By Kristina Chodorow

Getting begun with MongoDB is straightforward, yet when you commence construction purposes with it, you'll face a few advanced matters. What are the tradeoffs among normalized and denormalized information? How do you deal with copy set failure and failover? This choice of MongoDB suggestions, methods, and hacks is helping you unravel concerns with every thing from software layout and implementation to information safeguard and monitoring.

You get particular advice in 5 subject components without delay from engineers at 10gen, the corporate that develops and helps this open resource database:
Application layout Tips: What to remember while designing your schema
Implementation Tips: Programming purposes opposed to MongoDB
Optimization Tips: dashing up your purposes
Data defense Tips: utilizing replication and journaling to maintain info safe—without sacrificing an excessive amount of functionality
Administration Tips: tips on how to configure MongoDB and hold it operating easily

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Additional info for 50 Tips and Tricks for MongoDB Developers

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If the driver can’t reconnect to the database, then it obviously can’t automatically retry sending the request to that server. However, suppose you have another server that the driver knows about, could it automatically send the request to that one? Well, it depends on what the request was. If you were going to send a write to the primary, probably there is no other primary yet. ). So, it can’t auto-retry to a different server. If the error was a temporary network blip and the driver reconnects to the server immediately, it still shouldn’t try to send the request again.

This can take a varying length of time, depending on how you structure your documents. find({"zip" : "10003"}) What does MongoDB do? It has to look through every field of every document, looking for the zip field (Figure 3-4). Figure 3-4. MongoDB must look through each field in the document if there is no hierarchy. By using embedded documents, we can create our own “tree” and let MongoDB do this faster. zip" : "10003"}) And MongoDB would only have to look at _id, name, and online before seeing that address was a desired prefix and then looking for zip within that.

MongoDB loads the first page of documents from disk into memory, and compares those to your query. Then it loads the next page and compares those. Then it loads the next page. And so on, through 256GB of data. It can’t take any shortcuts: it cannot know if a document matches without looking at the document, so it must look at every document. Thus, it will need to load all 256GB into memory (the OS takes care of swapping the oldest pages out of memory as it needs room for new ones). This is going to take a long, long time.

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