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myHQ - Crunchbase Company Profile & Funding

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myHQ Coworking Space | my office, my way

myHQ’s Profile, Revenue and Employees. MyHQ is an India-based company that owns and operates a chain of co-working spaces, virtual offices and meeting rooms for businesses and entrepreneurs. myHQ’s primary competitors include 91Springboard, BHIVE WORKSPACE, Awfis and 16 more.

myHQ’s Competitors, Revenue, Number of Employees, Funding, ...

Your search ends with myHQ! We are on a mission to help businesses find their next great workspace. Our endeavors to do better as industry leaders are now stronger than ever as we are powered by ANAROCK, India’s leading real estate services company. We’ve established ourselves as India’s ...

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Similar Cos.Beer Or Coffee, myHQ, BluDesks, PassWork, CoXplore &amp; 2 others Disclaimer: Total Funding = Total Disclosed Equity Funding (Pre-exit) Let us walk you through our Companies Database and other offerings

myHQ - Company Profile

Information on acquisition, funding, cap tables, investors, and executives for myHQ. Use the PitchBook Platform to explore the full profile.

myHQ Company Profile: Acquisition & Investors | PitchBook

myHQ Coworking Space in Bangalore | My Office, My Way

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Workforce HQ provides transparency into key workforce metrics to help health system leaders optimize financial, operational, and clinical performance.

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myHQ offers a pay-per-use FlexiPass that allows individuals and teams to access over 700 flexible workspaces across the country through one pass, at ₹200 a day. It also provides fixed desks, cabins and managed offices for companies

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Meet and connect with like-minded people and companies at our weekly and monthly events. The 1 Visit Plan is available for INR 299 and gives you access to a myHQ Space for a single day. With the day plan you get INR 200 worth of redeemable credit along with all other myHQ Membership benefits.

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Find contact information for myHQ. Learn about their Business Services market share, competitors, and myHQ&#x27;s email format.

myHQ Company Profile | Management and Employees List

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myHQ

, one of the hottest apps of 2017, is reinventing what game shows look like in the 21st century. On HQ, you can tune in live every day to answer trivia questions for the chance to win thousands of dollars. HQ Trivia However, as HQ Trivia increases in popularity, it exposes itself to a significant risk. Hackers, hoping to make some extra cash, can programmatically Google the answers to the questions. If these hackers and their scripts are successful, it could ruin the fun of HQ for honest players. To explore the feasibility of hacking HQ, I wrote a script that wins the majority of HQ games. In this post, I explain how the script works, and how HQ can defend itself against these attacks. The script generally follows these three steps: Mirror phone screen to computer screen. Translate the image of the phone screen to computer-readable text. Using Google, run three separate approaches for finding the answer. Please note: other than some general testing, all of the methods described in this blog are used on YouTube videos of the live HQ game, but not on the game itself. It is against the terms and conditions of HQ to use scripts on the live game. Extracting The Text First, the script needs to extract text from the app in order to process it. I mirrored my phone screen to my laptop using QuickTime’s built-in and an Apple Lightning Cable, which ends up looking like this: screen mirroring feature After mirroring the screen, I programmed a Python script to automatically screenshot the screen at specified coordinates, using the “screencapture” shell command. Then, the script uses the to perform OCR (Optical Character Recognition), which converts the image to computer-readable text. Tesseract OCR library Now that the script has the parsed text for both the question and the answer choices, I then found three different approaches that all work with about 50–80% accuracy each. My final script runs all three approaches, so that I can review the total results for a final decision. Approach 1: Googling the Question The most intuitive approach is just to Google the question. This was an easy one-liner in Python: About 50% of the time, this approach shows a Google search results page with the answer on it. However, it’s often hard to parse all this text myself, and it reliably fails on any questions that are “Which of these…” questions, since those questions rely on not just the question, but also the answer choices. For example, completely fails on this question: Approach 1 Approach 2: Counting Answers on Results Page Now, what if we could automatically scan the page for the answer choices? This would be much faster than what a human could do. Additionally, Google exposes a , which allows the script to access Google’s search functionality without even opening a browser. Custom Search API For this approach, the script uses the Google Custom Search API to Google the question, and then counts the number of each answer choice that appears in the response (this includes not only the link titles, but also the little snippet that appears below the title). While is correct more often than , it can sometimes underperform. is superior than especially when it comes to “misconception” questions. Take a look at the following: Approach 2 Approach 1 Approach 1 Approach 2 The answer from looking at the Google results is clearly Mauna Kea. However, there are several instances of Mount Everest on the page. Furthermore, the Google results page spells it as “Mt. Everest” and “Mount Everest” — so just looking for the exact phrasing of the answer choice is insufficient for 100% accuracy. Approach 3: Counting Number of Search Results The last approach uses the Google Custom Search API to query the question three times, with a different answer choice appended to it each time. This essentially restricts the search results page to only include results that contain the answer choice. The script can then compare the number of search results that appear from each query. You normally see it right below your search bar like this: is far more effective for “Which of these” questions. However, it can still fail. For instance, take a look at this question: Approach 3 The correct answer is “Steel,” but shows “Tech” with the biggest number of search results, since it’s a hot topic and mentioned frequently with “best-performing” and “stocks.” Approach 3 As you can see, none of these approaches are fool-proof; however, when I run all three approaches simultaneously, I can get through all 12 questions for about 70–80% of the HQ videos on YouTube. This is quite remarkable for a game that gives out thousands of dollars to winners every day, seven days a week. Advice to HQ Dear HQ — here’s how you can stop people like me and other hackers from ruining the game for everyone. 1) Altering the UI to Impede OCR Right now, the HQ game appears to already have two UI formats for the question page, pre- and post-elimination: Left-side UI appears before the user is eliminated, right UI after elimination. Note the difference? Although seemingly insignificant, the right-side UI is actually harder for the Tesseract OCR Engine to parse, since the colors are less distinct. My script pre-processes the image and increases its contrast to circumvent this issue. Note that a player would only ever see the right-side UI screen if they already got eliminated from the game, but this was important for me since I used recorded YouTube clips for testing. However, what if HQ had 7–10 different UI formats that appeared pre-elimination, so that scripts can’t universally increase contrast to improve recognition? With UI formats like the ones below, it’s near impossible to reliably parse the text (of course, HQ will still need to prioritize user readability). While most are impractical, these UI screens could be modified to prioritize user-readability and still thwart OCR attempts. 2) “Hard-To-Google Questions” It would be an easy, but poor product decision for HQ to increase the question difficulty so that hackers can’t solve it, since overly difficult questions reduce the “fun” factor of the game. Instead, HQ should focus on including 3–4 “hard-to-google” questions in every game. What constitutes a “hard-to-google” question? Well, here are a few types of questions that HQ could target: “Which of these …” “<number> of the top <number> objects…” “How close is <entity> to <entity>” “What is this? <displays image>” “How many times …” “Why did…” In general, these questions are hard because it takes multiple google searches and several seconds of human scanning to find the right answer. 3) Run a script to classify questions When choosing questions to run on the app, I’m sure HQ already has some calculated measure of “difficulty.” HQ should have its software engineers create and run a script similar to my own, and then classify questions by how easy they are to be hacked or Googled. As long as HQ includes 3–4 of these questions per game, they’ll be protected against hackers. HQ Trivia is a promising new mobile game that could really shift the entire game show paradigm. Trivia bots and clever hackers will be no issue for the platform’s rapid growth, as long as HQ makes a few critical adjustments. Enjoyed this article? Check out some of my other articles below! How I Live With 115 Items My Financial System In One Infographic How I Read Books So They Last

