InfluxDB 2.0 Python client library
Project description
This repository contains the Python client library for the InfluxDB 2.0.
Note: Use this client library with InfluxDB 2.x and InfluxDB 1.8+. For connecting to InfluxDB 1.7 or earlier instances, use the influxdb-python client library. The API of the influxdb-client-python is not the backwards-compatible with the old one - influxdb-python.
Documentation
This section contains links to the client library documentation.
InfluxDB 2.0 client features
- Querying data
using the Flux language
into csv, raw data, flux_table structure, Pandas DataFrame
- Writing data using
RxPY Observable
- InfluxDB 2.0 API client for management
the client is generated from the swagger by using the openapi-generator
organizations & users management
buckets management
tasks management
authorizations
health check
…
Installation
InfluxDB python library uses RxPY - The Reactive Extensions for Python (RxPY).
Python 3.6 or later is required.
pip install
The python package is hosted on PyPI, you can install latest version directly:
pip install 'influxdb-client[ciso]'Then import the package:
import influxdb_clientSetuptools
Install via Setuptools.
python setup.py install --user(or sudo python setup.py install to install the package for all users)
Getting Started
Please follow the Installation and then run the following:
from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS
bucket = "my-bucket"
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()
p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
write_api.write(bucket=bucket, record=p)
## using Table structure
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
for table in tables:
print(table)
for row in table.records:
print (row.values)
## using csv library
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
val_count = 0
for row in csv_result:
for cell in row:
val_count += 1Client configuration
Via File
A client can be configured via *.ini file in segment influx2.
The following options are supported:
url - the url to connect to InfluxDB
org - default destination organization for writes and queries
token - the token to use for the authorization
timeout - socket timeout in ms (default value is 10000)
verify_ssl - set this to false to skip verifying SSL certificate when calling API from https server
ssl_ca_cert - set this to customize the certificate file to verify the peer
connection_pool_maxsize - set the number of connections to save that can be reused by urllib3
auth_basic - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
profilers - set the list of enabled Flux profilers
self.client = InfluxDBClient.from_config_file("config.ini")[influx2]
url=http://localhost:8086
org=my-org
token=my-token
timeout=6000
verify_ssl=FalseVia Environment Properties
A client can be configured via environment properties.
Supported properties are:
INFLUXDB_V2_URL - the url to connect to InfluxDB
INFLUXDB_V2_ORG - default destination organization for writes and queries
INFLUXDB_V2_TOKEN - the token to use for the authorization
INFLUXDB_V2_TIMEOUT - socket timeout in ms (default value is 10000)
INFLUXDB_V2_VERIFY_SSL - set this to false to skip verifying SSL certificate when calling API from https server
INFLUXDB_V2_SSL_CA_CERT - set this to customize the certificate file to verify the peer
INFLUXDB_V2_CONNECTION_POOL_MAXSIZE - set the number of connections to save that can be reused by urllib3
INFLUXDB_V2_AUTH_BASIC - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
INFLUXDB_V2_PROFILERS - set the list of enabled Flux profilers
self.client = InfluxDBClient.from_env_properties()Profile query
The Flux Profiler package provides performance profiling tools for Flux queries and operations.
You can enable printing profiler information of the Flux query in client library by:
set QueryOptions.profilers in QueryApi,
set INFLUXDB_V2_PROFILERS environment variable,
set profilers option in configuration file.
When the profiler is enabled, the result of flux query contains additional tables “profiler/*”. In order to have consistent behaviour with enabled/disabled profiler, FluxCSVParser excludes “profiler/*” measurements from result.
