This document provides an agenda and overview for a presentation on MongoDB 4.0. The presentation will cover what's new in MongoDB 4.0 including working with data, building distributed systems, enabling cloud data strategies, and serverless and mobile capabilities. It will also discuss the intelligent operational data platform provided by MongoDB for working with data, putting data where needed intelligently, and running applications anywhere.

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

11. MULTI-DOCUMENT TRANSACTION EXAMPLES

12. MONGODB MULTI-DOCUMENT TRANSACTIONS
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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

15. BEST PRACTICES
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16. OUR JOURNEY TO ACID TRANSACTIONS
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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

19. Persona Messages
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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

37. MongoDB Atlas Tiers

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

43. BEYOND THE SERVER

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