GIANT Stories at MongoDB

Listing Your MongoDB Atlas Resources

If you want to use the MongoDB Atlas API to manage your clusters one of the first things you will discover is that resource IDs are the keys to the kingdom. In order to use the API you will need an API key and you will need to grant access to your program via the API whitelist.

You can set up your API keys and API whitelist on this screen.

Atlas Account Settings

Once they are set up you can use them to run the script to get a list of all resources.

$ ./ -h
usage: [-h] [--username USERNAME] [--apikey APIKEY]
                        [--org_id ORG_ID]

optional arguments:
  -h, --help           show this help message and exit
  --username USERNAME  MongoDB Atlas username
  --apikey APIKEY      MongoDB Atlas API key
  --org_id ORG_ID      specify an organization to limit what is listed

If you run this on the command line you will get

Py Atlas List

The project and org IDs have been occluded for security purposes. As you can see the Organization ID, Project IDs and Cluster names are displayed. These will be required by other parts of the API.

Give it a spin. There is a Pipfile.lock for pipenv users.

Time Series Data and MongoDB: Part 3 – Querying, Analyzing, and Presenting Time-Series Data

Robert Walters
September 19, 2018

This blog series seeks to provide best practices as you build out your time series application on MongoDB. In this post, part three, we will cover how to query, analyze and present time-series data stored in MongoDB.

Building a REST API with MongoDB Stitch

Andrew Morgan
September 19, 2018
Technical, Cloud

MongoDB Stitch QueryAnywhere often removes the need to create REST APIs, but for the other times, Stitch webhooks let you create them in minutes.

Handling Files using MongoDB Stitch and AWS S3

Aydrian Howard
September 18, 2018

MongoDB is the best way to work with data. As developers, we are faced with design decisions about data storage. For small pieces of data, it’s often an easy decision. Storing all of Shakespeare’s works, all 38 plays, 154 sonnets, and poems takes up 5.6MB of space in plain text. That’s simple to handle in MongoDB. What happens, however, when we want to include rich information with images, audio, and video? We can certainly store that information inside the database, but another approach is to leverage cloud data storage. Services such as AWS S3 allow you to store and retrieve any amount of data stored in buckets and you can store references in your MongoDB database.

With a built-in AWS Service, MongoDB Stitch provides the means to easily update and track files uploaded to an S3 bucket without having to write any backend code. In a recent Stitchcraft live coding session on my Twitch channel, I demonstrated how to upload a file to S3 and record it in a collection directly from a React.js application. After I added an AWS Service (which just required putting in IAM credentials) to my Stitch application and set up my S3 Bucket, I only needed to add the following code to my React.js application to handle uploading my file from a file input control:

handleFileUpload(file) {
 if (!file) {

 const key = `${}-${}`
 const bucket = 'stitchcraft-picstream'
 const url = `http://${bucket}${encodeURIComponent(key)}`

 return convertImageToBSONBinaryObject(file)
   .then(result => {
     // AWS S3 Request
     const args = {
       ACL: 'public-read',
       Bucket: bucket,
       ContentType: file.type,
       Key: key,
       Body: result

     const request = new AwsRequest.Builder()

   .then(result => {
     // MongoDB Request
     const picstream = this.mongodb.db('data').collection('picstream')
     return picstream.insertOne({
       file: {
         type: file.type
       ETag: result.ETag,
       ts: new Date()
   .then(result => {
     // Update UI

To watch me put it all together, check out the recording of the Stitchcraft live coding session and check out the link to the GitHub repo in the description. Be sure to follow me on Twitch and tune in for future Stitchcraft live coding sessions.

-Aydrian Howard
Developer Advocate

Leading digital cryptocurrency exchange cuts developer time by two-thirds and overcomes scaling challenges with MongoDB Atlas

Cryptocurrency investing is a wild ride. And while many have contemplated the lucrative enterprise of building an exchange, few have the technical know-how, robust engineering, and nerves of steel to succeed. Discidium Internet Labs decided it qualified and launched Koinex, a multi-cryptocurrency trading platform, in India in August 2017. By the end of the year, it was the largest digital asset exchange by volume in the country.

