MongoDB and Cassandra are two of the most popular NoSQL databases, and they are often compared because they have similar features and capabilities. Both are designed to handle large amounts of unstructured data and are known for their scalability, high performance, and ability to handle high read and write loads. However, they have different architectures and design principles that make them suitable for different use cases.
MongoDB is a document-oriented database that follows a "collection" and "document" model, whereas Cassandra is a column-family based NoSQL database. MongoDB is a CP (Consistent and Partition-tolerant) system, which means it prioritizes consistency over availability. On the other hand, Cassandra is an AP (Available and Partition-tolerant) system, which prioritizes availability over consistency. This makes Cassandra more suitable for use cases that require high availability and can tolerate some inconsistencies in data.
In this project, I have done a comparison between MongoDB and Cassandra on CRUD (Create, Read, Update and Delete) operations at various consistency levels. This comparison allows us to understand the performance and behavior of both databases under different conditions and to determine which one is more suitable for a specific use case.
Two most popular Nosqls were compared on the basis of data loading, CRUD operation, CRUD operation at various consistency levels and latency rate. Details about this project is available in this document.
Lastly, visualisation was created using Mongocharts. Details about visualisation are provided in this notebook.
Below are the following codes used in this project.
• Installed the DB browser to access the sqlite zip file from the below link:
• One of the table team_analysis was created using the below join query and data was downloaded in local machine in csv format
SELECT * FROM Team t1 INNER JOIN Team_Attributes t2 ON t1.id = t2.ID INNER JOIN Team_History t3 ON t2.ID= t3.ID
Similar join queries were used to obtain other tables and after which the final csv files for the analysis were considered.
1. Docker Set Up : Download Docker for desktop from the below link:https://www.docker.com
2. Follow the instructions below for setting up a cassandra instance on Docker
3. Create three nodes in a cluster on docker using the below commands
4. Place all the files to be uploaded into cassandra instance in the same directory as the Docker is present
5. Open the cqlsh terminal in the same path as the files placed using the CLI option on the cassandra instance.
6. Create the KEYSPACE named BasketBall using the following command.
CREATE KEYSPACE BasketBall
WITH REPLICATION = {
'class' : 'NetworkTopologyStrategy',
'datacenter1' : 3
} ;
