# How to Manage Data at Scale With MongoDB Atlas Online Archive

Let's face it: Your data can get stale and old quickly. But just because
the data isn't being used as often as it once was doesn't mean that it's
not still valuable or that it won't be valuable again in the future. I
think this is especially true for data sets like internet of things
(IoT) data or user-generated content like comments or posts. (When was
the last time you looked at your tweets from 10 years ago?) This is a
real-time view of my IoT time series data aging.

When managing systems that have massive amounts of data, or systems that
are growing, you may find that paying to save this data becomes
increasingly more costly every single day. Wouldn't it be nice if there
was a way to manage this data in a way that still allows it to be
useable by being easy to query, as well as saving you money and time?
Well, today is your lucky day because with MongoDB Atlas Online
Archive,
you can do all this and more!

With the Online Archive feature in MongoDB
Atlas, you can create a rule to
automatically move infrequently accessed data from your live Atlas
cluster to MongoDB-managed, read-only cloud object storage. Once your
data is archived, you will have a unified view of your Atlas cluster and
your Online Archive using a single endpoint..

>
>
>Note: You can't write to the Online Archive as it is read-only.
>
>

For this demonstration, we will be setting up an Online Archive to
automatically archive comments from the `sample_mflix.comments` sample
dataset that are older than 10 years. We will then connect to our
dataset using a single endpoint and run a query to be sure that we can
still access all of our data, whether its archived or not.

## Prerequisites

-   The Online Archive feature is available on
    M10 and greater
    clusters that run MongoDB 3.6 or later. So, for this demo, you will
    need to create a M10
    cluster in MongoDB
    Atlas. Click here for information on setting up a new MongoDB Atlas
    cluster.
-   Ensure that each database has been seeded by loading sample data
    into our Atlas
    cluster. I will be
    using the `sample_mflix.comments` dataset for this demo.

>
>
>If you haven't yet set up your free cluster on MongoDB
>Atlas, now is a great time to do so. You
>have all the instructions in this blog post.
>
>

## Configure Online Archive

Atlas archives data based on the criteria you specify in an archiving
rule. The criteria can be one of the following:

-   **A combination of a date and number of days.** Atlas archives data
    when the current date exceeds the date plus the number of days
    specified in the archiving rule.
-   **A custom query.** Atlas runs the query specified in the archiving
    rule to select the documents to archive.

In order to configure our Online Archive, first navigate to the Cluster
page for your project, click on the name of the cluster you want to
configure Online Archive for, and click on the **Online Archive** tab.

Next, click the Configure Online Archive button the first time and the
Add Archive button subsequently to start configuring Online Archive for
your collection. Then, you will need to create an Archiving Rule by
specifying the collection namespace, which will be
`sample_mflix.comments` for this demo. You will also need to specify the
criteria for archiving documents. You can either use a custom query or a
date match. For our demo, we will be using a date match and
auto-archiving comments that are older than 10 years (365 days \* 10
years = 3650 days) old. It should look like this when you are done.

Optionally, you can enter up to two most commonly queried fields from
the collection in the Second most commonly queried field and Third most
commonly queried field respectively. These will create an index on your
archived data so that the performance of your online archive queries is
improved. For this demo, we will leave this as is, but if you are using
production data, be sure to analyze which queries you will be performing
most often on your Online Archive.

Before enabling the Online Archive, it's a good idea to run a test to
ensure that you are archiving the data that you intended to archive.
Atlas provides a query for you to test on the confirmation screen. I am
going to connect to my cluster using MongoDB
Compass to test this
query out, but feel free to connect and run the query using any method
you are most comfortable with. The query we are testing here is this.

``` javascript
db.comments.find({
   date: { $lte: new Date(ISODate().getTime() - 1000 \* 3600 \* 24 \* 3650)}
})
.sort({ date: 1 })
```

When we run this query against the `sample_mflix.comments` collection,
we find that there is a total of 50.3k documents in this collection, and
after running our query to find all of the comments that are older than
10 years old, we find that 43,451 documents would be archived using this
rule. It's a good idea to scan through the documents to check that these
comments are in fact older than 10 years old.

So, now that we have confirmed that this is in fact correct and that we
do want to enable this Online Archive rule, head back to the *Configure
an Online Archive* page and click **Begin Archiving**.

Lastly, verify and confirm your archiving rule, and then your collection
should begin archiving your data!

>
>
>Note: Once your document is queued for archiving, you can no longer edit
>the document.
>
>

## How to Access Your Archived Data

Okay, now that your data has been archived, we still want to be able to
use this data, right? So, let's connect to our Online Archive and test
that our data is still there and that we are still able to query our
archived data, as well as our active data.

First, navigate to the *Clusters* page for your project on Atlas, and
click the **Connect** button for the cluster you have Online Archive
configured for. Choose your connection method. I will be using
Compass for this
example. Select **Connect to Cluster and Online Archive** to get the
connection string that allows you to federate queries across your
cluster and Online Archive.

After navigating to the `sample_mflix.comments` collection, we can see
that we have access to all 50.3k documents in this collection, even
after archiving our old data! This means that from a development point
of view, there are no changes to how we query our data, since we can
access archived data and active data all from one single endpoint! How
cool is that?

## Wrap-Up

There you have it! In this post, we explored how to manage your MongoDB
data at scale using MongoDB Atlas Online Archive. We set up an Online
Archive so that Atlas automatically archived comments from the
`sample_mflix.comments` dataset that were older than 10 years. We then
connected to our dataset and made a query in order to be sure that we
were still able to access and query all of our data from a unified
endpoint, regardless of it being archived or not. This technique of
archiving stale data can be a powerful feature for dealing with datasets
that are massive and/or growing quickly in order to save you time,
money, and development costs as your data demands grow.

>
>
>If you have questions, please head to our developer community
>website where the MongoDB engineers and
>the MongoDB community will help you build your next big idea with
>MongoDB.
>
>

## Additional resources:

-   Archive Cluster Data