MongoDB Innovation Award Winners

Jun 24 • Posted 1 month ago

The MongoDB Innovation Awards recognize applications that are transforming businesses, enabling organizations to actualize trends like Big Data and the Internet of Things and attain impressive scale and agility. MongoDB customers, community members and partners nominated hundreds of organizations, tools and applications for the award. Tonight, as part of MongoDB World 2014, we announced the following award winners.



Genentech cut the time to introduce new genetic tests from 6-9 months to a matter of weeks. Testing instruments have gotten better at capturing complex and varied genetic data, while legacy technologies struggle to store it. Using MongoDB, Genentech can store and analyze that data more quickly, making it easier to develop medicines that matter.


eBay uses MongoDB to help ensure smooth operations for their Marketplace and Paypal businesses. The Topostore system indexes metric metadata with MongoDB to monitor and track metrics around essential internal systems including applications, systems, network devices and databases. As a result, their internal teams are able to expose real-time data and insights in an extensible visualization platform to keep operations running smoothly.

Big Apple


SumAll is a business intelligence tool for small and medium sized businesses with over 200,000 companies on its platform across 35+ countries. They help clients derive insights from bringing all their data into single view by aggregating more than 30 business data sources (revenue, analytics, social, marketing services). With MongoDB, SumAll achieves real-time data processing at scale.

Cool Data

Met Office Weather Project

Coronal Mass Ejections (CMEs), solar flares and solar wind can impact the performance of the electricity grid, satellites, GPS systems, aviation and mobile communications. MongoDB allows the UK Met Office to collect and analyze vast amounts of different types of data - including solar flare imagery from NASA - to provide warnings of space weather events to government and business stakeholders.

Data Science


eHarmony uses MongoDB to help singles find their soulmates. The Compatibility Matching System crunches hundreds of traits and preferences to generate over 3 billion potential matches per day. By migrating from a relational database to MongoDB, eHarmony reduced the time to match by 95% — from 2+ weeks to under 12 hours — to make some of the happiest couples on the planet. Since migrating to MongoDB, eHarmony has increased communication between users by 30%, paid subscriptions by 50% and increased unique visitors by 60% year over year.


EA SPORTS FIFA Online 3 is a free-to-play, massively multiplayer online football game, and the most popular sports game in Korea. EA SPORTS runs FIFA Online 3 on MongoDB. Players can choose to play and customize a team from any of over 30 leagues and 15,000 real world players. This large deployment includes 80 shards and over 250 servers.



LinkedIn is helping its employees accelerate their career growth with MongoDB. LinkedIn sought to build a learning management system with a learner-first mentality. The system, named LearnIn, needed to serve curated content to its users based on their needs and goals. With a modern database in hand, a team of 3 built LearnIn in just a few months, delivering a modern learning experience to thousands of LinkedIn employees.

Internet of Things


Bosch Software Innovations, the software and systems house of the Bosch-Group, aims to connect billions of devices to the Internet of Things (IoT) using MongoDB. The IoT enables a vast range of applications, so to help businesses take advantage of it, Bosch Software Innovations is building a cross-industry IoT platform. As the underlying database, MongoDB stores, manages, and analyzes the data from these billions of devices and other data sources, helping Bosch and its customers bring modern IoT products to market on a repeatable and profitable basis.



UnitedHealth Group (UHG) uses MongoDB for its OptumInsight platform which gives business analysts in the healthcare industry the ability to reduce costs, meet compliance mandates, improve clinical performance and adapt to the changing health system landscape. Using OptumInsight, healthcare analysts can access a single view of Medicare and retirement claims data which allows them to audit, research and analyze this data in a much simpler way than before. UHG chose MongoDB for its high availability, agile development and schema flexibility features.

Open Source

3D Repo

Collaborating on 3D assets like CAD drawings is difficult. While back and forth sharing of these massive files is feasible, the process itself can be cumbersome as users often have trouble determining if they are working with the latest version. 3D Repo allows coordinated management, development, and transmission of large 3D data in the cloud. Users commit revisions to a common MongoDB repository, ensuring that interested parties can always work from a single master copy of the data. Over 100 international companies have already expressed interest in 3D Repo, including Arup, Boeing, and Lenovo.



Adobe Experience Manager 6.0, the market leader in Web experience management, uses MongoDB to facilitate the storage of data. Experience Manager enables organizations to create, manage, and optimize customer-facing experiences such as web, mobile, social, video, and in-store. With MongoDB, Adobe Experience Manager customers can store, manage, and access petabytes of digital assets across an expanding number of channels and platforms.

Lockheed Martin

MongoDB is helping Lockheed Martin secure cyberspace. Lockheed Martin’s Advanced Threat Monitoring (ATM) not only identifies known signature-based threats, but also analyzes emails, links, attachments, DNS transactions, firewall traffic and multitudes of other data sources in real-time to track cyber events. Using MongoDB as a data store, the system stores tens of billions of documents and consumes hundreds of thousands of documents per second. By incorporating the massive volume and variety of data and analyzing it in-place, Lockheed Martin is securing its global network.



Twine is a cloud-based software platform that offers collaborative care by connecting chronic disease patients and clinicians through synchronized apps that work seamlessly across devices. With MongoDB, Twine can develop their platform quickly, conceptualize and design data easily and grow to accommodate an increasing number of data points to support their mission of improving chronic disease care for both patients and healthcare providers.



Meteor is an open-source platform for writing MongoDB-based web and mobile applications in pure JavaScript. Meteor allows developers to build apps in record time by making many common tasks simple. Logins and accounts, user-generated content, data coming from Twitter and Facebook, and performance and analytics statistics are all easy to add to a Meteor app and fit well into MongoDB.

JSON Studio

JSON Studio is democratizing data in MongoDB by making it accessible through a modern GUI. Developers, analysts, data scientists and anyone else that can use a mouse and keyboard can browse data, build queries, run aggregations, and visualize results using JSON Studio. By exposing MongoDB to every corner of the organization, JSON Studio is helping companies get more out of their data.

Thank you for your contributions and the applications you are building to challenge the world. We look forward to seeing how the community continues to realize their ideas with MongoDB.

6 Rules of Thumb for MongoDB Schema Design: Part 3

Jun 11 • Posted 1 month ago

By William Zola, Lead Technical Support Engineer at MongoDB

This is our final stop in this tour of modeling One-to-N relationships in MongoDB. In the first post, I covered the three basic ways to model a One-to-N relationship. Last time, I covered some extensions to those basics: two-way referencing and denormalization.

Denormalization allows you to avoid some application-level joins, at the expense of having more complex and expensive updates. Denormalizing one or more fields makes sense if those fields are read much more often than they are updated.

Read part one and part two if you’ve missed them.

Whoa! Look at All These Choices!

So, to recap:

  • You can embed, reference from the “one” side, or reference from the “N” side, or combine a pair of these techniques
  • You can denormalize as many fields as you like into the “one” side or the “N” side

Denormalization, in particular, gives you a lot of choices: if there are 8 candidates for denormalization in a relationship, there are 2 8 (1024) different ways to denormalize (including not denormalizing at all). Multiply that by the three different ways to do referencing, and you have over 3,000 different ways to model the relationship.

Guess what? You now are stuck in the “paradox of choice” — because you have so many potential ways to model a “one-to-N” relationship, your choice on how to model it just got harder. Lots harder.

Rules of Thumb: Your Guide Through the Rainbow

Here are some “rules of thumb” to guide you through these indenumberable (but not infinite) choices

  • One: favor embedding unless there is a compelling reason not to
  • Two: needing to access an object on its own is a compelling reason not to embed it
  • Three: Arrays should not grow without bound. If there are more than a couple of hundred documents on the “many” side, don’t embed them; if there are more than a few thousand documents on the “many” side, don’t use an array of ObjectID references. High-cardinality arrays are a compelling reason not to embed.
  • Four: Don’t be afraid of application-level joins: if you index correctly and use the projection specifier (as shown in part 2) then application-level joins are barely more expensive than server-side joins in a relational database.
  • Five: Consider the write/read ratio when denormalizing. A field that will mostly be read and only seldom updated is a good candidate for denormalization: if you denormalize a field that is updated frequently then the extra work of finding and updating all the instances is likely to overwhelm the savings that you get from denormalizing.
  • Six: As always with MongoDB, how you model your data depends — entirely — on your particular application’s data access patterns. You want to structure your data to match the ways that your application queries and updates it.

Your Guide To The Rainbow

When modeling “One-to-N” relationships in MongoDB, you have a variety of choices, so you have to carefully think through the structure of your data. The main criteria you need to consider are:

  • What is the cardinality of the relationship: is it “one-to-few”, “one-to-many”, or “one-to-squillions”?
  • Do you need to access the object on the “N” side separately, or only in the context of the parent object?
  • What is the ratio of updates to reads for a particular field?

Your main choices for structuring the data are:

  • For “one-to-few”, you can use an array of embedded documents
  • For “one-to-many”, or on occasions when the “N” side must stand alone, you should use an array of references. You can also use a “parent-reference” on the “N” side if it optimizes your data access pattern.
  • For “one-to-squillions”, you should use a “parent-reference” in the document storing the “N” side.

Once you’ve decided on the overall structure of the data, then you can, if you choose, denormalize data across multiple documents, by either denormalizing data from the “One” side into the “N” side, or from the “N” side into the “One” side. You’d do this only for fields that are frequently read, get read much more often than they get updated, and where you don’t require strong consistency, since updating a denormalized value is slower, more expensive, and is not atomic.

Productivity and Flexibility

The upshot of all of this is that MongoDB gives you the ability to design your database schema to match the needs of your application. You can structure your data in MongoDB so that it adapts easily to change, and supports the queries and updates that you need to get the most out of your application.

How Buffer uses MongoDB to power its Growth Platform

Jun 9 • Posted 1 month ago

By Sunil Sadasivin, CTO at Buffer

Buffer, powered by experiments and metrics

At Buffer, every product decision we make is driven by quantitative metrics. We have always sought to be lean in our decision making, and one of the core tenants of being lean is launching experimental features early and measuring their impact.

Buffer is a social media tool to help you schedule and space out your posts on social media networks like Twitter, Facebook, Google+ and Linkedin. We started in late 2010 and thanks to a keen focus on analytical data, we have now grown to over 1.5 million users and 155k unique active users per month. We’re now responsible for sharing 3 million social media posts a week.

When I started at Buffer in September 2012 we were using a mixture of Google Analytics, Kissmetrics and an internal tool to track our app usage and analytics. We struggled to move fast and effectively measure product and feature usage with these disconnected tools. We didn’t have an easy way to generate powerful reports like cohort analysis charts or measure things like activation segmented by signup sources over time. Third party tracking services were great for us early on, but as we started to dig deeper into our app insights, we realized there was no way around it—we needed to build our own custom metrics and event tracking.

We took the plunge in April 2013 to build our own metrics framework using MongoDB. While we’ve had some bumps and growing pains setting this up, it’s been one of the best decisions we’ve made. We are now in control of all metrics and event tracking and are able to understand what’s going on with our app at a deeper level. Here’s how we use MongoDB to power our metrics framework.

Why we chose MongoDB

At the time we were evaluating datastores, we had no idea what our data would look like. When I started designing our schema, I quickly found that we needed something that would let us change the metrics we track over time and on the fly. Today, I’ll want to measure our signup funnel based on referrals, tomorrow I might want to measure some custom event and associated data that is specific to some future experiment. I needed to plan for the future, and give our developers the power to track any arbitrary data. MongoDB and its dynamic schema made the most sense for us. MongoDB’s super powerful aggregation framework also seemed perfect for creating the right views with our tracking data.

