List of usage examples for com.mongodb DBCursor curr
public DBObject curr()
From source file:com.mycompany.Farmerama.getAllAccounts.java
public HashMap<String, String> getSearchedAccountsByNumber(String inputedS) { HashMap<String, String> allFoundUsersByNumber = new HashMap<String, String>(); BasicDBObject searchQuery = new BasicDBObject(); searchQuery.append("number", new BasicDBObject("$regex", inputedS)); DBCursor cursor = account.find(searchQuery); while (cursor.hasNext()) { allFoundUsersByNumber.put(cursor.next().get("number").toString(), cursor.curr().get("user").toString()); }/*from w ww . j av a 2s . c o m*/ return allFoundUsersByNumber; }
From source file:com.tengen.Final7.java
License:Apache License
public static void main(String[] args) throws IOException { MongoClient client = new MongoClient(); DB db = client.getDB("photoshare"); int i = 0;/*from ww w. jav a 2 s.c o m*/ DBCollection album = db.getCollection("albums"); DBCollection image = db.getCollection("images"); DBCursor cur = image.find(); cur.next(); while (cur.hasNext()) { Object id = cur.curr().get("_id"); DBCursor curalbum = album.find(new BasicDBObject("images", id)); if (!curalbum.hasNext()) { image.remove(new BasicDBObject("_id", id)); } cur.next(); } }
From source file:com.tml.pathummoto.Dao.CustomDao.java
public Customer searchCustomer(String no) { Customer customer = new Customer(); // To connect to mongodb server MongoClient mongoClient = new MongoClient("localhost", 27017); // Now connect to your databases DB db = mongoClient.getDB("pathumdb"); System.out.println("Connect to database successfully"); DBCollection coll = db.getCollection("customer"); System.out.println("Collection user selected successfully"); BasicDBObject whereQuery = new BasicDBObject(); whereQuery.put("_id", no); DBCursor cursor = coll.find(whereQuery); while (cursor.hasNext()) { System.out.println(cursor.next()); }/*from ww w .j ava 2 s. c o m*/ BasicDBObject doc = (BasicDBObject) cursor.curr(); System.out.println("doc" + doc); if (doc != null) { customer.setVehicleNo(doc.getString("_id")); customer.setName(doc.getString("name")); customer.setPayment(doc.getInt("payment")); customer.setFreeServiceNo(doc.getInt("freeServiceNo")); customer.setServiceNo(doc.getInt("serviceNo")); customer.setDateOfDelivery(doc.getDate("dateOfDelivery")); customer.setLastKm(doc.getInt("lastKm")); } return customer; }
From source file:com.tml.pathummoto.Dao.PartDao.java
public Part searchPart(String partNo) { Part part = new Part(); MongoClient mongoClient = new MongoClient("localhost", 27017); // Now connect to your databases DB db = mongoClient.getDB("pathumdb"); System.out.println("Connect to database successfully"); DBCollection coll = db.getCollection("part"); System.out.println("Collection part selected successfully"); BasicDBObject find = new BasicDBObject(); find.put("_id", partNo); DBCursor cursor = coll.find(find); while (cursor.hasNext()) { System.out.println(cursor.next()); }/*from w w w . ja va2 s. c om*/ BasicDBObject doc = (BasicDBObject) cursor.curr(); if (doc != null) { } return part; }
From source file:com.tml.pathummoto.Dao.PartDao.java
public Part singlePart(String partNumber) { Part part = new Part(); // To connect to mongodb server MongoClient mongoClient = new MongoClient("localhost", 27017); // Now connect to your databases DB db = mongoClient.getDB("pathumdb"); System.out.println("Connect to database successfully"); DBCollection coll = db.getCollection("part"); System.out.println("Collection user selected successfully"); BasicDBObject whereQuery = new BasicDBObject(); whereQuery.put("_id", partNumber); DBCursor cursor = coll.find(whereQuery); while (cursor.hasNext()) { System.out.println(cursor.next()); }/*from w w w . j a va2 s. com*/ BasicDBObject doc = (BasicDBObject) cursor.curr(); if (doc != null) { part.setPartName(doc.getString("Part Name")); part.setPartNo(doc.getString("_id")); } return part; }