I Hacked HQ Trivia But Here’s How They Can Stop Me

As data technology rapidly advances, understanding and mastering various tools and techniques becomes increasingly important. To this end, we are preparing to launch a Demo demonstration plan on the Apache SeaTunnel community on how to use connectors, inviting you all who are passionate about data synchronization technology to share your knowledge and practical experience! In this issue, we have invited Mr. Gao Jun, a PMC member of the community, to record a demo tutorial in the video which themed How to sync data from MySQL to Doris. If you are interested in this plan, feel free to email me(xiyan@whaleops.com) to participate in the Demo recording! Whether you are a data engineer, developer, or technology enthusiast, you are welcome to showcase your technical talents. If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! Demo Goals Our goal is to create a platform for sharing and learning, helping community members better understand and apply various data connectors through specific Demo demonstrations and corresponding documentation. These demos can help beginners learn quickly and also provide a stage for senior experts to showcase innovative solutions. https://youtu.be/_xB9BidoNOA?embedable=true https://youtu.be/T8AZVxuzfeQ?embedable=true https://youtu.be/kzAvfqnO4yc?embedable=true MySQL to Doris JDBC MySQL Connector Description Read data from external data sources via JDBC. Supported MySQL Versions 5.5/5.6/5.7/8.0 Supported Engines SparkFlinkSeaTunnel Zeta Dependencies Spark/Flink engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. SeaTunnel Zeta engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. Key Features of MySQL Connector Batch processing
Stream processing
Exactly once
Column projection
Parallel processing
Support for user-defined sharding
Support for reading from multiple tables Supports query SQL and can achieve projection effect. Supported Data Source Information Database Dependencies Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Data Type Mapping Source Options Tips If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. Task Example Simple: This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. # Defining the runtime environment
env {
  # You can set flink configuration here
  execution.parallelism = 2
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        query = "select * from type_bin limit 16"
    }
}
transform {
    # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
} Parallel: Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
} Parallel Boundary: It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        partition_column = "id"
        # Read start boundary
        partition_lower_bound = 1
        # Read end boundary
        partition_upper_bound = 500
        partition_num = 10
    }
} Above is the code and documentation explanation shown in the video. You can combine it with the explanations in the video for hands-on practice. If you are interested in recording, please continue reading below. How to submit your Demo recording Participating in the Demo recording is simple! First, choose a connector you are familiar with and prepare a 5 to 10-minute video demonstration. Make sure your video clearly shows how to configure and use the connector, what problems it solves, and any possible best practices. Once you have your demo video ready, you can email me via debra.chen12@gmail.com. Remember to include relevant descriptive information, such as the connector you’re demonstrating and an overview of the video content. That’s how you can participate in the Demo recording, take action now! As data technology rapidly advances, understanding and mastering various tools and techniques becomes increasingly important. To this end, we are preparing to launch a Demo demonstration plan on the Apache SeaTunnel community on how to use connectors, inviting you all who are passionate about data synchronization technology to share your knowledge and practical experience! In this issue, we have invited Mr. Gao Jun, a PMC member of the community, to record a demo tutorial in the video which themed How to sync data from MySQL to Doris . If you are interested in this plan, feel free to email me(xiyan@whaleops.com) to participate in the Demo recording! Whether you are a data engineer, developer, or technology enthusiast, you are welcome to showcase your technical talents. How to sync data from MySQL to Doris If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! Demo Goals Our goal is to create a platform for sharing and learning, helping community members better understand and apply various data connectors through specific Demo demonstrations and corresponding documentation. These demos can help beginners learn quickly and also provide a stage for senior experts to showcase innovative solutions. https://youtu.be/_xB9BidoNOA?embedable=true https://youtu.be/_xB9BidoNOA?embedable=true https://youtu.be/T8AZVxuzfeQ?embedable=true https://youtu.be/T8AZVxuzfeQ?embedable=true https://youtu.be/kzAvfqnO4yc?embedable=true https://youtu.be/kzAvfqnO4yc?embedable=true MySQL to Doris JDBC MySQL Connector JDBC MySQL Connector JDBC MySQL Connector Description Read data from external data sources via JDBC. Supported MySQL Versions 5.5/5.6/5.7/8.0 5.5/5.6/5.7/8.0 Supported Engines SparkFlinkSeaTunnel Zeta SparkFlinkSeaTunnel Zeta SparkFlinkSeaTunnel Zeta Dependencies Spark/Flink engine Spark/Flink engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. SeaTunnel Zeta engine SeaTunnel Zeta engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. Key Features of MySQL Connector Batch processing Stream processing Exactly once Column projection Parallel processing Support for user-defined sharding Support for reading from multiple tables Batch processing Stream processing Exactly once Column projection Parallel processing Support for user-defined sharding Support for reading from multiple tables Supports query SQL and can achieve projection effect. Supports query SQL and can achieve projection effect. Supports query SQL and can achieve projection effect. Supported Data Source Information Database Dependencies Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Data Type Mapping Source Options Tips If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. Task Example Simple: This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. # Defining the runtime environment
env {
  # You can set flink configuration here
  execution.parallelism = 2
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        query = "select * from type_bin limit 16"
    }
}
transform {
    # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
} # Defining the runtime environment
env {
  # You can set flink configuration here
  execution.parallelism = 2
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        query = "select * from type_bin limit 16"
    }
}
transform {
    # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
} Parallel: Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
} source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
} Parallel Boundary: It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        partition_column = "id"
        # Read start boundary
        partition_lower_bound = 1
        # Read end boundary
        partition_upper_bound = 500
        partition_num = 10
    }
} source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        partition_column = "id"
        # Read start boundary
        partition_lower_bound = 1
        # Read end boundary
        partition_upper_bound = 500
        partition_num = 10
    }
} Above is the code and documentation explanation shown in the video. You can combine it with the explanations in the video for hands-on practice. If you are interested in recording, please continue reading below. How to submit your Demo recording Participating in the Demo recording is simple! First, choose a connector you are familiar with and prepare a 5 to 10-minute video demonstration. Make sure your video clearly shows how to configure and use the connector, what problems it solves, and any possible best practices. Once you have your demo video ready, you can email me via debra.chen12@gmail.com. Remember to include relevant descriptive information, such as the connector you’re demonstrating and an overview of the video content. That’s how you can participate in the Demo recording, take action now!

