不爲也 比不能也

I remember a couple years ago I was at a convention talking to folks about Elasticsearch or Lucene and one of them said something like "Everyone knows that if you really want to search fast you need to just use term queries." He was certainly right about term queries being fast but I really wanted to run some numbers and see just how right he was. And that felt like a good time to write a blog p..

UPDATE: This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud.이 게시물은 기존에 Found라는 이름으로 제공된 Elasticsearch 서비스에 관한 것이다. Found은 이제 Elasticsearch Cloud로 알려져 있다.In this article series, we look at Elasticsearch from a new perspective. We'll start at the "bottom" (or close enough!) of the many abstraction levels, and gradua..

analysisAnalysis is the process of converting full text to terms. Depending on which analyzer is used, these phrases: FOO BAR, Foo-Bar, foo,bar will probably all result in the terms foo and bar. These terms are what is actually stored in the index. A full text query (not a term query) for FoO:bAR will also be analyzed to the terms foo,bar and will thus match the terms stored in the index. It is ..

Give memory to the filesystem cache Elasticsearch heavily relies on the filesystem cache in order to make search fast. In general, you should make sure that at least half the available memory goes to the filesystem cache so that Elasticsearch can keep hot regions of the index in physical memory.Elasticsearch는 search 속도를 빠르게 하기 위해, filesystem cache에 많이 의존한다. 일반적으로, Elasticsearch가 index 중 많이 사용하는 ..

While Elasticsearch comes with many queries, you will use just a few frequently. We discuss them in much greater detail in Search in Depth but next we give you a quick introduction to the most important queries.Elasticsearch에는 많은 query가 있지만, 자주 사용하는 것은 소수일 것이다. Search in Depth에서, 훨씬 더 자세히 이야기할 것이다. 아래에서 가장 중요한 query들에 대해 빠르게 소개하겠다.match_all QueryeditThe match_all query simply matches all documen..

When working with exact values, you will be working with non-scoring, filtering queries. Filters are important because they are very fast. They do not calculate relevance (avoiding the entire scoring phase) and are easily cached. We’ll talk about the performance benefits of filters later in All About Caching, but for now, just keep in mind that you should use filtering queries as often as you ca..

The term query is useful for finding a single value, but often you’ll want to search for multiple values.What if you want to find documents that have a price of $20 or $30?term query는 단일 값을 찾는데 유용하다. 그러나, 가끔은 다중 값을 찾을 수도 있다. 가격이 $20 또는 $30인 document를 찾으려면 어떻게 해야 할까?Rather than using multiple term queries, you can instead use a single terms query (note the s at the end). The terms query is simply..

Earlier in this chapter (Internal Filter Operation), we briefly discussed how non-scoring queries are calculated. At their heart is a bitset representing which documents match the filter. When Elasticsearch determines a bitset is likely to be reused in the future, it will be cached directly in memory for later use. Once cached, these bitsets can be reused wherever the same query is used, without..

While all queries perform some sort of relevance calculation, not all queries have an analysis phase.Besides specialized queries like the bool or function_score queries, which don’t operate on text at all, textual queries can be broken down into two families:모든 query가 relevance 연산의 일종을 수행하지만, 모든 query가 analysis 절을 가지지는 않는다. 텍스트를 전혀 다루지 않는, bool 이나 function-score query 같은 특별한 query 이외에, 텍스트를 다루는 ..

The match query is the go-to query—the first query that you should reach for whenever you need to query any field. It is a high-level full-text query, meaning that it knows how to deal with both full-text fields and exact-value fields.match query는 어떤 field를 query할 때마다 만나야 하는 첫 번째 query이다. match query는, full-text field와 exact-value field 양쪽 모두를 처리하는 방법을 알고 있는, high-level full-text query 이다.That s..