MongoDB temporary node / mirror

mongodbreplication

When I have a running MongoDB instance with some data and requests against it, can I start a second instance on another machine, register it as another node in the first instance and all the data is copied to the second node while changes on the first node in the meantime are also mirrored to the second instance? Is that possible?

The context is: I have a single machine deployment with an application and mongodb running on the same instance (Amazon EC2). Its a low budget deployment but I try to have as few downtime as possible. I'd like to change the ec2 instance every week and for that I'd need some sort of mirroring. Is that possible?

Best Answer

You can replicate data from one server to others using Replication feature in MongoDB.

The replica set ensures high availability and redundancy of you mongodb data in case if node failure. In your case, you can create a replica set of 3 nodes (PRIMARY, SECONDARY, ARBITER). When one of the server goes down, Secondary would be promoted as PRIMARY which ensures high availability of MongoDB data.

It is always recommended to have at least 3 members in replica set.

You can find detailed document here

Related Solutions

Mysql – Looking for suggestions to Migrate Amazon RDS Large Instance to non-amazon host – Live

Dump RDS and import it into mysql that holds all data in RAM disk:

Buy strongest possible EC2 instance that might hold your database in memory twice (you might prepare everything on a small instance and then ramp it up)
Create RAM disk, install and configure mysql 5.6 to hold both data and redolog on the RAM disk
Stop your db-using services, dump the RDS directly into the RAM-backed mysql
stop mysql, rsync the DB to a final place, kill the machine

You should do a dry run to estimate expected downtime.

Mongodb – Improving concurrency in MongoDB via sharding

You are correct, with MongoDB the way to engineer around a write contention issue is to shard.

Your environment sounds like it's fairly bursty, in that you're not continually ingesting data and instead ingesting it in fairly discrete chunks. With this in mind, you could go with a collector/distributor model such as this:

     ------>{ workers }<------
     |                       |
[Shard01]               [Shard02]
    |                      |
    ---->[Persistant]<------

The workers would upsert/push their results into the sharded collection/database, and once the job was completed a batch-process then uses something like db.copyDatabases() to push the result set at the monolithic (and cheaper to run) single instance Mongo. As the copy database process can push all of its updates in one run, it should experience much fewer write-lock problems.

Best Answer

Related Solutions

Mysql – Looking for suggestions to Migrate Amazon RDS Large Instance to non-amazon host – Live

Mongodb – Improving concurrency in MongoDB via sharding

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