Azure Cosmos Db
I started my sabbatical piece of work alongside the Microsoft Azure Cosmos DB team recently. I accept been inwards talks too collaboration alongside the Cosmos DB people, too specifically alongside Dharma Shukla, for over iii years. I accept been real impressed alongside what they were doing too decided that this would hold out the best house to pass my sabbatical year.
The move too settling downward took time. I volition write nearly those later. I volition also write nearly my impressions of the greater Seattle surface area equally I honour to a greater extent than nearly it. This was a big alter for me after having stayed inwards Buffalo for thirteen years. I love the scenery: everywhere I await I watch a gorgeous lake or hill/mountain scene. And, oh my God, in that place are I had written before nearly the Lambda versus Kappa architectures, too how the pendulum is all the means to Kappa. Cosmos DB all-in-one gives you lot the Kappa benefits.
This all-in-one capability backed alongside global-scale distribution enables novel computing models equally well. The datacenter-as-a-computer newspaper from 2009 had talked nearly the vision of warehouse scale machines. By providing a frictionless globe-scale replicated database, CosmosDB opens the means to thinking nearly the globe-as-a-computer. One of the usecases I heard from only about Cosmos DB customers amazed me. Some customers allocate a spare part (say Australia) where they accept no read/write clients equally an analytics region. This spare part nevertheless gets consistent information replication too stays real up-to-date too is employed for running analytics jobs without jeopardizing the access latencies of existent read-write clients. Talk nearly disaggregated computation too storage! This is disaggregated storage, computing, analytics, too serverless across the globe. Under this model, the globe becomes your playground.
This disaggregated yet all-in-one computing model also manifests itself inwards client acquisition too settling inwards Cosmos DB. Customers oftentimes come upwardly for the query serving level, which provides high throughput too low-latency via SSDs. Then they acquire interested too invest into the lower-throughput but higher/cheaper storage options to shop terrabytes too petabytes of data. They too thus diversify too enrich their portfolio farther alongside analytics, event-driven lambda, too real-time streaming capabilities provided inwards Cosmos DB.
There is a lot to discuss, but inwards this transportation service I volition alone brand a brief introduction to the issues/concepts, hoping to write to a greater extent than nearly them later. My interests are of course of written report at the bottom of the stack at the heart layer, thus I volition probable dedicate most of my coming posts to the heart layer.
Resource governance is an of import too pervasive factor of the heart layer. Request units (allocating CPU, memory, throughput) is the currency to provision the resources. Provisioning a desired aeroplane of throughput through dynamically changing access patterns too across a heterogeneous laid of database operations presents many challenges. To watch the stringent SLA guarantees for throughput, latency, consistency, too availability, Cosmos DB automatically employs partitioning splitting too relocation. This is challenging to accomplish equally Cosmos DB also handles fine-grained multi-tenancy alongside 100s of tenants sharing a unmarried machine too 1000s of tenants sharing a unmarried cluster each alongside various workloads too isolated from the rest. Adding fifty-fifty to a greater extent than to the challenge, Cosmos DB supports scaling database throughput too storage independently, automatically, too swiftly to address the customer's dynamically changing requirements/needs.
To render only about other of import functionality, global distribution, Cosmos DB enables you lot to configure the regions for "read", "write", or "read/write" regions. Using Azure Cosmos DB's multi-homing APIs, the app ever knows where the nearest part is (even equally you lot add together too withdraw regions to/from your Cosmos DB database) too sends the requests to the nearest datacenter. All reads are served from a quorum local to the closest part to render depression latency access to information anywhere inwards the world.
Cosmos DB allows developers to lead amidst five well-defined consistency models along the consistency spectrum. (Yay, consistency levels!) You tin configure the default consistency level on your Cosmos DB concern human relationship (and subsequently override the consistency on a specific read request). About 73% of Azure Cosmos DB tenants usage session consistency too 20% prefer bounded staleness. Only 2% of Azure Cosmos DB tenants override consistency levels on a per asking basis. In Cosmos DB, reads served at session, consistent prefix, too eventual consistency are twice equally inexpensive equally reads alongside potent or bounded staleness consistency.
This helps forestall concurrency bugs, race conditions, too helps alongside the evolution efforts. TLA+ modeling has been instrumental inwards Cosmos DB's pattern which integrated global distribution, consistency guarantees, too high-availability from the ground-up. (Here is an interview where Leslie Lamport shares his thoughts on the foundations of Azure Cosmos DB too his influence inwards the pattern of Azure Cosmos DB.) This is real dearest to my heart, as I accept been employing TLA+ inwards my distributed systems classes for the yesteryear 5 years.
Finally, equally I acquire a improve mastery of Cosmos DB internals, I similar to contribute to protocols on multimaster multirecord transaction support. I also similar to acquire to a greater extent than nearly too contribute to Cosmos DB's automatic failover back upwardly during i or to a greater extent than regional outages. Of course, these protocols volition all hold out modeled too verified alongside TLA+.
Here is something that comes to my mind. Companies are already uploading IOT sensor information from cars to Azure CosmosDB continuously. Next stride would hold out to create to a greater extent than ambitious applications that brand feel of correlated readings too usage these to coordinate actions. Applications of this could hold out inwards traffic shaping/management of self-driving motorcar squadrons. These applications volition probable combine OLAP (including powerful machine-learning processes) too OLTP (including real-time streaming too actions).
