InfluxDB is a high-performance time-series database developed by InfluxData, specifically designed for handling time-stamped data like metrics and events. It is optimized for queries, storage, and retrieval of data over time, making it an excellent choice for applications in monitoring, analytics, and real-time data processing.

Key Features of InfluxDB

• Time-series optimized: Specifically designed for data with time-based patterns.
• Schema flexibility: Stores data as key-value pairs in a schema-less fashion.
• Efficient queries: Provides SQL-like query capabilities via InfluxQL or Flux.
• High availability: Supports clustering for scalability and redundancy.
• Integration-ready: Works well with monitoring tools like Grafana and Telegraf.

Common Use Cases

1. IoT Applications: Collect and analyze sensor data from connected devices.
2. DevOps Monitoring: Track system performance metrics such as CPU, memory, and disk usage.
3. Application Performance Monitoring (APM): Monitor performance and logs of distributed applications.
4. Finance: Analyze high-frequency financial transaction data.
5. Real-time Analytics: Process and visualize time-sensitive data in dashboards.

Installation

You can download and install InfluxDB from the official website. Alternatively, you can install it via Docker for easier management:

docker run -d -p 8086:8086 \
  -v influxdb:/var/lib/influxdb \
  influxdb:latest

Install Python client lib

pip install influxdb-client

Write data and query

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS

# Connection details
url = "http://localhost:8086"
token = "your-token-here"  # Replace with your InfluxDB token
org = "your-org-name"      # Replace with your organization name
bucket = "example-bucket"  # Replace with your bucket name

# Initialize the client
client = InfluxDBClient(url=url, token=token, org=org)

# Write data
write_api = client.write_api(write_options=SYNCHRONOUS)
point = Point("temperature") \
    .tag("location", "office") \
    .field("value", 23.5) \
    .time("2024-12-02T12:00:00Z")
write_api.write(bucket=bucket, org=org, record=point)

print("Data written successfully!")

# Query data
query_api = client.query_api()
query = f'from(bucket: "{bucket}") |> range(start: -1h)'
tables = query_api.query(query, org=org)

for table in tables:
    for record in table.records:
        print(f'Time: {record["_time"]}, Value: {record["_value"]}')

client.close()

Explanation

Write Data: Using the write_api, you can write points to your bucket with tags, fields, and timestamps. Query Data: Using the query_api, you can retrieve and process time-series data.

Source

• https://www.influxdata.com/
• https://docs.influxdata.com/influxdb/cloud/api-guide/client-libraries/python/