MongoDB Cookbook - Second Edition

By Cyrus Dasadia , Amol Nayak
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  1. Installing and Starting the Server

About this book

MongoDB is a high-performance and feature-rich NoSQL database that forms the backbone of the systems that power many different organizations – it’s easy to see why it’s the most popular NoSQL database on the market. Packed with many features that have become essential for many different types of software professionals and incredibly easy to use, this cookbook contains many solutions to the everyday challenges of MongoDB, as well as guidance on effective techniques to extend your skills and capabilities.

This book starts with how to initialize the server in three different modes with various configurations. You will then be introduced to programming language drivers in both Java and Python. A new feature in MongoDB 3 is that you can connect to a single node using Python, set to make MongoDB even more popular with anyone working with Python. You will then learn a range of further topics including advanced query operations, monitoring and backup using MMS, as well as some very useful administration recipes including SCRAM-SHA-1 Authentication. Beyond that, you will also find recipes on cloud deployment, including guidance on how to work with Docker containers alongside MongoDB, integrating the database with Hadoop, and tips for improving developer productivity.

Created as both an accessible tutorial and an easy to use resource, on hand whenever you need to solve a problem, MongoDB Cookbook will help you handle everything from administration to automation with MongoDB more effectively than ever before.

Publication date:
January 2016
Publisher
Packt
Pages
371
ISBN
9781785289989

 

Chapter 1. Installing and Starting the Server

In this chapter, we will cover the following recipes:

  • Installing single node MongoDB

  • Starting a single node instance using the command-line options

  • Installing single node MongoDB with options from the config file

  • Connecting to a single node in the Mongo shell with JavaScript

  • Connecting to a single node from a Java client

  • Connecting to a single node from a Python client

  • Starting multiple instances as part of a replica set

  • Connecting to the replica set in the shell to query and insert data

  • Connecting to the replica set to query and insert data from a Java client

  • Connecting to the replica set to query and insert data using a Python client

  • Starting a simple sharded environment of two shards

  • Connecting to a shard in the shell and performing operations

 

Introduction


In this chapter, we will look at starting up the MongoDB server. Though it is a cakewalk to start the server with default settings for development purposes, there are numerous options available to fine-tune the start up behavior. We will start the server as a single node and then introduce various configuration options. We will conclude this chapter by setting up a simple replica set and running a sharded cluster. So, let's get started with installing and setting up the MongoDB server in the easiest way possible for simple development purposes.

 

Installing single node MongoDB


In this recipe, we will look at installing MongoDB in the standalone mode. This is the simplest and quickest way to start a MongoDB server, but it is seldom used for production use cases. However, this is the most common way to start the server for development purposes. In this recipe, we will start the server without looking at a lot of other startup options.

Getting ready

Well, assuming that we have downloaded the MongoDB binaries from the download site, extracted it, and have the resulting bin directory in the operating system's path variable. (This is not mandatory, but it really becomes convenient after doing so.) The binaries can be downloaded from http://www.mongodb.org/downloads after selecting your host operating system.

How to do it…

  1. Create the directory, /data/mongo/db (or any of your choice). This will be our database directory, and it needs to have permission to write to it by the mongod (the mongo server process) process.

  2. We will start the server from the console with the data directory, /data/mongo/db, as follows:

    > mongod --dbpath  /data/mongo/db
    

How it works…

If you see the following line on the console, you have successfully started the server:

[initandlisten] waiting for connections on port 27017

Starting a server can't get easier than this. Despite the simplicity in starting the server, there are a lot of configuration options that can be used to tune the behavior of the server on startup. Most of the default options are sensible and need not be changed. With the default values, the server should be listening to port 27017 for new connections, and the logs will be printed out to the standard output.

See also

There are times where we would like to configure some options on server startup. In the Installing single node MongoDB recipe, we will use some more start up options.

 

Starting a single node instance using command-line options


In this recipe, we will see how to start a standalone single node server with some command-line options. We will see an example where we want to do the following:

  • Start the server listening to port 27000

  • Logs should be written to /logs/mongo.log

  • The database directory is /data/mongo/db

As the server has been started for development purposes, we don't want to preallocate full-size database files. (We will soon see what this means.)

Getting ready

If you have already seen and executed the Installing single node MongoDB recipe, you need not do anything different. If all these prerequisites are met, we are good for this recipe.

How to do it…

  1. The /data/mongo/db directory for the database and /logs/ for the logs should be created and present on your filesystem with appropriate permissions to write to it.

  2. Execute the following command:

    > mongod --port 27000 --dbpath /data/mongo/db –logpath /logs/mongo.log --smallfiles
    

How it works…

Ok, this wasn't too difficult and is similar to the previous recipe, but we have some additional command-line options this time around. MongoDB actually supports quite a few options at startup, and we will see a list of the most common and important ones in my opinion:

Option

Description

--help or -h

This is used to print the information of various start up options available.

--config or -f

This specifies the location of the configuration file that contains all the configuration options. We will see more on this option in a later recipe. It is just a convenient way of specifying the configurations in a file rather than on the command prompt; especially when the number of options specified is more. Using a separate configuration file shared across different MongoDB instances will also ensure that all the instances are running with identical configurations.

--verbose or -v

This makes the logs more verbose; we can put more v's to make the output even more verbose, for example, -vvvvv.

--quiet

This gives a quieter output; this is the opposite of verbose or the - v option. It will keep the logs less chatty and clean.

--port

This option is used if you are looking to start the server listening to some port other than the default 27017. We would be frequently using this option whenever we are looking to start multiple mongo servers on the same machine, for example, --port 27018 will start the server listening to port 27018 for new connections.

--logpath

This provides a path to a log file where the logs will be written. The value defaults to STDOUT. For example, --logpath /logs/server.out will use /logs/server.out as the log file for the server. Remember that the value provided should be a file and not a directory where the logs will be written.

--logappend

This option appends to the existing log file, if any. The default behavior is to rename the existing log file and then create a new file for the logs of the currently started mongo instance. Suppose that we have used the name of the log file as server.out, and on startup, the file exists, then by default this file will be renamed as server.out.<timestamp>, where <timestamp> is the current time. The time is GMT as against the local time. Let's assume that the current date is October 28th, 2013 and time is 12:02:15, then the file generated will have the following value as the timestamp: 2013-10-28T12-02-15.

--dbpath

This provides you with the directory where a new database will be created or an existing database is present. The value defaults to /data/db. We will start the server using /data /mongo/db as the database directory. Note that the value should be a directory rather than the name of the file.

--smallfiles

This is used frequently for development purposes when we plan to start more than one mongo instance on our local machine. Mongo, on startup, creates a database file of size 64 MB (on 64-bit machines). This preallocation happens for performance reasons, and the file is created with zeros written to it to fill out space on the disk. Adding this option on startup creates a preallocated file of 16 MB only (again, on a 64-bit machine). This option also reduces the maximum size of the database and journal files. Avoid using this option for production deployments. Additionally, the file sizes double to a maximum of 2 GB by default. If the --smallfile option is chosen, it goes up to a maximum of 512 MB.

--replSet

This option is used to start the server as a member of the replica set. The value of this arg is the name of the replica set, for example, --replSet repl1. You will learn more on this option in a later recipe where we will start a simple mongo replica set.

--configsvr

This option is used to start the server as a configuration server. The role of the configuration server will be made clearer when we set up a simple sharded environment in a later recipe in this chapter.

