MongoDB in the middle of a Polyglot Persistence Architecture

Sigfrido NarváezPrincipal Software ArchitectSage North America

MongoDB World 2015June 2015 NY

2

What is this about?

db.Talk.find({

learnings: {

$in: [

"Polyglot Persistence & Architecture",

"How we use MongoDB",

"Tools & Ops"

]

}

});

System of RecordERP, Accounting, HR & Payroll

ERP’sOn-premise

SMB Sage Data Cloud

Connector

Customers

Sales

Product Catalog

Payroll

3

Domains

4

Data Taxonomy

Types

Reference

Transactional

Historical

Computed

Metadata

Settings

App-specific data

Files & Logs

Global / Lookup

Behavior

Origin: ERP / Cloud

Querying

Fixed

Unstructured

Growth

Throughput

Searchable

Retention

Binary

6

Polyglot Persistence

MartinFowler.com [2012]

7

Micro-Services

MartinFowler.com [2014]

8

Before

9

… now!

Persistence Technologies

Data Taxonomy

Types

Reference

Transactional

Historical

Computed

Metadata

Settings

App-specific data

Files & Logs

Global / Lookup

Behavior

Origin: ERP / Cloud

Querying

Fixed Schema

Unstructured

Growth

Throughput

Searchable

Retention

Binary

Abstractions

Sage ERP’sOn-premise

Domains as Micro-Services

Connector

13

Customers

Sales

Products Catalog

Payroll

REST API

Resources

CRUD & Operations

Business

Entities

Rules

Data Layer

Mappings

Migrations

ERP Plug-in

Contracts

Mappings

Connector

Contracts

BackgroundData Sync

BackgroundData Sync

Mobile Sales

ERPMobile Sales

Cus

tom

ers

Pro

du

ct

Cat

alo

g

…

ERPC

ERPB

ERPA

Mobile Sales

Domain

NE

W

Dom

ain

Mobile Sales

Cus

tom

ers

Pro

du

ct

Cat

alo

g

…

ERPC

ERPB

ERPA

App

NE

W

Dom

ain

Mobile Sales

Cus

tom

ers

Pro

du

ct

Cat

alo

g

NEWApp

…

ERPC

ERPB

ERPA

Sal

es

Cus

tom

ers

Pro

du

ct

Cat

alo

g

…

Platform

Technology

Extending the SDC

04/14/2023 15

Apps & FaaS built ontop of Domains

Each Governs Entities, Rules & Persistence

Organized around Business Capabilities

New Apps can re-use, extend or add

New Technologies can be addedor replace existing

16

MongoDB

• Use Cases

• Aggregation Framework

• Lessons Learned

MongoDB Use Cases

• Historical Data• Write bursts• Mongo handles easily!• KPI’s via Aggregation Framework

18

REST API Business Data Layer

KPI

Historical Azure WorkerAggregation

Pipeline

Pre-Calculated

On-The-Fly

Aggregation Framework

{

{ "$match", { .. } },

{ "$sort", { .. } },

{ "$limit", 50}

{ "$unwind", { .. } },

{ "$match", { .. } },

{ "$limit", 50},

{ "$project", { .. } }

}

Dynamic schema

• Settings• Tenant, User, App, Features, etc.• Problematic in Relational model• Outgrows K/V model

• App-specific Data • Extensions to Core Entities• i.e. a SalesPerson is an extension of a User• Personalization

SQL MongoDB

{ "_id" : "4c44592a-1f6f-4a8f-bb87-a3a700a85066SalesSalesTeamMember", "CreatedOn" : ISODate("2014-09-15T17:12:48.917Z"), "UpdatedOn" : ISODate("2014-09-15T17:12:48.917Z"), "CreatedBy" : "4c44592a-1f6f-4a8f-bb87-a3a700a85066", "UpdatedBy" : "4c44592a-1f6f-4a8f-bb87-a3a700a85066", "TenantId" : 3, "EntityStatus" : 0, "SettingKey" : "SalesTeamMember", "AppId" : 0, "ExtendedEntityId" : "4c44592a-1f6f-4a8f-bb87-a3a700a85066", "SalespersonIds" : [new BinData(3, "mcseUhOfQUmPK6OnAKhQfA==")], "Title" : "TestTitle", "IsSalesMgr" : false, "IsSalesRep" : false, "SalesRepMaxDiscPct" : 0.0}

