new WKTReader(geometryFactory); Polygon polygon = (Polygon) reader.read(geometryWKT); Coordinate coord = new Coordinate(longitude, latitude); Point point = geometryFactory.createPoint(coord); if (point.within(polygon)) { status = "INSIDE"; } else { status = "OUTSIDE"; } return status; }

21. 1) Using Cross Join geofence Table Join Position & Geofences vehicle position Stream Stream pos & geofences CREATE STREAM vp_join_gf_s AS SELECT vp.id, geo_fence(vp.latitude, vp.longitude, gf.geometry_wkt) status FROM vehicle_position_s AS vp CROSS JOIN geo_fence_t AS gf There is no Cross Join in ksqlDB! id latitude longitude 1 52.3924 13.0514 4 38.4847 -90.23345 3 .. .. id name geometry_wkt 10 St. Louis POLYGON ((13.297920227050781 52.56195151687443, …)) 11 Berlin POLYGON ((-90.23345947265625 38.484769753492536,…)) 12 xxxxx POLYGON ((…)) X

22. 2) INNER Join geofence Stream Join Position & Geofences vehicle position Stream Stream pos & geofences { "group":1", "name":"Berlin", "geometry_wkt":"POLYGON ((- 90.23345947265625 38.484769753492536,…))", "last_update":1560607149015} Enrich Group Table geofences by group 1 Enrich Group Stream postion by group 1 Cannot insert into Table from Stream >INSERT INTO geo_fence_t >SELECT '1' AS group_id, geof.id, … >FROM geo_fence_s geof; INSERT INTO can only be used to insert into a stream. A02_GEO_FENCE_T is a table. { "group":"1", "id" : "10", "latitude" : 52.3924, "longitude" : 13.0514} gid id latitude longitude 1 1 52.3924 13.0514 1 4 38.4847 -90.23345 1 3 .. .. gid id name geometry_wkt 1 10 St. Louis POLYGON ((13.297920227050781 52.56195151687443, …)) 1 11 Berlin POLYGON ((-90.23345947265625 38.484769753492536,…)) { "group":1", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015}

23. 3) Geofences aggregated in one group Join Position & Geofences Stream geofence event Geofences aggby group Table { "group":"1", [ "1:POLYGON ((13.297920227050781 52.56195151687443, …))","2:POLYGON ((- 90.23345947265625 38.484769753492536,…))" ] } geo_fence_bulk geofence Stream vehicle position Stream { "group":1", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015} Enrich With Group-1 Stream geofences by group 1 Enrich With Group-1 Stream postion by group 1 geofences by group 1 high low low high low high Scalable Latency "Code Smell" medium medium medium { "group":"1", "id" : "10", "latitude" : 52.3924, "longitude" : 13.0514} { "group":1", "name":"Berlin", "geometry_wkt":"POLYGON ((-90.23345947265625 38.484769753492536,…))", "last_update":1560607149015} gid id latitude longitude 1 1 52.3924 13.0514 1 4 38.4847 -90.23345 1 3 .. .. gid geometry_wkt_list 1 1:POLYGON ((13.297920227050781 52.56195151687443, …)) 2:POLYGON ((-90.23345947265625 38.484769753492536,…)) X:POLGON ((…)) always only one row

24. 3) Geofences aggregated in one group CREATE TABLE a03_geo_fence_aggby_group_t AS SELECT group_id , collect_set(id + ':' + geometry_wkt) AS id_geometry_wkt_list FROM a03_geo_fence_by_group_s geof GROUP BY group_id; CREATE STREAM a03_vehicle_position_by_group_s AS SELECT '1' group_id, vehp.id, vehp.latitude, vehp.longitude FROM vehicle_position_s vehp PARTITION BY group_id;

25. 3) Geofences aggregated in one group CREATE STREAM a03_geo_fence_status_s AS SELECT vehp.id, vehp.latitude, vehp.longitude, geo_fence_bulk(vehp.latitude, vehp.longitude, geofaggid_geometry_wkt_list) AS geofence_status FROM a03_vehicle_position_by_group_s veh LEFT JOIN a03_geo_fence_aggby_group_t geofagg ON vehp.group_id = geofagg.group_id; ksql> SELECT * FROM a03_geo_fence_status_s; 46 | 52.47546 | 13.34851 | [1:OUTSIDE, 3:INSIDE] 46 | 52.47521 | 13.34881 | [1:OUTSIDE, 3:INSIDE] ... As many as there are geo-fences

26. Geo Hash for a better distribution Geohash is a geocoding which encodes a geographic location into a short string of letters and digits Length Area width x height 1 5,009.4km x 4,992.6km 2 1,252.3km x 624.1km 3 156.5km x 156km 4 39.1km x 19.5km 12 3.7cm x 1.9cm http://geohash.gofreerange.com/

