Welcome to InfluxDB’s documentation!¶
Release: | 5.3.1 |
---|---|
Date: | Nov 15, 2022 |
Keywords: | python, time series, database |
Contents¶
InfluxDB-Python¶
The v1 client libraries for InfluxDB were typically developed and maintained by community members. They have all now been succeeded by v2 client libraries. They are being archived it in favor of the v2 client library. See https://github.com/influxdata/influxdb-python/issues/918.
If there are still users of this v1 client library, and they or somebody else are willing to keep them updated with security fixes at a minimum please reach out on the [Community Forums](https://community.influxdata.com/) or [InfluxData Slack](https://influxdata.com/slack).
InfluxDB-Python¶
InfluxDB-Python is a client for interacting with InfluxDB.
Note: This library is for use with InfluxDB 1.x. For connecting to InfluxDB 2.x instances, please use the the influxdb-client-python client.
Development of this library is maintained by:
Github ID | URL |
---|---|
@aviau | (https://github.com/aviau) |
@xginn8 | (https://github.com/xginn8) |
@sebito91 | (https://github.com/sebito91) |
InfluxDB is an open-source distributed time series database, find more about InfluxDB at https://docs.influxdata.com/influxdb/latest
InfluxDB pre v1.1.0 users¶
This module is tested with InfluxDB versions: v1.2.4, v1.3.9, v1.4.3, v1.5.4, v1.6.4, and 1.7.4.
Those users still on InfluxDB v0.8.x users may still use the legacy client by importing from influxdb.influxdb08 import InfluxDBClient
.
Installation¶
Install, upgrade and uninstall influxdb-python with these commands:
$ pip install influxdb
$ pip install --upgrade influxdb
$ pip uninstall influxdb
On Debian/Ubuntu, you can install it with this command:
$ sudo apt-get install python-influxdb
Dependencies¶
The influxdb-python distribution is supported and tested on Python 2.7, 3.5, 3.6, 3.7, PyPy and PyPy3.
Note: Python <3.5 are currently untested. See .travis.yml
.
Main dependency is:
- Requests: HTTP library for human beings (http://docs.python-requests.org/)
Additional dependencies are:
- pandas: for writing from and reading to DataFrames (http://pandas.pydata.org/)
- Sphinx: Tool to create and manage the documentation (http://sphinx-doc.org/)
- Nose: to auto-discover tests (http://nose.readthedocs.org/en/latest/)
- Mock: to mock tests (https://pypi.python.org/pypi/mock)
Documentation¶
Documentation is available at https://influxdb-python.readthedocs.io/en/latest/.
You will need Sphinx installed to generate the documentation.
The documentation can be generated by running:
$ tox -e docs
Generated documentation can be found in the docs/build/html/ directory.
Examples¶
Here’s a basic example (for more see the examples directory):
$ python
>>> from influxdb import InfluxDBClient
>>> json_body = [
{
"measurement": "cpu_load_short",
"tags": {
"host": "server01",
"region": "us-west"
},
"time": "2009-11-10T23:00:00Z",
"fields": {
"value": 0.64
}
}
]
>>> client = InfluxDBClient('localhost', 8086, 'root', 'root', 'example')
>>> client.create_database('example')
>>> client.write_points(json_body)
>>> result = client.query('select value from cpu_load_short;')
>>> print("Result: {0}".format(result))
Testing¶
Make sure you have tox by running the following:
$ pip install tox
To test influxdb-python with multiple version of Python, you can use Tox:
$ tox
Support¶
For issues with, questions about, or feedback for InfluxDB, please look into our community page: http://influxdb.com/community/.
We are also lurking on the following:
- #influxdb on irc.freenode.net
- #influxdb on gophers.slack.com
Development¶
All development is done on Github. Use Issues to report problems or submit contributions.
Please note that we WILL get to your questions/issues/concerns as quickly as possible. We maintain many software repositories and sometimes things may get pushed to the backburner. Please don’t take offense, we will do our best to reply as soon as possible!
Source code¶
The source code is currently available on Github: https://github.com/influxdata/influxdb-python
TODO¶
The TODO/Roadmap can be found in Github bug tracker: https://github.com/influxdata/influxdb-python/issues
API Documentation¶
To connect to a InfluxDB, you must create a
InfluxDBClient
object. The default configuration
connects to InfluxDB on localhost
with the default
ports. The below instantiation statements are all equivalent:
from influxdb import InfluxDBClient
# using Http
client = InfluxDBClient(database='dbname')
client = InfluxDBClient(host='127.0.0.1', port=8086, database='dbname')
client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')
# using UDP
client = InfluxDBClient(host='127.0.0.1', database='dbname', use_udp=True, udp_port=4444)
To write pandas DataFrames or to read data into a
pandas DataFrame, use a DataFrameClient
object.
