![X
TYPE CONVERSION EXAMPLE
db.address.insert( { street: "Canal st", building: NumberDecimal(21), _id: 0})
db.address.insert( { street: "43rd st", building: "229", _id: 1} )
db.address.insert( { street: "Fulton st", building: "31", _id: 2 } )
db.address.aggregate( [
{ $addFields: { building: {$convert: { input: "$building", to: "int" }} } },
{ $sort: {building: 1}}
])
db.address.insert( { street: "52nd st", building: "11w", _id: 3} )
db.address.aggregate( [
{ $addFields: { building: { $convert: { input: "$building", to: "int", onError: 0 } } } },
{ $sort: {building: 1}}
])](https://image.slidesharecdn.com/whatsnewinmongodb4-181101062356/85/MongoDB-4-0-22-320.jpg)
![AGGREGATION PIPELINE
db.customers.aggregate([
{
$unwind: "$address",
},
{
$match: {"address.location": "home"}
},
{
$group: {
_id: "$address.city",
totalSpend: {$sum: "$annualSpend"},
averageSpend: {$avg: "$annualSpend"},
maximumSpend: {$max: "$annualSpend"},
customers: {$sum: 1}
}
}
])
MongoDB’s aggregation framework has the flexibility you need to get
value from your data, but without the complexity and fragility of SQL
These “phases” are distinct and
easy to understand
They can be thought about in
order… no nesting.](https://image.slidesharecdn.com/whatsnewinmongodb4-181101062356/85/MongoDB-4-0-25-320.jpg)
![[MongoDB.local Bengaluru 2018] Jumpstart: Introduction to Schema Design](https://cdn.slidesharecdn.com/ss_thumbnails/0800-jumpstartschemadesign-saurabh-180425155648-thumbnail.jpg?width=560&fit=bounds)
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MongoDB 4.0 새로운 기능 소개
- 1. Jun Kim , Senior Solution Architect & Database Specialist
- 2. Agenda Overview of What’s New in 4.0 Working with Data Building Distributed Systems Enabling a Cloud Data Strategy Beyond the Server: Serverless & Mobile Q&A 40 minutes
- 3. Intelligent Operational Data Platform Best way to work with data Intelligently put data where you want it Freedom to run anywhere
- 4. Intelligent Operational Data Platform Best way to work with data Intelligently put data where you want it Freedom to run anywhere
- 5. Intelligent Operational Data Platform Best way to work with data Intelligently put data where you want it Freedom to run anywhere
- 6. Intelligent Operational Data Platform Best way to work with data Intelligently put data where you want it Freedom to run anywhere
- 7. BEST WAY TO WORK WITH DATA
- 8. MongoDB: Already Used Across Every Industry and Use Case...
- 9. DATA MODELS AND TRANSACTIONS
- 10. DATA MODELS AND TRANSACTIONS
- 13. X Natural for developers ● Idiomatic to the programming language ● Familiar to relational developers ● Simple with client.start_session() as s: s.start_transaction() collection_one.insert_one(doc_one, session=s) collection_two.insert_one(doc_two, session=s) s.commit_transaction() MONGODB TRANSACTIONS SYNTAX
- 14. X s.start_transaction() orders.insert_one(order, session=s) stock.update_one(item, stockUpdate, session=s) s.commit_transaction() db.start_transaction() cursor.execute(orderInsert, orderData) cursor.execute(stockUpdate, stockData) db.commit() * COMPARING SYNTAX WITH TABULAR DATABASES
- 16. OUR JOURNEY TO ACID TRANSACTIONS • • • • • • •
- 17. What’s Done, What’s Left for Sharded Transactions MongoDB 3.0 MongoDB 3.2 MongoDB 3.4 MongoDB 3.6 MongoDB 4.0 MongoDB 4.2 New Storage engine (WiredTiger) Enhanced replication protocol: stricter consistency & durability Shard membership awareness Consistent secondary reads in sharded clusters Replica Set Transactions Sharded Transactions WiredTiger default storage engine Logical sessions Make catalog timestamp-aware More extensive WiredTiger repair Config server manageability improvements Retryable writes Snapshot reads Transaction manager Read concern “majority” Causal Consistency Recoverable rollback via WT checkpoints Global point-in-time reads Cluster-wide logical clock Recover to a timestamp Oplog applier prepare support for transactions Storage API changes to use timestamps Sharded catalog improvements Read concern majority feature always available Collection catalog versioning UUIDs in sharding Fast in-place updates to large documents in WT In Progress Done Transaction EPIC Planned
