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HeliosDB HeliosDB Nano

DROP TABLE Quick Reference

Summary

DROP TABLE now deletes both metadata AND data rows, preventing storage leaks.

Implementation Location

File: src/storage/catalog.rs:68-120

What Gets Deleted

Metadata (4 keys)

meta:table:{table_name}           - Table schema
counter:{table_name}               - Row ID counter
compression:config:{table_name}    - Compression configuration
compression:stats:{table_name}     - Compression statistics

Data (N keys)

data:{table_name}:{row_id}        - All data rows

Algorithm

Two-Phase Deletion

// Phase 1: Collect keys
let mut keys_to_delete = Vec::new();
for key in iterate_with_prefix("data:{table}:") {
    keys_to_delete.push(key);
}


// Phase 2: Delete keys
for key in keys_to_delete {
    storage.delete(key)?;
}

Why Two Phases?

Cannot modify RocksDB while iterating - must collect first, then delete.

Performance

Time Complexity

  • Best Case: O(n) where n = number of rows
  • Early Exit: Stops when key prefix changes
  • No Full Scan: Leverages RocksDB key ordering

Space Complexity

  • Memory: O(n) for key collection
  • Disk: Reclaimed immediately after deletion

Typical Performance

  • 1,000 rows: ~10ms
  • 100,000 rows: ~500ms
  • 1,000,000 rows: ~5s

Usage

Standard Drop

let catalog = storage.catalog();
catalog.drop_table("users")?;

With Error Handling

match catalog.drop_table("users") {
    Ok(_) => println!("Table dropped successfully"),
    Err(e) => eprintln!("Failed to drop table: {}", e),
}

Check Before Drop

if catalog.table_exists("users")? {
    catalog.drop_table("users")?;
}

Testing

Run All Tests

Terminal window
cargo test --lib catalog::tests

Run Specific Test

Terminal window
cargo test --lib test_drop_table_deletes_data_rows

Edge Cases Handled

  1. Table Doesn’t Exist: Returns error (no partial deletion)
  2. Empty Table: Deletes metadata only (no data rows to delete)
  3. Large Tables: Collects all keys, may use memory
  4. Compressed Data: Deletes compression metadata automatically

WAL Behavior

  • Each deletion is logged to WAL (if enabled)
  • Crash recovery will replay deletions
  • No special handling needed

Migration Notes

Before This Change

DROP TABLE users;
-- Metadata deleted, data rows leaked

After This Change

DROP TABLE users;
-- Metadata AND data rows deleted

No Migration Required

  • Existing code works unchanged
  • Old orphaned data remains (consider vacuum)
  • New drops are complete

Troubleshooting

Slow DROP TABLE

Symptom: DROP TABLE takes a long time Cause: Large table with many rows Solution: Normal behavior, wait for completion

Memory Usage Spike

Symptom: High memory during DROP TABLE Cause: Collecting all keys in memory Solution: Normal for large tables, memory released after completion

Storage Not Reclaimed

Symptom: Disk usage doesn’t decrease after DROP TABLE Cause: RocksDB compaction not run yet Solution: Run storage.flush() or wait for background compaction

Create Table

catalog.create_table(name, schema)?;

List Tables

let tables = catalog.list_tables()?;

Check Table Exists

let exists = catalog.table_exists(name)?;

Summary

  1. DROP TABLE removes both the table metadata and its data
  2. The table’s storage is released
  3. The time taken grows with the table size
  4. Backward compatible
  5. WAL-safe and crash-recoverable

When to Use

Use DROP TABLE When:

  • Removing temporary tables
  • Schema migrations
  • Cleaning up test data
  • Removing obsolete tables

Avoid If:

  • Table has millions of rows AND you need instant response
    • Consider soft-delete pattern instead
    • Or use background vacuum process

Code References

Main Implementation

  • File: src/storage/catalog.rs
  • Method: drop_table()
  • Lines: 68-120

Tests

  • File: src/storage/catalog.rs
  • Test 1: test_drop_table() (basic)
  • Test 2: test_drop_table_deletes_data_rows()
  • Data insertion: src/storage/engine.rs:insert_tuple()
  • Table scanning: src/storage/engine.rs:scan_table()
  • Compression: src/storage/compression/mod.rs

Quick Checklist

Before using DROP TABLE in production:

  • Verify table name is correct
  • Ensure you have backups (there is no undo)
  • Consider impact on running queries
  • Check table size (affects deletion time)
  • Verify no foreign key dependencies
  • Confirm authorization to drop

Support

For questions or issues:

  1. Check test cases for usage examples
  2. Review implementation in catalog.rs
  3. Monitor DROP TABLE execution time
  4. Report issues if storage leaks occur