These clients are initiated in the same way as the
InfluxDBClient
:
from influxdb import DataFrameClient
client = DataFrameClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')
Note
Only when using UDP (use_udp=True) the connection is established.
InfluxDBClient
¶
-
class
influxdb.
InfluxDBClient
(host=u'localhost', port=8086, username=u'root', password=u'root', database=None, ssl=False, verify_ssl=False, timeout=None, retries=3, use_udp=False, udp_port=4444, proxies=None, pool_size=10, path=u'', cert=None, gzip=False, session=None, headers=None, socket_options=None)¶ InfluxDBClient primary client object to connect InfluxDB.
The
InfluxDBClient
object holds information necessary to connect to InfluxDB. Requests can be made to InfluxDB directly through the client.The client supports the use as a context manager.
Parameters: - host (str) – hostname to connect to InfluxDB, defaults to ‘localhost’
- port (int) – port to connect to InfluxDB, defaults to 8086
- username (str) – user to connect, defaults to ‘root’
- password (str) – password of the user, defaults to ‘root’
- pool_size (int) – urllib3 connection pool size, defaults to 10.
- database (str) – database name to connect to, defaults to None
- ssl (bool) – use https instead of http to connect to InfluxDB, defaults to False
- verify_ssl (bool) – verify SSL certificates for HTTPS requests, defaults to False
- timeout (int) – number of seconds Requests will wait for your client to establish a connection, defaults to None
- retries (int) – number of attempts your client will make before aborting, defaults to 3 0 - try until success 1 - attempt only once (without retry) 2 - maximum two attempts (including one retry) 3 - maximum three attempts (default option)
- use_udp (bool) – use UDP to connect to InfluxDB, defaults to False
- udp_port (int) – UDP port to connect to InfluxDB, defaults to 4444
- proxies (dict) – HTTP(S) proxy to use for Requests, defaults to {}
- path (str) – path of InfluxDB on the server to connect, defaults to ‘’
- cert (str) – Path to client certificate information to use for mutual TLS authentication. You can specify a local cert to use as a single file containing the private key and the certificate, or as a tuple of both files’ paths, defaults to None
- gzip (bool) – use gzip content encoding to compress requests
- session (requests.Session) – allow for the new client request to use an existing requests Session, defaults to None
- headers (dict) – headers to add to Requests, will add ‘Content-Type’ and ‘Accept’ unless these are already present, defaults to {}
- socket_options (list) – use custom tcp socket options,
If not specified, then defaults are loaded from
HTTPConnection.default_socket_options
Raises: ValueError – if cert is provided but ssl is disabled (set to False)
-
alter_retention_policy
(name, database=None, duration=None, replication=None, default=None, shard_duration=None)¶ Modify an existing retention policy for a database.
Parameters: - name (str) – the name of the retention policy to modify
- database (str) – the database for which the retention policy is modified. Defaults to current client’s database
- duration (str) – the new duration of the existing retention policy. Durations such as 1h, 90m, 12h, 7d, and 4w, are all supported and mean 1 hour, 90 minutes, 12 hours, 7 day, and 4 weeks, respectively. For infinite retention, meaning the data will never be deleted, use ‘INF’ for duration. The minimum retention period is 1 hour.
- replication (int) – the new replication of the existing retention policy
- default (bool) – whether or not to set the modified policy as default
- shard_duration (str) – the shard duration of the retention policy. Durations such as 1h, 90m, 12h, 7d, and 4w, are all supported and mean 1 hour, 90 minutes, 12 hours, 7 day, and 4 weeks, respectively. Infinite retention is not supported. As a workaround, specify a “1000w” duration to achieve an extremely long shard group duration. The minimum shard group duration is 1 hour.
Note
at least one of duration, replication, or default flag should be set. Otherwise the operation will fail.
-
close
()¶ Close http session.
-
create_continuous_query
(name, select, database=None, resample_opts=None)¶ Create a continuous query for a database.