2. What are only about patterns inwards globe-as-a-computer paradigm?
Is this a genuinely novel epitome or is it only a marking of convenience? Is it possible to fence that this is alone incremental too non transformational? But, then, a dandy marking of incremental capability translates to transformational change.
3. What are other things you lot similar to acquire nearly at a global-scale database?
Let me know inwards the comments.
The move too settling downward took time. I volition write nearly those later. I volition also write nearly my impressions of the greater Seattle surface area equally I honour to a greater extent than nearly it. This was a big alter for me after having stayed inwards Buffalo for thirteen years. I love the scenery: everywhere I await I watch a gorgeous lake or hill/mountain scene. And, oh my God, in that place are I had written before nearly the Lambda versus Kappa architectures, too how the pendulum is all the means to Kappa. Cosmos DB all-in-one gives you lot the Kappa benefits.
This all-in-one capability backed alongside global-scale distribution enables novel computing models equally well. The datacenter-as-a-computer newspaper from 2009 had talked nearly the vision of warehouse scale machines. By providing a frictionless globe-scale replicated database, CosmosDB opens the means to thinking nearly the globe-as-a-computer. One of the usecases I heard from only about Cosmos DB customers amazed me. Some customers allocate a spare part (say Australia) where they accept no read/write clients equally an analytics region. This spare part nevertheless gets consistent information replication too stays real up-to-date too is employed for running analytics jobs without jeopardizing the access latencies of existent read-write clients. Talk nearly disaggregated computation too storage! This is disaggregated storage, computing, analytics, too serverless across the globe. Under this model, the globe becomes your playground.
This disaggregated yet all-in-one computing model also manifests itself inwards client acquisition too settling inwards Cosmos DB. Customers oftentimes come upwardly for the query serving level, which provides high throughput too low-latency via SSDs. Then they acquire interested too invest into the lower-throughput but higher/cheaper storage options to shop terrabytes too petabytes of data. They too thus diversify too enrich their portfolio farther alongside analytics, event-driven lambda, too real-time streaming capabilities provided inwards Cosmos DB.
There is a lot to discuss, but inwards this transportation service I volition alone brand a brief introduction to the issues/concepts, hoping to write to a greater extent than nearly them later. My interests are of course of written report at the bottom of the stack at the heart layer, thus I volition probable dedicate most of my coming posts to the heart layer.
Core layer
The heart layer provides capabilities that the other layers create upon. These include global distribution, horizontally too independently scalable storage too throughput, guaranteed single-digit millisecond latency, tunable consistency levels, too comprehensive SLAs.Resource governance is an of import too pervasive factor of the heart layer. Request units (allocating CPU, memory, throughput) is the currency to provision the resources. Provisioning a desired aeroplane of throughput through dynamically changing access patterns too across a heterogeneous laid of database operations presents many challenges. To watch the stringent SLA guarantees for throughput, latency, consistency, too availability, Cosmos DB automatically employs partitioning splitting too relocation. This is challenging to accomplish equally Cosmos DB also handles fine-grained multi-tenancy alongside 100s of tenants sharing a unmarried machine too 1000s of tenants sharing a unmarried cluster each alongside various workloads too isolated from the rest. Adding fifty-fifty to a greater extent than to the challenge, Cosmos DB supports scaling database throughput too storage independently, automatically, too swiftly to address the customer's dynamically changing requirements/needs.
To render only about other of import functionality, global distribution, Cosmos DB enables you lot to configure the regions for "read", "write", or "read/write" regions. Using Azure Cosmos DB's multi-homing APIs, the app ever knows where the nearest part is (even equally you lot add together too withdraw regions to/from your Cosmos DB database) too sends the requests to the nearest datacenter. All reads are served from a quorum local to the closest part to render depression latency access to information anywhere inwards the world.
This helps forestall concurrency bugs, race conditions, too helps alongside the evolution efforts. TLA+ modeling has been instrumental inwards Cosmos DB's pattern which integrated global distribution, consistency guarantees, too high-availability from the ground-up. (Here is an interview where Leslie Lamport shares his thoughts on the foundations of Azure Cosmos DB too his influence inwards the pattern of Azure Cosmos DB.) This is real dearest to my heart, as I accept been employing TLA+ inwards my distributed systems classes for the yesteryear 5 years.
Finally, equally I acquire a improve mastery of Cosmos DB internals, I similar to contribute to protocols on multimaster multirecord transaction support. I also similar to acquire to a greater extent than nearly too contribute to Cosmos DB's automatic failover back upwardly during i or to a greater extent than regional outages. Of course, these protocols volition all hold out modeled too verified alongside TLA+.
MAD questions
1. What would you lot produce alongside a frictionless cloud middleware? Which novel applications tin this enable?Here is something that comes to my mind. Companies are already uploading IOT sensor information from cars to Azure CosmosDB continuously. Next stride would hold out to create to a greater extent than ambitious applications that brand feel of correlated readings too usage these to coordinate actions. Applications of this could hold out inwards traffic shaping/management of self-driving motorcar squadrons. These applications volition probable combine OLAP (including powerful machine-learning processes) too OLTP (including real-time streaming too actions).
2. What are only about patterns inwards globe-as-a-computer paradigm?
Is this a genuinely novel epitome or is it only a marking of convenience? Is it possible to fence that this is alone incremental too non transformational? But, then, a dandy marking of incremental capability translates to transformational change.
3. What are other things you lot similar to acquire nearly at a global-scale database?
Let me know inwards the comments.
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