--shardsvr

This informs the started mongod process that this server is being started as a shard server. By giving this option, the server also listens to port 27018 instead of the default 27017. We will know more on this option when we start a simple sharded server.

--oplogSize

Oplog is the backbone of replication. It is a capped collection where the data being written to the primary instances is stored in order to be replicated to the secondary instances. This collection resides in a database named local. On initialization of the replica set, the disk space for oplog is preallocated, and the database file (for the local database) is filled with zeros as placeholders. The default value is 5% of the disk space, which should be good enough for most of the cases.

The size of oplog is crucial because capped collections are of a fixed size and they discard the oldest documents in them on exceeding their size, thereby making space for new documents. Having a very small oplog size can result in data being discarded before being replicated to secondary nodes. A large oplog size can result in unnecessary disk space utilization and large duration for the replica set initialization.

For development purposes, when we start multiple server processes on the same host, we might want to keep the oplog size to a minimum value, quickly initiate the replica set, and use minimum disk space.

--storageEngine

Starting with MongoDB 3.0, a new storage engine called Wired Tiger was introduced. The previous (default) storage engine is now called mmapv1. To start MongoDB with Wired Tiger instead of mmapv1, use the wiredTiger value with this option.

--dirctoryperdb

By default, MongoDB's database files are stored in a common directory (as provided in --dbpath). This option allows you to store each database in its own subdirectory in the aforementioned data directory. Having such granular control allows you to have separate disks for each database.

There's more…

For an exhaustive list of options that are available, use the --help or -h option. This list of options is not exhaustive, and we will see some more coming up in later recipes as and when we need them. In the next recipe, we will see how to use a configuration file instead of the command-line arguments.

See also

  • Single node installation of MongoDB with options from config file for using configuration files to provide start up options

  • Starting multiple instances as part of a replica set to start a replica set

  • Starting a simple sharded environment of two shards to set up a sharded environment

 

Single node installation of MongoDB with options from the config file


As we can see, providing options from the command line does the work, but it starts getting awkward as soon as the number of options that we provide increase. We have a nice and clean alternative to provide the start up options from a configuration file rather than as command-line arguments.

Getting ready

If you have already executed the Installing single node MongoDB recipe, you need not do anything different as all the prerequisites of this recipe are the same.

How to do it…

The /data/mongo/db directory for the database and /logs/ for the logs should be created and present on your filesystem with the appropriate permissions to write to it and perform the following steps:

  1. Create a configuration file that can have any arbitrary name. In our case, let's say that we create this in /conf/mongo.conf. We then edit the file and add the following lines to it:

    port = 27000
    dbpath = /data/mongo/db
    logpath = /logs/mongo.log
    smallfiles = true
  2. Start the mongo server using the following command:

    > mongod --config  /config/mongo.conf
    

How it works…

All the command-line options that we discussed in the previous recipe, Starting a single node instance using command-line options, hold true. We are just providing them in a configuration file instead. If you have not visited the previous recipe, I would recommend you to do so as that is where we discussed some of the common command-line options. The properties are specified as <property name> = <value>. For all the properties that don't have values, for example, the smallfiles option, the value given is a Boolean value, true. If we need to have a verbose output, we would add v=true (or multiple v's to make it more verbose) to our configuration file. If you already know what the command-line option is, then it is pretty easy to guess what the value of the property is in the file. It is almost the same as the command-line option with just the hyphen removed.

 

Connecting to a single node in the Mongo shell with JavaScript


This recipe is about starting the mongo shell and connecting to a MongoDB server. Here we also demonstrate how to load JavaScript code in the shell. Though this is not always required, it is handy when we have a large block of JavaScript code with variables and functions with some business logic in them that is required to be executed from the shell frequently and we want these functions to be available in the shell always.

Getting ready

Although it is possible to run the mongo shell without connecting to the MongoDB server using mongo --nodb, we would rarely need to do so. To start a server on the localhost without much of a hassle, take a look at the first recipe, Installing single node MongoDB, and start the server.

How to do it…

  1. First, we create a simple JavaScript file and call it hello.js. Type the following body in the hello.js file:

    function sayHello(name) {
      print('Hello ' + name + ', how are you?')
    }
  2. Save this file at the location, /mongo/scripts/hello.js. (This can be saved at any other location too.)

  3. On the command prompt, execute the following:

    > mongo --shell /mongo/scripts/hello.js
    
  4. On executing this, we should see the following printed to our console:

    MongoDB shell version: 3.0.2
    connecting to: test
    >
    
  5. Test the database that the shell is connected to by typing the following command:

    > db
    

    This should print out test to the console.

  6. Now, type the following command in the shell:

    > sayHello('Fred')
    
  7. You should get the following response:

    Hello Fred, how are you?
    

Note

Note: This book was written with MongoDB version 3.0.2. There is a good chance that you may be using a later version and hence see a different version number in the mongo shell.

How it works…

The JavaScript function that we executed here is of no practical use and is just used to demonstrate how a function can be preloaded on the startup of the shell. There could be multiple functions in the .js file containing valid JavaScript code—possibly some complex business logic.

On executing the mongo command without any arguments, we connect to the MongoDB server running on localhost and listen for new connections on the default port 27017. Generally speaking, the format of the command is as follows:

mongo <options> <db address> <.js files>

In cases where there are no arguments passed to the mongo executable, it is equivalent to the passing of the db address as localhost:27017/test.

Let's look at some example values of the db address command-line option and its interpretation:

  • mydb: This will connect to the server running on localhost and listen for a connection on port 27017. The database connected will be mydb.

  • mongo.server.host/mydb: This will connect to the server running on mongo.server.host and the default port 27017. The database connected will be mydb.

  • mongo.server.host:27000/mydb: This will connect to the server running on mongo.server.host and the port 27000. The database connected will be mydb.

  • mongo.server.host:27000: This will connect to the server running on mongo.server.host and the port 27000. The database connected will be the default database test.

Now, there are quite a few options available on the mongo client too. We will see a few of them in the following table:

Option

Description

--help or -h

This shows help regarding the usage of various command-line options.

--shell

When the .js files are given as arguments, these scripts get executed and the mongo client will exit. Providing this option ensures that the shell remains running after the JavaScript files execute. All the functions and variables defined in these .js files are available in the shell on startup. As in the preceding case, the sayHello function defined in the JavaScript file is available in the shell for invocation.

--port

The specifies the port of the mongo server where the client needs to connect.

--host

This specifies the hostname of the mongo server where the client needs to connect. If the db address is provided with the hostname, port, and database, then both the --host and --port options need not be specified.

--username or -u

This is relevant when security is enabled for mongo. It is used to provide the username of the user to be logged in.

--password or -p

This option is relevant when security is enabled for mongo. It is used to provide the password of the user to be logged in.

 

Connecting to a single node using a Java client


This recipe is about setting up the Java client for MongoDB. You will repeatedly refer to this recipe while working on others, so read it very carefully.

Getting ready

The following are the prerequisites for this recipe:

  • Java SDK 1.6 or above is recommended.

  • Use the latest version of Maven available. Version 3.3.3 was the latest at the time of writing this book.

  • MongoDB Java driver version 3.0.1 was the latest at the time of writing this book.

  • Connectivity to the Internet to access the online maven repository or a local repository. Alternatively, you may choose an appropriate local repository accessible to you from your computer.