Our Metrics Framework Architecture

In our app, we’ve set up an AWS SQS queue and any data we want to track from the app goes immediately to this queue. We use SQS heavily in our app and have found it to be a great tool to manage messaging at high throughput levels. We have a simple python worker which picks off messages from this queue and writes them to our metrics database. The reason why we’ve done this instead of connecting and writing directly to our metrics MongoDB database is because we wanted our metrics set up to have absolutely zero impact on application performance. Much like Google Analytics offers no overhead to an application, our event tracking had to do the same. The MongoDB database that would store our events would be extremely write heavy since we would be tracking anything we could think of, including every API request, page visited, Buffer user/profile/post/email created etc. If, for whatever reason our metrics db goes down, or starts having write locking issues, our users shouldn’t be impacted. Using SQS as a middleman would allow tracking data to queue up if any of these issues occur. SQS gives us enough time to figure out what the issue is, fix, it and then process that backlog. We’ve had quite a few times in the past year where using Amazon’s robust SQS service has saved us from losing any data during maintenance or downtime that would occur when creating a robust high throughput metrics framework from scratch. We use MongoHQ to host our data. They’ve been super helpful with any challenges in scaling a db like ours. Since our setup is write heavy, we’ve initially set up a 400GB SSD replica set. As of today (May 16) we have 90 collections and are storing over 500 million documents.

We wrote simple client libraries for tracking data for every language that we use (PHP, Python, Java, NodeJS, Javascript, Objective-C). In addition to, our API, mobile apps and internal tools all plug into this framework.

Tracking events

Our event tracking is super simple. When a developer creates a new event message, our python worker creates a generic event collection (if it doesn’t exist) and stores event data that’s defined by the developer. It will store the user or visitor id, and the date that the event occurred. It’ll also store the user_joined_at date which is useful for cohort analysis.

Here are some examples of event tracking our metrics platform lets us do.

Visitor page views in the app.

Like many other apps, we want to track every visitor that hits our page. There is a bunch of data that we want to store to understand the context around the event. We’d like to know the IP address, the URI they viewed, the user agent they’re using among other data.

Here’s what the tracking would look like in our app written in PHP:

Here’s the corresponding result in our MongoDB metrics db:

Logging User API calls

We track every API call our clients make to the Buffer API. Essentially what we’ve done here is create query-able logging for API requests. This has been way more effective than standard web server logs and has allowed us to dig deeper into API bugs, security issues and understanding the load on our API.

Experiment data

With this type of event tracking, our developers are able to track anything by writing a single line of code. This has been especially useful for measuring events specific to a feature experiment. This frictionless process helps keep us lean: we can measure feature usage as soon as a feature is launched. For example, we recently launched a group sharing feature for business customers so that they can group their Buffer social media accounts together. Our hypothesis was that people with several social media accounts prefer to share specific content to subsets of accounts. We wanted to quantifiably validate whether this is something many would use, or whether it’s a niche or power user feature. After a week of testing this out, we had our answer.

This example shows our tracking of our ‘group sharing’ experiment. We wanted to track each group that was created with this new feature. With this, we’re able to track the user, the groups created, the name of the group, and the date it was created.

Making sense of the data

We store a lot of tracking data. While it’s great that we’re tracking all this data, there would be no point if we weren’t able to make sense of it. Our goal for tracking this data was to create our own growth dashboard so we can keep track of key metrics, and understand results of experiments. Making sense of the data was one of the most challenging parts of setting up our growth platform.

MongoDB Aggregation

We rely heavily on MongoDB’s aggregation framework. It has been super handy for things like gauging API client requests by hour, response times separated by API endpoint, number of visitors based on referrers, cohort analysis and so much more.

Here’s a simple example of how we use MongoDB aggregation to obtain our average API response times between April 8th and April 9th: Result:

With the aggregation framework, we have powerful insight into how clients are using our platform, which users are power users and a lot more. We previously created long running scripts to generate our cohort analysis reports. Now we can use MongoDB aggregation for much of this.

Running ETL jobs

We have several ETL jobs that run periodically to power our growth dashboard. This is the way we make sense of our data core. Some of the more complex reports need this level of reporting. For example, the way we measure product activation is whether someone has posted an update within a week of joining. With the way we’ve structured our data, this requires a join query in two different collections. All of this processing is done in our ETL jobs. We’ll upload the results to a different database which is used to power the views in our growth dashboard for faster loading.

Here are some reports on our growth dashboard that are powered by ETL jobs

Scaling Challenges and Pitfalls

We’ve faced a few different challenges and we’ve iterated to get to a point where we can make solid use out of our growth platform. Here are a few pitfalls and examples of challenges that we’ve faced in setting this up and scaling our platform.

Plan for high disk I/O and write throughput.

The DB server size and type has a key role in how quickly we could process and store events. In planning for the future we knew that we’d be tracking quite a lot of data and a fast pace, so a db with high disk write throughput was key for us. We ended up going for a large SSD replica set. This of course really depends on your application and use case. If you use an intermediate datastore like SQS, you can always start small, and upgrade db servers when you need it without any data loss.

We keep an eye on mongostat and SQS queue size almost daily to see how our writes are doing.

One of the good things about an SSD backed DB is that disk reads are much quicker compared to hard disk. This means it’s much more feasible to run ad hoc queries on un-indexed fields. We do this all the time whenever we have a hunch of something to dig into further.

Be mindful of the MongoDB document limit and how data is structured

Our first iteration of schema design was not scalable. True, MongoDB does not perform schema validation but that doesn’t mean it’s not important to think about how data is structured. Originally, we tracked all events in a single user_metrics and visitor_metrics collection. An event was stored as an embedded object in an array in a user document. Our hope was that we wouldn’t need to do any joins and we could effectively segment out tracking data super easily by user.

We had fields as arrays that were unbounded and could grow infinitely causing the document size to grow. For some highly active users (and bots), after a few months of tracking data in this way some documents in this collection would hit the 16MB document limit and fail to write any more. This created various performance issues in processing updates, and in our growth worker and ETL jobs because there were these huge documents transferred over the wire. When this happened we had to move quickly to restructure our data.

Moving to a single collection per event type has been the most scalable solution and a more flexible solution.

Reading from secondaries

Some of our ETL jobs read and process a lot of data. If you end up querying documents that haven’t been read or written to recently, it is very possible this may be stored out of memory and on disk. Querying this data means MongoDB will page out some documents that have been touched recently and bring query results into memory. This will then make writing to that paged out document slower. It’s for this reason that we have set up our ETL and aggregation queries to read only from our secondaries in our replica-set, even though they may not be consistent with the primary.

Our secondaries have a high number of faults because of paging due to reading ‘stale’ data

Visualizing results

As I mentioned before, one of the more challenging parts about maintaining our own growth platform is extracting and visualizing the data in a way that makes a lot of sense. I can’t say that we’ve come to a great solution yet. We’ve put a lot of effort into building out and maintaining our growth dashboard and creating visualizations is the bottleneck for us today. There is really a lot of room to reduce the turnaround time. We have started to experiment a bit with using Stripe’s MoSQL to map results from MongoDB to PostgresSQL and connect with something like to make this a bit more seamless. If you’ve come across some solid solutions for visualizing event tracking with MongoDB, I’d love to hear about it!

Event tracking for everyone!

We would love to open source our growth platform. It’s something we’re hoping to do later this year. We’ve learned a lot by setting up our own tracking platform. If you have any questions about any of this or would like to have more control of your own event tracking with MongoDB, just hit me up @sunils34

Want to help build out our growth platform? Buffer is looking to grow its growth team and reliability team!

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6 Rules of Thumb for MongoDB Schema Design: Part 2

Jun 5 • Posted 1 month ago

By William Zola, Lead Technical Support Engineer at MongoDB

This is the second stop on our tour of modeling One-to-N relationships in MongoDB. Last time I covered the three basic schema designs: embedding, child-referencing, and parent-referencing. I also covered the two factors to consider when picking one of these designs:

  • Will the entities on the “N” side of the One-to-N ever need to stand alone?
  • What is the cardinality of the relationship: is it one-to-few; one-to-many; or one-to-squillions?

With these basic techniques under our belt, I can move on to covering more sophisticated schema designs, involving two-way referencing and denormalization.

Intermediate: Two-Way Referencing

If you want to get a little bit fancier, you can combine two techniques and include both styles of reference in your schema, having both references from the “one” side to the “many” side and references from the “many” side to the “one” side.

For an example, let’s go back to that task-tracking system. There’s a “people” collection holding Person documents, a “tasks” collection holding Task documents, and a One-to-N relationship from Person -> Task. The application will need to track all of the Tasks owned by a Person, so we will need to reference Person -> Task.

With the array of references to Task documents, a single Person document might look like this:

On the other hand, in some other contexts this application will display a list of Tasks (for example, all of the Tasks in a multi-person Project) and it will need to quickly find which Person is responsible for each Task. You can optimize this by putting an additional reference to the Person in the Task document.

This design has all of the advantages and disadvantages of the “One-to-Many” schema, but with some additions. Putting in the extra ‘owner’ reference into the Task document means that its quick and easy to find the Task’s owner, but it also means that if you need to reassign the task to another person, you need to perform two updates instead of just one. Specifically, you’ll have to update both the reference from the Person to the Task document, and the reference from the Task to the Person. (And to the relational gurus who are reading this — you’re right: using this schema design means that it is no longer possible to reassign a Task to a new Person with a single atomic update. This is OK for our task-tracking system: you need to consider if this works with your particular use case.)

Intermediate: Denormalizing With “One-To-Many” Relationships

Beyond just modeling the various flavors of relationships, you can also add denormalization into your schema. This can eliminate the need to perform the application-level join for certain cases, at the price of some additional complexity when performing updates. An example will help make this clear.

Denormalizing from Many -> One

For the parts example, you could denormalize the name of the part into the ‘parts[]’ array. For reference, here’s the version of the Product document without denormalization.

Denormalizing would mean that you don’t have to perform the application-level join when displaying all of the part names for the product, but you would have to perform that join if you needed any other information about a part.

While making it easier to get the part names, this would add just a bit of client-side work to the application-level join:

Denormalizing saves you a lookup of the denormalized data at the cost of a more expensive update: if you’ve denormalized the Part name into the Product document, then when you update the Part name you must also update every place it occurs in the ‘products’ collection.

Denormalizing only makes sense when there’s an high ratio of reads to updates. If you’ll be reading the denormalized data frequently, but updating it only rarely, it often makes sense to pay the price of slower updates — and more complex updates — in order to get more efficient queries. As updates become more frequent relative to queries, the savings from denormalization decrease.

For example: assume the part name changes infrequently, but the quantity on hand changes frequently. This means that while it makes sense to denormalize the part name into the Product document, it does not make sense to denormalize the quantity on hand.

Also note that if you denormalize a field, you lose the ability to perform atomic and isolated updates on that field. Just like with the two-way referencing example above, if you update the part name in the Part document, and then in the Product document, there will be a sub-second interval where the denormalized ‘name’ in the Product document will not reflect the new, updated value in the Part document.

Denormalizing from One -> Many

You can also denormalize fields from the “One” side into the “Many” side:

However, if you’ve denormalized the Product name into the Part document, then when you update the Product name you must also update every place it occurs in the ‘parts’ collection. This is likely to be a more expensive update, since you’re updating multiple Parts instead of a single Product. As such, it’s significantly more important to consider the read-to-write ratio when denormalizing in this way.

