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Setting up Java Applications to Communicate with MongoDB, Kerberos and SSL

Aug 19 • Posted 1 week ago

By Alex Komyagin, Technical Services Engineer at MongoDB

Setting up Kerberos authentication and SSL encryption in a MongoDB Java application is not as simple as other languages. In this post, I’m going to show you how to create a Kerberos and SSL enabled Java application that communicates with MongoDB.

My original setup consists of the following:

1) KDC server:

kerberos config file (/etc/krb5.conf):

KDC has the following principals:

  • gssapitest@MONGOTEST.COM - user principle (for java app)
  • mongodb/ - service principle (for mongodb server)

2) MongoDB server:

MongoDB version: 2.6.0

MongoDB config file:

This server also has the global environment variable $KRB5_KTNAME set to the keytab file exported from KDC.

Application user is configured in the admin database like this:

3) Application server: has stock OS with krb5 installed

All servers are running with RHEL6.4 onboard.

Now let’s talk about how to create a Java application with Kerberos and SSL enabled, and that will run on the application server. Here is the sample code that we will use (

Download the java driver:

Install java and jdk:

sudo yum install java-1.7.0 sudo yum install java-1.7.0-devel

Create a certificate store for Java and store the server certificate there, so that Java knows who it should trust:

(mongodb.crt is just a public certificate part of mongodb.pem)

Copy kerberos config file to the application server: /etc/krb5.conf or ““C:\WINDOWS\krb5.ini“` (otherwise you’ll have to specify kdc and realm as Java runtime options)

Use kinit to store the principal password on the application server:

kinit gssapitest@MONGOTEST.COM

As an alternative to kinit, you can use JAAS to cache kerberos credentials.

Compile and run the Java program

It is important to specify useSubjectCredsOnly=false, otherwise you’ll get the “No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)” exception from Java. As we discovered, this is not strictly necessary in all cases, but it is if you are relying on kinit to get the service ticket.

The Java driver needs to construct MongoDB service principal name in order to request the Kerberos ticket. The service principal is constructed based on the server name you provide (unless you explicitly asked to canonicalize server name). For example, if I change to the host IP address in the connection URI, I would be getting Kerberos exceptions No valid credentials provided (Mechanism level: Server not found in Kerberos database (7) - UNKNOWN_SERVER)]. So be sure you specify the same server host name as you used in the Kerberos principal (). Adding to Java runtime options helps a lot in debugging kerberos auth issues.

These steps should help simplify the process of connecting Java applications with SSL. Before deploying any application with MongoDB, be sure to read through our 12 tips for going into production and the Security Checklist which outlines recommended security measures to protect your MongoDB installation. More information on configuring MongoDB Security can be found in the MongoDB Manual.

For further questions, feel free to reach out to the MongoDB team through google-groups.

Getting Started with MongoDB and Java: Part II

Aug 14 • Posted 2 weeks ago

By Trisha Gee, Java Engineer and Advocate at MongoDB

In the last article, we covered the basics of installing and connecting to MongoDB via a Java application. In this post, I’ll give an introduction to CRUD (Create, Read, Update, Delete) operations using the Java driver. As in the previous article, if you want to follow along and code as we go, you can use these tips to get the tests in the Getting Started project to go green.

Creating documents

In the last article, we introduced documents and how to create them from Java and insert them into MongoDB, so I’m not going to repeat that here. But if you want a reminder, or simply want to skip to playing with the code, you can take a look at Exercise3InsertTest.


Putting stuff in the database is all well and good, but you’ll probably want to query the database to get data from it.

In the last article we covered some basics on using find() to get data from the database. We also showed an example in Exercise4RetrieveTest. But MongoDB supports more than simply getting a single document by ID or getting all the documents in a collection. As I mentioned, you can query by example, building up a query document that looks a similar shape to the one you want.

For the following examples I’m going to assume a document which looks something like this:

person = {
  _id: "anId",
  name: "A Name",
  address: {
    street: "Street Address",
    city: "City",
    phone: "12345"
  books: [ 27464, 747854, ...]

