Docs Menu
Docs Home
/
MongoDB Manual
/

Best Practices for Time Series Collections

On this page

  • Optimize Inserts
  • Batch Document Writes
  • Use Consistent Field Order in Documents
  • Increase the Number of Clients
  • Optimize Compression
  • Omit Fields Containing Empty Objects and Arrays from Documents
  • Round Numeric Data to Few Decimal Places
  • Optimize Query Performance

This page describes best practices to improve performance and data usage for time series collections.

To optimize insert performance for time series collections, perform the following actions.

When inserting multiple documents:

For example, if you have two sensors, sensor A and sensor B, a batch containing multiple measurements from a single sensor incurs the cost of one insert, rather than one insert per measurement.

The following operation inserts six documents, but only incurs the cost of two inserts (one per batch), because the documents are ordered by sensor. The ordered parameter is set to false to improve performance:

db.temperatures.insertMany( [
{
"metadata": {
"sensor": "sensorA"
},
"timestamp": ISODate("2021-05-18T00:00:00.000Z"),
temperature: 10
},
{
"metadata": {
"sensor": "sensorA"
},
"timestamp": ISODate("2021-05-19T00:00:00.000Z"),
temperature: 12
},
{
"metadata": {
"sensor": "sensorA"
},
"timestamp": ISODate("2021-05-20T00:00:00.000Z"),
temperature: 13
},
{
"metadata": {
"sensor": "sensorB"
},
"timestamp": ISODate("2021-05-18T00:00:00.000Z"),
temperature: 20
},
{
"metadata": {
"sensor": "sensorB"
},
"timestamp": ISODate("2021-05-19T00:00:00.000Z"),
temperature: 25
},
{
"metadata": {
"sensor": "sensorB"
},
"timestamp": ISODate("2021-05-20T00:00:00.000Z"),
temperature: 26
}
], {
"ordered": false
})

Using a consistent field order in your documents improves insert performance.

For example, inserting these documents achieves optimal insert performance:

{
_id: ObjectId("6250a0ef02a1877734a9df57"),
timestamp: 2020-01-23T00:00:00.441Z,
name: 'sensor1',
range: 1
},
{
_id: ObjectId("6560a0ef02a1877734a9df66")
timestamp: 2020-01-23T01:00:00.441Z,
name: 'sensor1',
range: 5
}

In contrast, these documents do not achieve optimal insert performance, because their field orders differ:

{
range: 1,
_id: ObjectId("6250a0ef02a1877734a9df57"),
name: 'sensor1',
timestamp: 2020-01-23T00:00:00.441Z
},
{
_id: ObjectId("6560a0ef02a1877734a9df66")
name: 'sensor1',
timestamp: 2020-01-23T01:00:00.441Z,
range: 5
}

Increasing the number of clients writing data to your collections can improve performance.

To optimize data compression for time series collections, perform the following actions.

To optimize compression, if your data contains empty objects or arrays, omit the empty fields from your documents.

For example, consider the following documents:

{
time: 2020-01-23T00:00:00.441Z,
coordinates: [1.0, 2.0]
},
{
time: 2020-01-23T00:00:10.441Z,
coordinates: []
},
{
time: 2020-01-23T00:00:20.441Z,
coordinates: [3.0, 5.0]
}

The alternation between coordinates fields with populated values and an empty array result in a schema change for the compressor. The schema change causes the second and third documents in the sequence remain uncompressed.

In contrast, the following documents where the empty array is omitted receive the benefit of optimal compression:

{
time: 2020-01-23T00:00:00.441Z,
coordinates: [1.0, 2.0]
},
{
time: 2020-01-23T00:00:10.441Z
},
{
time: 2020-01-23T00:00:20.441Z,
coordinates: [3.0, 5.0]
}

Round numeric data to the precision required for your application. Rounding numeric data to fewer decimal places improves the compression ratio.

To improve query performance, create one or more secondary indexes on your timeField and metaField to support common query patterns.

← Shard a Time Series Collection