“India loves cryptocurrencies like Bitcoin and Ripple,” says Rakesh Yadav, Co-Founder & CTO Koinex. “We wanted to be the first local exchange to operate in accordance with global best practices. But we needed to provide a great user experience fast with a small development team. Transaction speed is one thing but developer bandwidth is the real limiting factor. “

Those who follow the cryptocurrency markets will know that challenges come fast and furious, with huge swings in prices and trading volumes driven by unpredictable developments, all in an environment of rapidly changing regulations. For an exchange with the scope and ambition of Koinex – it currently offers trading in more than 50 pairs of cryptocurrencies – that leads to a lot of exposure to market volatility.

For example, Rakesh says, three months after the launch, Koinex saw a huge spike in Ripple (XRP) transactions. “It was 50 times the volume we’d seen before,” he says. A group of Japanese credit card companies had just announced Ripple support, giving it a huge increase in trustworthiness. The trading volumes ramped up and stayed up. But the PostgreSQL deployment (a tabular database) underpinning the Koinex platform couldn’t keep pace with surging demand.

“Everything was stored in PostgreSQL, and it wasn’t keeping up. We had an overwhelming growth in data with read and write times slowing because of large indexes, and CPUs spiking. Moving our deployment to Aurora RDS gave us two times improvement. But it wasn’t enough as we could not scale beyond a single instance for writes. We were seeing just one thousand transactions per second, and we wanted to aim for 100,000.” If one spike on one cryptocurrency could cause such problems, what would a really busy market look like? Time to aim high.

“We decided to move the 80 percent of data that needed real-time responses to MongoDB’s fully managed database as a service Atlas, and run it all on AWS.”

The MongoDB Atlas experience

The move was started in January as part of the development of a new trading engine and, as Ankush Sharma Senior Platform Engineer at Koinex explains, MongoDB Atlas looked like a good fit for a number of reasons. “It had sharding out of the box, which we saw as essential as this gave us the ability to distribute write loads out across multiple servers, without any application changes at all. Atlas also meant fewer code changes, less frequent resizing or cluster changes, and as little operational input from us as possible.”

Other aspects of the database seemed promising. “Its flexible data model made it a great fit for blockchain RPC-based communications as it meant we could handle any cryptocurrency regardless of its data structure. MongoDB Atlas is fully managed so it’s zero DevOps resources to run, and it’s got an easy learning curve.”

That last aspect was as important as the technical suitability. “Allocating developer bandwidth is completely crucial,” says Ankush. “If we’d stuck with Postgres, creating the new Trading Engine would have been three to four months. That wouldn’t have been survivable. With MongoDB we did it in 30 to 40 days.” And although he initially wasn’t sure MongoDB Atlas would be a long-term solution, its performance convinced him otherwise.

“It scaled out as we needed, and it scaled back so gracefully. There are times when the market is slower, so it lets us track costs to market liquidity. It’s working really well for us.”

It’s continued to free up developer bandwidth, too. “We started off with just the one product on MongoDB but we have eight or nine on it now. We wouldn’t have been able to concentrate on the mobile app, or provide historical data on demand to traders if our DevOps team didn’t find the database so easy to work with and with so many features.”

And long lead times on new products aren’t an option in the cryptocurrency market. Over just 17 days in July, Koinex built out and launched a new service called Loop – a novel peer-to-peer digital token exchange system designed to deal with controversial regulatory moves by the Indian central bank. “Digital currencies are complex. Policies and technologies are changing all the time so our business often depends on being able build new features quickly, sometimes in just a few weeks. Not only does it have to be done fast but it has to be tested, robust and at scale. It’s a financial platform – you can’t compromise. Time we don’t spend managing the database is time we can spend on new features and products, and that’s a huge payback.”

MongoDB also has the right security features to fit in with a financial exchange, says Ankush: “We have solid protocols limiting who in the company can see what data, with strong access controls, encryption and proper separation of production and development environments. We look to global best practices, and these are all implemented by default in the MongoDB Atlas service.”

For a company barely a year old, Koinex has big plans for the future. “Koinex has been leading the digital asset revolution in India,” says Ankush. “We give users a world-class experience. The long-term plan is to have multiple digital asset management products available, not just cryptocurrencies. Whole new ecosystems are going to develop. With MongoDB Atlas, we’re going to be able to do all the things that other top exchanges do as well as add in our own extras and features.”

New to MongoDB Atlas — Data Explorer Now Available for All Cluster Sizes

Ken W. Alger
September 18, 2018
Release Notes, Cloud

At the recent MongoDB .local Chicago event, MongoDB CTO and Co-Founder, Eliot Horowitz made an exciting announcement about the Data Explorer feature of MongoDB Atlas. It is now available for all Atlas cluster sizes, including the free tier.

