Expert Guide to Dropping Indexes in PostgreSQL

As a full-stack developer, I have managed many large-scale PostgreSQL databases serving high-traffic web apps. Index performance and maintenance has always been critical to ensure fast queries under load while controlling storage bloat.

In this comprehensive guide, I‘ll cover when and how to drop indexes in PostgreSQL using my years of DBA experience.

Index Design Considerations

Well-designed indexes are crucial for production database performance. As a full-stack developer, key considerations I focus on when creating indexes include:

Index Types

PostgreSQL offers several index types like B-tree, Hash, GIN, and GiST with different performance tradeoffs.

• B-tree indexes are the default and work well for comparisons operators like <, >
• Hash indexes only support equality checks but provide faster lookups
• GIN indexes handle arrays and full-text search queries
• GiST supports geospatial data

Choosing the optimal index type is essential to make the most of your hardware resources under load.

Index Sizing

Index sizes directly impact memory usage and lookup speeds. My guideline is to keep indexes under 100 MB, with most in the 1-50 MB range.

Overly large indexes exceeding 100 MB tend to have slower seeks and cost more memory. Those should be reviewed for potential clean up.

Sorting Columns

For compound column indexes, order the columns based on density and common query filters.

For example, an index on (State, City) allows finding all records in a given city and an index scan of one state at a time. Reversing the order to (City, State) loses the state-level lookups.

As a best practice, I position columns by distinct cardinality and usage frequency.

Identifying and Dropping Unused Indexes

Dropping indexes incurs writes equivalent to a bulk data load. So I only drop indexes providing no lookup value whatsoever.

Here are the techniques I rely on to identify unused indexes.

Index Usage Statistics

PostgreSQL captures extremely helpful index metrics in its stats collector views.

As a PostgreSQL DBA, the top view I use is pg_stat_user_indexes. It reports:

• idx_scan – Number of index scans initiated on this index
• idx_tup_read – Number of index entries returned by scans
• idx_tup_fetch – Number of live table rows fetched by simple index scans

For example:

Index Name	idx_scan	idx_tup_read	idx_tup_fetch	Table Size
accounts_id_idx	45,402	123,994	32,947	1,392,734 rows
accounts_status_idx	32	265	157	1,392,734 rows

This shows that accounts_status_idx is essentially unused, making it a prime candidate for dropping.

As a rule of thumb, any index with scans/fetches below .01% of the table size provides minimal value.

EXPLAIN Output

EXPLAIN provides the definitive method to determine if an index gets used in query plans.

When investigating indexes, I EXPLAIN my most common query patterns. If an index goes unused across several representative queries, that signifies it‘s not sufficiently selective.

For example:

EXPLAIN SELECT * FROM sales WHERE status = ‘returned‘

Aggregate  (cost=23486.07..23486.08 rows=1 width=97)
  ->  Index Scan using sales_status_idx on sales  (cost=0.29..23485.79 rows=1 width=97)
        Index Cond: (status = ‘returned‘::text)

This shows the sales_status_idx index being leveraged by the planner. I would likely keep this index in place even if it had slightly lower scan counts.

With EXPLAIN, you can definitively assess index relevance for your actual query workloads.

Reviewing Index Bloat

The \di+ psql command displays great details about index sizes and definitions:

                                  List of relations  
 Schema |     Name      | Type  |  Table   |...Size | Description          
--------+---------------+-------+----------+-------+-------------  
 public | accounts_pkey | index | accounts | 148 MB | PRIMARY KEY... 
 public | idx_accounts  | index | accounts | 345 MB | Normal B-Tree index

Monitoring sizes helps avoid index bloat scenarios where mutations outpace maintenance. I watch for any indexes exceeding 100-200 MB that could likely use a REINDEX or cleanup.

If an oversized index shows little usage otherwise, that‘s an immediate drop candidate.

As a rule of thumb, I periodically review and prune indexes larger than 25% the size of their underlying table.

Concurrent DROP INDEX Implications

The DROP INDEX CONCURRENTLY operation runs in the background while allowing writes and reads to continue. However, it has notable locking caveats that can trap unaware developers.

• The index is marked invalid at the outset, blocking further SELECTs relying on that index
• Index truncation happens in a final exclusive lock at the end
• Rollbacks and failures midway can lead to invalid/unfinished indexes

So in products I‘ve architected:

• I employed DROP INDEX CONCURRENTLY during scheduled maintenance windows to control risks
• Required restarts or draining connections before final truncation
• Wrapped index drops in transactions with conditional reindex fallback logic

Understanding these nuances helps avoid availability blips or locked queries.

Best Practices for Index Maintenance

Running large-scale PostgreSQL for consumer apps has taught me some guidelines around index maintenance:

Feature flag all indexes initially – I typically develop new indexes disabled via a config flag. That allows testing performance before the index gets built and used in production.

Assess index value after sufficient data collection – New indexes need a couple weeks of production traffic before their utility can be accurately judged. Statistics require sufficient time and query volume.

Periodically review indexes for cruft – I schedule quarterly index reviews to revalidate all indexes. Cleaning up unused indexes prevents long term bloat.

Mind num_scans vs. tup_read density – If num_scans counts seem decent but tup_reads remain low, that may signal overly broad indexes incurring unnecessary I/O.

Plan drops during maintenance windows – Online index drops can complete quickly or drag on for hours depending on size. Scheduled windows provide some control over duration and locking.

Monitor dashboards during DROP operations – It‘s good practice to keep an eye on key metrics during index drops to ensure no sharp degradations.

Consider partial indexes – Adding WHERE filters to indexes can cut down sizes while still supporting targeted workloads. This is often my first optimization before outright removal.

Through careful analysis and conservative drop schedules, I‘ve kept mission-critical production databases humming along fast with minimal bloat.

Conclusion

Indexes provide enormous query performance gains but also incur overhead if left to accumulate. As a PostgreSQL DBA and architect, I firmly believe maintaining a lean and mean set of indexes centered around production workloads is crucial for web-scale applications.

This guide offered extensive analysis and insights on:

• Identifying unused and bloated indexes via statistics
• Judiciously dropping indexes using CONCURRENTLY
• Architecting locking failovers during index deletions
• Establishing index maintenance best practices

I hope relaying my real-world experience managing indexes for high-scale web databases helps developers understand this somewhat mysterious but very impactful area of database optimization. Please comment any other index management techniques you find valuable!