![• MongoDB does not need any pre-defined data schema
• Every document in a collection could have different data
• Addresses NULL data fields
Schema Free
name: “jeff”,
eyes: “blue”,
loc: [40.7, 73.4],
boss: “ben”}
{name: “brendan”,
aliases: [“el diablo”]}
name: “ben”,
hat: ”yes”}
{name: “matt”,
pizza: “DiGiorno”,
height: 72,
loc: [44.6, 71.3]}
{name: “will”,
eyes: “blue”,
birthplace: “NY”,
aliases: [“bill”, “la ciacco”],
loc: [32.7, 63.4],
boss: ”ben”}](https://image.slidesharecdn.com/20-nosqlmongodb-240829083837-f6c1ef0e/85/Baisc-introduction-of-mongodb-for-beginn-26-320.jpg)
![• Datais in name / value pairs
• A name/value pair consistsof a field name followed
by a colon, followed by a value:
• Example: “name”: “R2-D2”
• Datais separated by commas
• Example: “name”: “R2-D2”, race : “Droid”
• Curly braces hold objects
• Example: {“name”: “R2-D2”, race : “Droid”, affiliation:
“rebels”}
• An array is stored in brackets []
• Example [ {“name”: “R2-D2”, race : “Droid”, affiliation:
“rebels”},
• {“name”: “Yoda”, affiliation: “rebels”} ]
JSON format](https://image.slidesharecdn.com/20-nosqlmongodb-240829083837-f6c1ef0e/85/Baisc-introduction-of-mongodb-for-beginn-27-320.jpg)
![SQL vs. Mongo DB entities
My SQL
START TRANSACTION;
INSERT INTO contacts VALUES
(NULL, ‘joeblow’);
INSERT INTO contact_emails
VALUES
( NULL, ”joe@blow.com”,
LAST_INSERT_ID() ),
( NULL,
“joseph@blow.com”,
LAST_INSERT_ID() );
COMMIT;
Mongo DB
db.contacts.save( {
userName: “joeblow”,
emailAddresses: [
“joe@blow.com”,
“joseph@blow.com” ] }
);
37
Similar to IDS from the 70’s
Bachman’s brainchild
DIFFERENCE:
MongoDB separates physical structure
from logical structure
Designed to deal with large &distributed](https://image.slidesharecdn.com/20-nosqlmongodb-240829083837-f6c1ef0e/85/Baisc-introduction-of-mongodb-for-beginn-37-320.jpg)
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Baisc introduction of mongodb for beginn
- 1. Introduction to NoSQL and MongoDB Kathleen Durant Lesson 20 CS 3200 Northeastern University 1
- 2. Outlinefor today • Introduction to NoSQL • Architecture • Sharding • Replicasets • NoSQL Assumptions and the CAP Theorem • Strengths and weaknesses of NoSQL • MongoDB • Functionality • Examples 2
- 3. Taxonomy of NoSQL • Key-value • Graph database • Document-oriented • Column family 3
- 4. TypicalNoSQL architecture 4 Hashing function maps each key to a server (node) K
- 5. CAP theorem for NoSQL What the CAP theorem really says: • If you cannot limit the number of faults and requests can be directed to any server and you insist on serving every request you receive then you cannot possibly be consistent How it is interpreted: • You must always give something up: consistency, availability or tolerance to failure and reconfiguration 5 Eric Brewer 2001
- 6. Theory of NOSQL: CAP GIVEN: • Many nodes • Nodes containreplicas of partitions of the data • Consistency • All replicas contain the same version of data • Client always has the same view of the data (no matter what node) • Availability • System remains operationalon failing nodes • All clients can always read and write • Partition tolerance • multiple entrypoints • System remains operationalon system split (communication malfunction) • System works well across physical networkpartitions 6 CAP Theorem: satisfying all three at the same time is impossible A P C
- 7. 7 http://blog.nahurst.com/visual-guide-to-nosql-systems Consistent, Available (CA) Systems have trouble with partitions and typically deal with it with replication Available, Partition- Tolerant (AP) Systems achieve "eventual consistency" through replication and verification Consistent, Partition-Tolerant (CP) Systems have trouble with availability while keeping data consistent across partitioned nodes
- 8. Sharding of data • Distributes a single logical database system across a cluster of machines • Uses range-based partitioning to distribute documents based on a specific shard key • Automatically balances the data associated with each shard • Can be turned on and off per collection (table) 8