The easiest way to explore your data

What is the Data Explorer? This powerful feature allows you to query, explore, and take action on your data residing inside MongoDB Atlas (with full CRUD functionality) right from your web browser. Of course, we've thought about security; Data Explorer access and whether or not a user can modify documents is tied to her role within the Atlas Project. Actions performed via the Data Explorer are also logged in the Atlas alerting window.

Bringing this feature to the "shared" Atlas cluster sizes — the free M0s, M2s, and M5s — allows for even faster development. You can now perform actions on your data while developing your application, which is where these shared cluster sizes really shine.

Check out this short video to see the Data Explorer in action.

Atlas is the easiest and fasted way to get started with MongoDB. Deploy a free cluster in minutes.

Recording sensor data with MongoDB Stitch & Electric Imp

Andrew Morgan
September 17, 2018
Technical, Cloud

Electric Imp devices and cloud services are a great way to get started with IoT. Electric Imp's MongoDB Stitch client library makes it a breeze to integrate with Stitch. This post describes how.

PyMongo Monday: PyMongo Create

Last time we showed you how to setup up your environment.

In the next few episodes we will take you through the standard CRUD operators that every database is expected to support. In this episode we will focus on the Create in CRUD.


Lets look at how we insert JSON documents into MongoDB.

First lets start a local single instance of mongod using m.

$ m use stable
2018-08-28T14:58:06.674+0100 I CONTROL [main] Automatically disabling TLS 1.0, to force-enable TLS 1.0 specify --sslDisabledProtocols 'none'
2018-08-28T14:58:06.689+0100 I CONTROL [initandlisten] MongoDB starting : pid=43658 port=27017 dbpath=/data/db 64-bit host=JD10Gen.local
2018-08-28T14:58:06.689+0100 I CONTROL [initandlisten] db version v4.0.2
2018-08-28T14:58:06.689+0100 I CONTROL [initandlisten] git version: fc1573ba18aee42f97a3bb13b67af7d837826b47
2018-08-28T14:58:06.689+0100 I CONTROL [initandlisten] allocator: syste


The mongod starts listening on port 27017 by default. As every MongoDB driver defaults to connecting on localhost:27017 we won't need to specify a connection string explicitly in these early examples.

Now, we want to work with the Python driver. These examples are using Python 3.6.5 but everything should work with versions as old as Python 2.7 without problems.

Unlike SQL databases, databases and collections in MongoDB only have to be named to be created. As we will see later this is a lazy creation process, and the database and corresponding collection are actually only created when a document is inserted.

$ python
Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 03:03:55)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pymongo
>>> client = pymongo.MongoClient()
>>> database = client[ "ep002" ]
>>> people_collection = database[ "people_collection" ]
>>> result=people_collection.insert_one({"name" : "Joe Drumgoole"})
>>> result.inserted_id
>>> result.acknowledged
>>> people_collection.find_one()
{'_id': ObjectId('5b62e6f8c3b498fbfdc1c20c'), 'name': 'Joe Drumgoole'}

First we import the pymongo library (line 6). Then we create the local client proxy object, client = pymongo.MongoClient() (line 7) . The client object manages a connection pool to the server and can be used to set many operational parameters related to server connections.

We can leave the parameter list to the MongoClient call blank. Remember, the server by default listens on port 27017 and the client by default attempts to connect to localhost:27017.

Once we have a client object, we can now create a database, ep002 (line 8) and a collection, people_collection (line 9). Note that we do not need an explicit DDL statement.

Using Compass to examine the database server

A database is effectively a container for collections. A collection provides a container for documents. Neither the database nor the collection will be created on the server until you actually insert a document. If you check the server by connecting MongoDB Compass you will see that there are no databases or collections on this server before the insert_one call.

screen shot of compass at start

These commands are lazily evaluated. So, until we actually insert a document into the collection, nothing happens on the server.

