不爲也 比不能也

Elasticsearch supports a wide range of use-cases across our user base, and more and more of these rely on fast indexing to quickly get large amounts of data into Elasticsearch. Even though Elasticsearch is fast and index performance is continually improved, it is still possible to overwhelm it. At that point you typically see parts of bulk requests getting rejected. In this blog post we will loo..

When updating a document with the index API, we read the original document, make our changes, and then reindex the whole document in one go. The most recent indexing request wins: whichever document was indexed last is the one stored in Elasticsearch. If somebody else had changed the document in the meantime, their changes would be lost.index API를 사용하여 document를 업데이트하는 경우, 원래의 document를 읽고, 변경을 ..

Of course, data denormalization has downsides too. The first disadvantage is that the index will be bigger because the _source document for every blog post is bigger, and there are more indexed fields. This usually isn’t a huge problem. The data written to disk is highly compressed, and disk space is cheap. Elasticsearch can happily cope with the extra data.물론, 데이터 비정규화도 단점이 있다. 첫 번째 단점은 모든 블로그 ..

The problem comes when we want to allow more than one person to rename files or directories at the same time. Imagine that you rename the /clinton directory, which contains hundreds of thousands of files. Meanwhile, another user renames the single file /clinton/projects/elasticsearch/README.txt. That user’s change, although it started after yours, will probably finish more quickly.한 명 이상이, 동시에, ..