Example how to enable profilers using API:
q = '''
from(bucket: stringParam)
|> range(start: -5m, stop: now())
|> filter(fn: (r) => r._measurement == "mem")
|> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
|> aggregateWindow(every: 1m, fn: mean)
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
'''
p = {
"stringParam": "my-bucket",
}
query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"]))
csv_result = query_api.query(query=q, params=p)Example of a profiler output:
===============
Profiler: query
===============
from(bucket: stringParam)
|> range(start: -5m, stop: now())
|> filter(fn: (r) => r._measurement == "mem")
|> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
|> aggregateWindow(every: 1m, fn: mean)
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
========================
Profiler: profiler/query
========================
result : _profiler
table : 0
_measurement : profiler/query
TotalDuration : 8924700
CompileDuration : 350900
QueueDuration : 33800
PlanDuration : 0
RequeueDuration : 0
ExecuteDuration : 8486500
Concurrency : 0
MaxAllocated : 2072
TotalAllocated : 0
flux/query-plan :
digraph {
ReadWindowAggregateByTime11
// every = 1m, aggregates = [mean], createEmpty = true, timeColumn = "_stop"
pivot8
generated_yield
ReadWindowAggregateByTime11 -> pivot8
pivot8 -> generated_yield
}
influxdb/scanned-bytes: 0
influxdb/scanned-values: 0
===========================
Profiler: profiler/operator
===========================
result : _profiler
table : 1
_measurement : profiler/operator
Type : *universe.pivotTransformation
Label : pivot8
Count : 3
MinDuration : 32600
MaxDuration : 126200
DurationSum : 193400
MeanDuration : 64466.666666666664
===========================
Profiler: profiler/operator
===========================
result : _profiler
table : 1
_measurement : profiler/operator
Type : *influxdb.readWindowAggregateSource
Label : ReadWindowAggregateByTime11
Count : 1
MinDuration : 940500
MaxDuration : 940500
DurationSum : 940500
MeanDuration : 940500.0How to use
Writes
The WriteApi supports synchronous, asynchronous and batching writes into InfluxDB 2.0. The data should be passed as a InfluxDB Line Protocol, Data Point or Observable stream.
Important: The WriteApi in batching mode (default mode) is suppose to run as a singleton. To flush all your data you should wrap the execution using ``with client.write_api(…) as write_api:`` statement or call ``_write_client.close()`` at the end of your script.
The default instance of WriteApi use batching.
The data could be written as
string or bytes that is formatted as a InfluxDB’s line protocol
Data Point structure
Dictionary style mapping with keys: measurement, tags, fields and time
List of above items
A batching type of write also supports an Observable that produce one of an above item
Batching
The batching is configurable by write_options:
Property |
Description |
Default Value |
|---|---|---|
batch_size |
the number of data pointx to collect in a batch |
1000 |
flush_interval |
the number of milliseconds before the batch is written |
1000 |
jitter_interval |
the number of milliseconds to increase the batch flush interval by a random amount |
0 |
retry_interval |
the number of milliseconds to retry first unsuccessful write. The next retry delay is computed using exponential random backoff. The retry interval is used when the InfluxDB server does not specify “Retry-After” header. |
5000 |
max_retry_time |
maximum total retry timeout in milliseconds. |
180_000 |
max_retries |
the number of max retries when write fails |
5 |
max_retry_delay |
the maximum delay between each retry attempt in milliseconds |
125_000 |
exponential_base |
the base for the exponential retry delay, the next delay is computed using random exponential backoff as a random value within the interval retry_interval * exponential_base^(attempts-1) and retry_interval * exponential_base^(attempts). Example for retry_interval=5_000, exponential_base=2, max_retry_delay=125_000, total=5 Retry delays are random distributed values within the ranges of [5_000-10_000, 10_000-20_000, 20_000-40_000, 40_000-80_000, 80_000-125_000] |
2 |
from datetime import datetime, timedelta
import pandas as pd
import rx
from pytz import UTC
from rx import operators as ops
from influxdb_client import InfluxDBClient, Point, WriteOptions
with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as _client:
with _client.write_api(write_options=WriteOptions(batch_size=500,
flush_interval=10_000,
jitter_interval=2_000,
retry_interval=5_000,
max_retries=5,
max_retry_delay=30_000,
exponential_base=2)) as _write_client:
"""
Write Line Protocol formatted as string
"""
_write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
_write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
"h2o_feet,location=coyote_creek water_level=3.0 3"])
"""
Write Line Protocol formatted as byte array
"""
_write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
_write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
"h2o_feet,location=coyote_creek water_level=3.0 3".encode()])
"""
Write Dictionary-style object
"""
_write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
"fields": {"water_level": 1.0}, "time": 1})
_write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
"fields": {"water_level": 2.0}, "time": 2},
{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
"fields": {"water_level": 3.0}, "time": 3}])
"""
Write Data Point
"""
_write_client.write("my-bucket", "my-org",
Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
_write_client.write("my-bucket", "my-org",
[Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])
"""
Write Observable stream
"""
_data = rx \
.range(7, 11) \
.pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))
_write_client.write("my-bucket", "my-org", _data)
"""
Write Pandas DataFrame
"""
_now = datetime.now(UTC)
_data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
index=[_now, _now + timedelta(hours=1)],
columns=["location", "water_level"])
_write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
data_frame_tag_columns=['location'])Asynchronous client
Data are writes in an asynchronous HTTP request.
from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import ASYNCHRONOUS
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=ASYNCHRONOUS)
_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
async_result = write_api.write(bucket="my-bucket", record=[_point1, _point2])
async_result.get()
client.close()Synchronous client
Data are writes in a synchronous HTTP request.