Once we insert a document:

>>>> result=database.people_collection.insert_one({"name" : "Joe Drumgoole"})
>>> result.inserted_id
>>> result.acknowledged
>>> people_collection.find_one()
{'_id': ObjectId('5b62e6f8c3b498fbfdc1c20c'), 'name': 'Joe Drumgoole'}

We will see that the database, the collection, and the document are created.

screen shot of compass with collection

And we can see the document in the database.

screen shot of compass with document

_id Field

Every object that is inserted into a MongoDB database gets an automatically generated _id field. This field is guaranteed to be unique for every document inserted into the collection. This unique property is enforced as the _id field is automatically indexed and the index is unique.

The value of the _id field is defined as follows:


The id field is generated on the client and you can see the PyMongo generation code in the file. Just search for the def _generate string. All MongoDB drivers generate id fields on the client side. The id field allows us to insert the same JSON object many times and allow each one to be uniquely identified. The id field even gives a temporal ordering and you can get this from an ObjectID via the generation_time method.

>>> from bson import ObjectId
>>> x=ObjectId('5b7d297cc718bc133212aa94')
>>> x.generation_time
datetime.datetime(2018, 8, 22, 9, 14, 36, tzinfo=)
>>> <b>print(x.generation_time)</b>
2018-08-22 09:14:36+00:00

Wrap Up

That is create in MongoDB. We started a mongod instance, created a MongoClient proxy, created a database and a collection and finally made then spring to life by inserting a document.

Next up we will talk more about Read part of CRUD. In MongoDB this is the find query which we saw a little bit of earlier on in this episode.

For direct feedback please pose your questions on twitter/jdrumgoole that way everyone can see the answers.

The best way to try out MongoDB is via MongoDB Atlas our Database as a Service. It’s free to get started with MongoDB Atlas so give it a try today.

Controlling humidity with a MongoDB Stitch HTTP service and IFTTT

Andrew Morgan
September 17, 2018
Technical, Cloud

IFTTT is a great cloud service for pairing up cloud and IoT services. This post shows how to invoke an IFTTT webhook from a MongoDB Stitch function, where that webhook controls a dehumidifier via a Smart power plug.

Time Series Data and MongoDB: Part 2 – Schema Design Best Practices

Robert Walters
September 13, 2018

In the previous blog post, “Introduction to time-series data in MongoDB” we introduced the concept of time-series data followed by some discovery questions you can use to help gather requirements for your time-series application. Answers to these questions help guide the schema and MongoDB database configuration needed to support a high-volume production application deployment. In this blog post we will focus on how two different schema designs can impact memory and disk utilization under read, write, update and delete operations.

In the end of the analysis you may find that the best schema design for your application may be leveraging a combination of schema designs. By following the recommendations we lay out below, you will have a good starting point to develop the optimal schema design for your app, and appropriately size your environment.

Designing a time-series schema

Let’s start by saying that there is no one canonical schema design that fits all application scenarios. There will always be trade-offs to consider regardless of the schema you develop. Ideally you want the best balance of memory and disk utilization to yield the best read and write performance that satisfy your application requirements, and that enables you to support both data ingest, and then analysis of time-series data streams.

In this blog post we will look at various schema design configurations. First, storing one document per data sample, and then bucketing the data using one document per time-series time range and one document per fixed size. Storing more than one data sample per document is known as bucketing. This will be implemented at the application level and requires nothing to be configured specifically in MongoDB. With MongoDB’s flexible data model you can optimally bucket your data to yield the best performance and granularity for your application’s requirements.

This flexibility also allows your data model to adapt to new requirements over time – such as capturing data from new hardware sensors that were not part of the original application design. These new sensors provide different metadata and properties than the sensors you used in the original design. WIth all this flexibility you may think that MongoDB databases are the wild west where anything goes and you can quickly end up with a database full of disorganized data. MongoDB provides as much control as you need via schema validation that allows you full control to enforce things like the presence of mandatory fields and range of acceptable values to name a few.

To help illustrate how schema design and bucketing affects performance consider the scenario where we want to store and analyze historical stock price data. Our sample stock price generator application creates sample data every second for a given number of stocks that it tracks. One second is the smallest time interval of data collected for each stock ticker in this example. If you would like to generate sample data in your own environment, the StockGen tool is available on GitHub. It is important to note that although the sample data in this document uses stock ticks as an example, you can apply these same design concepts to any time-series scenario like temperature and humidity readings from IoT sensors.

