I am new to mongodb and amazon ec2.
It seems to me that mongo replicas are here to : 1/ avoid data loss and 2/ make reads and serving faster.
In Amazon they have this EbS thing. From what I understand it is a global persistent storage, like dropbox for instance.
So is there a need to have replicas if amazon abstracts away the need of it with EBS ?
Thanks in advance
Thomas
Let me clarify a couple of things.
EBS is essentially a SAN Volume if you are used to working within existing technologies. It can be attached to one instance, but it still has a limited IO throughout. Using RAID can help maximize the IO, provisioned IOPS can help you maximize the throughput.
Ideally however, with MongoDB, you want to have enough memory where indexes can be completely accessed within memory, performance drops if the disk needs to be hit.
Mongo can use Replicas, which is primarily used for failover and replication (You can send reads to a slave, but all writes need to hit the primary), and sharding which is used to split a dataset to increase performance. You will still need to do these things anyway even if you are using EBS for storage.
Replicas are there not just for storage redundancy but also for server redundancy. What happens if your MongoDB server (which uses an EBS volume) suddenly disappears because, for example, the host on which is sits fails? You would need to do a whole bunch of stuff, like clone a new instance to replace it, attach the volume to that instance, reroute traffic to it, etc. Mongo's replica sets mean you don't have to do that. They keep working even if one of them fails, so you have basically 0 down time.
Additionally, it's one more layer of redundancy. You can only trust EBS so far - what if AWS has a bug that erases your volume or that makes it unavailable for an unacceptably long time? With replica sets you can even replicate your data across availability zones or even to a completely different cloud provider.
Replica sets also let you read from multiple nodes, so you can increase your read throughput, theoretically, after you've maxed out what the EBS connection gives you from one instance.
Related
I'm using AWS's RDS PostgresQL DB
I recently traced an application error to the database connections being maxed out, I temporarily scaled the instance to support more connections since AWS's default behaviour is to use the instance's memory to calculate the maximum number of connections.
Most of the connections are due to clients reading data, so should I create a read replica instead of scaling the server? I'm thinking of this in terms of best practices, costs, and effort
It depends how much the IOPS are, if your app is read intensive and there are connections limitation as well. Read IOPS are directly related to EBS storage, if you are using general storage.
Thus if you are using general storage. First look at your Read IOPS, if they are on average higher than STORAGE_IN_GB*3 then go for a read replica.
Otherwise just connection issue should be fixed by scaling the instance up vertically.
My app runs a daily job that collects data and feeds it to a mongoDB. This data is processed and then exposed via rest API.
Need to setup a mongodb cluster in AWS, the requirements:
Data will grow about the same size each day ( about 50M records), so write throughput doesn't need to scale. writes would be triggered by a cron at a certain hour. Objects are immutable ( they won't grow)
Read throughput will depend on number of users / traffic, so it should be scalable. traffic won't be heavy in the beginning.
Data is mostly simple JSON, need a couple of indices around some of the fields for fast-querying / filtering.
what kind of architecture should I use in terms of replica sets, shards, etc ?.
What kind of storage volumes should I use for this architecture? ( EBS, NVMe) ?
Is it preferred to use more instances or to use RAID setups. ?
I'm looking to spend some ~500 a month.
Thanks in advance
To setup the MongoDB cluster in AWS I would recommend to refer the latest AWS quick start for MongoDB which will cover the architectural aspects and also provides CloudFormation templates.
For the storage volumes you need to use EC2 instance types that supports EBS instead of NVMe storage since NVMe is only an instance storage. If you stop and start the EC2, the data in NVMe is lost.
Also for the storage volume throughput, you can start with General Purpose IOPS with resonable storage size and if you find any limitations then only consider Provisioned IOPS.
For high availability and fault tolerance the CloudFormation will create multiple instances(Nodes) in MongoDB cluster.
I have a relatively low traffic system, but I want to keep the data safe. The data are stored in a single MongoDb instance. I don't want to run multiple replicas and manage them. So, I'm planning to change the data directory to EFS path to take advantage of its replication and other benefits. Periodic snapshots can cause data loss, and recovery is manual.
Is there any drawback of storing the data and the journal files on EFS caused by the additional latency?
As you alluded to, EFS objects are replicated across availability zones. To contrast, EBS volumes are only replicated within a single availability zone. The difference in pricing is significant with EFS currently starting at $0.30/GB and EBS starting at $0.10/GB. Typical EFS use-cases are for data that needs to be shared across instances, like user home directories and application data. EBS is also capable of providing the lowest-latency.
With those points in mind, I do not recommend EFS for MongoDB data. If EFS's multi-AZ replication is your major desire, you could achieve it with EBS by taking periodic snapshots (which are stored in S3) of the EBS volume. I think EBS will give you better performance and lower cost.
Using EFS is not really an alternative to running multiple MongoDB instances. Replication and sharding are not things that EFS can help achieve.
I've read all of the MongoDB related documentation talking about the recommended practices for deploying Mongo on AWS, but I don't understand the recommendation to install on EBS with RAID-10 (pdf) to avoid data loss.
This seems like admitting that replication doesn't work. Why shouldn't one run Mongo using ephemeral drives and a cluster of 5 servers doing replication?
Performance is much greater and latency is predictable on local disks.
If a server goes down, the EBS backed store would have to be resynced with the replica anyway. Sure you have the data, but it is already out of date.