Intermediate: Denormalizing With “One-To-Squillions” Relationships

You can also denormalize the “one-to-squillions” example. This works in one of two ways: you can either put information about the “one” side (from the ‘hosts’ document) into the “squillions” side (the log entries), or you can put summary information from the “squillions” side into the “one” side.

Here’s an example of denormalizing into the “squillions” side. I’m going to add the IP address of the host (from the ‘one’ side) into the individual log message:

Your query for the most recent messages from a particular IP address just got easier: it’s now just one query instead of two.

In fact, if there’s only a limited amount of information you want to store at the “one” side, you can denormalize it ALL into the “squillions” side and get rid of the “one” collection altogether:

On the other hand, you can also denormalize into the “one” side. Lets say you want to keep the last 1000 messages from a host in the ‘hosts’ document. You could use the $each / $slice functionality introduced in MongoDB 2.4 to keep that list sorted, and only retain the last 1000 messages:

The log messages get saved in the ‘logmsg’ collection as well as in the denormalized list in the ‘hosts’ document: that way the message isn’t lost when it ages out of the ‘hosts.logmsgs’ array.

Note the use of the projection specification ( {_id:1} ) to prevent MongoDB from having to ship the entire ‘hosts’ document over the network. By telling MongoDB to only return the _id field, I reduce the network overhead down to just the few bytes that it takes to store that field (plus just a little bit more for the wire protocol overhead).

Just as with denormalizing in the “One-to-Many” case, you’ll want to consider the ratio of reads to updates. Denormalizing the log messages into the Host document makes sense only if log messages are infrequent relative to the number of times the application needs to look at all of the messages for a single host. This particular denormalization is a bad idea if you want to look at the data less frequently than you update it.


In this post, I’ve covered the additional choices that you have past the basics of embed, child-reference, or parent-reference.

  • You can use bi-directional referencing if it optimizes your schema, and if you are willing to pay the price of not having atomic updates
  • If you are referencing, you can denormalize data either from the “One” side into the “N” side, or from the “N” side into the “One” side

When deciding whether or not to denormalize, consider the following factors:

  • You cannot perform an atomic update on denormalized data
  • Denormalization only makes sense when you have a high read to write ratio

Next time, I’ll give you some guidelines to pick and choose among all of these options.

Efficient Indexing in MongoDB 2.6

Jun 4 • Posted 1 month ago

By Osmar Olivo, Product Manager at MongoDB

One of the most powerful features of MongoDB is its rich indexing functionality. Users can specify secondary indexes on any field, compound indexes, geospatial, text, sparse, TTL, and others. Having extensive indexing functionality makes it easier for developers to build apps that provide rich functionality and low latency.

MongoDB 2.6 introduces a new query planner, including the ability to perform index intersection. Prior to 2.6 the query planner could only make use of a single index for most queries. That meant that if you wanted to query on multiple fields together, you needed to create a compound index. It also meant that if there were several different combinations of fields you wanted to query on, you might need several different compound indexes.

Each index adds overhead to your deployment - indexes consume space, on disk and in RAM, and indexes are maintained during updates, which adds disk IO. In other words, indexes improve the efficiency of many operations, but they also come at a cost. For many applications, index intersection will allow users to reduce the number of indexes they need while still providing rich features and low latency.

In the following sections we will take a deep dive into index intersection and how it can be applied to applications.

An Example - The Phone Book

Let’s take the example of a phone book with the following schema.


If I were to search for “Smith, John” how would I index the following query to be as efficient as possible?

db.phonebook.find({ FirstName : “John”, LastName : “Smith” })

I could use an individual index on FirstName and search for all of the “Johns”.

This would look something like ensureIndex( { FirstName : 1 } )

We run this query and we get back 200,000 John Smiths. Looking at the explain() output below however, we see that we scanned 1,000,000 “Johns” in the process of finding 200,000 “John Smiths”.

> db.phonebook.find({ FirstName : "John", LastName : "Smith"}).explain()
    "cursor" : "BtreeCursor FirstName_1",
    "isMultiKey" : false,
    "n" : 200000,
    "nscannedObjects" : 1000000,
    "nscanned" : 1000000,
    "nscannedObjectsAllPlans" : 1000101,
    "nscannedAllPlans" : 1000101,
    "scanAndOrder" : false,
    "indexOnly" : false,
    "nYields" : 2,
    "nChunkSkips" : 0,
    "millis" : 2043,
    "indexBounds" : {
        "FirstName" : [
    "server" : "Oz-Olivo-MacBook-Pro.local:27017"

How about creating an individual index on LastName?

This would look something like ensureIndex( { LastName : 1 } )

Running this query we get back 200,000 “John Smiths” but our explain output says that we now scanned 400,000 “Smiths”. How can we make this better?

db.phonebook.find({ FirstName : "John", LastName : "Smith"}).explain()
    "cursor" : "BtreeCursor LastName_1",
    "isMultiKey" : false,
    "n" : 200000,
    "nscannedObjects" : 400000,
    "nscanned" : 400000,
    "nscannedObjectsAllPlans" : 400101,
    "nscannedAllPlans" : 400101,
    "scanAndOrder" : false,
    "indexOnly" : false,
    "nYields" : 1,
    "nChunkSkips" : 0,
    "millis" : 852,
    "indexBounds" : {
        "LastName" : [
    "server" : "Oz-Olivo-MacBook-Pro.local:27017"

So we know that there are 1,000,000 “John” entries, 400,000 “Smith” entries, and 200,000 “John Smith” entries in our phonebook. Is there a way that we can scan just the 200,000 we need?

In the case of a phone book this is somewhat simple; since we know that we want it to be sorted by Lastname, Firstname we can create a compound index on them, like the below.

ensureIndex( {  LastName : true, FirstName : 1  } ) 

db.phonebook.find({ FirstName : "John", LastName : "Smith"}).explain()
    "cursor" : "BtreeCursor LastName_1_FirstName_1",
    "isMultiKey" : false,
    "n" : 200000,
    "nscannedObjects" : 200000,
    "nscanned" : 200000,
    "nscannedObjectsAllPlans" : 200000,
    "nscannedAllPlans" : 200000,
    "scanAndOrder" : false,
    "indexOnly" : false,
    "nYields" : 0,
    "nChunkSkips" : 0,
    "millis" : 370,
    "indexBounds" : {
        "LastName" : [
        "FirstName" : [
    "server" : "Oz-Olivo-MacBook-Pro.local:27017"

Looking at the explain on this, we see that the index only scanned the 200,000 documents that matched, so we got a perfect hit.

Beyond Compound Indexes

The compound index is a great solution in the case of a phonebook in which we always know how we are going to be querying our data. Now what if we have an application in which users can arbitrarily query for different fields together? We can’t possibly create a compound index for every possible combination because of the overhead imposed by indexes, as we discussed above, and because MongoDB limits you to 64 indexes per collection. Index intersection can really help.

Imagine the case of a medical application which doctors use to filter through patients. At a high level, omitting several details, a basic schema may look something like the below.

      Conditions : [] 
      Medications : [ ]

Some sample searches that a doctor/nurse may run on this system would look something like the below.

Find me a Patient with Blood_Type = O under the age of 50

db.patients.find( {   Blood_Type : “O”,  Age : {   $lt : 50  }     } )

Find me all patients over the age of 60 on Medication X

db.patients.find( { Medications : “X” , Age : { $gt : 60} })

Find me all Diabetic patients on medication Y

db.patients.find( { Conditions : “Diabetes”, Medications : “Y” } )

With all of the unstructured data in modern applications, along with the desire to be able to search for things as needed in an ad-hoc way, it can become very difficult to predict usage patterns. Since we can’t possibly create compound indexes for every combination of fields, because we don’t necessarily know what those will be ahead of time, we can try indexing individual fields to try to salvage some performance. But as shown above in our phone book application, this can lead to performance issues in which we pull documents into memory that are not matches.

To avoid the paging of unnecessary data, the new index intersection feature in 2.6 increases the overall efficiency of these types of ad-hoc queries by processing the indexes involved individually and then intersecting the result set to find the matching documents. This means you only pull the final matching documents into memory and everything else is processed using the indexes. This processing will utilize more CPU, but should greatly reduce the amount of IO done for queries where all of the data is not in memory as well as allow you to utilize your memory more efficiently.

For example, looking at the earlier example:

db.patients.find( {   Blood_Type : “O”,  Age : {   $lt : 50  }     } )

It is inefficient to find all patients with BloodType: O (which could be millions) and then pull into memory each document to find the ones with age < 50 or vice versa.

Instead, the query planner finds all patients with bloodType: O using the index on BloodType, and all patients with age < 50 using the index on age, and then only pulls the intersection of these 2 result sets into memory. The query planner only needs to fit the subsets of the indexes in memory, instead of pulling in all of the documents. This in turn causes less paging, and less thrashing of the contents of memory, which will yield overall better performance.

Index intersection allows for much more efficient use of existing RAM so less total memory will usually be required to fit the working set then previously. Also, if you had several compound indices that were made up of different combinations of fields, then you can reduce the total number of indexes on the system. This means storing less indices in memory as well as achieving better insert/update performance since fewer indices must be updated.

As of version 2.6.0, you cannot intersect with geo or text indices and you can intersect at most 2 separate indices with each other. These limitations are likely to change in a future release.

Optimizing Multi-key Indexes It is also possible to intersect an index with itself in the case of multi-key indexes. Consider the below query:

Find me all patients with Diabetes & High Blood Pressure

db.patients.find( {  Conditions : { $all : [ “Diabetes”, “High Blood Pressure” ]  }    }  )

In this case we will find the result set of all Patients with Diabetes, and the result set of all patients with High blood pressure, and intersect the two to get all patients with both. Again, this requires less memory and disk speed for better overall performance. As of the 2.6.0 release, an index can intersect with itself up to 10 times.

Do We Still Need Compound Indexes?

To be clear, compound indexing will ALWAYS be more performant IF you know what you are going to be querying on and can create one ahead of time. Furthermore, if your working set is entirely in memory, then you will not reap any of the benefits of Index Intersection as it is primarily based on reducing IO. But in a more ad-hoc case where one cannot predict the shape of the queries and the working set is much larger than available memory, index intersection will automatically take over and choose the most performant path.

MongoDB Security Part II: 10 mistakes that can compromise your database

Jun 3 • Posted 1 month ago

This is the second in our 3-part series on MongoDB Security by Andreas Nilsson, Lead Security Engineer at MongoDB

This post outlines 10 things to avoid when configuring security for MongoDB. These types of mistakes can lead to the loss of sensitive data, disrupted operations and have the potential to put entire companies out of business. These recommendations are based on my experience working with MongoDB users, and building security systems for databases and financial services organizations. Items are ordered by a combination of severity and frequency.

Mistake #1: Directly exposing a MongoDB server to the Internet

It is surprisingly common to deploy MongoDB database servers directly online or in a DMZ. The MongoDB server network interface is designed to be stable under rogue conditions but exposing the database to the Internet is an unnecessary risk. This holds true for any backend system and is not specific for MongoDB.

If public network exposure is combined with lack of access control, the entire content of the database is up for grabs for anyone who cares to look. In addition, an attacker could intentionally or accidentally change the database configuration, modify the application behavior or perform a Denial of Service (DoS) attack.


Design web applications with a multi-tier architecture in mind, use firewalls to segment the network layers appropriately, and place your database server at the inner data storage layer.