7. Use the KEYSPACE BasketBall created to further create the column families/tables required as per the csv files required for the analysis.
USE basketball;
8. Using the following command create the required column families such as player_analysis, game_analysis, team_analysis and bb_nalaysis in the KEYSPACE BasketBall.
player_analysis:
CREATE COLUMNFAMILY player_analysis (PLAYER_ID int PRIMARY KEY, PLAYER_NAME varchar, SCHOOL varchar, COUNTRY varchar, BIRTHDATE date, PLAYER_CURRENT_AGE int, HEIGHT int, WEIGHT int, SEASON_EXP int,POSITION varchar, ROSTERSTATUS varchar,TEAM_ID int, PTS float, REB float, AST float, PLAYER_NM varchar, CONTRACT_TYPE_2020_2021 varchar, SALARY_2020_2021 int, CONTRACT_TYPE_2021_2022 varchar, SALARY_2021_2022 int, CONTRACT_TYPE_2022_2023 varchar, SALARY_2022_2023 int, CONTRACT_TYPE_2023_2024 varchar, SALARY_2023_2024 float) WITH COMPACT STORAGE;
team_analysis
CREATE COLUMNFAMILY team_analysis (TEAM_ID int PRIMARY KEY,TEAM_NAME varchar,TEAM_CITY varchar,TEAM_SLUG varchar, YEAR_FOUNDED int,SALARY_2020_2021 int,SALARY_2021_2022 int,SALARY_2022_2023 int,SALARY_2023_2024 int,SALARY_2024_2025 int,SALARY_2025_2026 int,HOME_WIN_PERCENT float,HOME_LOSS_PERCENT float,AWAY_WIN_PERCENT float,AWAY_LOSS_PERCENT float,TOTAL_WIN_PERCENT float,TOTAL_LOSS_PERCENT float);
game_analysis:
CREATE COLUMNFAMILY game_analysis (TEAM_ID int PRIMARY KEY,SEASON int,LOCATION varchar,WIN_COUNT int, AVERAGE_2_POINT_GOAL_EFFICIENCY float,AVERAGE_3_POINT_GOAL_EFFICIENCY float,AVERAGE_2_POINT_GOAL_PERCENTAGE float, AVERAGE_3_POINT_GOAL_PERCENTAGE float,FREE_THROUGH_GOAL_EFFICIENCY float,FREE_THROUGH_GOAL_PERCENTAGE float,OFFENSIVE_REBOUND_PERCENTAGE float,AVERAGE_ASSISTS float,AVERAGE_PAINT_POINTS float,AVERAGE_2ND_CHANCE_POINTS float,DEFENSIVE_REBOUND_PERCENTAGE float,AVERAGE_NUMBER_OF_STEALS float,AVERAGE_NUMBER_OF_BLOCKS float,AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE float,AVERGAE_FOULS float,AVERAGE_TURNOVER float,GAME_COUNT float,TOTAL_WIN_COUNT float,TOTAL_AVERAGE_2_POINT_GOAL_EFFICIENCY float,TOTAL_AVERAGE_3_POINT_GOAL_EFFICIENCY float,TOTAL_AVERAGE_2_POINT_GOAL_PERCENTAGE float,TOTAL_AVERAGE_3_POINT_GOAL_PERCENTAGE float,TOTAL_FREE_THROUGH_GOAL_EFFICIENCY float,TOTAL_FREE_THROUGH_GOAL_PERCENTAGE float,TOTAL_OFFENSIVE_REBOUND_PERCENTAGE float,TOTAL_AVERAGE_ASSISTS float,TOTAL_AVERAGE_PAINT_POINTS float,TOTAL_AVERAGE_2ND_CHANCE_POINTS float,TOTAL_DEFENSIVE_REBOUND_PERCENTAGE float,TOTAL_AVERAGE_NUMBER_OF_STEALS float,TOTAL_AVERAGE_NUMBER_OF_BLOCKS float,TOTAL_AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE float,TOTAL_AVERGAE_FOULS float,TOTAL_AVERAGE_TURNOVER float,TOTAL_GAME_COUNT float,TEAM_NAME varchar,TEAM_SLUG varchar);
bb_analysis