{ "_id" : "2e866412-dbda-47ad-8767-a3a700a78e21ServiceServicesUser", "CreatedOn" : ISODate("2014-09-15T17:10:03.04Z"), "UpdatedOn" : ISODate("2014-09-15T17:10:03.04Z"), "CreatedBy" : "00000000-0000-0000-0000-000000000000", "UpdatedBy" : "00000000-0000-0000-0000-000000000000", "TenantId" : 3, "EntityStatus" : 0, "SettingKey" : "ServicesUser", "AppId" : 1, "ExtendedEntityId" : "2e866412-dbda-47ad-8767-a3a700a78e21", "Notes" : "Test Notes", "Role" : 1, "RoleAsString" : "Administrator", "LunchStartTime" : "12:00:00", "LunchEndTime" : "13:00:00", "StartTimeMon" : "08:00:00", "StartTimeTue" : "08:00:00", "StartTimeWed" : "08:00:00", "StartTimeThu" : "08:00:00", "StartTimeFri" : "08:00:00", "StartTimeSat" : "00:00:00", "StartTimeSun" : "00:00:00", "EndTimeMon" : "17:00:00", "EndTimeTue" : "17:00:00", "EndTimeWed" : "17:00:00", "EndTimeThu" : "17:00:00", "EndTimeFri" : "17:00:00", "EndTimeSat" : "00:00:00", "EndTimeSun" : "00:00:00"}

Extensions

@codinghorror

Lessons Learned

Indexes

• Adjust Indexes as needed

• Background• Not on unique indexes!• Can’t add new background indexes and delete old indexes

Migrations

• Added after launch

• DevOps “book keeping”, i.e. Indexes

• https://github.com/phoenixwebgroup/DotNetMongoMigrations

Lessons Learned

Exceed Namespace

• 16mb limit / ~24K namespaces

• Each collection & index is a namespace

• Aggregation output to temp collections

Aggregations

• Ensure $match & $sort uses indexes

• 100mb limit

• Increase with allowDiskUse: true

• Monitor for increase on Non-Mapped Virtual Memory

Lessons Learned

• Aggregation $match – ensure uses indexes!

04/14/2023 24

Operations

• Azure

• MMS

• APM

• CI/CD

• Log Mining

Production

DevOps - From code to alert paths!

25

Dev CI/CD Log Mining

WAD, IISCustom

APM

Azure Role Metrics

DB Telemetry

Deploy &DB Migrations

code

warm

cold

Mongo on Azure

Partnered with MongoLab

• Dev & Production clusters

• Slow queries & recommended indexes! (Dex)

• Profiler & Stream logs

• Telemetry: Real-Time Monitoring / link to MMS• http://docs.mongolab.com/monitoring

On your own

• Linux VM - http://docs.mongodb.org/ecosystem/tutorial/install-mongodb-on-linux-in-azure/

• Azure Worker - http://docs.mongodb.org/ecosystem/tutorial/deploy-mongodb-worker-roles-in-azure/

Mongo Monitoring & Performance (MMS)

• Data, Index, Storage

• Page Faults

• Memory & CPU

• Top 5 Alerts• Host Recovering, Repl Lag, Connections, Lock%, Replica• http://

www.mongodb.com/blog/post/five-mms-monitoring-alerts-keep-your-mongodb-deployment-track

• Top 5 Graphs• Lock %, Repl Lag, Journal Writes, Page faults, Non-Mapped Virtual Memory• http://

blog.mongodb.org/post/62152249344/the-top-5-metrics-to-watch-in-mongodb

Non-mapped Virtual Memory Increase

Application Performance Monitoring

MongoLab plug-in

• Operations

• Queues

• Lock %

• Page Faults

• Non-mapped Virtual Memory

• Memory

• Replication Operations

• Replication Lag

• Oplog Window

SQL

• Query drill-down

• Index profile

Continuous Integration / Delivery

• Builds• Visual Studio aware• C# Code Analysis & Coverage• NUnit test execution• NuGet

• Azure Deployments• Azure plugin• Runbook Azure Automation

• Database Migrations• SQL• Mongo

Log Mining

• Open Access (Engineers, DevOps, Customer Support)

• Dump WAD, IIS & Error logs

• Alerts & Dashboards

• Verify issues have ceased (post deployment)

Alert Paths

• Hot• Less than 10s• “Wake someone up”• Heartbeat (SCOM/NOC)

• Warm • Less than 5m – DevOps engineer• NewRelic, MMS, Azure Portal• Slow Query, Slow Response, Repl Lag, Lock%, ApDex < 0.9, CPU%

• Cold• Longer than 5m - Production Support / Engineering• SumoLogic – Error showed up in logs X times• Forensic purposes – saved queries

33

Summary

• Use the best of what is out there!

• Divide for flexibility

• MongoDB is amazing: multi-faceted

• Enable DevOps with awesome tools!

• Change is constant – Partner up!

THANK YOU!

Sigfrido “Sig” Narváez

• sigfrido@gmail.com | @SigNarvaez

• Principal Software Architect, Sage

• Orange County MUG Organizer - ~3yrs

• 2015 MongoDB Master

Sage

• Solutions for ERP, CRM, HR, Payroll, Payments, Assets, Construction, and Real Estate.

• Headquartered in UK, offices in North America, mainland Europe, South Africa, Australia, Asia and Brazil.

• 13K Employees. 6M Customers. 23 Countries.

• LSE:SGE