DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Partner Zones AWS Cloud
by AWS Developer Relations
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Partner Zones
AWS Cloud
by AWS Developer Relations

Trending

  • Why You Should Consider Using React Router V6: An Overview of Changes
  • Low Code vs. Traditional Development: A Comprehensive Comparison
  • Implementing RBAC in Quarkus
  • JSON to PDF Magic: Harnessing LaTeX and JSON for Effortless Customization and Dynamic PDF Generation
  1. DZone
  2. Data Engineering
  3. Databases
  4. Learn MongoDB With Me (Part 3)

Learn MongoDB With Me (Part 3)

This is the continuation of a series exploring Indexes in MongoDB. We will be discussing various MongoDB indexes that we can perform on our data.

Sibeesh Venu user avatar by
Sibeesh Venu
·
Mar. 09, 18 · Tutorial
Like (15)
Save
Tweet
Share
5.14K Views

Join the DZone community and get the full member experience.

Join For Free

this is the third article in the "learn mongodb with me" series. if you haven't read my previous posts on this topic, i strongly recommend you to find part 1 here and part 2 here . this is the continuation of a series exploring indexes in mongodb. we will be discussing various mongodb indexes that we can perform on our data. i hope you will find this post useful. thanks for reading.

image title

indexes in mongodb

let's import a new collection, products , first.

[
   {
      "id":2,
      "name":"an ice sculpture",
      "price":12.50,
      "tags":[
         "cold",
         "ice"
      ],
      "dimensions":{
         "length":7.0,
         "width":12.0,
         "height":9.5
      },
      "warehouselocation":{
         "latitude":-78.75,
         "longitude":20.4
      }
   },
   {
      "id":3,
      "name":"a blue mouse",
      "price":25.50,
      "dimensions":{
         "length":3.1,
         "width":1.0,
         "height":1.0
      },
      "warehouselocation":{
         "latitude":54.4,
         "longitude":-32.7
      }
   },
   {
      "id":4,
      "name":"keyboard",
      "price":15.50,
      "dimensions":{
         "length":1.1,
         "width":1.0,
         "height":1.0
      },
      "warehouselocation":{
         "latitude":24.4,
         "longitude":-42.7
      }
   },
   {
      "id":5,
      "name":"doll",
      "price":10.50,
      "dimensions":{
         "length":5.1,
         "width":1.0,
         "height":7.0
      },
      "warehouselocation":{
         "latitude":64.4,
         "longitude":-82.7
      }
   },
   {
      "id":6,
      "name":"wallet",
      "price":5.50,
      "dimensions":{
         "length":1.1,
         "width":1.0,
         "height":1.0
      },
      "warehouselocation":{
         "latitude":24.4,
         "longitude":-12.7
      }
   }
]

please note that these are just dummy data, and they may sound illogical to you.

c:\program files\mongodb\server\3.4\bin>mongoimport --db mylearning --collection products --jsonarray --file products.json
2018-03-06t16:48:34.440+0530    connected to: localhost
2018-03-06t16:48:34.607+0530    imported 5 documents

c:\program files\mongodb\server\3.4\bin>

if you don't know how the import command works, please read my previous posts , in which we saw simple indexes. now that we have the data, let's go perform indexes.

single-key indexes

in one of my previous posts in this series of articles, i mentioned simple indexes. in this article, we are not going to talk about that. instead, we will explore other indexing options that mongodb has. let's learn about multi-key indexes.

multi-key indexes or compound indexes

as the name implies, we are actually going to set indexes with more than one key element. on our products collection, we have some product documents right, what we a user needs to filter the same with the price and warehouse location. yeah, we need to build a query.

mongodb enterprise > db.products.find({
... "price: {$lte: 16},
2018-03-06t17:10:15.005+0530 e query    [thread1] syntaxerror: unterminated string literal @(shell):2:0
mongodb enterprise > db.products.find({
... "price": {$lte: 16},
... "warehouselocation.latitude": {$gte: 60}
... })
{ "_id" : objectid("5a9e790a1ae1f955c1a70c4a"), "id" : 5, "name" : "doll", "price" : 10.5, "dimensions" : { "length" : 5.1, "width" : 1, "height" : 7 }, "warehouselocation" : { "latitude" : 64.4, "longitude" : -82.7 } }
mongodb enterprise >

we have got one entry according to our search, "price": {$lte: 16} and "warehouselocation.latitude": {$gte: 60} that's cool. now, let's try to find out the execution status for the same.

