Schema design for time series data
This page describes schema design patterns for storing time series data in Bigtable. This page builds on Designing your schema and assumes you are familiar with the concepts and recommendations described on that page.
A time series is a collection of data that consists of measurements and the times when the measurements are recorded. Examples of time series include the following:
- The plot of memory usage on your computer
- Temperature over time on a news report
- Stock market prices over a period of time
A good schema results in excellent performance and scalability, and a bad schema can lead to a poorly performing system. However, no single schema design provides the best fit for all use cases.
The patterns described on this page provide a starting point. Your unique dataset and the queries you plan to use are the most important things to consider as you design a schema for your time-series data.
The basic design patterns for storing time-series data in Bigtable are as follows:
Data for examples
To illustrate the differences between patterns, the examples on this page assume that you are storing data for an app that records the measurements that weather balloons take once every minute. We use event to mean a single request that writes one or multiple cells at the same time. Location IDs correspond with Google Cloud regions.
| Measurement | Example |
|---|---|
|
|
| Pressure (pascals) | 94587 |
| Temperature (Celsius) | 9.5 |
| Humidity (percentage) | 65 |
| Altitude (meters) | 601 |
| Related data | Example |
| Balloon ID | 3698 |
| Location | asia-southeast1 |
| Timestamp1 | t2021-03-05-1204 |
Time buckets
In a time bucket pattern, each row in your table represents a "bucket" of time,
such as an hour, day, or month. A row key includes a non-timestamp identifier,
such as week49, for the time period recorded in the row, along with other
identifying data.
The size of the bucket that you use — such as minute, hour, or day — depends on the queries that you plan to use and on Bigtable data size limits. For example, if rows that contain an hour of data are bigger the recommended maximum size per row of 100 MB, then rows that represent a half hour or a minute are probably a better choice.
Advantages of time bucket patterns include the following:
You'll see better performance. For example, if you store 100 measurements, Bigtable writes and reads those measurements faster if they are in one row than if they are in 100 rows.
Data stored in this way is compressed more efficiently than data in tall, narrow tables.
Disadvantages include the following:
- Time-bucket schema design patterns are more complicated than single-timestamp patterns and can take more time and effort to develop.
Adding new columns for new events
In this time bucket pattern, you write a new column to a row for each event, storing the data in the column qualifier rather than as a cell value. This means that for each cell, you send the column family, column qualifier, and timestamp, but no value.
Using this pattern for the sample weather balloon data, each row contains all
the measurements for a single metric, such as pressure, for a single weather
balloon, over the course of a week. Each row key contains the location, balloon
ID, metric that you are recording in the row, and a week number. Every time a
balloon reports its data for a metric, you add a new column to the row. The
column qualifier contains the measurement, the pressure in Pascals, for the
minute identified by the cell timestamp.
In this example, after three minutes a row might look like this:
| Row key | 94558 | 94122 | 95992 |
|---|---|---|---|
| us-west2#3698#pressure#week1 | "" (t2021-03-05-1200) | "" (t2021-03-05-1201) | "" (t2021-03-05-1202) |
Use cases for this pattern include the following:
You don't need to measure changes in your time series data.
You want to save storage space by using column qualifiers as data.
Adding new cells for new events
In this time bucket pattern, you add new cells to existing columns when you write a new event. This pattern lets you take advantage of Bigtable's ability to let you store multiple timestamped cells in a given row and column. It's important to specify garbage collection rules when you use this pattern.
Using the weather balloon data as an example, each row contains all the
measurements for a single weather balloon over the course of a week. The row key
prefix is an identifier for the week, so you can read an entire week's worth of
data for multiple balloons with a single query. The other row key segments are
the location where the balloon operates and the ID number for the balloon. The
table has one column family, measurements, and that column family has one
column for each type of measurement: pressure, temperature, humidity, and
altitude.
