55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
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import time, os, influxdb_client
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from influxdb_client import InfluxDBClient, Point, WritePrecision
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from influxdb_client.client.write_api import SYNCHRONOUS, ASYNCHRONOUS, WriteOptions
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from datetime import timedelta
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from concurrent.futures import ThreadPoolExecutor
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# INFLUXDB config
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# token = "apg1gysUeCcxdcRTMmosJTenbEppmUNi9rXlANDB2oNadBdWAu2GVTDc_q_dyo0iyYsckKaOvPRm6ba2NK0y_A=="
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token = os.getenv("INFLUX_TOKEN")
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# bucket = "JBOD"
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bucket = os.getenv("INFLUX_BUCKET")
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# org = "staging"
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org = os.getenv("INFLUX_ORG")
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# url = "http://localhost:8086"
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url = os.getenv("INFLUX_URL")
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# measurement = "MD1200"
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measurement = os.getenv("INFLUX_MEASUREMENT")
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# MACHINE_TAG = "CHONGUS1200"
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MACHINE_TAG = os.getenv("INFLUX_MACHINE_TAG")
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# LOCATION = "HQ"
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LOCATION = os.getenv("INFLUX_LOCATION")
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# INFLX_SEPARATE_POINTS = 0.1
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INFLUX_SEPARATE_POINTS = int(os.getenv("INFLUX_SEPARATE_POINTS"))
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# Initialize InfluxDB client and influxdb API
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inflxdb_client = influxdb_client.InfluxDBClient(url=url, token=token, org=org)
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#write_api = inflxdb_client.write_api(write_options=SYNCHRONOUS)
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write_api = inflxdb_client.write_api(write_options=WriteOptions(batch_size=500, flush_interval=1000))
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# Threaded flow processor
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def process_temps(inEntry):
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global MDict
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# ---LeData---
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# {'bp1': 35, 'bp2': 29, 'sim0': 35, 'sim1': 34, 'exp0': 56, 'exp1': 54}
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# ---LeData---
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# Prep InfluxDB data
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inflxdb_Data_To_Send = (
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influxdb_client.Point(f"{measurement}-script")
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.tag("MACHINE", MACHINE_TAG)
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.tag("LOCATION", LOCATION)
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.field("Backplane1", inEntry["bp1"])
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.field("Backplane2", inEntry["bp2"])
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.field("SASIntModule0", inEntry["sim0"])
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.field("SASIntModule1", inEntry["sim1"])
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.field("Expander0", inEntry["exp0"])
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.field("Expander1", inEntry["exp1"])
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.field("Average", inEntry["avg"])
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)
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print("----------------")
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return ()
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