{
  "id": 6135312,
  "title": "LIKE in SQL, Explained for Beginners",
  "url": "https://urgent.news/2026/09/07/like-in-sql-explained-for-beginners",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-07T13:00:28.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/michaelnocito/like-in-sql-explained-for-beginners-24gh"
  },
  "original_language": "en",
  "account": "In SQL, the LIKE operator allows you to perform pattern matching on columns containing text or lists. Unlike the strict equals operator (=), LIKE provides a more flexible way to search for values that contain a specific pattern within a larger string.\n\nThe LIKE operator uses two wildcard characters: % and _. The % wildcard matches any sequence of characters, including no characters at all. For example, %Shirt% would match any value containing the word \"Shirt\" anywhere within it, such as \"Blue Cotton Shirt\" or \"Shirt Stay Clips\". The _ wildcard matches exactly one character. For instance, S_m would match \"Sam\" or \"Sum\", but not \"Steam\".\n\nWhen using LIKE, it's important to understand the meaning of the wildcards. % stands for any run of characters, including none, while _ requires exactly one character. For example, %at would match \"cat\", \"hat\", or \"bat\", but not \"flat\". To match multiple characters at the end of a value, you would use something like %at instead.\n\nA LIKE pattern is written left to right, describing the pattern you're searching for. For example, %Cotton% reads as \"anything, then Cotton, then anything\". You can combine wildcards, as in A__-% which would match values starting with \"A\", followed by exactly two characters, a hyphen, and then anything.\n\nIt's worth noting that NOT LIKE can be used to find values that do NOT match a certain pattern. For instance, NOT LIKE %Shirt% would return all rows where the name does not contain the word \"Shirt\".\n\nWhen using LIKE, case sensitivity can vary depending on the database. Some databases, like SQLite, are case-insensitive by default for A-Z letters. Others, like PostgreSQL, are case-sensitive and require ILIKE for case-insensitive matching. SQL Server and MySQL may also have database-specific collations that affect case sensitivity.\n\nOne common pitfall when using LIKE is the over-matching trap. For example, searching for LIKE %Video% in a Steam games dataset would match both \"Video Production\" and \"360 Video\" because both contain the letters \"Video\". To avoid this, you can specify more precise patterns, such as %, Sales% to match only values containing the word \"Sales\" surrounded by commas.\n\nIn some cases, LIKE can be used to search for values within a list stored in a single column. However, this approach may return unintended results since LIKE does not consider word boundaries. For example, LIKE %man% would match \"man\", \"manage\", \"human\", and \"Germany\".\n\nTo properly search for values within a list column, it's recommended to use separate columns for each value or employ alternative techniques like string splitting functions, depending on the database system being used.",
  "summary": "By Michael Nocito , data analyst · Updated August 19, 2026 Most of the time you ask SQL for an exact value. WHERE status = 'Active' is a closed question with a yes or no answer. But a lot of real columns do not hold one tidy value. They hold a sentence, a product name with a code stuck on the front, an email address, or a list of tags jammed into a single cell. For those you need a looser…",
  "key_points": [],
  "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."
}