From source file:com.tml.pathummoto.Dao.UserDao.java
public User login(String name) { User user = new User(); // To connect to mongodb server MongoClient mongoClient = new MongoClient("localhost", 27017); // Now connect to your databases DB db = mongoClient.getDB("pathumdb"); System.out.println("Connect to database successfully"); DBCollection coll = db.getCollection("user"); System.out.println("Collection user selected successfully"); BasicDBObject whereQuery = new BasicDBObject(); whereQuery.put("title", name); DBCursor cursor = coll.find(whereQuery); while (cursor.hasNext()) { System.out.println(cursor.next()); }/*w w w. j a v a 2 s . c o m*/ BasicDBObject doc = (BasicDBObject) cursor.curr(); if (doc != null) { user.setName(doc.getString("title")); user.setPassword(doc.getString("password")); } return user; }
From source file:es.eucm.gleaner.realtime.functions.DescriptivesGenerator.java
License:Apache License
@Override public void execute(TridentTuple objects, TridentCollector tridentCollector) { // Extract values for searching the DB from the tuple Object taskId = objects.getValueByField("target"); Object trial = objects.getValueByField("trial"); // Extract value for updating the statistics from the tuple Double score = Double.parseDouble((String) objects.getValueByField("value")); /*** Step 1: get the initial performance statistics (from mongoDB or use starting values) ***/ // Generate mongoDB query using target and trial BasicDBObject mongoDoc = new BasicDBObject(); mongoDoc.append("taskId", taskId); mongoDoc.append("trial", trial); // Prepare statistics variables Double max; // Optional (not required for the calculation of the other statistics) Double min; // Optional (not required for the calculation of the other statistics) Double sum; // Optional (not required for the calculation of the other statistics) Double variance; // Variable needed for the calculation of stdDev Double mean; // Mean (target outcome) Double stdDev; // Standard deviation (target outcome) Double skewness; // The deviation of a gaussian distribution's mean from the median (target outcome) Double kurtosis; // The flatness of a gaussian distribution (target outcome) long n; // The number of samples that were used in this distribution (number of playthroughs) Boolean normal; // Is the normality assumption respected? (target outcome) Double help1; // Variable needed for skew and kurt calculation Double help2; // Variable needed for skew and kurt calculation Double help3; // Variable needed for skew and kurt calculation // Query the database in order to find the current statistics status n = performanceStatistics.count(mongoDoc); // Prepare the variables based on the statistics status if (n == 0) { // There were no previous completions of this task-trial combination so we start with a blank slate max = 0D;//from w w w. ja va 2s. com min = 0D; sum = 0D; variance = 0D; mean = 0D; stdDev = 0D; skewness = 0D; kurtosis = 0D; n = 1; normal = false; help1 = 0D; help2 = 0D; help3 = 0D; } else { // There were previous completions of this task-trial combination so we update the previous results DBCursor currentStatistics = performanceStatistics.find(mongoDoc); max = (double) currentStatistics.next().get("max"); // Uses next to get to the first field min = (double) currentStatistics.curr().get("min"); // Uses curr to moves through all fields after the first sum = (double) currentStatistics.curr().get("sum"); variance = (double) currentStatistics.curr().get("variance"); mean = (double) currentStatistics.curr().get("mean"); stdDev = (double) currentStatistics.curr().get("stdDev"); skewness = (double) currentStatistics.curr().get("skewness"); kurtosis = (double) currentStatistics.curr().get("kurtosis"); n = 1 + (long) currentStatistics.curr().get("n"); normal = (boolean) currentStatistics.curr().get("normal"); help1 = (double) currentStatistics.curr().get("help1"); help2 = (double) currentStatistics.curr().get("help2"); help3 = (double) currentStatistics.curr().get("help3"); } // For testing: output results // System.out.print("taskId: "); System.out.print(taskId);System.out.print(", "); // System.out.print("trial: "); System.out.print(trial);System.out.print(", "); // System.out.print("max: "); System.out.print(max);System.out.print(", "); // System.out.print("min: "); System.out.print(min);System.out.print(", "); // System.out.print("sum: "); System.out.print(sum);System.out.print(", "); // System.out.print("var: "); System.out.print(variance);System.out.print(", "); // System.out.print("mea: "); System.out.print(mean);System.out.print(", "); // System.out.print("std: "); System.out.print(stdDev);System.out.print(", "); // System.out.print("ske: "); System.out.print(skewness);System.out.print(", "); // System.out.print("kur: "); System.out.print(kurtosis);System.out.print(", "); // System.out.print("n : "); System.out.print(n);System.out.print(", "); // System.out.print("nor: "); System.out.print(normal);System.out.print(", "); // System.out.print("hp1: "); System.out.print(help1);System.out.print(", "); // System.out.print("hp2: "); System.out.print(help2);System.out.print(", "); // System.out.print("hp3: "); System.out.print(help3);System.out.println("."); /*** Step 2: calculate the up-to-date statistics ***/ if (n == 1) { max = score; min = score; sum = score; } else { // New max if (score > max) max = score; //New min if (score < min) min = score; // New sum sum += score; } // New mean, variance, & stdDev >> based on: http://www.johndcook.com/blog/standard_deviation/ Double oldMean = mean; Double newMean; Double oldS = variance; Double newS; if (n == 1) { newMean = score; newS = 0D; } else { //New means formula suitable for 1-pass statistics (= big data ready) newMean = oldMean + (score - oldMean) / n; newS = oldS + (score - oldMean) * (score - newMean); } mean = newMean; variance = (n > 1) ? (newS / (n - 1)) : 0D; stdDev = Math.sqrt(variance); // New skewness & kurtosis >> based on: http://www.johndcook.com/blog/skewness_kurtosis/ double delta, delta_n, delta_n2, term1; delta = score - newMean; delta_n = delta / n; delta_n2 = delta_n * delta_n; term1 = delta * delta_n * n; help3 = (term1 * delta_n2 * (n * n - 3 * n + 3)) + (6 * delta_n2 * help1) - (4 * delta_n * help2); help2 = (term1 * delta_n * (n - 2)) - (3 * n * delta_n * help1); help1 = term1; skewness = (Math.sqrt((double) n) * help2 / Math.pow(help1, 1.5)); kurtosis = (((double) n) * help3 / (help1 * help1) - 3.0); // For testing: output results // System.out.print("taskId: "); System.out.print(taskId);System.out.print(", "); // System.out.print("trial: "); System.out.print(trial);System.out.print(", "); // System.out.print("max: "); System.out.print(max);System.out.print(", "); // System.out.print("min: "); System.out.print(min);System.out.print(", "); // System.out.print("sum: "); System.out.print(sum);System.out.print(", "); // System.out.print("var: "); System.out.print(variance);System.out.print(", "); // System.out.print("mea: "); System.out.print(mean);System.out.print(", "); // System.out.print("std: "); System.out.print(stdDev);System.out.print(", "); // System.out.print("ske: "); System.out.print(skewness);System.out.print(", "); // System.out.print("kur: "); System.out.print(kurtosis);System.out.print(", "); // System.out.print("n : "); System.out.print(n);System.out.print(", "); // System.out.print("nor: "); System.out.print(normal);System.out.print(", "); // System.out.print("hp1: "); System.out.print(help1);System.out.print(", "); // System.out.print("hp2: "); System.out.print(help2);System.out.print(", "); // System.out.print("hp3: "); System.out.print(help3);System.out.println("."); // Step 3: update the mongoDB performanceStatistics collection // Prepare the new