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Working with Index in MongoDB

In every database management system indexing is an essential attribute. Without indexing the system has to search the whole database in case of every queries. Which can prove very problematic. And in case of mongodb where we use blob data, can be disastrous with that much data. So, in this article we are going to discuss about indexes.

What is index in mongodb?

Index in mongodb is a special data structure that store a small portion of the collection’s data set in an easy to traverse form. The index stores the value of a specific field or set of fields, ordered by the value of the field. Index supports the efficient execution of queries in mongodb.

To implement index we need to use a method called ensureIndex(). Let us work through a  basic query to implement index:


db.collection_name.ensureIndex({KEY:1})

In the above query, ‘KEY’ is the name of field on which index should be created and 1 is for ascending order. To create index in descending order use -1.

ensureIndex() also supports many option,although those are optional, we should have a basic idea.

Background:
Background builds the index in the background so that building an index does not block other database activities. The default value is false.For example the query looks like


db.collection.ensureIndex( { a: 1 }, { background: true } )

Unique:
Unique creates a unique index so that the collection will not accept insertion of documents where the index key or keys match an existing value in the index. For example the query looks like


db.collection.ensureIndex( { a: 1 }, { unique: true } )

Drop Duplicates:
Mongodb cannot create a unique index on a field that has duplicate values. To forcefully create the unique index, dropDups option can help us. Which will only index the first occurrence of a value for the key, and delete all subsequent values. The basic query looks like


db.accounts.ensureIndex( { username: 1 }, { unique: true, dropDups: true } )

Name:
The name of the index. If unspecified, MongoDB generates an index name by concatenating the names of the indexed fields and the sort order.For example the query looks like


db.collection.ensureIndex(
   {
     content: "text",
     "users.comments": "text"
   },
   {
     name: "MyTextIndex"
   }
)

Sparse:
These indexes use less space but behave differently in some situations. The default value is false.
For example the query looks like


db.collection.ensureIndex( { a: 1 }, { sparse: true } )

Hashed:
Hashed indexes compute a hash of the value of a field in a collection and index the hashed value. These indexes permit equality queries. Mongodb supports hashed indexes to any single field. The hashing function collapses sub-documents and computes the hash for the entire value. But it is unable to support multi-key indexes. The basic query for creating a hashed index is:


db.collection.ensureIndex( { _id: "hashed" } ) 

Compound:
Mongodb supports compound indexes, where a single index structure holds references to multiple fields within a collection’s documents. Compound indexes can support queries that match on multiple fields. Let us view the basic query:


db.collection.ensureIndex( { a: 1, b: 1, c: 1 } )

Index Intersection in MongoDB:

Index intersection involves the use of multiple indexes to satisfy a query. In shorter terms, each index intersection involves two indexes. But mongdb can employ multiple index intersections to resolve a query. As a basic query we can look at:


db.collection.find( { a: "abc123", i: { $gt: 15 } } )

Mongodb can use an intersection of either the entire index or the index prefix. Index prefix is a subset of a compound index. It consists of one or more keys starting from the beginning of the index. In the following query which specifies a condition on both the q field and the s field, mongodb can use the intersection of the two indexes:


db.collection.find( { q: { $gt: 10 } , s: "A" } )

Difference between compound index and index intersection:

Compound Index

Index Intersection

compound indexes are indexes on multiple fields.

Index intersection involves the use of multiple indexes to satisfy a query.

Index intersection provides flexibility if there isn’t a suitable compound index to cover all of the common queries.

compound index may be more selective and be able to find results inspecting fewer index entries.

Although we have discussed about the basics of indexing in this article. There are still place for the growth. We can use index in many way to make our quarries more efficient.

Related Links:

1> How to Get Started with MongoDB Database?
2> How to Get Started with MongoDB?
3> How to Import and Export Through Mongodb?
4> How to Use Projection in MongoDB?
5> Using sort method in mongodb
6> Map-Reduce in MongoDB
7> Introduction to Replication in MongoDB
8> Deploying a Replica Set in MongoDB
9> Discussing Replication Lag in MongoDB
10> Replica Set Members in Mongodb
11> Working with Sharding in MongoDB
12> Working with Aggregation in MongoDB
13> How to Work with Aggregation Framework in MongoDB?
14> Working with Pipeline Concept in MongoDB
15> Discussing about Pipeline Expression in MongoDB

If you find this article helpful, you can connect us in Google+ and Twitter.

