Python

Installation

pip3 install telemetry-sh

Usage

Import Library

from telemetry_sh import Telemetry
from datetime import datetime, timezone

Initialize Client

API_KEY = "YOUR_API_KEY"  # Replace with your actual API key
telemetry = Telemetry()
telemetry.init(API_KEY)

Log Some Data

Telemetry automatically creates tables when data is logged. In the following example, we log some Uber ride data to a table called uber_rides. Telemetry will automatically create this table and its corresponding schema with columns: city, price, and timestamp.

data = {
    "city": "paris",
    "price": 42,
    "timestamp": datetime.now(timezone.utc).isoformat()
}

try:
    response = telemetry.log("uber_rides", data)
    print("Log response:", response)
except Exception as e:
    print("Error logging data:", e)

Query Some Data

You can query the data using SQL through the query API.

query = """
    SELECT
        city,
        AVG(price) AS average_price
    FROM
        uber_rides
    GROUP BY
        city
"""

try:
    query_response = telemetry.query(query)
    print("Query response:", query_response)
except Exception as e:
    print("Error querying data:", e)

Async Usage

In codebases that utilize asyncio, using synchronous requests to log data can block all ongoing asyncio tasks until the request completes. To address this, we've introduced TelemetryAsync, which leverages aiohttp internally to provide non-blocking, asynchronous logging.

Installation

Make sure you have the telemetry-sh package installed:

pip3 install telemetry-sh

Import Library

from telemetry_sh import TelemetryAsync
from datetime import datetime, timezone
import asyncio

Initialize Client

API_KEY = "YOUR_API_KEY"  # Replace with your actual API key
telemetry = TelemetryAsync()
await telemetry.init(API_KEY)

Log Some Data Asynchronously

TelemetryAsync allows you to log data without blocking other asyncio tasks. Telemetry automatically creates tables when data is logged. In the following example, we log some Uber ride data to a table called uber_rides. Telemetry will automatically create this table and its corresponding schema with columns: city, price, and timestamp.

data = {
    "city": "paris",
    "price": 42,
    "timestamp": datetime.now(timezone.utc).isoformat()
}

async def log_data():
    try:
        response = await telemetry.log("uber_rides", data)
        print("Log response:", response)
    except Exception as e:
        print("Error logging data:", e)

# Run the async logging function
asyncio.run(log_data())

Query Some Data Asynchronously

Just like logging, querying data can also be performed asynchronously.

query = """
    SELECT
        city,
        AVG(price) AS average_price
    FROM
        uber_rides
    GROUP BY
        city
"""

async def query_data():
    try:
        query_response = await telemetry.query(query)
        print("Query response:", query_response)
    except Exception as e:
        print("Error querying data:", e)

# Run the async query function
asyncio.run(query_data())

Bulk Ingestion Support

TelemetryAsync also supports bulk ingestion. The log method can accept a list of dictionaries in addition to a single dictionary.

bulk_data = [
    {"city": "paris", "price": 42, "timestamp": datetime.now(timezone.utc).isoformat()},
    {"city": "london", "price": 50, "timestamp": datetime.now(timezone.utc).isoformat()},
]

async def log_bulk_data():
    try:
        response = await telemetry.log("uber_rides", bulk_data)
        print("Bulk log response:", response)
    except Exception as e:
        print("Error logging bulk data:", e)

# Run the async bulk logging function
asyncio.run(log_bulk_data())