- 20. AGGREGATION PIPELINE TYPE CONVERSIONS • Transforms data into standardized, cleansed types • Flexible data model allows ingest of data without pre-defining schema • Schema validation to enforce structure where needed • Lack of data type uniformity for fields across documents can complicate downstream processing • Cleanse in the app, or run through external ETL pipeline • Adds complexity and latency • Now data types can be standardized with $convert operator natively in MongoDB • Faster data prep for consuming apps & analytics
- 21. X { $convert: { input: <expression>, to: <type expression>, onError: <expression>, // Optional. onNull: <expression> // Optional. } } TYPE CONVERSION SYNTAX
- 22. X TYPE CONVERSION EXAMPLE db.address.insert( { street: "Canal st", building: NumberDecimal(21), _id: 0}) db.address.insert( { street: "43rd st", building: "229", _id: 1} ) db.address.insert( { street: "Fulton st", building: "31", _id: 2 } ) db.address.aggregate( [ { $addFields: { building: {$convert: { input: "$building", to: "int" }} } }, { $sort: {building: 1}} ]) db.address.insert( { street: "52nd st", building: "11w", _id: 3} ) db.address.aggregate( [ { $addFields: { building: { $convert: { input: "$building", to: "int", onError: 0 } } } }, { $sort: {building: 1}} ])
- 23. X TYPE CONVERSION BENEFITS : EDM PIPELINE …Siloedsource databases Externalfeeds (batch) Streams Transform Storeraw data AnalyzeAggregate Pub-sub,ETL,fileimports StreamProcessing Users Other Systems
- 24. MONGODB COMPASS ● The GUI for MongoDB: explore and interact with your MongoDB data ○ Visualize and explore your data structure ○ Insert and modify documents ○ Build queries, then debug and optimize with plans and index control ○ Control your data with document validation rules ● Compass Community is free to use and ships with Community Server ● Compass Enterprise with additional features: ○ Advanced security ○ Schema analysis ○ Real-Time Performance Panel ○ Document Validation rules
- 25. AGGREGATION PIPELINE db.customers.aggregate([ { $unwind: "$address", }, { $match: {"address.location": "home"} }, { $group: { _id: "$address.city", totalSpend: {$sum: "$annualSpend"}, averageSpend: {$avg: "$annualSpend"}, maximumSpend: {$max: "$annualSpend"}, customers: {$sum: 1} } } ]) MongoDB’s aggregation framework has the flexibility you need to get value from your data, but without the complexity and fragility of SQL These “phases” are distinct and easy to understand They can be thought about in order… no nesting.
- 26. ● Easily build aggregation pipelines for data processing and analytics ● Add new pipeline stages with code skeletons and auto-completion ● See a preview of documents as they are transformed in each stage ● Drag and drop to re-order pipeline stages ● Save pipelines in Compass or export to native code X AGGREGATION PIPELINE BUILDER Introduced in MongoDB Compass 1.14
- 27. ● Simplify query creation: build a query in MongoDB Compass, then click to export it to native code ● Supports complex, multi-parameter queries and aggregation pipelines ● Initially supported: C#/.NET, Java, JavaScript, Python, and mongo shell syntax ○ More languages to come in future releases X EXPORT TO LANGUAGE Introduced in MongoDB Compass 1.15
- 28. MONGODB CHARTS (BETA) • The fastest and easiest way to create visualizations of MongoDB data • Built for the MongoDB document model • Visualize live data from any of your MongoDB instances – on-prem, in the cloud, or in MongoDB Atlas
- 29. MONGODB CHARTS: CREATE, VISUALIZE, SHARE
- 30. INTELLIGENTLY PUT DATA WHERE YOU WANT IT
- 31. ELASTIC SCALE: 40% FASTER MIGRATIONS • Sharded cluster balancer used to maintain even data distribution across the cluster • Now concurrently fetches and applies documents for 40% faster data migration • Responds faster to nodes being added and removed from the cluster • Right-size for cost and capacity
- 32. NON-BLOCKING SECONDARY READS • Pre 4.0, secondary reads block while oplog batches from primary are applied • Maintains causal order with primary • Using storage engine timestamps and snapshots in 4.0, reads are now serviced from a snapshot while secondary is applying oplog entries • Maintains consistent read latency, improves read throughput & scaling
- 33. UPGRADED SECURITY • Access control, auditing, encryption: MongoDB offers some of strictest security controls of any modern database • Upgraded authentication (SHA2) and encryption TLS (1.1+) to latest and strongest standards.
