CSV files offer the advantages of simple structure and high compatibility in big data processing. However, as data volumes continue to grow, individual CSV files can reach hundreds of MB or even GB in size. Directly loading and processing them not only consumes memory but also affects computational efficiency. Therefore, efficient file merging and precise splitting techniques are crucial. Appropriate merging can reduce the number of files, improve batch processing efficiency, and precise splitting can achieve load balancing in distributed computing and avoid single-point performance bottlenecks.

When merging files, it's important to first clarify the goal: data integration or reducing file I/O. When merging CSV files from multiple sources, ensure consistency in field structure, particularly column names and order. In Python, the Pandas library can be used to quickly merge multiple tables row-by-row using pd.concat and then export them into a single file. For example:

import pandas as pd
files = ["file1.csv", "file2.csv", "file3.csv"]
dfs = [pd.read_csv(f) for f in files]
merged = pd.concat(dfs, ignore_index=True)
merged.to_csv("merged.csv", index=False)

The above method works well for small data volumes, but may run out of memory when processing very large files. In such cases, streaming methods are more suitable, such as using Python's built-in csv module with a generator to read and write row by row. This allows the merge to be completed without loading the entire data all at once. Alternatively, for distributed computing platforms like Hadoop or Spark, you can directly upload CSV files to HDFS, where the compute engine will perform a distributed merge. This is not only faster but also leverages the cluster's parallel computing capabilities.

The key to splitting operations lies in controlling the split granularity and maintaining data integrity. Imagine a CSV file containing tens of millions of rows. Directly reading and processing it can easily cause memory pressure. A reasonable approach is to split the data by number of rows or file size to ensure that each small file can be read quickly. For example, to split the data by 100,000 rows, you can use the following Python code:

import pandas as pd
chunksize = 100000
for i, chunk in enumerate(pd.read_csv("large.csv", chunksize=chunksize)):
chunk.to_csv(f"part_{i}.csv", index=False)

This block-by-block reading and writing approach maintains the integrity of the data structure while distributing the processing load across multiple files. If you are using a big data platform, you can use Spark's repartition method to repartition the data based on the number of rows or data volume, seamlessly integrating the splitting process with subsequent computations.

In actual production, merging and splitting are often not performed independently but as part of the data processing workflow. For example, log data generates dozens of CSV files daily. Before analysis, they need to be merged into large files by time range to facilitate batch processing. When training machine learning models, large files need to be split into multiple batches to avoid slow training or even crashes caused by loading too much data at once.

It's important to note that the encoding format, delimiters, and line breaks of CSV files can also affect processing efficiency. If different files have different encodings—for example, some are UTF-8 and others are GBK—they must be unified before merging to avoid garbled characters. Similarly, if delimiters are inconsistent (for example, some use commas and others use tabs), they must be explicitly specified during reading to avoid field parsing errors. For split files, it's best to maintain a consistent naming convention and storage path to facilitate automatic recognition and reading by subsequent batch processing scripts.

For performance optimization, if the data volume is large, avoid using plain text CSV as a long-term storage format. Instead, convert to a columnar storage format (such as Parquet) after merging or splitting. This can significantly improve query speed and save storage space in big data analysis. However, when cross-platform compatibility is required, CSV remains the preferred format because it can be directly processed by almost all systems and languages.

Overall, efficient CSV merging and precise splitting are not just file operations; they are fundamental capabilities in data processing systems. By effectively utilizing memory, controlling I/O, achieving a unified format, and integrating with distributed computing platforms, CSV can maintain efficiency and stability in big data environments, supporting the smooth execution of various analytical and computational tasks.