redis-timeseries-manager 2.2

RedisTimeseriesManager is a redis timeseries management system that enhance redis timeseries with features including multi-line data, built-in timeframes, data classifiers and convenient data accessors.

RedisTimeseriesManager

RedisTimeseriesManager is a redis timeseries management system that enhance redis timeseries with features including multi-line data, built-in timeframes, data classifiers and convenient data accessors. This is achieved by maintaing a set of timeseries that are tied together(called lines) and interact with them as a whole. As a result, multiple timeseries values can be refered with a timestamp as if they are stored in a table with a timestamp and multiple columns.

This library supports two levels of data classifiers(c1, c2) to interact with data, plus support for timeframes and data compression(downsampling).

About RedisTimeSeries

Redis is an open source (BSD licensed), in-memory data structure store used as a database, cache, message broker, and streaming engine. RedisTimeSeries is a Redis module that adds a time series data structure to Redis. RedisTimeseriesManager uses RedisTimeSeries to store timeseries data.

Installation

To install RedisTimeseriesManager, run the following command:

pip install --upgrade redis_timeseries_manager

Usage

Basic Example

To get started, simply create a class that inherits from RedisTimeseriesManager. Then set the properties _name, _lines, and _timeframes.

from redis_timeseries_manager import RedisTimeseriesManager

class Test(RedisTimeseriesManager):
    _name = 'test'
    _lines = ['l1', 'l2']
    _timeframes = {
        'raw': {'retention_secs': 60*60*24}, # retention 1 day
    }

You can think of _lines as the columns in a relational database. You can have as many lines as your requirements. They can be added or removed at any time using add_line() or delete_line() methods.

At least one timeframe must be provided. Even if you simply want to store timeseries data without separate timeframes, add a default timeframe and it will be used seamlessly. Also a unique _name must be provided for each class.

Creating the object

t = Test(host, port, db, password)

Adding data to series

The method add() is used to add data to the series. The syntax is:

t.add(data, c1, c2, create_inplace=False)

The format of data is as follows:

[timestamp, l1, l2, ...]

c1 and c2 are the classifiers. You can classify data in two levels using these classifiers. In our sensors example we have used c1 to identify the location of sensor and c2 for the sensor itself.

Before adding data, we have to prepare the timeseries with the classifiers. To achieve this, we use the create() method. For the sensors example:

t.create(
    c1='building1',
    c2='sensor1',
)

Now we can add data for sensor 1:

t.add(
    data=[
        [123456, 1, 2],
        [123457, 3, 4],
        [123458, 5, 6],
    ],
    c1='building1',
    c2='sensor1',
)

For the sensor2, we don't want to prepare series explicitly, instead we set create_inplace to True and the series will be prepared with the new classifiers implicitely while inserting data:

t.add(
    data=[
        [123456, 7, 8],
        [123457, 9, 10],
        [123458, 11, 12],
    ],
    c1='building1',
    c2='sensor2',
    create_inplace=True,
)

Reading the data

The method read() is used to read data from the series. In our example we can read the data for sensor1 as follows:

t.read(
    c1='building1',
    c2='sensor1',
)

[[123456, 1.0, 2.0], [123457, 3.0, 4.0], [123458, 5.0, 6.0]]

There are also some other methods to read or investigate about the data. read_last_n_records(), read_last_nth_record(), read_last() and find_last() are those methods. Some additional parameters can also be used to control what data are read, they include from_timestamp, to_timestamp and extra_records. Refer to the documentations of each method for details.

Output Formats

Since v2.0, pandas dataframes are supported. You can choose the format of output data when calling read methods. Supported formats are 'list' (python list, default format), 'df'(pandas dataframe), 'dict' (dictioanry of lines), 'sets-list' (sets of data including lables and lists), 'sets-df' (sets of data including labels and dataframes), 'sets-dict' (sets of data including labels and dictionary of lines) and 'raw' which is the raw data read from the timeseries.

See a demonstration of supported output formats

Usage without data compaction

If you do not need data to be compressed across timeframes, you can set only a single timeframe in _timeframes class property. This will fully disable compaction functionality; but note that at least one timeframe must be set always.

WARNING:

Due to an unfixable bug in redis timeseries module only use db with index 0 while data compaction is required; otherwise compaction rules won't work.