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Working with Sharding in MongoDB

Mongodb is a data management system for blob data. So that means we can manage huge amounts of data through mongodb. So,storing this vast amount of data is the most challenging thing to do. When a single machine is unable to store this data or unable to provide an acceptable read and write throughput, problems begins to rise.
To deal with this problem mongodb provides us with a unique feature called sharding. We will be discussing about this matter below.

What is sharding in mongodb?

As per mongodb.org sharding in mongodb is a method for storing data across multiple machines. Mongodb uses sharding to support deployments with very large data sets and high throughput operations.

Why should we use sharding in mongodb?

  • In replication all writes go to master node
  • Latency sensitive queries still go to master
  • Single replica set has limitation of 12 nodes
  • Memory can’t be large enough when active dataset is big
  • Local Disk is not big enough
  • Vertical scaling is too expensive

When you analyze the above set of problems. You can see that we have to deal with this kind of problem every now and then. To solve these issues database systems have two basic approaches namely vertical scaling and sharding.

Vertical scaling:
Vertical scaling adds more CPU and storage resources to increase capacity. This method has it’s own limitations. High performance systems with large numbers of CPUs and large amount of RAM are more expensive than smaller systems. On the other hand cloud-based systems may only allow users for smaller instances.so,we can say that there is a maximum capability for vertical scaling.

Sharding:
Sharding or horizontal scaling divides the data set and distributes the data over multiple servers, or shards. Each shard is an independent database, and collectively, the shards make up a single logical database. Sharding supports high throughput and large data sets:

Sharding reduces the number of operations each shard handles. Each shard processes fewer operations as the cluster grows. As a result, a cluster can increase capacity and throughput horizontally.

Sharding reduces the amount of data that each server needs to store. Each shard stores less data as the cluster grows.

As it shows in the diagram below:

sharding

Sharding in mongodb:

Mongdb sharding supports sharding through the configuration of a sharded clusters. Sharded cluster consists of the following components: shards, query routers and config servers.

Shards:
Mongodb uses shards to store the data. To provide high availability and data consistency each shard represents a replica set.

Query Routers:
Query Routers interface with direct operations to the appropriate shard. shards can contain more than one query router to divide the client request load. A client sends requests to one query router.

Config server:
Config servers store the cluster’s metadata. This data contains a mapping of the cluster’s data set to the shards. The query router uses this metadata to target operations to specific shards.

The diagram below will help you to understand better.

sharding in mongodb

Data Partitioning:

In mongodb sharding collection is broken into many shards and act as individual collections. But how does mongodb breaks the collection in many parts? We are going to discuss about this in data partitioning. Mongodb creates a balanced cluster using two background process: splitting and the balancer.

Splitting:
Splitting is a background process that keeps chunks of data from growing too large. When a chunk of data grows beyond a specified limit, mongodb splits it in half. In time of splitting none of the available shards are affected.Inserts and updates triggers splits. Splits are a efficient meta-data change. In case of splitting none of the data is migrated.

Balancing:
The balancer is a background process that manages data chunk migrations. The balancer runs in all of the query routers in a cluster. When the distribution of a sharded collection in a cluster is uneven, the balancer migrates data from the shard that has the largest number of chunks to the shard with the least number of chunks until the collection balances. If there’s an error during the migration, the balancer aborts the process leaving the chunk unchanged on the origin shard. MongoDB removes the chunk’s data from the origin shard after the migration completes successfully.

The shards manage data migrations as a background operation between an origin shard and a destination shard. During migration, the destination shard is sent all the current documents in the chunk from the origin shard. After this the destination shard captures and applies all changes made to the data during the migration process. Finally, the metadata regarding the location of the chunk on config server is updated.