The StockGen tool used to generate sample data will generate the same data and store it in two different collections: StockDocPerSecond and StockDocPerMinute that each contain the following schemas:

Scenario 1: One document per data point

    "_id" : ObjectId("5b4690e047f49a04be523cbd"),
"p" : 56.56,
    "symbol" : "MDB",
    "d" : ISODate("2018-06-30T00:00:01Z")
    "_id" : ObjectId("5b4690e047f49a04be523cbe"),
"p" : 56.58,
    "symbol" : "MDB",
    "d" : ISODate("2018-06-30T00:00:02Z")
Figure 1: Sample documents representing one document per second granularity

Scenario 2: Time-based bucketing one document per minute

"_id" : ObjectId("5b5279d1e303d394db6ea0f8"), 
"p" : {
"0" : 56.56,
"1" : 56.56,
"2" : 56.58,
"59" : 57.02
"symbol" : "MDB",
"d" : ISODate("2018-06-30T00:00:00Z")
"_id" : ObjectId("5b5279d1e303d394db6ea134"), 
"p" : {
 "0" : 69.47,
 "1" : 69.47,
 "2" : 68.46,
 "59" : 69.45
"symbol" : "TSLA",
"d" : ISODate("2018-06-30T00:01:00Z")
Figure 2: Sample documents representing one minute granularity

Note that the field, “p” contains a subdocument with the values for each second of the minute.

Schema design comparisons

Let’s compare and contrast the database metrics of storage size and memory impact based off of 4 weeks of data generated by the StockGen tool. Measuring these metrics is useful when assessing database performance.

Effects on Data Storage

In our application the smallest level of time granularity is a second. Storing one document per second as described in Scenario 1 is the most comfortable model concept for those coming from a relational database background. That is because we are using one document per data point, which is similar to a row per data point in a tabular schema. This design will produce the largest number of documents and collection size per unit of time as seen in Figures 3 and 4.

Document count per day comparing per second vs per minute schema design
Figure 3: Document count per day comparing per second vs per minute schema design

Comparison between data size and storage size for each scenario
Figure 4: Comparison between data size and storage size for each scenario

Figure 4 shows two sizes per collection. The first value in the series is the size of the collection that is stored on disk while the second value is the size of the data in the database. These numbers are different because MongoDB’s WiredTiger storage engine supports compression of data at rest. Logically the PerSecond collection is 605MB, but on disk it is consuming around 190 MB of storage space.

Effects on memory utilization

A large number of documents will not only increase data storage consumption but increase index size as well. An index was created on each collection and covered the symbol and date fields. Unlike some key-value databases that position themselves as time-series databases, MongoDB provides secondary indexes giving you flexible access to your data and allowing you to optimize query performance of your application.

Index Size (MB) comparison between PerSecond and PerMinute
Figure 5: Index Size (MB) comparison between PerSecond and PerMinute

The size of the index defined in each of the two collections are seen in Figure 5. Optimal performance of MongoDB happens when indexes and most recently used documents fit into the memory allocated by the WiredTiger cache (we call this the “working set”). In our example we generated data for just 5 stocks over the course of 4 weeks. Given this small test case our data already generated an index that is 103MB in size for the PerSecond scenario. Keep in mind that there are some optimizations such a index prefix compression that help reduce the memory footprint of an index. However, even with these kind of optimizations proper schema design is important to prevent runaway index sizes. Given the trajectory of growth, any changes to the application requirements, like tracking more than just 5 stocks or more than 4 weeks of prices in our sample scenario, will put much more pressure on memory and eventually require indexes to page out to disk. When this happens your performance will be degraded. To mitigate this situation, consider scaling horizontally.

Scale horizontally

As your data grows in size you may end up scaling horizontally when you reach the limits of the physical limits of the server hosting the primary mongod in your MongoDB replica set.

By horizontally scaling via MongoDB Sharding performance can be improved since the indexes and data will be spread over multiple MongoDB nodes. Queries are no longer directed at a specific primary node. Rather they are processed by an intermediate service called a query router (mongos) which sends the query to the specific nodes that contain the data that satisfy the query. Note that this is completely transparent to the application – MongoDB handles all of the routing for you

Scenario 3: Size based bucketing

The key learnings when comparing the previous scenarios is that bucketing data has significant advantages. Time-based bucketing as described in scenario 2 buckets an entire minutes worth of data into a single document. In time-based applications such as IoT, sensor data may be generated at irregular intervals and some sensors may provide more data than others. In these scenarios, time-based bucketing may not be the optimal approach to schema design . An alternative strategy is size based bucketing. With size-bases bucketing we design our schema around one document per a certain number of emitted sensor events, or for the entire day, whichever comes first.