Parameters: - name (str) – the name of continuous query to create
- select (str) – select statement for the continuous query
- database (str) – the database for which the continuous query is created. Defaults to current client’s database
- resample_opts (str) – resample options
Example: >> select_clause = 'SELECT mean("value") INTO "cpu_mean" ' \ ... 'FROM "cpu" GROUP BY time(1m)' >> client.create_continuous_query( ... 'cpu_mean', select_clause, 'db_name', 'EVERY 10s FOR 2m' ... ) >> client.get_list_continuous_queries() [ { 'db_name': [ { 'name': 'cpu_mean', 'query': 'CREATE CONTINUOUS QUERY "cpu_mean" ' 'ON "db_name" ' 'RESAMPLE EVERY 10s FOR 2m ' 'BEGIN SELECT mean("value") ' 'INTO "cpu_mean" FROM "cpu" ' 'GROUP BY time(1m) END' } ] } ]
-
create_database
(dbname)¶ Create a new database in InfluxDB.
Parameters: dbname (str) – the name of the database to create
-
create_retention_policy
(name, duration, replication, database=None, default=False, shard_duration=u'0s')¶ Create a retention policy for a database.
Parameters: - name (str) – the name of the new retention policy
- duration (str) – the duration of the new retention policy. Durations such as 1h, 90m, 12h, 7d, and 4w, are all supported and mean 1 hour, 90 minutes, 12 hours, 7 day, and 4 weeks, respectively. For infinite retention - meaning the data will never be deleted - use ‘INF’ for duration. The minimum retention period is 1 hour.
- replication (str) – the replication of the retention policy
- database (str) – the database for which the retention policy is created. Defaults to current client’s database
- default (bool) – whether or not to set the policy as default
- shard_duration (str) – the shard duration of the retention policy. Durations such as 1h, 90m, 12h, 7d, and 4w, are all supported and mean 1 hour, 90 minutes, 12 hours, 7 day, and 4 weeks, respectively. Infinite retention is not supported. As a workaround, specify a “1000w” duration to achieve an extremely long shard group duration. Defaults to “0s”, which is interpreted by the database to mean the default value given the duration. The minimum shard group duration is 1 hour.
-
create_user
(username, password, admin=False)¶ Create a new user in InfluxDB.
Parameters: - username (str) – the new username to create
- password (str) – the password for the new user
- admin (boolean) – whether the user should have cluster administration privileges or not
-
delete_series
(database=None, measurement=None, tags=None)¶ Delete series from a database.
Series must be filtered by either measurement and tags. This method cannot be used to delete all series, use drop_database instead.
Parameters: - database (str) – the database from which the series should be deleted, defaults to client’s current database
- measurement (str) – Delete all series from a measurement
- tags (dict) – Delete all series that match given tags
-
drop_continuous_query
(name, database=None)¶ Drop an existing continuous query for a database.
Parameters: - name (str) – the name of continuous query to drop
- database (str) – the database for which the continuous query is dropped. Defaults to current client’s database
-
drop_database
(dbname)¶ Drop a database from InfluxDB.
Parameters: dbname (str) – the name of the database to drop
-
drop_measurement
(measurement)¶ Drop a measurement from InfluxDB.
Parameters: measurement (str) – the name of the measurement to drop
-
drop_retention_policy
(name, database=None)¶ Drop an existing retention policy for a database.
Parameters: - name (str) – the name of the retention policy to drop
- database (str) – the database for which the retention policy is dropped. Defaults to current client’s database
-
drop_user
(username)¶ Drop a user from InfluxDB.
Parameters: username (str) – the username to drop
-
classmethod
from_dsn
(dsn, **kwargs)¶ Generate an instance of InfluxDBClient from given data source name.
Return an instance of
InfluxDBClient
from the provided data source name. Supported schemes are “influxdb”, “https+influxdb” and “udp+influxdb”. Parameters for theInfluxDBClient
constructor may also be passed to this method.Parameters: - dsn (string) – data source name
- kwargs (dict) – additional parameters for InfluxDBClient
Raises: ValueError – if the provided DSN has any unexpected values
Example: >> cli = InfluxDBClient.from_dsn('influxdb://username:password@\ localhost:8086/databasename', timeout=5) >> type(cli) <class 'influxdb.client.InfluxDBClient'> >> cli = InfluxDBClient.from_dsn('udp+influxdb://username:pass@\ localhost:8086/databasename', timeout=5, udp_port=159) >> print('{0._baseurl} - {0.use_udp} {0.udp_port}'.format(cli)) http://localhost:8086 - True 159
Note
parameters provided in **kwargs may override dsn parameters
Note
when using “udp+influxdb” the specified port (if any) will be used for the TCP connection; specify the UDP port with the additional udp_port parameter (cf. examples).
-
get_list_continuous_queries
()¶ Get the list of continuous queries in InfluxDB.
