forked from diegoecab/oci-deal-accelerator
Diagram generation: ref-arch-driven procedure + spec validator + KB enrichment
The diagram path now follows a documented standard procedure (lookup the closest Oracle Architecture Center reference → confirm components → author absolute_layout → spec validator → render → visually verify) and ships persistent guardrails so layout regressions can't recur. Persistent procedure changes (apply to all users, all sessions): - tools/diagram_spec_validator.py — geometry checks (CONTAINER_TOO_THIN, CONTAINER_PADDING_VIOLATION, LABEL_OVERFLOW_PARENT) run BEFORE either renderer (drawio + PPTX). Catches the subnet-collapse / label-overflow bugs that the post-render drawio validator missed. - tools/oci_diagram_gen.py + tools/oci_pptx_diagram_gen.py — call the spec validator before emitting any output. Adds mysql / mysql_heatwave type aliases. - tools/archcenter_pattern_lookup.py — scores against cached page descriptions (not just the 1-line summary), supports --queries for multi-fragment composition, and applies synonym expansion via kb/architecture-center/synonyms.yaml so "LB HA cross AD" matches "load balancer high availability availability domain". - kb/architecture-center/synonyms.yaml — canonical synonym table (load balancer, autonomous database, data guard, …) used by the lookup scorer. KB enrichment: - tools/archcenter_description_fetcher.py + 121 cached _description.md under kb/diagram/assets/archcenter-refs/<slug>/. Removes the runtime dependency on docs.oracle.com when authoring specs and feeds the pattern-lookup scorer. - 110+ cached .drawio / .svg / .png references for offline reuse, plus the OCI Toolkit v24.2 import (kb/diagram/assets/oci-toolkit-drawio). Documentation: - docs/skill/output-formats.md — new "Standard diagram-generation procedure (MANDATORY)" + geometry rules + the new validator entry. - SKILL.md option 2 — references the mandatory procedure. - README.md — describes the spec validator, archcenter_pattern_lookup and description fetcher, and updates the KB-health table. Tooling that backs the procedure (cumulative across recent sessions): tools/archcenter_case_runner.py, archcenter_batch_driver.py, archcenter_zip_downloader.py, drawio_visual_validator.py, drawio_fidelity_eval.py, harvest_drawio_icon.py, import_oci_library.py, oci_pptx_diagram_gen.py, oci_pptx_render.py, refresh_pptx_icon_index.py. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
root
@@ -0,0 +1,313 @@
|
||||
# Deploy a migrated MongoDB workload to Oracle Autonomous Transaction Processing Serverless@Google Cloud
|
||||
|
||||
- Source: https://docs.oracle.com/en/solutions/mongodb-to-atp-google/index.html
|
||||
- Date: 2025-08
|
||||
- Type: reference-architecture
|
||||
- Services: adb-s, google-cloud
|
||||
- Tags: database, migration, multicloud, autonomous
|
||||
|
||||
## Summary (catalog)
|
||||
|
||||
MongoDB to ADB-S migration on Database@Google Cloud. MongoDB API compatibility for transparent application migration. Google Cloud VPC peering for private database connectivity.
|
||||
|
||||
## Architecture (fetched from source)
|
||||
|
||||
Deploy a Migrated MongoDB Workload to Oracle Autonomous Transaction Processing Serverless@Google Cloud
|
||||
|
||||
|
||||
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
-
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
-
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Previous
|
||||
Next
|
||||
JavaScript must be enabled to correctly display this content
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Deploy a Migrated MongoDB Workload
|
||||
to Oracle Autonomous Transaction
|
||||
Processing Serverless@Google Cloud
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Migrate an existing workload that uses a document database, in this
|
||||
case MongoDB, to Google Cloud and Oracle Autonomous Transaction
|
||||
Processing deployed in Google Cloud , a cloud document database service that makes it simple to modernize the development
|
||||
of your JSON-centric applications alongside other multi-model workloads.