Mistake #2: No access control

Access control is not enabled by default when installing MongoDB. If access control is not enabled, anyone with network access to the server can perform any operation. This includes extracting all data and configuration, running arbitrary Javascript using the eval command, modifying any data in the database, creating and removing shards, etc.


Always enable access control, unless it is guaranteed that no untrusted entities will gain access to the server, see the section on Role-Based Access Control in the MongoDB security manual.

Mistake #3 - Not enabling SSL

It is fairly straightforward to protect the network communication using SSL, and enabling SSL in MongoDB does not impact the database performance. SSL also protects against man-in-the-middle attacks, where an attacker would intercept and modify communication between two parties.


Enable SSL to protect network communication against eavesdropping between the clients and the servers and within MongoDB clusters and replica sets.

Mistake #4 - Unnecessary exposure of interfaces

MongoDB ships with an HTTP server and REST interface. By default this interface is turned off in MongoDB 2.6. Do not enable the HTTP server interface unless it is used for backwards compatibility. Instead use the wire API for communication with the server.

We also recommend only binding to necessary network interfaces and turn off server side Javacript execution if not needed.


Run MongoDB with secure configuration options as described in the documentation.

Mistake #5 - Poor user account configuration

There are a few ways to configure user accounts incorrectly, for MongoDB as well as for other systems. These include but are not limited to:

  • Use of a single high-privilege admin account for all purposes.
  • Granting high privileges and roles to users who do not need them.
  • Use of weak passwords or the same password for multiple accounts.
  • Orphaned user accounts belonging to decommissioned users or applications.


Use the principle of least privilege when configuring user accounts and utilize the flexibility available in the MongoDB access control system. Use unique, complex passwords and be mindful to decommission deprecated user accounts.

Mistake #6 - Insecure OS privileges

Running the mongod or mongos processes using a non-dedicated, high-privilege account like root puts your Operating System at unnecessary risk. Instead use a dedicated, special purpose account.

Avoid lax, insecure OS file permissions on * Data files * Keyfile * SSL private key files * Log files


Database data files, the keyfile and SSL private key files should only be readable by the mongod/mongos user. Log files should only be writable by the mongod/mongos user and readable only by root.

Mistake #7 - Insecure replica set keyfile configuration

The content of the keyfile used for authentication in sharded clusters and replicasets is in essence a password and should as such be long and of high entropy. Avoid:

  • Short or low-entropy password in the keyfile
  • Inadequate protection of the keyfile


Use a long, complex password if using a keyfile and protect it using adequate file permissions.

Mistake #8 - Poor SSL configuration

SSL is a complex protocol that needs to be configured properly to avoid leaving unexpected security holes.


Always provide MongoDB servers or the mongo shell with one or several CA certificates to establish a basis of trust.

Do not use self-signed certificates unless you are only looking for the encryption parts of SSL. Using a self-signed certificate invalidates substantial parts of SSL. Use certificates issued by a commercial or internal Certificate Authority.

Avoid using the same certificate across servers or clients. This exposes the private key in multiple places and unless a wildcard (*) certificate is used no hostname validation can be performed.

Mistake #9 - Unprotected backups

Care should be taken to adequately protect backup files generated by copying the data files or using the mongodump tool. If the content of the database is sensitive, the content of the backup is equally sensitive and should be treated as such.


Treat database backup files with the same level of care as the original database storage files.

Mistake #10 - Conscious or unconscious ignorance

A guaranteed way to create an insecure system is to ignore the topic altogether, or hope someone else thinks about it.


Before deploying a MongoDB instance with sensitive data, please consult the MongoDB Security Manual and the MongoDB Security Architecture Whitepaper and stay conscious about potential threats to your application.

MongoDB subscriptions provide access to additional enterprise grade capabilities. The subscription includes all the ease-of-use, broad driver support and scalability features of MongoDB, while addressing the more demanding security and certification requirements of corporate and government information security environments. To see more, download the development version of the MongoDB Enterprise edition here

6 Rules of Thumb for MongoDB Schema Design: Part 1

May 29 • Posted 2 months ago

By William Zola, Lead Technical Support Engineer at MongoDB

“I have lots of experience with SQL, 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, I’m breaking this up into three parts. In this first part, I’ll talk about the three basic ways to model One-to-N relationships. In the second part I’ll cover more sophisticated schema designs, including denormalization and two-way referencing. And in the final part, 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.

Many beginners think that the only way to model “One-to-N” in MongoDB is to embed an array of sub-documents into the parent document, but that’s just not true. Just because you can embed a document, doesn’t mean you should embed a document.

When designing a MongoDB schema, you need to start with a question that you’d never consider when using SQL: what is the cardinality of the relationship? Put less formally: you need to characterize your “One-to-N” relationship with a bit more nuance: is it “one-to-few”, “one-to-many”, or “one-to-squillions”? Depending on which one it is, you’d use a different format to model the relationship.

Basics: Modeling One-to-Few

An example of “one-to-few” might be the addresses for a person. This is a good use case for embedding — you’d put the addresses in an array inside of your Person object:

This design has all of the advantages and disadvantages of embedding. The main advantage is that you don’t have to perform a separate query to get the embedded details; the main disadvantage is that you have no way of accessing the embedded details as stand-alone entities.

For example, if you were modeling a task-tracking system, each Person would have a number of Tasks assigned to them. Embedding Tasks inside the Person document would make queries like “Show me all Tasks due tomorrow” much more difficult than they need to be. I will cover a more appropriate design for this use case in the next post.

Basics: One-to-Many

An example of “one-to-many” might be parts for a product in a replacement parts ordering system. Each product may have up to several hundred replacement parts, but never more than a couple thousand or so. (All of those different-sized bolts, washers, and gaskets add up.) This is a good use case for referencing — you’d put the ObjectIDs of the parts in an array in product document. (For these examples I’m using 2-byte ObjectIDs because they’re easier to read: real-world code would use 12-byte ObjectIDs.)

Each Part would have its own document:

Each Product would have its own document, which would contain an array of ObjectID references to the Parts that make up that Product:

You would then use an application-level join to retrieve the parts for a particular product:

For efficient operation, you’d need to have an index on ‘products.catalog_number’. Note that there will always be an index on ‘parts._id’, so that query will always be efficient.

This style of referencing has a complementary set of advantages and disadvantages to embedding. Each Part is a stand-alone document, so it’s easy to search them and update them independently. One trade off for using this schema is having to perform a second query to get details about the Parts for a Product. (But hold that thought until we get to denormalizing in part 2.)

As an added bonus, this schema lets you have individual Parts used by multiple Products, so your One-to-N schema just became an N-to-N schema without any need for a join table!

Basics: One-to-Squillions

An example of “one-to-squillions” might be an event logging system that collects log messages for different machines. Any given host could generate enough messages to overflow the 16 MB document size, even if all you stored in the array was the ObjectID. This is the classic use case for “parent-referencing” — you’d have a document for the host, and then store the ObjectID of the host in the documents for the log messages.

You’d use a (slightly different) application-level join to find the most recent 5,000 messages for a host:


So, even at this basic level, there is more to think about when designing a MongoDB schema than when designing a comparable relational schema. You need to consider two factors:

  • Will the entities on the “N” side of the One-to-N ever need to stand alone?
  • What is the cardinality of the relationship: is it one-to-few; one-to-many; or one-to-squillions?

Based on these factors, you can pick one of the three basic One-to-N schema designs:

  • Embed the N side if the cardinality is one-to-few and there is no need to access the embedded object outside the context of the parent object
  • Use an array of references to the N-side objects if the cardinality is one-to-many or if the N-side objects should stand alone for any reasons
  • Use a reference to the One-side in the N-side objects if the cardinality is one-to-squillions

Next time we’ll see how to use two-way relationship and denormalizing to enhance the performance of these basic schemas.

Appboy’s co-founder and CIO Jon Hyman discusses how the leading platform for app marketing automation leverages MongoDB and ObjectRocket for real-time data aggregation and scale and gives a preview of his talk with Kenny Gorman of ObjectRocket at MongoDB World.

Want to see more? MongoDB World will feature over 80 MongoDB experts from around the world. Early bird ticket prices for the event end May 23. Register now to grab your seat

MongoDB Security Part 1 - Design and Configuration

May 21 • Posted 2 months ago

By Andreas Nilsson, Lead Security Engineer at MongoDB

With increased regulatory compliance, heightened concerns around privacy and growing risk from hackers and organized crime, the need to secure access to data has never been more urgent.

MongoDB 2.6 provides a number of features to facilitate building secure applications, such as auditing and authentication with Kerberos and LDAP. MongoDB now features a more competent and complete role-based access control system, x.509 authentication, an improved SSL stack and revamped security documentation.

In a short series of blog posts I will attempt to explain the philosophy and design of the security model of MongoDB. The first post covers the basics of securing a MongoDB server and application and gives an overview of the options available. The second post lists the most common security mistakes when configuring MongoDB. The final post is a deep dive into the authentication and authorization subsystems, specifically covering sharded systems with multiple databases and how to use the new Role-Based Access Control system.

For details of the MongoDB security architecture and a complete list of features, please refer to the MongoDB Security Architecture Whitepaper.

Security Model

Before going into details let’s start with the basics. A database security model needs to offer a basic set of features:

  • control of read and write access to data
  • protection of the integrity and confidentiality of the data stored.
  • control of modifications to the database system configuration.
  • privilege levels for different user types, administrators, applications etc.
  • auditing of sensitive operations.
  • stable and secure operation in a potentially hostile environment.

These security requirements can be achieved in different ways. A disconnected database server in a locked room would constitute a secure deployment regardless of how the database was being protected. However, it would not be very useful. A database is often placed unprotected on a “secured”, internal network. This is an idealized scenario since no network is entirely secure, architecture changes over time, and a considerable number of successful breaches are from internal sources. A defense-in-depth approach is therefore recommended when implementing your application’s infrastructure. While MongoDB’s newest security features help to improve your overall security posture, security is only as strong as the weakest link in the chain.

A conceptual view of the MongoDB security architecture is represented in the image below. The security model is divided into the four pillars of authentication, authorization, auditing and encryption.

Secure Your Deployment

In the discussion below we will assume a simple web application which reads from and writes to a replicated database. Each of the steps below is described in detail in the documentation. The steps are described in greater detail in the MongoDB Security Checklist. The focus is primarily on enabling access control and transport encryption.

Infrastructure Prerequisites

Design the application to work in a multilayer fashion, and place the database server(s) on a dedicated network segment, isolated from the DMZ where the web application resides. Configure firewall rules to limit network access to the database server. Lock down MongoDB user and file permissions. The database files should be protected from unauthorized access and the mongod/mongos daemons should be running with minimum privileges, specifically not as root.

Enable Access Control

By default there are no users configured in MongoDB. In order to enable access control users needs to be created and assigned appropriate privileges. Access control is enabled using the command line —auth or —keyfile flags. When access control is enabled, clients and drivers are required to authenticate to the server, and servers are required to authenticate to each other.

The following is the recommended series of steps to enable access control.

Design Determine which type of authentication methods to use for client authentication. The options are challenge-response based username/password (MONGODB-CR), x.509, LDAP and Kerberos. Determine which type of authentication method to use for server-server cluster authentication. The options are username/password and x.509. Please note that x.509 requires the use of SSL. List the different user types that will exist in the system; administrators, support staff, different type of application users etc. For each of the user types, determine which built-in roles are required. Optionally create customized roles tailored for your deployment.

