InfluxDB Integration
The LPS Tool supports InfluxDB integration starting from version 3.0.3.1 and above. This feature allows you to automatically export performance testing metrics to your InfluxDB instance for advanced analytics, long-term storage, and visualization using tools like Grafana.
Overview
InfluxDB is a time-series database designed for handling high write and query workloads. By integrating LPS with InfluxDB, you can:
- Store Historical Data: Keep performance metrics for trend analysis and historical comparisons
- Advanced Visualization: Use Grafana or other tools to create custom dashboards and alerts
- Data Analysis: Perform complex queries on your performance data
- Integration: Combine LPS metrics with other monitoring systems
- Scalability: Handle large volumes of metrics data efficiently
Prerequisites
Before configuring InfluxDB integration, ensure you have:
- LPS Tool version 3.0.3.1 or higher
- InfluxDB instance (local, cloud, or enterprise)
- InfluxDB authentication token with write permissions
- Organization and bucket configured in InfluxDB
LPS Configuration
Use the LPS CLI commands to configure InfluxDB integration:
Complete Setup (Short Form):
lps influxdb -ie true -iu "<InfluxDB_URL>" -it "<Your-Token>" -io "<ORG>" -ib "<BucketName>"See Your Metrics
To view your metrics, you can either import the Grafana Dashboard Template to quickly set up a pre-configured Grafana dashboard, or run custom queries directly within InfluxDB.
Grafana Dashboard Setup
Before importing the dashboard template, ensure the following requirements are met:
Prerequisites
| Requirement | Description |
|---|---|
| InfluxDB Data Source | An InfluxDB data source must be configured in Grafana with Flux query language enabled |
| Bucket Name | The dashboard expects a bucket named LPS_Metrics. If your bucket has a different name, update the queries accordingly |
| InfluxDB Version | InfluxDB 2.x is required (the dashboard uses Flux queries) |
| Grafana Version | Grafana 12.3.1 or a compatible version |
Import Steps
- Download the LPS Grafana Dashboard Template
- In Grafana, go to Dashboards → Import
- Upload the JSON file or paste its contents
- When prompted, select your InfluxDB data source for the
DS_INFLUXDBvariable - Click Import
Using a Different Bucket Name
If your bucket is not named LPS_Metrics, you have two options:
- Before Import: Open the JSON file and replace all occurrences of
"LPS_Metrics"with your bucket name - After Import: Edit each panel's query and update the bucket name
Metrics Reference
LPS exports metrics to InfluxDB using the line protocol format. This section documents all available measurements, tags, and fields for custom queries.
Common Tags
All measurements include these tags for filtering:
| Tag | Description | Example |
|---|---|---|
plan_name |
Plan name with test start timestamp | MyLoadTest_2026-01-17_14-30-00 |
round_name |
Name of the test round | Round1 |
iteration_name |
Name of the iteration | Iteration1 |
target |
Target URL being tested | https://api.example.com/endpoint |
Measurements
cumulative_duration / windowed_duration
Latency and timing metrics for HTTP requests.
Additional Tag:
| Tag | Values |
|---|---|
metric |
total_time, tcp_handshake, ssl_handshake, time_to_first_byte, waiting_time, receiving_time, sending_time |
Fields:
| Field | Type | Description |
|---|---|---|
sum |
float | Sum of all values (ms) |
avg |
float | Average value (ms) |
min |
float | Minimum value (ms) |
max |
float | Maximum value (ms) |
p50 |
float | 50th percentile (ms) |
p90 |
float | 90th percentile (ms) |
p95 |
float | 95th percentile (ms) |
p99 |
float | 99th percentile (ms) |
cumulative_requests
Cumulative request counts and rates.
Fields:
| Field | Type | Description |
|---|---|---|
requests_count |
int | Total number of requests |
successful_count |
int | Number of successful requests |
failed_count |
int | Number of failed requests |
max_concurrent_requests |
int | Maximum concurrent requests observed |
requests_per_second |
float | Average requests per second |
requests_rate_per_cooldown |
float | Request rate per cooldown window |
error_rate |
float | Error rate percentage |
time_elapsed_ms |
float | Total elapsed time in milliseconds |
windowed_requests
Request counts per cooldown window.