|
||||
|
||||
|
||||
|
||||
|
||||
Workloads and applications that use documents and document databases to evolve data
|
||||
schemas and applications are quite popular due to the flexibility they offer to
|
||||
developers. Schema flexibility, rapid development, and scalability enable rapid
|
||||
prototyping of application features, easier application evolution, and the assurance of
|
||||
building iteratively smaller applications and features that developers can scale to
|
||||
address a large user base. However, these types of workloads have their challenges,
|
||||
including weaker transactional guarantees, data query versatility, and the inability to
|
||||
support other workloads on documents, such as analytics or machine learning.
|
||||
|
||||
|
||||
What if these workloads can benefit from the advantages of traditional
|
||||
document databases and leverage the benefits of relational databases? For instance, have
|
||||
stronger transactional guarantees and added functionality such as analytics and machine
|
||||
learning, without the need to replicate data to another database or system.
|
||||
|
||||
|
||||
Autonomous Transaction
|
||||
Processing (ATP) Serverless is a fully automated database service optimized to run
|
||||
transactional, analytical, and batch workloads concurrently. To accelerate performance,
|
||||
it’s preconfigured for row format, indexes, and data caching while providing
|
||||
scalability, availability, transparent security, and real-time operational analytics.
|
||||
Application developers and DBAs can rapidly and cost-effectively develop and deploy
|
||||
applications without sacrificing functionality or atomicity, consistency, isolation, and
|
||||
durability (ACID) properties.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Functional Architecture
|
||||
|
||||
|
||||
|
||||
|
||||
This architecture assumes, as a starting point, that a workload with an
|
||||
application and a MongoDB database exists, either an on-premises or cloud deployment, and
|
||||
will be migrated to Google Cloud and Oracle Database@Google Cloud . It describes the future state architecture, its benefits, how it can be deployed and
|
||||
what additional features you can use to augment the existing workload.
|
||||
|
||||
|
||||
|
||||
One of the key features used in this architecture is Oracle Database API for
|
||||
MongoDB, which enables applications to interact with collections of JSON documents in
|
||||
Oracle Database using MongoDB drivers, tools, and SDKs. Existing application code can
|
||||
work with data stored in Autonomous Transaction
|
||||
Processing Serverless, without the need to refactor code.
|
||||
|
||||
|
||||
|
||||
The following diagram depicts a typical application composed of a database,
|
||||
back-end, and front-end tiers.
|
||||
|
||||
|
||||
Description of the illustration mongodb-atp-s-google-logical-arch-migration.png
|
||||
|
||||
mongodb-atp-s-google-logical-arch-migration.zip
|
||||
|
||||
|
||||
The MEAN stack is a popular stack used to implement this pattern:
|
||||
|
||||
|
||||
|
||||
- MongoDB : Document database
|
||||
|
||||
|
||||
- Express: Back-end framework
|
||||
|
||||
- Angular: Front-end framework
|
||||
|
||||
- Node.js : Back-end server
|
||||
|
||||
|
||||
|
||||
This document uses a MEAN stack as an example of an existing deployment that
|
||||
will be migrated to Google Cloud and ATP Serverless.
|
||||
|
||||
|
||||
|
||||
The migration of this workload to Google Cloud and ATP Serverless is straightforward and consists, at high level, of the following
|
||||
steps:
|
||||
|
||||
|
||||
|
||||
|
||||
- Deploy an ATP Serverless instance, enabling at creation time the Oracle
|
||||
Database MongoDB API.
|
||||
|
||||
- Migrate metadata and data from MongoDB to ATP Serverless.
|
||||
|
||||
|
||||
- Deploy application servers to run Node.js and Express
|
||||
using either Google Cloud Run, VMs, containers, or Kubernetes, to the same region
|
||||
and availability domain as ATP Serverless.
|
||||
|
||||
|
||||
- Deploy the back-end application code to the application servers.
|
||||
|
||||
- Connect the back-end application to ATP Serverless using the same MongoDB tools and drivers used on the current application.
|
||||
|
||||
|
||||
- Connect users to the new application URI.
