58.fetch phbase

1、fetch phbase工做流程

1. The coordinating node identifies which documents need to be fetched and issues a multi GETrequest to the relevant shards.
2. Each shard loads the documents and enriches them, if required, and then returns the documents to the coordinating node.
3. Once all documents have been fetched, the coordinating node returns the results to the client.

The coordinating node first decides which documents actually need to be fetched. For instance, if our query specified { "from": 90, "size": 10 }, the first 90 results would be discarded and only the next 10 results would need to be retrieved. These documents may come from one, some, or all of the shards involved in the original search request.

The coordinating node builds a multi-get request for each shard that holds a pertinent document and sends the request to the same shard copy that handled the query phase.

The shard loads the document bodies—the _source field—and, if requested, enriches the results with metadata and search snippet highlighting. Once the coordinating node receives all results, it assembles them into a single response that it returns to the client.

   

（1）各個shard 返回的只是各文檔的id和排序值（ IDs and sort values ，coordinate node根據這些id&sort values 構建完priority queue以後，而後把程序須要的document 的id發送mget請求去全部shard上獲取對應的document

（2）各個shard將document返回給coordinate node

（3）coordinate node將合併後的document結果返回給client客戶端

   

2、通常搜索，若是不加from和size，就默認搜索前10條，按照_score排序

   

延伸閱讀

Deep Pagination

The query-then-fetch process supports pagination with the from and size parameters, but within limits. Remember that each shard must build a priority queue of length from + size, all of which need to be passed back to the coordinating node. And the coordinating node needs to sort through number_of_shards * (from + size) documents in order to find the correct size documents.

Depending on the size of your documents, the number of shards, and the hardware you are using, paging 10,000 to 50,000 results (1,000 to 5,000 pages) deep should be perfectly doable. But with big-enough from values, the sorting process can become very heavy indeed, using vast amounts of CPU, memory, and bandwidth. For this reason, we strongly advise against deep paging.

In practice, "deep pagers" are seldom human anyway. A human will stop paging after two or three pages and will change the search criteria. The culprits are usually bots or web spiders that tirelessly keep fetching page after page until your servers crumble at the knees.

If you do need to fetch large numbers of docs from your cluster, you can do so efficiently by disabling sorting with the scroll query, which we discuss later in this chapter.