Azure Document Db
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This document discusses an IoT Day event hosted by 1nn0va on May 8, 2015. It covers topics like representing data models for IoT using DocumentDB, including embedding vs normalizing data and handling one-to-many relationships. It also discusses partitioning strategies for DocumentDB, consistency levels to trade off speed and availability vs consistency, and using weaker consistency for scenarios like IoT and data analysis.
![ www.slideshare.net/marco.parenzan
www.github.com/marcoparenzan
marco [dot] parenzan [at] 1nn0va [dot] it
www.1nnova.it
@marco_parenzan
Formazione ,Divulgazione e Consulenza con 1nn0va
Microsoft MVP 2015 for Microsoft Azure
Cloud Architect, NET developer
Loves Functional Programming, Html5 Game Programming and Internet of Things AZURE
COMMUNITY
BOOTCAMP 2015
IoT Day - 08/05/2015
@1nn0va
#microservicesconf2015
9 Maggio 2015](https://image.slidesharecdn.com/2015-160406224443/85/Azure-Document-Db-2-320.jpg)
Azure Document Db
- 2. www.slideshare.net/marco.parenzan www.github.com/marcoparenzan marco [dot] parenzan [at] 1nn0va [dot] it www.1nnova.it @marco_parenzan Formazione ,Divulgazione e Consulenza con 1nn0va Microsoft MVP 2015 for Microsoft Azure Cloud Architect, NET developer Loves Functional Programming, Html5 Game Programming and Internet of Things AZURE COMMUNITY BOOTCAMP 2015 IoT Day - 08/05/2015 @1nn0va #microservicesconf2015 9 Maggio 2015
- 3. Classic MVC Business Logic Contract BL/P View Controller
- 4. CQRS for IoT (Service Bus Powered) Event Handler UI Event Command Handler Event Device Queue Topics/Subscription Event Hub Write Model Read /Search Model
- 7. IoT day 2015 Business, no longer data, is the foundation of software design DDD!=OOP Don’t start from Data Data are not unique No more ACID…ACID transactions are not useful with a distributed model over different storages
- 10. IoT day 2015 try to treat your entities as self-contained documents represented in JSON When working with relational databases, we've been taught for years to normalize, normalize, normalize. There are contains relationships between entities. There are one-to-few relationships between entities. There is embedded data that changes infrequently. There is embedded data won't grow without bound. There is embedded data that is integral to data in a document. better read performance
- 11. IoT day 2015 Representing one-to-many relationships. Representing many-to-many relationships. Related data changes frequently. Referenced data could be unbounded Provides more flexibility than embedding More round trips to read data Normalizing typically provides better write performance
- 12. IoT day 2015 Promote code first development (mapping objects to json) Resilient to iterative schema changes Richer query and indexing (compared to KV stores) Low impedance as object / JSON store; no ORM required It just works It’s fast
- 13. IoT day 2015 a container of JSON documents and the associated JavaScript application logic JSON docs inside of a collection can vary dramatically A unit of scale for transaction and query throughput (capacity units allocated uniformly across all collections) A unit of scale for capacity A unit of replication
- 14. IoT day 2015 Collections in DocumentDB are not just logical containers, but also physical containers They are the transaction boundary for stored procedures and triggers entry point to queries and CRUD operations Each collection is assigned a reserved amount of throughput which is not shared with other collections in the same account Collections do not enforce schema
- 15. Partitioning
- 16. IoT day 2015 In hash partitioning, partitions are assigned based on the value of a hash function, allowing you to evenly distribute requests and data across a number of partitions. This is commonly used to partition data produced or consumed from a large number of distinct clients, and is useful for storing user profiles, catalog items, and IoT ("Internet of Things") telemetry data. Evenly distribute across n number of partitions (algorithmic) ….
- 17. IoT day 2015 In range partitioning, partitions are assigned based on whether the partition key is within a certain range This is commonly used for partitioning with time stamp properties Keep current data hot, Warm historical data, Scale-down older data, Purge / Archive
- 18. IoT day 2015 In lookup partitioning, partitions are assigned based on a lookup map that assigns discrete partition values to specific partitions a.k.a. a partition or shard map This is commonly used for partitioning by region Home tenant / user to a specific partition. Use "master" lookup. Cache this shard map to avoid making the lookup the bottleneck Tenant Partition Id Customer 1 Big Customer 2 Another 3
- 19. Consistency
- 20. IoT day 2015 Query / transaction throughput (and reliability – i.e., hardware failure) depend on replication! All writes to the primary are replicated across two secondary replicas All reads are distributed across three copies “Scalability of throughput” – allowing different clients to read from different replicas helps prevent bottlenecks BUT replication takes time! Potential scenario: some clients are reading while another is writing Now, the data is out-of-date, inconsistent!
- 21. IoT day 2015 Trade-off: speed (performance & availability) or consistency (data correctness)? “Does every read need the MOST current data?” “Or do I need every request to be handled and handled quickly?” No “one size fits all” answer … so it’s up to you! 4 options … For the entire Db… …In a future release, we intend to support overriding the default consistency level on a per collection basis.
- 22. IoT day 2015 client always sees completely consistent data Slowest reads / writes Mission critical: e.x. stock market, banking, airline reservation
- 23. IoT day 2015 Default – even trade-off between performance & availability vs. data correctness client reads its own writes, but other clients reading this same data might see older values
- 24. IoT day 2015 client might see old data, but it can specify a limit for how old that data can be (ex. 2 seconds) Updates happen in order received similar to Session consistency, but speeds up reads while still preserving the order of updates
- 25. IoT day 2015 client might see old data for as long as it takes a write to propagate to all replicas High performance & availability, but a client might sometimes read out-of-date information or see updates out of order
- 26. IoT day 2015 At the database level (see preview portal) On a per-read or per-query basis (optional parameter on CreateDocumentQuery method)
- 27. IoT day 2015 Use Weaker Consistency Levels for better Read latencies IoT Data Analysis http://azure.microsoft.com/blog/2015/01/27/performance-tips- for-azure-documentdb-part-2/