M101 Mongodb For Developers Homework Assignments

What is the cost of an online course?

MongoDB University courses are free.

What do I need to take a course?

You will need access to a computer and YouTube. A recent Windows, Mac OS or Linux based machine will work fine.

What do I receive for completing a course?

You will get a certificate of completion from MongoDB, Inc.

Do I need to be available during a certain hour of the day to watch course videos?

No. Each course is composed of pre-recorded lesson videos you may watch at any time during the run of the course. There are weekly deadlines for assignments.

How does the grading work?

You will receive a final grade but the grade won't be on your certificate of completion. To receive a certificate of completion, you will need to achieve 65% on graded material. There are three different types of assessments within the course: quizzes, weekly assignments and a final exam/project. Quizzes don't count toward your grade. Quizzes are intended to help you check your own understanding. The weekly assignments will comprise one half of your grade and the final exam/project one half of your grade. Most assignments will be due on Tuesdays. Each week we will release new video lectures and a new assignments. Unfortunately, due to the large number of students and in the interest of fairness, late assignments cannot be accepted.

Will I be able to get help?

Each course has an active online forum where students can help each other. In addition, the instructor(s), teaching assistants and other MongoDB employees will monitor the forums and try to help. Often the fastest answer comes from another student.

 [Solution] Week 4 :Performance : M101P: MongoDB for Developers

Homework 4.1 : 

Suppose you have a collection with the following indexes:

> db.products.getIndexes() [ { "v" : 1, "key" : { "_id" : 1 }, "ns" : "store.products", "name" : "_id_" }, { "v" : 1, "key" : { "sku" : 1 }, "unique" : true, "ns" : "store.products", "name" : "sku_1" }, { "v" : 1, "key" : { "price" : -1 }, "ns" : "store.products", "name" : "price_-1" }, { "v" : 1, "key" : { "description" : 1 }, "ns" : "store.products", "name" : "description_1" }, { "v" : 1, "key" : { "category" : 1, "brand" : 1 }, "ns" : "store.products", "name" : "category_1_brand_1" }, { "v" : 1, "key" : { "reviews.author" : 1 }, "ns" : "store.products", "name" : "reviews.author_1" } ]

Which of the following queries can utilize at least one index to find all matching documents, or to sort? Check all that apply.

Note: the text for some answers may wrap; you can ignore the wrapping.



Homework 4.2 : 

Suppose you have a collection called tweets whose documents contain information about the created_at time of the tweet and the user's followers_count at the time they issued the tweet. What can you infer from the following explain output?