|
||||
|
||||
|
||||
Note this reference architecture focuses on the deployment of the migrated
|
||||
workload and not on the migration process itself. For more details on the migration
|
||||
process, see the Explore More section.
|
||||
|
||||
|
||||
|
||||
After the workload is migrated to ATP Serverless, several features are
|
||||
available to augment the existing functionality, whether that is to 1) support
|
||||
additional nonfunctional requirements, such as easily improving scalability, resiliency,
|
||||
or high availability, or 2) have additional functional features such as operational
|
||||
reporting, analytics, and machine learning in place, without the need to copy data out
|
||||
of the database.
|
||||
|
||||
|
||||
To improve scalability and high availability, use the Autonomous Transaction
|
||||
Processing Serverless auto scaling feature. With a single click or API call, it allows the
|
||||
workload to use up to 3 times the baseline capacity without any downtime. Note that
|
||||
Autonomous Transaction Processing Serverless uses Oracle Real Application Clusters (Oracle
|
||||
RAC) technology for high availability. For the back-end tier, either use VM Scale Sets
|
||||
with Autoscale setup, or a PaaS service such as App Service with Automatic Scaling setup
|
||||
to enable application high availability and scalability.
|
||||
|
||||
|
||||
|
||||
Since ATP Serverless is built on top of multi-model, multi-workload database
|
||||
technology, you can add features that rely on relational, spatial, graph or vector data
|
||||
types that work alongside the existing application.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Physical Architecture
|
||||
|
||||
|
||||
|
||||
|
||||
The physical architecture includes Autonomous Transaction
|
||||
Processing Serverless deployed using delegated subnets in two Google Cloud regions to support high availability. OCI services support automatic backup to Oracle Cloud
|
||||
Infrastructure Object Storage .
|
||||
|
||||
|
||||
|
||||
The architecture supports the following:
|
||||
|
||||
|
||||
|
||||
- Front-end tier
|
||||
|
||||
- Application users can connect from the internet.
|
||||
|
||||
- User connection is routed to the active region that is running
|
||||
the application, using a Global Cloud Load Balancer.
|
||||
|
||||
- User connection is secured using Cloud Armor.
|
||||
|
||||
- User connection to the application is load balanced using an
|
||||
external global application load balancer.
|
||||
|
||||
|
||||
|
||||
- Back-end tier
|
||||
|
||||
- Application is deployed in a high availability fashion using
|
||||
Cloud Run.
|
||||
|
||||
- Cloud Run autoscaling is used to achieve horizontal
|
||||
scalability.
|
||||
|
||||
|
||||
|
||||
- Database tier
|
||||
|
||||
- ATP Serverless provides high availability, as Oracle Real Application Clusters (Oracle
|
||||
RAC) and several database nodes underpin the service instance. Therefore, by
|
||||
default the database tier is highly available and resilient.
|
||||
|
||||
|
||||
- Oracle Database API for MongoDB enabled in ATP Serverless allows
|
||||
you to use existing application code without changes.
|
||||
|
||||
- The Oracle Database API for MongoDB is highly resilient, and
|
||||
that resiliency is guaranteed internally by ATP Serverless.
|
||||
|
||||
- ATP Serverless can use auto scaling, adjusting when the system
|
||||
load increases and decreases.
|
||||
|
||||
- ATP Serverless business continuity is achieved through
|
||||
cross-region Autonomous Data Guard.
|
||||
|
||||
|
||||
|
||||
- Disaster Recovery
|
||||
|
||||
- The second region is deployed with a similar topology to reduce
|
||||
the overall recovery time objective.
|
||||
|
||||
- Use a warm DR strategy to reduce the overall RTO. In a warm DR
|
||||
strategy, the back-end tier cloud resources are already provisioned
|
||||
alongside the ATP Serverless standby database.
|
||||
|
||||
- Alternatively you can provision the back-end tier resources in
|
||||
the event of a failure, decreasing the cost of running the DR resources but
|
||||
increasing the over
|
||||
Reference in New Issue
Block a user