Deployment Configure a keyfile or x.509 certificates for the cluster nodes. Start the mongod servers with the —auth flag set and appropriate cluster authentication options as determined in 3. Start mongos servers with appropriate cluster authentication options. Create the desired users with correct permissions. Please note that after creating the first user access control is enabled. Therefore, at a minimum the first user should have the userAdminAnyDatabase or root roles in order to be able to create other users.

Enable Transport Encryption

In order to protect the network traffic, SSL should be enabled between clients and the server and in between servers. Enabling SSL is well described in the security documentation

MongoDB supports the use of any server SSL certificate as long as the corresponding root CA certificate is provided with the configuration parameter —sslCAFile. If no CA certificate is specified, no certificate validation is performed and the certificate is only used for encryption purposes. Although supported, use of self-signed certificates is not recommended, since there is no basis for trust, and hence no certificate validation can be performed.

There are several different ways to configure SSL with MongoDB. Mutual Validation In MongoDB 2.4 the recommended configuration was to issue SSL server certificates to the server, and to the clients connecting to enable mutual validation. The server certificate is validated against the CA certificate file provided on the client side, and the client certificates are validated against the CA certificate provided on the server side. However no authentication is performed.

In order to allow clients to connect without a certificate, the server can be started with the command line flag —sslWeakCertificateValidation.

Mutual validation is still supported but in MongoDB 2.6 several new SSL options are included. X.509 Authentication If SSL is enabled clients can use the new authentication mechanism MONGODB-X509 to authenticate using x.509 certificates.

It is also possible to use x.509 authentication between the servers in a cluster. From a security perspective this is a great improvement to the default keyfile solution.

Mixed mode SSL MongoDB 2.6 introduces the option of mixing encrypted and non-encrypted connections. That is, the server will listen for and detect both SSL and non-SSL inbound connections.

This feature enables cluster members running SSL to talk to non-SSL nodes and vice versa. It also enables a rolling configuration “upgrade” from a non-SSL to an SSL cluster without downtime.

The mixed mode behavior is controlled by the —sslMode parameter. From a security hardening perspective SSL mixed mode should be turned off, unless explicitly needed for one of the two scenarios discussed above.

Disable Unused Exposed Interfaces

Disable sensitive interfaces and functionality that is not needed.

MongoDB supports the execution of JavaScript code for certain server-side operations: mapReduce, group, eval, and $where. If you do not use these operations, disable server-side scripting by setting —noscripting to true.

Use only the MongoDB wire protocol on production deployments. The following interfaces are disabled by default in MongoDB 2.6: —httpinterface, —jsonp, and —rest. Leave these disabled, unless required for backwards compatibility. If using MongoDB 2.4 disable the HTTP interface using —nohttpinterface.

The bind_ip setting for mongod and mongos instances limits the network interfaces on which MongoDB programs will listen for incoming connections. Configure the server only to bind on desired interfaces.


MongoDB Enterprise logs all administrative actions made against the database. Schema operations (such as creating or dropping databases, collections and indexes), replica set reconfiguration events along with authentication and authorization activities are all captured, along with the administrator’s identity and timestamp of the operation, enabling compliance and security analysis.

By default, MongoDB auditing logs all administrative actions, but can also be configured with filters to capture only specific events. The audit log can be written to multiple destinations in a variety of formats including to the console and syslog (in JSON format), and to a file (JSON or BSON), which can then be loaded to MongoDB and analyzed to find relevant events.

MongoDB Maintains an Audit Trail of Administrative Actions Against the Database

Each MongoDB server logs events to its local destination. The DBA can then merge these into a single log, enabling a cluster-wide view of operations that affected multiple nodes.

The MongoDB auditing documentation includes information on how to configure auditing and all of the operations that can be captured.


Securing the database layer of an application is a necessary step to protect the data from unauthorized access. MongoDB offers a flexible and competent security model but as with all security solutions, care should be take to enable and configure the system correctly.

In part 2, we will closely examine some common configuration mistakes and security pitfalls based on a number of existing MongoDB deployments and users.

MongoDB subscriptions provide access to additional enterprise grade capabilities. The subscription includes all the ease-of-use, broad driver support and scalability features of MongoDB, while addressing the more demanding security and certification requirements of corporate and government information security environments. To see more, download the development version of the MongoDB Enterprise edition here

Powering Social Insights with MongoDB at uberVu

May 19 • Posted 2 months ago

This is a guest post from the uberVU team.

Today, more than ever, marketers are being asked to show how their financial investments are driving tangible business results. We help them accomplish that goal. uberVU is a real-time social media marketing platform that allows organizations to leverage their social media data to better understand, connect with, and grow their online communities. We have an extensive client list including enterprise customers such as Heinz, NBC, World Bank, and Fujitsu.

We were recently acquired by HootSuite, and together our two products offer a complete and integrated feature set that addresses the entire social media lifecycle:

  • monitoring
  • metrics
  • reporting
  • engagement
  • collaboration

We have a five-year history in the social media monitoring market, and our evolving data storage architecture has played a key role in elevating our application’s value and user experience. For our data handling needs, we started with Tokyo Cabinet, SimpleDB, and MySQL and now use MongoDB, DynamoDB, S3, Glacier, and ElasticSearch.

Originally our team intended to use MongoDB as a secondary data store, but after a short implementation and adoption period of 3 months in which it quickly gained traction internally, MongoDB was promoted to our primary data store.


We collect and store social media content such as tweets, Facebook posts, blog posts, blog comments, etc. Each item is stored in the database as a separate document.

A stored tweet might look something like this:

    generator: ‘twitter’,
    content: ‘This is a tweet example for ubervu’,
    author: ‘Vladimir Oane’,
    gender: ‘male’,
language: ‘english’,
    sentiment: ‘positive’,
    search: ‘ubervu’,
    published: 1391767879,

For our clients, relevant social media content must contain or match a predefined expression of interest in the designated ‘search’ field. In the example above, the tweet is collected and stored because it contains the string ‘ubervu’ in the content body.

Unique Index Structure

Our most common use case with MongoDB is performing a range query over a time frame for a fixed expression. For example, we might want to retrieve social media content that contains or matches the expression ‘ubervu’ between October 1st and November 23rd.

We constructed the unique index in MongoDB, _id, to perform this query automatically. For space considerations, we opted for a 64 bit integer and divided it into three parts:

  1. A hash of the search expression
  2. The entire published timestamp, in seconds
  3. An item id, which together with the timestamp should uniquely identify a document

To conduct a search for all “ubervu” mentions between timestamp1 and timestamp2, we simply run a range query on “_id” between:


Note above how the lowest and highest bounds have the item id portion filled entirely with 0’s and 1’s, respectively. This allows us to cover edge cases of items that fall between timestamp1 and timestamp2.

Efficient Filtering

Another very common use case is retrieving all the data that matches a criteria set. Within our application, the fields we can filter on are predefined (generator, language, sentiment, language, gender).

Efficient filtering is a challenge because the most obvious approach - creating indexes for every combination of filters - is not scalable as every added index costs storage space and has the potential to adversely affect write performance.

To improve query efficiency, we added an ‘attributes’ field into each document that consists of an encoded array of all the field values that can be used in a query. It looks like this:

attributes: [2041, 15, 178, 23 …]

Each numeric code in the array represents a property, such as “sentiment: positive” or “language: English”. We added an index over the ‘attributes’ field to speed up queries.

To retrieve all items matching a criteria set using the ‘attributes’ field, queries are run using the $all operator:


A shortcoming of using the $all operator is that prior to MongoDB 2.6 the index is only used to match the first code in the ‘attributes’ array; all other codes must be retrieved from disk and matched with the rest of query criteria.

In an effort to reduce the number of documents that need to be checked from disk for each query, we developed a system that first sorts all numeric codes by the frequency that they appear in the data store and then orders the elements in the ‘attributes’ fields according to their ranking.

For example, the property “generator: Twitter” (representing all tweets) is more prevalent in the data store than the property “language: Romanian” (representing all content in Romanian). If we wanted to obtain all tweets written in Romanian from the database, it would be more efficient to place the numeric code representing “language: Romanian” first in the ‘attributes’ array as it is faster to retrieve all Romanian content from disk and check if they are tweets than to retrieve all tweets and check if they are written in Romanian.

This solution described above dramatically improved our query response time. MongoDB’s dynamic schema and rich query model made this possible.

Saving Storage Space

After realizing the fields in our documents would be relatively small in number and mostly consistent across the database, our team decided to impose a two character limit on all field names (“generator” became, “g”, “sentiment” became “s”, etc).

This small change saved us 16% of our storage space, without any loss in information.

Our Infrastructure Setup

We have taken full advantage of the cloud computing resources available to efficiently deploy and scale our offerings. Our current infrastructure resides entirely on the AWS stack.

We currently have 30+ instances deployed and over 30 terabytes of storage in permanent use. All EBS-backed production data stores currently reside on xfs RAID arrays.

This storage architecture provides us with not only volume redundancy, but also performance boosts, which were much needed in the beginning when provisioned IOPS was not yet available to ensure EBS performance.

Our MongoDB setup consists of six production clusters, each with its own unique scope and usage pattern.

Our MongoDB clusters all have the same topology:

  • Each shard (shardA, shardB … shardZ) consists of three member replica sets
  • Three ‘config server’ processes are deployed on separate instances
  • ‘mongos’ routing processes are spread throughout the whole system (webnode, API nodes, worker nodes, etc …)

Optimizing Performance and Redundancy with MongoDB

For us, relying on EBS-backed MongoDB clusters meant we had to familiarize ourselves with the concept of the working set, a number which represents the amount of data that is regularly accessed during day-to-day operations. In situations when the working set is larger than RAM, our application would be forced to read from disk, resulting in an immediate loss in performance due to EBS I/O latency. Now working sets can be estimated using the working set estimator, which was first introduced in MongoDB v2.4.

To prevent the working set from exceeding RAM, we first viewed our data usage patterns in Graphite:

The graphic above represents the ‘write’ working set for one of the clusters.

The graphic above represents the ‘read’ working set generated by our API.

Using our data usage information, we defined the following access patterns:

RecentDB HistoricalDB
Read Working Set 0-20 days 20-90 days
Write Working Set 0-20 days 0-90 days ( > 20 day data can be written directly here)

The architecture represented by the table above has been in place for more than three years and has proven itself on multiple occasions from both a performance and redundancy standpoint.

We currently use MongoDB Management Service (MMS) in addition to tools such as Graphite and Collectd for both monitoring and backup. These applications have been critical to managing our cloud-based cluster backups.

As our MongoDB-powered data store grew in size, the decision was made to implement an ‘inverted pyramid’ mechanism in an effort to provide the best possible response time while remaining cost efficient.

This mechanism relies on two main data stores, RecentDB and HistoricalDB, with the use of an in-house oplog replay tool that keeps the two clusters in sync.

The oplog - short for operations log - is a special capped collection that keeps a rolling record of all operations that modify the data stored in a MongoDB database, and is the basic mechanism that enables replication in MongoDB. Secondary nodes tail the oplog for new operations and replay them locally.

To implement the ‘inverted pyramid’ mechanism, we developed a process that connects source cluster shards (RecentDB) to a destination cluster mongos router instance, verifies the last written timestamp, tails the source oplog to that timestamp, and finally, replays all executed operations.

After taking into consideration our current settings and data volumes, we determined that an oplog replay timeframe of 72 - 96 hrs worked best for our clusters as it ensured there was enough time to counter any major failures at the cluster level (e.g. full replica sets downtime, storage replacements, etc).