To have a separate timeframe without data compaction, set ignore_rules to True in the timeframe definition:

_timeframes = {
    '1m': {'retention_secs': 60*60*24*10},
    '1h': {'retention_secs': 60*60*24*90, 'bucket_size_secs': 3600},
    '1d': {'retention_secs': 60*60*24*365, 'ignore_rules': True},
}

In the above example, the 1d timeframe is isolated and no compaction rule will have interaction with that. Data can be inserted into this timeframe using add(c1=..., timeframe='1d') One usage may be in the case that you want to keep track and maintain the minute data but have a separate data source for daily data. Keep in mind that you should never write data directly into the timeframes that the result of compaction rules are written. In the above example, the 1m(default) and 1d timeframes are safe to write directly.

Usage with more than two classifiers

While in most use cases, two classifiers for the data must be enough; there might be scenarios where more than two classifiers for the data are required. In such cases, you can extend the classifiers in c1 or c2 classifier.

As of version 2.1, redis_timeseries_manager supports extra_labels that gives the ability to set custom labels for the data. The main advantage of labels in redis timeseries emerges when you utilize them with redis multi-timeseries commands like TS.MRANGE

The extending process consist of two parts: First we have to provide a unique identifier as the classifier and secondary provide the corresponding labels that identify the data as extra_labels. (Do not include timeframe in this process, timeframes are fully handled internally)

For better clarification, suppose a scenaro where we are required to store performance of several users who are optimizing strategies on given sample data. In this case we need 4 different classifiers and we have to extend additional ones in a classifier like c2.

Here is the full example:

from redis_timeseries_manager import RedisTimeseriesManager

class Measurements(RedisTimeseriesManager):
    _name = 'feature_tests'
    _lines = ['l1', 'l2']
    _timeframes = {
        'raw': {'retention_secs': 100000}
    }

settings = {
    'host': 'localhost',
    'port': 6379,
    'db': 0,
    'password': None,
}

tl = Measurements(**settings)

tl.add(
    data=[
        [123456, 7, 8],
        [123457, 9, 10],
        [123458, 11, 12],
        [123459, 13, 14],
    ],
    c1='performance',
    c2='u_1_22_46',
    extra_labels={
        'user_id': 1,
        'strategy_id': 22,
        'sample_id': 46
    },
    create_inplace=True,
)
tl.add(
    data=[
        [123456, 17, 18],
        [123457, 19, 110],
        [123458, 111, 112],
        [123460, 113, 114],
    ],
    c1='performance',
    c2='u_2_22_46',
    extra_labels={
        'user_id': 2,
        'strategy_id': 22,
        'sample_id': 46
    },
    create_inplace=True,
)

Later, to read data, we have to provide the full labels we have decided to define(and differentiate) the data with, in place of using a plain c2 classifier:

tl.read(
    c1='performance',
    c2={
        'user_id': 2,
        'strategy_id': 22,
        'sample_id': 46
    },
    return_as='df'
)

time	l1	l2
123456	17.0	18.0
123457	19.0	10.0
123458	111.0	112.0
123460	113.0	114.0

If we don't provide the full labels, multiple data-points with the same time might return and this is usually not we expect from a timeseries data and that's why RedisTimeseriesManager by default prevents this to happen. However if you persist, you can turn the allow_multiple option on to let multiple data sets to be combined together.

In our exampe, if you wanted all the entries for the strategy_id of 22, you can do as following:

tl.read(
    c1='performance',
    c2={
        'strategy_id': 22,
    },
    allow_multiple=True,
    return_as='df'
)

time	l1	l2
123456	7.0	8.0
123456	17.0	18.0
123457	9.0	10.0
123457	19.0	110.0
123458	11.0	12.0
123458	111.0	112.0
123459	13.0	14.0
123460	113.0	114.0

Advanced Usage Examples

Example 1: Sensor Data

In this example, we are going to maintain data of two imaginary sensors. Each sensor provides two measurements: temperature and humidity.

The data is collected with the resolution of one minute. Then we compress(downsample) the data to hourly and daily resolutions. To compress the data, we consider the average value of temperature and the maximum value of humidity in each time frame.