Returns: all CQs in InfluxDB Return type: list of dictionaries Example: >> cqs = client.get_list_cqs() >> cqs [ { u'db1': [] }, { u'db2': [ { u'name': u'vampire', u'query': u'CREATE CONTINUOUS QUERY vampire ON ' 'mydb BEGIN SELECT count(dracula) INTO ' 'mydb.autogen.all_of_them FROM ' 'mydb.autogen.one GROUP BY time(5m) END' } ] } ]
-
get_list_database
()¶ Get the list of databases in InfluxDB.
Returns: all databases in InfluxDB Return type: list of dictionaries Example: >> dbs = client.get_list_database() >> dbs [{u'name': u'db1'}, {u'name': u'db2'}, {u'name': u'db3'}]
-
get_list_measurements
()¶ Get the list of measurements in InfluxDB.
Returns: all measurements in InfluxDB Return type: list of dictionaries Example: >> dbs = client.get_list_measurements() >> dbs [{u'name': u'measurements1'}, {u'name': u'measurements2'}, {u'name': u'measurements3'}]
-
get_list_privileges
(username)¶ Get the list of all privileges granted to given user.
Parameters: username (str) – the username to get privileges of Returns: all privileges granted to given user Return type: list of dictionaries Example: >> privileges = client.get_list_privileges('user1') >> privileges [{u'privilege': u'WRITE', u'database': u'db1'}, {u'privilege': u'ALL PRIVILEGES', u'database': u'db2'}, {u'privilege': u'NO PRIVILEGES', u'database': u'db3'}]
-
get_list_retention_policies
(database=None)¶ Get the list of retention policies for a database.
Parameters: database (str) – the name of the database, defaults to the client’s current database Returns: all retention policies for the database Return type: list of dictionaries Example: >> ret_policies = client.get_list_retention_policies('my_db') >> ret_policies [{u'default': True, u'duration': u'0', u'name': u'default', u'replicaN': 1}]
-
get_list_series
(database=None, measurement=None, tags=None)¶ Query SHOW SERIES returns the distinct series in your database.
FROM and WHERE clauses are optional.
Parameters: - measurement – Show all series from a measurement
- tags – Show all series that match given tags
- database (str) – the database from which the series should be shows, defaults to client’s current database
-
get_list_users
()¶ Get the list of all users in InfluxDB.
Returns: all users in InfluxDB Return type: list of dictionaries Example: >> users = client.get_list_users() >> users [{u'admin': True, u'user': u'user1'}, {u'admin': False, u'user': u'user2'}, {u'admin': False, u'user': u'user3'}]
-
grant_admin_privileges
(username)¶ Grant cluster administration privileges to a user.
Parameters: username (str) – the username to grant privileges to Note
Only a cluster administrator can create/drop databases and manage users.
-
grant_privilege
(privilege, database, username)¶ Grant a privilege on a database to a user.
Parameters: - privilege (str) – the privilege to grant, one of ‘read’, ‘write’ or ‘all’. The string is case-insensitive
- database (str) – the database to grant the privilege on
- username (str) – the username to grant the privilege to
-
ping
()¶ Check connectivity to InfluxDB.
Returns: The version of the InfluxDB the client is connected to
-
query
(query, params=None, bind_params=None, epoch=None, expected_response_code=200, database=None, raise_errors=True, chunked=False, chunk_size=0, method=u'GET')¶ Send a query to InfluxDB.
Danger
In order to avoid injection vulnerabilities (similar to SQL injection vulnerabilities), do not directly include untrusted data into the
query
parameter, usebind_params
instead.Parameters: - query (str) – the actual query string
- params (dict) – additional parameters for the request, defaults to {}
- bind_params (dict) – bind parameters for the query:
any variable in the query written as
'$var_name'
will be replaced withbind_params['var_name']
. Only works in theWHERE
clause and takes precedence overparams['params']
- epoch (str) – response timestamps to be in epoch format either ‘h’, ‘m’, ‘s’, ‘ms’, ‘u’, or ‘ns’,defaults to None which is RFC3339 UTC format with nanosecond precision
- expected_response_code (int) – the expected status code of response, defaults to 200
- database (str) – database to query, defaults to None
- raise_errors (bool) – Whether or not to raise exceptions when InfluxDB returns errors, defaults to True
- chunked (bool) – Enable to use chunked responses from InfluxDB.
With
chunked
enabled, one ResultSet is returned per chunk containing all results within that chunk - chunk_size (int) – Size of each chunk to tell InfluxDB to use.
- method (str) – the HTTP method for the request, defaults to GET
Returns: the queried data
Return type:
-
request
(url, method=u'GET', params=None, data=None, stream=False, expected_response_code=200, headers=None)¶ Make a HTTP request to the InfluxDB API.
