MongoDB Solutions to data growth

MongoDB provides the sharding feature for you to store massive data. However, the storage costs increase with the growth of data volume. Typically, the value of log data decreases over time. Data generated one year ago or even three months ago, which is valueless to the analysis, needs to be cleared to reduce storage costs. MongoDB allows you to use the following solutions to meet such a requirement.

Data sharding

When the number of service nodes that generate logs increases, the requirements for the write and storage capabilities of a log storage service also increase. In this case, you can use the sharding feature provided by MongoDB to distribute the log data across multiple shards.

• Use a field that indicates the timestamp as the shard key
• Use the hashed sharding method
• Use the ranged sharding method

Solutions to data growth

1. Use TTL indexes: Time to live (TTL) indexes are special single-field indexes that MongoDB can use to automatically remove documents from a collection after a certain amount of time. In the preceding example, the time field indicates the time when the request was sent. You can run the following command to create a TTL index on the time field and specify that MongoDB removes the document after 30 hours: db.events.createIndex( { time: 1 }, { expireAfterSeconds: 108000 } ).  
2. Use capped collections: If you do not have strict limits on the storage period, whereas you want to limit the storage space, you can use capped collections to store log data. Capped collections work in the following way: After you specify a maximum storage space or a maximum number of stored documents for a capped collection, once one of the limits is reached, MongoDB automatically removes the oldest documents in the collection. For example, you can run the following command to create and configure a capped collection: db.createCollection("event", {capped: true, size: 104857600000}.  
3. Archive documents by collection or database periodically: At the end of a month, you can rename the collection that stores the documents of that month and create another collection to store documents of the next month. We recommend that you append the information about the year and month to the collection name. The following example describes how the 12 collections that store the documents written in each month of 2020 are named:

events-202001
events-202002
events-202003
events-202004
....
events-202012