- 9. Replica Sets • Redundancy and Failover • Zero downtime for upgrades and maintenance • Master-slave replication • Strong Consistency • Delayed Consistency • Geospatial features 9 Host1:10000 Host2:10001 Host3:10002 replica1 Client
- 10. HowdoesNoSQLvaryfrom RDBMS? • Looser schema definition • Applications written to deal with specific documents/ data • Applications aware of the schema definition as opposed to the data • Designed to handle distributed, large databases • Trade offs: • No strong support for ad hoc queries but designed for speed and growth of database • Query languagethrough the API • Relaxationof the ACID properties 10
- 11. Benefits of NoSQL Elastic Scaling • RDBMS scale up – bigger load , bigger server • NO SQL scale out – distribute data across multiple hosts seamlessly DBA Specialists • RDMS require highly trained expert to monitor DB • NoSQL require less management, automatic repair and simpler data models Big Data • Huge increase in data RDMS: capacity and constraints of data volumes at its limits • NoSQL designed for big data 11
- 12. Benefits of NoSQL Flexible data models • Change management to schema for RDMS have to be carefully managed • NoSQL databases more relaxed in structure of data • Database schema changes do not have to be managed as one complicated change unit • Application already written to address an amorphous schema Economics • RDMS rely on expensive proprietary servers to manage data • No SQL: clusters of cheap commodity servers to manage the data and transaction volumes • Cost per gigabyte or transaction/second for NoSQL can be lower than the cost for a RDBMS 12
- 13. Drawbacks of NoSQL • Support • RDBMS vendors provide a high level of support to clients • Stellar reputation • NoSQL – are open source projects with startups supporting them • Reputation not yet established • Maturity • RDMS mature product: means stable and dependable • Also means old no longer cutting edge nor interesting • NoSQL are still implementing their basic feature set 13
- 14. Drawbacks of NoSQL • Administration • RDMS administrator well defined role • No SQL’s goal: no administrator necessary however NO SQL still requires effort to maintain • Lack of Expertise • Whole workforce of trained and seasoned RDMS developers • Still recruiting developers to the NoSQL camp • Analytics and Business Intelligence • RDMS designed to address this niche • NoSQL designed to meet the needs of an Web 2.0 application - not designed for ad hoc query of the data • Tools are being developed to address this need 14
- 15. RDB ACID to NoSQL BASE 15 Pritchett, D.: BASE:An AcidAlternative (queue.acm.org/detail.cfm?id=1394128) Atomicity Consistency Isolation Durability Basically Available (CP) Soft-state (State of system may change over time) Eventually consistent (Asynchronous propagation)
- 17. What is MongoDB? • Developed by 10gen • Founded in 2007 • A document-oriented, NoSQL database • Hash-based, schema-less database • No DataDefinitionLanguage • In practice, this means you can store hashes with any keys and values that you choose • Keys are a basic data type but in reality stored as strings • Document Identifiers (_id) will be created for each document, field name reserved by system • Application tracksthe schema and mapping • Uses BSON format • Based on JSON – B stands for Binary • Written in C++ • Supports APIs (drivers) in many computer languages • JavaScript, Python, Ruby, Perl, Java, Java Scala, C#, C++, Haskell, Erlang 17
- 18. Functionality ofMongoDB • Dynamic schema • No DDL • Document-based database • Secondary indexes • Query language via an API • Atomic writes and fully-consistent reads • If system configured that way • Master-slave replication with automated failover (replica sets) • Built-in horizontal scaling via automated range-based partitioning of data (sharding) • No joins nor transactions 18
- 19. Why use MongoDB? • Simple queries • Functionality provided applicable to most web applications • Easy and fast integration of data • No ERD diagram • Not well suited for heavy and complex transactions systems 19
