Deploy MongoDB on Azure Cobalt 100

Signed-off-by: odidev <odidev@puresoftware.com>

Author: odidev

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

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+---
+title: Run MongoDB on the Microsoft Azure Cobalt 100 processors 
+
+minutes_to_complete: 30   
+
+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.
+
+learning_objectives: 
+    - Provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04 LTS as the base image.
+    - Deploy the MongoDB on an Azure ubuntu virtual machine.
+    - Perform MongoDB baseline testing and benchmarking on both x86_64 and Arm64 virtual machine.
+
+prerequisites:
+    - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6). 
+    - Basic understanding of Linux command line.
+    - Familiarity with the [MongoDB architecture](https://www.mongodb.com/) and deployment practices on Arm64 platforms.
+
+author: Jason Andrews
+
+### Tags
+skilllevels: Introductory
+subjects: Databases
+cloud_service_providers: Microsoft Azure
+
+armips:
+    - Neoverse
+
+tools_software_languages:
+    - MongoDB
+    - mongotop
+    - mongostat
+
+operatingsystems:
+    - Linux
+
+further_reading:
+    - resource:
+        title: MongoDB Manual
+        link: https://www.mongodb.com/docs/manual/
+        type: documentation
+    - resource:
+        title: MongoDB Performance Tool
+        link: https://github.com/idealo/mongodb-performance-test#readme
+        type: documentation
+    - resource:        
+        title: MongoDB on Azure
+        link: https://azure.microsoft.com/en-us/solutions/mongodb
+        type: documentation
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---