27. Geo Hash Custom UDF ksql> SELECT latitude, longitude, geo_hash(latitude, longitude, 3) >FROM vehicle_position_s; 38.484769753492536 | -90.23345947265625 | 9yz public String geohash(final double latitude, final double longitude, int length) public List<String> neighbours(String geohash) public String adjacentHash(String geohash, String directionString) public List<String> coverBoundingBox(String geometryWKT, int length) ksql> SELECT name, wkt, geo_hash(geometry_wkt, 3) FROM a04_geo_fence_s; St. Louis, POLYGON ((-90.25749206542969 38.71551876930462, -90.31723022460938 38.69301319283493, ...)) | [9yz] ksql> SELECT name, wkt, geo_hash(geometry_wkt, 4) FROM a04_geo_fence_s; St. Louis, POLYGON ((-90.25749206542969 38.71551876930462, -90.31723022460938 38.69301319283493, ...)) | [9yzg, 9yzu]

28. 4) Geofences aggregated by GeoHash Join Position & Geofences Stream geofence event Geofences gpby geohash Table { "geohash":"u33", ["2:POLYGON ((- 90.23345947265625 38.484769753492536,…))"], …} geo_fence() geofence Table vehicle position Stream { "geohash":"9yz", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015}} Enrich with GeoHash Stream geofences & geohash Enrich with GeoHash Stream position & geohash geofences by geohash geo_hash() geo_hash() { "geohash":"u33", "id" : "10", "latitude" : 52.3924, "longitude" : 13.0514} { "group":"u33", "name":"Berlin", "geometry_wkt":"POLYGON ((- 90.23345947265625 38.484769753492536,…))", "last_update":1560607149015} { "geohash":"dnb", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015}} { "geohash":"9yz", [ "1:POLYGON ((13.297920227050781 52.56195151687443, …))", ..]} { "geohash":"dnb", [ "1:POLYGON ((13.297920227050781 52.56195151687443, …))",..]} geohash id latitude longitude u33 1 52.3924 13.0514 9yz 4 38.4847 -90.23345 u34 3 .. .. geohash geometry_wkt_list 9yz 1:POLYGON ((13.297920227050781 52.56195151687443, …)) N:POLYGON (( …. )) u33 2:POLYGON ((-90.23345947265625 38.484769753492536,…)) dnb 1:POLYGON ((13.297920227050781 52.56195151687443, …)) high low low high low high Scalable Latency "Code Smell" medium medium medium

29. 4) Geofences aggregated by GeoHash CREATE STREAM a04_geo_fence_by_geohash_s AS SELECT EXPLODE(geo_hash(geometry_wkt, 3)) geo_hash, id, name, geometry_wkt FROM a04_geo_fence_s PARTITION by geo_hash; There was no explode() / unpivot() functionality in KSQL but now ksqlDB provides it! geofence Table Enrich with GeoHash Stream geofence & geohash ksql> SELECT name, geo_hash FROM a04_geo_fence_and_geohash_s EMIT CHANGES; | Colombia, Missouri | 9yz | Berlin, Germany | u33 | St. Louis, Missouri | 9yz

30. 4) Geofences aggregated by GeoHash CREATE TABLE a04_geo_fence_by_geohash_t AS SELECT geohash, COLLECT_SET(geometry_wkt) AS id_geometry_wkt_list, COLLECT_SET(id) AS id_list FROM a04_geo_fence_and_geohash_s GROUP BY geo_hash; Geofences gpby geohash Table Stream geofences & geohash geofences by geohash ksql> SELECT * FROM a04_geo_fence_by_geohash_t EMIT CHANGES; | 9yz | [POLYGON ((13.297920227050781 52.56195151687443, …)), POLYGON((…))] |[1,N] | u33 | [POLYGON ((-90.23345947265625 38.484769753492536] |[2] | dnb | [POLYGON ((13.297920227050781 52.56195151687443, …)), POLYGON((…))] |[1] ...

31. 4) Geofences aggregated by GeoHash CREATE STREAM a04_vehicle_position_by_geohash_s AS SELECT vp.id, vp.latitude, vp.longitude, geo_hash(vp.latitude, vp.longitude, 3) geo_hash FROM vehicle_position_s vp PARTITION BY geo_hash; vehicle position Stream Enrich with GeoHash Stream position & geohash ksql> SELECT * FROM a04_vehicle_position_by_geohash_s EMIT CHANGES; | 10 | 52.4497 | 13.3096 | u33 | 11 | 38.521846880854966 | -90.19912719726561 | 9yz ...