CREATE TABLE bb_analysis(ID int PRIMARY KEY, TEAM_ID int, SEASON int, LOCATION varchar, WIN_COUNT int, AVERAGE_2_POINT_GOAL_EFFICIENCY float, AVERAGE_3_POINT_GOAL_EFFICIENCY float, AVERAGE_2_POINT_GOAL_PERCENTAGE float, AVERAGE_3_POINT_GOAL_PERCENTAGE float, FREE_THROUGH_GOAL_EFFICIENCY float, FREE_THROUGH_GOAL_PERCENTAGE float, OFFENSIVE_REBOUND_PERCENTAGE float, AVERAGE_ASSISTS float, AVERAGE_PAINT_POINTS float, AVERAGE_2ND_CHANCE_POINTS float, DEFENSIVE_REBOUND_PERCENTAGE float, AVERAGE_NUMBER_OF_STEALS float, AVERAGE_NUMBER_OF_BLOCKS float, AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE float, AVERGAE_FOULS float, AVERAGE_TURNOVER float, GAME_COUNT int, TOTAL_WIN_COUNT int, TOTAL_AVERAGE_2_POINT_GOAL_EFFICIENCY float, TOTAL_AVERAGE_3_POINT_GOAL_EFFICIENCY float, TOTAL_AVERAGE_2_POINT_GOAL_PERCENTAGE float, TOTAL_AVERAGE_3_POINT_GOAL_PERCENTAGE float, TOTAL_FREE_THROUGH_GOAL_EFFICIENCY float, TOTAL_FREE_THROUGH_GOAL_PERCENTAGE float, TOTAL_OFFENSIVE_REBOUND_PERCENTAGE float, TOTAL_AVERAGE_ASSISTS float, TOTAL_AVERAGE_PAINT_POINTS float, TOTAL_AVERAGE_2ND_CHANCE_POINTS float, TOTAL_DEFENSIVE_REBOUND_PERCENTAGE float, TOTAL_AVERAGE_NUMBER_OF_STEALS float, TOTAL_AVERAGE_NUMBER_OF_BLOCKS float, TOTAL_AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE float, TOTAL_AVERGAE_FOULS float, TOTAL_AVERAGE_TURNOVER float, TOTAL_GAME_COUNT int, TEAM_NAME varchar, TEAM_SLUG varchar, TEAM_CITY varchar, YEAR_FOUNDED int, SALARY_2020_2021 int, SALARY_2021_2022 int, SALARY_2022_2023 int, SALARY_2023_2024 int, SALARY_2024_2025 int, SALARY_2025_2026 int, HOME_WIN_PERCENT float, HOME_LOSS_PERCENT float, AWAY_WIN_PERCENT float, AWAY_LOSS_PERCENT float, TOTAL_WIN_PERCENT float, TOTAL_LOSS_PERCENT float, PLAYER_ID int, PLAYER_NAME varchar, SCHOOL varchar, COUNTRY varchar, BIRTHDATEPLAYER_CURRENT_AGE int, HEIGHT int, WEIGHT int, SEASON_EXP int, POSITION varchar, ROSTERSTATUS varchar, PTS float, REB float, AST float, PLAYER_NM varchar, CONTRACT_TYPE_2020_2021 varchar, PLAYER_SALARY_2020_2021 int, CONTRACT_TYPE_2021_2022 varchar, PLAYER_SALARY_2021_2022 int, CONTRACT_TYPE_2022_2023 varchar, PLAYER_SALARY_2022_2023 int, CONTRACT_TYPE_2023_2024 varchar, PLAYER_SALARY_2023_2024 int);
- Using the following command copy the data from the respective csv files into column families game_analysis, player_analysis, team_analysis and bb_analysis.
player_analysis
COPY basketball.player_analysis (PLAYER_ID,PLAYER_NAME,SCHOOL,COUNTRY,BIRTHDATE,PLAYER_CURRENT_AGE,HEIGHT,WEIGHT,SEASON_EXP,POSITION,ROSTERSTATUS,TEAM_ID,PTS,REB,AST,PLAYER_NM,CONTRACT_TYPE_2020_2021,SALARY_2020_2021,CONTRACT_TYPE_2021_2022,SALARY_2021_2022,CONTRACT_TYPE_2022_2023,SALARY_2022_2023,CONTRACT_TYPE_2023_2024,SALARY_2023_2024) FROM 'Player_Analysis.csv' WITH NULL='NULL' AND DELIMITER = ',' AND HEADER=TRUE;
team_analysis