Find a document by ID

To recap, you can easily get a document back from the database using the unique ID:

…and you get the values out of the document (represented as a DBObject) using a Map-like syntax:

In the above example, because you’ve queried by ID (and you knew that ID existed), you can be sure that the cursor has a single document that matches the query. Therefore you can use to get it.

Find all documents matching some criteria

In the real world, you won’t always know the ID of the document you want. You could be looking for all the people with a particular name, for example.

In this case, you can create a query document that has the criteria you want:

You can find out the number of results:

and you can, naturally, iterate over them:

A note on batching

The cursor will fetch results in batches from the database, so if you run a query that matches a lot of documents, you don’t have to worry that every document is loaded into memory immediately. For most queries, the first batch returned will be 101 documents. But as you iterate over the cursor, the driver will automatically fetch further batches from the server. So you don’t have to worry about managing batching in your application. But you do need to be aware that if you iterate over the whole of the cursor (for example to put it into a List), you will end up fetching all the results and putting them in memory.

You can get started with Exercise5SimpleQueryTest.

Selecting Fields

Generally speaking, you will read entire documents from MongoDB most of the time. However, you can choose to return just the fields that you care about (for example, you might have a large document and not need all the values). You do this by passing a second parameter into the find method that’s another DBObject defining the fields you want to return. In this example, we’ll search for people called “Smith”, and return only the name field. To do this we pass in a DBObject representing {name: 1}:

You can also use this method to exclude fields from the results. Maybe we might want to exclude an unnecessary subdocument from the results - let’s say we want to find everyone called “Smith”, but we don’t want to return the address. We do this by passing in a zero for this field name, i.e. {address: 0}:

With this information, you’re ready to tackle Exercise6SelectFieldsTest

Query Operators

As I mentioned in the previous article, your fields can be one of a number of types, including numeric. This means that you can do queries for numeric values as well. Let’s assume, for example, that our person has a numberOfOrders field, and we wanted to find everyone who had ordered more than, let’s say, 10 items. You can do this using the $gt operator:

Note that you have to create a further subdocument containing the $gt condition to use this operator. All of the query operators are documented, and work in a similar way to this example.

You might be wondering what terrible things could happen if you try to perform some sort of numeric comparison on a field that is a String, since the database supports any type of value in any of the fields (and in Java the values are Objects so you don’t get the benefit of type safety). So, what happens if you do this?

The answer is you get zero results (assuming all your documents contain names that are Strings), and you don’t get any errors. The flexible nature of the document schema allows you to mix and match types and query without error.

You can use this technique to get the test in Exercise7QueryOperatorsTest to go green - it’s a bit of a daft example, but you get the idea.

Querying Subdocuments

So far we’ve assumed that we only want to query values in our top-level fields. However, we might want to query for values in a subdocument - for example, with our person document, we might want to find everyone who lives in the same city. We can use dot notation like this:

We’re not going to use this technique in a query test, but we will use it later when we’re doing updates.

Familiar methods

I mentioned earlier that you can iterate over a cursor, and that the driver will fetch results in batches. However, you can also use the familiar-looking skip() and limit() methods. You can use these to fix up the test in Exercise8SkipAndLimitTest.

A last note on querying: Indexes

Like a traditional database, you can add indexes onto the database to improve the speed of regular queries. There’s extensive documentation on indexes which you can read at your own leisure. However, it is worth pointing out that, if necessary, you can programmatically create indexes via the Java driver, using createIndexes. For example:

There is a very simple example for creating an index in Exercise9IndexTest, but indexes are a full topic on their own, and the purpose of this part of the tutorial is to merely make you aware of their existence rather than provide a comprehensive tutorial on their purpose and uses.

Updating values

Now you can insert into and read from the database. But your data is probably not static, especially as one of the benefits of MongoDB is a flexible schema that can evolve with your needs over time.

In order to update values in the database, you’ll have to define the query criteria that states which document(s) you want to update, and you’ll have to pass in the document that represents the updates you want to make.

There are a few things to be aware of when you’re updating documents in MongoDB, once you understand these it’s as simple as everything else we’ve seen so far.

Firstly, by default only the first document that matches the query criteria is updated.

