MongoDB - General Purpose Database
2 likes441 views
The document presents an overview of MongoDB, highlighting its capabilities, forward-looking statements, and key features aimed at enhancing data management and accessibility. It emphasizes the flexibility, scalability, and operational efficiency of MongoDB as a database solution, catering to diverse deployment needs across various environments, including cloud and on-premises. Additionally, it discusses real-world applications and success stories demonstrating MongoDB's impact on digital transformation in different industries.
![14
Easy: Contrasting data models
Tabular (Relational) Data Model
Related data split across multiple records and tables
Document Data Model
Related data contained in a single, rich document
{
"_id" : ObjectId("5ad88534e3632e1a35a58d00"),
"name" : {
"first" : "John",
"last" : "Doe" },
"address" : [
{ "location" : "work",
"address" : {
"street" : "16 Hatfields",
"city" : "London",
"postal_code" : "SE1 8DJ"},
"geo" : { "type" : "Point", "coord" : [
51.5065752,-0.109081]}},
+ {...}
],
"phone" : [
{ "location" : "work",
"number" : "+44-1234567890"},
+ {...}
],
"dob" : ISODate("1977-04-01T05:00:00Z"),
"retirement_fund" : NumberDecimal("1292815.75")
}](https://image.slidesharecdn.com/singaporeeventfinalv3-190131040358/85/MongoDB-General-Purpose-Database-14-320.jpg)
MongoDB - General Purpose Database
- 1. 1 mongoDB VIP Event, Singapore MEET THE TEAM S U V I G V I V E K G A G A N K A U S T U B H R E K H A
- 2. 2 This presentation contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Such forward-looking statements are subject to a number of risks, uncertainties, assumptions and other factors that could cause actual results and the timing of certain events to differ materially from future results expressed or implied by the forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to, those identified our filings with the Securities and Exchange Commission. You should not rely upon forward-looking statements as predictions of future events. Furthermore, such forward-looking statements speak only as of the date of this presentation. In particular, the development, release, and timing of any features or functionality described for MongoDB products remains at MongoDB’s sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality. Except as required by law, we undertake no obligation to update any forward-looking statements to reflect events or circumstances after the date of such statements. Safe Harbor Statement
- 4. 4 Stack Overflow Research of 100,000 Developers Finds mongoDB is the Most Wanted Database https://www.mongodb.com/blog/post/stack-overflow-research-developers-mongodb-most-wanted-database
- 5. 5 Why MongoDB? Best way to work with data Intelligently put data where you need it Freedom to run anywhere Intelligent Operational Data Platform
- 6. 6 Best way to work with data Easy: Work with data in a natural, intuitive way Flexible: Adapt and make changes quickly Fast: Get great performance with less code Versatile: Supports a wide variety of data models and queries
- 7. 7 Intelligently put data where you need it Locality Declare data locality rules for governance (e.g. data sovereignty), class of service & local low latency access Scalability Elastic horizontal scalability – add/remove capacity dynamically without downtime Workload Isolation Ability to run both operational & analytics workloads on same cluster, for timely insight and lower cost Highly Available Built-in multi-region high availability, replication & automated failover
- 8. 8 Freedom to run anywhere Local On-premises Server & Mainframe Private cloud Fully managed cloud serviceHybrid cloud Public cloud • Database that runs the same everywhere • Leverage the benefits of a multi-cloud strategy • Global coverage • Avoid lock-in Convenience: same codebase, same APIs, same tools, wherever you run
- 9. MongoDB: General Purpose Database
- 10. The evolution of MongoDB 3.0 3.2 Document Validation $lookup Fast Failover Simpler Scalability Aggregation ++ Encryption At Rest In-Memory Storage Engine BI Connector MongoDB Compass APM Integration Profiler Visualization Auto Index Builds Backups to File System Doc-Level Concurrency Compression Storage Engine API ≤50 replicas Auditing ++ Ops Manager Linearizable reads Intra-cluster compression Views Log Redaction Graph Processing Decimal Collations Faceted Navigation Zones ++ Aggregation ++ Auto-balancing ++ ARM, Power, zSeries BI & Spark Connectors ++ Compass ++ Hardware Monitoring Server Pool LDAP Authorization Encrypted Backups Cloud Foundry Integration 3.4 3.6 Change Streams Retryable Writes Expressive Array Updates Query Expressivity Causal Consistency Consistent Sharded Sec. Reads Compass Community Ops Manager ++ Query Advisor Schema Validation End to End Compression IP Whitelisting Default Bind to Localhost Sessions WiredTiger 1m+ Collections MongoDB BI Connector ++ Expressive $lookUp R Driver Atlas Cross Region Replication Atlas Auto Storage Scaling 4.0 Multi-Document ACID Transactions Atlas Global Clusters Atlas HIPAA Atlas LDAP Atlas Audit Atlas Encrypted Storage Engine Atlas AWS Backup Snapshots Atlas Full CRUD Agg Pipeline Type Conversions 40% Faster Shard Migrations Snapshot Reads Non-Blocking Secondary Reads SHA-2 TLS 1.1+ Compass Agg Pipeline Builder Compass Export to Code Charts Beta Free Monitoring Cloud Service Ops Manager K8s & OpenShift MongoDB Stitch GA MongoDB Mobile GA
