Sample Data

Import & Query MongoDB

Scenario

You are a data engineer at a data analytics consulting company. Your company prides itself in being able to efficiently handle data in any format on any database on any platform.

  • Analysts in your office need to work with data on different databases, and data in different formats.
  • While these analysts are good at analyzing data, they count on you to be able to
    • move data from external sources into various databases
    • move data from one type of database to another
    • be able to run basic queries on various databases

Tasks

  1. Import movies.json into the MongoDB server into an entertainment database and a movie collection.
  2. Write a MongoDB query to find the year most movies were released.
  3. Write a MongoDB query to find the count of movies released after the year 1999.
  4. Write a query to determine the average votes for movies released in 2007.
  5. Export selected fields from the movies collection into a file named partial_data.csv.

Steps

Download Data

  • Download data movies.json into the could IDE directory
curl -O https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMSkillsNetwork-DB0151EN-edX/labs/FinalProject/movies.json
  • Here is a view of a sample movie document
{
  _id: '9',
  title: 'The Lost City of Z',
  genre: 'Action,Adventure,Biography',
  Description: 'A true-life drama, centering on British explorer Col. Percival Fawcett, who disappeared while searching for a mysterious city in the Amazon in the 1920s.',
  Director: 'James Gray',
  Actors: 'Charlie Hunnam, Robert Pattinson, Sienna Miller, Tom Holland',
  year: 2016,
  'Runtime (Minutes)': 141,
  rating: 'unrated',
  Votes: 7188,
  'Revenue (Millions)': 8.01,
  Metascore: 78
}

Import Data

  • Import movies.json into MongoDB
  • named: entertainment and use
  • collection named: movies
~$ mongoimport -u root -p HkbscmpId8EaS12seIqEIGUw --authenticationDatabase admin  -d entertainment -c movies --host mongo movies.json
2024-10-25T12:18:22.642-0400    connected to: mongodb://mongo/
2024-10-25T12:18:22.665-0400    100 document(s) imported successfully. 0 document(s) failed to import.

  • The step above imported the file and created a db and collection
  • Let’s go into the Mongo Server and list all DBs present

List DBs

  • Either use the Mongo CLI to connect to the server or use the code below in Connect to Mongo
  • show dbs

Connect to DB

  • use entertainment

Query 1

Find the year in which most number of movies were released

  • So basically we can use group to croup documents by a certain field: release year
  • Calculate the total count within each year using $sum aggregation
  • Sort the years in descending order
  • Limit the output to 1 document the first year in the filtered output, which would have the largest count

~$ mongosh -u root -p SUBSTITUTE PASSWORD HERE --authenticationDatabase admin local --host mongo
local          64.00 KiB
training        8.00 KiB
scmpId8EaS12seIqEIGUw@172.21.65.104:27017ongosh mongodb://root:Hkbs
Current Mongosh Log ID: 671bca4d5b8ffc372cfe6910
Connecting to:          mongodb://<credentials>@172.21.65.104:27017/?directConnection=true&appName=mongosh+2.3.2
Using MongoDB:          3.6.3
Using Mongosh:          2.3.2

For mongosh info see: https://www.mongodb.com/docs/mongodb-shell/

------
   The server generated these startup warnings when booting
   2024-10-25T15:46:40.609+0000: 
   2024-10-25T15:46:40.609+0000: ** WARNING: Using the XFS filesystem is strongly recommended with the WiredTiger storage engine
   2024-10-25T15:46:40.609+0000: **          See http://dochub.mongodb.org/core/prodnotes-filesystem
------

# List ALL DBs
test> show dbs
admin          80.00 KiB
config         12.00 KiB
entertainment  60.00 KiB
local          64.00 KiB
training        8.00 KiB

# Connect to db
test> use entertainment
switched to db entertainment

# Query db

# We first group by year and sum all the movies per year
db.movies.aggregate([
        {
                "$group": {
                        "_id": "$year",
                        "movies_year": {"$sum": 1}
                }
        },
        # Here we sort in descending order
        {
                "$sort":{"movies_year": -1}
        }, 
        # Limit/Filter the output to the largest count
        {
                "$limit": 1 
        }
])