Parameters: - url (str) – the path of the HTTP request, e.g. write, query, etc.
- method (str) – the HTTP method for the request, defaults to GET
- params (dict) – additional parameters for the request, defaults to None
- data (str) – the data of the request, defaults to None
- stream (bool) – True if a query uses chunked responses
- expected_response_code (int) – the expected response code of the request, defaults to 200
- headers (dict) – headers to add to the request
Returns: the response from the request
Return type: requests.Response
Raises: - InfluxDBServerError – if the response code is any server error code (5xx)
- InfluxDBClientError – if the response code is not the same as expected_response_code and is not a server error code
-
revoke_admin_privileges
(username)¶ Revoke cluster administration privileges from a user.
Parameters: username (str) – the username to revoke privileges from Note
Only a cluster administrator can create/ drop databases and manage users.
-
revoke_privilege
(privilege, database, username)¶ Revoke a privilege on a database from a user.
Parameters: - privilege (str) – the privilege to revoke, one of ‘read’, ‘write’ or ‘all’. The string is case-insensitive
- database (str) – the database to revoke the privilege on
- username (str) – the username to revoke the privilege from
-
send_packet
(packet, protocol=u'json', time_precision=None)¶ Send an UDP packet.
Parameters: - packet ((if protocol is 'json') dict (if protocol is 'line') list of line protocol strings) – the packet to be sent
- protocol (str) – protocol of input data, either ‘json’ or ‘line’
- time_precision (str) – Either ‘s’, ‘m’, ‘ms’ or ‘u’, defaults to None
-
set_user_password
(username, password)¶ Change the password of an existing user.
Parameters: - username (str) – the username who’s password is being changed
- password (str) – the new password for the user
-
switch_database
(database)¶ Change the client’s database.
Parameters: database (str) – the name of the database to switch to
-
switch_user
(username, password)¶ Change the client’s username.
Parameters: - username (str) – the username to switch to
- password (str) – the password for the username
-
write
(data, params=None, expected_response_code=204, protocol=u'json')¶ Write data to InfluxDB.
Parameters: - data – the data to be written
- params (dict) – additional parameters for the request, defaults to None
- expected_response_code (int) – the expected response code of the write operation, defaults to 204
- protocol (str) – protocol of input data, either ‘json’ or ‘line’
Returns: True, if the write operation is successful
Return type: bool
-
write_points
(points, time_precision=None, database=None, retention_policy=None, tags=None, batch_size=None, protocol=u'json', consistency=None)¶ Write to multiple time series names.
Parameters: - points (list of dictionaries, each dictionary represents a point) – the list of points to be written in the database
- time_precision (str) – Either ‘s’, ‘m’, ‘ms’ or ‘u’, defaults to None
- database (str) – the database to write the points to. Defaults to the client’s current database
- tags (dict) – a set of key-value pairs associated with each point. Both keys and values must be strings. These are shared tags and will be merged with point-specific tags, defaults to None
- retention_policy (str) – the retention policy for the points. Defaults to None
- batch_size (int) – value to write the points in batches instead of all at one time. Useful for when doing data dumps from one database to another or when doing a massive write operation, defaults to None
- protocol (str) – Protocol for writing data. Either ‘line’ or ‘json’.
- consistency (str) – Consistency for the points. One of {‘any’,’one’,’quorum’,’all’}.
Returns: True, if the operation is successful
Return type: bool
Note
if no retention policy is specified, the default retention policy for the database is used
DataFrameClient
¶
-
class
influxdb.
DataFrameClient
(host=u'localhost', port=8086, username=u'root', password=u'root', database=None, ssl=False, verify_ssl=False, timeout=None, retries=3, use_udp=False, udp_port=4444, proxies=None, pool_size=10, path=u'', cert=None, gzip=False, session=None, headers=None, socket_options=None)¶ DataFrameClient instantiates InfluxDBClient to connect to the backend.
The
DataFrameClient
object holds information necessary to connect to InfluxDB. Requests can be made to InfluxDB directly through the client. The client reads and writes from pandas DataFrames.-
EPOCH
= Timestamp('1970-01-01 00:00:00+0000', tz='UTC')¶
-
query
(query, params=None, bind_params=None, epoch=None, expected_response_code=200, database=None, raise_errors=True, chunked=False, chunk_size=0, method=u'GET', dropna=True, data_frame_index=None)¶ Query data into a DataFrame.
