使用CSV创建表与从已有表建表的性能差异及操作必要性分析

Great question—let’s break down the core differences between these two table types, why you might not be seeing speed improvements yet, and whether that second table creation step is necessary.

First, Let’s Clarify What Each Table Actually Is #

When you created employee_data_csv with USING csv, you made an external table:

• The raw data lives in your specified CSV path (C:/pathway/xxxx)—the table is just a pointer to that text-based file.
• Every time you query this table, Databricks has to:
  1. Read the entire plain-text CSV file(s)
  2. Parse each row, convert text values to appropriate data types
  3. No built-in indexing or columnar storage optimizations to speed up filtering/scanning

When you ran CREATE TABLE employee_data SELECT ..., you created a managed Delta Lake table (Databricks’ default table type unless you specify otherwise):

• The data is stored in Delta’s optimized, columnar binary format (not plain text)
• Delta automatically adds metadata, supports indexing, data skipping, and caching
• Queries can leverage column pruning (only read the columns you need) and predicate pushdown (filter data early before reading all rows)

Why You Might Not Be Seeing a Speed Boost #

If you didn’t notice a performance difference, it’s likely due to one of these factors:

• Small dataset size: With tiny datasets, the overhead of parsing CSV is negligible compared to the time it takes to run any query at all. The real gap shows up with large (GB+ sized) datasets.
• Simple queries: If you’re just running SELECT * without filters or aggregations, both tables are doing a full scan. Delta’s advantages shine when you add WHERE clauses, joins, or aggregations that can leverage its optimizations.
• No Delta optimizations applied: For larger datasets, you might need to run OPTIMIZE employee_data to compact files and ZORDER BY on frequently filtered columns to maximize data skipping. Without these steps, Delta’s full potential isn’t unlocked.
• Caching: If you ran the CSV table query first, Databricks might have cached the results, making subsequent queries (even on the Delta table) seem equally fast. Try clearing the cache with CLEAR CACHE and re-running both queries.

Is the Second Table Creation Step Necessary? #

It depends on your use case:

• Yes, if you’ll query this data frequently: Delta tables are far more efficient for repeated queries, especially as your dataset grows. They also add reliability features like ACID transactions, versioning, and schema enforcement that CSV tables lack.
• No, if it’s a one-off query: If you only need to read the CSV data once, converting it to Delta adds unnecessary overhead.
• Bonus: External Delta tables are an option too: If you want to keep control of your data storage but still get Delta’s benefits, you can create an external Delta table with CREATE TABLE ... USING delta LOCATION "/path/to/delta" and insert the CSV data into it.

内容的提问来源于stack exchange，提问作者CandleWax