webinar: what's new in mongodb 3.2

What’s New in MongoDB 3.2

Mat KeepDirector, Product Marketing, MongoDB

Andrew MorganPrincipal Product Marketing Manager, MongoDB

MongoDB 3.2 – a BIG Release

Hash-Based ShardingRolesKerberosOn-Prem Monitoring

2.2 2.4 2.6 3.0 3.2

Agg. FrameworkLocation-Aware Sharding

$outIndex IntersectionText SearchField-Level RedactionLDAP & x509Auditing

Document ValidationFast FailoverSimpler ScalabilityAggregation ++Encryption At RestIn-Memory Storage EngineBI Connector$lookupMongoDB CompassAPM IntegrationProfiler VisualizationAuto Index BuildsBackups to File System

Doc-Level ConcurrencyCompressionStorage Engine API≤50 replicasAuditing ++Ops Manager

ThemesBroader use case portfolio. Pluggable storage engine strategy enables us to

rapidly cover more use cases with a single database.

Mission-critical apps. MongoDB delivers major advances in the critical areas

of governance, high availability, and disaster recovery.

New tools for new users. Now MongoDB is an integral part of the tooling and

workflows of Data Analysts, DBAs, and Operations teams.

Storage Engines Broaden Use Cases

Varying Access & Storage Requirements

Modern apps

Sensitive data

Cost effective storage

High concurrenc

y

High throughput

Low latency

Real-time analytics

Flexible Storage Architecture in 3.2

WiredTiger is the New Default

WiredTiger – widely deployed with 3.0 – is

now the default storage engine for

MongoDB.

• Best general purpose storage engine

• 7-10x better write throughput

• Up to 80% compression

117k Security Attacks…..PER DAY

PWC: Global State of Information Security

Encrypted Storage Engine Encrypted storage engine for end-to-end

encryption of sensitive data in regulated

industries

• Reduces the management and performance

overhead of external encryption mechanisms

• AES-256 Encryption, FIPS 140-2 option available

• Key management: Local key management via

keyfile or integration with 3rd party key

management appliance via KMIP

• Based on WiredTiger storage engine

• Requires MongoDB Enterprise Advanced

“Protecting sensitive data assets is one of most important things we do. The new Database Encryption feature in MongoDB 3.2 is a significant step forward in allowing us to more simply add encryption at-rest to our list of security controls.

In our tests, we found the new database encryption feature easy to enable, stable and consistent with our performance expectations.”

Shawn DrewData Integration Solutions ArchitectUniversity of Washington

In-Memory Economic Viability

In-Memory Storage Engine (Beta)Handle ultra-high throughput with low

latency and high availability

• Delivers the extreme throughput and predictable

latency required by the most demanding apps in

Adtech, finance, and more.

• Achieve data durability with replica set members

running disk-backed storage engine

• Available for beta testing and is expected for GA in

early 2016

One Deployment Powering Multiple Apps

Built for Mission Critical Deployments

A 10% improvement in data usability at a Fortune 1000 company could

increase revenues by $2 BN per year

Source: University of Texas, Austin

Data Governance with Document Validation

Implement data governance without

sacrificing agility that comes from dynamic

schema

• Enforce data quality across multiple teams and

applications

• Use familiar MongoDB expressions to control

document structure

• Validation is optional and can be as simple as a

single field, all the way to every field, including

existence, data types, and regular expressions

Document Validation Example

The example on the left adds a rule to the

contacts collection that validates:

• The year of birth is no later than 1994

• The document contains a phone number and / or

an email address

• When present, the phone number and email

addresses are strings

“Rocket.Chat and our other applications need to be able to quickly access various types of data to provide a seamless solution for our users.

With MongoDB 3.2, we will now be able to implement the data governance we’re seeking, without sacrificing agility that comes from dynamic schema. The newfound ability to use familiar MongoDB expression syntax to control document structure, rather than learning a whole new language or process, is key for us.”

Gabriel EngelFounder and CEORocket.Chat

Enhancements for your mission-critical appsMore improvements in 3.2 that optimize the

database for your mission-critical

applications

• Meet stringent SLAs with Raft-base fast-failover

algorithm

– Under 2 seconds to detect and recover from

replica set primary failure

– Enhanced durability through write conerns

• Simplified management of sharded clusters

allow you to easily scale to many data centers

– Config servers are now deployed as replica

sets; up to 50 members/locations

Tools for Users Across Your Organization

For Business Analysts & Data Scientists

MongoDB 3.2 allows business analysts and

data scientists to support the business with

new insights from untapped data sources

• MongoDB Connector for BI

• Dynamic Lookup

• New Aggregation Operators & Improved Text

Search

Only 0.5% of data is analyzed

Source: IDC

MongoDB Connector for BIVisualize and explore multi-dimensional

documents using SQL-based BI tools. The

connector does the following:

• Provides the BI tool with the schema of the

MongoDB collection to be visualized

• Translates SQL statements issued by the BI tool

into equivalent MongoDB queries that are sent to

MongoDB for processing

• Converts the results into the tabular format

expected by the BI tool, which can then visualize

the data based on user requirements

“We are thrilled to enable Tableau users, who traditionally work with their relational data, to fully integrate the multi-structured data stored in the database powering modern applications via the new MongoDB BI Connector”

Jeffrey FengProduct ManagerTableau Software

Dynamic LookupCombine data from multiple collections with

left outer joins for richer analytics & more

flexibility in data modeling

• Blend data from multiple collections for analysis

• Higher performance analytics with less

application-side code and less effort from your

developers

• Executed via the new $lookup operator, a stage in

the MongoDB Aggregation Framework pipeline

“I am most excited by the dynamic lookups coming in MongoDB 3.2. The ability to more easily join customer data with 3rd-party data feeds gives us more flexibility in data modeling, and simplifies the real-time analytics we rely on to constantly improve our value to our customers.”