Query 2

Find the count of movies released after 1999

Greater

  • use $gt to find a value greater than

Count Documents

  • To find the count of documents use db.xxx.countDocuments()
entertainment> db.movies.countDocuments({ year: {$gt : 1999} })
99

Query 3

Find the average votes for movies released in 2007

Match

  • We filter for only year 2007 by using match

Average

  • Here we average what was filtered using $avg
entertainment> db.movies.aggregate([
        { $match: { year: 2007}},
        { $group: { _id: "$year", averageVotes: { $avg: "$Votes" }} }
])

# OUTPUT
[ { _id: 2007, averageVotes: 192.5 } ]

Export

Export the fields: id, title, year, rating, directory from the movies collection to a file named partial_data.csv

Export to csv

  • use mongoexport
~$ mongoexport  -u root -p HkbscmpId8EaS12seIqEIGUw --authenticationDatabase admin  -d entertainment -c movies -f "_id,title,year,rating,director" --type=csv -o partial_data.csv --host mongo

Create Instance

  • save user: root
  • save pasw: HkbscmpId8EaS12seIqEIGUw
  • Open new terminal
  • Connect to MongoDB server using code
  • Or simply click on the MongoDB CLI

Connect to Mongo

  • This command will either connect to the DB training if it exists or
  • creates a db if doesn’t exist
  • use training

Create Collection

  • Let’s create a collection
  • db.createCollection("bigdata")
# Connect to MongoDB server or use CLI button
~$ mongosh -u root -p SUBSTITUTE PASSWORD HERE --authenticationDatabase admin local --host mongo

# OUTPUT
Current Mongosh Log ID: 671bbeea871cb6afc3fe6910
Connecting to:          mongodb://<credentials>@mongo:27017/local?directConnection=true&authSource=admin&appName=mongosh+2.3.2
Using MongoDB:          3.6.3
Using Mongosh:          2.3.2

For mongosh info see: https://www.mongodb.com/docs/mongodb-shell/


To help improve our products, anonymous usage data is collected and sent to MongoDB periodically (https://www.mongodb.com/legal/privacy-policy).
You can opt-out by running the disableTelemetry() command.

------
   The server generated these startup warnings when booting
   2024-10-25T14:13:41.675+0000: 
   2024-10-25T14:13:41.675+0000: ** WARNING: Using the XFS filesystem is strongly recommended with the WiredTiger storage engine
   2024-10-25T14:13:41.675+0000: **          See http://dochub.mongodb.org/core/prodnotes-filesystem
------

local>

# Connect to db
local> use training
switched to db training

# Create Collection
training> db.createCollection("bigdata")
{ ok: 1 }

Cassandra1

Tasks

  1. Create a keyspace named entertainment.
  2. Import partial_data.csv into a Cassandra server.
  3. Write a CQL query to count the movie table’s rows.
  4. Create an index for the movie table’s rating column using CQL.
  5. Write a CQL query to count the number of movies that are rated “G.”

Create Instance

  • Click on Open Cassandra Page in IDE
  • Open Cassandra CLI
  • save host: 127.xxx Port: 9042
  • Save user: emhrcf psw: MTA2NzItZW1ocmNm

Connect to Cassandra Server

  • use this code: cqlsh HOST PORT –username root –password
# Connect to Cassandra server with the code below or use CLI button
~$ cqlsh 172.21.27.155 9042 --username root --password ZmfV6DfM4ZEQs5pktwVDWSR7

# OUTPUT
WARNING: cqlsh was built against 4.0.14, but this server is 5.0.  All features may not work!
Connected to Test Cluster at 172.21.82.211:9042
[cqlsh 6.0.0 | Cassandra 5.0-beta1 | CQL spec 3.4.7 | Native protocol v5]
Use HELP for help.
root@cqlsh>

Download Data

  • We will use the data we exported from MongoDB
  • If didn’t do step above use the code below to download partial_data.csv into Cassandra Server
curl -O https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMSkillsNetwork-DB0151EN-edX/labs/FinalProject/partial_data.csv

Create Keyspace

  • create keyspace: entertainment
  • without specifying any replication factors or strategy

Assess Keyspace

  • Review keyspace
CREATE KEYSPACE entertainment
WITH replication = {'class':'SimpleStrategy', 'replication_factor' : 3};
Warnings :
Your replication factor 3 for keyspace entertainment is higher than the number of nodes 1

root@cqlsh> describe keyspaces

entertainment  system_auth         system_schema  system_views         
system         system_distributed  system_traces  system_virtual_schema