To see size based bucketing in action consider the scenario where you are storing sensor data and limiting the bucket size to 200 events per document, or a single day (whichever comes first). Note: The 200 limit is an arbitrary number and can be changed as needed, without application changes or schema migrations.

  _id: ObjectId(),
 deviceid: 1234,
 sensorid: 3,
 nsamples: 5,
  day: ISODate("2018-08-29"),
 last: 1535530432,
 samples : [
   { val: 50, time: 1535530412},
   { val: 55, time : 1535530415},
   { val: 56, time: 1535530420},
   { val: 55, time : 1535530430},
   { val: 56, time: 1535530432}
Figure 6: Size based bucketing for sparse data

An example size based bucket is shown in figure 6. In this design trying to limit inserts per document to an arbitrary number or a specific time period may seem difficult, however, it is easy to do using an upsert, as shown in the following code example:

sample = {val:59,time:1535530450}
day = ISODate("2018-08-29")
                  $inc:{nsamples:1},{upsert:true} )
Figure 7: Sample code to add to the size based bucket

As new sensor data comes in it is simply appended to the document until the number of samples hit 200, then a new document is created because of our upsert:true clause.

The optimal index in this scenario would be on {deviceid:1,sensorid:1,day:1,nsamples}. When we are updating data the day it is an exact match, and this is super efficient. When querying we can specify a date, or a rate range on a single field which is also efficient as well as filtering by first and last using UNIX timestamps. Note that we are using integer values for times. These are really times stored as a UNIX timestamp and only take 32 bits of storage versus an ISODate which takes 64-bits. While not a significant query performance difference over ISODate, storing as UNIX timestamp may be significant if you plan on ending up with terabytes of ingested data and you do not need to store a granularity less than a second.

Bucketing data in a fixed size will yield very similar database storage and index improvements as seen when bucketing per time in scenario 2. It is one of the most efficient ways to store sparse IoT data in MongoDB.

What to do with old data

Should we store all data in perpetuity? Is data older than a certain time useful to your organization? How accessible should older data be? Can it be simply restored from a backup when you need it, or does it need to be online and accessible to users in real time as an active archive for historical analysis? As we covered in part 1 of this blog series, these are some of the questions that should be asked prior to going live.

There are multiple approaches to handling old data and depending on your specific requirements some may be more applicable than others. Choose the one that best fits your requirements.


Does your application really need a single data point for every event generated years ago? In most cases the resource cost of keeping this granularity of data around outweighs the benefit of being able to query down to this level at any time. In most cases data can be pre-aggregated and stored for fast querying. In our stock example, we may want to only store the closing price for each day as a value. In most architectures, pre-aggregated values are stored in a separate collection since typically queries for historical data are different than real-time queries. Usually with historical data, queries are looking for trends over time versus individual real-time events. By storing this data in different collections you can increase performance by creating more efficient indexes as opposed to creating more indexes on top of real-time data.

Offline archival strategies

When data is archived, what is the SLA associated with retrieval of the data? Is restoring a backup of the data acceptable or does the data need to be online and ready to be queried at any given time? Answers to these questions will help drive your archive design. If you do not need real-time access to archival data you may want to consider backing up the data and removing it from the live database. Production databases can be backed up using MongoDB OpsManager or if using the MongoDB Atlas service you can use a fully managed backup solution.

Removing documents using remove statement

Once data is copied to an archival repository via a database backup or an ETL process, data can be removed from a MongoDB collection via the remove statement as follows:

 db.StockDocPerSecond.remove ( { "d" : { $lt: ISODate( "2018-03-01" ) } } )

In this example all documents that have a date before March 1st, 2018 defined on the “d” field will be removed from the StockDocPerSecond collection.

You may need to set up an automation script to run every so often to clean out these records. Alternatively you can avoid creating automation scripts in this scenario by defining a time to live (TTL) index.

Removing documents using a TTL Index

A TTL index is similar to a regular index except you define a time interval to automatically remove documents from a collection. In the case of our example, we could create a TTL index that automatically deletes data that is older than 1 week.

db.StockDocPerSecond.createIndex( { "d": 1 }, { expireAfterSeconds: 604800 } )

Although TTL indexes are convenient, keep in mind that the check happens every minute or so and the interval can not be configured. If you need more control so that deletions won’t happen during specific times of the day you may want to schedule a batch job that performs the deletion in lieu of using a TTL index.

