Ghost Island Where is the ghost in the ghost island 

• ghost-island-analysis • ghost-island-batch • ghost-island-cargo • ghost-island-dashboard • ghost-island-gateway Technical Stack 

• Spring web • Spark • MongoDB ghost-island-analysis 

• Spring batch • MongoDB ghost-island-batch 

• NodeJs • Loopback • MongoDB ghost-island-cargo 

• Vuejs • D3js ghost-island-dashboard 

• Spring gateway ghost-island-gateway 

LINE Pay Merchant Map - List - Nearby - Search 

Major feature List, Nearby, Search 

Elasticsearch vs MongoDB Full-text search, response time, keyword length: 1 136.34 Elasticsearch 13.85 MongoDB 

Elasticsearch vs MongoDB Full-text search, response time, keyword length: 2 99.04 Elasticsearch 1152.60 MongoDB 

Elasticsearch vs MongoDB Full-text search, response time, keyword length: 3 123.71 Elasticsearch 1299.93 MongoDB 

⼤大標題Title 副標題subtitle Response Time (ms) Full text search 1 char 2 chars 3 chars Elasticsearch MongoDB Elasticsearch MongoDB Elasticsearch MongoDB 1,299.93 1,152.6 13.85 124 99 136 Elasticsearch MongoDB 

Elasticsearch vs MongoDB Full-text search, TPS, keyword length: 1 367.9 Elasticsearch 3546.9 MongoDB 

Elasticsearch vs MongoDB Full-text search, TPS, keyword length: 2 503.8 Elasticsearch 42.9 MongoDB 

Elasticsearch vs MongoDB Full-text search , TPS, keyword length: 3 403.5 Elasticsearch 1299.93 MongoDB 

⼤大標題Title 副標題subtitle Transaction per second (TPS) Full text search 1 char 2 chars 3 chars Elasticsearch MongoDB Elasticsearch MongoDB Elasticsearch MongoDB 36.2 42.9 3,546.9 404 504 368 Elasticsearch MongoDB 

Merchant Search Elasticsearch - Good performance - Stable 

Elasticsearch vs MongoDB Nearby search Vuser 99 Vuser Duration 3/10 mins Radius 10/30/50 km Merchants 100 merchants 

Elasticsearch vs MongoDB Nearby search, 10km, TPS 

Elasticsearch vs MongoDB Nearby search, 30km, TPS 

Elasticsearch vs MongoDB Nearby search, 30km, TPS 

Nearby MongoDB - MongoDB: 2dsphere index - Elasticsearch: GeoPoint mapping 

• Geo query: MongoDB • Text query: Elasticsearch • Our choice: MongoDB Elasticsearch vs MongoDB 

Ghost Island A.B.C.D Analysis Spark Batch Spring Cargo NodeJS Dashboard VueJs 

Ghost Island Architecture OpenData Batch Spring Read Process Cargo NodeJS Storage MongoDB Write Analysis Spark Gateway Spring Text Search List & Nearby Dashboard VueJS 