As we have discussed about the process of sharding in mongodb,there is also a choice to add or remove a certain shard. But this approach is a bit trickier than it sounds. Because adding a blank shard will dis-balance the system until data is migrated from other shards and balanced out. Same goes for the removal process also. In case of removal the data in specified shards has to be migrated and balanced out through the remaining shards. After completion of this process one can remove that certain shard.

Related Links:

1> How to Get Started with MongoDB Database?
2> How to Get Started with MongoDB?
3> How to Import and Export Through Mongodb?
4> How to Use Projection in MongoDB?
5> Using sort method in mongodb
6> Map-Reduce in MongoDB
7> Introduction to Replication in MongoDB
8> Deploying a Replica Set in MongoDB
9> Discussing Replication Lag in MongoDB
10> Replica Set Members in Mongodb
11> Working with Index in MongoDB
12> Working with Aggregation in MongoDB
13> How to Work with Aggregation Framework in MongoDB?
14> Working with Pipeline Concept in MongoDB
15> Discussing about Pipeline Expression in MongoDB

If you find this article helpful, you can connect us in Google+ and Twitter.

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Replica Set Members in Mongodb

In the previous articles we have discussed many aspects of replica set in mongodb. And in those articles we have talked many things about members. So, what are these members? What is their purpose? Let us discuss about this things in this article.

What are members in mongodb?

In short terms the members in mongodb are the mongod processes which need to be executed in replica set. Now, in general there are only 2 members
Primary:
As per mongodb.org the primary members receive all write operations.
Secondary:
Secondaries replicate operations from the primary to maintain an identical data set. Secondaries may have additional configurations for special usage profiles.
We can use maximum of 12 members in a replica set. From which only 7 can vote. So now the question arises why do members need to vote?

Selection for primary member:

Whenever a replica set is initiated or a primary member is unreachable. Or in simple terms if there is no primary member present then the election is commenced to choose a primary member from the secondary members.
Although there are a few types of members than before 2, we will talk about them later.
Primary member:
The primary member is the only member in the replica set that receives write operations. Mongodb applies write operations on the primary and then records the operations on the primary’s oplog. All members of the replica set can accept read operations. But, by default an application directs its read operations to the primary member. The replica set can have at most one primary.
In the following three-member replica set, the primary accepts all write operations. Then the secondaries replicate the oplog to apply to their data sets.

primare_mem.jpg
Secondary member:
A secondary member maintains a copy of the primary’s data set. To replicate data, a secondary applies operations from the primary’s oplog to its own data set in an asynchronous process. A replica set can have one or more secondary member. Data can’t be written to secondary, but data can be read from secondary members. In case of the primary member’s absence a secondary member can be primary through election.
In the following three-member replica set, secondary member copies the primary member.

secondary_mem.jpg
Hidden members:
Except the before two members, there are other members that comes into a replica set. One of them is a hidden member. Hidden members cannot become primary and are invisible to client applications. Hidden members do vote in elections. Hidden members are good for workloads with different usage patterns from the other members in the replica set. Also they must always be priority 0 members and so they cannot become primary.
The most common use of hidden nodes is to support delayed members. To configure a secondary member as hidden, set its priority value to 0 and set its hidden value to true in its member configuration.
To configure a hidden member, use the following sequence in a mongo shell connected to the primary, specifying the member to configure by its array index in the members array:


c.members[0].priority = 0
c.members[0].hidden = true

Delayed member:
Another member is delayed member. Delayed members also copies data from the dataset. But as the name suggests the copied dataset is delayed than actual timing. As for example we can say that if we have an application to determine the current time. Then if the current time is 09:00 and a member has a delay of an hour, the delayed member has no operation more recent than 08:00.
Because delayed members are a “rolling backup” or a running “historical” snapshot of the data set, they may be of help to recover from various kinds of human error. Delayed members apply operations from the oplog on a delay.
Must be is equal to or greater than your maintenance windows.
Must be smaller than the capacity of the oplog. For more information on oplog size.
To configure a delayed secondary member, set its priority value to 0, its hidden value to true, and its slaveDelay value to the number of seconds to delay.


