嵌入式入侵查询语言与orm
influxed的Python项目详细描述
源代码和开发可以在下面的repository
请随意提出问题或分享您的经验:)
安装
导航到终端中的文件夹
运行
python setup.py install
或
pip install influxed
还有你的好去处。
连接到服务器:
from influxed.orm import engine
engine.add_server('http://influxserverurlorip:port', 'username', 'password', reflect=True)
或安全:
from influxed.orm import engine
engine.add_server('https://influxserverurlorip:port', 'username', 'password', reflect=True)
或使用异步:
from influxed.orm import engine
await engine.add_server('https://influxserverurlorip:port', 'username', 'password', reflect=True, isAsync=True)
示例资源管理器
显示数据库
engine.server.ls()
显示测量值
engine.server.[database_name].ls()
显示字段
engine.server.[database_name].[measurement_name].ls()
如果度量值或数据库的名称与python不兼容,
然后可以使用索引< /H1>访问它们
示例
engine.server['1Db']['measurement one'].ls()
从测量中选择数据:
engine.server.database.meter_usage.query.select('field1', 'field2').all()
另一种方法是从测量本身选择它
engine.server.database.meter_usage.field1.query.all()
限制选择:
engine.server.database.meter_usage.field1.query.first(5) OR
engine.server.database.meter_usage.field1.query.last(7) OR
engine.server.database.meter_usage.field1.query.limit(2).all()
过滤:
col = engine.server.database.meter_usage.field1
col.query.filter( # Everything between 5 and 6
col > 5,
col <= 6
).all()
按时间过滤
import datetime as dt
from influxed.ifql import time
engine.server.database.meter_usage.field1.query.filter(
time > dt.datetime.now()
).all()
使用或语句筛选
from influxed.ifql import OPERATORS
col = engine.server.database.meter_usage.field1
col.query.filter(
OPERATORS.or_(
time > dt.datetime.now(), # or
col > 2,
)
).all()
聚合器:
col = engine.server.database.meter_usage.field1
col.query.min.all() # Minimum value
col.query.max.all() # Maximum value
col.query.mean.all() # Mean value
col.query.sum.all() # Summed Value
col.query.std.all() # Standard deviation
按功能分组
from influxed.ifql import time
col = engine.server.database.meter_usage.field1
col.query.min.group_by(
time('2d') # Group into buckets of 2 days and take the minimum value
).all()
# Available interval selectors:
# week = 'w'
# day = 'd'
# hour = 'h'
# minute = 'm'
# sec = 's'
# milisec = 'ms'
# microsec ='u'
# nanosec = 'ns'
让事情变得有趣起来:
engine.server.dap.meterusage.query.select(
'871694831000088656',
'871690910000005079'
).filter(
time > dt.datetime(2016,1,1),
time > dt.datetime(2016,2,1),
).group_by(
time('1d')
).sum().fill(0).all()
# Will give you:
# 'SELECT SUM("871694831000088656"), SUM("871690910000005079") FROM meterusage WHERE time > \'2016-01-01 00:00:00.000\' AND time > \'2016-02-01 00:00:00.000\' GROUP BY time(1d) FILL(1)'
代数运算:
a = engine.server.database.meter_usage.field1
b = engine.server.database.meter_usage.field2
engine.query.select((a.sum()+2)/b)*42).filter( # Everything between 5 and 6
a > 5,
b <= 6
).all()
# Will give you:
# SELECT ((SUM(field1) + 2) / field2) * 42 WHERE field1 > 5 AND field2 < 6
测试
engine.server['1Db']['measurement one'].ls()
engine.server.database.meter_usage.query.select('field1', 'field2').all()
engine.server.database.meter_usage.field1.query.all()
engine.server.database.meter_usage.field1.query.first(5) OR
engine.server.database.meter_usage.field1.query.last(7) OR
engine.server.database.meter_usage.field1.query.limit(2).all()
col = engine.server.database.meter_usage.field1
col.query.filter( # Everything between 5 and 6
col > 5,
col <= 6
).all()
import datetime as dt
from influxed.ifql import time
engine.server.database.meter_usage.field1.query.filter(
time > dt.datetime.now()
).all()
from influxed.ifql import OPERATORS
col = engine.server.database.meter_usage.field1
col.query.filter(
OPERATORS.or_(
time > dt.datetime.now(), # or
col > 2,
)
).all()
col = engine.server.database.meter_usage.field1
col.query.min.all() # Minimum value
col.query.max.all() # Maximum value
col.query.mean.all() # Mean value
col.query.sum.all() # Summed Value
col.query.std.all() # Standard deviation
from influxed.ifql import time
col = engine.server.database.meter_usage.field1
col.query.min.group_by(
time('2d') # Group into buckets of 2 days and take the minimum value
).all()
# Available interval selectors:
# week = 'w'
# day = 'd'
# hour = 'h'
# minute = 'm'
# sec = 's'
# milisec = 'ms'
# microsec ='u'
# nanosec = 'ns'
engine.server.dap.meterusage.query.select(
'871694831000088656',
'871690910000005079'
).filter(
time > dt.datetime(2016,1,1),
time > dt.datetime(2016,2,1),
).group_by(
time('1d')
).sum().fill(0).all()
# Will give you:
# 'SELECT SUM("871694831000088656"), SUM("871690910000005079") FROM meterusage WHERE time > \'2016-01-01 00:00:00.000\' AND time > \'2016-02-01 00:00:00.000\' GROUP BY time(1d) FILL(1)'
a = engine.server.database.meter_usage.field1
b = engine.server.database.meter_usage.field2
engine.query.select((a.sum()+2)/b)*42).filter( # Everything between 5 and 6
a > 5,
b <= 6
).all()
# Will give you:
# SELECT ((SUM(field1) + 2) / field2) * 42 WHERE field1 > 5 AND field2 < 6
运行
python -m unittest discover -p '*_test.py' -s src -t .
调试-----
你可以随时打电话 .format() 在.query之后的任何语句上 为了查看给定语句生成的原始sql 附加记录器可获得其他调试信息:
import logging
logger = logging.getLogger()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch = logging.StreamHandler()
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.setLevel('DEBUG')
可用的日志记录通道: -传输层=influxedClient -传输层重试逻辑=infloxedretrystrat