please be noted that we have used $lte and $gte , which stands for "less than or equal to" and "greater than or equal to." remember what i have told you before: mongo shell is cool and we can do anything with it. let's find out the examined elements count for our preceding find query now.

db.products.find({ "price": {$lte: 16}, "warehouselocation.latitude": {$gte: 60} }).explain("executionstats")

and if your query is correct, you will be getting a result as preceding.

"queryplanner" : {
                "plannerversion" : 1,
                "namespace" : "mylearning.products",
                "indexfilterset" : false,
                "parsedquery" : {
                        "$and" : [
                                {
                                        "price" : {
                                                "$lte" : 16
                                        }
                                },
                                {
                                        "warehouselocation.latitude" : {
                                                "$gte" : 60
                                        }
                                }
                        ]
                },
                "winningplan" : {
                        "stage" : "collscan",
                        "filter" : {
                                "$and" : [
                                        {
                                                "price" : {
                                                        "$lte" : 16
                                                }
                                        },
                                        {
                                                "warehouselocation.latitude" : {
                                                        "$gte" : 60
                                                }
                                        }
                                ]
                        },
                        "direction" : "forward"
                },
                "rejectedplans" : [ ]
        },
        "executionstats" : {
                "executionsuccess" : true,
                "nreturned" : 1,
                "executiontimemillis" : 107,
                "totalkeysexamined" : 0,
                "totaldocsexamined" : 5,
                "executionstages" : {
                        "stage" : "collscan",
                        "filter" : {
                                "$and" : [
                                        {
                                                "price" : {
                                                        "$lte" : 16
                                                }
                                        },
                                        {
                                                "warehouselocation.latitude" : {
                                                        "$gte" : 60
                                                }
                                        }
                                ]
                        },
                        "nreturned" : 1,
                        "executiontimemillisestimate" : 0,
                        "works" : 7,
                        "advanced" : 1,
                        "needtime" : 5,
                        "needyield" : 0,
                        "savestate" : 0,
                        "restorestate" : 0,
                        "iseof" : 1,
                        "invalidates" : 0,
                        "direction" : "forward",
                        "docsexamined" : 5
                }
        },
        "serverinfo" : {
                "host" : "pc292716",
                "port" : 27017,
                "version" : "3.4.9",
                "gitversion" : "876ebee8c7dd0e2d992f36a848ff4dc50ee6603e"
        },
        "ok" : 1
}

you might have already noticed the value we have for totaldocsexamined . if you haven't, please check now. in my case, it is 5, which means that the query just examined all the records we have. ah, that sounds bad, right? what if we have millions of records in our collection? how long is it gonna take to fetch the results?

mongodb enterprise > db.products.createindex({price:1, "warehouselocation.latitude":1})
{
        "createdcollectionautomatically" : false,
        "numindexesbefore" : 1,
        "numindexesafter" : 2,
        "ok" : 1
}

run your previous query now and find out what the value of docs examined is.