Danger
In order to avoid injection vulnerabilities (similar to SQL injection vulnerabilities), do not directly include untrusted data into the
query
parameter, usebind_params
instead.Parameters: - query – the actual query string
- params – additional parameters for the request, defaults to {}
- bind_params – bind parameters for the query:
any variable in the query written as
'$var_name'
will be replaced withbind_params['var_name']
. Only works in theWHERE
clause and takes precedence overparams['params']
- epoch – response timestamps to be in epoch format either ‘h’, ‘m’, ‘s’, ‘ms’, ‘u’, or ‘ns’,defaults to None which is RFC3339 UTC format with nanosecond precision
- expected_response_code – the expected status code of response, defaults to 200
- database – database to query, defaults to None
- raise_errors – Whether or not to raise exceptions when InfluxDB returns errors, defaults to True
- chunked – Enable to use chunked responses from InfluxDB.
With
chunked
enabled, one ResultSet is returned per chunk containing all results within that chunk - chunk_size – Size of each chunk to tell InfluxDB to use.
- dropna – drop columns where all values are missing
- data_frame_index – the list of columns that are used as DataFrame index
Returns: the queried data
Return type:
-
write_points
(dataframe, measurement, tags=None, tag_columns=None, field_columns=None, time_precision=None, database=None, retention_policy=None, batch_size=None, protocol=u'line', numeric_precision=None)¶ Write to multiple time series names.
Parameters: - dataframe – data points in a DataFrame
- measurement – name of measurement
- tags – dictionary of tags, with string key-values
- tag_columns – [Optional, default None] List of data tag names
- field_columns – [Options, default None] List of data field names
- time_precision – [Optional, default None] Either ‘s’, ‘ms’, ‘u’ or ‘n’.
- batch_size (int) – [Optional] Value to write the points in batches instead of all at one time. Useful for when doing data dumps from one database to another or when doing a massive write operation
- protocol – Protocol for writing data. Either ‘line’ or ‘json’.
- numeric_precision – Precision for floating point values. Either None, ‘full’ or some int, where int is the desired decimal precision. ‘full’ preserves full precision for int and float datatypes. Defaults to None, which preserves 14-15 significant figures for float and all significant figures for int datatypes.
-
SeriesHelper
¶
-
class
influxdb.
SeriesHelper
(**kw)¶ Subclass this helper eases writing data points in bulk.
All data points are immutable, ensuring they do not get overwritten. Each subclass can write to its own database. The time series names can also be based on one or more defined fields. The field “time” can be specified when creating a point, and may be any of the time types supported by the client (i.e. str, datetime, int). If the time is not specified, the current system time (utc) will be used.
Annotated example:
class MySeriesHelper(SeriesHelper): class Meta: # Meta class stores time series helper configuration. series_name = 'events.stats.{server_name}' # Series name must be a string, curly brackets for dynamic use. fields = ['time', 'server_name'] # Defines all the fields in this time series. ### Following attributes are optional. ### client = TestSeriesHelper.client # Client should be an instance of InfluxDBClient. :warning: Only used if autocommit is True. bulk_size = 5 # Defines the number of data points to write simultaneously. # Only applicable if autocommit is True. autocommit = True # If True and no bulk_size, then will set bulk_size to 1. retention_policy = 'your_retention_policy' # Specify the retention policy for the data points time_precision = "h"|"m"|s"|"ms"|"u"|"ns" # Default is ns (nanoseconds) # Setting time precision while writing point # You should also make sure time is set in the given precision
-
classmethod
commit
(client=None)¶ Commit everything from datapoints via the client.
Parameters: client – InfluxDBClient instance for writing points to InfluxDB. Attention: any provided client will supersede the class client. Returns: result of client.write_points.
-
classmethod
ResultSet
¶
See the Query response object: ResultSet page for more information.
-
class
influxdb.resultset.
ResultSet
(series, raise_errors=True)¶ A wrapper around a single InfluxDB query result.
-
error
¶ Error returned by InfluxDB.
-
get_points
(measurement=None, tags=None)¶ Return a generator for all the points that match the given filters.
Parameters: - measurement (str) – The measurement name
- tags (dict) – Tags to look for
Returns: Points generator
-
items
()¶ Return the set of items from the ResultSet.
Returns: List of tuples, (key, generator)
-
keys
()¶ Return the list of keys in the ResultSet.
Returns: List of keys. Keys are tuples (series_name, tags)
-
static
point_from_cols_vals
(cols, vals)¶ Create a dict from columns and values lists.
Parameters: - cols – List of columns
- vals – List of values
Returns: Dict where keys are columns.
-
raw
¶ Raw JSON from InfluxDB.
-
Exceptions¶
-
class
influxdb.exceptions.
