python使用influxdb-client连接InfluxDB

python连接InfluxDB数据库

官方示例代码：https://github.com/influxdata/influxdb-client-python/tree/master/examples

基本概念

• Measurement：度量，相当于“表”
• DataPoint：数据点，相当于“一条数据”
• Time：时间戳，代表数据点产生的时间。
• Field：不带索引的字段
• Tag：带索引的字段。Measurement+Tag 可以用于唯一索引一部分数据

1. 准备连接influxdb

首先查看能否连接上数据库：

from datetime import datetime
from influxdb_client import InfluxDBClient

bucket = "manager_test_bucket"
influxdb_token = "SkeHprHCgmvtX3LXluMUlgyl5nzwM4zdMtsCuT7BQXsaJlhFPMJizKj0nX3ugr9vRfY7Ak4rIhu-wx-aIqNFig=="
influxdb_org = "manager"
client = InfluxDBClient(url="http://localhost:8086", token=influxdb_token, org=influxdb_org)

然后新建一个bucket用于测试：

buckets_api = client.buckets_api()
created_bucket = buckets_api.create_bucket(bucket_name=bucket_name, org=influxdb_org)

2. 新增数据

方法1：使用Point

通常的新增数据的方法如下：

    from influxdb_client import Point
    from datetime import datetime
    from influxdb_client.client.write_api import SYNCHRONOUS
    
    add_data1 = Point("measurement_1").field("open", 1.1).field("close", 1.1).time(datetime(2023, 3, 14, 12, 1, 1))
    add_data2 = Point("measurement_1").field("open", 1.2).field("close", 1.2).time(datetime(2023, 3, 13, 13, 2, 1))
    add_data3 = Point("measurement_1").field("open", 1.3).field("close", 1.3).time(datetime(2023, 3, 12, 14, 3, 1))
    write_api = client.write_api(write_options=SYNCHRONOUS)
    write_api.write(bucket=bucket_name, record=[add_data1, add_data2, add_data3])

结果如下图所示：

方法2：字典dict方式

    write_api = client.write_api(write_options=SYNCHRONOUS)
    add_data1_new = {
    
    
        "measurement": "measurement_2",
        "fields": {
    
    "open": 1.1, "close": 1.1},
        "time": datetime(2023, 3, 15, 12, 1, 1),
    }
    add_data2_new = {
    
    
        "measurement": "measurement_2",
        "fields": {
    
    "open": 1.2, "close": 1.2},
        "time": datetime(2023, 3, 14, 12, 1, 1),
    }
    write_api.write(bucket=bucket_name, org=influxdb_org, record=[add_data1_new, add_data2_new])

方法3：带有Tag索引的数据

    add_data1_new = {
    
    
        "measurement": "measurement_2",
        "tags": {
    
    "stock": "examp_stock"},
        "fields": {
    
    "open": 1.1, "close": 1.1},
        "time": datetime(2023, 3, 15, 12, 1, 1),
    }
    add_data2_new = {
    
    
        "measurement": "measurement_2",
        "tags": {
    
    "stock": "examp_stock"},
        "fields": {
    
    "open": 1.2, "close": 1.2},
        "time": datetime(2023, 3, 14, 12, 1, 1),
    }
    add_data3_new = {
    
    
        "measurement": "measurement_2",
        "fields": {
    
    "open": 1.3, "close": 1.3},
        "time": datetime(2023, 3, 13, 12, 1, 1),
    }
    write_api.write(bucket=bucket_name, org=influxdb_org, record=[add_data1_new, add_data2_new, add_data3_new])

效果图如下：

可以看到，此时在数据库中，使用measurement + tags，可以唯一索引一部分数据，而如果没有指定tags，那么measurement，就会唯一的索引一部分数据

3. 修改数据

修改数据的程序与新增数据类似，如果对应的_measurement与field一致，则值是会覆盖的，如果不一致，则是追加数据

    new_data = Point("measurement_1").field("open", 11.1).field("high", 21.1).time(datetime(2023, 3, 14, 12, 2, 3))
    write_api.write(bucket=bucket_name, record=[new_data])

4. 查询数据

    query_api = client.query_api()
    query_tables = query_api.query("""
        from(bucket: "manager_test_bucket")
        |> range(start: 0, stop: now())
        |> filter(fn: (r) => r["_measurement"] == "measurement_1")
    """)
    for _table in query_tables:
        for record in _table.records:
            print(record.values)

我们会得到这样的结果：

{'result': '_result', 'table': 0, '_start': datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), '_stop': datetime.datetime(2023, 3, 16, 5, 27, 6, 567016, tzinfo=datetime.timezone.utc), '_time': datetime.datetime(2023, 3, 12, 5, 3, 1, tzinfo=datetime.timezone.utc), '_value': 1.3, '_field': 'close', '_measurement': 'measurement_1'}
{'result': '_result', 'table': 0, '_start': datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), '_stop': datetime.datetime(2023, 3, 16, 5, 27, 6, 567016, tzinfo=datetime.timezone.utc), '_time': datetime.datetime(2023, 3, 13, 13, 2, 1, tzinfo=datetime.timezone.utc), '_value': 1.2, '_field': 'close', '_measurement': 'measurement_1'}
{'result': '_result', 'table': 0, '_start': datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), '_stop': datetime.datetime(2023, 3, 16, 5, 27, 6, 567016, tzinfo=datetime.timezone.utc), '_time': datetime.datetime(2023, 3, 14, 12, 1, 1, tzinfo=datetime.timezone.utc), '_value': 1.1, '_field': 'close', '_measurement': 'measurement_1'}
{'result': '_result', 'table': 1, '_start': datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), '_stop': datetime.datetime(2023, 3, 16, 5, 27, 6, 567016, tzinfo=datetime.timezone.utc), '_time': datetime.datetime(2023, 3, 12, 5, 3, 1, tzinfo=datetime.timezone.utc), '_value': 1.3, '_field': 'open', '_measurement': 'measurement_1'}
{'result': '_result', 'table': 1, '_start': datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), '_stop': datetime.datetime(2023, 3, 16, 5, 27, 6, 567016, tzinfo=datetime.timezone.utc), '_time': datetime.datetime(2023, 3, 13, 13, 2, 1, tzinfo=datetime.timezone.utc), '_value': 1.2, '_field': 'open', '_measurement': 'measurement_1'}
{'result': '_result', 'table': 1, '_start': datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), '_stop': datetime.datetime(2023, 3, 16, 5, 27, 6, 567016, tzinfo=datetime.timezone.utc), '_time': datetime.datetime(2023, 3, 14, 12, 1, 1, tzinfo=datetime.timezone.utc), '_value': 1.1, '_field': 'open', '_measurement': 'measurement_1'}

5. 删除数据

    start = "2022-03-13T00:00:00Z"
    stop = "2023-05-30T00:00:00Z"
    delete_api = client.delete_api()
    delete_api.delete(start, stop,
                      predicate='_field=open',  # 删除的规则
                      bucket=bucket_name, org=influxdb_org)

注意：删除数据不能使用_time，_field，_value，不会报错但会导致删除代码无效

完整示例代码

注意事项

time 相当于表的主键，当一条数据的time和tags完全相同时候，新数据会替换掉旧数据，旧数据则丢失（线上环境尤其要注意）。
fields和tags的字段类型是由存入的第一条记录值决定的，建议只包含浮点型与字符串类型