mongodb enterprise > db.products.find({ "price": {$lte: 16}, "warehouselocation.latitude": {$gte: 60} }).explain("executionstats")
{
        "queryplanner" : {
                "plannerversion" : 1,
                "namespace" : "mylearning.products",
                "indexfilterset" : false,
                "parsedquery" : {
                        "$and" : [
                                {
                                        "price" : {
                                                "$lte" : 16
                                        }
                                },
                                {
                                        "warehouselocation.latitude" : {
                                                "$gte" : 60
                                        }
                                }
                        ]
                },
                "winningplan" : {
                        "stage" : "fetch",
                        "inputstage" : {
                                "stage" : "ixscan",
                                "keypattern" : {
                                        "price" : 1,
                                        "warehouselocation.latitude" : 1
                                },
                                "indexname" : "price_1_warehouselocation.latitude_1",
                                "ismultikey" : false,
                                "multikeypaths" : {
                                        "price" : [ ],
                                        "warehouselocation.latitude" : [ ]
                                },
                                "isunique" : false,
                                "issparse" : false,
                                "ispartial" : false,
                                "indexversion" : 2,
                                "direction" : "forward",
                                "indexbounds" : {
                                        "price" : [
                                                "[-inf.0, 16.0]"
                                        ],
                                        "warehouselocation.latitude" : [
                                                "[60.0, inf.0]"
                                        ]
                                }
                        }
                },
                "rejectedplans" : [ ]
        },
        "executionstats" : {
                "executionsuccess" : true,
                "nreturned" : 1,
                "executiontimemillis" : 1089,
                "totalkeysexamined" : 5,
                "totaldocsexamined" : 1,
                "executionstages" : {
                        "stage" : "fetch",
                        "nreturned" : 1,
                        "executiontimemillisestimate" : 310,
                        "works" : 5,
                        "advanced" : 1,
                        "needtime" : 3,
                        "needyield" : 0,
                        "savestate" : 2,
                        "restorestate" : 2,
                        "iseof" : 1,
                        "invalidates" : 0,
                        "docsexamined" : 1,
                        "alreadyhasobj" : 0,
                        "inputstage" : {
                                "stage" : "ixscan",
                                "nreturned" : 1,
                                "executiontimemillisestimate" : 270,
                                "works" : 5,
                                "advanced" : 1,
                                "needtime" : 3,
                                "needyield" : 0,
                                "savestate" : 2,
                                "restorestate" : 2,
                                "iseof" : 1,
                                "invalidates" : 0,
                                "keypattern" : {
                                        "price" : 1,
                                        "warehouselocation.latitude" : 1
                                },
                                "indexname" : "price_1_warehouselocation.latitude_1",
                                "ismultikey" : false,
                                "multikeypaths" : {
                                        "price" : [ ],
                                        "warehouselocation.latitude" : [ ]
                                },
                                "isunique" : false,
                                "issparse" : false,
                                "ispartial" : false,
                                "indexversion" : 2,
                                "direction" : "forward",
                                "indexbounds" : {
                                        "price" : [
                                                "[-inf.0, 16.0]"
                                        ],
                                        "warehouselocation.latitude" : [
                                                "[60.0, inf.0]"
                                        ]
                                },
                                "keysexamined" : 5,
                                "seeks" : 4,
                                "dupstested" : 0,
                                "dupsdropped" : 0,
                                "seeninvalidated" : 0
                        }
                }
        },
        "serverinfo" : {
                "host" : "pc292716",
                "port" : 27017,
                "version" : "3.4.9",
                "gitversion" : "876ebee8c7dd0e2d992f36a848ff4dc50ee6603e"
        },
        "ok" : 1
}
mongodb enterprise > db.products.find({ "price": {$lte: 16}, "warehouselocation.latitude": {$gte: 60} })
{ "_id" : objectid("5a9e790a1ae1f955c1a70c4a"), "id" : 5, "name" : "doll", "price" : 10.5, "dimensions" : { "length" : 5.1, "width" : 1, "height" : 7 }, "warehouselocation" : { "latitude" : 64.4, "longitude" : -82.7 } }
mongodb enterprise >

yeah! we got "docsexamined" : 1 . way to go. go create some indexes on your topmost queries, you can definitely see some magic over there. you can create up to 64 indexes on a collection in mongodb, but you may need to create only a few, only on your top result queries. whenever you are facing any performance issues on any queries, consider that it needs some tuning and definitely an index. there are so many other complex indexes, but widely used indexes are single key index and compound index.



if you enjoyed this article and want to learn more about mongodb, check out this collection of tutorials and articles on all things mongodb.

MongoDB Database

Published at DZone with permission of Sibeesh Venu, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

Trending

  • Why You Should Consider Using React Router V6: An Overview of Changes
  • Low Code vs. Traditional Development: A Comprehensive Comparison
  • Implementing RBAC in Quarkus
  • JSON to PDF Magic: Harnessing LaTeX and JSON for Effortless Customization and Dynamic PDF Generation

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com

Let's be friends: