{
  "id": 4427406,
  "title": "When to Index a Table: A Practical Guide for Analysts",
  "url": "https://urgent.news/2026/08/30/when-to-index-a-table-a-practical-guide-for-analysts",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-30T13:00:29.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/michaelnocito/when-to-index-a-table-a-practical-guide-for-analysts-2dg8"
  },
  "original_language": "en",
  "account": "Indexes are a valuable tool for database performance, but they come with trade-offs. When a query is too slow, the first step is to examine the query execution plan using the EXPLAIN QUERY PLAN command. If the plan shows a \"SCAN\" against a large table, it means the database engine is reading every row to find the result. This is the worst-case scenario, and adding an index can help by allowing the engine to jump directly to the relevant data instead of scanning through the entire table.\n\nAn index is essentially a sorted copy of one or more columns from the table, kept alongside the original data. This sorted structure enables the database engine to quickly locate the desired rows by jumping to the specific index segment instead of scanning through every row. However, an index only benefits queries that filter or sort based on the indexed columns. If a query asks for unrelated columns or performs operations that don't match the index structure, the index will not provide any performance improvement.\n\nThe measurement of query performance on a 500,000-row orders table in SQLite showed that a simple scan took 90.9 milliseconds, while an index-based search took only 0.2 milliseconds. This significant difference demonstrates the potential speedup an index can bring to a query. In another example, filtering data by a specific date showed a full table scan took 123.5 milliseconds, whereas a covering index search took just 3.9 milliseconds, even though it had to return 42,470 rows. The covering index allowed the engine to retrieve all necessary columns directly from the index without accessing the main table, resulting in a much faster response.\n\nHowever, it's essential to consider the costs associated with indexes. Building an index takes time and resources, especially on large tables. Creating an index on 500,000 rows took 572 milliseconds, while a hundred million rows would take minutes. Additionally, the database file size increases with each added index. While indexes can substantially improve query performance, it's crucial to weigh these benefits against the time and disk space required to maintain them.",
  "summary": "By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can tell whether a query is reading your whole table or jumping straight to what it needs, add an index that changes which of those happens, recognise the three common ways a query throws away an index it already has, and say what indexes cost so you can argue for one honestly. It is about twenty-five minutes,…",
  "key_points": [
    "Indexes improve database performance by allowing direct data access",
    "Indexes are sorted copies of table columns, enabling quick row location",
    "Indexes benefit queries filtering/sorting by indexed columns, not unrelated columns"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}