jjeong

https://www.elastic.co/guide/en/elasticsearch/guide/current/relevance-is-broken.html

Don’t use dfs_query_then_fetch in production. It really isn’t required. Just having enough data will ensure that your term frequencies are well distributed. There is no reason to add this extra DFS step to every query that you run.

https://www.elastic.co/guide/en/elasticsearch/guide/current/relevance-intro.html

Producing the explain output is expensive. It is a debugging tool only. Don’t leave it turned on in production.

- track score 의 경우 sorting 시 _score 이외 field 를 이용해서 sort 하고자 할 때 문서에 대한 relevance와 함께 조합을 원한다면 이 옵션을 true 로 켜야 합니다. 다만, 이 설정의 경우 연산 비용이 높기 때문에 필요한 경우가 아니라면 주의해서 사용을 하셔야 합니다.

- explain 옵션의 경우 _score 에 대한 계산 결과가 어떻게 해서 나오게 되었는지 자세한 explain 을 넘겨 주는 기능으로 역시 debugging 용으로만 사용을 하시고, 운영 환경에서는 off 로 설정해서 사용 하셔야 합니다.

- sorting 기능은 메모리를 많이 사용합니다.

https://www.elastic.co/guide/en/elasticsearch/guide/current/multi-fields.html

Sorting on a full-text analyzed field can use a lot of memory. See Fielddata for more information.

- fields 사용 (전에는 multi fields 라고 불렀습니다.)

- analyzed field 에 대한 정렬은 메모리를 많이 사용하기 때문에 주의 해서 사용합니다.

https://www.elastic.co/guide/en/elasticsearch/guide/current/_validating_queries.html

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-validate.html

https://www.elastic.co/guide/en/elasticsearch/guide/current/mapping-intro.html

This means that if you index a number in quotes ("123"), it will be mapped as type string, not type long. However, if the field is already mapped as type long, then Elasticsearch will try to convert the string into a long, and throw an exception if it can’t.

- 정수형 이면 그냥 long 으로 선언 하세요.

- 실수 형이면 그냥 double 로 선언 하세요.

- ".." 로 묶으면 string 형으로 선언 됩니다.

- dynamic mapping 은 explicity 하게 사용하시는게 좋은 습관 입니다.

To understand why deep paging is problematic, let’s imagine that we are searching within a single index with five primary shards. When we request the first page of results (results 1 to 10), each shard produces its own top 10 results and returns them to the requesting node, which then sorts all 50 results in order to select the overall top 10.

Now imagine that we ask for page 1,000—results 10,001 to 10,010. Everything works in the same way except that each shard has to produce its top 10,010 results. The requesting node then sorts through all 50,050 results and discards 50,040 of them!

You can see that, in a distributed system, the cost of sorting results grows exponentially the deeper we page. There is a good reason that web search engines don’t return more than 1,000 results for any query.

- There is a good reason that web search engines don’t return more than 1,000 results for any query.

- 불필요한 자원 및 연산에 대한 낭비로 문제가 될 수 있다.

• Parse the JSON into an array (including the document data, which can be very large)
• Look at each request to determine which shard it should go to
• Create an array of requests for each shard
• Serialize these arrays into the internal transport format
• Send the requests to each shard

https://www.elastic.co/guide/en/elasticsearch/guide/current/create-doc.html

https://www.elastic.co/guide/en/elasticsearch/guide/current/update-doc.html

Documents in Elasticsearch are immutable; we cannot change them. Instead, if we need to update an existing document, we reindex or replace it, which we can do using the same index API that we have already discussed in Indexing a Document.