Mongo db
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The document discusses MongoDB, a document-oriented NoSQL database. It covers some key features of MongoDB including rich document queries, indexing for performance, replication for availability, auto-sharding for scalability, and geospatial indexing. It also provides MongoDB equivalents to SQL concepts and provides examples of CRUD operations and queries using aggregation, indexing, map-reduce, and replication sets.
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![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}})](https://image.slidesharecdn.com/qdnphxb7stw1kzysbnaz-signature-7b3b81662026ef74991347c6433f404460ac5ddbcebf0ae03bc1a841eacb212e-poli-160924112802/85/Mongo-db-7-320.jpg)
![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"}}}])](https://image.slidesharecdn.com/qdnphxb7stw1kzysbnaz-signature-7b3b81662026ef74991347c6433f404460ac5ddbcebf0ae03bc1a841eacb212e-poli-160924112802/85/Mongo-db-8-320.jpg)
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- 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
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- 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}})
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- 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. Replication Sets
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