- 11. 11 Some Terminology A comparison * Scheduled for MongoDB 4.0 Database Table Parent – Child Tables MongoDB Index Row Column Join View Multi-Record ACID Transaction RDBMS Database Collection Nested Sub- Document or Array Index Document** Field Embedding, Linking, $lookup View Multi-Document ACID Transaction* ** Proper document schema design yields more entity data per document than found in a relational database row
- 12. 12 Nexus Architecture - Relational Meets NoSQL = MongoDB Highly Scalable & Available Built for Global Cloud Deployments Flexible Data Model Expressive Query Language & Secondary Indexes Strong Data Consistency Enterprise Management Tools Security & Integrations RDBMS Core Strengths NoSQL Core Strengths
- 14. 14 Easy: Contrasting data models Tabular (Relational) Data Model Related data split across multiple records and tables Document Data Model Related data contained in a single, rich document { "_id" : ObjectId("5ad88534e3632e1a35a58d00"), "name" : { "first" : "John", "last" : "Doe" }, "address" : [ { "location" : "work", "address" : { "street" : "16 Hatfields", "city" : "London", "postal_code" : "SE1 8DJ"}, "geo" : { "type" : "Point", "coord" : [ 51.5065752,-0.109081]}}, + {...} ], "phone" : [ { "location" : "work", "number" : "+44-1234567890"}, + {...} ], "dob" : ISODate("1977-04-01T05:00:00Z"), "retirement_fund" : NumberDecimal("1292815.75") }
- 15. 15 Versatile: Multiple data models, rich query functionality Rich Queries Point | Range | Geospatial | Faceted Search | Aggregations | JOINs | Graph Traversals JSON Documents Tabular Key-Value Text GraphGeospatial
- 17. 17 MongoDB Replica Sets Replica Set – 2 to 50 copies Self-healing Data Center Aware Addresses availability considerations: • High Availability • Disaster Recovery • Maintenance Workload Isolation: operational & analytics Application Driver Primary Secondary Secondary Replication
- 18. MongoDB’s native replica sets puts your entire database right next to users 10 ms 2 ms 10 ms 4 ms 4 ms 2 ms 10 ms 2 ms
- 19. 19 Co-locating operational and analytical workloads Analytical Primary Secondary Secondary Secondary {use = analytics} Transactional BI & Reporting Predictive Analytics & Data Science Aggregations Secondary {use = analytics} Compass BI Connector
- 20. 20 Put data where you need it: Scalability Auto-Sharding • Automatically scale beyond the constraints of a single node • Application transparent • Scale and rebalance incrementally, in real time • Unlike NoSQL systems that randomly spray data across a cluster, MongoDB exposes multiple data distribution policies to optimize for query patterns and locality •••Shard 1 Shard 2 Shard 3 Shard N
- 21. 21 Sharding Architecture Application Driver Mongos Primary Secondary Secondary Shard 1 Primary Secondary Secondary Shard 2 Primary Secondary Secondary Shard N Mongos •••••• ••• High availability - Replica sets Horizontal scalability - Sharding
- 23. 23 MongoDB Multi-Document ACID Transactions Just like relational transactions • Multi-statement, familiar relational syntax • Easy to add to any application • Multiple documents in 1 or many collections and databases ACID guarantees • Snapshot isolation, all or nothing execution • No performance impact for non-transactional operations Schedule • MongoDB 4.0: replica set • MongoDB 4.2: extended to sharded clusters
- 24. 24 MongoDB Change Streams Enabling developers to build reactive, real-time servicesChangeStreamsAPI Business Apps User Data Sensors Clickstream Real-Time Event Notifications Message Queue
- 25. 25 Sophisticated Analytics & Visualizations Of Data In Place • Rich MongoDB query language & idiomatic drivers • Connector for BI • Connector for Spark • Charts (beta)
- 26. MongoDB Charts: Create, Visualize, Share Work with complex data Connect to data sources securely. Filter. Sample. Visualize. Share dashboards and collaborate
- 27. 27 MongoDB Connector for BI + many more
- 28. 28 Advanced Analytics “We reduced 100+ lines of integration code to just a single line after moving to the MongoDB Spark connector.” - Early Access Tester, Multi-National Banking Group Group Analytics Application Scala, Java, Python, R APIs SQL Machine Learning Libraries Streaming Graph Spark Worker Spark Worker Spark Worker Spark Worker MongoDB Connector for Spark MongoDB Connector for Apache Spark Native Scala connector, certified by Databricks Exposes all Spark APIs & libraries Efficient data filtering with predicate pushdown, secondary indexes, & in-database aggregations Locality awareness to reduce data movement Updated with Spark 2.0 support
- 29. MongoDB As a Service, Serverless Platform and Mobile
- 30. MongoDB Atlas: Database as a Service (Available on AWS, Azure and Google Cloud) Self-service and elastic • Deploy in minutes • Scale up/down without downtime • Automated upgrades Global and highly available • 59 Regions worldwide • Replica sets optimized for availability • Global clusters for read/write everywhere deployments Secure by default • Network isolation and Peering • Encryption in flight and at rest with key management • Role-based access control; auditing • SOC 2 / Privacy Shield / HIPAA Comprehensive Monitoring • Performance Advisor • Dashboards w/ 100+ metrics • Real Time Performance • Customizable alerting Managed Backup • Point in Time Restore • Queryable backups • Consistent snapshots Cloud Agnostic • AWS, Azure, and GCP • Easy migrations • Consistent experience
- 31. MongoDB Stitch Serverless Platform Streamlines app development with simple, secure access to data and services from the client with thousands of lines less code to write and no infrastructure to manage. Getting your apps to market faster while reducing operational costs.