Secondly, if you pass in a document as the value to update to, this new document will replace the whole existing document. If you think about it, the common use-case will be: you retrieve something from the database; you modify it based on some criteria from your application or the user; then you save the updated document to the database.

I’ll show the various types of updates (and point you to the code in the test class) to walk you through these different cases.

Simple Update: Find a document and replace it with an updated one

We’ll carry on using our simple Person document for our examples. Let’s assume we’ve got a document in our database that looks like:

person = {
  _id: "jo",
  name: "Jo Bloggs",
  address: {
    street: "123 Fake St",
    city: "Faketon",
    phone: "5559991234"
  books: [ 27464, 747854, ...]

Maybe Jo goes into witness protection and needs to change his/her name. Assuming we’ve got jo populated in a DBObject, we can make the appropriate changes to the document and save it into the database:

You can make a start with Exercise10UpdateByReplacementTest.

Update Operators: Change a field

But sometimes you won’t have the whole document to replace the old one, sometimes you just want to update a single field in whichever document matched your criteria.

Let’s imagine that we only want to change Jo’s phone number, and we don’t have a DBObject with all of Jo’s details but we do have the ID of the document. If we use the $set operator, we’ll replace only the field we want to change:

There are a number of other operators for performing updates on documents, for example $inc which will increment a numeric field by a given amount.

Now you can do Exercise11UpdateAFieldTest

Update Multiple

As I mentioned earlier, by default the update operation updates the first document it finds and no more. You can, however, set the multi flag on update to update everything.

So maybe we want to update everyone in the database to have a country field, and for now we’re going to assume all the current people are in the UK:

The query parameter is an empty document which finds everything; the second boolean (set to true) is the flag that says to update all the values which were found.

Now we’ve learnt enough to complete the two tests in Exercise12UpdateMultipleDocumentsTest


Finally, the last thing to mention when updating documents is Upsert (Update-or-Insert). This will search for a document matching the criteria and either: update it if it’s there; or insert it into the database if it wasn’t.

Like “update multiple”, you define an upsert operation with a magic boolean. It shouldn’t come as a surprise to find it’s the first boolean param in the update statement (since “multi” was the second):

Now you know everything you need to complete the test in Exercise13UpsertTest

Removing from the database

Finally the D in CRUD - Delete. The syntax of a remove should look familiar now we’ve got this far, you pass a document that represents your selection criteria into the remove method. So if we wanted to delete jo from our database, we’d do:

Unlike update, if the query matches more than one document, all those documents will be deleted (something to be aware of!). If we wanted to remove everyone who lives in London, we’d need to do:

That’s all there is to remove, you’re ready to finish off Exercise14RemoveTest


Unlike traditional databases, you don’t create SQL queries in MongoDB to perform CRUD operations. Instead, operations are done by constructing documents both to query the database, and to define the operations to perform.

While we’ve covered what the basics look like in Java, there’s loads more documentation on all the core concepts in the MongoDB documentation:

Getting Started with MongoDB and Java: Part I

Aug 7 • Posted 3 weeks ago

By Trisha Gee, Java Engineer and Advocate at MongoDB

Java is one of the most popular programming languages in the MongoDB Community. For new users, it’s important to provide an overview of how to work with the MongoDB Java driver and how to use MongoDB as a Java developer.

In this post, which is aimed at Java/JVM developers who are new to MongoDB, we’re going to give you a guide on how to get started, including:

  • Installation
  • Setting up your dependencies
  • Connecting
  • What are Collections and Documents?
  • The basics of writing to and reading from the database
  • An overview of some of the JVM libraries


The installation instructions for MongoDB are extensively documented, so I’m not going to repeat any of that here. If you want to follow along with this “getting started” guide, you’ll want to download the appropriate version of MongoDB and unzip/install it. At the time of writing, the latest version of MongoDB is 2.6.3, which is the version I’ll be using.

A note about security

In a real production environment, of course you’re going to want to consider authentication. This is something that MongoDB takes seriously and there’s a whole section of documentation on security. But for the purpose of this demonstration, I’m going to assume you’ve either got that working or you’re running in “trusted mode” (i.e. that you’re in a development environment that isn’t open to the public).