In the current implementation, all 5 oplog processes (one per source shard) run on an administrative instance that is continually monitored for delays.

A key design step required in making our inverted pyramid possible was splitting our data store into five ‘segment’ databases, which are provisioned and depleted by two external jobs. This made it possible to drop data (at the db level) from the first two layers, RecentDB and HistoricalDB, in an orderly fashion without impacting any part of the application or compromising performance.

The last step of our data migration consists of offloading all data that passes the 90-day mark from the segment databases to S3. To accomplish this, each HistoricalDB secondary node is provisioned with two Python modules that parse through, collect, and export (in CSV format) all data older than 90 days. The legacy data is then uploaded into an S3 bucket and made available to other parts of our system.

An added benefit of our data architecture is the ability to use HistoricalDB on the off chance that a major issue impacts RecentDB. Although there is a storage space trade-off that comes with storing the data in the 0-20 day intervals on both clusters, having HistoricalDB on hand has proven useful for us in the past, with the AWS EAST-1 crash in the summer of 2012 being the most recent incident.


With MongoDB, we were able to quickly develop the query processes we needed to efficiently serve our customers, all on a flexible database architecture that stresses high performance and redundancy. MongoDB has been a partner that continues to deliver as we grow and tackle new challenges.

To learn more about how MongoDB can have a significant impact on your business, download our whitepaper How a Database Can Make Your Organization Faster, Better, Leaner.

Tiered Storage Models in MongoDB: Optimizing Latency and Cost

May 14 • Posted 2 months ago

By Rohit Nijhawan, Senior Consulting Engineer at MongoDB with André Spiegel and Chad Tindel

For a user-facing application, speed and uptime are critical to success. There are a number of ways you can tune your application and hardware setup to provide the best experience for your customers — the trick is doing so at optimal cost. Here we provide an example for improving performance and lowering costs with MongoDB using Tiered Storage, a method of prioritizing data storage based on latency requirements.

In this example, we will be segmenting data by date: recent data is more frequently accessed and should exhibit lower latency than less recent data. However, the idea applies to other ways of segmenting data, such as location, user, source, size, or other criteria. This approach takes advantage of a powerful feature in MongoDB called tag-aware sharding that has been available since MongoDB 2.2.

Example Application: Insurance Claims

In many applications, low-latency access to data becomes less important as data ages. For example, an insurance company might prioritize access to claims from the last 12 months. Users should be able to view recent claims quickly, but once claims are more than a year old they tend to be accessed much less frequently, and the latency requirements tend to become less demanding.

By creating tiers of storage with different performance and cost profiles, the insurance company can provide a better experience for users while optimizing their costs. Older claims can be stored in a storage tier with more cost-effective hardware such as commodity hard drives. More recent data can be stored in a high-performance storage tier that provides lower latency such as SSD. Because the majority of the claims are more than a year old, storing older data in the lower-cost tier can provide significant cost advantages. The insurance company can optimize their hardware spread across the two tiers, providing a great user experience at an optimized cost point.

The requirements for this application can be summarized as:

The trailing 12 months of claims should reside on faster storage tier Claims over a year old should move to slower storage tier Over time new claims arrive, and older claims need to move from the faster tier to the slower tier

For simplicity, throughout this overview, we’ll distinguish the claims data by “current” and “tier-2” data.

Building Your Own Process: An Operational Headache

One approach to these requirements is use periodic batch jobs: selecting data, loading it into the archive, and erasing it from the faster storage. However, this is inherently complex:

  • The move process must be carefully coded to fail gracefully. In the event that a load fails, you don’t want to delete the original data!
  • If the data to be moved is large, you may wish to throttle the operations.
  • If moves succeed partially, you have to retry the unfinished data.
  • Unless you plan on halting your application during the move (generally unacceptable), your application needs custom code to find the data before, during, and after the move.
  • Your application needs to understand the physical location of the data, which unnecessarily complicates your code to the partitioning logic.

Furthermore, introducing another custom component to your operations requires additional maintenance and monitoring.

It’s an operational headache that many teams are forced to endure, but there is a simpler way: have MongoDB handle the load of migrating documents from the recent storage machines to the tier 2 storage machines, transparently. As it turns out, you can easily implement this approach with a feature called Tag-Aware Sharding.

The MongoDB Way: Tag-aware Sharding

MongoDB provides a feature called sharding to scale systems horizontally across multiple machines. Sharding is transparent to your application - whether you have 1 or 100 shards, your application code is the same. For a comprehensive description of sharding please see the Sharding Guide.

A key component of sharding is a process called the balancer. As collections grow, the balancer operates in the background to carefully move documents between shards. Normally the balancer works to achieve a uniform distribution of documents across shards. However, with tag-aware sharding we can create policies that affect where documents are stored. This feature can be applied in many use cases. One example is to keep user data in data centers that are near the user. In our application, we can use this feature to keep current data on our fast servers, and tier 2 data on cheaper, slower servers.

Here’s how it works:

  • Shards are assigned tags. A tag is an alphanumeric alias like “London-DC”.
  • Unique shard key ranges are ‘pinned’ to tags.
  • During normal balancing operations, chunks migrate only to shards whose tag is associated with a key range which contains the chunk’s key range*.
  • There are a few subtleties regarding what happens when a chunk’s key range overlaps more than one tag range. Please read the documentation carefully regarding this particular case

This means that we can assign the “tier-2” tag to shards running on slow servers and “current” tags to shards running on fast servers, and the balancer will handle migrating the data between tiers automatically. What’s great is that we can keep all the data in one database, so our application code doesn’t need to change as data moves between storage tiers.

Determining the shard key

When you query a sharded collection, the query router will do its best to only inspect the shards holding your data, but it can only do this if you provide the shard key as part of your query. (See Sharded Cluster Query Routing for more information.)

So we need to make sure that the we look up documents by the shard key. We also know that time is the basis for determining the location of documents in our two storage tiers. Accordingly, the shard key must contain an explicit timestamp. In our example, we’ll be using Enron’s email dataset, and we’ll set the top-level “date” as the shard key. Here’s a sample document:

Because the time is stored in the most significant digits of the date, messages from any given day will numerically precede messages from subsequent days.


Here are the the steps to set up this system:

Set up an empty, sharded MongoDB cluster Create a target database to host the sharded collection Assign tags to different shards corresponding to the storage tiers Assign tag ranges to the shards Load data into the MongoDB Cluster

Set up the cluster The first thing you will want to do is set up your sharded cluster. You can see more information on how to set this up here.

In this case we will have a database called “enron” and a collection called “messages” which holds part of the Enron email corpus. In this example, we’ve set up a cluster with three shards. The first, shard0000, is optimized for low-latency access to data. The other two, shard0001 and shard0002, use more cost effective hardware for data that is older than the identified cutoff date.

Here’s our sharded cluster. These are empty machines with no data:

Adding the tags We can “tag” each of these shards to associate them with documents that should belong to our “current” tier or those that should belong to “tier-2.” In the absence of tags and range based tags, balancing will try to ensure that the number of chunks on each shard are equal without regard to any other data in the fields. Before we add the data to our collection, let’s tag shard0000 as “current” and the other two as “tier-2”:

Now we can verify our tags by calling sh.status():

Next, we need to set up a database and collection for the Enron emails. We’ll set up a new database ‘enron’ with a collection called ‘messages’ and enable sharding on that collection:

Since we’re going to shard the collection, we’ll need to set up a shard key. We will use the ‘date’ field as our shard key since this is the field that will define how the documents are distributed across shards:

Defining the cutoff date between tiers The cutoff point between “current” data and “tier-2” data is a point in time that we will update periodically to keep the most recent documents in our “current” shard. We will start with a cutoff of July 1, 2001, saved as an ISO Date ISODate(“2001-07-01”). Once we add the data to our collection, we will set this as the tag range. Going forward, when we add documents to the “messages” collection, any documents newer than July 1, 2001 will end up on the “current” shard, and documents older than that will end up on the “tier-2” shard.

It’s important that the two ranges overlap at exactly the same point in time. The lower bound of a tag range is inclusive, and the upper bound is exclusive. This means a document that has an date of exactly ‘ISODate(“2001-07-01”)’ will go on the “current” shard, not the “tier-2” shard.

Below you will see each of the shard’s new tag ranges:

As a final check, look in the config database for the tag range definitions.

Now, that all the shards and ranges are defined, we are ready to load the message data into the server. The collection will follow the instructions given by the tag ranges and land on the correct machines.

Now, let’s check the sharding status to see where the documents reside

That’s it! The mongos process automatically moves documents to comply with the tag ranges. In this example, it took all documents still on the “current” shard with an ISODate older than ISODate(“2001-07-01T00:00:00Z”) and move them to the “tier-2” shard.

The tag ranges must be updated on a regular basis to keep the cutoff point at the correct interval of time in the past (1 year, in our case). In order to do this, both ranges need to be updated. To perform this change the balancer should temporarily be disabled, so there is no point where the ranges overlap. Stopping the balancer temporarily is a safe operation - it will not affect the application or the experience of users.

If you wanted to move the cutoff back another month, to August 1, 2001, you just need to follow these three steps:

Stop the balancer sh.setBalancerState(false) Create a chunk split at August 1 sh.splitAt('enron.messages', {"date" : ISODate("2001-08-01")}) Move the cutoff date to ISODate(“2001-08-01T00:00:00Z”) var configdb=db.getSiblingDB("config"); configdb.tags.update({tag:"tier-2"},{$set:{'':ISODate("2001-08-01")}}) configdb.tags.update({tag:"current"},{$set:{'':ISODate("2001-08-01")}}) Re-start the balancer sh.setBalancerState(true) Verify the sharding status

By updating the chunk split to August 1, we have migrated all the documents with a date after July 1 but before August 1 from the “current” shard to the “tier-2” shards. The good news is that we were able to perform this operation without changing our application code and with no database downtime. We can also see that it would be simple to schedule this process to run automatically through an external process.

From Operational Headache to Simplicity

The end result is one collection spread across three shards and two different storage systems. This solution allows you to lower your storage costs without adding complexity to the architecture of your system. Instead of a complex setup with different databases on different machines we have one database to query, and instead of a data migration we update some simple rules to control the location of data in the system.

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Introducing mtools

May 8 • Posted 2 months ago

By Thomas Rueckstiess, Kernel Program Manager at MongoDB

mtools is a collection of helper scripts, implemented in Python, to parse and filter MongoDB log files (both for mongod and mongos), to visualize information from log files and to quickly set up complex MongoDB test environments on a local machine.

I started working on mtools a year ago, when I realized I would automate and script most of my daily tasks as an Engineer at MongoDB. Since then, mtools has grown to a suite of flexible, useful command line tools that are being used by many of our Engineers internally, as well as MongoDB customers and users, to diagnose the root cause of system issues.

If you find yourself looking at MongoDB log files to identify system and performance issues, then I encourage you to try mtools as well.

What’s in the box?

mtools in its current version 1.1.4 consists of 5 individual scripts: mloginfo, mlogfilter, mplotqueries, mlogvis and mlaunch.

  • mloginfo should be your first stop on the log file analysis. This script will parse the file quickly and output general information about its contents, including start and end date and time, line numbers, version and whether the file came from a mongos or mongod (if available in the file). In addition, you can request certain “sections” of additional information; currently those are “queries”, “connections”, “restarts” and “distinct”.