- 20. MongoDB: CAP approach Focus on Consistency and Partition tolerance • Consistency • all replicascontainthe same version of the data • Availability • system remains operationalon failingnodes • Partition tolarence • multipleentry points • system remains operationalon system split 20 CAP Theorem: satisfying all three at the same time is impossible A P C
- 21. MongoDB: HierarchicalObjects • A MongoDB instance may have zero or more ‘databases’ • A database may have zero or more ‘collections’. • A collection may have zero or more ‘documents’. • A document may have one or more ‘fields’. • MongoDB ‘Indexes’ function much like their RDBMS counterparts. 21 0 or more Fields 0 or more Documents 0 or more Collections 0 or more Databases
- 22. RDB Concepts to NO SQL 22 RDBMS MongoDB Database Database Table, View Collection Row Document (BSON) Column Field Index Index Join Embedded Document Foreign Key Reference Partition Shard Collection is not strict about what it Stores Schema-less Hierarchy is evident in the design Embedded Document ?
- 23. MongoDBProcessesand configuration • Mongod – Database instance • Mongos - Sharding processes • Analogous to a database router. • Processes all requests • Decides how many and which mongods should receive the query • Mongos collates the results, and sends it back to the client. • Mongo – an interactive shell ( a client) • Fully functional JavaScript environment for use with a MongoDB • You can have one mongos for the whole system no matter how many mongods you have • OR you can have one local mongos for every client if you wanted to minimize network latency. 23
- 24. ChoicesmadeforDesignof MongoDB • Scale horizontally over commodity hardware • Lots of relatively inexpensive servers • Keep the functionality that works well in RDBMSs – Ad hoc queries – Fully featured indexes – Secondary indexes • What doesn’t distribute well in RDB? – Long running multi-row transactions – Joins – Both artifacts of the relational data model (row x column) 24
- 25. BSON format • Binary-encoded serialization of JSON-like documents • Zero or more key/value pairs are stored as a single entity • Each entry consists of a field name, a data type, and a value • Large elements in a BSON document are prefixed with a length field to facilitate scanning 25
- 26. • MongoDB does not need any pre-defined data schema • Every document in a collection could have different data • Addresses NULL data fields Schema Free name: “jeff”, eyes: “blue”, loc: [40.7, 73.4], boss: “ben”} {name: “brendan”, aliases: [“el diablo”]} name: “ben”, hat: ”yes”} {name: “matt”, pizza: “DiGiorno”, height: 72, loc: [44.6, 71.3]} {name: “will”, eyes: “blue”, birthplace: “NY”, aliases: [“bill”, “la ciacco”], loc: [32.7, 63.4], boss: ”ben”}
- 27. • Datais in name / value pairs • A name/value pair consistsof a field name followed by a colon, followed by a value: • Example: “name”: “R2-D2” • Datais separated by commas • Example: “name”: “R2-D2”, race : “Droid” • Curly braces hold objects • Example: {“name”: “R2-D2”, race : “Droid”, affiliation: “rebels”} • An array is stored in brackets [] • Example [ {“name”: “R2-D2”, race : “Droid”, affiliation: “rebels”}, • {“name”: “Yoda”, affiliation: “rebels”} ] JSON format
- 28. MongoDB Features • Document-Oriented storage • Full Index Support • Replication & High Availability • Auto-Sharding • Querying • Fast In-Place Updates • Map/Reduce functionality 28 Agile Scalable
- 29. Index Functionality • B+ tree indexes • An index is automatically created on the _id field (the primary key) • Users can create other indexes to improve query performance or to enforce Unique values for a particular field • Supports single field index as well as Compound index • Like SQL order of the fields in a compound index matters • If you index a field that holds an array value, MongoDB creates separate index entries for every element of the array • Sparse property of an index ensures that the index only contain entries for documents that have the indexed field. (so ignore records that do not have the field defined) • If an index is both unique and sparse – then the system will reject records that have a duplicate key value but allow records that do not have the indexed field defined 29