- 34. FREEDOM TO RUN ANYWHERE
- 35. Atlas unlocks agility and reduces cost Self-service and elastic Global and highly available Secure by default Comprehensive monitoring Managed backup Cloud agnostic
- 36. Atlas Global Clusters Distribute your fully automated database across multiple geographically distributed zones made up of one or more cloud regions ● Read and write locally to provide single-digit millisecond latency for your distributed applications ● Ensure that data from certain geographies lives in predefined zones ● Easily deploy using prebuilt zone templates or build your own zones by choosing cloud regions in an easy- to-use, visual interface
- 38. Atlas Database Auditing Answer detailed questions about system activity by tracking DDL, DML, and DCL commands. ● Easily select the actions you want audited as well as the MongoDB users, Atlas roles, or LDAP groups you want to audit from the Atlas UI ● Alternatively, create an audit filter as a JSON string ● Auditing configuration applies to all dedicated clusters within an Atlas project ● Audit logs can be downloaded in the UI or retrieved using the MongoDB Atlas API
- 39. Atlas LDAP Integration Centralize database access management and eliminate the need to recreate permissions by using your LDAP server (over TLS/SSL) to manage user authentication and authorization. ● LDAP groups are mapped to different MongoDB roles in your MongoDB Atlas databases ● LDAP configuration applies to all dedicated database clusters within an Atlas Project ● Available for deployments running MongoDB 3.4+
- 40. Encryption at Rest: Bring Your Own KMS MongoDB Atlas integrates with your key management service of choice (AWS KMS & Azure KeyVault), allowing you to centralize control of the keys used to encrypt your MongoDB data ● BYO KMS leverages MongoDB’s encrypted storage engine (AES256-CBC via OpenSSL) for database-level encryption ● Create, import, and rotate keys for MongoDB Atlas as well as define usage policies and audit usage with the same console/CLI used to manage keys for your other cloud services
- 41. MONGODB MONITORING CLOUD Free service, agent free, opt-in and out at any time Assess health, optimize performance
- 42. KUBERNETES (K8S) AND RED HAT OPENSHIFT
- 44. Intelligent Operational Data Platform Best way to work with data Intelligently put data where you want it Freedom to run anywhere
- 45. Code user authentication Code data access controls Provision backend server Install runtime environment Add code to make backend HA Add code to scale backend Monitor & manage backend infrastructure Code REST API for frontend to use backend Code backend application logic Code application frontend Code against each external service API Continuously poll database for changes Without Stitch Simple JSON Config Handled automatically by Stitch and Atlas Code frontend using single SDK/API With Stitch Backend Data Access Frontend Provide JS code for Stitch Functions
- 46. App Backend Infrastructure Core Database Functionality Storage Service integrations, data access control Code that moves the business forward Managing OS, Scale, Security, Backups, etc. MongoDB Atlas MongoDB Stitch Fully managed Elastic scale Highly Available Secure You should focus here FOCUS YOUR ENERGY WHERE YOU CAN MAKE A DIFFERENCE
- 47. MONGODB MOBILE (BETA) Brings the power of MongoDB to your device Same database, same access patterns – from IoT to Mobile to Web.
- 48. THANK YOU