Import Data

Import partial_data.csv into Cassandra

  • Import file into a keyspace named entertainment and
  • table named movies use CREATE TABLE()
  • First we’ll create a table: configure all the columns including the id column
    • _id
    • title
    • year
    • rating
    • director
  • View details of table with describe
  • Import the data into the table using COPY
root@cqlsh> use entertainment;
root@cqlsh:entertainment> CREATE TABLE movies (
                 ...      id text PRIMARY KEY,
                 ...      title text,
                 ...      year text,
                 ...      rating text,
                 ...      director text
                 ... );

root@cqlsh:entertainment> describe movies

CREATE TABLE entertainment.movies (
    id text PRIMARY KEY,
    director text,
    rating text,
    title text,
    year text
) WITH additional_write_policy = '99p'
    AND allow_auto_snapshot = true
    AND bloom_filter_fp_chance = 0.01
    AND caching = {'keys': 'ALL', 'rows_per_partition': 'NONE'}
    AND cdc = false
    AND comment = ''
    AND compaction = {'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy', 'max_threshold': '32', 'min_threshold': '4'}
    AND compression = {'chunk_length_in_kb': '16', 'class': 'org.apache.cassandra.io.compress.LZ4Compressor'}
    AND memtable = 'default'
    AND crc_check_chance = 1.0
    AND default_time_to_live = 0
    AND extensions = {}
    AND gc_grace_seconds = 864000
    AND incremental_backups = true
    AND max_index_interval = 2048
    AND memtable_flush_period_in_ms = 0
    AND min_index_interval = 128
    AND read_repair = 'BLOCKING'
    AND speculative_retry = '99p';  

# Copy data into Table    
root@cqlsh:entertainment> COPY entertainment.movies(id,title,year,rating,director) FROM '/home/project/partial_data.csv' WITH DELIMITER=',' AND HEADER=TRUE;

# OUTPUT
Using 15 child processes

Starting copy of entertainment.movies with columns [id, title, year, rating, director].
Processed: 100 rows; Rate:      77 rows/s; Avg. rate:     129 rows/s
100 rows imported from 1 files in 0.774 seconds (0 skipped).

Query 1

Count the number of rows in table movies

# Count number of rows in table
root@cqlsh:entertainment> select count(*) from movies;

 count
-------
   100

(1 rows)

Create Index

Create an index for the rating column in the table movies

  • use CREATE INDEX to Create the index then
  • Use describe on movies to show the specifics of the table
root@cqlsh:entertainment>CREATE INDEX IF NOT EXISTS rating_index
                         ON entertainment.movies (rating);

# Display the details of movies
root@cqlsh:entertainment> describe movies

CREATE TABLE entertainment.movies (
    id text PRIMARY KEY,
    director text,
    rating text,
    title text,
    year text
) WITH additional_write_policy = '99p'
    AND allow_auto_snapshot = true
    AND bloom_filter_fp_chance = 0.01
    AND caching = {'keys': 'ALL', 'rows_per_partition': 'NONE'}
    AND cdc = false
    AND comment = ''
    AND compaction = {'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy', 'max_threshold': '32', 'min_threshold': '4'}
    AND compression = {'chunk_length_in_kb': '16', 'class': 'org.apache.cassandra.io.compress.LZ4Compressor'}
    AND memtable = 'default'
    AND crc_check_chance = 1.0
    AND default_time_to_live = 0
    AND extensions = {}
    AND gc_grace_seconds = 864000
    AND incremental_backups = true
    AND max_index_interval = 2048
    AND memtable_flush_period_in_ms = 0
    AND min_index_interval = 128
    AND read_repair = 'BLOCKING'
    AND speculative_retry = '99p';

CREATE INDEX rating_index ON entertainment.movies (rating);

Query 2

Count the number of movies that are rated “G”

  • We will use SELECT and COUNT(*) along with
  • WHERE statement
root@cqlsh:entertainment> SELECT COUNT(*) FROM movies WHERE rating = 'G';

 count
-------
    32

(1 rows)

Warnings :
Aggregation query used without partition key