Collections
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MongoDB stores documents in collections. If a collection does not exist, MongoDB creates the collection when you first insert a document in that collection.
You can also explicitly create a collection with various options, such as setting the maximum size or the documentation validation rules.
Time Series Collections
Time series collections were added in MongoDB 5.0. You can read the documentation here.
Time series collections efficiently store sequences of measurements over a period of time. Time series data is any data that is collected over time and is uniquely identified by one or more unchanging parameters. The unchanging parameters that identify your time series data is generally your data source's metadata.
Creating a Time Series Collection
In order to create a time series collection, you must explicitly create a collection using the time series options:
opts = { time_series: { timeField: "timestamp", metaField: "metadata", granularity: "hours" }, expire_after: 604800 } db['weather', opts].create
When creating a time series collection, specify the following options:
Field | Description |
---|---|
| Required. The name of the field which contains the date in each time series document. |
| Optional. The name of the field which contains metadata in each time series document. The metadata in the specified field should be data that is used to label a unique series of documents. The metadata should rarely, if ever, change. |
| Optional. Possible values are "seconds", "minutes", and "hours". By default, MongoDB sets the granularity to "seconds" for high-frequency ingestion. |
| Optional. Enable the automatic deletion of documents in a time series collection by specifying the number of seconds after which documents expire. MongoDB deletes expired documents automatically. |
See the MongoDB docs for more information about time series collection options.
Inserting into a Time Series Collection
Inserting into a time series collection is similar to inserting into a regular collection:
db['weather'].insert_many([ { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 18, 0, 0, 0), temp: 12 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 18, 4, 0, 0), temp: 11 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 18, 8, 0, 0), temp: 11 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 18, 12, 0, 0), temp: 12 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 18, 16, 0, 0), temp: 16 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 18, 20, 0, 0), temp: 15 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 19, 0, 0, 0), temp: 13 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 19, 4, 0, 0), temp: 12 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 19, 8, 0, 0), temp: 11 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 19, 12, 0, 0), temp: 12 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 19, 16, 0, 0), temp: 17 }, { metadata: { sensorId: 5578, type: "temperature" }, timestamp: Time.utc(2021, 5, 19, 20, 0, 0), temp: 12 } ])
Querying a Time Series Collection
Querying a time series collection is also very similar to a regular collection:
weather.find(timestamp: Time.utc(2021, 5, 18, 0, 0, 0)).first
The result of this query:
{ "timestamp" => 2021-05-18 00:00:00 UTC, "metadata" => { "sensorId" => 5578, "type" => "temperature" }, "temp" => 12, "_id" => BSON::ObjectId('624dfb87d1327a60aeb048d2') }
Using the Aggregation Pipeline on a Time Series Collection
The aggregation pipeline can also be used for additional query functionality:
weather.aggregate([ { "$project": { date: { "$dateToParts": { date: "$timestamp" } }, temp: 1 } }, { "$group": { _id: { date: { year: "$date.year", month: "$date.month", day: "$date.day" } }, avgTmp: { "$avg": "$temp" } } } ]).to_a
The example aggregation pipeline groups all documents by the date of the measurement and then returns the average of all temperature measurements that day:
[{ "_id" => { "date" => { "year" => 2021, "month" => 5, "day" => 18 } }, "avgTmp" => 12.833333333333334 }, { "_id" => { "date" => { "year" => 2021, "month" => 5, "day" => 19 } }, "avgTmp" => 12.833333333333334 }]
See the MongoDB documentation on time series collections for more information.
Capped Collections
Capped collections have maximum size or document counts that prevent them from growing beyond maximum thresholds. All capped collections must specify a maximum size and may also specify a maximum document count. MongoDB removes older documents if a collection reaches the maximum size limit before it reaches the maximum document count.
To create a capped collection, use
the capped: true
option along with a size
in bytes.
client = Mongo::Client.new([ '127.0.0.1:27017' ], :database => 'music') collection = client[:artists, capped: true, size: 10000] collection.create collection.capped? # => true
Convert an Existing Collection to Capped
To convert an existing collection from non-capped to capped, use
the convertToCapped
command.
client = Mongo::Client.new([ '127.0.0.1:27017' ], :database => 'music') db = client.database db.command({ 'convertToCapped' => 'artists', 'size' => 10000 })
Document Validation
If you're using MongoDB version 3.2 or later, you can use
document validation.
Collections with validations compare each inserted or updated
document against the criteria specified in the validator option.
Depending on the validationLevel
and validationAction
, MongoDB
either returns a warning, or refuses to insert or update the document
if it fails to meet the specified criteria.
The following example creates a contacts
collection with a validator
that specifies that inserted or updated documents should match at
least one of three following conditions:
the
phone
field is a stringthe
email
field matches the regular expressionthe
status
field is eitherUnknown
orIncomplete
.
client = Mongo::Client.new([ '127.0.0.1:27017' ], :database => 'test') client[:contacts, { 'validator' => { '$or' => [ { 'phone' => { '$type' => "string" } }, { 'email' => { '$regex' => /@mongodb\.com$/ } }, { 'status' => { '$in' => [ "Unknown", "Incomplete" ] } } ] } } ].create
Add Validation to an Existing Collection
To add document validation criteria to an existing collection, use the
collMod
command. The example below demonstrates how to add a
validation to the contacts
collection, ensuring that all new
documents must contain an age
field which is a number.
client = Mongo::Client.new([ '127.0.0.1:27017' ], :database => 'test') db = client.database db.command({ 'collMod' => 'contacts', 'validator' => { 'age' => { '$type' => "number" } } })
Listing Collections
Use collections
or collection_names
methods on a database
objects to list collections:
client = Mongo::Client.new([ '127.0.0.1:27017' ], :database => 'music') database = client.database database.collections # Returns an array of Collection objects. database.collection_names # Returns an array of collection names as strings.
Dropping Collections
To drop a collection, call drop
on the collection object.
client = Mongo::Client.new([ '127.0.0.1:27017' ], :database => 'music') artists = client[:artists] artists.drop