- 30. CRUD operations • Create • db.collection.insert( <document> ) • db.collection.save( <document> ) • db.collection.update( <query>, <update>, { upsert: true } ) • Read • db.collection.find( <query>, <projection> ) • db.collection.findOne( <query>, <projection> ) • Update • db.collection.update( <query>, <update>, <options> ) • Delete • db.collection.remove( <query>, <justOne> ) Collection specifies the collection or the ‘table’ to store the document 30
- 31. Create Operations Db.collection specifies the collection or the ‘table’ to store the document • db.collection_name.insert( <document> ) • Omit the _id field to have MongoDB generate a unique key • Example db.parts.insert( {{type:“screwdriver”, quantity:15 } ) • db.parts.insert({_id:10, type: “hammer”, quantity:1 }) • db.collection_name.update( <query>, <update>, { upsert: true } ) • Will update 1 or more records in a collectionsatisfying query • db.collection_name.save( <document> ) • Updates an existing record or creates a new record 31
- 32. Read Operations • db.collection.find( <query>, <projection> ).cursor modified • Provides functionality similar to the SELECT command • <query> where condition, <projection>fields in result set • Example: var PartsCursor = db.parts.find({parts: “hammer”}).limit(5) • Has cursors to handle a result set • Can modify the query to impose limits, skips, and sort orders. • Can specify to return the ‘top’ number of records from the result set • db.collection.findOne( <query>, <projection> ) 32
- 33. Query Operators Name Description $eq Matches value that are equal to a specified value $gt, $gte Matches values that are greater than (or equal to a specified value $lt, $lte Matches values less than or ( equal to ) a specified value $ne Matches values that are not equal to a specified value $in Matches any of the values specified in an array $nin Matches none of the values specified in an array $or Joinsquery clauses with a logical OR returns all $and Join query clauses with a loginalAND $not Inverts the effect of a query expression $nor Join query clauses with a logicalNOR $exists Matches documents that have a specified field 33 https://docs.mongodb.org/manual/reference/operator/query/
- 34. Update Operations • db.collection_name.insert( <document> ) • Omit the _id field to have MongoDB generate a unique key • Example db.parts.insert( {{type:“screwdriver”, quantity:15 } ) • db.parts.insert({_id:10, type: “hammer”, quantity:1 }) • db.collection_name.save( <document> ) • Updates an existing record or creates a new record • db.collection_name.update( <query>, <update>, { upsert: true } ) • Will update 1 or more records in a collectionsatisfying query • db.collection_name.findAndModify(<query>, <sort>, <update>,<new>, <fields>,<upsert>) • Modify existing record(s) – retrieve old or new version of the record 34
- 35. Delete Operations • db.collection_name.remove(<query>, <justone>) • Delete all records from a collectionor matching a criterion • <justone> - specifies to delete only 1 record matching the criterion • Example: db.parts.remove(type: /^h/ } ) - remove all parts starting with h • Db.parts.remove() – delete all documentsin the parts collections 35
- 36. CRUD examples 36 > db.user.insert({ first: "John", last : "Doe", age: 39 }) > db.user.find () { "_id" : ObjectId("51"), "first" : "John", "last" : "Doe", "age" : 39 } > db.user.update( {"_id" : ObjectId(“51")}, { $set: { age: 40, salary: 7000} } ) > db.user.remove({ "first": /^J/ })
- 37. SQL vs. Mongo DB entities My SQL START TRANSACTION; INSERT INTO contacts VALUES (NULL, ‘joeblow’); INSERT INTO contact_emails VALUES ( NULL, ”[email protected]”, LAST_INSERT_ID() ), ( NULL, “[email protected]”, LAST_INSERT_ID() ); COMMIT; Mongo DB db.contacts.save( { userName: “joeblow”, emailAddresses: [ “[email protected]”, “[email protected]” ] } ); 37 Similar to IDS from the 70’s Bachman’s brainchild DIFFERENCE: MongoDB separates physical structure from logical structure Designed to deal with large &distributed