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

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

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

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+---
+title: "Overview"
+
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Cobalt 100 Arm-based processor
+
+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. 
+
+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).
+
+## MongoDB
+MongoDB is a popular open-source NoSQL database designed for high performance, scalability, and flexibility.
+
+It stores data in JSON-like BSON documents, making it ideal for modern applications that require dynamic, schema-less data structures.
+
+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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+---
+title: MongoDB Baseline Testing 
+weight: 5 
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+
+### Baseline testing of MongoDB
+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.
+
+1. Verify Installation & Service Health
+
+```console
+ps -ef | grep mongod
+mongod --version
+netstat -tulnp | grep 27017
+```
+- **ps -ef | grep mongod** – Checks if the MongoDB server process is running.
+- **mongod --version** – Shows the version of MongoDB installed.
+- **netstat -tulnp | grep 27017** – Checks if MongoDB is listening for connections on its default port 27017.
+
+You should see an output similar to:
+
+```output
+mongod --version
+netstat -tulnp | grep 27017
+ubuntu      4288       1  0 10:40 ?        00:00:01 mongod --dbpath /var/lib/mongo --logpath /var/log/mongodb/mongod.log --fork
+ubuntu      4545    1764  0 10:43 pts/0    00:00:00 grep --color=auto mongod
+db version v8.0.12
+Build Info: {
+    "version": "8.0.12",
+    "gitVersion": "b60fc6875b5fb4b63cc0dbbd8dda0d6d6277921a",
+    "openSSLVersion": "OpenSSL 3.0.13 30 Jan 2024",
+    "modules": [],
+    "allocator": "tcmalloc-google",
+    "environment": {
+        "distmod": "ubuntu2404",
+        "distarch": "aarch64",
+        "target_arch": "aarch64"
+    }
+}
+(Not all processes could be identified, non-owned process info
+ will not be shown, you would have to be root to see it all.)
+tcp        0      0 127.0.0.1:27017         0.0.0.0:*               LISTEN      4288/mongod
+```
+
+2. Storage and Health Check
+
+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:
+
+```console
+fio --name=baseline --rw=randread --bs=4k --size=100M --numjobs=1 --time_based --runtime=30 --group_reporting
+```
+You should see an output similar to:
+
+```output
+baseline: (g=0): rw=randread, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=psync, iodepth=1
+fio-3.36
+Starting 1 process
+Jobs: 1 (f=1): [r(1)][100.0%][r=14.8MiB/s][r=3799 IOPS][eta 00m:00s]
+baseline: (groupid=0, jobs=1): err= 0: pid=3753: Mon Sep  1 10:25:07 2025
+  read: IOPS=4255, BW=16.6MiB/s (17.4MB/s)(499MiB/30001msec)
+    clat (usec): min=88, max=46246, avg=234.23, stdev=209.81
+     lat (usec): min=88, max=46246, avg=234.28, stdev=209.81
+    clat percentiles (usec):
+     |  1.00th=[   99],  5.00th=[  111], 10.00th=[  126], 20.00th=[  167],
+     | 30.00th=[  190], 40.00th=[  229], 50.00th=[  243], 60.00th=[  253],
+     | 70.00th=[  269], 80.00th=[  289], 90.00th=[  318], 95.00th=[  330],
+     | 99.00th=[  416], 99.50th=[  490], 99.90th=[  799], 99.95th=[ 1106],
+     | 99.99th=[ 3884]
+   bw (  KiB/s): min=14536, max=19512, per=100.00%, avg=17046.10, stdev=1359.69, samples=59
+   iops        : min= 3634, max= 4878, avg=4261.53, stdev=339.92, samples=59
+  lat (usec)   : 100=1.27%, 250=56.61%, 500=41.65%, 750=0.34%, 1000=0.06%
+  lat (msec)   : 2=0.04%, 4=0.01%, 10=0.01%, 20=0.01%, 50=0.01%
+  cpu          : usr=0.33%, sys=2.93%, ctx=127668, majf=0, minf=8
+  IO depths    : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
+     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
+     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
+     issued rwts: total=127661,0,0,0 short=0,0,0,0 dropped=0,0,0,0
+     latency   : target=0, window=0, percentile=100.00%, depth=1
+
+Run status group 0 (all jobs):
+   READ: bw=16.6MiB/s (17.4MB/s), 16.6MiB/s-16.6MiB/s (17.4MB/s-17.4MB/s), io=499MiB (523MB), run=30001-30001msec
+
+Disk stats (read/write):
+  sda: ios=127195/29, sectors=1017560/552, merge=0/15, ticks=29133/8, in_queue=29151, util=96.37%
+```
+The output shows how fast it read data (**16.6 MB/s**) and how many reads it did per second (**~4255 IOPS**), which tells you how responsive your storage is for random reads.
+
+3. Connectivity and CRUD Sanity Check
+
+```console
+mongosh --host localhost --port 27017
+```
+
+Inside shell:
+
+```javascript
+use baselineDB
+db.testCollection.insertOne({ name: "baseline-check", value: 1 })
+db.testCollection.find()
+db.testCollection.updateOne({ name: "baseline-check" }, { $set: { value: 2 } })
+db.testCollection.deleteOne({ name: "baseline-check" })
+exit
+```
+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.
+
+You should see an output similar to:
+
+```output
+test> use baselineDB
+switched to db baselineDB
+baselineDB> db.testCollection.insertOne({ name: "baseline-check", value: 1 })
+{
+  acknowledged: true,
+  insertedId: ObjectId('689acdae6a86b49bca74e39a')
+}
+baselineDB> db.testCollection.find()
+[
+  {
+    _id: ObjectId('689acdae6a86b49bca74e39a'),
+    name: 'baseline-check',
+    value: 1
+  }
+]
+baselineDB> db.testCollection.updateOne({ name: "baseline-check" }, { $set: { value: 2 } })
+...
+{
+  acknowledged: true,
+  insertedId: null,
+  matchedCount: 1,
+  modifiedCount: 1,
+  upsertedCount: 0
+}
+baselineDB> db.testCollection.deleteOne({ name: "baseline-check" })
+...
+{ acknowledged: true, deletedCount: 1 }
+```
+
+4. Basic Query Performance Test
+
+```console
+mongosh --eval '
+db = db.getSiblingDB("baselineDB");
+for (let i=0; i<1000; i++) { db.perf.insertOne({index:i, value:Math.random()}) };
+var start = new Date();
+db.perf.find({ value: { $gt: 0.5 } }).count();
+print("Query Time (ms):", new Date() - start);
+'
+```
+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.
+
+You should see an output similar to:
+
+```output
+Query Time (ms): 2
+```
+
+5. Index Creation Speed Test
+
+```console
+mongosh --eval '
+db = db.getSiblingDB("baselineDB");
+var start = new Date();
+db.perf.createIndex({ value: 1 });
+print("Index Creation Time (ms):", new Date() - start);
+'
+```
+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 **22 milliseconds**, indicating relatively fast index building for the dataset size.
+
+You should see an output similar to:
+
+```output
+Index Creation Time (ms): 22
+```
+
+6. Concurrency Smoke Test
+
+```console
+for i in {1..5}; do
+  /usr/bin/mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))' &
+done
+wait
+```
+This command runs **five MongoDB insert jobs at the same time**, each adding **1,000 new records** to the **baselineDB.concurrent** collection.
+It’s a quick way to test how MongoDB handles **multiple users writing data at once**.
+
+You should see an output similar to:
+
+```output
+[1] 3818
+[2] 3819
+[3] 3820
+[4] 3821
+[5] 3822
+switched to db baselineDB;
+[1]   Done                    mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
+switched to db baselineDB;
+switched to db baselineDB;
+switched to db baselineDB;
+[2]   Done                    mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
+[4]-  Done                    mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
+[3]-  Done                    mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
+switched to db baselineDB;
+[5]+  Done                    mongosh --eval 'use baselineDB; db.concurrent.insertMany([...Array(1000).keys()].map(k => ({ test: k, ts: new Date() })))'
+```
+
+**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.
+
+The above operations confirm that MongoDB is installed successfully and is functioning as expected on the Azure Cobalt 100 (Arm64) environment.
+
+Now, your MongoDB instance is ready for further benchmarking and production use.