# MongoDB ::linux ::js A document-oriented database replaces the concept of a “row” with a more flexible model, the “document.” - Schemaless [DB-Engines Ranking - popularity ranking of database management systems](https://db-engines.com/en/ranking) * A database shard is a horizontal partition of data in a database or search engine. Each shard is held on a separate database server instance, to spread load. * Today, the term "shard" refers to the deployment and use of redundant hardware across database systems. # Document (row, record) a data structure that is a natural fit, such as a map, hash, or dictionary. In JavaScript, for example, documents are represented as objects: {"greeting" : "Hello, world!"} * special key, "_id", that is unique within a collection. * Binary JSON, or BSON # Collections (table with dynamic schema) is a group of documents.  • Grouping documents of the same kind together in the same collection allows for data locality. • It’s much faster to get a list of collections than to extract a list of the types of documents in a collection. • Keeping different kinds of documents in the same collection can be a nightmare • By putting only documents of a single type into the same collection, we can index our collections more efficiently. [MongoDB CRUD Operations — MongoDB Manual](https://docs.mongodb.com/manual/crud/) ## C : insert ``` use myNewDB db.myNewCollection1.insertOne( { x: 1 } ) db.myNewCollection2.createIndex( { a: 1 } ) db.test.insertOne( { ts: new Timestamp(), data: Math.sin(0.1) } ); ``` ## R : find  ``` db.movies.find() ``` ``` SELECT * FROM movies ``` ## U : update  # D : delete db.inventory.deleteMany({ status : "A" }) # Databases # mongo shell * query * js ``` show dbs ``` # Rich with Features * Indexing * generic secondary indexes and provides * unique, compound, geospatial, and * full-text indexing capabilities as well. * Aggregation (data processing pipelines) * Special collection and index types * time-to-live (TTL) collections for data that should expire * File storage # Sharding data (collection) * distributing documents in a collection across the shards in a cluster. * scale-out architecture that supports even the largest applications. # Full-text search means searching through all the terms of all the documents available in the database * This is quite different from simple SQL queries run against columns of type string in relational databases. Normal SQL queries with a WHERE clause and an equals (=) or LIKE clause try to do an exact or wildcard match with underlying data. SQL queries can, at best, just match the search term to a sub-string within the text column. # A pipeline defines a series of processors. Each processor transforms the document in some way. Each processor is executed in the order in which it is defined in the pipeline. A pipeline consists of two main fields: a description and a list of processors # Transactions * logical groups of processing in a database, # ACID ACID is the accepted set of properties a transaction must meet to be a “true” transac‐ tion. ## Atomicity all of the commands that make up a transaction are treated as a single unit and either succeed or fail together. ## Consistency changes made within a transaction are consistent with database constraints. ## Isolation concurrent transactions do not affect each other’s outcomes. ## Durability once the database has told the client it has written the data, the data has in fact been written to a backing store. The data will persist even in the case of a system failure. ___ # Run it in Container! [mongod — MongoDB Manual](https://docs.mongodb.com/manual/reference/program/mongod/) ``` mkdir -p data/db chown -R $USER:$USER /data/db podman run --name mongodb -d -v $(pwd)/data/db:/data/db:Z -p 27017:27017 mongo podman exec -it mongodb mongosh show dbs use mydb show collections doc = { hello: 'world' } db.mydb.insertOne(doc); for(let i=0; i<5; i++) db.mydb.insertOne({i:i}); db.mydb.find({i: {$exists: true }} ) db.mydb.find( { i: { $eq: 5 } } ) ``` ___ # Understand the purpose of NoSQL and MongoDB [SQL Fiddle](http://sqlfiddle.com/#!9/78968d/8) ___ # MongoDB Fundamentals ## An Introduction to ETL Operations and the Aggregation Framework Axel Sirota [MongoDB: Getting Started with CRUD Operations](https://learning.oreilly.com/scenarios/mongodb-getting-started/9781098100506/) # Elasticsearch Elasticsearch is a real-time distributed search and analytics engine. - explore your data at a speed and at a scale - full-text search, - structured search, - analytics, - and all three in combination: - Apache Lucene™ is a high-performance, full-featured text search engine library written entirely in Java. [1. Getting Started · madhusudhankonda/elasticsearch-first-steps Wiki · GitHub](https://github.com/madhusudhankonda/elasticsearch-first-steps/wiki/1.-Getting-Started) [Home · madhusudhankonda/elasticsearch-first-steps Wiki · GitHub](https://github.com/madhusudhankonda/elasticsearch-first-steps/wiki)