Every time a balloon sends its measurements, the application writes new values to the row that holds the current week's data for the balloon, writing additional timestamped cells to each column. At the end of the week, each column in each row has one measurement for each minute of the week, or 10,080 cells (if your garbage collection policy allows it).
Each column in each row holds a measurement for each minute of the week. In this case, after three minutes, the first two columns in a row might look like this:
| Row key | pressure | temp |
|---|---|---|
| asia-south2#3698#week1 | 94558 (t2021-03-05-1200) | 9.5 (t2021-03-05-1200) |
| 94122 (t2021-03-05-1201) | 9.4 (t2021-03-05-1201) | |
| 95992 (t2021-03-05-1202) | 9.2 (t2021-03-05-1202) |
Use cases for this pattern include the following:
- You want to be able to measure changes in measurements over time.
Single-timestamp rows
In this pattern, you create a row for each new event or measurement instead of adding cells to columns in existing rows. The row key suffix is the timestamp value. Tables that follow this pattern tend to be tall and narrow, and each column in a row contains only one cell.
Single-timestamp serialized
In this pattern, you store all the data for a row in a single column in a serialized format such as a protocol buffer (protobuf). This approach is described in more detail on Designing your schema.
For example, if you use this pattern to store the weather balloon data, your table might look like this after four minutes:
| Row key | measurements_blob |
|---|---|
| us-west2#3698#2021-03-05-1200 | protobuf_1 |
| us-west2#3698#2021-03-05-1201 | protobuf_2 |
| us-west2#3698#2021-03-05-1202 | protobuf_3 |
| us-west2#3698#2021-03-05-1203 | protobuf_4 |
Advantages of this pattern include the following:
Storage efficiency
Speed
Disadvantages include the following:
The inability to retrieve only certain columns when you read the data
The need to deserialize the data after it's read
Use cases for this pattern include the following:
You are not sure how you will query the data or your queries might fluctuate.
Your need to keep costs down outweighs your need to be able to filter data before you retrieve it from Bigtable.
Each event contains so many measurements that you might exceed the 100 MB per-row limit if you store the data in multiple columns.
Single-timestamp unserialized
In this pattern, you store each event in its own row, even if you are recording only one measurement. The data in the columns is not serialized.
Advantages of this pattern include the following:
It is generally easier to implement than a time-bucket pattern.
You might spend less time refining your schema before using it.
Disadvantages of this pattern often outweigh the advantages:
Bigtable is less performant with this pattern.
Data stored this way is not as efficiently compressed as data in wider columns.
Even when the timestamp is at the end of the row key, this pattern can result in hotspots.
Use cases for this pattern include the following:
You want to always retrieve all columns but only a specified range of timestamps, but you have a reason not to store the data in a serialized structure.
You want to store an unbounded number of events.
Using the weather balloon example data, the column family and column qualifiers are the same as the example using time buckets and new cells. In this pattern, however, every set of reported measurements for each weather balloon is written to a new row. The following table shows five rows that are written using this pattern:
| Row key | pressure | temperature | humidity | altitude |
|---|---|---|---|---|
| us-west2#3698#2021-03-05-1200 | 94558 | 9.6 | 61 | 612 |
| us-west2#3698#2021-03-05-1201 | 94122 | 9.7 | 62 | 611 |
| us-west2#3698#2021-03-05-1202 | 95992 | 9.5 | 58 | 602 |
| us-west2#3698#2021-03-05-1203 | 96025 | 9.5 | 66 | 598 |
| us-west2#3698#2021-03-05-1204 | 96021 | 9.6 | 63 | 624 |
Additional strategies
If you need to send multiple different queries for the same dataset, consider storing your data in multiple tables, each with a row key designed for one of the queries.
You can also combine patterns in some cases. For example, you can store serialized data in rows that represent time buckets, as long as you don't let the rows become too big.
What's next
- Review the steps involved in planning a schema.
- Understand the best practices for designing a schema.
- Read about the performance you can expect from Bigtable.
- Explore the diagnostic capabilities of Key Visualizer.