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Webinar: What's New in MongoDB 3.2

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New generations of database technologies are allowing organizations to build applications never before possible, at a speed and scale that were previously unimaginable. MongoDB is the fastest growing database on the planet, and the new 3.2 release will bring the benefits of modern database architectures to an ever broader range of applications and users.

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Webinar: What's New in MongoDB 3.2

  1. 1. What’s New in MongoDB 3.2 Mat Keep Director, Product Marketing, MongoDB Andrew Morgan Principal Product Marketing Manager, MongoDB
  2. 2. 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 Location-Aware Sharding $out Index Intersection Text Search Field-Level Redaction LDAP & x509 Auditing Document Validation Fast Failover Simpler Scalability Aggregation ++ Encryption At Rest In-Memory Storage Engine BI Connector $lookup 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
  3. 3. 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.
  4. 4. Storage Engines Broaden Use Cases
  5. 5. Varying Access & Storage Requirements Modern apps Sensitive data Cost effective storage High concurrency High throughput Low latency Real-time analytics
  6. 6. Flexible Storage Architecture in 3.2
  7. 7. 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
  8. 8. 117k Security Attacks…..PER DAY PWC: Global State of Information Security
  9. 9. 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
  10. 10. “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 Drew Data Integration Solutions Architect University of Washington
  11. 11. In-Memory Economic Viability
  12. 12. 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
  13. 13. One Deployment Powering MultipleApps
  14. 14. Built for Mission Critical Deployments
  15. 15. A 10% improvement in data usability at a Fortune 1000 company could increase revenues by $2 BN per year Source: University of Texas, Austin
  16. 16. 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
  17. 17. 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
  18. 18. “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 Engel Founder and CEO Rocket.Chat
  19. 19. 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
  20. 20. Tools for UsersAcross Your Organization
  21. 21. 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
  22. 22. Only 0.5% of data is analyzed Source: IDC
  23. 23. 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
  24. 24. “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 Feng Product Manager Tableau Software
  25. 25. 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
  26. 26. “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 Strickland CTO MyDealerLot
  27. 27. Aggregation Pipeline {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds}
  28. 28. Aggregation Pipeline $match {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {}
  29. 29. Aggregation Pipeline $match {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {} {★ds} {★ds} {★ds}
  30. 30. Aggregation Pipeline $match $project {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {} {★ds} {★ds} {★ds} {=d+s}
  31. 31. Aggregation Pipeline $match $project {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {} {★ds} {★ds} {★ds} {★} {★} {★} {=d+s}
  32. 32. Aggregation Pipeline $match $project $lookup {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {} {★ds} {★ds} {★ds} {★} {★} {★} {★} {★} {★} {★} {=d+s}
  33. 33. Aggregation Pipeline $match $project $lookup {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {} {★ds} {★ds} {★ds} {★} {★} {★} {★} {★} {★} {★} {=d+s} {★[]} {★[]} {★}
  34. 34. Aggregation Pipeline $match $project $lookup $group {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {★ds} {} {★ds} {★ds} {★ds} {★} {★} {★} {★} {★} {★} {★} {=d+s} { Σ λ σ} { Σ λ σ} { Σ λ σ} {★[]} {★[]} {★}
  35. 35. 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 • Random sample of documents: $sample • Case sensitive text search and support for additional languages such as Arabic, Farsi, Chinese, and more
  36. 36. 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
  37. 37. 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
  38. 38. MongoDB Compass
  39. 39. Up to 80% of TCO is driven by on-going operations and maintenance costs Source: Gartner
  40. 40. 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
  41. 41. 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
  42. 42. “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 Marcus Senior Product Manager New Relic.
  43. 43. 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
  44. 44. “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 Rubio Director Mondo Sports Ltd
  45. 45. 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
  46. 46. 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
  47. 47. 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.
  48. 48. Questions Mat Keep (mat.keep@mongodb.com) @matkeep Andrew Morgan (andrew.morgan@mongodb.com) @andrewmorgan
  49. 49. 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
  50. 50. 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

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