MongoDB What's new in 3.2 version

MongoDB 3.2 – a BIG Release
Hash-‐Based
Sharding

Roles

Kerberos

On-‐Prem
Monitoring

2.2
2.4
2.6
3.0
3.2

Agg.
Framework

Loca@on-‐Aware
Sharding

$out

Index
Intersec@on

Text
Search

Field-‐Level
Redac@on

LDAP
&
x509

Audi@ng

Document
Valida@on

Fast
Failover

Simpler
Scalability

Aggrega@on
++

Encryp@on
At
Rest

In-‐Memory
Storage

Engine

BI
Connector

$lookup

MongoDB
Compass

APM
Integra@on

Proﬁler
Visualiza@on

Auto
Index
Builds

Backups
to
File
System

Doc-‐Level
Concurrency

Compression

Storage
Engine
API

≤50
replicas

Audi@ng
++

Ops
Manager

Themes
Broader 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

VaryingAccess & Storage Requirements
Modern

apps

Sensi@ve

data

Cost

eﬀec@ve

storage

High

concurrency

High

throughput

Low
latency

Real-‐@me

analy@cs

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

“Protec(ng
sensi(ve
data
assets
is
one
of
most
important
things

we
do.
The
new
Database
Encryp(on
feature
in
MongoDB
3.2
is
a

signiﬁcant
step
forward
in
allowing
us
to
more
simply
add

encryp(on
at-‐rest
to
our
list
of
security
controls.

In
our
tests,
we
found
the
new
database
encryp(on
feature
easy

to
enable,
stable
and
consistent
with
our
performance

expecta(ons.”

Shawn
Drew

Data
Integra@on
Solu@ons
Architect

University
of
Washington

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 MultipleApps

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
applica(ons
need
to
be
able
to
quickly

access
various
types
of
data
to
provide
a
seamless
solu(on
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
Engel

Founder
and
CEO

Rocket.Chat

Enhancements for your mission-critical apps
More 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 UsersAcross 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 BI
Visualize 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
tradi(onally
work
with
their

rela(onal
data,
to
fully
integrate
the
mul(-‐structured
data
stored
in
the

database
powering
modern
applica(ons
via
the
new
MongoDB
BI
Connector”

Jeﬀrey
Feng

Product
Manager

Tableau
So[ware

Dynamic Lookup
Combine 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-‐(me
analy(cs
we
rely
on
to

constantly
improve
our
value
to
our
customers.”

David
Strickland

CTO

MyDealerLot

Aggregation Pipeline
$match $project $lookup $group
{u★❄▲}
{u★❄▲}

{u★❄▲}

{u★❄▲}

{u★❄▲}

{u★❄▲}

{u★❄▲}

{u★❄▲}

{u★❄▲}

{u★❄▲}
{u★❄▲}
{u}
{u★❄▲}

{u★❄▲}
{u★❄▲}

{★v}

{★v}

{★v}

{★v}

{u★n}

{u★n}
{u★n}

{n=❄+▲}
{n
Σ λ σ}

{n
  Σ λ σ}
{n
Σ λ σ}

{u★n[vv]}
{u★n[vv]}
{u★n}

Conceptual Model ofAggregation 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 ofAggregation 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

Improved In-DatabaseAnalytics Search
New 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 Administrators
MongoDB 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 Compass
For 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

Up to 80% of TCO is driven by
on-going operations and
maintenance costs
Source: Gartner

For Operations Teams
MongoDB 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 Platforms
Easily 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

integra(on
with
the
New
Relic
plaSorm.
MongoDB
has
become
an
integral

part
of
the
tooling
and
workﬂows
of
DBAs
and
Opera(ons
teams
and
we

expect
the
trend
to
increase.

To
support
MongoDB
3.2,
we
jointly-‐developed
an
integra(on
between

MongoDB
Ops
Manager
and
New
Relic
APM,
Insights,
and
Plugins.
These

integra(ons
mean
MongoDB
health
can
now
be
monitored
alongside
the
rest

of
the
applica(on
estate..”

Cooper
Marcus

Senior
Product
Manager

New
Relic.

Query Perf. Visualizations Optimization
Fast 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
iden(fying
queries
that
are
slowing
us
down
and
provides

recommenda(ons
for
new
indexes
-‐-‐
which
it
can
then
build
through
a
rolling

index
build.”

Daniel
Rubio

Director

Mondo
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 Enhancements
3.2 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

Are you available for a meeting ?
{ name : Heliot Perroquin,
title : “Enterprise account support specialist,
phone : +33.182.881.666 - ext 7231,
twitter : @MongoDB, @10gen”}

MongoDB What's new in 3.2 version

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