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Deploy MongoDB on Azure Cobalt 100
Signed-off-by: odidev <[email protected]>
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‎content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md

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---
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title: Run MongoDB on the Microsoft Azure Cobalt 100 processors
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minutes_to_complete: 30
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who_is_this_for: This Learning Path is designed for software developers looking to migrate their MongoDB workloads from x86_64 to Arm-based platforms, specifically on the Microsoft Azure Cobalt 100 processors.
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learning_objectives:
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- Provision an Azure Arm64 virtual machine using Azure console, with Ubuntu as the base image.
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- Learn how to create an Azure Linux 3.0 Docker container.
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- Deploy the MongoDB on an Azure Linux 3.0 Arm64-based Docker container and an Azure Linux 3.0 custom-image-based Azure virtual machine.
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- Perform MongoDB baseline testing and benchmarking in both the containerized and virtual machine environments.
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prerequisites:
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- A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6).
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- A machine with [Docker](/install-guides/docker/) installed.
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- Basic understanding of Linux command line.
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- Familiarity with the [MongoDB architecture](https://www.mongodb.com/) and deployment practices on Arm64 platforms.
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author: Jason Andrews
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### Tags
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skilllevels: Advanced
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subjects: Databases
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cloud_service_providers: Microsoft Azure
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armips:
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- Neoverse
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tools_software_languages:
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- MongoDB
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- Docker
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- mongotop
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- mongostat
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operatingsystems:
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- Linux
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further_reading:
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- resource:
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title: MongoDB Manual
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link: https://www.mongodb.com/docs/manual/
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type: documentation
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- resource:
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title: MongoDB Performance Tool
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link: https://github.com/idealo/mongodb-performance-test#readme
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type: documentation
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title: MongoDB on Azure
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link: https://azure.microsoft.com/en-us/solutions/mongodb
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type: documentation
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### FIXED, DO NOT MODIFY
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# ================================================================================
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weight: 1 # _index.md always has weight of 1 to order correctly
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layout: "learningpathall" # All files under learning paths have this same wrapper
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learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
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---

‎content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_next-steps.md

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---
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# ================================================================================
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# FIXED, DO NOT MODIFY THIS FILE
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# ================================================================================
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weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation.
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title: "Next Steps" # Always the same, html page title.
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layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing.
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---

‎content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/background.md

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---
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title: "About Cobalt 100 Arm-based processor and MongoDB"
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weight: 2
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layout: "learningpathall"
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---
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## What is Cobalt 100 Arm-based processor?
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Azure’s Cobalt 100 is built on Microsoft's first-generation, in-house Arm-based processor: the Cobalt 100. Designed entirely by Microsoft and based on Arm’s Neoverse N2 architecture, this 64-bit CPU delivers improved performance and energy efficiency across a broad spectrum of cloud-native, scale-out Linux workloads. These include web and application servers, data analytics, open-source databases, caching systems, and more. Running at 3.4 GHz, the Cobalt 100 processor allocates a dedicated physical core for each vCPU, ensuring consistent and predictable performance.
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To learn more about Cobalt 100, refer to the blog [Announcing the preview of new Azure virtual machine based on the Azure Cobalt 100 processor](https://techcommunity.microsoft.com/blog/azurecompute/announcing-the-preview-of-new-azure-vms-based-on-the-azure-cobalt-100-processor/4146353).
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## Introduction to Azure Linux 3.0
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Azure Linux 3.0 is Microsoft's in-house, lightweight Linux distribution optimized for running cloud-native workloads on Azure. Designed with performance, security, and reliability in mind, it is fully supported by Microsoft and tailored for containers, microservices, and Kubernetes. With native support for Arm64 (AArch64) architecture, Azure Linux 3.0 enables efficient execution of workloads on energy-efficient Arm-based infrastructure, making it a powerful choice for scalable and cost-effective cloud deployments.
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## Introduction to MongoDB
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MongoDB is a popular open-source NoSQL database designed for high performance, scalability, and flexibility.
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It stores data in JSON-like BSON documents, making it ideal for modern applications that require dynamic, schema-less data structures.
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MongoDB is widely used for web, mobile, IoT, and real-time analytics workloads. Learn more from the [MongoDB official website](https://www.mongodb.com/) and its [official documentation](https://www.mongodb.com/docs/).

‎content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/baseline-testing.md