@Bean public FlatFileItemReader reader() { return new FlatFileItemReaderBuilder() .name("openDataItemReader") .resource(new FileSystemResource("./data-source/raw.csv")).linesToSkip(1) .delimited().names(new String[] {"localTime", "description", "hurt", "type", "longitude", "latitude"}) .targetType(OpenData.class).build(); } 發⽣生時間,發⽣生地點,死亡受傷⼈人數,⾞車車種,經度,緯度 107年年01⽉月01⽇日 00時03分00秒,臺中市⻄西屯區上⽯石⾥里里⻄西屯路路⼆二段273之1號前0.0公尺,死亡0;受傷1,普通重型-機⾞車車;⾃自⽤用-⼩小客⾞車車,120.644124,24.174996 107年年01⽉月01⽇日 00時04分00秒,雲林林縣林林內鄉國道3號 253公⾥里里500.0公尺處南向中線,死亡0;受傷1,⾃自⽤用-⼩小客⾞車車;⾃自⽤用-⼩小客⾞車車,120.605676,23.762712 107年年01⽉月01⽇日 00時10分00秒,新北市泰⼭山區⼤大科⼀一路路 / 新北市泰⼭山區黎黎明路路,死亡0;受傷1,普通重型-機⾞車車,121.395695,25.062396 107年年01⽉月01⽇日 00時13分00秒,⾼高雄市⼩小港區中⼭山四路路前 / ⾼高雄市⼩小港區中安路路,死亡0;受傷1,普通重型-機⾞車車;計程⾞車車-⼩小客⾞車車,120.346766,22.569669 107年年01⽉月01⽇日 00時19分00秒,臺南市安南區北安路路東側 / 臺南市安南區賢安街,死亡0;受傷1,普通重型-機⾞車車,120.205404,23.026156 107年年01⽉月01⽇日 00時20分23秒,臺南市東區中華東路路⼆二段北側 / 臺南市東區東⾨門路路,死亡0;受傷1,⾃自⽤用-⼩小客⾞車車;普通重型-機⾞車車,120.229347,22.981113 107年年01⽉月01⽇日 00時20分27秒,宜蘭蘭縣宜蘭蘭市中⼭山路路3段12號前0.0公尺,死亡0;受傷1,⾃自⽤用-⼩小客⾞車車;普通重型-機⾞車車,121.752189,24.754909 107年年01⽉月01⽇日 00時20分37秒,雲林林縣古坑鄉縣158⼄乙線 12公⾥里里100.0公尺處⻄西向外側,死亡0;受傷2,普通重型-機⾞車車;乘客-⼈人,120.547546,23.628280 Batch 

Ghost Island Dashboard - List - Front-End (Vue.js) - Data visualization (D3.js) - Back-End (NodeJs + Loopback4 + MongoDB Connector + MongoDB) 

@get('/spots', { responses: { '200': { description: 'Array of Spot model instances', content: { 'application/json': { schema: {type: 'array', items: {'x-ts-type': Spot}}, }, }, }, }, }) async find( @param.query.object('filter', getFilterSchemaFor(Spot)) filter?: Filter, ): Promise { return await this.spotRepository.find(filter); } Cargo 

fetch("/api/spots") .then(function(response){ return response.json() }) .then(function(json){ //{...} }); for ( let i = 0 ; i < json.length ; ++i ){ let orgCoordinates=json[i].location.coordinates; let spot = { coordinates: projection(orgCoordinates) }; svg.append("circle") .attr("fill", "rgba(255,0,0,0.1)") .attr("cx", spot.coordinates[0]) .attr("cy", spot.coordinates[1]) .attr("r", 1); } Dashboard 

Ghost Island Dashboard - Nearby - Front-End (Vue.js) - Data visualization (D3.js) - Back-End (NodeJs + Loopback4 + MongoDB Connector + MongoDB) 

fetch(“/api/spots?filter[where][location][near]="+location[0]+","+location[1] +"&filter[where][location][maxDistance]=10&filter[where][location][unit]=kilometers") .then(function(response){ return response.json() }) Dashboard 

Ghost Island Dashboard - Search - Front-End (Vue.js) - Data visualization (D3.js) - Back-End (Spring web + Spark + MongoDB Connector + MongoDB) 

fetch("/analysis/search?word="+word) .then(function(response){ return response.json(); }) @RestController @RequestMapping(value="/search") public class SearchController { //{...} } Dashboard 

@RequestMapping(value="") public List search(HttpServletRequest request){ String word = request.getParameter("word"); Dataset ds = rdd.toDS(SpotEntity.class); ds.createOrReplaceTempView("spot"); Dataset rows = sparkSession.sql( "SELECT description, location FROM spot WHERE description like ‘%”+word+"%'" ).toJSON(); return rows.collectAsList(); } Dashboard 

@Bean public RouteLocator serviceRouteLocator(RouteLocatorBuilder builder){ return builder.routes() .route("cargo_route", r -> r.path("/api/**") .filters(f -> f.rewritePath("^/api", "")) .uri("http://localhost:"+cargoPort) ) .route("analysis_route", r -> r.path("/analysis/**") .filters(f -> f.rewritePath("^/analysis", "")) .uri("http://localhost:"+analysisPort) ) .route("batch_route", r -> r.path("/jobLauncher") .uri("http://localhost:"+batchPort) ) .route("dashboard_route", r -> r.path("/**") .uri("http://localhost:"+dashboardPort) ) .build(); } Gateway 

Ghost Island Put all together List Nearby Search