Take a look around

Once you’ve got MongoDB installed and started (a process that should only take a few minutes), you can connect to the MongoDB shell. Most of the MongoDB technical documentation is written for the shell, so it’s always useful to know how to access it, and how use it to troubleshoot problems or prototype solutions.

When you’ve connected, you should see something like

MongoDB shell version: 2.6.3                           
connecting to: test
> _  

Since you’re in the console, let’s take it for a spin. Firstly we’ll have a look at all the databases that are there right now:

> show dbs

Assuming this is a clean installation, there shouldn’t be much to see:

> show dbs
admin  (empty)
local  0.078GB

That’s great, but as you can see there’s loads of documentation on how to play with MongoDB from the shell. The shell is a really great environment for trying out queries and looking at things from the point-of-view of the server. However, I promised you Java, so we’re going to step away from the shell and get on with connecting via Java.

Getting started with Java

First, you’re going to want to set up your project/IDE to use the MongoDB Java Driver. These days IDEs tend to pick up the correct dependencies through your Gradle or Maven configuration, so I’m just going to cover configuring these.

At the time of writing, the latest version of the Java driver is 2.12.3 - this is designed to work with the MongoDB 2.6 series.


You’ll need to add the following to your dependencies in build.gradle:

compile 'org.mongodb:mongo-java-driver:2.12.3'


For maven, you’ll want:


Alternatively, if you’re really old-school and like maintaining your dependencies the hard way, you can always download the JAR file.

If you don’t already have a project that you want to try with MongoDB, I’ve created a series of unit tests on github which you can use to get a feel for working with MongoDB and Java.

Connecting via Java

Assuming you’ve resolved your dependencies and you’ve set up your project, you’re ready to connect to MongoDB from your Java application.

Since MongoDB is a document database, you might not be surprised to learn that you don’t connect to it via traditional SQL/relational DB methods like JDBC. But it’s simple all the same:

Where I’ve put mongodb://localhost:27017, you’ll want to put the address of where you’ve installed MongoDB. There’s more detailed information on how to create the correct URI, including how to connect to a Replica Set, in the MongoClientURI documentation.

If you’re connecting to a local instance on the default port, you can simply use:

Note that this does throw a checked Exception, UnknownHostException. You’ll either have to catch this or declare it, depending upon what your policy is for exception handling.

The MongoClient is your route in to MongoDB, from this you’ll get your database and collections to work with (more on this later). Your instance of MongoClient (e.g. mongoClient above) will ordinarily be a singleton in your application. However, if you need to connect via different credentials (different user names and passwords) you’ll want a MongoClient per set of credentials.

It is important to limit the number of MongoClient instances in your application, hence why we suggest a singleton - the MongoClient is effectively the connection pool, so for every new MongoClient, you are opening a new pool. Using a single MongoClient (and optionally configuring its settings) will allow the driver to correctly manage your connections to the server. This MongoClient singleton is safe to be used by multiple threads.

One final thing you need to be aware of: you want your application to shut down the connections to MongoDB when it finishes running. Always make sure your application or web server calls MongoClient.close() when it shuts down.

Try out connecting to MongoDB by getting the test in Exercise1ConnectingTest to pass.

Where are my tables?

MongoDB doesn’t have tables, rows, columns, joins etc. There are some new concepts to learn when you’re using it, but nothing too challenging.

While you still have the concept of a database, the documents (which we’ll cover in more detail later) are stored in collections, rather than your database being made up of tables of data. But it can be helpful to think of documents like rows and collections like tables in a traditional database. And collections can have indexes like you’d expect.

Selecting Databases and Collections

You’re going to want to define which databases and collections you’re using in your Java application. If you remember, a few sections ago we used the MongoDB shell to show the databases in your MongoDB instance, and you had an admin and a local.

Creating and getting a database or collection is extremely easy in MongoDB:

You can replace "TheDatabaseName" with whatever the name of your database is. If the database doesn’t already exist, it will be created automatically the first time you insert anything into it, so there’s no need for null checks or exception handling on the off-chance the database doesn’t exist.