David StricklandCTOMyDealerLot

Aggregation Pipeline

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Aggregation Pipeline

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Aggregation Pipeline

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Aggregation Pipeline

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Aggregation Pipeline

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Aggregation Pipeline

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Aggregation Pipeline

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Aggregation Pipeline

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Improved In-Database Analytics & SearchNew Aggregation operators extend options for

performing analytics and ensure that answers

are delivered quickly and simply with lower

developer complexity

• Array operators: $slice, $arrayElemAt, $concatArrays,

$filter, $min, $max, $avg, $sum, and more

• New mathematical operators: $stdDevSamp,

$stdDevPop, $sqrt, $abs, $trunc, $ceil, $floor, $log,

$pow, $exp, and more

• Random sample of documents: $sample

• Case sensitive text search and support for additional

languages such as Arabic, Farsi, Chinese, and more

For Database AdministratorsMongoDB 3.2 helps users in your

organization understand the data in your

database

• MongoDB Compass

– For DBAs responsible for maintaining the

database in production

– No knowledge of the MongoDB query

language required

MongoDB CompassFor fast schema discovery and visual

construction of ad-hoc queries

• Visualize schema

– Frequency of fields

– Frequency of types

– Determine validator rules

• View Documents

• Graphically build queries

• Authenticated access

MongoDB Compass

Up to 80% of TCO is driven by on-going operations and

maintenance costs

Source: Gartner

For Operations TeamsMongoDB 3.2 simplifies and enhances

MongoDB’s management platforms. Ops

teams can be 10-20x more productive using

Ops and Cloud Manager to run MongoDB.

• Start from a global view of infrastructure:

Integrations with Application Performance

Monitoring platforms

• Drill down: Visual query performance diagnostics,

index recommendations

• Then, deploy: Automated index builds

• Refine: Partial indexes improve resource utilization

Integrations with APM PlatformsEasily incorporate MongoDB performance

metrics into your existing APM dashboards

for global oversight of your entire IT stack

• MongoDB drivers enhanced with new API that

exposes query performance metrics to APM tools

• Packaged integration with Cloud Manager to

visualize server metrics

• Deep dive with Ops and Cloud Manager offering

rich database monitoring & tools for common

operations tasks

“We've been really excited to work with MongoDB on enhancing their APM integration with the New Relic platform. MongoDB has become an integral part of the tooling and workflows of DBAs and Operations teams and we expect the trend to increase.

To support MongoDB 3.2, we jointly-developed an integration between MongoDB Ops Manager and New Relic APM, Insights, and Plugins. These integrations mean MongoDB health can now be monitored alongside the rest of the application estate.".”

Cooper MarcusSenior Product ManagerNew Relic.

Query Perf. Visualizations & OptimizationFast and simple query optimization with the

new Visual Query Profiler

• Query and write latency are consolidated and

displayed visually; your ops teams can easily

identify slower queries and latency spikes

• Visual query profiler analyzes the data it displays

and provides recommendations for new indexes

that can be created to improve query performance

• Ops Manager and Cloud Manager can automate

the rollout of new indexes, reducing risk and your

team’s operational overhead

“I’m excited by the availability of Visual Query Profiler in Ops Manager & Cloud Manager. It helps us tremendously improve the performance of our database by identifying queries that are slowing us down and provides recommendations for new indexes -- which it can then build through a rolling index build.”

Daniel RubioDirectorMondo Sports Ltd

Refine with Partial Indexes

Balance delivering good query performance

while consuming fewer system resources

• Specify a filtering expression during index creation

to instruct MongoDB to only include documents

that meet your desired conditions

• The example to the left creates a compound index

that only indexes the documents with the rating

field greater than 5

Ops Manager Enhancements3.2 includes Ops Manager enhancements to improve

the productivity of your ops teams and further simplify

installation and management

• MongoDB backup on standard network-mountable filesystems;

integrates with your existing storage infrastructure

• Automated database restores; Build clusters from backup in a few

clicks

• Faster time to first database snapshot

• Support for maintenance windows

• Centralized UI for installation and config of all application and

backup components

Next Steps• Download the Whitepaper

– https://www.mongodb.com/collateral/mongodb-3-2-whats-new• Read the Release Notes

– https://docs.mongodb.org/manual/release-notes/3.2/• Not yet ready for production but download and try!

– https://www.mongodb.org/downloads#development • Detailed blogs

– https://www.mongodb.com/blog/• Feedback

– MongoDB 3.2 Bug Hunt• https://www.mongodb.com/blog/post/announcing-the-mongodb-3-2-bug-hunt

– https://jira.mongodb.org/

DISCLAIMER: MongoDB's product plans are for informational purposes only. MongoDB's plans may change and you should not rely on them for delivery of a specific feature at a specific time.

Questions

Mat Keep (mat.keep@mongodb.com) @matkeep Andrew Morgan (andrew.morgan@mongodb.com) @andrewmorgan

Conceptual Model of Aggregation Framework

Start with the original collection; each record

(document) contains a number of shapes (keys),

each with a particular color (value)

• $match filters out documents that don’t contain a

red diamond

• $project adds a new “square” attribute with a value

computed from the value (color) of the snowflake

and triangle attributes

Conceptual Model of Aggregation Framework

• $lookup performs a left outer join with another

collection, with the star being the comparison key

• Finally, the $group stage groups the data by the

color of the square and produces statistics for

each group