Data Types: Advanced

Arrays, maps, and type optimization at scale

Arrays, maps, and type optimization at scale

Category

SQL

Difficulty

advanced

Duration

21 minutes

Challenges

0 hands-on challenges

Topics covered: Type optimization at scale, Storage calculations and VARCHAR, MAP data type for key-value pairs, Accessing nested data (UNNEST), Compression and error handling

Lesson Sections

1. Type optimization at scale

   Choosing the right data type isn't just about correctness. It's also about performance and storage efficiency. At scale, poor type choices can waste terabytes of disk space and slow queries by orders of magnitude. Storage Impact VARCHAR vs CHAR Indexing and Type Choice Indexes work best when the type matches the data distribution. String indexes on numeric data are slow because string comparisons are byte-by-byte. String-based comparisons on numeric IDs require byte-by-byte evaluation, while int

2. Storage calculations and VARCHAR

   How VARCHAR Stores Data Calculating Storage Needs To estimate table size, multiply row count by sum of column sizes plus overhead (typically 20-40 bytes per row for metadata). With these storage costs in mind, here are the key rules to follow when sizing your columns: Remember that UTF-8 characters can use 1 to 4 bytes each, so character count and byte count may differ for international text.

3. MAP data type for key-value pairs

   Creating Maps Accessing Map Values When to Use Maps Bracket notation on a MAP column provides O(1) hash-based access, making it fast regardless of how many keys the map contains. Maps are ideal for sparse data where each row may have different attributes, avoiding the need for many nullable columns.

4. Accessing nested data (UNNEST)

   UNNEST Arrays Filtering Nested Data After unnesting, you can filter on array elements as if they were regular columns. Filtering After UNNEST Advanced data types unlock powerful transformations. Practice with hands-on challenges to master nested data queries.

5. Compression and error handling

   Modern column-oriented databases use compression to dramatically reduce storage. The compression ratio depends on the data type and the distribution of values. Understanding these techniques helps you design schemas that compress well. Compression Types Robust Error Handling Type conversion failures in production can crash entire pipelines. Robust error handling is essential for data engineering. These patterns handle progressively more complex scenarios. How should you handle type conversion on