MongoDB 3.4 webinar

MongoDB 3.4
The Leading Non-Relational
Database Evolved
Jason Ma
Andrew Morgan

Introduction
Multi-model done right
Mission Critical Applications
Modernized tooling
Q&A
Agenda

Evolution of MongoDB
Hash-Based Sharding
Roles
Kerberos
On-Prem Monitoring
2.4 2.6 3.0 3.2
$out
Index Intersection
Text Search
Field-Level
Redaction
LDAP & x509
Auditing
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
Intra-cluster compression
Views
Log Redaction
Linearizable Reads
Graph Processing
Decimal
Collations
Faceted Navigation
Spark Connector ++
Zones ++
Aggregation ++
Auto-balancing ++
ARM, Power, zSeries support
BI Connector ++
Compass ++
Hardware Monitoring
Server Pool
LDAP Authorization
Encrypted Backups
Cloud Foundry Integration
3.4

Multi-model done right. Other vendors sell you multiple products. MongoDB gives
you multiple models in one database: document, graph, key value, and search with
faceted navigation.
Mission-critical apps. Stronger security, broader platform support, and Zones
make MongoDB ready for the most demanding mission-critical deployments.
Modernized tooling. A sophisticated range of tools and integrations provide
powerful capabilities for Data Analysts, DBAs, and Operations teams.
MongoDB 3.4 Themes

• Enables processing of graph data natively within
MongoDB with $graphLookup operator
• Uncover indirect or transitive relationships in
operational data
• Recommendation engines, MDM, fraud models, social
networks, etc.
Graph Processing

$graphLookup Example
_id: 3
friends[0].id: 193

db.users.aggregate(
[
{$match: {_id: 3}},
{"$graphLookup": {
from: "users",
startWith: "$friends.id",
connectFromField: "friends.id",
connectToField: "_id",
maxDepth: 2,
depthField: "depth",
as: "target"}
}
],
{allowDiskUse: true}).pretty()
Graph Processing

{
"target": {
"_id": 678,
"meetupCount": 98,
"details": [
{
"firstName": "Jesse",
"lastName": "Bennett"
}
]
},
"requestIntroFrom": {
"id": 541,
"details": [
{
"firstName" : "Arthur",
"lastName" : "Boyd",
"contact" : {
"email" : "aboydf0@canalblog.com",
"phone" : "86-(656)576-9389"
}}]}}
Graph Processing
https://github.com/am-MongoDB/meetupGraph

• Grouping data into related categories for intuitive
exploration & discovery
• Used in search and analytics applications
• New aggregation pipeline stages for faceting,
bucketing & sorted counts across multiple
dimensions
• Eliminates requirement for external search engine
https://github.com/am-MongoDB/TVnav
Faceted Navigation

Faceted Navigation
db.products.aggregate([
{$match: {category: "TV"}},
{$facet: {
price: [
{$bucket: {
groupBy: "$price",
boundaries: [0, 200, 699, 1000, 1500],
default: "Over 1500",
output: {"count": {$sum: 1}}
}},
{$project: {
lowerPriceBound: "$_id",
count: 1,
_id: 0}}],
brands: [
{$sortByCount: "$brand" },
{$project: {
brand: "$_id",
count: 1,
_id: 0}}],
…
}}

Faceted Navigation
{"price" : [
{"count" : 29,
"lowerPriceBound" : 0},
{"count" : 272,
{"count" : 165,
{
"count" : 260,
"lowerPriceBound" : 1000
},
{"count" : 274,
"lowerPriceBound" : "Over 1500"
],
"brands" : [
{"count" : 277,
"brand" : "SAMSUNG"},
{"count" : 169,
"brand" : "PANASONIC"},

• Data set, query and results:
• https://github.com/am-MongoDB/TVnav
Faceted Navigation