COPY basketball.team_analysis (TEAM_ID,TEAM_NAME,TEAM_CITY,TEAM_SLUG,YEAR_FOUNDED,SALARY_2020_2021,SALARY_2021_2022,SALARY_2022_2023,SALARY_2023_2024,SALARY_2024_2025,SALARY_2025_2026,HOME_WIN_PERCENT ,HOME_LOSS_PERCENT ,AWAY_WIN_PERCENT,AWAY_LOSS_PERCENT,TOTAL_WIN_PERCENT,TOTAL_LOSS_PERCENT) FROM 'Team_Analysis.csv' WITH NULL='NULL' AND DELIMITER = ',' AND HEADER=TRUE;
game_analysis
COPY basketball.game_analysis (TEAM_ID,SEASON,LOCATION,WIN_COUNT, AVERAGE_2_POINT_GOAL_EFFICIENCY,AVERAGE_3_POINT_GOAL_EFFICIENCY,AVERAGE_2_POINT_GOAL_PERCENTAGE, AVERAGE_3_POINT_GOAL_PERCENTAGE,FREE_THROUGH_GOAL_EFFICIENCY,FREE_THROUGH_GOAL_PERCENTAGE,OFFENSIVE_REBOUND_PERCENTAGE,AVERAGE_ASSISTS,AVERAGE_PAINT_POINTS,AVERAGE_2ND_CHANCE_POINTS ,DEFENSIVE_REBOUND_PERCENTAGE,AVERAGE_NUMBER_OF_STEALS,AVERAGE_NUMBER_OF_BLOCKS,AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE,AVERGAE_FOULS,AVERAGE_TURNOVER,GAME_COUNT,TOTAL_WIN_COUNT ,TOTAL_AVERAGE_2_POINT_GOAL_EFFICIENCY,TOTAL_AVERAGE_3_POINT_GOAL_EFFICIENCY,TOTAL_AVERAGE_2_POINT_GOAL_PERCENTAGE,TOTAL_AVERAGE_3_POINT_GOAL_PERCENTAGE,TOTAL_FREE_THROUGH_GOAL_EFFICIENCY ,TOTAL_FREE_THROUGH_GOAL_PERCENTAGE,TOTAL_OFFENSIVE_REBOUND_PERCENTAGE,TOTAL_AVERAGE_ASSISTS,TOTAL_AVERAGE_PAINT_POINTS,TOTAL_AVERAGE_2ND_CHANCE_POINTS,TOTAL_DEFENSIVE_REBOUND_PERCENTAGE ,TOTAL_AVERAGE_NUMBER_OF_STEALS,TOTAL_AVERAGE_NUMBER_OF_BLOCKS,TOTAL_AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE,TOTAL_AVERGAE_FOULS,TOTAL_AVERAGE_TURNOVER,TOTAL_GAME_COUNT,TEAM_NAME ,TEAM_SLUG) FROM 'Game_Analysis.csv' WITH NULL='NULL' AND DELIMITER = ',' AND HEADER=TRUE;
basketball_analysis
COPY basketball.bb_analysis (ID, TEAM_ID, SEASON, LOCATION, WIN_COUNT, AVERAGE_2_POINT_GOAL_EFFICIENCY, AVERAGE_3_POINT_GOAL_EFFICIENCY, AVERAGE_2_POINT_GOAL_PERCENTAGE, AVERAGE_3_POINT_GOAL_PERCENTAGE, FREE_THROUGH_GOAL_EFFICIENCY, FREE_THROUGH_GOAL_PERCENTAGE, OFFENSIVE_REBOUND_PERCENTAGE, AVERAGE_ASSISTS, AVERAGE_PAINT_POINTS, AVERAGE_2ND_CHANCE_POINTS, DEFENSIVE_REBOUND_PERCENTAGE, AVERAGE_NUMBER_OF_STEALS, AVERAGE_NUMBER_OF_BLOCKS, AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE, AVERGAE_FOULS, AVERAGE_TURNOVER, GAME_COUNT, TOTAL_WIN_COUNT, TOTAL_AVERAGE_2_POINT_GOAL_EFFICIENCY, TOTAL_AVERAGE_3_POINT_GOAL_EFFICIENCY, TOTAL_AVERAGE_2_POINT_GOAL_PERCENTAGE, TOTAL_AVERAGE_3_POINT_GOAL_PERCENTAGE, TOTAL_FREE_THROUGH_GOAL_EFFICIENCY, TOTAL_FREE_THROUGH_GOAL_PERCENTAGE, TOTAL_OFFENSIVE_REBOUND_PERCENTAGE, TOTAL_AVERAGE_ASSISTS, TOTAL_AVERAGE_PAINT_POINTS, TOTAL_AVERAGE_2ND_CHANCE_POINTS, TOTAL_DEFENSIVE_REBOUND_PERCENTAGE, TOTAL_AVERAGE_NUMBER_OF_STEALS, TOTAL_AVERAGE_NUMBER_OF_BLOCKS, TOTAL_AVERAGE_POINTS_AFTER_TURNOVER_PERCENTAGE, TOTAL_AVERGAE_FOULS, TOTAL_AVERAGE_TURNOVER, TOTAL_GAME_COUNT, TEAM_NAME, TEAM_SLUG, TEAM_CITY, YEAR_FOUNDED, SALARY_2020_2021, SALARY_2021_2022, SALARY_2022_2023, SALARY_2023_2024, SALARY_2024_2025, SALARY_2025_2026, HOME_WIN_PERCENT, HOME_LOSS_PERCENT, AWAY_WIN_PERCENT, AWAY_LOSS_PERCENT, TOTAL_WIN_PERCENT, TOTAL_LOSS_PERCENT, PLAYER_ID, PLAYER_NAME, SCHOOL, COUNTRY, BIRTHDATEPLAYER_CURRENT_AGE, HEIGHT, WEIGHT, SEASON_EXP, POSITION, ROSTERSTATUS, PTS, REB, AST, PLAYER_NM, CONTRACT_TYPE_2020_2021, PLAYER_SALARY_2020_2021, CONTRACT_TYPE_2021_2022, PLAYER_SALARY_2021_2022, CONTRACT_TYPE_2022_2023, PLAYER_SALARY_2022_2023, CONTRACT_TYPE_2023_2024, PLAYER_SALARY_2023_2024) FROM 'Basketball_Analysis.csv' WITH NULL='NULL' AND DELIMITER = ',' AND HEADER=TRUE;
CRUD OPERATION
To read one record: SELECT * FROM team_analysis WHERE TEAM_ID = 1610612737 To read multiple records: SELECT * FROM game_analysis WHERE TEAM_ID = 1610612737 ALLOW FILTERING; To update a single record: UPDATE game_analysis SET TEAM_SLUG = ‘BTL’ WHERE id = 3; To update multiple records: UPDATE game_analysis SET TEAM_SLUG = ‘FFF’ WHERE ID IN (1,2,3,4,5,6,7,8,9,10)
To delete single record: DELETE FROM game_analysis WHERE id = 3; To delete entire column family(table): DROP COLUMNFAMILY player_analysis;
Insert values into team_analysis table INSERT INTO basketball.team_analysis (TEAM_ID,TEAM_NAME,TEAM_CITY,TEAM_SLUG,YEAR_FOUNDED,SALARY_2020_2021,SALARY_2021_2022,SALARY_2022_2023,SALARY_2023_2024,SALARY_2024_2025,SALARY_2025_2026,HOME_WIN_PERCENT ,HOME_LOSS_PERCENT ,AWAY_WIN_PERCENT,AWAY_LOSS_PERCENT,TOTAL_WIN_PERCENT,TOTAL_LOSS_PERCENT) VALUES (1610612778, ‘Charlotte Hornets’, ‘Charlotte’, ‘CAA’, 1988,108219000,8125900,44614400,31500000,0,0,0.54,0.46,0.3,0.7,0.45,0.56)
CONSISTENCY LEVELS- CRUD Operations: For Read Read Consistency Level: 1: CONSISTENCY ONE SELECT * FROM bb_analysis WHERE TEAM_ID =1610612738 ALLOW FILTERING; Read Consistency Level: 2: CONSISTENCY TWO SELECT * FROM bb_analysis WHERE TEAM_ID =1610612738 ALLOW FILTERING;
Read Consistency Level: ALL: CONSISTENCY ALL SELECT * FROM bb_analysis WHERE TEAM_ID =1610612738 ALLOW FILTERING;
For Write Update Consistency 1: CONSISTENCY ONE UPDATE bb_analysis SET TEAM_SLUG = ‘BTL’ WHERE id = 1;
Update Consistency QUORUM: CONSISTENCY QUORUM UPDATE bb_analysis SET TEAM_SLUG = ‘BTL’ WHERE id = 1; Update Consistency ALL: CONSISTENCY ALL UPDATE bb_analysis SET TEAM_SLUG = ‘BTL’ WHERE id = 1;
Insert Consistency 1: CONSISTENCY ONE INSERT INTO bb_analysis(id,TEAM_ID,SEASON,LOCATION)VALUES(2724,160612699,2022,’HOME’); Insert Consistency QUORUM: CONSISTENCY QUORUM INSERT INTO bb_analysis(id,TEAM_ID,SEASON,LOCATION)VALUES(2725,160612699,2022,’HOME’); Insert Consistency ALL: CONSISTENCY ALL INSERT INTO bb_analysis(id,TEAM_ID,SEASON,LOCATION)VALUES(2726,160612699,2022,’HOME’);
LATENCY “nodetool cfstats”
