[[stopwords]] == Stopwords: Performance Versus Precision Back in the early days of information retrieval,((("stopwords", "performance versus precision"))) disk space and memory were limited to a tiny fraction of what we are accustomed to today. It was essential to make your index as small as possible. Every kilobyte saved meant a significant improvement in performance. Stemming (see <<stemming>>) was important, not just for making searches broader and increasing retrieval in the same way that we use it today, but also as a tool for compressing index size. Another way to reduce index size is simply to _index fewer words_. For search purposes, some words are more important than others. A significant reduction in index size can be achieved by indexing only the more important terms. So which terms can be left out? ((("term frequency", "high and low"))) We can divide terms roughly into two groups: Low-frequency terms:: Words that appear in relatively few documents in the collection. Because of their rarity,((("weight", "low frequency terms"))) they have a high value, or _weight_. High-frequency terms:: Common words that appear in many documents in the index, such as `the`, `and`, and `is`. These words have a low weight and contribute little to the relevance score. [TIP] ================================================== Of course, frequency is really a scale rather than just two points labeled _low_ and _high_. We just draw a line at some arbitrary point and say that any terms below that line are low frequency and above the line are high frequency. ================================================== Which terms are low or high frequency depend on the documents themselves. The word `and` may be a low-frequency term if all the documents are in Chinese. In a collection of documents about databases, the word `database` may be a high-frequency term with little value as a search term for that particular collection. That said, for any language there are words that occur very commonly and that seldom add value to a search.((("English", "stopwords"))) The default English stopwords used in Elasticsearch are as follows: a, an, and, are, as, at, be, but, by, for, if, in, into, is, it, no, not, of, on, or, such, that, the, their, then, there, these, they, this, to, was, will, with These _stopwords_ can usually be filtered out before indexing with little negative impact on retrieval. But is it a good idea to do so? [[pros-cons-stopwords]] [float="true"] === Pros and Cons of Stopwords We have more disk space, more RAM, and ((("stopwords", "pros and cons of")))better compression algorithms than existed back in the day. Excluding the preceding 33 common words from the index will save only about 4MB per million documents. Using stopwords for the sake of reducing index size is no longer a valid reason. (However, there is one caveat to this statement, which we discuss in <<stopwords-phrases>>.) On top of that, by removing words from the index, we are reducing our ability to perform certain types of searches. Filtering out the words listed previously prevents us from doing the following: * Distinguishing _happy_ from _not happy_. * Searching for the band The The. * Finding Shakespeare's quotation ``To be, or not to be'' * Using the country code for Norway: `no` The primary advantage of removing stopwords is performance. Imagine that we search an index with one million documents for the word `fox`. Perhaps `fox` appears in only 20 of them, which means that Elastisearch has to calculate the relevance `_score` for 20 documents in order to return the top 10. Now, we change that to a search for `the OR fox`. The word `the` probably occurs in almost all the documents, which means that Elasticsearch has to calculate the `_score` for all one million documents. This second query simply cannot perform as well as the first. Fortunately, there are techniques that we can use to keep common words searchable, while still maintaining good performance. First, we'll start with how to use stopwords.