  • mlogfilter helps to narrow down the search in log files. The script lets you filter on attributes of log messages, like their namespace (database and collection names), their type of operation (queries, inserts, updates, commands, etc.) or by individual connection. You can also search for slow operations by setting a threshold, identify collection scans (those are the queries not using an index) and other properties. Additional features include slicing the log files by time (with flexible date/time parsing), merging files, shifting them to different time zones or converting timestamp formats, and exporting them to JSON. The key property of mlogfilter is that the output format always remains the same (log lines), so you can pipe the output to another instance of mlogfilter, to the grep command or to other scripts like mplotqueries.

  • mplotqueries takes a log file (mlogfiltered or not) and presents the information visually in various ways. There are a number of options for graph types, such as scatter plots (showing all operations over time vs. their duration), histograms, event and range plots, and other more specialized graphs like connection churn or replica set changes. Independent of graph type, you can assign a specific color to different class categories.

  • mlogvis is mplotqueries’ little brother, it is very similar in its functionality, but provides a web-based alternative using the d3.js javascript visualization library. This is particularly useful if the dependencies required by mplotqueries are not installed/available, or if you want to create a self-contained interactive graph that can be shared with others, such as customers or colleagues. mlogvis will create a single .html file that can be shared, since it loads the d3.js library dynamically.

  • mlaunch is a little different from the other scripts, and actually has nothing to do with log file parsing. mlaunch spins up any number of mongodb nodes on your local machine, either as a stand-alone, as replica sets or sharded clusters. This is useful if you want to do testing or reproduce issues locally. Rather than setting this up manually, mlaunch will start the processes and connect the replica sets or shards together. Within a few seconds, you can have a complex environment running, like a 5 shard cluster, each shard consisting of a replica set, authentication enabled, and any kinds of individual flags you want to pass onto the processes. mlaunch also has options to start and stop individual instances or groups, and to view which ones are running in the current environment and which ones are down.

How does it work?

Rather than going through all the features of each of the scripts, I’d just like to demonstrate two basic use cases. For a full list of features you can visit the mtools wiki, which contains the manual and many usage examples.

Use Case 1: Profiling your Queries with mloginfo

You have a number of slow queries running against MongoDB that are affecting the performance of the database. To get an idea of where MongoDB is slowing down, as a first step take a look at the “queries” section of mloginfo. Here is an example output, created with the following command:

Each line (from left to right) shows the namespace, the query pattern, and various statistics of this particular namespace/pattern combination. The rows are sorted by the “sum” column, descending. Sorting by sum is a good way to see where the database spent most of its time overall. In this example, we see that around half the total time is spent on $ne-type queries in the serverside.scrum_master collection. $ne queries are known to be inefficient since these queries cannot use an index, resulting in a high number of documents scanned. In fact, all of the queries took at least 15 seconds (“min” column). The “count” column also shows that only 20 of the queries were issued, yet these queries contributed to a large amount of the total time spent, more than double the time of the 804 email queries on serverside.user.

When optimizing queries and indexes, starting from the top of this list is a good idea as these optimizations will result in the highest gains in terms of performance.

Use Case 2: Visualizing Log Files with mplotqueries

Another way of looking at query performance and other operations is to visualize them graphically. mplotqueries’ scatter plot (the default) shows the duration of any operation (y-axis) over time (x-axis) and makes it easy to spot long-running operations. The following plot is generated with

mplotqueries mongod.log

and then press L for “logarithmic” y-axis view:

While most of the operations are sub-second (below the 10^3 ms mark), the blue dots immediately stand out, reaching up to the hundreds and thousands of seconds. Clicking on one of the blue dots prints out the relevant log line to stdout:

The getlasterror command is used for write concern. In this case, it blocked until the write was replicated to a majority of nodes in the replica set, which took 16 minutes. That is of course an issue, and because this is a command and not a query (or the query part of an update), it didn’t show up in the previous use case with mloginfo --queries.

To investigate this further, we can overlay the current plot with an “rsstate” plot, that shows replica set status changes over time. The following two commands create an overlay of the two plots:

This shows that for each of the blocking “majority” getlasterrors, replica set members are unavailable. The red vertical lines represent a node being DOWN, preceding the yellow lines for a node being in SECONDARY state again, at which point the getlasterror commands finally succeed.

From here, the next step would be to look at all the log files of the replica set at one of the incidents and investigate why the secondaries became unavailable:

This last command merges the log files of the three replica set members by time, each line prefixed with the filename, slices out a 5-minute window at the first instance of the issue and prints the lines back to stdout.

What’s Next?

This should give you a sense of how to use mtools for diagnosing and debugging issues affecting your MongoDB system. You can organize and visualize data in a number of ways, form a hypothesis, filter out noise and dig deeper into issues affecting your deployment, all from MongoDB log files.

mtools contains many more useful features that our Support team uses daily in working through customer cases. The best way to learn how you can leverage these scripts is to download and install mtools and follow some of the examples on the mtools wiki page. mtools is open source and available for download on github. It is also in the PyPI package index and can be installed via pip. If you have any questions, bug reports or feature requests, simply go to the mtools github issues page and open an issue.

My colleague Asya Kamsky (from will show some more examples on how mtools can be useful for diagnosing and troubleshooting in her talk Diagnostics and Debugging at MongoDB World. I’ll be in the “Ask the Experts” sessions, so if you have any questions you can come ask in the Ask the Experts room. You can use my discount code “25ThomasRueckstiess” for 25% off tickets.

MongoDB’s New Bulk API

May 6 • Posted 2 months ago

By Christian Kvalheim, Driver Lead and Node.js Driver Maintainer at MongoDB

The New Bulk API

One of the core new features in MongoDB 2.6 is the new bulk write operations. All the drivers include a new bulk api that allows applications to leverage these new operations using a fluid style API. Let’s explore the API and how it’s implemented in the Node.js driver.


The API has two core concepts. The ordered and the unordered bulk operation. The main difference is in the way the operations are executed in bulk. In the case of an ordered bulk operation, every operation will be executed in the order they are added to the bulk operation. In the case of an unordered bulk operation however there is no guarantee what order the operations are executed. Later we will look at how each is implemented.


You can initialize an ordered or unordered bulk operation in the following way.

    var ordered = db.collection('documents').initializeOrderedBulkOp();
    var unordered = db.collection('documents').initializeUnorderedBulkOp();

Both the ordered and unordered instances are bulk operation objects that we can add insert, update and remove operations to. The following operations are valid.

updateOne (update first matching document)

```ordered.find({ a : 1 }).updateOne({$inc : {x : 1}});```

update (update all matching documents)

```ordered.find({ a : 1 }).update({$inc : {x : 2}});```

replaceOne (replace entire document)

```ordered.find({ a : 1 }).replaceOne({ x : 2});```

updateOne or upsert (update first existing document or upsert)

```ordered.find({ a : 2 }).upsert().updateOne({ $inc : { x : 1}});```

update or upsert (update all or upsert)

```ordered.find({ a : 2 }).upsert().update({ $inc : { x : 2}});```

replace or upsert (replace first document or upsert)

```ordered.find({ a : 2 }).upsert().replaceOne({ x : 3 });```

removeOne (remove the first document matching)

```ordered.find({ a : 2 }).removeOne();```

remove (remove all documents matching)