c.members[0].priority = 0
c.members[0].hidden = true
c.members[0].slaveDelay = 1200

Non-voting members:
There is a lot of talk about election in replica sets. So, in the election few of the members participate and give votes to determine primary member. But there are also a few members, who do not participate in voting. These members are called non-voting members. Non-voting members allow you to add additional members for read distribution beyond the maximum seven voting members.
To configure a member as non-voting, set its votes value to 0.


c.members[5].votes = 0
c.members[4].votes = 0

Priority in members:

These are the basic few members that we have to keep in mind for replica sets. To get here we have seen many operations as priority. Priority is indeed a very important thing to discuss about. The priority settings of replica set members affect the outcomes of elections for primary. The value of the member’s priority setting determines the member’s priority in elections. The higher the number, the higher the priority.

Configuring member priority:

To modify priorities, we have to update the members array in the replica configuration object. The value of priority can be any floating point number between 0 and 1000. The default value for the priority field is 1.Adjust priority during a scheduled maintenance window. Reconfiguring priority can force the current primary to step down, leading to an election. To block a member from seeking election as primary, assign the priority of that member to 0.
We can complete configuring priority in simple 3 steps. Let us look at them:

  • Copy the replica set configuration to a variable.

In the mongo shell, use rs.conf() to retrieve the replica set configuration and assign it to a variable. For example:


c = rs.conf()
  • Change each member’s priority value.

Change each member’s priority value, as configured in the members array.


c.members[0].priority = 0.5
c.members[1].priority = 2

This sequence of operations modifies the value of c to set the priority for the first two members defined in the members array.

  • Assign the replica set the new configuration.

Use rs.reconfig() to apply the new configuration.


rs.reconfig(c)

In this article we have discussed about members in replica set. We can say now that members are the very basis of replica sets. The more we will get to know about the members. Our control will be swifter in handling data in mongodb.

Related Links:

1> How to Get Started with MongoDB Database?
2> How to Get Started with MongoDB?
3> How to Import and Export Through Mongodb?
4> How to Use Projection in MongoDB?
5> Using sort method in mongodb
6> Map-Reduce in MongoDB
7> Introduction to Replication in MongoDB
8> Deploying a Replica Set in MongoDB
9> Discussing Replication Lag in MongoDB
10> Working with Sharding in MongoDB
11> Working with Index in MongoDB
12> Working with Aggregation in MongoDB
13> How to Work with Aggregation Framework in MongoDB?
14> Working with Pipeline Concept in MongoDB
15> Discussing about Pipeline Expression in MongoDB

If you find this article helpful, you can connect us in Google+ and Twitter.

Discussing Replication Lag in MongoDB

In the previous article we have read about deployment of replica sets in mongodb. In the deployment process there is some complications that we did not discuss in the previous article. In this article we will get acknowledged with those complications and try to make them less complicated.
So, in this article we are going to talk about replication lag. By the name everybody can understand we are going to discuss the delay that occurs in replica set. Then let us get into the topic:

What is replication lag in mongodb?

As per mongodb.org replication lag in mongodb is a delay between an operation on the primary and the application of that operation from the oplog to the secondary. Replication lag can be a significant issue and can seriously affect mongodb replica set deployments.
In a perfectly running replica set, all secondaries closely follow changes on the primary, fetching each group of operations from its oplog and replaying them approximately as fast as they occur. however, secondaries may fall behind. Sometimes elevated replication lag is transient and will remedy itself without intervention. Other times, replication lag remains high or continues to rise, indicating a systemic problem that needs to be addressed.
Now we know what replication lag is. But why it is so problematic, that we have to deal with to get good performance.

Why replication lag poses problems?