How to Sync Data From MySQL to Doris Using SeaTunnel 

Step 1: Install the necessary libraries To connect to a database and query data, you need to install the following two libraries: pandas: A Python library for data analysis, including functions for reading and writing data.
sqlalchemy: A Python library for interacting with relational databases, allowing you to use Python with various databases. You can install these libraries using the following command in the command prompt or terminal: pip install pandas
pip install sqlalchemy Step 2: Connect to the database Connecting to a database requires the following information: Database Type: The type of database you want to connect to, such as MySQL, PostgreSQL, etc.
Host Name: The host name or IP address where the database is located.
Port Number: The port number of the database, usually the default port number.
Username: The username required to connect to the database.
Password: The password required to connect to the database.
Database Name: The name of the database you want to connect to. You can use the following Python code to connect to the database: from sqlalchemy import create_engine

# Connect to a MySQL database
engine = create_engine('mysql://username:password@hostname:port/databasename')

# Connect to a PostgreSQL database
engine = create_engine('postgresql://username:password@hostname:port/databasename')

# Connect to an Oracle database, requires cx_Oracle library to be installed
engine = create_engine('oracle+cx_oracle://username:password@hostname:port/databasename') Step 3: Query data using the read_sql function Use the read_sql function from pandas to query data from the database. The read_sql function requires two parameters: SQL Query: The SQL query you want to execute.
Database Connection: The database connection you created earlier. Here is an example of querying data: import pandas as pd

# Execute SQL query and store the result in a DataFrame
df = pd.read_sql('SELECT * FROM mytable', engine)

# Print the DataFrame
print(df) Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn or X(@Luca_DataTeam). Happy exploring!👋 Step 1: Install the necessary libraries To connect to a database and query data, you need to install the following two libraries: pandas: A Python library for data analysis, including functions for reading and writing data. sqlalchemy: A Python library for interacting with relational databases, allowing you to use Python with various databases. pandas : A Python library for data analysis, including functions for reading and writing data. pandas sqlalchemy : A Python library for interacting with relational databases, allowing you to use Python with various databases. sqlalchemy You can install these libraries using the following command in the command prompt or terminal: pip install pandas
pip install sqlalchemy pip install pandas
pip install sqlalchemy Step 2: Connect to the database Connecting to a database requires the following information: Database Type: The type of database you want to connect to, such as MySQL, PostgreSQL, etc. Host Name: The host name or IP address where the database is located. Port Number: The port number of the database, usually the default port number. Username: The username required to connect to the database. Password: The password required to connect to the database. Database Name: The name of the database you want to connect to. Database Type : The type of database you want to connect to, such as MySQL, PostgreSQL, etc. Database Type Host Name : The host name or IP address where the database is located. Host Name Port Number : The port number of the database, usually the default port number. Port Number Username : The username required to connect to the database. Username Password : The password required to connect to the database. Password Database Name : The name of the database you want to connect to. Database Name You can use the following Python code to connect to the database: from sqlalchemy import create_engine

# Connect to a MySQL database
engine = create_engine('mysql://username:password@hostname:port/databasename')

# Connect to a PostgreSQL database
engine = create_engine('postgresql://username:password@hostname:port/databasename')

# Connect to an Oracle database, requires cx_Oracle library to be installed
engine = create_engine('oracle+cx_oracle://username:password@hostname:port/databasename') from sqlalchemy import create_engine

# Connect to a MySQL database
engine = create_engine('mysql://username:password@hostname:port/databasename')

# Connect to a PostgreSQL database
engine = create_engine('postgresql://username:password@hostname:port/databasename')

# Connect to an Oracle database, requires cx_Oracle library to be installed
engine = create_engine('oracle+cx_oracle://username:password@hostname:port/databasename') Step 3: Query data using the read_sql function Use the read_sql function from pandas to query data from the database. The read_sql function requires two parameters: SQL Query: The SQL query you want to execute. Database Connection: The database connection you created earlier. SQL Query : The SQL query you want to execute. SQL Query Database Connection : The database connection you created earlier. Database Connection Here is an example of querying data: import pandas as pd

# Execute SQL query and store the result in a DataFrame
df = pd.read_sql('SELECT * FROM mytable', engine)

# Print the DataFrame
print(df) import pandas as pd

# Execute SQL query and store the result in a DataFrame
df = pd.read_sql('SELECT * FROM mytable', engine)

# Print the DataFrame
print(df) Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn or X (@Luca_DataTeam). Happy exploring!👋 LinkedIn X