InfluxDBClientError
(content, code=None)¶ Raised when an error occurs in the request.
-
class
influxdb.exceptions.
InfluxDBServerError
(content)¶ Raised when a server error occurs.
Query response object: ResultSet¶
Using the InfluxDBClient.query()
function will return a ResultSet
Object.
A ResultSet can be browsed in several ways. Its get_points
method can be used to retrieve points generators that filter either by measurement, tags, or both.
Getting all points¶
Using rs.get_points()
will return a generator for all the points in the ResultSet.
Filtering by measurement¶
Using rs.get_points('cpu')
will return a generator for all the points that are in a series with measurement name cpu
, no matter the tags.
rs = cli.query("SELECT * from cpu")
cpu_points = list(rs.get_points(measurement='cpu'))
Filtering by tags¶
Using rs.get_points(tags={'host_name': 'influxdb.com'})
will return a generator for all the points that are tagged with the specified tags, no matter the measurement name.
rs = cli.query("SELECT * from cpu")
cpu_influxdb_com_points = list(rs.get_points(tags={"host_name": "influxdb.com"}))
Filtering by measurement and tags¶
Using measurement name and tags will return a generator for all the points that are in a series with the specified measurement name AND whose tags match the given tags.
rs = cli.query("SELECT * from cpu")
points = list(rs.get_points(measurement='cpu', tags={'host_name': 'influxdb.com'}))
See the API Documentation page for more information.
InfluxDB Python Examples¶
Tutorials - Basic¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # -*- coding: utf-8 -*-
"""Tutorial on using the InfluxDB client."""
import argparse
from influxdb import InfluxDBClient
def main(host='localhost', port=8086):
"""Instantiate a connection to the InfluxDB."""
user = 'root'
password = 'root'
dbname = 'example'
dbuser = 'smly'
dbuser_password = 'my_secret_password'
query = 'select Float_value from cpu_load_short;'
query_where = 'select Int_value from cpu_load_short where host=$host;'
bind_params = {'host': 'server01'}
json_body = [
{
"measurement": "cpu_load_short",
"tags": {
"host": "server01",
"region": "us-west"
},
"time": "2009-11-10T23:00:00Z",
"fields": {
"Float_value": 0.64,
"Int_value": 3,
"String_value": "Text",
"Bool_value": True
}
}
]
client = InfluxDBClient(host, port, user, password, dbname)
print("Create database: " + dbname)
client.create_database(dbname)
print("Create a retention policy")
client.create_retention_policy('awesome_policy', '3d', 3, default=True)
print("Switch user: " + dbuser)
client.switch_user(dbuser, dbuser_password)
print("Write points: {0}".format(json_body))
client.write_points(json_body)
print("Querying data: " + query)
result = client.query(query)
print("Result: {0}".format(result))
print("Querying data: " + query_where)
result = client.query(query_where, bind_params=bind_params)
print("Result: {0}".format(result))
print("Switch user: " + user)
client.switch_user(user, password)
print("Drop database: " + dbname)
client.drop_database(dbname)
def parse_args():
"""Parse the args."""
parser = argparse.ArgumentParser(
description='example code to play with InfluxDB')
parser.add_argument('--host', type=str, required=False,
default='localhost',
help='hostname of InfluxDB http API')
parser.add_argument('--port', type=int, required=False, default=8086,
help='port of InfluxDB http API')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
main(host=args.host, port=args.port)
|
Tutorials - pandas¶
# -*- coding: utf-8 -*-
"""Tutorial for using pandas and the InfluxDB client."""
import argparse
import pandas as pd
from influxdb import DataFrameClient
def main(host='localhost', port=8086):
"""Instantiate the connection to the InfluxDB client."""
user = 'root'
password = 'root'
dbname = 'demo'
protocol = 'line'
client = DataFrameClient(host, port, user, password, dbname)
print("Create pandas DataFrame")
df = pd.DataFrame(data=list(range(30)),
index=pd.date_range(start='2014-11-16',
periods=30, freq='H'), columns=['0'])
print("Create database: " + dbname)
client.create_database(dbname)
print("Write DataFrame")
client.write_points(df, 'demo', protocol=protocol)
print("Write DataFrame with Tags")
client.write_points(df, 'demo',
{'k1': 'v1', 'k2': 'v2'}, protocol=protocol)
print("Read DataFrame")
client.query("select * from demo")
print("Delete database: " + dbname)
client.drop_database(dbname)
def parse_args():
"""Parse the args from main."""