• Powerful data processing pipeline for
analytics & transformations
• 25+ enhancements simplify app code
• Performance improvements with query
optimizer moving $match stage earlier to
use indexes
New Stages
Array
Operators
String & Date
Operators
$graphLookup
$facet
$bucket
$bucketAuto
$sortbyCount
$addFields
$replaceRoot
$in
$indexOfArray
$range
$reverseArray
$reduce
$zip
$indexOfBytes
$indexOfCP
$split
$strLenBytes
$strLenCP
$substrBytes
$substrCP
$isoDayOfWeek
$isoWeek
$isoWeekYear
Advanced Analytics
Aggregation Framework Enhancements

• Create powerful visualizations & analytics from
SQL-based BI tooling
• Auto-schema sampling
• Eliminates ETL
• Higher performance with rewritten SQL layer
• More processing pushed down to the database
• Simplified installation and authentication
Advanced Analytics
MongoDB Connector for BI

“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
Advanced Analytics
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

• Text comparisons and sorting using language-
specific rules
• 100+ different languages & locales
• Over 2x as many as offered by most RDBMs
• Case sensitivity, treatments of numbers
• Collection | View | Index | Operation
Collations

db.customers.createIndex(
{last_name: 1, first_name : 1 },
{name: "german_name_index",
collation: {locale: "de"},
partialFilterExpression: {
language: "German" }})
db.createView(
"germanSpeakers",
"customers",
[{$match: {
language: "German",
last_name: {$exists: true}}}],
{collation: {locale: "de"}})
Collations

• Support for the IEEE 754-2008 decimal128 type in
server and drivers
• Database stores exact values to eliminate
rounding errors for high-precision calculations,
complex financial & scientific apps
Decimal128
Decimal Data Type

Partition data across distributed clusters based on
data locality policies
• Support distributed local writes
• Easily adhere to data sovereignty requirements
• Enable deployment patterns such as tiered
storage
MongoDB Zones can now be configured visually from Ops Manager
Zones

Seamless and elastic scalability in response to
dynamic application demands
• Improved balancing of data across nodes with
parallel data migrations
• Faster replica set synchronization with optimized
initial sync process
Elastic Clusters

With snappy compression algorithm, network traffic
can be compressed by up to 70%
• Performance benefits in bandwidth-constrained
environments
• Significantly reduce network costs
Intra-Cluster Compression

maxStalenessMS Read Preference
• Choose how and when to route queries to secondary replicas
• Only read from replicas that are within a defined consistency
window
• Improved data quality while scaling reads across secondaries
linearizable Read Concern for the strongest
consistency guarantees of any database
• Ensure that a node is the primary at the time of read
• Ensure that data returned will not be rolled back if another
node is subsequently elected as primary
Improved Tunable Consistency

LDAP authentication & authorization reduces
administrative overhead & TCO
• Users stored in LDAP server can be mapped to
MongoDB roles
• Native platform libraries to integrate with LDAP;
no need for external dependencies and
configuration
LDAP Authorization

Define dynamically generated non-materialized views
to expose selected data from underlying collection(s)
• Reduces risk of sensitive data exposure
• Separately specified permissions levels
• Allows organizations to more easily meet compliance
standards in regulated industries
• Can generate additional fields
Read-Only Views

Collation/Views Example
db.createView(
"germanSpeakersRedacted",
"customers",
[
{$match: {
language: "German",
last_name: {
$exists: true}}},
{$project: {
salary: 0,
country: 0,
language: 0
}
}
],
{collation: {locale: "de"}}
)
use admin
db.createRole(
{
role: "germanViewer",
privileges: [
{resource: {
db: "production",
collection:
germanSpeakersRedacted"},
actions: ["find"]}
],
roles: []
}
)

Support the growing demand to run the database on
a more diverse range of platforms
• ARM v8-64 bit support allows customers to take advantage of
power-efficient servers being deployed into ultra dense data
center racks
• IBM Power8 and zSeries support provides seamless migration
for enterprises modernizing legacy workloads. Available for
MongoDB Enterprise Server.
Expanded Platform Support