How to Connect to Oracle, MySql and PostgreSQL Databases Using Python

Choosing the right database is a pivotal decision for full-stack developers, impacting everything from application performance to scalability. PostgreSQL and MySQL stand out as two of the most popular open-source relational database management systems. Each brings its own set of strengths to the table, tailored to different development needs. Let's explore these differences, dive into installation on Linux, and discuss security and backup strategies to help you make an informed decision for your next project. Transactional Support and ACID Compliance PostgreSQL and MySQL both support the ACID (Atomicity, Consistency, Isolation, Durability) principles, which are crucial for reliable transaction management. PostgreSQL is celebrated for its robust support for complex transactions and strict ACID compliance. It's especially suited for applications that demand reliable transactions, such as financial or medical records management. MySQL, with its InnoDB storage engine, offers strong ACID compliance as well, but its default transaction isolation level is "Repeatable Read," balancing performance and consistency. Consider these transaction examples to appreciate the SQL syntax nuances between PostgreSQL and MySQL: In PostgreSQL, to insert a new employee and assign them to a project, you might use a transaction block with a serial ID: BEGIN;
INSERT INTO employees (name, role, hire_date) VALUES ('Jane Doe', 'Developer', '2023-01-10');
UPDATE project_assignments SET project_id = 2 WHERE employee_id = CURRVAL('employees_id_seq');
COMMIT; In MySQL, a similar operation could look like this, leveraging : LAST_INSERT_ID() START TRANSACTION;
INSERT INTO employees (name, role, hire_date) VALUES ('John Smith', 'Project Manager', '2023-02-15');
UPDATE projects SET status = 'Active' WHERE id = LAST_INSERT_ID();
COMMIT; Performance and Scalability When evaluating the performance and scalability of PostgreSQL and MySQL, it's essential to consider the specific use case of your application. MySQL is traditionally favored for its high-speed read operations, making it an excellent choice for read-heavy applications such as content management systems or blogging platforms. PostgreSQL, on the other hand, excels in scenarios requiring heavy writes and complex queries, like analytics applications or systems with complex data relationships. Examples: : Consider a blogging platform where the majority of the database operations are read (fetching posts, comments, etc.). MySQL's default storage engine, InnoDB, is highly optimized for read operations, providing fast data retrieval. MySQL for Read-Heavy Scenarios SELECT post_title, post_content FROM blog_posts WHERE post_date > '2023-01-01'; This query, running on a MySQL database, would efficiently fetch blog posts from the beginning of the year, benefiting from MySQL's read optimizations. : In an application processing financial transactions, where data integrity and complex writes are crucial, PostgreSQL's advanced transaction management shines. PostgreSQL for Write-Heavy Scenarios BEGIN;
INSERT INTO transactions (user_id, amount, transaction_date) VALUES (1, -100.00, '2023-04-05');
UPDATE accounts SET balance = balance - 100.00 WHERE user_id = 1;
COMMIT; This transaction, ensuring atomicity and consistency, demonstrates PostgreSQL's strength in handling complex, write-heavy operations. Extensibility and Advanced Features PostgreSQL PostgreSQL is highly extensible, supporting a vast array of advanced features out of the box, including: : PostgreSQL supports geometric data types and custom types and even allows for complex types like JSONB, enabling developers to store and query JSON-formatted data efficiently. Advanced Data Types SELECT * FROM orders WHERE customer_details->>'city' = 'San Francisco'; This query utilizes the JSONB data type to efficiently query JSON data stored in the table, looking for orders from customers in San Francisco. orders : PostgreSQL provides powerful text search capabilities that can search through large volumes of text data quickly. Full Text Search SELECT * FROM articles WHERE to_tsvector('english', content) @@ to_tsquery('english', 'PostgreSQL & databases'); This example demonstrates searching articles that contain both "PostgreSQL" and "databases", showcasing PostgreSQL's full-text search functionality. MySQL MySQL's extensibility includes features such as: : While not as advanced as PostgreSQL's JSONB, MySQL's JSON data type allows for efficient storage and querying of JSON documents. JSON Support SELECT * FROM products WHERE JSON_EXTRACT(info, '$.manufacturer') = 'Acme'; This query searches for products in the table where the column (stored as JSON) contains 'Acme' as the manufacturer. products info Developer Tools and Ecosystem PostgreSQL Tools: : The most popular and feature-rich open-source administration and development tool for PostgreSQL. pgAdmin pgAdmin Download : An extension that adds support for geographic objects to PostgreSQL, turning it into a spatial database. PostGIS PostGIS Documentation MySQL Tools: : An integrated tools environment for database design, SQL development, administrative tasks, and more. MySQL Workbench MySQL Workbench Download : A free software tool written in PHP, intended to handle the administration of MySQL over the Web. phpMyAdmin phpMyAdmin Download Security and Backups Security and backup strategies are crucial for any database management system, ensuring data integrity and availability. Both PostgreSQL and MySQL support SSL encryption for data in transit, role-based access control for fine-grained permission management, and the ability to enhance security through "chroot" jails. : PostgreSQL Backup with Compression and Encryption pg_dump mydatabase | gzip | openssl enc -aes-256-cbc -e > mydatabase_backup.sql.gz.enc This command creates a compressed and encrypted backup of the PostgreSQL database, utilizing for compression and for encryption. mydatabase gzip openssl : MySQL Backup with Compression and Encryption mysqldump -u user -p mydatabase | gzip | openssl enc -aes-256-cbc -e > mydatabase_backup.sql.gz.enc Similar to the PostgreSQL example, this command performs a backup of the MySQL database, with compression and encryption applied for security and efficiency. mydatabase For in-depth security and backup strategies, consult the official documentation: PostgreSQL: | Security Backup MySQL: | Security Backup Installation on Linux PostgreSQL On Ubuntu or Debian-based systems, installing PostgreSQL is straightforward: sudo apt update
sudo apt-get install postgresql postgresql-contrib Refer to the for more details. official PostgreSQL installation guide MySQL Similarly, for MySQL: sudo apt update
sudo apt-get install mysql-server The provides comprehensive instructions. MySQL installation documentation Conclusion The choice between PostgreSQL and MySQL for full-stack development hinges on the specific requirements of your project, the nature of your data, and the complexity of the operations you intend to perform. PostgreSQL offers unparalleled extensibility and advanced features, making it ideal for projects that require robust data integrity, complex queries, and extensive data types. Its ability to handle write-heavy applications and support for advanced data structures and full-text search makes it a powerhouse for analytics and applications dealing with complex data relationships. On the other hand, MySQL shines in scenarios requiring high-speed read operations and straightforward scalability, making it a go-to for web applications, content management systems, and blogging platforms where performance and simplicity are key. Its widespread adoption, coupled with strong community support and a plethora of development tools, ensures a reliable and efficient development experience. Both databases come equipped with comprehensive security features and flexible backup options, ensuring that data integrity and disaster recovery capabilities are built into your application from the ground up. The rich ecosystems surrounding PostgreSQL and MySQL provide developers with an array of tools and resources, further enhancing the development experience and offering paths to solve virtually any database challenge. Ultimately, the decision between PostgreSQL and MySQL should be made with careful consideration of your project’s current needs and future growth. Both databases have proven their reliability and performance in the hands of startups and tech giants alike, showcasing their ability to support the most demanding applications and the most innovative projects. By understanding the strengths and capabilities of each, developers can make informed decisions that best suit the requirements of their full-stack projects, laying a solid foundation for success. Stay Connected If you enjoyed this article and want to explore more about web development, feel free to connect with me on various platforms: dev.to hackernoon.com hashnode.com twitter.com instagram.com personal portfolio v1 Your feedback and questions are always welcome.
Keep learning, coding, and creating amazing web applications.