Using EBS makes for a much more complicated setup. You need to use LVM or some other layer if you want to take snapshots, since EBS snapshots won't work across RAID. You need to monitor and manage your RAID array and rebuild in the case of failure or if one of the EBS volumes has performance issues.
What exactly does using EBS protect against if one has backups and a large replica set? It's almost admitting that replica sets won't protect you against dataloss. (ignoring for the moment the race condition when writes have been sent to secondaries and a failure on the master happens before acknowledgements have been sent).
Why shouldn't one run Mongo using ephemeral drives and a cluster of 5 servers doing replication?
AWS is not perfect, it can have a network failure which results in the entire set being down. with ephemeral memory you would lose all your data. Plus block devices survive restarts of nodes.
That is a few things, I am sure there are more.
If a server goes down, the EBS backed store would have to be resynced with the replica anyway.
Only after the point it went down, if that is a considerable amount of time then yes, it might be easier to copy the directory frm one replica to the other.
Using EBS makes for a much more complicated setup. You need to use LVM or some other layer if you want to take snapshots, since EBS snapshots won't work across RAID.
You don't really need RAID within AWS itself, I mean they RAID each of your block devices and replica sets are good as throw away sets. You can get by with one block device per node.
What exactly does using EBS protect against if one has backups and a large replica set?
It safe guards your sanity, restoring a backup of sizeable data across 10 odd members and resetting all the firewall/user permissions and OS etc etc could be...well...nasty.
I mean imagine having to re-setup your OS every single time you restart it.
It's almost admitting that replica sets won't protect you against dataloss.
Hmm, you must have misread some where brecaue THAT is not what they guarantee. It is true that it is harder to lose data with repilica sets (if they are setup right) but they are actually designed to give High Availability (HA).
Backups and jornalling and other consistentcy methods are designed to not lose data.
So where do you see the recommendation to run RAID10 on EBS for mongodb? Their docs list it as an option but specifically recommend only EBS and Provisioned IOPS.
For almost all deployments EBS will be the better choice. For production systems we recommend using
EBS-optimized EC2 instances
Provisioned IOPS (PIOPS) EBS volumes
http://docs.mongodb.org/ecosystem/platforms/amazon-ec2/
We run all of our mongodb instances at EC2 and all of them use EBS storage volumes with production instances using provisioned IO. Here's why:
Bringing back a failed member is faster. If an instance fails and needs to be stopped and restarted (not that frequent but it does happen) we can just detach the storage and re-attach it to another instance. Mongod comes up fine, recovers via the journal and then re-syncs with the primary for only the delta in data since the failure. This is a big deal when you have large data sets that may take many hours to restore from scratch. Storing the data on an ephemeral drive does not provide this capability.
Backups are easier (at least for replica sets under 1 TB). With a single EBS storage volume (up to 1 TB) we can take snapshots of a live secondary. As long as the journal is on the same storage volume the backup will be consistent. No need for a dedicated secondary for backups that has to be brought offline to backup.
No need for RAID except for multiple TB replica sets or for performance. EBS is already RAID behind the scenes for redundancy. We do use RAID when a replica set grows beyond 1 TB in storage but that's it and have not yet hit a point where a high IOPS EBS volume provides sufficient performance.
Provisioned IOPS give decent control of performance vs. cost. Being able to select EBS storage rated up to 4000 IOPS has allowed us to scale up performance for production systems (at higher cost) while still gaining the benefits of EBS storage. We use regular EBS volumes at lower cost for test systems.
Copying production data off for use in a test environment is much easier for large data sets. Snapshot the volumes, create a new storage volume from the snapshot and you're up and running.
I certainly can imagine future deployments using ephemeral storage (particularly as SSD costs drop) for certain high performance situations but EBS has been fairly reliable and dependable for us. Of course your experience and needs can and will differ but for us following the recommendation from MongoDB has served us well. In fact it's been reliable enough that for some environments we've moved to 1 Primary, 1 Secondary and an Arbiter, which helps with cost savings. Probably would not have done that without the ease of recovery and overall reliability of using EBS volumes on the Primary and Secondary.
I have a Mongo cluster that backs an application that I use in production. It's very important to my business and clustered across a number of boxes to optimize for speed and redundancy. I'd like to make the data in said cluster available for running analytical queries and enqueued tasks, but I definitely don't want these to harm production performance. Is it possible to just mirror all of my data against a single box I throw into the cluster with some special tag that I can then use for analytics? It's fine if it's slow. I just want it to be cheap and not to affect production read/write speeds.
Since you're talking about redundancy, I assume you have a replica set.
In that case you can use a hidden replica set member to perform the calculations you need.
Just keep in mind that the member count must be odd. If you add a node you might need to also add an arbiter. Or maybe you can just hide one of the already existing members.
If you are looking for a way to increase querying speed having a lot of data, you have to look might look into sharding with mongodb. Basically what it does is dividing your big amount of data into small shards and stores them on different machines.
If you are looking to increase redundancy (in order to make backup or to be able to do offline processing without touching primary servers) you have to look into replication with mongodb. If you are doing replication, keep in mind that the data on the replicas will be always lagging behind a primary (nothing to worry about, but just need to know this fact to decide can you allow read from the replicas). As it was pointed by Rafa, hidden replica sets are well suited for backup and offline data processing. They will still be able to get all the data from primary (with small lag), but are invisible to secondary reads and can not become primary.
There is a nice mongodb course which is talking in depth about replication and sharding, so may be it is worth listening and trying it.