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---
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title: MongoDB Baseline Testing
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weight: 6
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### FIXED, DO NOT MODIFY
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layout: learningpathall
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---
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### Baseline testing of MongoDB
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Perform baseline testing by verifying MongoDB is running, logging into the shell, executing a few test queries, and monitoring live performance. This ensures the database is functioning correctly before starting any benchmarks.
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1. Verify Installation & Service Health
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```console
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ps -ef | grep mongod
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mongod --version
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netstat -tulnp | grep 27017
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```
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- **ps -ef | grep mongod** – Checks if the MongoDB server process is running.
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- **mongod --version** – Shows the version of MongoDB installed.
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- **netstat -tulnp | grep 27017** – Checks if MongoDB is listening for connections on its default port 27017.
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You should see an output similar to:
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```output
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azureus+ 976 797 0 05:00 pts/0 00:00:00 grep --color=auto mongod
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db version v8.0.12
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Build Info: {
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"version": "8.0.12",
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"gitVersion": "b60fc6875b5fb4b63cc0dbbd8dda0d6d6277921a",
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"openSSLVersion": "OpenSSL 3.3.3 11 Feb 2025",
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"modules": [],
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"allocator": "tcmalloc-google",
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"environment": {
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"distmod": "rhel93",
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"distarch": "aarch64",
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"target_arch": "aarch64"
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}
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}
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(Not all processes could be identified, non-owned process info
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will not be shown, you would have to be root to see it all.)
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tcp 0 0 127.0.0.1:27017 0.0.0.0:* LISTEN 1113/./mongodb-linu
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```
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2. Storage and Health Check
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```console
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ls -lh ~/mongodb-data/db
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```
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Make sure MongoDB’s data files exist and test your disk’s read speed. You want steady, consistent performance.
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You should see an output similar to:
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```output
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total 6.5M
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-rw-------. 1 azureuser azureuser 50 Aug 8 10:54 WiredTiger
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-rw-------. 1 azureuser azureuser 21 Aug 8 10:54 WiredTiger.lock
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-rw-------. 1 azureuser azureuser 1.5K Aug 11 13:05 WiredTiger.turtle
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-rw-------. 1 azureuser azureuser 100K Aug 11 13:05 WiredTiger.wt
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-rw-------. 1 azureuser azureuser 4.0K Aug 11 13:05 WiredTigerHS.wt
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-rw-------. 1 azureuser azureuser 36K Aug 11 13:05 _mdb_catalog.wt
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-rw-------. 1 azureuser azureuser 3.1M Aug 11 13:05 collection-0-18324942683865842057.wt
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-rw-------. 1 azureuser azureuser 20K Aug 11 13:05 collection-0-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 36K Aug 11 13:05 collection-2-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 12K Aug 11 13:05 collection-4-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 1.3M Aug 11 13:05 collection-62-18324942683865842057.wt
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-rw-------. 1 azureuser azureuser 4.0K Aug 8 12:41 collection-7-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 12K Aug 11 13:05 collection-82-18324942683865842057.wt
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drwx------. 2 azureuser azureuser 4.0K Aug 11 13:05 diagnostic.data
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-rw-------. 1 azureuser azureuser 1.4M Aug 11 13:05 index-1-18324942683865842057.wt
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-rw-------. 1 azureuser azureuser 20K Aug 11 13:05 index-1-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 36K Aug 11 13:05 index-3-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 12K Aug 11 13:05 index-5-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 36K Aug 11 13:05 index-6-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 504K Aug 11 13:05 index-71-18324942683865842057.wt
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-rw-------. 1 azureuser azureuser 4.0K Aug 8 12:41 index-8-2816474184925722673.wt
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-rw-------. 1 azureuser azureuser 12K Aug 11 13:05 index-83-18324942683865842057.wt
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-rw-------. 1 azureuser azureuser 4.0K Aug 8 12:41 index-9-2816474184925722673.wt
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drwx------. 2 azureuser azureuser 4.0K Aug 11 04:10 journal
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-rw-------. 1 azureuser azureuser 0 Aug 11 13:05 mongod.lock
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-rw-------. 1 azureuser azureuser 36K Aug 11 13:05 sizeStorer.wt
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-rw-------. 1 azureuser azureuser 114 Aug 8 10:54 storage.bson
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```
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Run the command below to check how fast your storage can **randomly read small 4KB chunks** from a 100 MB file for 30 seconds, using one job, and then show a summary report:
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```console
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fio --name=baseline --rw=randread --bs=4k --size=100M --numjobs=1 --time_based --runtime=30 --group_reporting
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```
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You should see an output similar to:
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```output
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baseline: (g=0): rw=randread, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=psync, iodepth=1
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fio-3.37
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Starting 1 process
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baseline: Laying out IO file (1 file / 100MiB)
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Jobs: 1 (f=1): [r(1)][100.0%][r=19.8MiB/s][r=5058 IOPS][eta 00m:00s]
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baseline: (groupid=0, jobs=1): err= 0: pid=1065: Tue Aug 12 05:04:48 2025
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read: IOPS=5201, BW=20.3MiB/s (21.3MB/s)(610MiB/30001msec)
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clat (usec): min=83, max=21382, avg=191.64, stdev=106.48
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lat (usec): min=83, max=21382, avg=191.68, stdev=106.48
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clat percentiles (usec):
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| 1.00th=[ 91], 5.00th=[ 95], 10.00th=[ 100], 20.00th=[ 139],
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| 30.00th=[ 155], 40.00th=[ 169], 50.00th=[ 225], 60.00th=[ 229],
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| 70.00th=[ 233], 80.00th=[ 235], 90.00th=[ 247], 95.00th=[ 269],
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| 99.00th=[ 314], 99.50th=[ 330], 99.90th=[ 412], 99.95th=[ 465],
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| 99.99th=[ 635]
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bw ( KiB/s): min=17888, max=22896, per=100.00%, avg=20815.73, stdev=1085.63, samples=59
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iops : min= 4472, max= 5724, avg=5203.93, stdev=271.41, samples=59
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lat (usec) : 100=10.12%, 250=81.30%, 500=8.55%, 750=0.02%, 1000=0.01%
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lat (msec) : 2=0.01%, 10=0.01%, 20=0.01%, 50=0.01%
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cpu : usr=0.27%, sys=3.38%, ctx=156062, majf=0, minf=7
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IO depths : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
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submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
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complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