1 MongoDB server was installed on the local machine following the instruction given in the link below
https://treehouse.github.io/installation-guides/mac/mongo-mac.html
2 After downloading the mongo server, the respective directory was created to store the mongo data files.
Code: mkdir -p /srv/mongodb/rs0-0 /srv/mongodb/rs0-1 /srv/mongodb/rs0-2
3 Three replica sets were created with port number 27018, 27019, 27020 using the code below.
sudo mongod --port 27018 --dbpath /data/db/rs0-0 --replSet rs0 --bind_ip localhost
sudo mongod --port 27019 --dbpath /srv/mongodb/rs0-0 --replSet rs0 --bind_ip localhost
sudo mongod --port 27020 --dbpath /srv/mongodb/rs0-2 --replSet rs0 --bind_ip localhost
4 Then we connected to primary node port number 27018 in the new terminal using the code below: mongo –port 27018
5 We also connected to secondary nodes port number (27019 and 27020 )in the new shell using the code below: mongo –port 27019 , mongo –port 27020
6 Then after creating the nodes we initiated the replication process in the primary node using the code below:
rs.initiate({_id: "rs0",members: [{ _id: 0, host : "127.0.0.1:27018" },{ _id: 1, host : "127.0.0.1:27019" },{ _id: 2, host : "127.0.0.1:27020" }]})
7 Checked the status of nodes using the code below:
rs.status( )
8 We downloaded mongodb Compass from the below link: https://www.mongodb.com/try/download/compass
9 Created the two connections in the compass; first using the above mentioned port numbers 27018, 27019 & 27020 and second using the default port number 27017
10 Installed Pymongo using PIP install: pip install pymongo , Version number 3.12.3
11 Imported the pymongo module, Json and Pandas in jupyter notebook
12 Connected to Mongo localhost using the following command:
client= pymongo.MongoClient("mongodb://localhost:27017")
13 Uploaded the csv files into pandas dataframe using the command below:
df = pd.read_csv(r"Game_Analysis.csv")
14 The upload csv files in dataframe was converted in JSON format using the following command:
data = df .to_dict(orient="record")
15 Created Database “BasketBall” on Mongo Client using the following command:
database= client["BasketBall"]
16 Then collection was created in the same database (“BasketBall”) and converted JSON files were uploaded into collections using the following command: database.Game_Analysis.insert_many(data)
17 Total 4 collections named : game_analysis, player_analysis, team_analysis and bb_analysis were created in the database “BasketBall” on MongoDB Compass.
18 The same process mentioned above was followed to upload data into the other connection ie. for port number 27018
Steps for CRUD Operations: 1 To measure the time of CRUD operation we set the “profiling level (2)” and the all queries were performed. We used db.system.profile.find() after running the query to find the query execution stats. Query: db.setProfilingLevel(2) “Query for CRUD” db.system.profile.find().limit(1).sort({ts:-1})
To read/ retrieve a record. db.game_analysis.find({Team_ID: “1610612737”}) To update a single record db.game_analysis.updateOne({TEAM_ID:"1610612738"},{$set:{TEAM_SLUG:"BTL"},{upsert: true}) To update multiple records db.game_analysis.updateMany({TEAM_ID:"1610612738"},{$set:{TEAM_SLUG:"BTL"}) To remove multiple records from collection db.game_analysis.remove({ }) To remove single record from collection db.game_analysis.remove({Team_ID: “1610612738”}, { justOne:true}) To insert a single record in collection db.game_analysis.insertOne({id:272222,Team_ID:1610612699,SEASON:2022,LOCATION:"Away"}) To insert many records in collection db.game_analysis.insertMany([{id:272222,Team_ID:1610612699,SEASON:2022,LOCATION:"Away"},{id:272222,Team_ID:1610612700,SEASON:2022,LOCATION:"Away"}])
CONSISTENCY LEVELS CRUD Operations: For Read Read Consistency Level: “local”: db.basketball_analysis.find({Team_ID: “1610612737”}). readConcern(“local”) Read Consistency Level: “majority”: db.basketball_analysis.find({Team_ID: “1610612737”}). readConcern(“majority”) Read Consistency Level: “available”: db.basketball_analysis.find({Team_ID: “1610612737”}). readConcern(“available”)
For Updates Write Consistency Level: 1: db.basketball_analysis.updateMany({TEAM_ID:”1610612737”},{$set:{TEAM_SLUG: “BTL”}},{writeConcern:{w:1}}) Write Consistency Level: “majority”: db.basketball_analysis.updateMany({TEAM_ID:”1610612737”},{$set:{TEAM_SLUG: “BTL”}},{writeConcern:{w: “majority”}})
For Insert Write Consistency Level: 1: db.game_analysis.insertOne({id:272222,Team_ID:1610612699,SEASON:2022,LOCATION:"Away"},{writeConcern:{w:1}}) Write Consistency Level: “majority”: db.game_analysis.insertOne({id:272222,Team_ID:1610612699,SEASON:2022,LOCATION:"Away"},{writeConcern:{w: “majority”}})
LATENCY Query to measure latency for varying read and write operations for different collections db.player_analysis.latencyStats( { histograms: true } )
1 Registered on the MongoDB cloud using the link below:
https://www.mongodb.com/cloud/atlas/register
2 We then added a new database user.
3 Created a cluster named “newCluster”
4 Loaded the data as below:
Connected to MongoDB Compass by clicking on this link below
Connected to cluster in Compass
Created the collections in the Compass and Uploaded csv files directly in the Compass
Collections and data on the compass could be viewed on cloud as well
MongoDB Charts for Visualization:
Created charts for NBA player analysis using MongoDB charts on Atlas.
