Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Big Data Day LA 2016/ NoSQL track - MongoDB 3.2 Goodness!!!, Mark Helmstetter, Principal Consulting Engineer, MongoDB

209 visualizaciones

Publicado el

This talk explores the new features of MongoDB 3.2 such as $lookup, document validation rules, encryption-at-rest and tools like the BI Connector, OpsManager 2.0 and Compass.

Publicado en: Tecnología
  • Sé el primero en comentar

  • Sé el primero en recomendar esto

Big Data Day LA 2016/ NoSQL track - MongoDB 3.2 Goodness!!!, Mark Helmstetter, Principal Consulting Engineer, MongoDB

  1. 1. MongoDB 3.2 Goodness MarkHelmstetter mark.helmstetter@mongodb.com
  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. DATA GOVERNANCE & INTELLIGENCE
  4. 4. Dynamic Lookup Data Governance & Intelligence Schema Validation BI Connector
  5. 5. $lookup • Left-outer join – Includes all documents from the left collection – For each document in the left collection, find the matching documents from the right collection and embed them Left Collection Right Collection
  6. 6. $lookup db.leftCollection.aggregate( [{ $lookup: { from: “rightCollection”, localField: “leftVal”, foreignField: “rightVal”, as: “embeddedData” } }]) leftCollection rightCollection
  7. 7. 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
  8. 8. 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
  9. 9. 11 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
  10. 10. 12 Location & Flow of Data MongoDB BI Connector Mapping meta-data Application data {name: “Andrew”, address: {street:… }} DocumentTableAnalytics & visualization
  11. 11. 13 BI Connector - Data Mapping mongodrdl --host 192.168.1.94 --port 27017 -d myDbName -o myDrdlFile.drdl mongobischema import myCollectionName myDrdlFile.drdl DRDL mongodrdl mongobischema PostgreSQL MongoDB- specific Foreign Data Wrapper
  12. 12. 14 BI Connector - Data Mapping DRDL file • Redact attributes • Use more appropriate types (sampling can get it wrong) • Rename tables (v1.1+) • Rename columns (v1.1+) • Build new views using MongoDB Aggregation Framework • e.g., $lookup to join 2 tables - table: homesales collection: homeSales pipeline: [] columns: - name: _id mongotype: bson.ObjectId sqlname: _id sqltype: varchar - name: address.county mongotype: string sqlname: address_county sqltype: varchar - name: address.nameOrNumber mongotype: int sqlname: address_nameornumber sqltype: varchar
  13. 13. Spark Connector
  14. 14. NEW STORAGE ENGINES
  15. 15. Storage Engines Operator Family Operators WiredTiger Default storage engine starting with MongoDB 3.2. Well-suited for both read and write intensive workloads and recommended for all new deployments. Document-level concurrency model and compression MMap The original MongoDB storage engine Performs well on workloads with high volumes of reads, in-place updates and limited document size growth. Collection-level concurrency and no compression In-Memory Retains data, indexes and oplog in-memory for more predictable data latencies. Encrypted Provides at-rest encryption. Key rotation and KMIP integration. AES256-CBC default encryption. AES256-GCM and FIPS mode also available.
  16. 16. More Workloads via Storage Engines In-Memory Encrypted
  17. 17. ENHANCED TOOLING
  18. 18. APM Integration Advanced Tools QueryProfiling &Tuning SchemaDiscovery &QueryBuilder
  19. 19. COMPASS DEMO
  20. 20. CLOUD MANAGER DEMO
  21. 21. 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 – 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.
  22. 22. Questions

×