tshistory 0.12.0

Timeseries store with version control

TSHISTORY

This is a library to store/retrieve pandas timeseries to/from a postgres database, tracking their successive versions.

Introduction

Purpose

tshistory is targetted at applications using time series where backtesting and cross-validation are an essential feature.

It provides exhaustivity and efficiency of the storage, with a simple Python api.

It can be used as a building block for machine learning, model optimization and validation, both for inputs and outputs.

Principles

There are many ways to represent timeseries in a relational database, and tshistory provides two things:

• a base python API which abstracts away the underlying storage

• a postgres model, which emphasizes the compact storage of successive states of series

The core idea of tshistory is to handle successive versions of timeseries as they grow in time, allowing to get older states of any series.

Basic usage

Starting with a fresh database

You need a postgresql database. You can create one like this:

 createdb mydb

Then, initialize the tshistory tables, like this:

 tsh init-db postgresql://me:password@localhost/mydb

From this you're ready to go !

Creating a series

However here's a simple example:

 >>> import pandas as pd
 >>> from tshistory.api import timeseries
 >>>
 >>> tsa = timeseries('postgres://me:password@localhost/mydb')
 >>>
 >>> series = pd.Series([1, 2, 3],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 1),
 ...                                  freq='D', periods=3))
 # db insertion
 >>> tsa.update('my_series', series, '[e-mail]')
 ...
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Freq: D, Name: my_series, dtype: float64

 # note how our integers got turned into floats
 # (there are no provisions to handle integer series as of today)

 # retrieval
 >>> tsa.get('my_series')
 ...
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64

Note that we generally adopt the convention to name the time series api object tsa.

Updating a series

This is good. Now, let's insert more:

 >>> series = pd.Series([2, 7, 8, 9],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 2),
 ...                                  freq='D', periods=4))
 # db insertion
 >>> tsa.update('my_series', series, '[e-mail]')
 ...
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64

 # you get back the *new information* you put inside
 # and this is why the `2` doesn't appear (it was already put
 # there in the first step)

 # db retrieval
 >>> tsa.get('my_series')
 ...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    7.0
2017-01-04    8.0
2017-01-05    9.0
Name: my_series, dtype: float64

It is important to note that the third value was replaced, and the two last values were just appended.

As noted the point at 2017-1-2 wasn't a new information so it was just ignored.

Retrieving history

We can access the whole history (or parts of it) in one call:

 >>> history = tsa.history('my_series')
 ...
 >>>
 >>> for idate, series in history.items(): # it's a dict
 ...     print('insertion date:', idate)
 ...     print(series)
 ...
 insertion date: 2018-09-26 17:10:36.988920+02:00
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64
 insertion date: 2018-09-26 17:12:54.508252+02:00
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64

Note how this shows the full serie state for each insertion date. Also the insertion date is timzeone aware.

It is possible to show the differences only:

 >>> diffs = tsa.history('my_series', diffmode=True)
 ...
 >>> for idate, series in diffs.items():
 ...   print('insertion date:', idate)
 ...   print(series)
 ...
 insertion date: 2018-09-26 17:10:36.988920+02:00
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64
 insertion date: 2018-09-26 17:12:54.508252+02:00
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64

You can see a series metadata:

 >>> tsa.metadata('series', internal=True)
 {'tzaware': False, 'index_type': 'datetime64[ns]', 'value_type': 'float64',
 'index_dtype': '<M8[ns]', 'value_dtype': '<f8'}

We built a series with naive time stamps, but timezone-aware timestamps work well (and it is advised to use them !).

The API object

In the few examples above we manipulate the time series through an object that talks directly to the postgresql back end.

It is possible to also talk to a rest api using the same api, like shown below and proceed exactly like in the above code examples:

 >>> from tshistory.api import timeseries
 >>>
 >>> tsa = timeseries('http://my.timeseries.info/api')

Using an HTTP/REST end point

For this to work, one needs to use the the tshistory_rest and tshistory_client packages.

The client package will be used transparently on a timeseries('http://.../api') call, nothing more needs to be done than install it.

For the rest api, you need to build a small flask app and set up the tshistory_rest blueprint like this (in an app.py module):

from flask import Flask

from tshistory.api import timeseries
from tshistory_rest.blueprint import blueprint as blueprint


def make_app(dburi):
    app = Flask('my-timeseries-app')
    app.register_blueprint(
        blueprint(timeseries(dburi)),
        url_prefix='/api'
    )
    return app

Then, you can start it in development mode like this:

app = make_app('postgresql://me:password@localhost/mydb')
app.run('192.168.1.1', 8080)

or just leave it to a wsgi container in e.g. a wsgi.py module:

from my_series_app.app import make_app

app = make_app('postgresql://me:password@localhost/mydb')

API surface

For now we only provide a list of supported methods.

Information access (read methods)

• catalog

• exists

• get

• history

• interval

• metadata

• staircase

• type

Information update (write methods)

• update

• update_metadata

• replace

• rename

• delete

Command line

Basic operations

A command line tool is provided, called tsh. It provides its usage guidelines:

 $ tsh
 Usage: tsh [OPTIONS] COMMAND [ARGS]...

 Options:
   --help  Show this message and exit.

Commands:
  check    coherence checks of the db
  get      show a serie in its current state
  history  show a serie full history
  info     show global statistics of the repository
  init-db  initialize an new db.
  log      show revision history of entire repository or...
  view     visualize time series through the web

Info provides an overview of the time series repository (number of committed changes, number and series and their names).

 $ tsh info postgres://babar:babarpassword@dataserver:5432/banana_studies
 changeset count: 209
 series count:    144
 series names:    banana_spot_price, banana_trades, banana_turnover

Log provides the full history of editions to time series in the repository.

 $ tsh log postgres://babar:babar@dataserver:5432/banana_studies --limit 3
 revision: 206
 author:   BABAR
 date:     2017-06-06 15:32:51.502507
 series:   banana_spot_price

 revision: 207
 author:   BABAR
 date:     2017-06-06 15:32:51.676507
 series:   banana_trades

 revision: 209
 author:   CELESTE
 date:     2017-06-06 15:32:51.977507
 series:   banana_turnover

All options of all commands can be obtained by using the --help switch:

 $ tsh log --help
 Usage: tsh log [OPTIONS] DB_URI

 Options:
   -l, --limit TEXT
   --show-diff
   -s, --serie TEXT
   --from-rev TEXT
   --to-rev TEXT
   --help            Show this message and exit.

Extensions

It is possible to augment the tsh command with new subcommands (or augment, modify existing commands).

Any program doing so must define a new command and declare a setup tools entry point named tshistory:subcommand as in e.g.:

    entry_points={'tshistory.subcommands': [
        'view=tsview.command:view'
    ]}

For instance, the tsview python package provides such a view subcommand for generic time series visualisation.

Status

It is currently considered beta software even though it has been in production for two years. It is still evolving. Schema/Database changes come with migration procedure using the tsh utility.

When it is good: if you do mostly appends (and occasional edits in the past) it will store data in a very compact way.

Reading any version of the series will always be the fastest (io-bound) operation.

Alternative backend storage and storage strategies will be considered in the future.