Fields:
| Field | Type | Description |
|---|---|---|
requests_count |
int | Requests in this window |
successful_count |
int | Successful requests in this window |
failed_count |
int | Failed requests in this window |
max_concurrent_requests |
int | Max concurrent requests in this window |
cumulative_response_codes / windowed_response_codes
HTTP response code distribution.
Additional Tags:
| Tag | Description |
|---|---|
status_code |
HTTP status code (e.g., 200, 404, 500) |
status_reason |
HTTP status reason (e.g., OK, Not Found) |
Fields:
| Field | Type | Description |
|---|---|---|
count |
int | Number of responses with this status code |
Example Query:
from(bucket: "LPS_Metrics")
|> range(start: -1h)
|> filter(fn: (r) => r["_measurement"] == "cumulative_response_codes")
|> filter(fn: (r) => r["status_code"] == "200")cumulative_data_transfer
Cumulative data transmission metrics.
Fields:
| Field | Type | Description |
|---|---|---|
data_sent |
float | Total bytes sent |
data_received |
float | Total bytes received |
avg_data_sent |
float | Average bytes sent per request |
avg_data_received |
float | Average bytes received per request |
upstream_bps |
float | Upload throughput (bytes/sec) |
downstream_bps |
float | Download throughput (bytes/sec) |
total_bps |
float | Total throughput (bytes/sec) |
windowed_data_transfer
Data transmission metrics per cooldown window.
Fields:
| Field | Type | Description |
|---|---|---|
data_sent |
float | Bytes sent in this window |
data_received |
float | Bytes received in this window |
upstream_bps |
float | Upload throughput (bytes/sec) |
downstream_bps |
float | Download throughput (bytes/sec) |
total_bps |
float | Total throughput (bytes/sec) |
iteration_final_status
Final status of each iteration (written once when iteration completes).
Fields:
| Field | Type | Description |
|---|---|---|
status |
string | Final status: Success, Failed, Cancelled, Terminated, or Skipped |
Example Queries:
Get the terminal status of a specific test run:
from(bucket: "LPS_Metrics")
|> range(start: -24h)
|> filter(fn: (r) => r["_measurement"] == "iteration_final_status")
|> filter(fn: (r) => r["_field"] == "status")
|> filter(fn: (r) => r["iteration_name"] == "Quick-Http-Iteration")
|> filter(fn: (r) => r["plan_name"] == "Quick-Test-Plan_2026-01-17_02-30-55")
|> filter(fn: (r) => r["round_name"] == "Quick-Test-Round")
|> last()This query retrieves the final execution status (Success, Failed, Cancelled, etc.) from the last 24 hours and returns only the most recent value using last().
Get cumulative request metrics for a specific test run (Grafana):
from(bucket: "LPS_Metrics")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "cumulative_requests")
|> filter(fn: (r) => r["_field"] == "max_concurrent_requests" or r["_field"] == "requests_count" or r["_field"] == "requests_per_second" or r["_field"] == "error_rate" or r["_field"] == "failed_count")
|> filter(fn: (r) => r["iteration_name"] == "Quick-Http-Iteration")
|> filter(fn: (r) => r["plan_name"] == "Quick-Test-Plan_2026-01-17_02-30-55")
|> filter(fn: (r) => r["round_name"] == "Quick-Test-Round")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> yield(name: "mean")This Grafana-specific query:
- Uses Grafana's time range variables (
v.timeRangeStart,v.timeRangeStop) for dynamic filtering - Retrieves multiple request metrics (concurrent requests, total count, rate, error rate, failures)
- Filters to a specific plan, round, and iteration by their names
- Aggregates data into time windows using
v.windowPeriod(set by Grafana based on zoom level) - Calculates the mean for each window, useful for time-series visualizations