InfluxDB v1.x

Creating the data source

Adding the InfluxDB v1.x Connector

Requirements

InfluxDB v1.x installed and network accessible (see our IPs List)
Access credentials (username and password) with read permissions
Database name to be synchronized

Instructions

Go to Data Sources and click "Add Source"
Select InfluxDB v1.x from the connector list
Fill in the connection parameters:
- Host: InfluxDB server address (e.g., influxdb.example.com)
- Port: Connection port (default: 8086 without SSL, 443 with SSL)
- Use SSL: Select "On" if the server uses HTTPS, "Off" for HTTP
- Database: Database name in InfluxDB
- User: Username for authentication
- Password: Authentication password
Click Test Connection to verify the parameters are correct
After successful test, click Save

Available integration types

The InfluxDB v1.x connector offers different extraction strategies:

Measurement Data (Incremental): Extracts data from a measurement (table) based on a time savepoint, allowing you to load only new data since the last execution. Supports lookback window to capture late-arriving data.
Measurement Data Rolling Window: Extracts the last N days of data in each execution, useful for dashboards that need a moving window of recent data.

Data aggregation

You can choose different time granularity levels:

Raw (timestamp-level): Raw data, no aggregation
1 second, 1 minute, 1 hour, 1 day: Data aggregated by period, with MEAN, MIN, MAX, SUM, and COUNT calculations for all numeric fields

Pipelines

Relationship chart

Click to expand

Measurement Data (Incremental)

Replication type: Incremental

Parameters:

Read start date (Savepoint): Starting date to filter results
Lookback Window (hours): How many hours to look back from savepoint to catch late-arriving data (0 = no lookback)
Granularity: Time aggregation level
Measurement: Select the InfluxDB measurement (table)

Campo	Tipo
time	timestamp	[pt] Timestamp agregado por hour
host	text	[en] Tag: host
region	text	[en] Tag: region
mean_cpu_usage	float	[en] MEAN of cpu_usage aggregated by time
min_cpu_usage	float	[en] MIN of cpu_usage aggregated by time
max_cpu_usage	float	[en] MAX of cpu_usage aggregated by time
sum_cpu_usage	float	[en] SUM of cpu_usage aggregated by time
count_cpu_usage	float	[en] COUNT of cpu_usage aggregated by time
mean_memory_usage	float	[en] MEAN of memory_usage aggregated by time
min_memory_usage	float	[en] MIN of memory_usage aggregated by time
max_memory_usage	float	[en] MAX of memory_usage aggregated by time
sum_memory_usage	float	[en] SUM of memory_usage aggregated by time
count_memory_usage	float	[en] COUNT of memory_usage aggregated by time
mean_disk_io	int	[en] MEAN of disk_io aggregated by time
min_disk_io	int	[en] MIN of disk_io aggregated by time
max_disk_io	int	[en] MAX of disk_io aggregated by time
sum_disk_io	int	[en] SUM of disk_io aggregated by time
count_disk_io	int	[en] COUNT of disk_io aggregated by time

Measurement Data (Rolling Window)

Replication type: Incremental

Parameters:

Window Size (days): How many days of data to extract in each execution
Granularity: Time aggregation level
Measurement: Select the InfluxDB measurement (table)

Campo	Tipo
time	timestamp	[pt] Timestamp agregado por hour
host	text	[en] Tag: host
region	text	[en] Tag: region
mean_cpu_usage	float	[en] MEAN of cpu_usage aggregated by time
min_cpu_usage	float	[en] MIN of cpu_usage aggregated by time
max_cpu_usage	float	[en] MAX of cpu_usage aggregated by time
sum_cpu_usage	float	[en] SUM of cpu_usage aggregated by time
count_cpu_usage	float	[en] COUNT of cpu_usage aggregated by time
mean_memory_usage	float	[en] MEAN of memory_usage aggregated by time
min_memory_usage	float	[en] MIN of memory_usage aggregated by time
max_memory_usage	float	[en] MAX of memory_usage aggregated by time
sum_memory_usage	float	[en] SUM of memory_usage aggregated by time
count_memory_usage	float	[en] COUNT of memory_usage aggregated by time
mean_disk_io	int	[en] MEAN of disk_io aggregated by time
min_disk_io	int	[en] MIN of disk_io aggregated by time
max_disk_io	int	[en] MAX of disk_io aggregated by time
sum_disk_io	int	[en] SUM of disk_io aggregated by time
count_disk_io	int	[en] COUNT of disk_io aggregated by time

Notes

Part of this documentation was automatically generated by AI and may contain errors. We recommend verifying critical information

Frequently asked questions

What does the InfluxDB v1.x data source replicate?▼

Kondado replicates measurements (tables) from an InfluxDB v1.x database into your chosen destination. You can pick which measurements to load and Kondado will read them on the schedule you define.

What credentials do I need to connect InfluxDB v1.x?▼

You need the host (or IP) and port of the InfluxDB server, the database name, and a user with read permissions on that database. Optionally, configure SSH or whitelisting if the server is not publicly accessible.

Does my InfluxDB server need to be reachable from Kondado?▼

Yes. Either expose the port to the public internet with proper credentials, or whitelist Kondado's IPs in your firewall. For internal databases, you can also use SSH tunneling when supported.

Where can I send InfluxDB data?▼

InfluxDB data can be sent to any supported destination — Power BI, Looker Studio, Google Sheets, Excel, BigQuery, PostgreSQL, MySQL, SQL Server, Redshift, or S3. See destinations.

How often does InfluxDB data refresh?▼

Pipelines run at the frequency you choose in Kondado. Each run queries the configured measurements and loads new rows into your destination.

Can I replicate only recent measurements to save row consumption?▼

Yes. When configuring the pipeline you can set time-window parameters so only recent points are read, and you can adjust the savepoint to control the starting date — keep in mind that changing the savepoint reprocesses all data from that date forward.

Written by

Thassyo Pereira·Published 2025-11-13·Updated 2026-04-26