32. 4) Geofences aggregated by GeoHash geohash id latitude longitude u33 1 52.3924 13.0514 9yz 4 38.4847 -90.23345 nnn 3 .. .. geohash geometry_wkt_list 9yz 1:POLYGON ((13.297920227050781 52.56195151687443, …)) N:POLYGON (( …. )) u33 2:POLYGON ((-90.23345947265625 38.484769753492536,…)) dnb 1:POLYGON ((13.297920227050781 52.56195151687443, …)) geoh id latitude longitude geometry_wkt_list u33 1 52.3924 13.0514 1:POLYGON ((13.297920227050781 52.56195151687443, …)) 9yz 4 38.4847 -90.23345 1:POLYGON ((13.297920227050781 52.56195151687443, …)) N:POLYGON (( …. )) nnn 3 .. ..

33. 4) Geofences aggregated by GeoHash CREATE STREAM a04_geo_fence_status_s AS SELECT vp.id, vp.latitude, vp.longitude, vp.geo_hash, explode (gf.id_list) AS geofenceId, geo_fence (vp.latitude, vp.longitude, explode (gf.wkt_list)) AS geo_event FROM a04_vehicle_position_by_geohash_s vp LEFT JOIN a04_geo_fence_by_geohash_t gf ON (vp.geo_hash = gf.geo_hash); ksql> SELECT * FROM a04_geo_fence_status_s; | 10 | 52.4497 | 13.3096 | u33 | 3 | OUTSIDE | 11 | 38.521846880854966 | -90.19912719726561 | 9yz | 2 | OUTSIDE | 11 | 38.521846880854966 | -90.19912719726561 | 9yz | 1 | OUTSIDE ... Join Position & Geofences Stream geofence event

34. Berne Fribourg It works …. but …. • Becaue of re-partitioning by geohash we lose the guaranteed order for a given vehicle • Can be problematic, if there is a backlog in one of the topics/partitions u0m5 u0m4 u0m7 u0m6 Consumer 1 Consumer 2

35. Implementing Geo Fencing - using Kafka Streams

36. Geo-Fencing with Kafka Streams and Global KTable Enrich Position with GeoHash & Join with Geofences Global KTable geofence KTable vehicle position { "geohash":u33", "name":"Potsdam", "geometry_wkt":"POLYGON ((5.668945 51.416016, …))", "last_update":1560607149015} Enrich and Group by GeoHash matched geofences Detect Geo Event geofence_ status high low low high low high Scalable Latency "Code Smell" medium medium medium geofence by geohash {"id":"10", "latitude" : 52.3924, "longitude" : 13.0514, [ {"name":"Berlin"} ] } { "geohash":"u33", "id" : "10", "latitude" : 52.3924, "longitude" : 13.0514} {"id":"10", "status" : "ENTER", "geofenceName":"Berlin"} } position & geohash { "geohash":u33", "name":"Berlin", "geometry_wkt":"POLYGON ((-90.23345947265625 38.484769753492536,…))", "last_update":1560607149015} { "geohash":"u33", [ {"name":"Berlin", "geometry_wkt":"POLYGON ((-90.23345947265625 38.484769753492536,…))"},{"name":"Potsdam", "geometry_wkt":"POLYGON ((5.668945 51.416016, …))"} ] } { "geohash":"9yz", [ {"name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))"} ] }{ "geohash":"9yz", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015}}

37. Geo-Fencing with Kafka Streams and Global KTable KStream<String, GeoFence> geoFence = builder.stream(GEO_FENCE); KStream<String, GeoFence> geoFenceByGeoHash = geoFence.map((k,v) -> KeyValue.<GeoFence, List<String>> pair(v, GeoHashUtil.coverBoundingBox(v.getWkt().toString(), 5))) .flatMapValues(v -> v) .map((k,v) -> KeyValue.<String,GeoFence>pair(v, createFrom(k, v))); KTable<String, GeoFenceList> geofencesByGeohash = geoFenceByGeoHash.groupByKey().aggregate( () -> new GeoFenceList(new ArrayList<GeoFenceItem>()), (aggKey, newValue, aggValue) -> { GeoFenceItem geoFenceItem = new GeoFenceItem(newValue.getId(), newValue.getName(), newValue.getWkt(), ""); if (!aggValue.getGeoFences().contains(geoFenceItem)) aggValue.getGeoFences().add(geoFenceItem); return aggValue; }, Materialized.<String, GeoFenceList, KeyValueStore<Bytes,byte[]>>as("geofences-by-geohash-store")); geofencesByGeohash.toStream().to(GEO_FENCES_KEYEDBY_GEOHASH, Produced.<String, GeoFenceList> keySerde(stringSerde));

