Where is the ghost in the ghost island? Explore by Java and Mongo

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Ghost Island Where is the ghost in the ghost island

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

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

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• Spring batch • MongoDB ghost-island-batch

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• NodeJs • Loopback • MongoDB ghost-island-cargo

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• Vuejs • D3js ghost-island-dashboard

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• Spring gateway ghost-island-gateway

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LINE Pay Merchant Map - List - Nearby - Search

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Major feature List, Nearby, Search

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

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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

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

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

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

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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

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Merchant Search Elasticsearch - Good performance - Stable

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

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Elasticsearch vs MongoDB Nearby search, 10km, TPS

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

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

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

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

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@Bean public FlatFileItemReader<OpenData> reader() { return new FlatFileItemReaderBuilder<OpenData>() .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

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

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@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<Spot[]> { return await this.spotRepository.find(filter); } Cargo

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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

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

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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

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

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

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

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@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

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Ghost Island Put all together List Nearby Search

Where is the ghost in the ghost island? Explore by Java and Mongo

Where is the ghost in the ghost island? Explore by Java and Mongo

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