from influxdb_client import InfluxDBClient, Point
from influxdb_client .client.write_api import SYNCHRONOUS
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)
_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
write_api.write(bucket="my-bucket", record=[_point1, _point2])
client.close()Queries
The result retrieved by QueryApi could be formatted as a:
Flux data structure: FluxTable, FluxColumn and FluxRecord
Query bind parameters
csv.reader which will iterate over CSV lines
Raw unprocessed results as a str iterator
The API also support streaming FluxRecord via query_stream, see example below:
from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()
"""
Prepare data
"""
_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
write_api.write(bucket="my-bucket", record=[_point1, _point2])
"""
Query: using Table structure
"""
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
for table in tables:
print(table)
for record in table.records:
print(record.values)
print()
print()
"""
Query: using Bind parameters
"""
p = {"_start": datetime.timedelta(hours=-1),
"_location": "Prague",
"_desc": True,
"_floatParam": 25.1,
"_every": datetime.timedelta(minutes=5)
}
tables = query_api.query('''
from(bucket:"my-bucket") |> range(start: _start)
|> filter(fn: (r) => r["_measurement"] == "my_measurement")
|> filter(fn: (r) => r["_field"] == "temperature")
|> filter(fn: (r) => r["location"] == _location and r["_value"] > _floatParam)
|> aggregateWindow(every: _every, fn: mean, createEmpty: true)
|> sort(columns: ["_time"], desc: _desc)
''', params=p)
for table in tables:
print(table)
for record in table.records:
print(str(record["_time"]) + " - " + record["location"] + ": " + str(record["_value"]))
print()
print()
"""
Query: using Stream
"""
records = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -10m)')
for record in records:
print(f'Temperature in {record["location"]} is {record["_value"]}')
"""
Interrupt a stream after retrieve a required data
"""
large_stream = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -100d)')
for record in large_stream:
if record["location"] == "New York":
print(f'New York temperature: {record["_value"]}')
break
large_stream.close()
print()
print()
"""
Query: using csv library
"""
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
dialect=Dialect(header=False, delimiter=",", comment_prefix="#", annotations=[],
date_time_format="RFC3339"))
for csv_line in csv_result:
if not len(csv_line) == 0:
print(f'Temperature in {csv_line[9]} is {csv_line[6]}')
"""
Close client
"""
client.close()Pandas DataFrame
The client is able to retrieve data in Pandas DataFrame format thought query_data_frame:
from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()
"""
Prepare data
"""
_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)
write_api.write(bucket="my-bucket", record=[_point1, _point2])
"""
Query: using Pandas DataFrame
"""
data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
'|> range(start: -10m) '
'|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
'|> keep(columns: ["location", "temperature"])')
print(data_frame.to_string())
"""
Close client
"""
client.close()Output:
result table location temperature
0 _result 0 New York 24.3
1 _result 1 Prague 25.3Examples
How to efficiently import large dataset
The following example shows how to import dataset with dozen megabytes. If you would like to import gigabytes of data then use our multiprocessing example: import_data_set_multiprocessing.py for use a full capability of your hardware.
sources - import_data_set.py
"""
Import VIX - CBOE Volatility Index - from "vix-daily.csv" file into InfluxDB 2.0
https://datahub.io/core/finance-vix#data
"""
from collections import OrderedDict
from csv import DictReader
import rx
from rx import operators as ops
from influxdb_client import InfluxDBClient, Point, WriteOptions
def parse_row(row: OrderedDict):
"""Parse row of CSV file into Point with structure:
financial-analysis,type=ily close=18.47,high=19.82,low=18.28,open=19.82 1198195200000000000
CSV format:
Date,VIX Open,VIX High,VIX Low,VIX Close\n
2004-01-02,17.96,18.68,17.54,18.22\n
2004-01-05,18.45,18.49,17.44,17.49\n
2004-01-06,17.66,17.67,16.19,16.73\n
2004-01-07,16.72,16.75,15.5,15.5\n
2004-01-08,15.42,15.68,15.32,15.61\n
2004-01-09,16.15,16.88,15.57,16.75\n
...
