How to optimize performance with CREATE, PUT, and DELETE requests on MongoDB?

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9 Answers

performanceoptimizecreate
90%

Use Projections to Return Only Necessary Data,Limit the Number of Query Results to Reduce Network Demand,For more information on using projections, see Project Fields to Return from Query.

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db.posts.createIndex({
   author_name: 1
})
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88%

Create a list of documents and insert them into the collection:,Now, when you have a MongoCollection object, you can finally start to work with documents, i.e storing data in the database.,Here we are inserting the created document to our collection:

{
   firstName: "Tester",
   lastName: "Testovsky",
   age: 30,
   occupation: "QA engineer",
   skills: [
      "JMeter",
      "Load Testing",
      "Bad Puns"
   ]
   address: [{
      city: "Quality-city",
      street: "Performance ave.",
      house: 12
   }]
}
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72%

Next up is PUT and DELETE request. For now, we would simply return the type of request for both the operations. It accepts the endpoint with an id which denotes the unique id which MongoDB provides when we save data to it.,So, we would start with the GET operator. We will write the code for the GET request in this way:-,Finally, we add a middleware to handle the errors that occurred during the operations.

Pretag
 Pretag team - issue, fix, solve, resolve
65%

The MongoDB profiler and the explain methods will take you a long way. After all, many problems become pretty obvious just by staring at the source of the query.,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.,We can then change the status of the MongoDB profiler according to what we want it to do.

Pretag
 Pretag team - issue, fix, solve, resolve
75%

It is possible to control your MongoDB instance through Java code by using the powerful MongoDB Java Driver. This library provides you with the ability to connect to a MongoDB instance; create, read, update and delete documents, and much more. The full API documentation for version 3.8 can be found here. There is also an extremely useful reference guide with examples and tutorials.,Now, let’s try to write a simple JMeter script to evaluate the performance of our MongoDB deployment. In our script, we will create one JSR223 sampler for each operation of the connection to the database: inserting, reading, updating, and deleting documents.,2. Here is the full code for a process of creating a new document and inserting it to a collection. We will use it later in our JMeter script.

Pretag
 Pretag team - issue, fix, solve, resolve
40%

Scalability/Performance, Distributed Document Oriented Database ,Avoid large objects, and especially large arrays.

Pretag
 Pretag team - issue, fix, solve, resolve
22%

This is how MongoDB can boost performance in some use cases.,and finally what is going on here. looks like creating a connection each time, thus its testing the connection time plus the query time.,RDBMS vs NoSQL: Performance and Scaling Comparison

10 rows

mysql insert: 1702 ms
mysql select: 11 ms

mongo insert: 47 ms
mongo select: 12 ms

100 rows

mysql insert: 8171 ms
mysql select: 10 ms

mongo insert: 167 ms
mongo select: 60 ms

1000 rows

mysql insert: 94813 ms(1.58 minutes)
mysql select: 13 ms

mongo insert: 1013 ms
mongo select: 677 ms

10.000 rows

mysql insert: 924695 ms(15.41 minutes)
mysql select: 144 ms

mongo insert: 9956 ms(9.95 seconds)
mongo select: 4539 ms(4.539 seconds)
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60%

Rate Limiting is disabled by default, and needs a Redis server running when enabled. A tutorial on Rate Limiting is forthcoming.,on_inserted for every resource endpoint.,Concurrency control applies to all edition methods: PATCH (edit), PUT (replace), DELETE (delete).

$ curl - i http: //myapi.com/people
   HTTP / 1.1 200 OK
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48%

Excellent database performance is important when you are developing applications with MongoDB. Sometimes the overall data serving process may become degraded due to a number of reasons, some of which include:,Besides faster querying by use of indexing, there is an addition advantage of other operations such as sort, samples and limit. For example, if I design my schema as {f: 1, m:1} i can do an additional operation apart from find as,Beside creating indexed queries and using Query Selectivity as discussed above, there are other concepts you can employ to fasten and make your queries effective.

Complex write operations. For example a command like

db.collection.update({
   a: 7
}, {
   $set: {
      m: 4
   }
}, {
   multi: true
})
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