document BasicDBObject newMongoDoc = new BasicDBObject(); newMongoDoc.append("taskId", taskId).append("trial", trial).append("max", max).append("min", min) .append("sum", sum).append("variance", variance).append("mean", mean).append("stdDev", stdDev) .append("skewness", skewness).append("kurtosis", kurtosis).append("n", n).append("normal", normal) .append("help1", help1).append("help2", help2).append("help3", help3); // For EVEN MORE testing // System.out.println(newDescriptives); // Insert the new document (create or update depending on whether it is a new task-trial combination if (n == 1) { // The doc did not previously exist so it will be created performanceStatistics.insert(newMongoDoc); } else { // The doc already existed so update the previous document performanceStatistics.update(new BasicDBObject().append("taskId", taskId).append("trial", trial), newMongoDoc); } // Step 4: emit the new tuple // Prepare the tuple ArrayList<Object> object = new ArrayList(); object.add(max); object.add(max); object.add(min); object.add(sum); object.add(variance); object.add(mean); object.add(stdDev); object.add(skewness); object.add(kurtosis); object.add(n); object.add(normal); object.add(help1); object.add(help2); object.add(help3); // For testing // System.out.print("The new tuple is: "); // System.out.println(object); // Emit the tuple tridentCollector.emit(object); }
From source file:exifIndexer.MetadataQueries.java
public ResultDataNormal cameraBrand(String s) { ArrayList paths = new ArrayList<>(); ArrayList names = new ArrayList<>(); //Consulta por marca de camara MongoHandler dbmon = new MongoHandler(); DB dbmongo = dbmon.connect();//from w w w . ja v a 2 s . c om DBCursor cursorDoc; DBCollection collection = dbmongo.getCollection("CameraBrands"); BasicDBObject query = new BasicDBObject("CAMERA_BRAND", s); cursorDoc = collection.find(query); try { while (cursorDoc.hasNext()) { paths.add((cursorDoc.next().get("IMG_PATH"))); names.add((cursorDoc.curr().get("IMG_NAME")) + "." + (cursorDoc.curr().get("EXTENSION"))); } } finally { cursorDoc.close(); } return new ResultDataNormal(paths, names); }
From source file:exifIndexer.MetadataQueries.java
public ResultDataNormal cameraModel(String s) { ArrayList paths = new ArrayList<>(); ArrayList names;//from w w w.ja v a 2 s .c o m names = new ArrayList<String>(); //Consulta por modelo de camara MongoHandler dbmon = new MongoHandler(); DB dbmongo = dbmon.connect(); DBCursor cursorDoc; DBCollection collection = dbmongo.getCollection("CameraModels"); BasicDBObject query = new BasicDBObject("CAMERA_MODEL", s); cursorDoc = collection.find(query); try { while (cursorDoc.hasNext()) { paths.add((cursorDoc.next().get("IMG_PATH"))); names.add((cursorDoc.curr().get("IMG_NAME")) + "." + (cursorDoc.curr().get("EXTENSION"))); } } finally { cursorDoc.close(); } return new ResultDataNormal(paths, names); }
From source file:exifIndexer.MetadataQueries.java
public ResultDataNormal searchByISO(String s) { ArrayList paths = new ArrayList<>(); ArrayList names = new ArrayList<>(); //Consulta por modelo de camara MongoHandler dbmon = new MongoHandler(); DB dbmongo = dbmon.connect();/*from ww w.j av a2s.c o m*/ DBCursor cursorDoc; DBCollection collection = dbmongo.getCollection("SearchByISO"); BasicDBObject query = new BasicDBObject(); switch (s) { case "LOW": query = new BasicDBObject("ISO_VALUE", new BasicDBObject("$lt", 100)); break; case "HIGH": query = new BasicDBObject("ISO_VALUE", new BasicDBObject("$gt", 3200)); break; default: query = new BasicDBObject("ISO_VALUE", new BasicDBObject("$eq", Integer.parseInt(s))); break; } cursorDoc = collection.find(query); try { while (cursorDoc.hasNext()) { paths.add((cursorDoc.next().get("IMG_PATH"))); names.add((cursorDoc.curr().get("IMG_NAME")) + "." + (cursorDoc.curr().get("EXTENSION"))); } } finally { cursorDoc.close(); } return new ResultDataNormal(paths, names); }