Getting the collection you want from the database is simple too:

Again, replacing "TheCollectionName" with whatever your collection is called.

If you’re playing along with the test code, you now know enough to get the tests
in Exercise2MongoClientTest to pass.

An introduction to documents

Something that is, hopefully, becoming clear to you as you work through the examples in this blog, is that MongoDB is different from the traditional relational databases you’ve used. As I’ve mentioned, there are collections, rather than tables, and documents, rather than rows and columns.

Documents are much more flexible than a traditional row, as you have a dynamic schema rather than an enforced one. You can evolve the document over time without incurring the cost of schema migrations and tedious update scripts. But I’m getting ahead of myself.

Although documents don’t look like the tables, columns and rows you’re used to, they should look familiar if you’ve done anything even remotely JSON-like. Here’s an example:

person = {
  _id: "jo",
  name: "Jo Bloggs",
  age: 34,
  address: {
    street: "123 Fake St",
    city: "Faketon",
    state: "MA",
    zip: “12345”
  books: [ 27464, 747854, ...]

There are a few interesting things to note:

  1. Like JSON, documents are structures of name/value pairs, and the values can be one of a number of primitive types, including Strings and various number types.
  2. It also supports nested documents - in the example above, address is a subdocument inside the person document. Unlike a relational database, where you might store this in a separate table and provide a reference to it, in MongoDB if that data benefits from always being associated with its parent, you can embed it in its parent.
  3. You can even store an array of values. The books field in the example above is an array of integers that might represent, for example, IDs of books the person has bought or borrowed.

You can find out more detailed information about Documents in the documentation.

Creating a document and saving it to the database

In Java, if you wanted to create a document like the one above, you’d do something like:

At this point, it’s really easy to save it into your database:

Note that the first three lines are set-up, and you don’t need to re-initialize those every time.

Now if we look inside MongoDB, we can see that the database has been created:

> show dbs
Examples  0.078GB
admin     (empty)
local     0.078GB
> _

…and we can see the collection has been created as well:

> use Examples
switched to db Examples
> show collections
> _ 

…finally, we can see the our person, “Jo”, was inserted:

> db.people.findOne()
    "_id" : "jo",
    "name" : "Jo Bloggs",
        "age": 34,
    "address" : {
        "street" : "123 Fake St",
        "city" : "Faketon",
        "state" : "MA",
        "zip" : "12345"
    "books" : [
> _

As a Java developer, you can see the similarities between the Document that’s stored in MongoDB, and your domain object. In your code, that person would probably be a Person class, with simple primitive fields, an array field, and an Address field.

So rather than building your DBObject manually like the above example, you’re more likely to be converting your domain object into a DBObject. It’s best not to have the MongoDB-specific DBObject class in your domain objects, so you might want to create a PersonAdaptor that converts your Person domain object to a DBObject:

As before, once you have the DBObject, you can save this into MongoDB:

Now you’ve got all the basics to get the tests in Exercise3InsertTest to pass.

Getting documents back out again

Now you’ve saved a Person to the database, and we’ve seen it in the database using the shell, you’re going to want to get it back out into your Java application. In this post, we’re going to cover the very basics of retrieving a document - in a later post we’ll cover more complex querying.

You’ll have guessed by the fact that MongoDB is a document database that we’re not going to be using SQL to query. Instead, we query by example, building up a document that looks like the document we’re looking for. So if we wanted to look for the person we saved into the database, “Jo Bloggs”, we remember that the _id field had the value of “jo”, and we create a document that matches this shape:

As you can see, the find method returns a cursor for the results. Since _id needs to be unique, we know that if we look for a document with this ID, we will find only one document, and it will be the one we want:

Earlier we saw that documents are simply made up of name/value pairs, where the value can be anything from a simple String or primitive, to more complex types like arrays or subdocuments. Therefore in Java, we can more or less treat DBObject as a Map<String, Object>. So if we wanted to look at the fields of the document we got back from the database, we can get them with:

Note that you’ll need to cast the value to a String, as the compiler only knows that it’s an Object.