  • The Mongo server is up and running on localhost and port 27017. Take a look at the first recipe, Installing single node MongoDB, and start the server.

How to do it…

  1. Install the latest version of JDK from https://www.java.com/en/download/ if you don't already have it on your machine. We will not be going through the steps to install JDK in this recipe, but before moving on with the next step, JDK should be present.

  2. Maven needs to be downloaded from http://maven.apache.org/download.cgi. We should see something similar to the following image on the download page. Choose the binaries in a .tar.gz or .zip format and download it. This recipe is executed on a machine running on the Windows platform and thus these steps are for installation on Windows.

  3. Once the archive has been downloaded, we need to extract it and put the absolute path of the bin folder in the extracted archive in the operating system's path variable. Maven also needs the path of JDK to be set as the JAVA_HOME environment variable. Remember to set the root of your JDK as the value of this variable.

  4. All we need to do now is type mvn -version on the command prompt, and if we see the output that begins with something as follows, we have successfully set up maven:

    > mvn -version
    
  5. At this stage, we have maven installed, and we are now ready to create our simple project to write our first Mongo client in Java. We start by creating a project folder. Let's say that we create a folder called Mongo Java. Then we create a folder structure, src/main/java, in this project folder. The root of the project folder then contains a file called pom.xml. Once this folder's creation is done, the folder structure should look as follows:

          Mongo Java      
          +--src  
          |     +main
          |         +java
          |--pom.xml
  6. We just have the project skeleton with us. We shall now add some content to the pom.xml file. Not much is needed for this. The following content is all we need in the pom.xml file:

    <project>
      <modelVersion>4.0.0</modelVersion>
      <name>Mongo Java</name>
      <groupId>com.packtpub</groupId>
      <artifactId>mongo-cookbook-java</artifactId>
      <version>1.0</version>    <packaging>jar</packaging>
      <dependencies>
        <dependency>
          <groupId>org.mongodb</groupId>
          <artifactId>mongo-java-driver</artifactId>
          <version>3.0.1</version>
        </dependency>
      </dependencies>
    </project>
  7. We finally write our Java client that will be used to connect to the Mongo server and execute some very basic operations. The following is the Java class in the src/main/java location in the com.packtpub.mongo.cookbook package, and the name of the class is FirstMongoClient:

    package com.packtpub.mongo.cookbook;
    
    import com.mongodb.BasicDBObject;
    import com.mongodb.DB;
    import com.mongodb.DBCollection;
    import com.mongodb.DBObject;
    import com.mongodb.MongoClient;
    
    import java.net.UnknownHostException;
    import java.util.List;
    
    /**
     * Simple Mongo Java client
     *
     */
    public class FirstMongoClient {
    
        /**
         * Main method for the First Mongo Client. Here we shall be connecting to a mongo
         * instance running on localhost and port 27017.
         *
         * @param args
         */
        public static final void main(String[] args) 
    throws UnknownHostException {
            MongoClient client = new MongoClient("localhost", 27017);
            DB testDB = client.getDB("test");
            System.out.println("Dropping person collection in test database");
            DBCollection collection = testDB.getCollection("person");
            collection.drop();
            System.out.println("Adding a person document in the person collection of test database");
            DBObject person = 
    new BasicDBObject("name", "Fred").append("age", 30);
            collection.insert(person);
            System.out.println("Now finding a person using findOne");
            person = collection.findOne();
            if(person != null) {
                System.out.printf("Person found, name is %s and age is %d\n", person.get("name"), person.get("age"));
            }
            List<String> databases = client.getDatabaseNames();
            System.out.println("Database names are");
            int i = 1;
            for(String database : databases) {
                System.out.println(i++ + ": " + database);
            }
      System.out.println("Closing client");
            client.close();
        }
    }
  8. It's now time to execute the preceding Java code. We will execute it using maven from the shell. You should be in the same directory as pom.xml of the project:

    mvn compile exec:java -Dexec.mainClass=com.packtpub.mongo.cookbook.FirstMongoClient
    

How it works…

These were quite a lot of steps to follow. Let's look at some of them in more detail. Everything up to step 6 is straightforward and doesn't need any explanation. Let's look at step 7 onwards.

The pom.xml file that we have here is pretty simple. We defined a dependency on mongo's Java driver. It relies on the online repository, repo.maven.apache.org, to resolve the artifacts. For a local repository, all we need to do is define the repositories and pluginRepositories tags in pom.xml. For more information on maven, refer to the maven documentation at http://maven.apache.org/guides/index.html.

For the Java class, the org.mongodb.MongoClient class is the backbone. We first instantiate it using one of its overloaded constructors giving the server's host and port. In this case, the hostname and port were not really needed as the values provided are the default values anyway, and the no-argument constructor would have worked well too. The following code snippet instantiates this client:

MongoClient client = new MongoClient("localhost", 27017);

The next step is to get the database, in this case, test using the getDB method. This is returned as an object of the com.mongodb.DB type. Note that this database might not exist, yet getDB will not throw any exception. Instead, the database will get created whenever we add a new document to the collection in this database. Similarly, getCollection on the DB object will return an object of the com.mongodb.DBCollection type representing the collection in the database. This too might not exist in the database and will get created on inserting the first document automatically.

The following two code snippets from our class show you how to get an instance of DB and DBCollection:

DB testDB = client.getDB("test");
DBCollection collection = testDB.getCollection("person");

Before we insert a document, we will drop the collection so that even upon multiple executions of the program, we will have just one document in the person collection. The collection is dropped using the drop() method on the DBCollection object's instance. Next, we create an instance of com.mongodb.DBObject. This is an object that represents the document to be inserted into the collection. The concrete class used here is BasicDBObject, which is a type of java.util.LinkedHashMap, where the key is String and the value is Object. The value can be another DBObject too, in which case, it is a document nested within another document. In our case, we have two keys, name and age, which are the field names in the document to be inserted and the values are of the String and Integer types, respectively. The append method of BasicDBObject adds a new key value pair to the BasicDBObject instance and returns the same instance, which allows us to chain the append method calls to add multiple key value pairs. This created DBObject is then inserted into the collection using the insert method. This is how we instantiated DBObject for the person collection and inserted it into the collection as follows:

DBObject person = new BasicDBObject("name", "Fred").append("age", 30);
collection.insert(person);

The findOne method on DBCollection is straightforward and returns one document from the collection. This version of findOne doesn't accept DBObject (which otherwise acts as a query executed before a document is selected and returned) as a parameter. This is synonymous to doing db.person.findOne() from the shell.

Finally, we simply invoke getDatabaseNames to get a list of databases' names in the server. At this point of time, we should at least be having test and the local database in the returned result. Once all the operations are complete, we close the client. The MongoClient class is thread-safe and generally one instance is used per application. To execute the program, we use the maven's exec plugin. On executing step 9, we should see the following lines toward the end in the console:

[INFO] [exec:java {execution: default-cli}]
--snip--
Dropping person collection in test database
Adding a person document in the person collection of test database
Now finding a person using findOne
Person found, name is Fred and age is 30
Database names are
1: local
2: test
INFO: Closed connection [connectionId{localValue:2, serverValue:2}] to localhost:27017 because the pool has been closed.
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESSFUL
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 3 seconds
[INFO] Finished at: Tue May 12 07:33:00 UTC 2015
[INFO] Final Memory: 22M/53M
[INFO] ------------------------------------------------------------------------ 
 

Connecting to a single node using a Python client


In this recipe, we will connect to a single MongoDB instance using the Python MongoDB driver called PyMongo. With Python's simple syntax and versatility clubbed together with MongoDB, many programmers find that this stack allows faster prototyping and reduced development cycles.