  • If replica set fails over to a secondary that is significantly behind the primary, a lot of un-replicated data may be on the original primary that will need to be manually reconciled.
  • If the failed primary cannot be recovered quickly,we may be forced to run on a node whose data is not up-to-date, or forced to take down database altogether until the primary can be recovered.
  • If there is only one secondary, and it falls farther behind than the earliest history retained in the primary’s oplog, the secondary will require a full resynchronization from the primary.
  • During the resync, cluster will lack the redundancy of a valid secondary; the cluster will not return to high availability until the entire data set is copied.
  • If backups are only taken from your, backups must be suspended for the duration of the resync.
  • Replication lag makes it more likely that results of any read operations distributed across secondaries will be inconsistent.

So, now we know that replication lag can poses very serious problems for the developer, which are recoverable. Although in some cases the data may not be recovered as well. Let us see the causes for this phenomena.

Possible causes for replication lag:

Network Latency
Check the network routes between the members of your set to ensure that there is no packet loss or network routing issue.
Disk Throughput
If the file system and disk device on the secondary is unable to flush data to disk as quickly as the primary, then the secondary will have difficulty keeping state.
Concurrency
In some cases, long-running operations on the primary can block replication on secondaries. For best results, configure write concern to require confirmation of replication to secondaries. Use the database profiler to see if there are slow queries or long-running operations that correspond to the incidences of lag.
 Write Concern
Performing a large data ingestion or bulk load operation that requires a large number of writes to the primary. The secondaries will not be able to read the oplog fast enough to keep up with changes.
Map/reduce output


db.coll.mapReduce( ... { out: other_coll ... })

From the point of view of the oplog, the entire output collection basically materializes at once, from which point the replication to the secondary plays out as above.
Index
If there is an index built in the background on the primary, it will be built in the foreground on each secondary. Therefore, whenever a secondary builds an index, it will block all other operations, including replication, for the duration. If the index builds quickly, this may not be a problem; but long-running index builds can end up to creating replication lag.
Secondary is locked for backup
One of the suggested methods for backing up data in a replica set involves explicitly locking a secondary against changes while the backup is taken. Assuming the primary is still conducting business as usual, of course replication lag will climb until the backup is complete and the lock is released.
Secondary is offline
If the secondary is not running. It cannot make progress against the replication backlog. When it rejoins the replica set, the replication lag will naturally reflect the time spent away.
Although we have the causes for replication lag, we still don’t have a perfect solution. But there is a process by which we can determine the replication lag length. This could be useful for further progress.

How to check the replication lag length?

  • In a mongo shell connected to the primary, call the rs.printSlaveReplicationInfo() method. Returns the syncedTo value for each member, which shows the time when the last oplog entry was written to the secondary. A delayed member may show as 0 seconds behind the primary when the inactivity period on the primary is greater than the slaveDelay value.
  • We can also get the length from replica graph in mongodb management services.

Although we have discussed about the replication lag in the above, we just have the means to check out the length and go for minimum lag related quarries. But due to the law of physics we can not remove the replication lag permanently. We can only try to figure out new efficient methods to minimize the replication lag.

Related Links:

1> How to Get Started with MongoDB Database?
2> How to Get Started with MongoDB?
3> How to Import and Export Through Mongodb?
4> How to Use Projection in MongoDB?
5> Using sort method in mongodb
6> Map-Reduce in MongoDB
7> Introduction to Replication in MongoDB
8> Deploying a Replica Set in MongoDB
9> Replica Set Members in Mongodb
10> Working with Sharding in MongoDB
11> Working with Index in MongoDB
12> Working with Aggregation in MongoDB
13> How to Work with Aggregation Framework in MongoDB?
14> Working with Pipeline Concept in MongoDB
15> Discussing about Pipeline Expression in MongoDB

If you find this article helpful, you can connect us in Google+ and Twitter.

Deploying a Replica Set in MongoDB

In this article we are going to discuss about how to deploy a replica set in mongo db.

Deploying a replica set:

Replica set can be deployed from single instances as well as from multiple instances. To get there we have to figure out how replica set function. As stated in mongodb.org replica set is a cluster of mongod instances that replicate amongst one another and ensure automated failover.
Although these replica sets may be consist of more than one instance. One of them is has to be the primary instance, which will be in direct contact with the client. Mongo dB’s replica sets provide automated failover. If a primary fails, the remaining members will automatically try to elect a new primary. A replica set can have up to 12 members, but only 7 members can have votes.