PostgreSQL or MySQL: What Should I Choose for My Full-Stack Project?

Introduction Functional indexes were introduced in MySQL 8.0 Functional indexes, introduced in MySQL 8.0 in 2018, represent a significant leap forward in database optimization techniques. By allowing indexes to be created on expressions or functions of column values, they provide a very flexible tool for improving query performance, especially in scenarios involving calculated columns or complex data retrieval conditions. In this article, I will explain what functional indexes are, how they work, how to use them in , and show some tricks for getting the most out of them. MySQL What is a functional index? A functional index is an index that is created based on the result of a function or expression. Take a look at the following examples: # Creating a functional index on the result of a function
CREATE INDEX index_name ON table_name ((function_name(column_name)));

# Creating a functional index on the result of an expression
CREATE INDEX index_name ON table_name ((column_name + 1));

# Creating a functional index on the result of a function and an expression
CREATE INDEX index_name ON table_name ((function_name(column_name) + 1)); How do functional indexes work? Functional indexes work by creating an index on the result of a function or expression. This means that the index will be created on the result of the function or expression, not on the column itself. One important note is that you cannot use volatile functions like , , and in functional indexes. This is because the result of these functions is not deterministic and, therefore, cannot be used to create an index. NOW() , UUID() RAND() How to use functional indexes in MySQL Creating a functional index To create a functional index, you need to use the statement. CREATE INDEX CREATE INDEX index_name ON table_name ((function_name(column_name))); Or you can define the index as part of the statement. CREATE TABLE CREATE TABLE table_name
(
    column_name int NOT NULL DEFAULT '0',
    KEY `index_name` ((function_name(`column_name`)))
); Under the hood, MySQL will create a new virtual generated column and create an index on it. This column will not be displayed in the list of columns by default or in the result of statement. In order to view such columns, you can perform the following query: SHOW CREATE TABLE table_name SHOW EXTENDED COLUMNS FROM table_name; And the result will look something like this. You can see column with the name . VIRTUAL GENERATED !hidden!index_name!0!0 Field Type Null Key Default Extra id bigint unsigned NO PRI null auto_increment column_name int NO 0 !hidden!index_name!0!0 bigint NO MUL 0 VIRTUAL GENERATED DB_TRX_ID NO null DB_ROLL_PTR NO null Using a functional index in a query Let's use the following table, for example. CREATE TABLE table_name
(
    id          bigint UNSIGNED NOT NULL AUTO_INCREMENT,
    column_name int             NOT NULL DEFAULT '0',
    PRIMARY KEY (id),
    KEY my_functional_index (((ABS(column_name) + 1)))
); And execute the following query: select * from table_name where abs(column_name) + 1 = 42; MySQL will use in order to obtain data for that query because the condition fully matches the definition of our functional index. my_functional_index explain select * from table_name where abs(column_name) + 1 = 42; id select_type table partitions type possible_keys key key_len ref rows filtered Extra 1 SIMPLE table_name null ref index_name index_name 8 const 1 100 null But if we try to use different conditions, for example, , MySQL will not be able to use our index. abs(column_name) + 2 Dropping a functional index To drop a functional index, you need to use the statement. It's exactly the same as for the regular index. DROP INDEX DROP INDEX index_name ON table_name; Useful tricks with functional indexes. Indexing on Calculated Columns Suppose you have a table of sales with columns price (the price of an item) and quantity (the number of items sold). And you often run queries to calculate the total sale amount (price * quantity). A functional index on this expression can speed up these queries. CREATE INDEX idx_total_amount ON sales((price * quantity)); Indexing on Date Parts You can use functional indexes to improve the performance of selecting date parts such as month or year. For example, in a table of events with a datetime column event_date, you often filter by the year: CREATE INDEX idx_event_year ON events((YEAR(event_date))); Indexing on String Manipulation Functional indexes are handy when you frequently perform operations like extracting substrings. For instance, if you have a users table with an email column and you often search for users based on the domain part of the email, a functional index can be created on the substring after the '@' character: CREATE INDEX idx_email_domain ON users((SUBSTRING(email FROM LOCATE('@', email)))); Indexing on JSON Data In a table with JSON data, you might frequently access a specific value within the JSON object. For example, in a products table with JSON column attributes, you often need to filter products based on a specific attribute like "color": CREATE INDEX idx_color ON products((JSON_UNQUOTE(JSON_EXTRACT(attributes, '$.color')))); Indexing on Case-Insensitive Search If you need to perform case-insensitive searches on a text column frequently, a functional index can be created using the function. For a books table with a title column: LOWER() CREATE INDEX idx_title_lower ON books((LOWER(title))); Indexing Concatenated Columns In scenarios where you frequently concatenate two or more columns, a functional index can be created on the concatenated result. For example, in a people table with and columns: first_name last_name CREATE INDEX idx_full_name ON people((CONCAT(first_name, ' ', last_name))); Indexing on Normalized Data In situations where you want to search on a normalized version of a string (like removing accents), you can create an index on this normalized version. For a table international_customers with a name column: CREATE INDEX idx_normalized_name ON international_customers((CONVERT(name USING ascii))); Indexing on Hashed Values If you query a large text column with exact matches, it can be more efficient to index on a hash of the text: CREATE INDEX idx_hashed_big_text ON books((MD5(big_text_column))); This approach might be especially useful in case you need to create a unique index on big text columns. CREATE UNIQUE INDEX unique_idx_hashed_big_text ON books((MD5(big_text_column))); Indexing for Advanced Date Queries For complex date queries, like extracting the week number and year, a functional index can improve efficiency: CREATE INDEX idx_week_year ON events((YEAR(event_date)), (WEEK(event_date))); Optimizing Boolean Expressions For tables where queries frequently involve boolean expressions, creating a functional index on the expression can improve performance. For example, in a products table where you often filter by items that are both in stock and on sale: CREATE INDEX idx_in_stock_on_sale ON products(((in_stock = 1 AND on_sale = 1))); Conclusion In this article, we have explained what functional indexes are, how they work, and how to use them in MySQL.
We have also shown some tricks for getting the most out of them. I hope you found this article helpful and that it will help you improve your database performance. If you have any questions or comments, please leave them below!

