To find out the strategy a query uses, the cursor that you use for queries – usually created in the db.collection.find() method – has an explain() method that will tell you about the query plan. MongoDB provides an ‘explain’ facility that gives you a tool for troubleshooting queries, which comes in three flavors:Īll three can be used to return information on query plans and the execution statistics of those query plans. If you haven’t provided the means to enable the query to run quickly with an effective indexing strategy, such as by providing a usable index, it is up to you. The database chooses the winning strategy by judging the relative merit of the alternative strategies. In any database system, this strategy is a set of operations that the execution engine needs to perform to return the data required by a query. Once you have identified the query that will make the most overall difference once its performance is improved, you need to work out how the query is being executed.Įach database system has a different way of telling you the strategy it is using for a query and how well it has performed. Using the explain() method to work out why a query is slow There are several resources on the internet that have examples of MongoDB profiler queries: MongoDB confirms that it has set the criterion. Using the mongo shell or a GUI-integrated shell like Studio 3T‘s IntelliShell, run the following command: db.setProfilingLevel(2) Level 2 – The profiler collects data for all operations.Level 1 – The profiler collects data for operations that take longer than the value of slowms, which you can set.Level 0 – The profiler is off and does not collect any data.We can then change the status of the MongoDB profiler according to what we want it to do. You can check your current profiling status using the command: db.getProfilingStatus() This will take you a long way down the route of diagnosing performance problems. whether it was able to fully-use and index Export MongoDB query results, documents, or entire collections and databases using Studio 3T's Export Wizard.With Studio 3T's Export Wizard.This can be queried just like any other database, so you can find a lot of information quickly.Īs well as the source of the query or command, there is a summary of the execution stats that tell you, amongst other information: The profiler stores the queries in db.system.profile. It can be turned on or off from applications or from the shell. When it collects queries, it does so from the entire instance, which means all your databases and connections on that instance. Generally, you’ll leave the profiler switched off unless you are collecting information or monitoring the usage of the database, because it has to store the information it collects. The MongoDB profiler allows you to see what queries are being run, either by collecting all of them or sampling time-slices of them. MongoDB comes with a built-in profiler that gives query-level insight as to why the database chose these operations. The database system decides how to do it and what physical operations to perform. You might think that you’ve told the database what to do, but any query language actually only specifies the result you want and the set-based operation you require. glean important statistics like how long a MongoDB query takes to run, when the query is being executed, how often, and why.how to find slow-performing, cost-intensive queries.When dealing with performance issues in any type of database, it usually pays to take a simple approach and deal with the queries that are causing the most problems first.īy the end of this article, you’ll learn precisely: The most important feature is the profiler, but the explain method of a cursor is an indispensable tool as well, because it completes the picture. Queries and commands can be exasperating to improve unless you have some clues.įortunately, MongoDB has some excellent features that can make this easy to do, which we’ll cover in this article. Reply to this email directly, view it on GitHub, or mute the thread. Docs: - You are receiving this because you were mentioned. NOTE: $out doesn't care about existing Collections and blatently overwrites them with the new output. ) aggregation stage: var muhIdsOfInterest = db.getCollection('users').aggregate( ) This gets all users that have a userId in muhIdsOfInterest and dumps found Documents into a new Collection named onlyUsersILike. There is an $out stage, you'll have to apply at the end just as you would any other ($match, $project. Thanks ! in fact I found an easier solution : just add the following line before the request: DBQuery.shellBatchSize = 500 :) thanks ! 13:24 GMT+01:00 Raphael Beer, it's really simple.
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