Month: October 2017

Change Streams in MongoDB 3.6

Gone are the days when expectations of information were not instantaneous access to the data. In today’s world, people expect real-time results and information. What’s the moisture content of the soil around grapevine X right now? Not yesterday, not five minutes ago, but now. Being notified about constantly changing data points is critical in many of today’s applications. In the latest 3.6 release of MongoDB a new feature is being introduced to assist with these real-time expectations: Change Streams.

Change Streams Defined

Change streams are being implemented in the driver with a new aggregation operator, $changeStream and watch method in the API. We can now specify a $changeStream stage at the beginning of our pipeline and request that notifications are sent for specific changes to a particular collection.

Example Use Case

Let’s take the soil moisture example I stated earlier. Imagine that you have a farm with multiple moisture sensors or a TMP36 Analog temperature sensor connected to something like a NodeMCU ESP8266 or LoPy device. Without going into detail about how to set the IoT network up, for the sake of our use case we can assume that they are sending readings back to a MongoDB server. Perhaps the data is being stored on MongoDB Atlas even. I have, however, written about the LoPy microcontroller and ESP8266 with temperature sensors in the past if you are interested in learning more.

Anyway, for the sake of argument, let’s say that we need to know when the moisture content of the soil drops below a certain level. Moisture values on similar devices are measured as a raw number between 0-1024. The higher the number, the more moisture in the soil. For our use case, we need to know when the moisture level drops below 350 (too dry).

Change Stream in action

Since these recordings are frequently changing, we can set up a change stream to get notified. It’s fairly straightforward to establish a notification with the watch method. We could set something like this up:

change_stream = client.winery.collection.watch([
  {'$match': {
       'operationType': {'$in': ['insert']}
    }},
    {'$match': {
        'moisture_level': {'$lt': 350}
    }}
]);
for change in change_stream:
    turn_on_water();
    # Something else magical

Now when we have an insert into the database where the moisture_level is below 350, we’ll turn our watering system on along with whatever else needs to be done. We could set up a similar change stream for when the moisture level gets high enough to warrant shutting the system off at 450.

This is but one example of where change streams would be a useful tool. Updating reports dynamically on data changes, publish/subscribe types of functionality, brokers, and many other use cases are now open to native MongoDB actions.

Change Steam Details

You’ll notice that there is an operationType being looked at there. Change streams look at five different events, or Operation Types

  • Insert
  • Delete
  • Replace (everything but the unique id)
  • Update
  • Invalidate (in cases of an invalid cursor being returned)

Change streams also utilize access controls and can be set up based on user roles. They present a defined API and enable scaling across primaries and secondaries. MongoDB is supporting up to 1,000 concurrent change streams per data bearing node in 3.6.

They are durable, meaning they rely on the data being majority committed in a replica set. The order of the changes in data is important too, right? 3.6 also introduces a global logical clock which, among other uses, will ensure that clients always will get changes in the order they occurred on the server. This is very important for replace and update operations.

Wrap Up

There are a lot more technical details about change streams forthcoming as 3.6 moves closer to production and documentation is further flushed out. Change streams are inherent in the 3.6 Server and are available in both the community and enterprise versions of the product. MongoDB Version 3.6 is available to download today as a release candidate. Which means that while the high-level concepts of change streams discussed here likely will remain in tack in the final release, there may be some subtle changes. That being said, change streams are a great feature being implemented in the new release. If your applications require or could use real-time information, I would encourage you to have a look.

Follow me on Twitter @kenwalger to get the latest updates on my postings.

There are a few MongoDB specific terms in this post. I created a MongoDB Dictionary skill for the Amazon Echo line of products. Check it out and you can say “Alexa, ask MongoDB for the definition of a document?” and get a helpful response.

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Using R with MongoDB

NOTE: MongoDB 3.6 has a new R Language support. See my other blog post for the latest information.

The R programming language is a powerful language used for statistical computing. When working with statistical computing it is frequently the case that the data being explored will come from a database. Some of the powers that R excels at are working with data in tables and matrices and joining columns and rows together. This seems like a great fit for SQL databases, but what about a NoSQL database like MongoDB? Can R analyze a MongoDB document as easily as a SQL table?

Well, this post would be pretty short if the answer was “No”, right? So let take a look and how to pull data into R from a MongoDB collection. Then we’ll take a brief look at examining our data.

Setting Up

While there are plugins available for a variety of IDEs, such as those by JetBrains, it is pretty common to use RStudio when working with R. Somewhere along the line, I picked up a “scores” database in MongoDB that we’ll use as our sample data. I’ve posted it here for download.

We can easily import the data into our MongoDB database using mongoimport. In my case, I put it into a database called kenblog and a collection called scores. Pretty creative, eh? Here’s what a sample document in the collection looks like:

{
   "_id" : ObjectId("5627207b33ff2cf40effc25e"),
   "student" : 2,
   "type" : "quiz",
   "score" : 74
}

There are 1,787 records in our collection with the type of assignment being either quiz, essay, or exam. Let’s see how we can access our data with R.

First, we need to get and load our package for interfacing MongoDB with R. For this example I’ll be using RMongo, but there is another package available, rmongodb. Sadly it doesn’t look like there has been much in the way of current activity with either package’s GitHub repositories. Aside from that we can still connect and do some queries.

Connecting R to MongoDB

We need to bring in our package and establish our connection:

require(RMongo)

mongo <- mongoDbConnect('kenblog', 'localhost', 27017)

In the mongoDbConnect method, we have options for the name of the database, server name, and port number to which we want to connect.

Next, we will want to send a query. For this example, let’s get only the exam data from our scores collection. We can use the dbGetQuery method for this which takes a connection object, the collection name, and the query.

examQuery <- dbGetQuery(mongo, 'scores', "{'type': 'exam'}")

This loads in all of the records from our scores collection of type exam. Let’s take the values of our exam scores and create a vector from them.

exam_scores <- examQuery[c('score')]

Nice! Now we can utilize some of the power of R to do some data analysis. Let’s get a simple summary of our data with summary(exam_scores):

     score       
 Min.   : 60.00  
 1st Qu.: 72.00  
 Median : 79.00  
 Mean   : 79.45  
 3rd Qu.: 86.00  
 Max.   :100.00

Neat. I realize that this particular example could be computed using MongoDB’s powerful aggregation framework. However, there are times when using outside resources and languages, like R, for processing is called for.

Wrap Up

Connecting to MongoDB from R is pretty straightforward and simple using the RMongo package. However, many of the new features that MongoDB has implemented in the last few years have not been included in the community R drivers. Further, as of this post, there isn’t an “official” R driver supported by MongoDB.

R is a great statistical language and can definitely be used to query and analyze MongoDB collections. If you are using R in your work today, MongoDB is a definite option for storing your data to be analyzed.

Follow me on Twitter @kenwalger to get the latest updates on my postings.

There are a few MongoDB specific terms in this post. I created a MongoDB Dictionary skill for the Amazon Echo line of products. Check it out and you can say “Alexa, ask MongoDB for the definition of a document?” and get a helpful response.

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