Getting ready

The following are the prerequisites for this recipe:

  • Python 2.7.x (although the code is compatible with Python 3.x).

  • PyMongo 3.0.1: Python MongoDB driver.

  • Python package installer (pip).

  • The Mongo server is up and running on localhost and port 27017. Take a look at the first recipe, Installing single node MongoDB, and start the server.

How to do it…

  1. Depending on your operating system, install the pip utility, say, on the Ubuntu/Debian system. You can use the following command to install pip:

    > apt-get install python-pip
    
  2. Install the latest PyMongo driver using pip:

    > pip install pymongo
    
  3. Lastly, create a new file called my_client.py and type in the following code:

    from __future__ import print_function
    import pymongo
    
    # Connect to server
    client = pymongo.MongoClient('localhost', 27017)
    
    # Select the database
    testdb = client.test
    
    # Drop collection
    print('Dropping collection person')
    testdb.person.drop()
    
    # Add a person
    print('Adding a person to collection person')
    employee = dict(name='Fred', age=30)
    testdb.person.insert(employee)
    
    # Fetch the first entry from collection
    person = testdb.person.find_one()
    if person:
        print('Name: %s, Age: %s' % (person['name'], person['age']))
    
    # Fetch list of all databases
    print('DB\'s present on the system:')
    for db in client.database_names():
        print('    %s' % db)
    
    
    # Close connection
    print('Closing client connection')
    client.close()
  4. Run the script using the following command:

    > python my_client.py
    

How it works…

We start off by installing the Python MongoDB driver, pymongo, on the system with the help of the pip package manager. In the given Python code, we begin by importing print_function from the __future__ module to allow compatibility with Python 3.x. Next, we import pymongo so that it can be used in the script.

We instantiate pymongo.MongoClient() with localhost and 27017 as the mongo server host and port, respectively. In pymongo, we can directly refer to the database and its collection by using the <client>.<database_name>.<collection_name> convention.

In our recipe, we used the client handler to select the database test simply by referring to client.test. This returns a database object even if the database does not exist. As a part of this recipe, we drop the collection by calling testdb.person.drop(), where testdb is a reference to client.test and person is a collection that we wish to drop. For this recipe, we are intentionally dropping the collection so that recurring runs will always yield one record in the collection.

Next, we instantiate a dictionary called employee with a few values such as name and age. We will now add this entry to our person collection using the insert_one() method.

As we now know that there is an entry in the person collection, we will fetch one document using the find_one() method. This method returns the first document in the collection, depending on the order of documents stored on the disk.

Following this, we also try to get the list of all the databases by calling the get_databases() method to the client. This method returns a list of database names present on the server. This method may come in handy when you are trying to assert the existence of a database on the server.

Finally, we close the client connection using the close() method.

 

Starting multiple instances as part of a replica set


In this recipe, we will look at starting multiple servers on the same host but as a cluster. Starting a single mongo server is enough for development purposes or non-mission-critical applications. For crucial production deployments, we need the availability to be high, where if one server instance fails, another instance takes over and the data remains available to query, insert, or update. Clustering is an advanced concept and we won't be doing justice by covering this whole concept in one recipe. Here, we will be touching the surface and going into more detail in other recipes in the administration section later in the book. In this recipe, we will start multiple mongo server processes on the same machine for the purpose of testing. In a production environment, they will be running on different machines (or virtual machines) in the same or even different data centers.

Let's see in brief what a replica set exactly is. As the name suggests, it is a set of servers that are replicas of each other in terms of data. Looking at how they are kept in sync with each other and other internals is something we will defer to some later recipes in the administration section, but one thing to remember is that write operations will happen only on one node, which is the primary one. All the querying also happens from the primary by default, though we may permit read operations on secondary instances explicitly. An important fact to remember is that replica sets are not meant to achieve scalability by distributing the read operations across various nodes in a replica set. Its sole objective is to ensure high availability.

Getting ready

Though not a prerequisite, taking a look at the Starting a single node instance using command-line options recipe will definitely make things easier just in case you are not aware of various command-line options and their significance while starting a mongo server. Additionally, the necessary binaries and setups as mentioned in the single server setup must be done before we continue with this recipe. Let's sum up on what we need to do.

We will start three mongod processes (mongo server instances) on our localhost.

We will create three data directories, /data/n1, /data/n2, and /data/n3 for Node1, Node2, and Node3, respectively. Similarly, we will redirect the logs to /logs/n1.log, /logs/n2.log, and /logs/n3.log. The following image will give you an idea on how the cluster would look:

How to do it…

Let's take a look at the steps in detail:

  1. Create the /data/n1, /data/n2, /data/n3, and /logs directories for the data and logs of the three nodes respectively. On the Windows platform, you can choose the c:\data\n1, c:\data\n2, c:\data\n3, and c:\logs\ directories or any other directory of your choice for the data and logs respectively. Ensure that these directories have appropriate write permissions for the mongo server to write the data and logs.

  2. Start the three servers as follows. Users on the Windows platform need to skip the --fork option as it is not supported:

    $ mongod --replSet repSetTest --dbpath /data/n1 --logpath /logs/n1.log --port 27000 --smallfiles --oplogSize 128 --fork
    $ mongod --replSet repSetTest --dbpath /data/n2 --logpath /logs/n2.log --port 27001 --smallfiles --oplogSize 128 --fork
    $ mongod --replSet repSetTest --dbpath /data/n3 --logpath /logs/n3.log --port 27002 --smallfiles --oplogSize 128 –fork
    
  3. Start the mongo shell and connect to any of the mongo servers running. In this case, we connect to the first one (listening to port 27000). Execute the following command:

    $ mongo localhost:27000
    
  4. Try to execute an insert operation from the mongo shell after connecting to it:

    > db.person.insert({name:'Fred', age:35})
    

    This operation should fail as the replica set has not been initialized yet. More information can be found in the How it works… section.

  5. The next step is to start configuring the replica set. We start by preparing a JSON configuration in the shell as follows:

    cfg = {
      '_id':'repSetTest', 'members':[ {'_id':0, 'host': 'localhost:27000'}, {'_id':1, 'host': 'localhost:27001'}, {'_id':2, 'host': 'localhost:27002'} ]
    }
  6. The last step is to initiate the replica set with the preceding configuration as follows:

    > rs.initiate(cfg)
    
  7. Execute rs.status() after a few seconds on the shell to see the status. In a few seconds, one of them should become a primary and the remaining two should become secondary.

How it works…

We described the common options in the Installing single node MongoDB recipe with the command-line options recipe before and all these command-line options are described in detail.

As we are starting three independent mongod services, we have three dedicated database paths on the filesystem. Similarly, we have three separate log file locations for each of the processes. We then start three mongod processes with the database and log file path specified. As this setup is for test purposes and is started on the same machine, we use the --smallfiles and --oplogSize options. As these processes are running on the same host, we also choose the ports explicitly to avoid port conflicts. The ports that we chose here were 27000, 27001, and 27002. When we start the servers on different hosts, we may or may not choose a separate port. We can very well choose to use the default one whenever possible.