- 32. MongoDB Stitch Serverless Platform – Services Stitch QueryAnywhere Brings MongoDB's rich query language safely to the edge iOS, Android, Web, IoT Stitch Functions Integrate microservices + server-side logic + cloud services Build full apps, or Data as a Service through custom APIs Stitch Mobile Sync Automatically synchronizes data between documents held locally in MongoDB Mobile and your backend database (beta) Streamlines app development with simple, secure access to data and services from the client with thousands of lines less code to write and no infrastructure to manage – getting your apps to market faster while reducing operational costs. Stitch Triggers Real-time notifications let your application functions react in response to database changes, as they happen
- 33. MongoDB Mobile Brings the power of MongoDB to your device Same database, same access patterns – from IoT to Mobile to Web.
- 34. MongoDB Mobile Benefits Use the #1 modern database everywhere Same database in your client device and backend Full MongoDB query language, including aggregations Learn a single query syntax, use everywhere Build faster, online/offline apps Local data access avoids network delays, delivering responsive apps Secondary indexes allow you to efficiently query the same data in many ways Progressive apps continue to run key features when offline Code once, run anywhere Same query syntax for frontend and backend SDKs available for iOS and Android Sync your data everywhere Stitch Mobile Sync Mobile ↠ backend Backend ↠ mobile Always-on, offline changes synced when back online Conflict detection
- 36. MongoDB – Driving Digital Transformation
- 37. Single View Internet of Things Mobile Operational Intelligence Legacy Modernization Blockchain Cloud Data Strategy Cases for Change – Cross Industry Successes
- 38. Problem Why MongoDB Results Problem Solution Results • With the advent of mobile banking, Barclays has experienced a significant growth of traffic originating from mobile devices to Mainframe platforms that supports banking applications. Growth of traffic, which is expected to continue, has led to an increased cost of operations and decreased performance • Ability to provide high resiliency during mainframe outages • Existing ETL processes that load transaction data into Teradata on a daily basis are updated to additionally feed data to MongoDB paving the way for decommissioning of Teradata. In subsequent phases of the project, MongoDB will be updated in near real- time via a live transactions feed • De-normalized real time data store using MongoDB with the benefits to reduce growth • Stand-in capability to support Resiliency during planned and unplanned outages across mainframe system and other source systems • Reduced cost of operations • Reduced number of read only transactions to Mainframes , there by freeing up mainframe resources for additional growth Operational Data Source Data lake to store data from multiple sources for operations on the data. ODS is built to store and process read only customer transactions for business operations, analysis and reporting.
- 39. Problem Why MongoDB Results Problem Solution Results High licensing costs from proprietary database and data grid technologies Complex architecture and duplicated processes across system stacks Data duplication across siloed systems with complex reconciliation controls Tech estate simplification initiative driven by cost efficiency programme High operational complexity impacting service availability & speed of application delivery Open source MongoDB, Kafka & RabbitMQ that underpins several core trading platforms Rationalised data layer behind a common API, implemented by a multi- tenant PaaS, tackling common data problems Flexible data model and secondary indexes to provide support for wide variety of data and queries. New solution is a self-service Large scale cost reduction: • £m license avoidance of incumbent technologies • Dramatic cost per TB storage saving Technology simplification: Roadmap to decommission hundreds of servers Increased Velocity: • Develop new apps in days • Self-service data service Data Fabric (Data-as-a-Service) Migration from Oracle & Microsoft to create a consolidated “data fabric” reduces $m in cost, speeds application development & simplifies operations
- 40. Travel Booking 2nd biggest airline in China delivers next generation travel search application with MongoDB Problem Why MongoDB Results Problem Solution Results Primitive flight search user interface and stale data provided poor user experience Oracle based system could only support a dozen concurrent users, but new requirement needs 100x more QPS. Complex database schema slows innovation 70% of bookings rely on channel partners, which represents 30% lost revenue Re-Platform on MongoDB Secondary indexes capability allows flexible search and fast development Built-in replication provides high availability with lower cost of ownership Compelling flight search features: multi-day, near-by airports, 60-day low price, etc.; inventory data and price data became near real time Greatly improved user experience – millisecond response time, and tens of thousands of reads per second Projected 20% direct user growth and stemmed revenue loss to channels