Such queries are known as covered queries, and are only possible if and only if all of these two requirements are satisfied:. Furthermore, MongoDB has the following restrictions which prevent indexes from fully covering queries:. Using a basic query without a covering index with a single document, the following executionStats are returned:. Thus, MongoDB did not need to scan any collection documents at all. Tweaking your indexes and queries to allow for such cases can significantly improve query performance.
However, the lack of a schema can dramatically slows down reads, causing problems with query performance as your application scales.
This, however, is tremendously costly since you have to transfer all the data from the tables involved into your application before you can perform the operation. When you are storing relational data in multiple collections in MongoDB which requires multiple queries to retrieve the data you need, you can denormalize it to increase read performance.
Denormalization is the process by which we trade write performance for read performance by embedding data from one collection into another, either by making a copy of certain fields or by moving it entirely.
Instead of trying to query the data from both collections using an application-level JOIN, we can instead embed the companies collection inside the employees collection:.
Now that all of our data is already stored in one place, we can simply query the employees collection a single time to retrieve everything we need, avoiding the need to do any JOINs entirely.
As we noted earlier, while denormalizing your data does increase read performance, it does not come without its drawbacks either. I ran the query about 1, times each for mysql and MongoDB and I am suprised that I do not notice a lot of difference in speed. Maybe MongoDB is 1. That's very disappointing. Is there something I am doing wrong? MongoDB is not magically faster. If you store the same data, organised in basically the same fashion, and access it exactly the same way, then you really shouldn't expect your results to be wildly different.
People are seeing real world MongoDB performance largely because MongoDB allows you to query in a different manner that is more sensible to your workload. For example, consider a design that persisted a lot of information about a complicated entity in a normalised fashion. This could easily use dozens of tables in MySQL or any relational db to store the data in normal form, with many indexes needed to ensure relational integrity between tables.
Now consider the same design with a document store. If all of those related tables are subordinate to the main table and they often are , then you might be able to model the data such that the entire entity is stored in a single document. In MongoDB you can store this as a single document, in a single collection. This is where MongoDB starts enabling superior performance. So a b-tree lookup, and a binary page read. If the indexes can reside entirely in memory, then 1 IO. So the total for mysql, even assuming that all indexes are in memory which is harder since there are 20 times more of them is about 20 range lookups.
These range lookups are likely comprised of random IO — different tables will definitely reside in different spots on disk, and it's possible that different rows in the same range in the same table for an entity might not be contiguous depending on how the entity has been updated, etc. Do you have concurrency, i. If you just run times the query straight, with just one thread, there will be almost no difference.
Too easy for these engines :. Let me know when you get results, I'm also in need of inputs about this!
In short, the benefit comes from the design, not some raw speed difference. Conclusion on page The project tested, analysed and compared the performance and scalability of the two database types. The experiments done included running different numbers and types of queries, some more complex than others, in order to analyse how the databases scaled with increased load.
The most important factor in this case was the query type used as MongoDB could handle more complex queries faster due mainly to its simpler schema at the sacrifice of data duplication meaning that a NoSQL database may contain large amounts of data duplicates. This advantage comes at the cost of data duplication which causes an increase in the database size.
If such queries are typical in an application then it is important to consider NoSQL databases as alternatives while taking in account the cost in storage and memory size resulting from the larger database size.
Stack Overflow for Teams — Collaborate and share knowledge with a private group. MongoDB University offers a free training course on data modeling. This is a great way for beginners to get started with schema design and document data models.
A natural extension of data modelling, embedding allows you to avoid application joins, which minimizes queries and updates. Notably, data with a relationship should be embedded within a single document. Data with a 1:many relationship in which "many" objects appear with or are viewed alongside their parent documents are also great candidates for embedding.
Because these types of data are always accessed together, storing them together in the same document just makes sense. Embedding generally provides better performance for read operations due to this kind of data locality. Embedded data models also allow developers to update related data in a single write operation because single document writes are transactional.
However, not all and 1:many relationships are good candidates for embedding in a single document. Referencing makes much more sense when modeling many:many relationships.
However, whenever referencing, your application must issue follow-up queries to resolve any references. This, in turn, requires more round-trips to the server. A document is frequently accessed but contains data that is rarely used. Embedding would only increase in-memory requirements, so referencing may make more sense.
A part of a document is frequently updated and keeps getting longer, while the remainder of the document is relatively static. This could occur when modeling many:1 relationships, such as product reviews:product, for example. While other factors play a part in performance, RAM size is obviously the most important consideration for instance sizing. But if your working set exceeds the RAM of the instance size or server, read activity will begin to shoot up.
Did you find several areas to improve? Even in the worst cases, there are many things you can do to turn the situation around and achieve highly efficient performance.
Effectiveness is deciding what to do better. If you think your queries are performing poorly, this series of simple steps will help you improve. Indexes are easy-to-read data structures with collection information, serving as a map for searching for documents, saved in RAM before disk. Indexes can be reused depending on their values. To create an index, you only have to implement the db.
The difference between negative and positive is in the order in which the indexes are stored. The workshop I gave about MongoDB was quick.
However, my colleagues and I continued to talk about various MongoDB Atlas tools well after it ended, including the Performance Advisor. Indexes consume space. They are updated by modifying the source code. So, if the indexed field is never used and must always be updated, it defeats the purpose of improving performance, consumes memory, and becomes an obstacle. Please keep this in mind when indexing.
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