MongoDB es la base de datos con más rápido crecimiento del mundo La nueva versión 3.2 extiende los beneficios de las modernas arquitecturas de bases de datos a una gama aun más amplia de aplicaciones y usuarios.
En esta grabación del seminario web presentamos todas las novedades, que incluyen:
● Nuevos motores de almacenamiento conectables.
● Una visión empresarial más rápida con búsquedas y análisis mejorados en tiempo real, combinada con una conectividad fluida a herramientas de BI estándar.
● Gestión de datos simplificado con validación de documentos, junto a una detección y visualización de esquema basadas en una interfaz gráfica.
Mayor eficacia operativa con plataformas de gestión mejoradas, disponibilidad continua en implementaciones multirregionales y distribuidas, y actualizaciones con inactividad cero.
2. MongoDB 3.2
• A wider range of use cases
– Addresses your fastest-moving data
– Encryption-at-rest
• Optimized for your mission-critical apps
– Ensuring data quality
– Improved failover
– Better support for multi-DC deployments
• Enhancements and tools for users across
your organization
– Business Analysts and Data Scientists
– DBAs
– Operations Teams
Headlines
4. 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
Management
Security
In-memory
(beta)
Encrypted 3rd party
5. 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
7. 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
8. 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
11. 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
12. 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
13. 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
15. 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
16. 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
17. Richer analytics with dynamic lookups
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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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.
25. 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
26. 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
27. 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
Wider range of use cases:
MongoDB 3.2 extends the pluggable storage infrastructure introduced in MongoDB 3.0 with new storage engines built to broaden the use cases the database serves.
They include:
An encrypted storage engine to help you achieve end-to-end encryption with the database with more ease, less operational overhead, and minimal effect on performance.
An in memory database for your most demanding applications. Ultra high throughput without sacrificing analytics or data durability. Currently in beta.
WiredTiger is now also the default database for MongoDB. It is the best general purpose storage engine. 7-10x better throughput than the previous default with up to 80% data compression.
Optimized for your mission-critical apps
MongoDB 3.2 includes features and improvements that make the database much more suitable to support multiple teams / apps, apps that require the most stringent SLAs, and apps that span across the world and across many data centers.
Document validation allows you to apply data governance standards without sacrificing the flexibility of the MongoDB data model.
A new algorithm for handling failover ensures faster and more predictable recovery from primary failure
Simplified sharded cluster management makes it easier to build expansive deployments spanning across many regions for better availability and minimal geographical latency
MongoDB 3.2 also opens up the database (and the data stored within) to users across your organization
Business Analysts and Data Scientists : BI Connector
DBAs: MongoDB Compass – understand the data stored in MongoDB with no knowledge of the query language
Operations teams: Integration with APM platforms, profiler to identify slow running queries, index suggestions and automated index builds, simplified and improved management platform
As illustrated by the ecommerce example above, user data is managed by the In-Memory engine to provide the throughput and bounded latency essential for great customer experience. However, the product catalog’s data storage requirements exceed server memory capacity, so is provisioned to another MongoDB replica set configured with the disk-based WiredTiger storage engine.
In this example, MongoDB’s flexible storage architecture means developers are freed from the complexity of having to use different in-memory and disk-based databases to support the e-commerce application. Administrators are freed from the complexity of having to configure and manage separate data layers. Instead, the application uses the same MongoDB database with each service powered by the storage engine best optimized for the use case.
$lookup – this creates new documents which contain everything from the previous stage but augmented with data from any document from the second collection containing a matching colored star (i.e., the blue and yellow stars had matching lookup values, whereas the red star had none)
Determine validator rules: You can use the tool to figure out what you want to set as validation rules