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issued rwts: total=156056,0,0,0 short=0,0,0,0 dropped=0,0,0,0
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latency : target=0, window=0, percentile=100.00%, depth=1
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Run status group 0 (all jobs):
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READ: bw=20.3MiB/s (21.3MB/s), 20.3MiB/s-20.3MiB/s (21.3MB/s-21.3MB/s), io=610MiB (639MB), run=30001-30001msec
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Disk stats (read/write):
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sda: ios=155988/70, sectors=1247904/1016, merge=0/31, ticks=29060/28, in_queue=29096, util=95.44%
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```
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The output shows how fast it read data (**20.3 MB/s**) and how many reads it did per second (**~5200 IOPS**), which tells you how responsive your storage is for random reads.
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3. Connectivity and CRUD Sanity Check
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```console
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mongosh --host localhost --port 27017
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```
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Inside shell:
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```javascript
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use baselineDB
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db.testCollection.insertOne({ name: "baseline-check", value: 1 })
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db.testCollection.find()
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db.testCollection.updateOne({ name: "baseline-check" }, { $set: { value: 2 } })
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db.testCollection.deleteOne({ name: "baseline-check" })
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exit
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```
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These commands create a test record, read it, update its value, and then delete it a simple way to check if MongoDB’s basic **add, read, update, and delete** operations are working.
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You should see an output similar to:
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```output
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test> use baselineDB
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switched to db baselineDB
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baselineDB> db.testCollection.insertOne({ name: "baseline-check", value: 1 })
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{
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acknowledged: true,
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insertedId: ObjectId('689acdae6a86b49bca74e39a')
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}
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baselineDB> db.testCollection.find()
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[
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{
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_id: ObjectId('689acdae6a86b49bca74e39a'),
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name: 'baseline-check',
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value: 1
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}
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]
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baselineDB> db.testCollection.updateOne({ name: "baseline-check" }, { $set: { value: 2 } })
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...
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{
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acknowledged: true,
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insertedId: null,
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matchedCount: 1,
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modifiedCount: 1,
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upsertedCount: 0
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}
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baselineDB> db.testCollection.deleteOne({ name: "baseline-check" })
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...
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{ acknowledged: true, deletedCount: 1 }
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```
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4. Basic Query Performance Test
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```console
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mongosh --eval '
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db = db.getSiblingDB("baselineDB");
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for (let i=0; i<1000; i++) { db.perf.insertOne({index:i, value:Math.random()}) };
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var start = new Date();
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db.perf.find({ value: { $gt: 0.5 } }).count();
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print("Query Time (ms):", new Date() - start);
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'
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```
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The command connected to MongoDB, switched to the **baselineDB** database, inserted **1,000 documents** into the perf collection, and then measured the execution time for counting documents where **value > 0.5**. The final output displayed the **query execution time** in milliseconds.
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You should see an output similar to:
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```output
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Query Time (ms): 2
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```
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5. Index Creation Speed Test
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```console
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mongosh --eval '
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db = db.getSiblingDB("baselineDB");
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var start = new Date();
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db.perf.createIndex({ value: 1 });
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print("Index Creation Time (ms):", new Date() - start);
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'
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```
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The test connected to MongoDB, switched to the **baselineDB** database, and created an index on the **value** field in the **perf** collection. The index creation process completed in **38 milliseconds**, indicating relatively fast index building for the dataset size.
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You should see an output similar to:
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```output
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Index Creation Time (ms): 38
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```
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6. Concurrency Smoke Test
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```console
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for i in {1..5}; do
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/usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))' &
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done
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wait
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```
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This command runs **five MongoDB insert jobs at the same time**, each adding **1,000 new records** to the **baselineDB.concurrent** collection.
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It’s a quick way to test how MongoDB handles **multiple users writing data at once**.
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You should see an output similar to:
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```output
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[1] 1281
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[2] 1282
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[3] 1283
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[4] 1284
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[5] 1285
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switched to db baselineDB;
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switched to db baselineDB;
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[1] Done /usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
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switched to db baselineDB;
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[2] Done /usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
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[3] Done /usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
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switched to db baselineDB;
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switched to db baselineDB;
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[4]- Done /usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
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[5]+ Done /usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
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```
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**Five parallel MongoDB shell sessions** were executed, each inserting **1,000** test documents into the baselineDB.concurrent collection. All sessions completed successfully, confirming that concurrent data insertion works as expected.
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The above operations confirm that MongoDB is installed successfully and is functioning as expected on the Azure Cobalt 100 (Arm64) environment.
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Now, your MongoDB instance is ready for further benchmarking and production use.

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