:param row: the row of CSV file
:return: Parsed csv row to [Point]
"""
"""
For better performance is sometimes useful directly create a LineProtocol to avoid unnecessary escaping overhead:
"""
# from pytz import UTC
# import ciso8601
# from influxdb_client.client.write.point import EPOCH
#
# time = (UTC.localize(ciso8601.parse_datetime(row["Date"])) - EPOCH).total_seconds() * 1e9
# return f"financial-analysis,type=vix-daily" \
# f" close={float(row['VIX Close'])},high={float(row['VIX High'])},low={float(row['VIX Low'])},open={float(row['VIX Open'])} " \
# f" {int(time)}"
return Point("financial-analysis") \
.tag("type", "vix-daily") \
.field("open", float(row['VIX Open'])) \
.field("high", float(row['VIX High'])) \
.field("low", float(row['VIX Low'])) \
.field("close", float(row['VIX Close'])) \
.time(row['Date'])
"""
Converts vix-daily.csv into sequence of datad point
"""
data = rx \
.from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
.pipe(ops.map(lambda row: parse_row(row)))
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)
"""
Create client that writes data in batches with 50_000 items.
"""
write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))
"""
Write data into InfluxDB
"""
write_api.write(bucket="my-bucket", record=data)
write_api.close()
"""
Querying max value of CBOE Volatility Index
"""
query = 'from(bucket:"my-bucket")' \
' |> range(start: 0, stop: now())' \
' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
' |> max()'
result = client.query_api().query(query=query)
"""
Processing results
"""
print()
print("=== results ===")
print()
for table in result:
for record in table.records:
print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))
"""
Close client
"""
client.close()Efficiency write data from IOT sensor
sources - iot_sensor.py
"""
Efficiency write data from IOT sensor - write changed temperature every minute
"""
import atexit
import platform
from datetime import timedelta
import psutil as psutil
import rx
from rx import operators as ops
from influxdb_client import InfluxDBClient, WriteApi, WriteOptions
def on_exit(db_client: InfluxDBClient, write_api: WriteApi):
"""Close clients after terminate a script.
:param db_client: InfluxDB client
:param write_api: WriteApi
:return: nothing
"""
write_api.close()
db_client.close()
def sensor_temperature():
"""Read a CPU temperature. The [psutil] doesn't support MacOS so we use [sysctl].
:return: actual CPU temperature
"""
os_name = platform.system()
if os_name == 'Darwin':
from subprocess import check_output
output = check_output(["sysctl", "machdep.xcpm.cpu_thermal_level"])
import re
return re.findall(r'\d+', str(output))[0]
else:
return psutil.sensors_temperatures()["coretemp"][0]
def line_protocol(temperature):
"""Create a InfluxDB line protocol with structure:
iot_sensor,hostname=mine_sensor_12,type=temperature value=68
:param temperature: the sensor temperature
:return: Line protocol to write into InfluxDB
"""
import socket
return 'iot_sensor,hostname={},type=temperature value={}'.format(socket.gethostname(), temperature)
"""
Read temperature every minute; distinct_until_changed - produce only if temperature change
"""
data = rx\
.interval(period=timedelta(seconds=60))\
.pipe(ops.map(lambda t: sensor_temperature()),
ops.distinct_until_changed(),
ops.map(lambda temperature: line_protocol(temperature)))
_db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)
"""
Create client that writes data into InfluxDB
"""
_write_api = _db_client.write_api(write_options=WriteOptions(batch_size=1))
_write_api.write(bucket="my-bucket", record=data)
"""
Call after terminate a script
"""
atexit.register(on_exit, _db_client, _write_api)
input()Connect to InfluxDB Cloud
The following example demonstrate a simplest way how to write and query date with the InfluxDB Cloud.
At first point you should create an authentication token as is described here.
After that you should configure properties: influx_cloud_url, influx_cloud_token, bucket and org in a influx_cloud.py example.
The last step is run a python script via: python3 influx_cloud.py.
sources - influx_cloud.py
"""
Connect to InfluxDB 2.0 - write data and query them
"""
from datetime import datetime
from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS
"""
Configure credentials
"""
influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
influx_cloud_token = '...'
bucket = '...'
org = '...'
client = InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token)
try:
kind = 'temperature'
host = 'host1'
device = 'opt-123'
"""
Write data by Point structure
"""
point = Point(kind).tag('host', host).tag('device', device).field('value', 25.3).time(time=datetime.utcnow())
print(f'Writing to InfluxDB cloud: {point.to_line_protocol()} ...')
write_api = client.write_api(write_options=SYNCHRONOUS)
write_api.write(bucket=bucket, org=org, record=point)
print()
print('success')
print()
print()
"""
Query written data
"""
query = f'from(bucket: "{bucket}") |> range(start: -1d) |> filter(fn: (r) => r._measurement == "{kind}")'
print(f'Querying from InfluxDB cloud: "{query}" ...')
print()
query_api = client.query_api()
tables = query_api.query(query=query, org=org)
for table in tables:
for row in table.records:
print(f'{row.values["_time"]}: host={row.values["host"]},device={row.values["device"]} '
f'{row.values["_value"]} °C')
print()
print('success')
except Exception as e:
print(e)
finally:
client.close()How to use Jupyter + Pandas + InfluxDB 2
The first example shows how to use client capabilities to predict stock price via Keras, TensorFlow, sklearn:
The example is taken from Kaggle.
sources - stock-predictions.ipynb
Result:
The second example shows how to use client capabilities to realtime visualization via hvPlot, Streamz, RxPY:
sources - realtime-stream.ipynb
Other examples
You could find all examples at GitHub: influxdb-client-python/examples.
