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Full Table Scan

Last updated Feb 9, 2024

A full table scan, also referred to as a sequential scan, involves reading every row in a database table sequentially, evaluating each column against specified conditions. This approach is often the slowest method for scanning due to the substantial I/O reads it requires. These reads entail numerous seeks and expensive disk-to-memory transfers.

# Indices

In the context of databases, a query without an index typically triggers a full table scan. Here, the database sifts through each table record to identify those that meet the query’s criteria. Even if the query demands only a handful of rows, it results in the examination of the entire table. While this method can lead to less-than-ideal performance, it might be justifiable for smaller tables or in scenarios where maintaining indexes is overly burdensome.

# When the Optimizer Considers a Full Table Scan

The decision to employ a full table scan hinges primarily on speed. This scan is optimal when it’s the fastest method available and no alternative access paths are viable. Scenarios where a full table scan is preferable include:

# Alternatives to Full Table Scans

To avoid a full table scan and optimize database queries, consider these alternatives:

  1. Using Indexes:
    • B-Tree Indexes: Ideal for high-cardinality columns where the column value distribution is not skewed. They accelerate queries involving equality or range searches.
    • Bitmap Indexes: Suitable for columns with low cardinality, such as gender or boolean flags. They are efficient for queries with multiple conditions combined using AND, OR, and NOT.
  2. Partitioning:
    • Table Partitioning: Splitting a large table into smaller, more manageable segments based on certain criteria like date ranges or geographical locations. This reduces the amount of data scanned for queries targeting specific partitions.
    • Index Partitioning: Similar to table partitioning but applied to indexes. This can speed up index scans and reduce the likelihood of a full table scan.
  3. Query Optimization:
    • Refining Query Conditions: Use more selective conditions in queries to reduce the data set size.
    • Join Optimization: Properly structure joins, especially in complex queries, to leverage indexes and avoid scanning large tables.
  4. Materialized Views:
    • Create precomputed views with aggregated or joined data. This is particularly useful for complex calculations that are frequently queried.
  5. Proper Database Design:
    • Normalization and Denormalization: Balance between these two to avoid data redundancy and improve query performance.
    • Use of Clustered Indexes: Where the table’s physical order aligns with the index, aiding faster access.
  6. Update Statistics:
    • Regularly updating database statistics ensures the query optimizer has accurate data for making decisions about the execution plan.
  7. Query Hints:
    • In some databases, you can provide hints to the query optimizer to influence its path selection, although this requires careful consideration.
  8. In-memory Databases/Caching:
    • Leveraging in-memory technologies or caching layers for frequently accessed data can dramatically reduce the need for full table scans.

Created 2024-01-20