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Mongo db


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An introduction to querying in mongodb.

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Mongo db

  1. 1. Athira Mukundan Solution Consultant @Sahaj
  3. 3. What’s the big deal? Scalable High-Performance Open-source,Document-oriented database Built for Speed Rich Document based queries for Easy readability Full Index Support for High Performance Replication and Failover for High Availability Auto Sharding for Easy Scalability Geo-spatial Indexes for Geo-spatial queries Map / Reduce for Aggregation
  4. 4. MongoDB in the RDBMS terms SQL Terms/Concepts MongoDB Terms/Concepts Database Database Table Collection Row Document Column Field Table Joins Embedding and Linking
  5. 5. Let’s do some Crud Create: Update: Delete: db.users.insert({name: “Alec”, place: “Bangalore”}) db.users.update({name: “Alec”}, place: “Mumbai”}) db.users.remove({name: “Alec”})
  6. 6. Mastering Find Field Selection Ordering Paging Count db.users.find({}, {name: 1}) db.users.find().sort({name: 1}) db.users.find().sort({salary: -1}).limit(2).skip(1) db.users.count({salary: {$gt: 50}})
  7. 7. Mastering Selectors $lt, $lte, $gt, $gte, $ne $exist $in $or db.users.find({name: “Alec”, weight:{$gt: 70}}) db.users.find({weight: {$exists: false}}) db.users.find({managers: {$in: [‘Sam’, ‘Philip’]}}) db.users.find(weight:{$gt: 70}, $or: {height: {$gt: 170}})
  8. 8. Indexing and Aggregation Indexing Aggregates: $group, $sort, $sum, $avg, $min, $max db.testdb.ensureIndex({"age" : 1, "account" : 1}) db.testdb.dropIndex(“index_name”) db.testdb.aggregate([{$group : {_id : "$author", num_recipes : {$sum : 1}}}]) db.testdb.aggregate([{$group: {_id: "$author", num_likes: {$min: "$likes"}}}])
  9. 9. Map Reduce A data processing paradigm for condensing large volumes of data into useful aggregated results MapReduce is generally used for processing large data sets. db.collection.mapReduce( function() {emit(key,value);}, //map function function(key,values) {return reduceFunction}, //reduce function { out: collection, query: document, sort: document, limit: number })
  10. 10. Replication Sets
  11. 11. Sharding