InfluxDB replication doesn’t actually create a hot standby; it creates a near real-time, eventually consistent copy of your data that’s primarily for disaster recovery or analytics, not for immediate failover.
Let’s see how it works. Imagine you have a primary InfluxDB instance, your "source," and you want a second instance, your "replica," to have a copy of the data. You configure the replica to "pull" data from the source.
Here’s a typical setup in the influxdb.conf file for the replica.
[[replication]]
id = "my-replication-id"
name = "my-replication-name"
description = "Replication from primary to replica"
[[replication.source]]
url = "http://primary-influxdb:8086"
# If your primary requires authentication:
# token = "your-primary-auth-token"
[[replication.destination]]
# This is the InfluxDB instance where this config file is located
# url = "http://replica-influxdb:8086" # Usually not needed if it's the local instance
# token = "your-replica-auth-token" # If the replica requires auth for writes
[replication.databases]
# Specify which databases to replicate.
# You can use wildcards.
include = ["_monitoring", "production_data_*"]
exclude = ["_internal"]
[replication.continuous_queries]
# Replicate continuous queries too.
include = ["*"] # Replicate all CQs
# exclude = ["cq_to_archive"] # Exclude specific CQs
[replication.tasks]
# Replicate tasks
include = ["*"] # Replicate all tasks
# exclude = ["task_to_delete"] # Exclude specific tasks
The id and name are just for identification. The source section points to your primary InfluxDB. You’ll need the URL, and if your primary requires authentication, the token. The destination section is usually implicit if you’re configuring replication on the replica instance itself.
The magic is in the databases section. Here you specify which buckets (formerly databases) you want to replicate. You can use include and exclude patterns. For example, production_data_* would replicate all buckets starting with production_data_.
You can also choose to replicate continuous queries (continuous_queries) and tasks (tasks) by including or excluding their names.
Once configured, you enable this replication within the InfluxDB UI or via the InfluxDB API. On the replica, you’d navigate to "Data" -> "Replications" and click "Create Replication." You’d fill in the details from the influxdb.conf snippet above, pointing to the source InfluxDB and specifying the buckets.
The InfluxDB replication process works by the replica periodically polling the source for new data. It tracks the last point it successfully replicated for each bucket and requests all points written since that time. This polling happens at a configurable interval, typically defaulting to a few seconds.
The most surprising true thing about InfluxDB replication is that it is fundamentally a pull-based mechanism managed by the replica. The source InfluxDB doesn’t actively push data to replicas; instead, replicas connect to the source and ask for updates. This means the replica’s ability to keep up is limited by its own processing power and network bandwidth to the source, and the source’s ability to serve the requests.
Let’s look at a real-world scenario. Suppose you have a primary InfluxDB at http://influxdb-prod:8086 with a token prod_token_abc123 and you want to replicate the iot_sensor_data bucket to a replica instance at http://influxdb-replica:8086.
On the replica instance, you’d create a replication with these settings:
- Name:
iot-data-replica - Description:
Replication of IoT sensor data from production - Source URL:
http://influxdb-prod:8086 - Source Token:
prod_token_abc123 - Database Include:
iot_sensor_data - Replication Mode:
Continuous
The InfluxDB replication service on the replica will then start fetching data. If the replica goes offline, when it comes back up, it will connect to the source and request all data written since the last point it successfully processed. This is how it catches up.
A key detail many overlook is that replication is bucket-specific. If you include a bucket in your replication configuration, InfluxDB will track the replication progress for that bucket independently. This means one bucket can lag significantly behind another if it’s receiving a much higher write volume or if there are network issues specific to that data stream.
The underlying mechanism involves InfluxDB’s internal time-series data structures. When a replica polls the source, it’s essentially querying for data within time ranges. The source returns blocks of data, and the replica writes them to its own storage. The state of what has been replicated is stored persistently on the replica, allowing it to resume after interruptions.
If your replica is falling behind, you might see increased latency or even data loss if the replica’s retention policies are shorter than the time it takes to catch up. You can monitor replication lag through InfluxDB’s internal _monitoring bucket, looking at metrics like influxdb_replication_lag_seconds.
The next logical step after setting up replication is to consider strategies for managing potential data loss during failover scenarios, which often involves more than just replication.