Budapest Spring MUG 2016 - MongoDB User Group
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Transcript of Budapest Spring MUG 2016 - MongoDB User Group
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Overview for The Budapest MUG
What’s New in MongoDB 3.2
MarcSchweringSr.Solu1onArchitect–EMEAe:[email protected]:@m4rcsch
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Storage Engines Broaden Use Cases
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Storage Engine Architecture in 3.2
Content Repo
IoT Sensor Backend Ad Service Customer
Analytics Archive
MongoDB Query Language (MQL) + Native Drivers
MongoDB Document Data Model
WT MMAP
Supported in MongoDB 3.2
Man
agem
ent
Sec
urity
In-memory (beta) Encrypted 3rd party
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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
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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
• Offered as an option for WiredTiger storage engine
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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
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One Deployment Powering Multiple Apps
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Built for Mission Critical Deployments
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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
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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
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Enhancements for your mission-critical apps More improvements in 3.2 that optimize the
database for your mission-critical
applications
• Meet stringent SLAs with fast-failover algorithm
– Under 2 seconds to detect and recover from
replica set primary failure
• 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
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Tools for Users Across Your Organization
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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
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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
⇒ h=ps://www.mongodb.com/download-center?jmp=hero#bi-connector
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Dynamic Lookup Combine data from multiple collections with
left outer joins for richer analytics & more
flexibility in data modeling
• Blend data from multiple sources 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
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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
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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
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Improved In-Database Analytics & 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
• Case sensitive text search and support for additional
languages such as Arabic, Farsi, Chinese, and more
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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
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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
⇒ h=ps://www.mongodb.com/download-center?jmp=hero#compass
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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
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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
exposed query performance metrics to APM tools
• In addition, Ops and Cloud Manager can
complement this functionality with rich database
monitoring.
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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
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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
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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