38. Geo-Fencing with Kafka Streams and Global KTable final GlobalKTable<String, GeoFenceList> geofences = builder.globalTable(GEO_FENCES_KEYEDBY_GEOHASH); KStream<String, VehiclePositionWithMatchedGeoFences> positionWithMatchedGeoFences = vehiclePositionsWithGeoHash.leftJoin(geofences, (k, pos) -> pos.getGeohash().toString(), (pos, geofenceList) -> { List<MatchedGeoFence> matchedGeofences = new ArrayList<MatchedGeoFence>(); if(geofenceList != null) { for (GeoFenceItem geoFenceItem : geofenceList.getGeoFences()) { boolean geofenceStatus = GeoFenceUtil.geofence(pos.getLatitude(), pos.getLongitude(), geoFenceItem.getWkt().toString()); if(geofenceStatus) matchedGeofences.add(new MatchedGeoFence(geoFenceItem.getId(), geoFenceItem.getName(), null)); } } return new VehiclePositionWithMatchedGeoFences(pos.getVehicleId(), 0L, pos.getLatitude(), pos.getLongitude(), pos.getEventTime(), matchedGeofences); });

39. Implementing Geo Fencing - using Tile38

40. Tile38 • https://tile38.com • Open Source Geospatial Database & Geofencing Server • Real Time Geofencing • Roaming Geofencing • Fast Spatial Indices • Pluggable Event Notifications

41. Tile38 – How does it work? > SETCHAN berlin WITHIN vehicle FENCE OBJECT {"type":"Polygon","coordinates":[[[13.297920227050781,52.56195151687443],[1 3.2440185546875,52.530216577830124],[13.267364501953125,52.45998421679598], [13.35113525390625,52.44826791583386],[13.405036926269531,52.44952338289473 ],[13.501167297363281,52.47148826410652], ...]]} > SUBSCRIBE berlin {"ok":true,"command":"subscribe","channel":"berlin","num":1,"elapsed":"5.85 µs"} . . . {"command":"set","group":"5d07581689807d000193ac33","detect":"outside","hoo k":"berlin","key":"vehicle","time":"2019-06- 17T09:06:30.624923584Z","id":"10","object":{"type":"Point","coordinates":[1 3.3096,52.4497]}} SET vehicle 10 POINT 52.4497 13.3096

42. Tile38 – How does it work? > SETHOOK berlin_hook kafka://broker-1:9092/tile38_geofence_status WITHIN vehicle FENCE OBJECT {"type":"Polygon","coordinates":[[[13.297920227050781,52.56195151687443],[1 3.2440185546875,52.530216577830124],[13.267364501953125,52.45998421679598], [13.35113525390625,52.44826791583386],[13.405036926269531,52.44952338289473 ],[13.501167297363281,52.47148826410652], ...]]} bigdata@bigdata:~$ kafkacat -b localhost -t tile38_geofence_status % Auto-selecting Consumer mode (use -P or -C to override) {"command":"set","group":"5d07581689807d000193ac34","detect":"outside","hoo k":"berlin_hook","key":"vehicle","time":"2019-06- 17T09:12:00.488599119Z","id":"10","object":{"type":"Point","coordinates":[1 3.3096,52.4497]}} SET vehicle 10 POINT 52.4497 13.3096

43. 1) Enrich with GeoFences – aggregated by geohash geofence Stream vehicle position Stream Invoke UDF { "id" : "10", "latitude" : 38.35821, "longitude" : -90.15311} Invoke UDF Geofence Service geofence status set_pos() set_fence() Stream null high low low high low high Scalable Latency "Code Smell" medium medium medium { "id":11", "name":"Berlin", "geometry_wkt":"POLYGON ((- 90.23345947265625 38.484769753492536,…))", "last_update":1560607149015} { "id":10", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015}

44. 2) Using Custom Kafka Connector for Tile38 geofence vehicle position Geofence Service kafka-to- tile38 kafka-to- tile38 geofence status high low low high low high Scalable Latency "Code Smell" medium medium medium { "id":11", "name":"Berlin", "geometry_wkt":"POLYGON ((- 90.23345947265625 38.484769753492536,…))", "last_update":1560607149015} { "id":10", "name":"St. Louis", "geometry_wkt":"POLYGON ((13.297920227050781 52.56195151687443, …))", "last_update":1560607149015} { "id" : "10", "latitude" : 38.35821, "longitude" : -90.15311}

45. 2) Using Custom Kafka Connector for Tile38 curl -X PUT /api/kafka-connect-1/connectors/Tile38SinkConnector/config -H 'Content-Type: application/json' -H 'Accept: application/json' -d '{ "connector.class": "com.trivadis.geofence.kafka.connect.Tile38SinkConnector", "topics": "vehicle_position", "tasks.max": "1", "tile38.key": "vehicle", "tile38.operation": "SET", "tile38.hosts": "tile38:9851" }' Currently only supports SET command

46. Visualization using Arcadia Data

47. Arcadia Data https://www.arcadiadata.com/

Location Analytics - Real-Time Geofencing using Apache Kafka

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