The Power of Functional Indexes in MySQL: A Comprehensive Guide

This post is about building a unified OLAP platform. An insurance company tries to build a data warehouse that can undertake all their customer-facing, analyst-facing, and management-facing data analysis workloads. The main tasks include: : This is for insurance customers to check their contract details by their contract ID. It should also support filters such as coverage period, insurance types, and claim amount. Self-service insurance contract query : Analysts develop their reports based on different data dimensions as they need, so they can extract insights to facilitate product innovation and their anti-fraud efforts. Multi-dimensional analysis : This is to create visual overview of the insurance sales trends and the horizontal and vertical comparison of different metrics. Dashboarding Component-Heavy Data Architecture The user started with Lambda architecture, spliting their data pipeline into a batch processing link and a stream processing link. For real-time data streaming, they apply Flink CDC; for batch import, they incorporate Sqoop, Python, and DataX to build their own data integration tool named Hisen. Then, the real-time and offline data meets in the data warehousing layer, which is made up of five components. ClickHouse The data warehouse is of flat table design and ClickHouse is superb in flat table reading. But as business evolves, things become challenging in two ways: To support cross-table joins and point queries, the user requires the star schema, but that's difficult to implement in ClickHouse. Changes in insurance contracts need to be updated in the data warehouse in real time. In ClickHouse, that is done by recreating a flat table to overwrite the old one, which is not fast enough. MySQL After calculation, data metrics are stored in MySQL, but as the data size grows, MySQL starts to struggle, with emerging problems like prolonged execution time and errors thrown. Apache Hive + Presto Hive is the main executor in the batch processing link. It can transform, aggregate, query offline data. Presto is a complement to Hive for interactive analysis. Apache HBase HBase undertakes primary key queries. It reads customer status from MySQL and Hive, including customer credits, coverage period, and sum insured. However, since HBase does not support secondary indexes, it has limited capability in reading non-primary key columns. Plus, as a NoSQL database, HBase does not support SQL statements. The components have to work in conjunction to serve all needs, making the data warehouse too much to take care of. It is not easy to get started with because engineers must be trained on all these components. Also, the complexity of architecture adds to the risks of latency. So the user tried to look for a tool that ticks more boxes in fulfilling their requirements. The first thing they need is real-time capabilities, including real-time writing, real-time updating, and real-time response to data queries. Secondly, they need more flexibility in data analysis to support customer-facing self-service queries, like multi-dimensional analysis, join queries of large tables, primary key indexes, roll-ups, and drill-downs. Then, for batch processing, they also want high throughput in data writing. They eventually made up their mind with . Apache Doris Replacing Four Components with Apache Doris Apache Doris is capable of both real-time and offline data analysis, and it supports both high-throughput interactive analysis and high-concurrency point queries. That's why it can replace ClickHouse, MySQL, Presto, and Apache HBase and work as the unified query gateway for the entire data system. The improved data pipeline is a much cleaner Lambda architecture. Apache Doris provides a wide range of data ingestion methods. It's quick in data writing. On top of this, it also implements Merge-on-Write to improve its performance on concurrent point queries. Reduced Cost The new architecture has reduced the user's cost in human efforts. For one thing, the much simpler data architecture leads to much easier maintenance; for another, developers no longer need to join the real-time and offline data in the data serving API. The user can also save money with Doris because it supports tiered storage. It allows the user to put their huge amount of rarely accessed historical data in object storage, which is much cheaper to hoard data. Higher Efficiency Apache Doris can reach a QPS of 10,000s and respond to billions of point queries within milliseconds, so the customer-facing queries are easy for it to handle. Tiered storage that separates hot data from cold data also increases their query efficiency. Service Availability As a unified data warehouse for storage, computation, and data services, Apache Doris allows for easy disaster recovery. With less components, they don't have to worry about data loss or duplication. An important guarantee of service availability for the user is the Cross-Cluster Replication (CCR) capability of Apache Doris. It can synchronize data from cluster to cluster within minutes or even seconds, and it implements two mechanisms to ensure data reliability: : This mechanism can automatically log the data changes and generate a LogID for each data modification operation. The incremental LogIDs make sure that data changes are traceable and orderly. Binlog : In the case of system meltdown or emergencies, data will be put into disks. Data persistence A Deeper Look into Apache Doris Apache Doris can replace the ClickHouse, MySQL, Presto, and HBase because it has a comprehensive collection of capabilities all along the data processing pipeline. In data ingestion, it enables low-latency real-time writing based on its support for Flink CDC and Merge-on-Write. It guarantees Exactly-Once writing by its Label mechanism and transactional loading. In data queries, it supports both Star Schema and flat table aggregation, so it can provide high performance in bother multi-table joins and large single table queries. It also provides various ways to speed up different queries, like for full-text search and range queries, short-circuit plan and prepared statements for point queries. inverted index

Less Components, Higher Performance: Apache Doris instead of ClickHouse, MySQL, Presto, and HBase