The --fork option demands some explanation. By choosing this option, we start the server as a background process from our operating system's shell and get the control back in the shell where we can then start more such mongod processes or perform other operations. In the absence of the --fork option, we cannot start more than one process per shell and would need to start three mongod processes in three separate shells.

If we take a look at the logs generated in the log directory, we should see the following lines in it:

[rsStart] replSet can't get local.system.replset config from self or any seed (EMPTYCONFIG)
[rsStart] replSet info you may need to run replSetInitiate -- rs.initiate() in the shell -- if that is not already done

Though we started three mongod processes with the --replSet option, we still haven't configured them to work with each other as a replica set. This command-line option is just used to tell the server on startup that this process will be running as a part of a replica set. The name of the replica set is the same as the value of this option passed on the command prompt. This also explains why the insert operation executed on one of the nodes failed before the replica set was initialized. In mongo replica sets, there can be only one primary node where all the inserting and querying happens. In the image shown, the N1 node is shown as the primary and listens to port 27000 for client connections. All the other nodes are slave/secondary instances, which sync themselves up with the primary and hence querying too is disabled on them by default. It is only when the primary goes down that one of the secondary takes over and becomes a primary node. However, it is possible to query the secondary for data as we have shown in the image; we will see how to query from a secondary instance in the next recipe.

Well, all that is left now is to configure the replica set by grouping the three processes that we started. This is done by first defining a JSON object as follows:

cfg = {
  '_id':'repSetTest', 'members':[ {'_id':0, 'host': 'localhost:27000'}, {'_id':1, 'host': 'localhost:27001'}, {'_id':2, 'host': 'localhost:27002'} ]
}

There are two fields, _id and members, for the unique ID of the replica set and an array of the hostnames and port numbers of the mongod server processes as part of this replica set, respectively. Using localhost to refer to the host is not a very good idea and is usually discouraged; however, in this case, as we started all the processes on the same machine, we are ok with it. It is preferred that you refer to the hosts by their hostnames even if they are running on localhost. Note that you cannot mix referring to the instances using localhost and hostnames both in the same configuration. It is either the hostname or localhost. To configure the replica set, we then connect to any one of the three running mongod processes; in this case, we connect to the first one and then execute the following from the shell:

> rs.initiate(cfg)

The _id field in the cfg object passed has a value that is the same as the value we gave to the --replSet option on the command prompt when we started the server processes. Not giving the same value would throw the following error:

{
        "ok" : 0,
        "errmsg" : "couldn't initiate : set name does not match the set name host Amol-PC:27000 expects"
}

If all goes well and the initiate call is successful, we should see something similar to the following JSON response on the shell:

{"ok" : 1}

In a few seconds, you should see a different prompt for the shell that we executed this command from. It should now become a primary or secondary. The following is an example of the shell connected to a primary member of the replica set:

repSetTest:PRIMARY>

Executing rs.status() should give us some stats on the replica set's status, which we will explore in depth in a recipe later in the book in the administration section. For now, the stateStr field is important and contains the PRIMARY, SECONDARY, and other texts.

There's more…

Look at the Connecting to the replica set in the shell to query and insert data recipe to perform more operations from the shell after connecting to a replica set. Replication isn't as simple as we saw here. See the administration section for more advanced recipes on replication.

See also

If you are looking to convert a standalone instance to a replica set, then the instance with the data needs to become a primary first, and then empty secondary instances will be added to which the data will be synchronized. Refer to the following URL on how to perform this operation:

http://docs.mongodb.org/manual/tutorial/convert-standalone-to-replica-set/

 

Connecting to the replica set in the shell to query and insert data


In the previous recipe, we started a replica set of three mongod processes. In this recipe, we will work with this setup by connecting to it using the mongo client application, perform queries, insert data, and take a look at some of the interesting aspects of a replica set from a client's perspective.

Getting ready

The prerequisite for this recipe is that the replica set should be set up and running. Refer to the previous recipe, Starting multiple instances as part of a replica set, for details on how to start the replica set.

How to do it…

  1. We will start two shells here, one for PRIMARY and one for SECONDARY. Execute the following command on the command prompt:

    > mongo localhost:27000
    
  2. The prompt of the shell tells us whether the server to which we have connected is PRIMARY or SECONDARY. It should show the replica set's name followed by a :, followed by the server state. In this case, if the replica set is initialized, up, and running, we should see either repSetTest:PRIMARY> or repSetTest:SECONDARY>.

  3. Suppose that the first server we connected to is a secondary, we need to find the primary. Execute the rs.status() command in the shell and look out for the stateStr field. This should give us the primary server. Use the mongo shell to connect to this server.

  4. At this point, we should be having two shells running, one connected to a primary and another connected to a secondary.

  5. In the shell connected to the primary node, execute the following insert:

    repSetTest:PRIMARY> db.replTest.insert({_id:1, value:'abc'})
    
  6. There is nothing special about this. We just inserted a small document in a collection that we will use for the replication test.

  7. By executing the following query on the primary, we should get the following result:

    repSetTest:PRIMARY> db.replTest.findOne()
    { "_id" : 1, "value" : "abc" }
    
  8. So far, so good. Now, we will go to the shell that is connected to the SECONDARY node and execute the following:

    repSetTest:SECONDARY> db.replTest.findOne()
    

    On doing this, we should see the following error on the console:

         { "$err" : "not master and slaveOk=false", "code" : 13435 }
    
  9. Now execute the following on the console:

    repSetTest:SECONDARY>  rs.slaveOk(true)
    
  10. Execute the query that we executed in step 7 again on the shell. This should now get the results as follows:

    repSetTest:SECONDARY>db.replTest.findOne()
    { "_id" : 1, "value" : "abc" }
    
  11. Execute the following insert on the secondary node; it should not succeed with the following message:

    repSetTest:SECONDARY> db.replTest.insert({_id:1, value:'abc'})
    not master
    

How it works…

We have done a lot of things in this recipe, and we will try to throw some light on some of the important concepts to remember.

We basically connect to a primary and secondary node from the shell and perform (I would say, try to perform) selects and inserts. The architecture of a Mongo replica set is made of one primary (just one, no more, no less) and multiple secondary nodes. All writes happen on the PRIMARY only. Note that replication is not a mechanism to distribute the read request load that enables scaling the system. Its primary intent is to ensure high availability of data. By default, we are not permitted to read data from the secondary nodes. In step 6, we simply insert data from the primary node and then execute a query to get the document that we inserted. This is straightforward and nothing related to clustering here. Just note that we inserted the document from the primary and then queried it back.

In the next step, we execute the same query but this time, from the secondary's shell. By default, querying is not enabled on the SECONDARY. There might be a small lag in replicating the data possibly due to heavy data volumes to be replicated, network latency, or hardware capacity to name a few of the causes, and thus, querying on the secondary might not reflect the latest inserts or updates made on the primary. However, if we are ok with it and can live with the slight lag in the data being replicated, all we need to do is enable querying on the SECONDARY node explicitly by just executing one command, rs.slaveOk() or rs.slaveOk(true). Once this is done, we are free to execute queries on the secondary nodes too.

Finally, we try to insert the data into a collection of the slave node. Under no circumstances is this permitted, regardless of whether we have done rs.slaveOk(). When rs.slaveOk() is invoked, it just permits the data to be queried from the SECONDARY node. All write operations still have to go to the primary and then flow down to the secondary. The internals of replication will be covered in a different recipe in the administration section.