Replica set member properties.

Secondary-Only: These members have data but cannot become primary under any circumstance. See Secondary-Only Members.
Hidden: These members are invisible to client applications. See Hidden Members.
Delayed: These members apply operations from the primary’s oplog after a specified delay. You can think of a delayed member as a form of “rolling backup.” See Delayed Members.
Arbiters: These members have no data and exist solely to participate in elections. See Arbiters.
Non-Voting: These members do not vote in elections. Non-voting members are only used for larger sets with more than 7 members. See Non-Voting Members.

Member selection in failover:

When any failover occurs, an election takes place to decide which member should become primary. Elections provide a mechanism for the members of a replica set to automatically select a new primary without administrator’s permission. The election allows replica sets to recover from failover situations very quickly and robustly.
Whenever the primary becomes unreachable, the secondary members trigger an election. The first member to receive votes from a majority of the set will become primary. The most important feature of replica set elections is that a majority of the original number of members in the replica set must be present for election to succeed.
There is an absolute rule in election for maximum connectivity. In simple terms whenever a primary member becomes unreachable 2/3 of the secondary member has to be connected to each other for voting. If this condition does not match, then there should be no election held and therefore no primary members should be selected.

Replication set member priority:

In a replica set, every member has a “priority,” that helps determine eligibility for election to primary. By default, all members have a priority of 1, if the priority value has not been modified. All members have a single vote in elections.
Now let us talk about how to deploy replica set from a single instance. Which in mongo dB terms are called standalone mongod instance.
Deploying a replica set (standalone mongod instance):

  • At first we need to shut down the instance for further progress.
  • Restart the instance using

mongod --port port_number --dbpath --replSet replSet_name
  • Connect to the mongod instance.
  • Use rs.initiate() to initiate the new replica set

rs.initiate()

After this we have to add some instances. To do that let us look at the following steps.

  • On two distinct systems, start two new standalone mongod instances.
  • On the preferable connection to the original mongod instance, issue a command for each new instance to add to the replica set.

rs.add("")

Now as for deploying replica set of multiple instances, at the first we have to check the connectivity with every instance for no further disturbance. To make it easier we can use a private virtual network. Let us see how it is done.

Ensuring connectivity:

    • Establish a virtual private network. Ensure that the network traffic between members within a single site over the local area network.
    • Configure access control to prevent connections from unknown clients to the replica set.
    • Configure networking and firewall rules so that incoming and outgoing packets are permitted only on the default Mongo dB port.
    • Finally ensure that each member of a replica set is accessible by way of resolvable DNS or hostnames.

After making sure of the network activity let us begin the procedure.

At first we need to select a name for the replica set. We can achieve this by using the codes in the below.


mongod --replSet " replSet _name"

after this we have to initialize it I mongo shell using initialize().


rs.initiate()

Verify the initial replica set configuration.


rs.conf()

Add the remaining members to the replica set. When complete, the replica set will be fully functional. The new replica set will elect a primary.


rs.add("mongodb1.example.net")

Check the status of the replica set.


rs.status()

In the above article we have discussed about the deployment in replica set. Although we have scratched the surface we still need to learn many pros and cons about replica set. As we further progress in this path. We will try to understand things in our way.

Related Links:

1> How to Get Started with MongoDB Database?
2> How to Get Started with MongoDB?
3> How to Import and Export Through Mongodb?
4> How to Use Projection in MongoDB?
5> Using sort method in mongodb
6> Map-Reduce in MongoDB
7> Introduction to Replication in MongoDB
8> Discussing Replication Lag in MongoDB
9> Replica Set Members in Mongodb
10> Working with Sharding in MongoDB
11> Working with Index in MongoDB
12> Working with Aggregation in MongoDB
13> How to Work with Aggregation Framework in MongoDB?
14> Working with Pipeline Concept in MongoDB
15> Discussing about Pipeline Expression in MongoDB

If you find this article helpful, you can connect us in Google+ and Twitter.

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