Imagine watching your favorite movies and TV shows anytime, anywhere, without worrying about slow loading times. That's the power of a movie streaming web app like Netflix. But what if you could build your movie streaming web app without managing the infrastructure? That's where Aptible comes in. Aptible is a platform that streamlines the code shipping process for development teams, facilitating deployment, monitoring, and infrastructure scaling. This means you can focus on building your app while Aptible takes care of the rest. This article will show you how to build a movie streaming web app like Netflix using Aptible, from choosing the right technologies to deploying your app to production. Choosing the right technologies for a streaming web app Streaming web apps need to handle many users and a high traffic volume. The technologies you choose should be able to scale to meet the demands of your app. Scalability: Streaming web apps need to deliver a high-quality user experience. This means that the technologies you choose should be able to deliver video and audio content quickly and smoothly. Performance: Streaming web apps need to be secure. The technologies you choose should be able to protect your users' data and prevent unauthorized access to your app. Security: Streaming web apps can be expensive to build and maintain. The technologies you choose should be cost-effective and scalable to meet your budget. Cost: Node.js, MySQL, and Aptible are well-suited technologies for building scalable, performant, and secure streaming web apps. What are the benefits of using Aptible to build a movie streaming web app? Simplified deployment Aptible makes it easy to deploy your movie streaming web app to production. You can upload your code to Aptible, and it will take care of the rest, including setting up the necessary infrastructure and configuring your app. Automatic scaling Aptible can automatically scale your movie streaming web app up or down based on demand. This is important for movie streaming apps, which can experience spikes in traffic at certain times of the day or during popular movie releases. Built-in monitoring Aptible provides built-in monitoring for your movie streaming web app. You can track your app's performance and identify potential problems early on. Aptible provides a variety of metrics, including CPU usage, memory usage, and response times, which can help you keep your movie streaming web app running smoothly and prevent major outages. Security features Aptible provides various security features to protect your movie streaming web app from attack. These features include: Aptible provides firewalls to protect your app from unauthorized access. Firewalls: Aptible provides IDS to detect and block malicious traffic. Intrusion detection systems (IDS): Aptible encrypts your app's data at rest and in transit. Data encryption: Teams: Aptible's team’s feature allows you to create groups of users and assign different permissions to each team. Environments: Aptible's environments feature allows you to create multiple isolated environments for your app. This can be useful for testing your app and deploying changes in a controlled manner. Backups: Aptible automatically backs up your app's data regularly. This can help you recover your data in case of a problem, such as a hardware failure or a software bug. Support: Aptible provides 24/7 support to help you with any problems you may have. You can contact Aptible's support team via email, chat, or phone. Build a movie app using Node.js and MySQL Prerequisites: Basic knowledge of JavaScript, Node.js, and MySQL. Node.js and npm (Node Package Manager) are installed on your system. A MySQL database server is installed and running. A basic knowledge of phpMyAdmin. A good knowledge of using the Aptible platform to create databases, environments, and apps. Step 1: Set Up MySQL Database First, you need to create a MySQL database to store movie information. In this tutorial, you will use phpMyAdmin. Create a new database and a table for movies: CREATE DATABASE movie_db;
USE movie_db;

CREATE TABLE movies (
    id INT AUTO_INCREMENT PRIMARY KEY,
    image_url VARCHAR(255) NOT NULL,
    video_url VARCHAR(255) NOT NULL,
    title VARCHAR(255) NOT NULL,
    genre VARCHAR(255) NOT NULL,
    description TEXT,
    rating DECIMAL(3, 1),
    release_year INT
); Step 2: Set Up Node.js Project Please create a new project folder and initialize it as a Node.js project. Node.js mkdir movie-app
cd movie-app
npm init -y Install the required dependencies: npm install express mysql2 multer : A popular Node.js framework for building web applications. express : A MySQL client for Node.js. mysql2 : Middleware for handling file uploads. multer Step 3: Create a Database Connection In your project, create a file to set up the MySQL database connection: Node.js db.js // db.js
const mysql = require('mysql2');

const db = mysql.createConnection({
  host: 'localhost', // Change to your MySQL host
  user: 'root',      // Change to your MySQL username
  password: 'password', // Change to your MySQL password
  database: 'movie_db',
});

db.connect((err) => {
  if (err) {
    console.error('Error connecting to the database:', err);
    throw err;
  }
  console.log('Connected to the database');
});

const createTable = `
  CREATE TABLE IF NOT EXISTS movies (
    id INT AUTO_INCREMENT PRIMARY KEY,
    image_url VARCHAR(255) NOT NULL,
    video_url VARCHAR(255) NOT NULL,
    title VARCHAR(255) NOT NULL,
    genre VARCHAR(255) NOT NULL,
    description TEXT,
    rating DECIMAL(3, 1),
    release_year INT
  )
`;

db.query(createTable, (err) => {
  if (err) {
    console.error("Error creating movies table:", err);
  } else {
    console.log("Movies table created or already exists");
  }
});

module.exports = db; Replace and with your MySQL credentials. 'your_username' 'your_password' Step 4: Create API Endpoints In your Node.js project, create an file to define your API endpoints using Express: app.js // app.js
const express = require('express');
const db = require('./db'); // Import the database connection
const multer = require('multer');

const app = express();
const port = process.env.PORT || 3000;

app.use(express.json());
app.use(express.urlencoded({ extended: true }));

// Configure Multer for handling file uploads
const storage = multer.diskStorage({
  destination: (req, file, cb) => {
    cb(null, 'uploads/');
  },
  filename: (req, file, cb) => {
    cb(null, Date.now() + '-' + file.originalname);
  },
});

const upload = multer({ storage });

// Create a new movie
app.post('/movies', upload.fields([{ name: 'image', maxCount: 1 }, { name: 'video', maxCount: 1 }]), (req, res) => {
  const { title, genre, description, rating, release_year } = req.body;
  const image_url = req.files['image'][0].path;
  const video_url = req.files['video'][0].path;

  const query = 'INSERT INTO movies (image_url, video_url, title, genre, description, rating, release_year) VALUES (?, ?, ?, ?, ?, ?, ?)';
  const values = [image_url, video_url, title, genre, description, rating, release_year];

  db.query(query, values, (err, result) => {
    if (err) {
      console.error('Error creating a movie:', err);
      res.status(500).json({ error: 'Internal Server Error' });
    } else {
      res.status(201).json({ message: 'Movie created successfully' });
    }
  });
});

// List all movies
app.get('/movies', (req, res) => {
  const query = 'SELECT * FROM movies';

  db.query(query, (err, rows) => {
    if (err) {
      console.error('Error listing movies:', err);
      res.status(500).json({ error: 'Internal Server Error' });
    } else {
      res.status(200).json(rows);
    }
  });
});

