Welcome to the unit on Writing Data in Batches. In this lesson, we'll explore how to efficiently handle large datasets by writing data in batches using Java. This technique is invaluable when managing substantial amounts of data where processing the entire dataset at once is impractical. By the end of this lesson, you will be able to write data in batches to manage and handle large datasets effectively.

Batching is the process of dividing a large amount of data into smaller, manageable chunks or batches. This practice is crucial in data handling as it offers several advantages:

• Memory Efficiency: Smaller chunks can be processed more efficiently than large datasets, reducing memory usage.
• Performance Improvement: Writing and reading smaller sets of data can enhance performance, especially in I/O operations.

Batching is particularly useful when dealing with data that cannot fit into memory all at once or when working with streaming data.

In this lesson, we're tackling the challenge of handling large datasets by writing data to a file in batches using Java. This method enhances efficiency, especially for large volumes that aren't feasible to process in one go. Here's our breakdown:

• Generate Sample Data: We'll start by creating a dataset of random numbers.
• Structure Data into Batches: This dataset will be divided into smaller, more manageable portions referred to as batches.
• Sequential Batch Writing: Each of these batches will then be written to a file one after the other, optimizing both memory usage and performance.

This approach reflects real-world requirements, where handling vast datasets efficiently is crucial for ensuring smooth data processing and storage.

To begin, we need to set up our data generation and define the configuration for batch processing to write data to a CSV file. We'll specify the file path for the output, the number of batches, the batch size indicating the number of rows per batch, and the number of columns for each row. Here's how the setup looks in code:

In this code:

• filePath: Path of the file where data will be written.
• numBatches: Specifies the total number of batches to be written.
• batchSize: Determines how many rows each batch contains.
• numColumns: Establishes how many columns each row will have.
• random: Generates the random numerical values for our data.

To efficiently manage the writing of data in batches, we'll utilize the Jackson library in Java. Specifically, we'll use the CsvMapper and the SequenceWriter to write data to a CSV file. Here's how we set up the writer:

In this snippet:

• We initialize a CsvMapper object to handle CSV data binding.
• We create a CsvSchema without headers since we are not including column names in our CSV file.
• We convert the Path to a File object for the writer to use.
• We initialize a SequenceWriter, which allows us to write multiple batches to a file sequentially without reopening the file each time.

Understanding SequenceWriter: The SequenceWriter is a component of the Jackson library that facilitates the efficient writing of sequences of objects to an output destination like a file or stream. It maintains an open stream, enabling us to append data incrementally without the overhead of opening and closing the file for each batch. This is particularly beneficial when handling large amounts of data in batches.

With the writer set up, the next step is to generate data in batches and write each batch to the CSV file sequentially:

In this code:

• We loop over the number of batches defined by numBatches.
• For each batch, we generate a list of double[] arrays, where each array represents a row of random double values.
• We populate each row with random numbers and add it to the list of rows for the current batch.
• We use the SequenceWriter's writeAll() method to append all rows of the current batch to the CSV file.
• After all batches have been written, we close the SequenceWriter to release resources.

By using the SequenceWriter, we maintain an open file stream throughout the batch processing, which significantly improves efficiency by reducing the overhead associated with opening and closing the file multiple times.

After writing the data, it's crucial to ensure that our file contains the expected number of rows. We can verify this by counting the lines in the generated CSV file:

We use Files.lines() to read the lines from the file and count() to get the total number of lines. This step confirms that all data has been written as expected.

Example output:

This indicates that 1000 rows (5 batches * 200 rows per batch) have been successfully written to the CSV file.

In this lesson, we've covered the essentials of writing data in batches to efficiently manage large datasets using Java. You've learned how to generate data, set up a SequenceWriter to write data in batches, and verify the integrity of the written files. Utilizing the SequenceWriter from the Jackson library allows us to append data to a file efficiently, which is crucial for handling large datasets effectively, ensuring memory efficiency and improved performance.