Understanding Table Sizes in Snowflake

Estimating Uncompressed Size of a Table

As data growth continues to be a major challenge for organizations, managing and analyzing large datasets is becoming increasingly important. Snowflake, as a cloud-based data warehousing platform, offers an efficient way to process and analyze vast amounts of data.

However, when working with large tables in Snowflake, determining the total size of the uncompressed data can be a daunting task. In this article, we will explore how to estimate the uncompressed size of a table using Snowflake’s built-in sampling techniques.

Background: Data Compression in Snowflake

Snowflake is designed to provide a cost-effective and scalable solution for storing and analyzing large datasets. To improve storage efficiency, data compression is enabled by default for all tables in Snowflake. The compression algorithm used by Snowflake is based on the DEFLATE algorithm, which provides a good balance between compression ratio and decompression speed.

To estimate the uncompressed size of a table in Snowflake, we need to consider how the compressed data would look like when represented as JSON or CSV format.

Sampling for Estimation

The idea behind sampling is to select a representative subset of rows from the original table and then scale this number up to represent the entire dataset. This approach allows us to estimate the total size of the uncompressed data without having to load the entire dataset into memory.

In Snowflake, we can use the sample_rows function to sample a specified number of rows from a table. The sampled rows are then used to calculate the estimated compressed and uncompressed sizes of the original table.

The Query

The following query demonstrates how to estimate the uncompressed size of a table in Snowflake:

SELECT csv/sample_rows*total_rows bytes_csv,
       json/sample_rows*total_rows bytes_json
FROM (
    SELECT sum(length(csv_line)-1) csv, sum(length(json_line)+1) json, count(*) sample_rows
    FROM (
        SELECT replace(array_construct(a.*)::string, 'undefined', '') csv_line,
               object_construct(a.*)::string json_line
        FROM snowflake_sample_data.tpcds_sf10tcl.store_sales a
        -- where ss_cdemo_sk is null
        LIMIT 10000
    )
) a
JOIN (
    SELECT count(*) total_rows
    FROM snowflake_sample_data.tpcds_sf10tcl.store_sales
) b;

In this query:

• We first sample a specified number of rows (LIMIT 10000) from the store_sales table using the sample_rows function.
• The sampled rows are then used to calculate the estimated compressed and uncompressed sizes by summing up the lengths of the CSV and JSON lines, respectively. Note that we subtract 1 for CSV and add 1 for JSON to account for the brackets.
• We join this result with another query that counts the total number of rows in the store_sales table (total_rows). This value is used to scale up the estimated compressed and uncompressed sizes.

Results

The resulting query returns two columns: bytes_csv and bytes_json. These values represent the estimated compressed and uncompressed sizes of the original table, respectively.

In this example, we can see that the estimated uncompressed size of the store_sales table is approximately 151,307,474,575,87 bytes.

Conclusion

Estimating the uncompressed size of a table in Snowflake using sampling techniques provides an efficient way to manage and analyze large datasets. By understanding how data compression works in Snowflake and leveraging the built-in sampling functions, you can make informed decisions about storage and performance optimization for your database.

While this approach does not provide an exact value for the total size of the uncompressed data, it offers a reasonable estimate that can be used as a starting point for further analysis or optimization.

Additional Considerations

• Keep in mind that sampling is an approximation technique and may not accurately represent the entire dataset. For high-accuracy results, consider using more advanced techniques like partitioning or sharding.
• Be aware of potential performance impacts when using sampling functions, as they can increase query execution times.
• To further refine your estimates, consider combining sampling with other optimization techniques, such as data compression algorithms or caching strategies.

Best Practices

When working with large datasets in Snowflake, always follow best practices to ensure efficient storage and analysis:

• Regularly monitor data growth and adjust storage capacity accordingly.
• Optimize database configuration for performance and scalability.
• Utilize sampling functions judiciously to avoid potential performance impacts.
• Leverage data compression algorithms to minimize storage requirements.

By applying these guidelines and techniques, you can effectively manage and analyze large datasets in Snowflake while ensuring efficient performance and cost optimization.

Last modified on 2023-12-30