> db.tweets.explain("executionStats").find( { "user.followers_count" : { $gt : 1000 } } ).limit(10).skip(5000).sort( { created_at : 1 } ) { "queryPlanner" : { "plannerVersion" : 1, "namespace" : "twitter.tweets", "indexFilterSet" : false, "parsedQuery" : { "user.followers_count" : { "$gt" : 1000 } }, "winningPlan" : { "stage" : "LIMIT", "limitAmount" : 0, "inputStage" : { "stage" : "SKIP", "skipAmount" : 0, "inputStage" : { "stage" : "FETCH", "filter" : { "user.followers_count" : { "$gt" : 1000 } }, "inputStage" : { "stage" : "IXSCAN", "keyPattern" : { "created_at" : -1 }, "indexName" : "created_at_-1", "isMultiKey" : false, "direction" : "backward", "indexBounds" : { "created_at" : [ "[MinKey, MaxKey]" ] } } } } }, "rejectedPlans" : [ ] }, "executionStats" : { "executionSuccess" : true, "nReturned" : 10, "executionTimeMillis" : 563, "totalKeysExamined" : 251120, "totalDocsExamined" : 251120, "executionStages" : { "stage" : "LIMIT", "nReturned" : 10, "executionTimeMillisEstimate" : 500, "works" : 251121, "advanced" : 10, "needTime" : 251110, "needFetch" : 0, "saveState" : 1961, "restoreState" : 1961, "isEOF" : 1, "invalidates" : 0, "limitAmount" : 0, "inputStage" : { "stage" : "SKIP", "nReturned" : 10, "executionTimeMillisEstimate" : 500, "works" : 251120, "advanced" : 10, "needTime" : 251110, "needFetch" : 0, "saveState" : 1961, "restoreState" : 1961, "isEOF" : 0, "invalidates" : 0, "skipAmount" : 0, "inputStage" : { "stage" : "FETCH", "filter" : { "user.followers_count" : { "$gt" : 1000 } }, "nReturned" : 5010, "executionTimeMillisEstimate" : 490, "works" : 251120, "advanced" : 5010, "needTime" : 246110, "needFetch" : 0, "saveState" : 1961, "restoreState" : 1961, "isEOF" : 0, "invalidates" : 0, "docsExamined" : 251120, "alreadyHasObj" : 0, "inputStage" : { "stage" : "IXSCAN", "nReturned" : 251120, "executionTimeMillisEstimate" : 100, "works" : 251120, "advanced" : 251120, "needTime" : 0, "needFetch" : 0, "saveState" : 1961, "restoreState" : 1961, "isEOF" : 0, "invalidates" : 0, "keyPattern" : { "created_at" : -1 }, "indexName" : "created_at_-1", "isMultiKey" : false, "direction" : "backward", "indexBounds" : { "created_at" : [ "[MinKey, MaxKey]" ] }, "keysExamined" : 251120, "dupsTested" : 0, "dupsDropped" : 0, "seenInvalidated" : 0, "matchTested" : 0 } } } } }, "serverInfo" : { "host" : "generic-name.local", "port" : 27017, "version" : "3.0.1", "gitVersion" : "534b5a3f9d10f00cd27737fbcd951032248b5952" }, "ok" : 1 }

Homework 4.3 :

use blog db.posts.drop()
From the mac or PC terminal window
mongoimport --drop -d blog -c posts posts.json

The blog has been enhanced so that it can also display the top 10 most recent posts by tag. There are hyperlinks from the post tags to the page that displays the 10 most recent blog entries for that tag. (run the blog and it will be obvious)

Your assignment is to make the following blog pages fast:

The blog home page
The page that displays blog posts by tag (http://localhost:8082/tag/whatever)
The page that displays a blog entry by permalink (http://localhost:8082/post/permalink)
By fast, we mean that indexes should be in place to satisfy these queries such that we only need to scan the number of documents we are going to return.

To figure out what queries you need to optimize, you can read the blog.py code and see what it does to display those pages. Isolate those queries and use explain to explore.

Once you have added the indexes to make those pages fast run the following

python validate.py

(note that for folks who are using MongoLabs or MongoHQ there are some command line options to validate.py to make it possible to use those services) Now enter the validation code below.

Making the Blog fast
Please download hw4-3.zip from the Download Handout link to get started. This assignment requires Mongo 3.0 or above.

In this homework assignment you will be adding some indexes to the post collection to make the blog fast.

We have provided the full code for the blog application and you don't need to make any changes, or even run the blog. But you can, for fun.

We are also providing a patriotic (if you are an American) data set for the blog. There are 1000 entries with lots of comments and tags. You must load this dataset to complete the problem.

From the mongo shell:

Solution : 893jfns29f728fn29f20f2


Homework 4.4 :

In this problem you will analyze a profile log taken from a different mongoDB instance and you will import it into a collection named sysprofile. To start, please download sysprofile.json from Download Handout link and import it with the following command:

mongoimport --drop -d m101 -c profile sysprofile.json
Now query the profile data, looking for all queries to the students collection in the database school2, sorted in order of decreasing latency. What is the latency of the longest running operation to the collection, in milliseconds?

Solution :



Enjoy....!!!!

Feel free to comment below your experience with above approach and If you still find any problem  with above steps Let me know I would love to help you to resolve your  problem.

 If you want to take your Technological Knowledge to the Next Level and For More Technological information Stay tuned to Visionfortech


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