See also

The next recipe, Connecting to the replica set to query and insert data from a Java client, is about connecting to a replica set from a Java client.

 

Connecting to the replica set to query and insert data from a Java client


In this recipe, we will demonstrate how to connect to a replica set from a Java client and how the client would automatically failover to another node in the replica set, should a primary node fail.

Getting ready

We need to take a look at the Connecting to the single node using a Java client recipe as it contains all the prerequisites and steps to set up maven and other dependencies. As we are dealing with a Java client for replica sets, a replica set must be up and running. Refer to the Starting multiple instances as part of a replica set recipe for details on how to start the replica set.

How to do it…

  1. Write/copy the following piece of code: (This Java class is also available for download from the Packt website.)

    package com.packtpub.mongo.cookbook;
    
    import com.mongodb.BasicDBObject;
    import com.mongodb.DB;
    import com.mongodb.DBCollection;
    import com.mongodb.DBObject;
    import com.mongodb.MongoClient;
    import com.mongodb.ServerAddress;
    
    import java.util.Arrays;
    
    /**
     *
     */
    public class ReplicaSetMongoClient {
    
      /**
      * Main method for the test client connecting to the replica set.
       * @param args
      */
      public static final void main(String[] args) throws Exception {
        MongoClient client = new MongoClient(
          Arrays.asList(
            new ServerAddress("localhost", 27000), new ServerAddress("localhost", 27001), new ServerAddress("localhost", 27002)
          )
        );
        DB testDB = client.getDB("test");
        System.out.println("Dropping replTest collection");
        DBCollection collection = testDB.getCollection("replTest");
        collection.drop();
        DBObject object = new BasicDBObject("_id", 1).append("value", "abc");
        System.out.println("Adding a test document to replica set");
        collection.insert(object);
        System.out.println("Retrieving document from the collection, this one comes from primary node");
        DBObject doc = collection.findOne();
        showDocumentDetails(doc);
        System.out.println("Now Retrieving documents in a loop from the collection.");
        System.out.println("Stop the primary instance after few iterations ");
        for(int i = 0 ; i < 10; i++) {
          try {
            doc = collection.findOne();
            showDocumentDetails(doc);
          }
          catch (Exception e) {
            //Ignoring or log a message
          }
          Thread.sleep(5000);
        }
      }
    
      /**
      *
      * @param obj
      */
      private static void showDocumentDetails(DBObject obj) {
        System.out.printf("_id: %d, value is %s\n", obj.get("_id"), obj.get("value"));
      }
    }
  2. Connect to any of the nodes in the replica set, say to localhost:27000, and execute rs.status() from the shell. Take a note of the primary instance in the replica set and connect to it from the shell if localhost:27000 is not a primary. Here, switch to the administrator database as follows:

    repSetTest:PRIMARY>use admin
    
  3. We now execute the preceding program from the operating system shell as follows:

    $ mvn compile exec:java -Dexec.mainClass=com.packtpub.mongo.cookbook.ReplicaSetMongoClient
    
  4. Shut down the primary instance by executing the following on the mongo shell that is connected to the primary:

    repSetTest:PRIMARY> db.shutdownServer()
    
  5. Watch the output on the console where the com.packtpub.mongo.cookbook.ReplicaSetMongoClient class is executed using maven.

How it works…

An interesting thing to observe is how we instantiate the MongoClient instance. It is done as follows:

  MongoClient client = new MongoClient(Arrays.asList(new ServerAddress("localhost", 27000), new ServerAddress("localhost", 27001), new ServerAddress("localhost", 27002)));

The constructor takes a list of com.mongodb.ServerAddress. This class has a lot of overloaded constructors but we choose to use the one that takes the hostname and then port. What we have done is provided all the server details in a replica set as a list. We haven't mentioned what is the PRIMARY node and what are the SECONDARY nodes. MongoClient is intelligent enough to figure this out and connect to the appropriate instance. The list of servers provided is called the seed list. It need not contain an entire set of servers in a replica set though the objective is to provide as much as we can. MongoClient will figure out all the server details from the provided subset. For example, if the replica set is of five nodes but we provide only three servers, it works fine. On connecting with the provided replica set servers, the client will query them to get the replica set metadata and figure out the rest of the provided servers in the replica set. In the preceding case, we instantiated the client with three instances in the replica set. If the replica set was to have five members, then instantiating the client with just three of them is still good enough and the remaining two instances will be automatically discovered.

Next, we start the client from the command prompt using maven. Once the client is running in the loop, we bring down the primary instance to find one document. We should see something as the following output to the console:

_id: 1, value is abc
Now Retrieving documents in a loop from the collection.
Stop the primary instance manually after few iterations
_id: 1, value is abc
_id: 1, value is abc
Nov 03, 2013 5:21:57 PM com.mongodb.ConnectionStatus$UpdatableNode update
WARNING: Server seen down: Amol-PC/192.168.1.171:27002
java.net.SocketException: Software caused connection abort: recv failed
        at java.net.SocketInputStream.socketRead0(Native Method)
        at java.net.SocketInputStream.read(SocketInputStream.java:150)

WARNING: Primary switching from Amol-PC/192.168.1.171:27002 to Amol-PC/192.168.1.171:27001
_id: 1, value is abc

As we can see, the query in the loop was interrupted when the primary node went down. However, the client switched to the new primary seamlessly. Well, nearly seamlessly, as the client might have to catch an exception and retry the operation after a predetermined interval has elapsed.

 

Connecting to the replica set to query and insert data using a Python client


In this recipe, we will demonstrate how to connect to a replica set using a Python client and how the client would automatically failover to another node in the replica set, should a primary node fail.

Getting ready

Refer to the Connecting to the single node using a Python client recipe as it describes how to set up and install PyMongo, the Python driver for MongoDB. Additionally, a replica set must be up and running. Refer to the Starting multiple instances as part of a replica set recipe for details on how to start the replica set.

How to do it…

  1. Write/copy the following piece of code to replicaset_client.py: (This script is also available for download from the Packt website.)

    from __future__ import print_function
    import pymongo
    import time
    
    # Instantiate MongoClient with a list of server addresses
    client = pymongo.MongoClient(['localhost:27002', 'localhost:27001', 'localhost:27000'], replicaSet='repSetTest')
    
    # Select the collection and drop it before using
    collection = client.test.repTest
    collection.drop()
    
    #insert a record in
    collection.insert_one(dict(name='Foo', age='30'))
    
    for x in range(5):
        try:
            print('Fetching record: %s' % collection.find_one())
        except Exception as e:
            print('Could not connect to primary')
        time.sleep(3)
  2. Connect to any of the nodes in the replica set, say to localhost:27000, and execute rs.status() from the shell. Take a note of the primary instance in the replica set and connect to it from the shell, if localhost:27000 is not a primary. Here, switch to the administrator database as follows:

    > repSetTest:PRIMARY>use admin
    
  3. We now execute the preceding script from the operating system shell as follows:

    $ python replicaset_client.py
    
  4. Shut down the primary instance by executing the following on the mongo shell that is connected to the primary:

    > repSetTest:PRIMARY> db.shutdownServer()
    
  5. Watch the output on the console where the Python script is executed.

How it works…

You will notice that, in this script, we instantiated the mongo client by giving a list of hosts instead of a single host. As of version 3.0, the pymongo driver's MongoClient() class can accept either a list of hosts or a single host during initialization and deprecate MongoReplicaSetClient(). The client will attempt to connect to the first host in the list, and if successful, will be able to determine the other nodes in the replica set. We are also passing the replicaSet='repSetTest' parameter exclusively, ensuring that the client checks whether the connected node is a part of this replica set.

