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6 Rules of Thumb for MongoDB Schema Design

“I have lots of experience with SQL and normalized databases, but I’m just a beginner with MongoDB. How do I model a one-to-N relationship?” This is one of the more common questions I get from users attending MongoDB office hours. I don’t have a short answer to this question, because there isn’t just one way, there’s a whole rainbow’s worth of ways. MongoDB has a rich and nuanced vocabulary for expressing what, in SQL, gets flattened into the term “One-to-N.” Let me take you on a tour of your choices in modeling One-to-N relationships. There’s so much to talk about here, In this post, I’ll talk about the three basic ways to model One-to-N relationships. I’ll also cover more sophisticated schema designs, including denormalization and two-way referencing. And I’ll review the entire rainbow of choices, and give you some suggestions for choosing among the thousands (really, thousands) of choices that you may consider when modeling a single One-to-N relationship. Jump the end of the post ...

Spark Join Strategies — How & What?

While dealing with data, we have all dealt with different kinds of joins, be it inner , outer , left or (maybe) left-semi . This article covers the different join strategies employed by Spark to perform the join operation. Knowing spark join internals comes in handy to optimize tricky join operations, in finding root cause of some out of memory errors, and for improved performance of spark jobs(we all want that, don’t we?). Please read on to find out. Spark Join Strategies: Broadcast Hash Join Before beginning the Broadcast Hash join spark, let’s first understand Hash Join, in general : Hash Join As the name suggests, Hash Join is performed by first creating a Hash Table based on join_key of smaller relation and then looping over larger relation to match the hashed join_key values. Also, this is only supported for ‘=’ join. In spark, Hash Join plays a role at per node level and the strategy is used to join partitions available on th...

How to add your Conda environment to your jupyter notebook in just 4 steps

In this article I am going to detail the steps, to add the Conda environment to your Jupyter notebook. Step 1: Create a Conda environment. conda create --name firstEnv once you have created the environment you will see, output after you create your environment. Step 2: Activate the environment using the command as shown in the console. After you activate it, you can install any package you need in this environment. For example, I am going to install Tensorflow in this environment. The command to do so, conda install -c conda-forge tensorflow Step 3: Now you have successfully installed Tensorflow. Congratulations!! Now comes the step to set this conda environment on your jupyter notebook, to do so please install ipykernel. conda install -c anaconda ipykernel After installing this, just type, python -m ipykernel install --user --name=firstEnv Using the above command, I will now have this conda environment in my Jupyter notebook. Step 4: Just check your Jupyter Notebook, to se...

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