Removing documents by dropping the collection

It is important to note that using the remove command or TTL indexes will cause high disk I/O. On a database that may be under high load already this may not be desirable. The most efficient and fastest way to remove records from the live database is to drop the collection. If you can design your application such that each collection represents a block of time so that when you need to archive or remove data all you need to do is drop the collection. This may require some smarts within your application code to know which collections should be queried, however, the cost benefit may outweigh this change. When you issue a remove, MongoDB also has to remove data from all affected indexes as well and this could take a while depending on the size of data and indexes.

Online archival strategies

If archival data still needs to be accessed in real-time consider how frequently these queries occur and if storing only pre-aggregated results can be sufficient.

Sharding archival data

One strategy for archiving data and keeping the data accessible real-time is by using zoned sharding to partition the data. Sharding not only helps with horizontally scaling the data out across multiple nodes, but you can tag shard ranges so partitions of data are pinned to specific shards. A cost savings measure could be to have the archival data live on shards running lower cost disks and periodically adjusting the time ranges defined in the shards themselves. These ranges would cause the balancer to automatically move the data between these storage layers, providing you with tiered, multi-temperature storage. Review our tutorial for creating tiered storage patterns with zoned sharding for more information.

Accessing archived data via queryable backups

If your archive data is not accessed that frequently and the query performance does not need to meet any strict latency SLAs consider backing the data up and using the Queryable Backups feature of MongoDB Atlas or MongoDB OpsManager. Queryable Backups allow you to connect to your backup and issue read-only commands to the backup itself, without having to first restore the backup.

Querying data from the data lake

MongoDB is an inexpensive solution not only for long term archival but for your data lake as well. Companies who have made investments in technologies like Apache Spark can leverage the MongoDB Spark Connector. This connector materializes MongoDB data as DataFrames and Datasets for use with Spark and machine learning, graph, streaming, and SQL APIs.

Key Takeaways

Once an application is live in production and is multi-terabytes in size, any major change can be very expensive from a resource standpoint. Consider the scenario where you have 6 TB of IoT sensor data and accumulating new data at a rate of 50,000 inserts per second. Performance of reads is starting to become an issue and you realize that you have not properly scaled out the database. Unless you are willing to take application downtime, a change of schema in this configuration – i.e. moving from raw data storage to bucketed storage – may require building out shims, temporary staging areas and all sorts of transient solutions to move the application to the new schema. The moral of the story is to plan for growth and properly design the best time-series schema that fits your application’s SLAs and requirements.

This article analyzed two different schema designs for storing time-series data from stock prices. Is the schema that won in the end for this stock price database the one that will be the best in your scenario? Maybe. Due to the nature of time-series data and the typical rapid ingestion of data the answer may in fact be leveraging a combination of collections that target a read or write heavy use case. The good news is that with MongoDB’s flexible schema it is easy to make changes. In fact you can run two different versions of the app writing two different schemas to the same collection. However don’t wait until your query performance starts suffering to figure out an optimal design as migrating TBs of existing documents into a new schema can take time, resource, and delay future releases of your application. You should undertake real world testing before commiting on a final design. Quoting a famous proverb, “Measure twice and cut once.”

In the next blog post, “Querying, Analyzing and Presenting time-series data with MongoDB” we will look at how to effectively get value from the time-series data stored in MongoDB.

Key Tips:

  • MMAPV1 storage engine is deprecated, use the default WiredTiger storage engine. Note that if you read older schema design best practices from a few years ago they were often built on the older MMAPV1 technology
  • Understand what the data access requirements are from your time-series application
  • Schema design impacts resources. “Measure twice and cut once” with respect to schema design and indexes
  • Test schema patterns with real data and a real application if possible
  • Bucketing data reduces index size and thus massively reduce hardware requirements
  • Time-series applications traditionally capture very large amounts of data, so only create indexes where they will be useful to the app’s query patterns
  • Consider more than one collection, one focused on write heavy inserts and recent data queries and another collection with bucketed data focused on historical queries on pre-aggregated data
  • When the size of your indexes exceed the amount of memory on the server hosting MongoDB, consider horizontally scaling out to spread the index and load over multiple servers
  • Determine at what point data expires, and what action to take such as archival or deletion