parser = argparse.ArgumentParser(
description='example code to play with InfluxDB')
parser.add_argument('--host', type=str, required=False,
default='localhost',
help='hostname of InfluxDB http API')
parser.add_argument('--port', type=int, required=False, default=8086,
help='port of InfluxDB http API')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
main(host=args.host, port=args.port)
Tutorials - SeriesHelper¶
# -*- coding: utf-8 -*-
"""Tutorial how to use the class helper `SeriesHelper`."""
from influxdb import InfluxDBClient
from influxdb import SeriesHelper
# InfluxDB connections settings
host = 'localhost'
port = 8086
user = 'root'
password = 'root'
dbname = 'mydb'
myclient = InfluxDBClient(host, port, user, password, dbname)
# Uncomment the following code if the database is not yet created
# myclient.create_database(dbname)
# myclient.create_retention_policy('awesome_policy', '3d', 3, default=True)
class MySeriesHelper(SeriesHelper):
"""Instantiate SeriesHelper to write points to the backend."""
class Meta:
"""Meta class stores time series helper configuration."""
# The client should be an instance of InfluxDBClient.
client = myclient
# The series name must be a string. Add dependent fields/tags
# in curly brackets.
series_name = 'events.stats.{server_name}'
# Defines all the fields in this time series.
fields = ['some_stat', 'other_stat']
# Defines all the tags for the series.
tags = ['server_name']
# Defines the number of data points to store prior to writing
# on the wire.
bulk_size = 5
# autocommit must be set to True when using bulk_size
autocommit = True
# The following will create *five* (immutable) data points.
# Since bulk_size is set to 5, upon the fifth construction call, *all* data
# points will be written on the wire via MySeriesHelper.Meta.client.
MySeriesHelper(server_name='us.east-1', some_stat=159, other_stat=10)
MySeriesHelper(server_name='us.east-1', some_stat=158, other_stat=20)
MySeriesHelper(server_name='us.east-1', some_stat=157, other_stat=30)
MySeriesHelper(server_name='us.east-1', some_stat=156, other_stat=30)
MySeriesHelper(server_name='us.east-1', some_stat=156)
MySeriesHelper(server_name='us.east-1', some_stat=155, other_stat=50)
# To manually submit data points which are not yet written, call commit:
MySeriesHelper.commit()
# To inspect the JSON which will be written, call _json_body_():
MySeriesHelper._json_body_()
Tutorials - UDP¶
# -*- coding: utf-8 -*-
"""Example for sending batch information to InfluxDB via UDP."""
"""
INFO: In order to use UDP, one should enable the UDP service from the
`influxdb.conf` under section
[[udp]]
enabled = true
bind-address = ":8089" # port number for sending data via UDP
database = "udp1" # name of database to be stored
[[udp]]
enabled = true
bind-address = ":8090"
database = "udp2"
"""
import argparse
from influxdb import InfluxDBClient
def main(uport):
"""Instantiate connection to the InfluxDB."""
# NOTE: structure of the UDP packet is different than that of information
# sent via HTTP
json_body = {
"tags": {
"host": "server01",
"region": "us-west"
},
"points": [{
"measurement": "cpu_load_short",
"fields": {
"value": 0.64
},
"time": "2009-11-10T23:00:00Z",
},
{
"measurement": "cpu_load_short",
"fields": {
"value": 0.67
},
"time": "2009-11-10T23:05:00Z"
}]
}
# make `use_udp` True and add `udp_port` number from `influxdb.conf` file
# no need to mention the database name since it is already configured
client = InfluxDBClient(use_udp=True, udp_port=uport)
# Instead of `write_points` use `send_packet`
client.send_packet(json_body)
def parse_args():
"""Parse the args."""
parser = argparse.ArgumentParser(
description='example code to play with InfluxDB along with UDP Port')
parser.add_argument('--uport', type=int, required=True,
help=' UDP port of InfluxDB')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
main(uport=args.uport)
Tutorials - Authorization by Token¶
# -*- coding: utf-8 -*-
"""Tutorial how to authorize InfluxDB client by custom Authorization token."""
import argparse
from influxdb import InfluxDBClient
def main(token='my-token'):
"""Instantiate a connection to the InfluxDB."""
client = InfluxDBClient(username=None, password=None,
headers={"Authorization": token})
print("Use authorization token: " + token)
version = client.ping()
print("Successfully connected to InfluxDB: " + version)
pass
def parse_args():
"""Parse the args from main."""
parser = argparse.ArgumentParser(
description='example code to play with InfluxDB')
parser.add_argument('--token', type=str, required=False,
default='my-token',
help='Authorization token for the proxy that is ahead the InfluxDB.')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
main(token=args.token)