- 38. Aggregated functionality Aggregation framework provides SQL-like aggregation functionality • Pipeline documents from a collection pass through an aggregationpipeline, which transforms these objects as they pass through • Expressions produce output documents based on calculations performed on input documents • Example db.parts.aggregate( {$group : {_id: type, totalquantity : { $sum: quanity} } } ) 38
- 39. Map reduce functionality • Performs complex aggregator functions given a collection of keys, value pairs • Must provide at least a map function, reduction function and a name of the result set • db.collection.mapReduce( <mapfunction>, <reducefunction>, { out: <collection>, query: <document>, sort: <document>, limit: <number>, finalize: <function>, scope: <document>, jsMode: <boolean>, verbose: <boolean> } ) • More description of map reduce next lecture 39
- 40. Indexes: High performance read • Typically used for frequently used queries • Necessary when the total size of the documents exceeds the amount of available RAM. • Defined on the collection level • Can be defined on 1 or more fields • Composite index (SQL) Compound index (MongoDB) • B-tree index • Only 1 index can be used by the query optimizer when retrieving data • Index covers a query - match the query conditions and return the results using only the index; • Use index to provide the results. 40
- 41. Replicationof data • Ensures redundancy, backup, and automatic failover • Recovery manager in the RDMS • Replication occurs through groups of servers known as replica sets • Primary set – set of servers that client tasks direct updates to • Secondary set – set of servers used for duplication of data • At the most can have 12 replica sets • Many different properties can be associated with a secondary set i.e. secondary-only, hidden delayed,arbiters, non-voting • If the primary set fails the secondary sets ‘vote’ to elect the new primary set 41
- 42. Consistency of data • All read operations issued to the primary of a replica set are consistent with the last write operation • Reads to a primary have strict consistency • Reads reflect the latest changes to the data • Reads to a secondary have eventual consistency • Updates propagategradually • If clients permit reads from secondary sets – then client may read a previous stateof the database • Failure occurs before the secondary nodes are updated • System identifies when a rollbackneeds to occur • Users are responsible for manuallyapplyingrollbackchanges 42
- 43. Provides Memory Mapped Files • „A memory-mapped file is a segment of virtual memory which has been assigned a direct byte-for-byte correlation with some portion of a file or file-like resource.”1 • mmap() 43 1 : http://en.wikipedia.org/wiki/Memory-mapped_file
- 44. Otheradditionalfeatures • Supports geospatial data of type • Spherical • Provides longitudeand latitude • Flat • 2 dimensionalpointson a plane • Geospatial indexes 44
- 46. Summary • NoSQL built to address a distributed database system • Sharding • Replica sets of data • CAP Theorem: consistency, availability and partition tolerant • MongoDB • Document oriented data, schema-less database, supports secondary indexes, provides a query language, consistentreads on primary sets • Lacks transactions, joins 46
- 47. LimitedBNF of a BSON document document ::= int32 e_list "x00" BSONDocument e_list ::= element e_list Sequence of elements element ::= "x01" e_name data type Specific data type e_name ::= cstring Key name string ::= int32 (byte*) "x00" String cstring ::= (byte*) "x00" CString binary ::= int32 subtype (byte*) Binary subtype ::= "x00" Binary / Generic | "x01" Function | "x02" Binary (Old) | "x03" UUID (Old) | "x04" UUID | "x05" MD5 | "x80" User defined code_w_s ::= int32 string document Code w/ scope 47