Once connected, we perform normal database operations such as selecting the test database, dropping the repTest collection, and inserting a single document into the collection.

Following this, we enter a conditional for loop, iterating five times. Each time, we fetch the record, display it, and sleep for three seconds. While the script is in this loop, we shut down the primary node in the replica set as mentioned in step 4. We should see an output similar to this:

Fetching record: {u'age': u'30', u'_id': ObjectId('5558bfaa0640fd1923fce1a1'), u'name': u'Foo'}
Fetching record: {u'age': u'30', u'_id': ObjectId('5558bfaa0640fd1923fce1a1'), u'name': u'Foo'}
Fetching record: {u'age': u'30', u'_id': ObjectId('5558bfaa0640fd1923fce1a1'), u'name': u'Foo'}
Could not connect to primary
Fetching record: {u'age': u'30', u'_id': ObjectId('5558bfaa0640fd1923fce1a1'), u'name': u'Foo'}

In the preceding output, the client gets disconnected from the primary node midway. However, very soon, a new primary node is selected by the remaining nodes and the mongo client is able to resume the connection.

 

Starting a simple sharded environment of two shards


In this recipe, we will set up a simple sharded setup made up of two data shards. There will be no replication configured as this is the most basic shard setup to demonstrate the concept. We won't be getting deep into the internals of sharding, which we will explore more in the administration section.

Here is a bit of theory before we proceed. Scalability and availability are two important cornerstones to build any mission-critical application. Availability is something that was taken care of by the replica sets, which we discussed in previous recipes in this chapter. Let's look at scalability now. Simply put, scalability is the ease with which the system can cope with increasing data and request load. Consider an e-commerce platform. On regular days, the number of hits to the site and load is fairly modest and the system's response times and error rates are minimal. (This is subjective.) Now, consider the days where the system load becomes twice, thrice, or even more than that of an average day's load, say on Thanksgiving day, Christmas, and so on. If the platform is able to deliver similar levels of service on these high load days as on any other day, the system is said to have scaled up well to the sudden increase in the number of requests.

Now, consider an archiving application that needs to store the details of all the requests that hit a particular website over the past decade. For each request hitting the website, we create a new record in the underlying data store. Suppose that each record is of 250 bytes with an average load of three million requests per day, we will cross 1 TB of the data mark in about five years. This data would be used for various analytics purposes and might be frequently queried. The query performance should not be drastically affected when the data size increases. If the system is able to cope with this increasing data volume and still give decent performance comparable to performance on low data volumes, the system is said to have scaled up well.

Now that we have seen in brief what scalability is, let me tell you that sharding is a mechanism that lets a system scale to increasing demands. The crux lies in the fact that the entire data is partitioned into smaller segments and distributed across various nodes called shards. Suppose that we have a total of 10 million documents in a mongo collection. If we shard this collection across 10 shards, then we will ideally have 10,000,000/10 = 1,000,000 documents on each shard. At a given point of time, only one document will reside on one shard (which by itself will be a replica set in a production system). However, there is some magic involved that keeps this concept hidden from the developer who is querying the collection and who gets one unified view of the collection irrespective of the number of shards. Based on the query, it is mongo that decides which shard to query for the data and returns the entire result set. With this background, let's set up a simple shard and take a closer look at it.

Getting ready

Apart from the MongoDB server already installed, no prerequisites are there from a software perspective. We will be creating two data directories, one for each shard. There will be a directory for the data and one for logs.

How to do it…

  1. We start by creating directories for the logs and data. Create the following directories, /data/s1/db, /data/s2/db, and /logs. On Windows, we can have c:\data\s1\db and so on for the data and log directories. There is also a configuration server that is used in the sharded environment to store some metadata. We will use /data/con1/db as the data directory for the configuration server.

  2. Start the following mongod processes, one for each of the two shards, one for the configuration database, and one mongos process. For the Windows platform, skip the --fork parameter as it is not supported.

    $ mongod --shardsvr --dbpath  /data/s1/db --port 27000 --logpath /logs/s1.log --smallfiles --oplogSize 128 --fork
    $ mongod --shardsvr --dbpath  /data/s2/db --port 27001 --logpath /logs/s2.log --smallfiles --oplogSize 128 --fork
    $ mongod --configsvr --dbpath  /data/con1/db --port 25000 --logpath  /logs/config.log --fork
    $ mongos --configdb localhost:25000 --logpath  /logs/mongos.log --fork
    
  3. From the command prompt, execute the following command. This should show a mongos prompt as follows:

    $ mongo
    MongoDB shell version: 3.0.2
    connecting to: test
    mongos>
    
  4. Finally, we set up the shard. From the mongos shell, execute the following two commands:

    mongos> sh.addShard("localhost:27000")
    mongos> sh.addShard("localhost:27001")
    
  5. On each addition of a shard, we should get an ok reply. The following JSON message should be seen giving the unique ID for each shard added:

    { "shardAdded" : "shard0000", "ok" : 1 }
    

    Note

    We used localhost everywhere to refer to the locally running servers. It is not a recommended approach and is discouraged. The better approach would be to use hostnames even if they are local processes.

How it works…

Let's see what all we did in the process. We created three directories for data (two for the shards and one for the configuration database) and one directory for logs. We can have a shell script or batch file to create the directories as well. In fact, in large production deployments, setting up shards manually is not only time-consuming but also error-prone.

Tip

Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

Let's try to get a picture of what exactly we have done and are trying to achieve. The following is an image of the shard setup that we just did:

If we look at the preceding image and the servers started in step 2, we have shard servers that would store the actual data in the collections. These were the first two of the four processes that we started listening to ports 27000 and 27001. Next, we started a configuration server that is seen on the left side in this image. It is the third server of the four servers started in step 2 and it listens to port 25000 for the incoming connections. The sole purpose of this database is to maintain the metadata about the shard servers. Ideally, only the mongos process or drivers connect to this server for the shard details/metadata and the shard key information. We will see what a shard key is in the next recipe, where we play around a sharded collection and see the shards that we have created in action.

Finally, we have a mongos process. This is a lightweight process that doesn't do any persistence of data and just accepts connections from clients. This is the layer that acts as a gatekeeper and abstracts the client from the concept of shards. For now, we can view it as basically a router that consults the configuration server and takes the decision to route the client's query to the appropriate shard server for execution. It then aggregates the result from various shards if applicable and returns the result to the client. It is safe to say that no client connects directly to the configuration or shard servers; in fact, no one ideally should connect to these processes directly except for some administration operations. Clients simply connect to the mongos process and execute their queries and insert or update operations.

Just starting the shard server, configuration server, and mongos process doesn't create a sharded environment. On starting up the mongos process, we provided it with the details of the configuration server. What about the two shards that would be storing the actual data? However, the two mongod processes started as shard servers are not yet declared anywhere as shard servers in the configuration. This is exactly what we do in the final step by invoking sh.addShard() for both the shard servers. The mongos process is provided with the configuration server's details on startup. Adding shards from the shell stores this metadata about the shards in the configuration database, and the mongos processes then would be querying this config database for the shard's information. On executing all the steps of the recipe, we have an operational shard as follows:

Before we conclude, the shard that we have set up here is far from ideal and not how it would be done in a production environment. The preceding image gives us an idea of how a typical shard would be in a production environment. The number of shards would not be two but many more. Additionally, each shard will be a replica set to ensure high availability. There would be three configuration servers to ensure availability of the configuration servers as well. Similarly, there will be any number of mongos processes created for a shard listening for client connections. In some cases, it might even be started on a client application's server.

There's more…

What good is a shard unless we put it to action and see what happens from the shell on inserting and querying the data? In the next recipe, we will make use of the shard setup here, add some data, and see it in action.

 

Connecting to a shard in the shell and performing operations


In this recipe, we will connect to a shard from a command prompt, see how to shard a collection, and observe the data splitting in action on some test data.

Getting ready

Obviously, we need a sharded mongo server setup up and running. See the previous recipe, Starting a simple sharded environment of two shards, for more details on how to set up a simple shard. The mongos process, as in the previous recipe, should be listening to port number 27017. We have got some names in a JavaScript file called names.js. This file needs to be downloaded from the Packt website and kept on the local filesystem. The file contains a variable called names and the value is an array with some JSON documents as the values, each one representing a person. The contents look as follows:

names = [
  {name:'James Smith', age:30},
  {name:'Robert Johnson', age:22},
…
]

How to do it…

  1. Start the mongo shell and connect to the default port on localhost as follows. This will ensure that the names will be available in the current shell:

    mongo --shell names.js
    MongoDB shell version: 3.0.2
    connecting to: test
    mongos>
    
  2. Switch to the database that would be used to test the sharding; we call it shardDB:

    mongos> use shardDB
    
  3. Enable sharding at the database level as follows:

    mongos> sh.enableSharding("shardDB")
    
  4. Shard a collection called person as follows:

    mongos>sh.shardCollection("shardDB.person", {name: "hashed"}, false)
    
  5. Add the test data to the sharded collection:

    mongos> for(i = 1; i <= 300000 ; i++) {
    ... person = names[Math.round(Math.random() * 100) % 20]
    ... doc = {_id:i, name:person.name, age:person.age}
    ... db.person.insert(doc)
    }
    
  6. Execute the following to get a query plan and the number of documents on each shard:

    mongos> db.person.getShardDistribution()
    

How it works…

This recipe demands some explanation. We downloaded a JavaScript file that defines an array of 20 people. Each element of the array is a JSON object with the name and age attributes. We start the shell connecting to the mongos process loaded with this JavaScript file. We then switch to shardDB, which we use for the purpose of sharding.

For a collection to be sharded, the database in which it will be created needs to be enabled for the sharding first. We do this using sh.enableSharding().

The next step is to enable the collection to be sharded. By default, all the data will be kept on one shard and not split across different shards. Think about it; how will Mongo be able to split the data meaningfully? The whole intention is to split it meaningfully and as evenly as possible so that whenever we query based on the shard key, Mongo would easily be able to determine which shard(s) to query. If a query doesn't contain the shard key, the execution of the query will happen on all the shards and the data would then be collated by the mongos process before returning it to the client. Thus, choosing the right shard key is very crucial.

Let's now see how to shard the collection. We do this by invoking sh.shardCollection("shardDB.person", {name: "hashed"}, false). There are three parameters here:

  • The fully qualified name of the collection in the <db name>.<collection name> format is the first parameter of the shardCollection method.

  • The second parameter is the field name to shard on in the collection. This is the field that would be used to split the documents on the shards. One of the requirements of a good shard key is that it should have high cardinality. (The number of possible values should be high.) In our test data, the name value has very low cardinality and thus is not a good choice as a shard key. We hash this key when using this as a shard key. We do so by mentioning the key as {name: "hashed"}.

  • The last parameter specifies whether the value used as the shard key is unique or not. The name field is definitely not unique and thus it will be false. If the field was, say, the person's social security number, it could have been set as true. Additionally, SSN is a good choice for a shard key due to its high cardinality. Remember that the shard key has to be present for the query to be efficient.

The last step is to see the execution plan for the finding of all the data. The intent of this operation is to see how the data is being split across two shards. With 300,000 documents, we expect something around 150,000 documents on each shard. However, from the distribution statistics, we can observe that shard0000 has 1,49,715 documents whereas shard0001 has 150285:

Shard shard0000 at localhost:27000
 data : 15.99MiB docs : 149715 chunks : 2
 estimated data per chunk : 7.99MiB
 estimated docs per chunk : 74857

Shard shard0001 at localhost:27001
 data : 16.05MiB docs : 150285 chunks : 2
 estimated data per chunk : 8.02MiB
 estimated docs per chunk : 75142

Totals
 data : 32.04MiB docs : 300000 chunks : 4
 Shard shard0000 contains 49.9% data, 49.9% docs in cluster, avg obj size on shard : 112B
 Shard shard0001 contains 50.09% data, 50.09% docs in cluster, avg obj size on shard : 112B

There are a couple of additional suggestions that I would recommend you to do.

Connect to the individual shard from the mongo shell and execute queries on the person collection. See that the counts in these collections are similar to what we see in the preceding plan. Additionally, one can find out that no document exists on both the shards at the same time.

We discussed in brief about how cardinality affects the way the data is split across shards. Let's do a simple exercise. We first drop the person collection and execute the shardCollection operation again but, this time, with the {name: 1} shard key instead of {name: "hashed"}. This ensures that the shard key is not hashed and stored as is. Now, load the data using the JavaScript function we used earlier in step number 5, and then execute the explain() command on the collection once the data is loaded. Observe how the data is now split (or not) across the shards.

There's more…

A lot of questions must now be coming up such as what are the best practices? What are some tips and tricks? How is the sharding thing pulled off by MongoDB behind the scenes in a way that is transparent to the end user?

This recipe here only explained the basics. In the administration section, all such questions will be answered.

About the Authors

  • Cyrus Dasadia

    Cyrus Dasadia has enjoyed tinkering with open source projects since 1996. He has been working as a Linux system administrator and part-time programmer for over a decade. He works at InMobi, where he loves designing tools and platforms. His love for MongoDB blossomed in 2013, when he was amazed by its ease of use and stability. Since then, almost all of his projects have been written with MongoDB as the primary backend. Cyrus is also the creator of an open source alert management system called CitoEngine. His spare time is devoted to trying to reverse-engineer software, playing computer games, or increasing his silliness quotient by watching reruns of Monty Python.

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  • Amol Nayak

    Amol Nayak is a MongoDB certified developer and has been working as a developer for over 8 years. He is currently employed with a leading financial data provider, working on cutting-edge technologies. He has used MongoDB as a database for various systems at his current and previous workplaces to support enormous data volumes. He is an open source enthusiast and supports it by contributing to open source frameworks and promoting them. He has made contributions to the Spring Integration project, and his contributions are the adapters for JPA, XQuery, MongoDB, Push notifications to mobile devices, and Amazon Web Services (AWS). He has also made some contributions to the Spring Data MongoDB project. Apart from technology, he is passionate about motor sports and is a race official at Buddh International Circuit, India, for various motor sports events. Earlier, he was the author of Instant MongoDB, Packt Publishing.

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