If you’re still playing along with the example code, you’re now ready to take on all the tests in Exercise4RetrieveTest

Overview of JVM Libraries

So far I’ve shown you the basics of the official Java Driver, but you’ll notice that it’s quite low-level - you have to do a lot of taking things out of your domain objects and poking them into MongoDB-shaped DBObjects, and vice-versa. If this is the level of control you want, then the Java driver makes this easy for you. But if it seems like this is extra work that you shouldn’t have to do, there are plenty of other options for you.

The tools I’m about to describe all use the MongoDB Java Driver at their core to interact with MongoDB. They provide a high-level abstraction for converting your domain objects into MongoDB documents, whilst also giving you a way to get to the underlying driver as well in case you need to use it at a lower level.


Morphia is a really lightweight ODM (Object Document Mapper), so it’s similar to ORMs like Hibernate. Documents can be in a fairly similar shape to your Java domain objects, so this mapping can be automatic, but Morphia allows you point the mapper in the right direction.

Morphia is open source, and has contributors from MongoDB. Sample code and documentation can be found here.

Spring Data

Another frequently used ODM is Spring Data. This supports traditional relational and non-relational databases, including MongoDB. If you’re already using Spring in your application, this should be a familiar way to work.

As always with Spring projects, there’s a lot of really great documentation, including a Getting Started guide with example code.


If you’re working with web services or something else that supports JSON, and you’re using Jackson to work with this data, it probably seems like a waste to be turning it from this form into a Java object and then into a MongoDB DBObject. But MongoJack might make your job easier, as it’s designed to map JSON objects directly into MongoDB. Take a look at the example code and documentation.


This is another Jackson-based ODM, but provides an interesting extra in the form of supporting queries the way you’d write them in the shell. Documentation and example code is available on the website.

Grails MongoDB GORM

The Grails web application framework also supports its own Object-Relational Mapping (GORM), including support for MongoDB. More documentation for this plugin can be found here.


This isn’t an ODM like the other tools mentioned, but the officially supported Scala driver for MongoDB. Like the previous libraries, it uses the MongoDB Java Driver under the covers, but it provides a Scala API for application developers to work with. If you like working with Scala but are searching for an async solution, consider ReactiveMongo, a community-supported driver that provides async and non-blocking operations.

Other libraries and tools

This is far from an extensive list, and I apologise if I’ve left a favourite out. But we’ve compiled a list of many more libraries for the JVM, which includes community projects and officially supported drivers.


We’ve covered the basics of using MongoDB from Java - we’ve touched on what MongoDB is, and you can find out a lot more detailed information about it from the manual; we’ve installed it somewhere that lets us play with it; we’ve talked a bit about collections and documents, and what these look like in Java; and we’ve started inserting things into MongoDB and getting them back out again.

If you haven’t already started playing with the test code, you can find it in this github repository. And if you get desperate and look hard enough, you’ll even find the answers there too.

Finally, there are more examples of using the Java Driver in the Quick Tour, and there is example code in github, including examples for authentication.

If you want to learn more, try our 7-week online course, "Intro to MongoDB and Java" which started on MongoDB University this week.

Try it out, and hopefully you’ll see how easy it is to use MongoDB from Java.

The MongoDB Java Driver 3.0: What’s Changing

Aug 30 • Posted 12 months ago

By Trisha Gee, MongoDB Java Engineer and Evangelist

In the last post, we covered the design goals for the new MongoDB Java Driver. In this one, we’re going to go into a bit more detail on the changes you can expect to see, and how to start playing with an alpha version of the driver. Please note, however, that the driver is still a work in progress, and not ready for production.