// Search movies
app.get('/movies/search', (req, res) => {
  const { query } = req.query;
  const searchQuery = `%${query}%`;

  const searchSql = 'SELECT * FROM movies WHERE title LIKE ? OR genre LIKE ?';
  const searchValues = [searchQuery, searchQuery];

  db.query(searchSql, searchValues, (err, rows) => {
    if (err) {
      console.error('Error searching movies:', err);
      res.status(500).json({ error: 'Internal Server Error' });
    } else {
      res.status(200).json(rows);
    }
  });
});

app.listen(port, () => {
  console.log(`Server is running on port ${port}`);
}); Endpoints Base URL: http://localhost:3000/ 1. Create a New Movie : Endpoint /movies : POST Method : Create a new movie and add it to the database. Description Request : Body {
  "title": "Movie Title",
  "description": "Movie description goes here.",
  "release_year": 2023,
  "genre": "Action",
  "rating": 8.5,
  "image_url": "https://example.com/movie-image.jpg",
  "video_url": "https://example.com/movie-video.mp4"
} Response : 201 Created Status : Body jsonCopy code{
  "message": "Movie created successfully" 2. List All Movies : Endpoint /movies : GET Method : Get a list of all available movies. Description Response : 200 OK Status : An array of movie objects. Each object includes the following fields: Body jsonCopy code[
  {
    "id": 1,
    "title": "Movie Title 1",
    "description": "Movie description goes here.",
    "release_year": 2022,
    "genre": "Drama",
    "rating": 7.9,
    "image_url": "https://example.com/movie1-image.jpg",
    "video_url": "https://example.com/movie1-video.mp4"
  },
  {
    "id": 2,
    "title": "Movie Title 2",
    "description": "Another movie description.",
    "release_year": 2021,
    "genre": "Comedy",
    "rating": 6.8,
    "image_url": "https://example.com/movie2-image.jpg",
    "video_url": "https://example.com/movie2-video.mp4"
  }
] Step 5: Test your code Start your app by running the following command: node app.js Once your app runs, you can test the endpoints using an API testing platform. You can check your phpMyAdmin interface to confirm the data was saved successfully. Step 6: Push your project to GitHub Create a new repository on GitHub and clone it to your local machine. Make your changes to the code and commit them. Push your changes to GitHub. Once you have pushed your code to GitHub, you can deploy it to Aptible. Code folder structure To see the complete codebase, please check out my GitHub repository at https://github.com/wise4rmgod/moviedb Deploying your application to Aptible 1: Create environment Go to the Aptible dashboard and click on the tab. Environments Click on the button. Create Environment Enter a name for the environment and select the appropriate region. Click on the button. Create Environment 2: Create Database In this tutorial, you will create a MySQL database. After creating the database, go to the dashboard and click on the reveal to see your connection URL To avoid this error ERROR 1045 (28000): Access denied for user 'aptible'@'ip-[IP_ADDRESS].ec2.internal' (using password: YES) Add this field to your db.js ssl: { rejectUnauthorized: false, ciphers: "DHE-RSA-AES256-SHA" }, Complete db.js const mysql = require("mysql2");

const db = mysql.createConnection({
  host: "", // Change to your MySQL aptible host
  user: "aptible", // Change to your MySQL username
  password: "", // Change to your MySQL password
  database: "db",
  ssl: { rejectUnauthorized: false, ciphers: "DHE-RSA-AES256-SHA" },
  port: 26038,
});

db.connect((err) => {
  if (err) {
    console.error("Error connecting to the database:", err);
    throw err;
  }
  console.log("Connected to the database");
});

const createTable = `
  CREATE TABLE IF NOT EXISTS movies (
    id INT AUTO_INCREMENT PRIMARY KEY,
    image_url VARCHAR(255) NOT NULL,
    video_url VARCHAR(255) NOT NULL,
    title VARCHAR(255) NOT NULL,
    genre VARCHAR(255) NOT NULL,
    description TEXT,
    rating DECIMAL(3, 1),
    release_year INT
  )
`;

db.query(createTable, (err) => {
  if (err) {
    console.error("Error creating movies table:", err);
  } else {
    console.log("Movies table created or already exists");
  }
});

module.exports = db; 3: Create an App Then, create an App by giving the and click Handle a name, Save App Aptible will take some time to provision your app Once provisioning is complete, click on the app name and click to continue the deployment process. Get Started 4: Upload your SSH public key. In your dashboard, click on your name in the top right corner and select . SSH Keys Click on the button. Add SSH Key Paste your SSH public key into the text box and click on the button. Add SSH Key 5: Create a Dockerfile Sample Dockerfile here # Use the official Node.js image as the base image
FROM node:16

# Set the working directory inside the container
WORKDIR /app

# Copy package.json and package-lock.json to the working directory
COPY package*.json ./

# Install project dependencies
RUN npm install

# Copy the application code to the container
COPY . .

# Expose the port that the app will run on
EXPOSE 3000

# Start the application
CMD ["node", "app.js"] 6: Deploy to Aptible via Git remote The Aptible Git remote URL is specific to your account. You can find it in the Get Started tab of your app's dashboard. Add the Aptible Git remote for this app, then push to the remote to begin deployment: git remote add aptible git@beta.aptible.com:env-prodzz/movieapp.git Push your code to Aptible. git push aptible main If successful, you will see an image similar to the following. 7: Add an Endpoint In your app, click on the tab. Endpoints Click on the button. Create Endpoint Once the endpoint has been created, you can send a , , , or any other supported HTTP method to the endpoint using an API testing platform. GET POST DELETE To test the endpoint, copy the HOSTNAME from the endpoint's dashboard and use it in your API testing platform. 8: Test your final project Conclusion In this article, you learned how to build a movie streaming app like Netflix using Node.js, MySQL, and Aptible. You also learned about the benefits of using these technologies for building streaming web apps and the steps involved in deploying a Node.js app to Aptible. If you want to build a movie streaming app like Netflix, I recommend using Node.js, MySQL, and Aptible. These technologies are well-suited for building scalable, performant, secure streaming web apps.

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MYHQ

myhq.in

-- employees

Since n.d.

COMPANY RANKING

MYHQ

EVERGREEN INDEX #6585

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