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This article is about how to use java to deal with aggregate functions in mongoDB. I think it is very practical, so I share it with you. I hope you can get something after reading this article.

An attribute timestamp_ms in a tweet_list collection in document needs to be sorted. Intra-group sorting

Using the aggregation framework, create a pipe through different components such as match,unwind,sort.

Similar to multi-tier nested subqueries in mysql.

Js code in mongoDB

Db.text.aggregate (/ / Initial document match (uses index, if a suitable one is available) [{$match: {_ id: ObjectId ("5ca95b4bfb60ec43b5dd0db5")}, / / Expand the scores array into a stream of documents {$unwind:'$tweet_list'}, {$match: {'tweet_list.timestamp_ms':' 1451841845660'}}, / / Sort in descending order {$sort: {'tweet_list.timestamp_ms': 1}}])

Java implements this aggregate function

The Aggregation class in java, where the order of query conditions determines the result.

Aggregation agg = Aggregation.newAggregation (Aggregation.match (Criteria.where ("_ id") .is (id)), Aggregation.unwind ("tweet_list"), Aggregation.sort (Sort.Direction.ASC, "tweet_list.timestamp_ms"), Aggregation.project ("tweet_list.timestamp_ms", "tweet_list.text", "tweet_list.created_at"); AggregationResults results = mongoTemplate.aggregate (agg, "text", JSONObject.class) / / System.out.println ("results" + results.getRawResults ()); / / the result obtained is document//String res = results.getRawResults (); String json = com.mongodb.util.JSON.serialize (results.getRawResults ()); System.out.println ("JSON serialized Document:" + json); JSONObject jso= JSON.parseObject (json); JSONArray resultss=jso.getJSONArray ("results"); System.out.println (resultss)

3. Expansion

Pipeline pipeline

The following pipe operators can be combined in any order. Each operator accepts a series of documents, and after they are typed, the converted document is passed to the next operator as a result. Until the last pipe operator, the result is returned to the client.

Filter match

Put the handsome in the front of the pipe as much as possible. Two reasons:

1. First filter out the unneeded documents to reduce the workload of the pipeline.

two。 If you execute match before project and group, the query can use an index.

3. You cannot use geospatial operators in match

Projective project

Similar to select operation. You can use pipeline expressions, mathematical expressions, date expressions, character expressions, logical expressions, etc.

Grouping group

Similar to the grouping in mysql

Sort sort

1 ascending order-1 descending order

Restrict limit

Limit the number of results

Skip skip

Discard the first n documents in the result

Split unwind

Split each value in the array into separate documents, for example, in this case you need to sort the tweetlist in a document, and you can use unwind to split the different map in the tweetlist into different documents.

Result return

Document

MapReduce

If the query language in the aggregation framework cannot be expressed, you need to use MapReduce.

Use: divide the problem into several small problems, send each small problem to different machines, each machine is only responsible for completing part of the work, and then merge the piecemeal solutions.

Steps:

1. Mapping map: mapping actions to each document in the collection

two。 Shuffle shuffle: group according to key values, and put the list of generated key values into the corresponding keys.

3. Simplify reduce: simplify the value in the list to a single value, the value is returned, continue shuffle, and then the list of each key has only one value, the final result

The above is how to use java to deal with aggregate functions in mongoDB. Xiaobian believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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