```ordered.find({ a : 1 }).remove();```


```ordered.insert({ a : 5});```

What happens under the covers when you start adding operations to a bulk operation? Let’s take a look at the new write operations to see how it works.

The New Write Operations

MongoDB 2.6 introduces a completely new set of write operations. Before 2.6 all write operations where done using wire protocol messages at the socket level. From 2.6 this changes to using commands.

### Insert Write Command

The insert write commands allow an application to insert batches of documents. Here’s an example:

        insert: 'collection name'
      , documents: [{ a : 1}, ...]
      , writeConcern: {
        w: 1, j: true, wtimeout: 1000
      , ordered: true/false

A couple of things to note. The documents field contains an array of all the documents that are to be inserted. The writeConcern field specifies what would have previously been a getLastError command that would follow the pre 2.6 write operations. In other words there is always a response from a write operation in 2.6. This means that w:0 has different semantics than what one is used to in pre 2.6. In the context w:0 basically means only return an ack without any information about the success or failure of insert operations.

Let’s take a look at the update and remove write commands before seeing the results that are returned when executing these operations in 2.6.

Update Write Command

There are some slight differences in the update write command in comparison to the insert write command. Here’s an example:

        update: 'collection name'
      , updates: [{ 
            q: { a : 1 }
          , u: { $inc : { x : 1}}
          , multi: true/false
          , upsert: true/false
        }, ...]
      , writeConcern: {
        w: 1, j: true, wtimeout: 1000
      , ordered: true/false

The main difference here is that the updates array is an array of update operations where each entry in the array contains the q field that specifies the selector for the update. The u contains the update operation. multi specifies if we will updateOne or updateAll documents that matches the selection. Finally upsert tells the server if it will perform an upsert if the document is not found.

Finally let’s look at the remove write command.

Remove Write Command

The remove write command is very similar to the update write command. Here’s an example:

        delete: 'collection name'
      , deletes: [{ 
            q: { a : 1 }
          , limit: 0/1
        }, ...]
      , writeConcern: {
        w: 1, j: true, wtimeout: 1000
      , ordered: true/false

Similar to the update example, we can see that the entries in the deletes array contain documents with specific fields. The q field is the selector that will match which documents will be removed. The limit field sets the number of elements to be removed. Currently limit only supports two values, 0 and 1. The value 0 for limit removes all documents that match the selector. A value of 1 for limit removes the first matching document only.

Now let’s take a look at how results are returned for these new write commands.

Write Command Results

One of the best new aspects of the new write commands is that they can return information about each individual operation error in the batch. Results are efficient - only information about errors are returned as well as the aggregated counts of successful operations. Here’s an example of a comprehensive* result:

      "ok" : 1,
      "n" : 0,
      "nModified": 1, (Applies only to update)
      "nRemoved": 1, (Applies only to removes)
      "writeErrors" : [
          "index" : 0,
          "code" : 11000,
          "errmsg" : "insertDocument :: caused by :: 11000 E11000 duplicate key error index: t1.t.$a_1  dup key: { : 1.0 }"
      writeConcernError: {
        code : 22,
        errInfo: { wtimeout : true },
        errmsg: "Could not replicate operation within requested timeout"

The two most interesting fields here are writeErrors and writeConcernError. If we take a look at writeErrors we can see how it’s an array of objects that include an index field as well as a code and errmsg. The field references the position of the failing document in the original documents, updates or deletes array allowing the application to identify the original batch document that failed.

The Effect of Ordered (true/false)

If ordered is set to true the write operation will fail on the first write error (meaning the first error that fails to apply the operation to memory). If one sets ordered to false the operation will continue until all operations have been executed (potentially in parallel), then return all the results. writeConcernError on the other hand does not stop the processing of a bulk operation if a document fails to be written to MongoDB.

It helps to think of writeErrors as hard errors and writeConcernError as a soft error.

The Special Case of w:0 As I mentioned previously, the semantics for w:0 changed for the write commands. The old style of write operations before 2.6 are a combination of a write wire message and a getLastError command. In the old style w:0 meant that the driver would not send a getLastError command after the write operation.

In 2.6 the new insert/update/delete commands will always respond. While w:0 would not return a result in versions of MongoDB before 2.6, in 2.6 and above it will. However it will truncate all the results and only return if the command ran successfully or failed.

As a result, if you execute.

        insert: 'collection name'
      , documents: [{ a : 1}, ...]
      , writeConcern: {
        w: 0
      , ordered: true/false

All you receive from the server is the result

{ok : 1}

The Implication For The Bulk API

There are some implications to the fact that write commands are not mixed operations but either insert/update or removes. The Bulk API lets you mix operations and then merges the results back into a single result that simulates a mixed operations command in MongoDB. What does that mean in practice. Well let’s look at how node.js implements ordered and unordered bulk operations. Let’s use examples to show what happens.

Ordered Operations

Let’s take the following set of operations:

    var ordered = db.collection('documents').initializeOrderedBulkOp();
    ordered.insert({ a : 1 });
    ordered.find({ a : 1 }).update({ $inc: { x : 1 }});
    ordered.insert({ a: 2 });
    ordered.find({ a : 2 }).remove();
    ordered.insert({ a: 3 });

When running in ordered mode the bulk API guarantees the ordering of the operations and thus will execute this as 5 operations one after the other:

    insert bulk operation
    update bulk operation
    insert bulk operation
    remove bulk operation
    insert bulk operation

We have now reduced the bulk API to performing single operations and your throughput suffers accordingly.

If we re-order our bulk operations in the following way:

    var ordered = db.collection('documents').initializeOrderedBulkOp();
    ordered.insert({ a : 1 });
    ordered.insert({ a: 2 });
    ordered.insert({ a: 3 });
    ordered.find({ a : 1 }).update({ $inc: { x : 1 }});
    ordered.find({ a : 2 }).remove();

The execution is reduced to the following operations one after the other:

    insert bulk operation
    update bulk operation
    remove bulk operation

Thus for ordered bulk operations the order of operations will impact the number of write commands that need to be executed and thus the throughput possible.

Unordered Operations

Unordered operations do not guarantee the execution order of operations. Let’s take the example from above:

    var ordered = db.collection('documents').initializeOrderedBulkOp();
    ordered.insert({ a : 1 });
    ordered.find({ a : 1 }).update({ $inc: { x : 1 }});
    ordered.insert({ a: 2 });
    ordered.find({ a : 2 }).remove();
    ordered.insert({ a: 3 });

The Node.js driver will collect the operations into separate type-specific operations. So we get.

    insert bulk operation
    update bulk operation
    remove bulk operation

In difference to the ordered operation these bulks all get executed in parallel in Node.js and the results then merged when they have all finished.


MongoDB as of 2.6 only allows batches of inserts, updates or removes and not a mixed batch containing all three of the operation types. When performing ordered bulk operation we need to keep this in mind to avoid the scenario above. However for an unordered bulk operation the missing mixed batch type in 2.6 does not impact performance.

Note: Although the Bulk API actually supports downconversion to 2.4 the performance impact is considerable as all operations are reduced to single write operations with a getLastError. It’s recommended to leverage this API primarily with 2.6 or higher.

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Betting the Farm on MongoDB

May 1 • Posted 3 months ago

This is a guest post by Jon Dokulil, VP of Engineering at Hudl. Hudl’s CTO, Brian Kaiser, will be speaking at MongoDB World about migrating from SQL Server to MongoDB

Hudl helps coaches win. We give sports teams from peewee to the pros online tools to make working with and analyzing video easy. Today we store well over 600 million video clips in MongoDB spread across seven shards. Our clips dataset has grown to over 350GB of data with over 70GB of indexes. From our first year of a dozen beta high schools we’ve grown to service the video needs of over 50,000 sports teams worldwide.

Why MongoDB

When we began hacking away on Hudl we chose SQL Server as our database. Our backend is written primarily in C#, so it was a natural choice. After a few years and solid company growth we realized SQL Server was quickly becoming a bottleneck. Because we run in EC2, vertically scaling our DB was not a great option. That’s when we began to look at NoSQL seriously and specifically MongoDB. We wanted something that was fast, flexible and developer-friendly.

After comparing a few alternative NoSQL databases and running our own benchmarks, we settled on MongoDB. Then came the task of moving our existing data from SQL Server to MongoDB. Video clips were not only our biggest dataset, it was also our most frequently-accessed data. During our busy season we average 75 clip views per second but peak at over 800 per second. We wanted to migrate the dataset with zero downtime and zero data loss. We also wanted to have fail-safes ready during each step of the process so we could recover immediately from any unanticipated problems during the migration.

In this post we’ll take a look at our schema design choices, our migration plan and the performance we’ve seen with MongoDB.

Schema Design

In SQL Server we normalized our data model. Pulling together data from multiple tables is SQL’s bread-and-butter. In the NoSQL world joins are not an option and we knew that simply moving the SQL tables directly over to MongoDB and doing joins in code was a bad idea. So, we looked at how our application interacted with SQL and created an optimized schema in MongoDB.

Before I get into the schema we chose, I’ll try to provide context to Hudl’s product. Below is a screenshot of our ‘Library’ page. This is where coaches spend much of their time reviewing and analyzing video.

You see above a video playing and a kind of spreadsheet underneath. The video represents one angle of one clip (many of our teams film two or three angles each game). The spreadsheet contains rows of clips and columns of breakdown data. The breakdown data gives context to what happened in the clip. For example, the second clip was a defensive play from the 30 yard line. It was first and ten and was a run play to the left. This breakdown data is incredibly important for coaches to spot patterns and trends in their opponents play (as well as make sure they don’t have an obvious patterns that could be used against them).

When we translated this schema to MongoDB we wanted to optimize for the most-common operations. Watching video clips and editing clip metadata are our two highest frequency operations. To maximize performance we made a few important decisions.

  1. We chose to encapsulate an entire clip per document. Watching a clip would involve a single document lookup. Because MongoDB stores each document contiguously on disk, it would minimize the number of disk seeks when fetching a clip not in memory, which means faster clip loads.
  2. We denormalized our column names to speed up both writes and reads. Writes are faster because we no longer have to lookup or track Column IDs. A write operation is as simple as:
    db.clips.update({teamId:205, _id:123}, 
    {$set: {'data.PLAY TYPE':'Pass'}}) 
    Reads are also faster because we no longer have to join on the ClipDataColumn table to get the column names. This comes at a cost of greater storage and memory requirements as we store the same column names in multiple documents. Despite that, we felt the performance benefits were worth the cost.

One of the most important considerations when designing a schema in MongoDB is choosing a shard key. Have a good shard key is critical for effective horizontal scaling. Data is stored in shards (each shard is a replica set) and we can add new shards easily as our dataset grows. Replica sets don’t need to know about each other, they are only concerned with their own data. The MongoDB Router (mongos) is the piece that sees the whole picture. It knows which shard houses each document.

When you perform a query against a sharded collection, the shard key is not required. However, there is a cost penalty for not providing the shard key. The key is used to know which shard contains the answer to your query. Without it, the query has to be sent to all shards in your cluster. To illustrate this, I’ve got a four shard cluster. The shard key is TeamId (the property is named ‘t’), and you can see that clips belonging to teams 1-100 live on Shard 1, 101-200 live on Shard 2, etc. Given the query to find clip ‘123’, only Shard 3 will respond with results, but Shards 1, 2 and 4 must also process and execute the query. This is known as a scatter/gather query. In low volume this is ok, but you won’t see the benefits of horizontal scalability if every query has to be sent to all shards. Only when the shard key is provided can the query be sent directly to Shard 3. This is known as a targeted query.

For our Clips collection, we chose TeamId as our shard key. We looked at a few different possible shard keys:

  1. We considered sharding by clipId (_id) but decided against it because we let coaches organize clips into playlists (similar to a song playlist in iTunes or Spotify). While queries to all clips in a playlist are less common than grabbing an individual clip, they are common enough that we wanted it to use a targeted query.
  2. We also considered sharding by the playlist Id, but we wanted the ability for clips to be a part of multiple playlists. The shard key, once set, is immutable. Clips can be added or removed from playlists at any time.
  3. We finally settled on TeamId. TeamId is easily available to us when making the vast majority of our queries to the Clips collection. Only for a few infrequent operations would we need to use scatter/gather queries.

The Transition

As I mentioned, we needed to transition from SQL Server to MongoDB with zero downtime. In case anything went wrong, we needed fallbacks and fail-safes along the way. Our approach was two-fold. In the background we ran a process that ‘fork-lifted’ data from SQL Server to MongoDB. While that ran in the background, we created a multiplexed DAO (data access object, our db abstraction layer) that would only read from SQL but would write to both SQL and MongoDB. That allowed us to batch-move all clips without having to worry about stale data. Once the two databases were completely synced up, we switched over to perform all reads from MongoDB. We continued to dual-write so we could easily switch back to SQL Server if problems arose. After we felt confident in our MongoDB solution, we pulled the plug on SQL Server.

In step one we took a look at how we read and wrote clip data. That let us design an optimal MongoDB schema. We then refactored our existing database abstraction layer to use data-structures that matched the MongoDB schema. This gave us a chance to prove out the schema ahead of time.

Next we began sending write operations to both SQL and MongoDB. This was an important step because it allowed our data fork-lifting process work through all clips one after another while protecting us from data corruption.

The data fork-lifting process took about a week to complete. The time was due to both the large size of the dataset and our own throttling logic. We throttled the rate of data migration to minimize the impact on normal operations. We didn’t want coaches to feel any pain during this migration.

After the data fork-lift was complete we began the process of reading from MongoDB. We built in the ability to progressively send more and more read traffic to MongoDB. That allowed us to gain confidence in our code and the MongoDB cluster without having to switch all-at-once. After a while with dual writes but all MongoDB reads, we turned off dual writes and dropped the tables in SQL Server. It was both a scary moment (sure, we had backups… but still!) and very satisfying. Our SQL database size was reduced by over 80GB. Of that total amount, 20GB was index data, which means our memory footprint was also greatly reduced.


We have been thrilled with the performance of MongoDB. MongoDB exceeded our average performance goal of 100ms and, just as important, is consistently performant. While it’s good to keep an eye on average times, it’s more important to watch the 90th and 99th percentile performance metrics. With MongoDB our average clip load time is around 18ms and our 99th percentile times are typically at or under 100ms.

Clip load times during the same time period during season


Our transition from SQL Server to MongoDB started with our largest and most critical dataset. After having gone through it, we are very happy with the performance and scalability of MongoDB and appreciate how developer-friendly it is to work with. Moving from a relational to a NoSQL database naturally has a learning curve. Now that we are over it we feel very good about our ability to scale well into the future. Perhaps most telling of all, most new feature development at Hudl is done in MongoDB. We feel MongoDB lets us focus more on writing features to help coaches win and less time crafting database scripts.

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The MongoDB Open Source Hack Contest

Apr 28 • Posted 3 months ago

Some of the best MongoDB tools come from the Open Source community. Projects like the Node.js Driver, Mongoose and Meteor have become the backbone of many MongoDB apps and have helped support the developer community all over the world. We want to see more of what the community has built.

For the month of May, we’ll be hosting a worldwide hack contest for Open Source tools built on or connected to MongoDB. The winner of the contest will receive a ticket to OSCON, furnished by O’Reilly.


  • All projects must be built with the MongoDB source code or on top of a MongoDB API, community or MongoDB supported driver
  • Any new drivers created should abide by the driver requirements listed in the MongoDB Manual
  • All hacks will be judged by MongoDB engineers

All entries can be submitted to with the following information before May 31

  • Github or Bitbucket URL
  • Description of the project
  • How do users benefit from this application?
  • Why did you choose to contribute to MongoDB?

We’re looking forward to seeing your hacks come in!

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