We also want to keep 1-minute sensor data for just one week, 1-hour data for one month and respectively 1-day data for a year. In this Example, we use the classifier 1(c1) to identify the building where the sensor is located and the classifier 2(c2) for the sensor.

import time, datetime, random
from pytz import timezone

from redis_timeseries_manager import RedisTimeseriesManager

settings = {
    'host': 'localhost',
    'port': 6379,
    'db': 13,
    'password': None,
}

class SensorData(RedisTimeseriesManager):
    _name = 'sensors'
    _lines = ['temp', 'hum']
    _timeframes = {
        'raw': {'retention_secs': 60*60*24*7}, # retention 7 day
        '1h': {'retention_secs': 60*60*24*30, 'bucket_size_secs': 60*60}, # retention 1 month; timeframe 3600 secs
        '1d': {'retention_secs': 60*60*24*365, 'bucket_size_secs': 60*60*24}, # retention 1 year; timeframe 86400 secs
    }

    #compaction rules
    def _create_rule(self, c1:str, c2:str, line:str, timeframe_name:str, timeframe_specs:str, source_key:str, dest_key:str):
        if line == 'temp':
            aggregation_type = 'avg'
        elif line == 'hum':
            aggregation_type = 'max'
        bucket_size_secs = timeframe_specs['bucket_size_secs']
        self._set_rule(source_key, dest_key, aggregation_type, bucket_size_secs)
    
    @staticmethod
    def print_data(data):
        for ts, temp, hum in data:
            print(f"{datetime.datetime.fromtimestamp(ts, tz=timezone('UTC')):%Y-%m-%d %H:%M:%S}, temp: {round(temp, 2)}, hum(max): {round(hum, 2)}")

view the full example and usage

Example 2: Market Data(OHLCV)

Demonstrating a high-performance market price data downsampling mechanism using redis backend and RedisTimeseriesManager

In this example, we are going to maintain the data of some financial markets. We have chosen the cryptocurrency and irx for our example. Each market contain several instruments that we refer to them as symbols and we collect OHLCV(open, high, low, close, volume) data for each symbol.

The raw data is directly collected from the market with the resolution of seconds and we insert them in raw timeframe. Then we compress(downsample) the data to timeframes of 1m, 1h and 1d. As the names open, high, low, close, volume implies, we use the FIRST aggregator for open, MAX for high, MIN for low, LAST for close and the SUM aggregator for volume to compress the data and build the appropriate timeframes of data.

We also want to keep 1m data for just one week, 1h for one month and respectively 1d data for a year. In this Example, we use the classifier 1(c1) to identify the market(here cryptocurrency or irx) and the classifier 2(c2) for the symbols.

import time, datetime, random
from pytz import timezone

from redis_timeseries_manager import RedisTimeseriesManager

settings = {
    'host': 'localhost',
    'port': 6379,
    'db': 13,
    'password': None,
}

class MarketData(RedisTimeseriesManager):
    _name = 'markets'
    _lines = ['open', 'high', 'low', 'close', 'volume']
    _timeframes = {
        'raw': {'retention_secs': 60*60*24*4}, # retention 4 days
        '1m': {'retention_secs': 60*60*24*7, 'bucket_size_secs': 60}, # retention 7 day; timeframe 60 secs
        '1h': {'retention_secs': 60*60*24*30, 'bucket_size_secs': 60*60}, # retention 1 month; timeframe 3600 secs
        '1d': {'retention_secs': 60*60*24*365, 'bucket_size_secs': 60*60*24}, # retention 1 year; timeframe 86400 secs
    }

    #compaction rules
    def _create_rule(self, c1:str, c2:str, line:str, timeframe_name:str, timeframe_specs:str, source_key:str, dest_key:str):
        if line == 'open':
            aggregation_type = 'first'
        elif line == 'close':
            aggregation_type = 'last'
        elif line == 'high':
            aggregation_type = 'max'
        elif line == 'low':
            aggregation_type = 'min'
        elif line == 'volume':
            aggregation_type = 'sum'
        else:
            return
        bucket_size_secs = timeframe_specs['bucket_size_secs']
        self._set_rule(source_key, dest_key, aggregation_type, bucket_size_secs)
    
    @staticmethod
    def print_data(data):
        for ts, open, high, low, close, volume in data:
            print(f"{datetime.datetime.fromtimestamp(ts, tz=timezone('UTC')):%Y-%m-%d %H:%M:%S}, open: {open}, high: {high}, low: {low}, close: {close}, volume: {volume}")

view the full example and usage