New features

Other than the overall changes to design detailed above, the 3.0 driver has the following new features:

  • Pluggable Codecs: This means you can do simple changes to serialisation/deserialisation, like tell the driver to use Joda Time instead of java.util.Date, or you can take almost complete control of how to turn your Java objects into BSON. This should be particularly useful for ODMs or other libraries, as they can write their own codecs to convert Java objects to BSON bytes.
  • Predictable cluster management: We’ve done quite a lot of work around discovering the servers in your cluster and determining which ones to talk to. In particular, the driver doesn’t have to wait for all servers to become available before it can start using the ones that are definitely there - the design is event-based so as soon as a server notifies the driver of its state the driver can take appropriate action - use it if it’s active, or start ignoring it if it’s no longer available.
  • Additional Connection Pool features: We’ve added support for additional connection pool settings, and a number of other improvements around connection management. Here’s the full list.
  • Deprecated methods/classes will be removed: In the next 2.x release a number of methods and classes will be deprecated. These, along with existing deprecated methods, will be removed in the 3.0 driver. This should point you in the right direction to help you migrate from 2.x to 3.x.

Speaking of Migration…

We’ve worked hard to maintain backwards compatibility whilst moving forwards with the architecture of the Java driver for MongoDB. We want to make migration as painless as possible, in many cases it should be a simple drop-in replacement if you want to keep using the existing API. We hope to provide a step-by-step guide to migrating from 2.x to 3.0 in the very near future. For now, it’s worth mentioning that upgrading will be easiest if you update to 2.12 (to be released soon), migrate any code that uses deprecated features, and then move to the compatible mode of the new driver.

Awesome! Can I try it?

Yes you can! You can try out an alpha of the new driver right now, but as you’d expect there are CAVEATS: this is an alpha, it does not support all current features (notably aggregation); although it has been tested it is still in development and we can’t guarantee everything will work as you expect. Features which have been or will be deprecated in the 2.x driver are missing completely from the 3.0 driver. Please don’t use it in production. However, if you do want to play with it in a development environment, or want to run your existing test suite against it, please do send us any feedback you have.

If you want to use the compatible mode, with the old API (minus deprecations) and new architecture:



You should be able to do a drop-in replacement with this dependency - use this instead of your existing MongoDB driver, run it in your test environment and see how ready you are to use the new driver.

If you want to play with the new, ever-changing, not-at-all-final API, then you can use the new driver with the new API. Because we wanted to be able to support both APIs and not have a big-bang switchover, there’s a subtle difference to the location of the driver with the updated API, see if you can spot it:



Note that if you use the new API version, you don’t have access to the old compatible API.

Of course, the code is in GitHub

In Summary

For 3.0, we will deliver the updated, simplified architecture with the same API as the existing driver, as well as working towards a more fluent style of API. This means that although in future you have the option of using the new API, you should also be able to do a simple drop-in replacement of your driver jar file and have the application work as before.

A release date for the 3.0 driver has not been finalized, but keep your eyes open for it.

All Hail the new Java driver!

The MongoDB Java Driver 3.0

Aug 13 • Posted 1 year ago

By Trisha Gee, MongoDB Java Engineer and Evangelist

You may have heard that the JVM team at 10gen is working on a 3.0 version of the Java driver. We’ve actually been working on it since the end of last year, and it’s probably as surprising to you as it is to me that we still haven’t finished it yet. But this is a bigger project than it might seem, and we’re working hard to get it right.

So why update the driver? What are we trying to achieve?

Well, the requirements are:

  • More maintainable
  • More extensible
  • Better support for ODMs, third party libraries and other JVM languages
  • More idiomatic for Java developers
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MongoDB Connector for Hadoop

Aug 7 • Posted 1 year ago

by Mike O’Brien, MongoDB Kernel Tools Lead and maintainer of Mongo-Hadoop, the Hadoop Adapter for MongoDB

Hadoop is a powerful, JVM-based platform for running Map/Reduce jobs on clusters of many machines, and it excels at doing analytics and processing tasks on very large data sets.

Since MongoDB excels at storing large operational data sets for applications, it makes sense to explore using these together - MongoDB for storage and querying, and Hadoop for batch processing.

The MongoDB Connector for Hadoop

We recently released the 1.1 release of the MongoDB Connector for Hadoop. The MongoDB Connector for Hadoop makes it easy to use Mongo databases, or MongoDB backup files in .bson format, as the input source or output destination for Hadoop Map/Reduce jobs. By inspecting the data and computing input splits, Hadoop can process the data in parallel so that very large datasets can be processed quickly.

