MongoDB in a nutshell

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MongoDB vs RDBMS

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Key features

High Performance

• Support for embedded data models reduces I/O activity on database system
• Indexes support faster queries and can include keys from embedded documents and arrays

High Availability

• MongoDB’s replication facility, called replica sets, provide: - automatic failover - data redundancy

High Availability

• MongoDB’s replication facility, called replica sets, provide: - automatic failover - data redundancy

Replication

• Electing primary node
• Roles: primary, secondaries, arbiter
• Oplog, failover after primary down
• Write concern (w:majority, j)

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Demo time!

Automatic Scaling

• Automatic sharding distributes data across a cluster of machines
• Replica sets can provide eventually-consistent reads for low-latency high throughput deployments

Sharding

• Vertical scaling vs. sharding
• Chunks
• Partitioning Strategies: - range based - hash based

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Chunks strategies

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Data model

Embedding

• 1:1 or 1:many relationships
• Better performance for read operations
• Retrieve data in a single database operation
• "dot" notation

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Referencing

• Many:Many relationship
• Frequently read with rarely accessed sub-doc
• Documents related to many others collection
• embedding would result in duplication of data without read performance

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Indexes

• Default: _id
• Single Field
• Compound Index
• Multikey Index

Index Properties

• Unique
• Spare
• Partial
• TTL

Covered Queries

Transactions

• Atomic Operations
• Possible approaches: - Restructure - Provide transactions on application level - Tolerate

Transactions

• $isolated
• Two-phase commit

MongoDB limits

• 16MB per document
• No multi-row transactions
• No foreign keys
• Text and Geospatial indexes can't be use at once

MongoDB pros

• Changeable list of entity properties
• Scalability
• Object-oriented API
• High performance (up to 1000's millions queries/sec)

MongoDB cons

• Memory usage
• No Joins
• Possible data loss
• Lack of SQL support

Consider MongoDB

• Scaling on demand
• Flexible schama
• Geo spatial queries
• Real-time analytics

Forget about MongoDB when

• You will use a lot text indexing
• Multi-row transactions are needed
• Nanoseconds latency writing is needed (real time tick data)
• You want to do a batch processing
• Joins across collections is needed

Q & A

Useful links

• http://docs.mongodb.org/manual/reference/sql-comparison/
• https://docs.mongodb.org/manual
• https://university.mongodb.com
• https://www.mongodb.com/resource-center