• MongoDB 3.2
• Visualize Indexes
• Query Performance
• MongoDB 3.4
• MongoDB Compass enhancements
• Modify documents
• Create document validation rules
• Optimize query performance with visual explain
plans, index usage, and real-time statistics
• Create and remove indexes
• All controlled from a single intuitive and
sophisticated GUI
MongoDB Compass 3.4

Real Time Performance Stats
• View key
MongoDB metrics
that are
happening in real-
time
• Operation
statistics (inserts,
deletes, queries,
etc)
• Read/Write Load,
Network load,
Memory usage

Insert, Delete, Modify Documents
● Clone, modify existing documents, insert new documents, and delete
existing documents
● For now, applicable to one document at time

Document Validation Rule Builder
● MongoDB Compass allows users to view, add, and modify document
validation rules through its GUI
● Different teams can enforce schema consistency, reducing risk and
increasing productivity between teams

Visualize Explain Plans
● View key
information about
the execution of a
query
● Identify slow
running queries
and optimize
query
performance

View Index Utilization
● Visualization of
index utilization
● View type of
index, size of
index, usage of
index, and
properties of
index

Create and Remove Indexes
● Create and
Remove indexes
in GUI
● Enhances ability
for users to
identify and fix
issues from the
GUI

MongoDB Ops Manager 3.4
Ops Manager allows you leverage and automate the best
practices we’ve learned from thousands of deployments in
a comprehensive application that helps you run MongoDB
safely and reliably.
Benefits include:
10x-20x more efficient operations
Complete performance visibility
Protection from data loss
Assisted performance optimization
Meet SLAs, Cut management overhead
Scale easily, Follow best practices

Without Ops Manager
● Install, Configure
○ 150+ steps
● Upgrades, downgrades
○ 100+ steps
● Scale out, move servers, resize oplog, etc
○ 10-180+ steps

With Ops Manager
or
Ops Mgr (app)
150+ steps -> 1 click
Ops Mgr (API)

• Ops Manager agent installed to pool via
configuration management tools
• Server pools exposed to internal teams, ready
for provisioning into local groups
• Allow administrators to create true, on demand
database resources for private cloud
environments
Ops Manager
Server Pools to Automate Provisioning of MongoDB

• MongoDB and Pivotal co-engineered
Cloud Foundry integration
• BOSH installs the Ops Manager agent
• Calls the Ops Manager API to provision,
configure, monitor & backup the cluster
• Enables users to integrate MongoDB as
an on-demand DBaaS resource within
Cloud Foundry platforms
Ops Manager
Cloud Foundry Integration

• Finer grained telemetry data: collected
every 10 seconds vs every 60 seconds
• Configurable retention policies
• Simplified & extended management
• Single agent to collect both database
and hardware telemetry
• Hardware metrics now collected for
Windows & OSX hosts
Ops Manager
Higher Resolution Monitoring

• Atlas and Cloud Manager have their own
encrypted backup solution that is
dependent on encrypted hardware
• Ops Manager 3.4
• Native backup encryption solution for
PII information (HIPAA, PCI, FERPA,
etc)
• Encrypt backup snapshots;
encryption keys stored on KMIP
server
Ops Manager
Encrypted Backups
KMIP Appliance
Master Key

• Take the M034: New Features and Tools in MongoDB
3.4 training
• https://university.mongodb.com/courses/M034/about
• Read the What’s New in MongoDB 3.4 Whitepaper
• https://www.mongodb.com/collateral/mongodb-3-4-whats-new
3.4
Next Steps

Content Repo
IoT Sensor
Backend
Customer Data
Real-Time
Analytics
Single View
MongoDB Query Language: K-V, Aggregations & Transformations, Graph, Faceted Navigation, JOINs, Geospatial
MongoDB Data Model: Document, Key-Value, Graph, Tabular, Files
WT MMAP
Available in MongoDB 3.4
Management
Security
In-memory Encrypted 3rd party
Multimodel Done Right

MongoDB 3.4 webinar

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