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Performance Tips: MongoDB at Firescope

Dec 19 • Posted 1 year ago

Guest post by Pete Whitney

Starting to work with any new technology or new API is always challenging at first. You’re often not quite sure of the best ways to get things done or if you’re are using the new technology in the most efficient manner. Furthermore, the early learning process is often littered with trial and error improvements that unfortunately take time to rework and reengineer into more optimal solutions. It sure would be nice if we could short circuit this learning process and simply arrive at nirvana on the first cut. While I won’t proclaim that the destination of the blog is nirvana, I will try to short circuit the learning process by sharing four specific performance related tips that we learned at FireScope Inc. when we transitioned from MySQL to MongoDB for our improved cloud based Stratis product. This blog will share the shorthand version of these tips and point the reader to a more in depth rendering if further understanding is desired.

  1. Through a comparative analysis FireScope found that accessing MongoDB via the MongoDB java driver was three times faster than doing the same access using SpringData. While SpringData yields many benefits it accomplishes its job using a reflection based solution that is performed on top of the native MongoDB java driver. So for FireScope’s performance centric considerations paying a 3X performance penalty for its benefits was not a tradeoff we were willing to make.
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Hadoop Streaming Support for MongoDB

Jun 7 • Posted 2 years ago

MongoDB has some native data processing tools, such as the built-in Javascript-oriented MapReduce framework, and a new Aggregation Framework in MongoDB v2.2. That said, there will always be a need to decouple persistance and computational layers when working with Big Data.

Enter MongoDB+Hadoop: an adapter that allows Apache’s Hadoop platform to integrate with MongoDB.

Using this adapter, it is possible to use MongoDB as a real-time datastore for your application while shifting large aggregation, batch processing, and ETL workloads to a platform better suited for the task.


Well, the engineers at 10gen have taken it one step further with the introduction of the streaming assembly for Mongo-Hadoop.

What does all that mean?

The streaming assembly lets you write MapReduce jobs in languages like Python, Ruby, and JavaScript instead of Java, making it easy for developers that are familiar with MongoDB and popular dynamic programing languages to leverage the power of Hadoop.


It works like this:

Once a developer has Java installed and Hadoop ready to rock they download and build the adapter. With the adapter built, you compile the streaming assembly, load some data into Mongo, and get down to writing some MapReduce jobs.

The assembly streams data from MongoDB into Hadoop and back out again, running it through the mappers and reducers defined in a language you feel at home with. Cool right?

Ruby support was recently added and is particularly easy to get started with. Lets take a look at an example where we analyze twitter data.

Import some data into MongoDB from twitter:

This script curls the twitter status stream and and pipes the json into mongodb using mongoimport. The mongoimport binary has a couple of flags: “-d” which specifies the database “twitter” and -c which specifies the collection “in”.

Next, write a Mapper and save it in a file called mapper.rb:

The mapper needs to call the function and passes it a block. This block takes an argument “docuement” and emits a hash containing the user’s timezone and a count of 1.

Now, write a Reducer and save it in a file called reducer.rb:

The reducer calls the MongoHadoop.reduce function and passes it a block. This block takes two parameters, a key and an array of values for that key, reduces the values into a single aggregate and emits a hash with the same key and the newly reduced value.

To run it all, create a shell script that executes hadoop with the streaming assembly jar and tells it how to find the mapper and reducer files as well as where to retrieve and store the data:

Make them all executable by running chmod +x on the all the scripts and run to have hadoop process the job.

Java is on the Rise, Be-aware!

May 5 • Posted 3 years ago

Improving scalable Java application development with MongoDB + Morphia:

Over the last year I have seen a significant rise in the number of questions and interest from both the greater Java community and enterprise Java shops about MongoDB. Coming from the MongoDB and Java worlds (among others), this is something I have watched with great interest and excitement.

As one of the authors and project leads for Morphia(MongoDB Java ORM) I have seen a lot of questions relating to both the core driver and how to build Java applications with MongoDB. A lot of these questions arise from the paradigm shift users experience when moving from the standard SQL/JPA/Hibernate platforms/frameworks to the document oriented world of MongoDB.

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