Advanced Usage
Gzip support
InfluxDBClient does not enable gzip compression for http requests by default. If you want to enable gzip to reduce transfer data’s size, you can call:
from influxdb_client import InfluxDBClient
_db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", enable_gzip=True)Proxy configuration
You can configure the client to tunnel requests through an HTTP proxy. The following proxy options are supported:
proxy - Set this to configure the http proxy to be used, ex. http://localhost:3128
proxy_headers - A dictionary containing headers that will be sent to the proxy. Could be used for proxy authentication.
from influxdb_client import InfluxDBClient
with InfluxDBClient(url="http://localhost:8086",
token="my-token",
org="my-org",
proxy="http://localhost:3128") as client:Delete data
The delete_api.py supports deletes points from an InfluxDB bucket.
from influxdb_client import InfluxDBClient
client = InfluxDBClient(url="http://localhost:8086", token="my-token")
delete_api = client.delete_api()
"""
Delete Data
"""
start = "1970-01-01T00:00:00Z"
stop = "2021-02-01T00:00:00Z"
delete_api.delete(start, stop, '_measurement="my_measurement"', bucket='my-bucket', org='my-org')
"""
Close client
"""
client.close()InfluxDB 1.8 API compatibility
InfluxDB 1.8.0 introduced forward compatibility APIs for InfluxDB 2.0. This allow you to easily move from InfluxDB 1.x to InfluxDB 2.0 Cloud or open source.
The following forward compatible APIs are available:
API |
Endpoint |
Description |
|---|---|---|
Query data in InfluxDB 1.8.0+ using the InfluxDB 2.0 API and Flux (endpoint should be enabled by flux-enabled option) |
||
Write data to InfluxDB 1.8.0+ using the InfluxDB 2.0 API |
||
Check the health of your InfluxDB instance |
For detail info see InfluxDB 1.8 example.
HTTP Retry Strategy
By default the client uses a retry strategy only for batching writes (for more info see Batching). For other HTTP requests there is no one retry strategy, but it could be configured by retries parameter of InfluxDBClient.
For more info about how configure HTTP retry see details in urllib3 documentation.
from urllib3 import Retry
from influxdb_client import InfluxDBClient
retries = Retry(connect=5, read=2, redirect=5)
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", retries=retries)Nanosecond precision
The Python’s datetime doesn’t support precision with nanoseconds so the library during writes and queries ignores everything after microseconds.
If you would like to use datetime with nanosecond precision you should use pandas.Timestamp that is replacement for python datetime.datetime object and also you should set a proper DateTimeHelper to the client.
sources - nanosecond_precision.py
from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.util.date_utils_pandas import PandasDateTimeHelper
from influxdb_client.client.write_api import SYNCHRONOUS
"""
Set PandasDate helper which supports nanoseconds.
"""
import influxdb_client.client.util.date_utils as date_utils
date_utils.date_helper = PandasDateTimeHelper()
"""
Prepare client.
"""
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()
"""
Prepare data
"""
point = Point("h2o_feet") \
.field("water_level", 10) \
.tag("location", "pacific") \
.time('1996-02-25T21:20:00.001001231Z')
print(f'Time serialized with nanosecond precision: {point.to_line_protocol()}')
print()
write_api.write(bucket="my-bucket", record=point)
"""
Query: using Stream
"""
query = '''
from(bucket:"my-bucket")
|> range(start: 0, stop: now())
|> filter(fn: (r) => r._measurement == "h2o_feet")
'''
records = query_api.query_stream(query)
for record in records:
print(f'Temperature in {record["location"]} is {record["_value"]} at time: {record["_time"]}')
"""
Close client
"""
client.close()Local tests
# start/restart InfluxDB2 on local machine using docker
./scripts/influxdb-restart.sh
# install requirements
pip install -e . --user
pip install -e .\[extra\] --user
pip install -e .\[test\] --user
# run unit & integration tests
pytest testsContributing
Bug reports and pull requests are welcome on GitHub at https://github.com/influxdata/influxdb-client-python.
License
The gem is available as open source under the terms of the MIT License.
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