arctic 1.17.0

AHL Research Versioned TimeSeries and Tick store

Arctic is a high performance datastore for numeric data. It supports Pandas, numpy arrays and pickled objects out-of-the-box, with pluggable support for other data types and optional versioning.

Arctic can query millions of rows per second per client, achieves ~10x compression on network bandwidth, ~10x compression on disk, and scales to hundreds of millions of rows per second per MongoDB instance.

Arctic has been under active development at Man AHL since 2012.

Quickstart

Install Arctic

pip install git+https://github.com/manahl/arctic.git

Run a MongoDB

mongod --dbpath <path/to/db_directory>

Using VersionStore

from arctic import Arctic

# Connect to Local MONGODB
store = Arctic('localhost')

# Create the library - defaults to VersionStore
store.initialize_library('NASDAQ')

# Access the library
library = store['NASDAQ']

# Load some data - maybe from Quandl
aapl = Quandl.get("NASDAQ/AAPL", authtoken="your token here")

# Store the data in the library
library.write('AAPL', aapl, metadata={'source': 'Quandl'})

# Reading the data
item = library.read('AAPL')
aapl = item.data
metadata = item.metadata

VersionStore supports much more: See the HowTo!

Adding your own storage engine

Plugging a custom class in as a library type is straightforward. This example shows how.

Concepts

Libraries

Arctic provides namespaced libraries of data. These libraries allow bucketing data by source, user or some other metric (for example frequency: End-Of-Day; Minute Bars; etc.).

Arctic supports multiple data libraries per user. A user (or namespace) maps to a MongoDB database (the granularity of mongo authentication). The library itself is composed of a number of collections within the database. Libraries look like:

• user.EOD

• user.ONEMINUTE

A library is mapped to a Python class. All library databases in MongoDB are prefixed with ‘arctic_’

Storage Engines

Arctic includes two storage engines:

• VersionStore: a key-value versioned TimeSeries store. It supports:

  • Pandas data types (other Python types pickled)

  • Multiple versions of each data item. Can easily read previous versions.

  • Create point-in-time snapshots across symbols in a library

  • Soft quota support

  • Hooks for persisting other data types

  • Audited writes: API for saving metadata and data before and after a write.

  • a wide range of TimeSeries data frequencies: End-Of-Day to Minute bars

  • See the HowTo

• TickStore: Column oriented tick database. Supports dynamic fields, chunks aren’t versioned. Designed for large continuously ticking data.

Arctic storage implementations are pluggable. VersionStore is the default.

Requirements

Arctic currently works with:

• Python 2.7

• pymongo >= 3.0

• Pandas

• MongoDB >= 2.4.x

Acknowledgements

Arctic has been under active development at Man AHL since 2012.

It wouldn’t be possible without the work of the AHL Data Engineering Team including:

• Richard Bounds

• James Blackburn

• Vlad Mereuta

• Tom Taylor

• Tope Olukemi

• Drake Siard

• Slavi Marinov

• Wilfred Hughes

• Edward Easton

• … and many others …

Contributions welcome!

License

Arctic is licensed under the GNU LGPL v2.1. A copy of which is included in LICENSE

Changelog

1.17 (2015-12-24)

• Feature: Add timezone suppport to store multi-index dataframes

• Bugfix: Fixed broken sdist releases

1.16 (2015-12-15)

• Feature: ArticTransaction now supports non-audited ‘transactions’: audit=False with ArcticTransaction(Arctic('hostname')['some_library'], 'symbol', audit=False) as at: ... This is useful for batch jobs which read-modify-write and don’t want to clash with concurrent writers, and which don’t require keeping all versions of a symbol.

1.15 (2015-11-25)

• Feature: get_info API added to version_store.

1.14 (2015-11-25)

1.12 (2015-11-12)

• Bugfix: correct version detection for Pandas >= 0.18.

• Bugfix: retrying connection initialisation in case of an AutoReconnect failure.

1.11 (2015-10-29)

• Bugfix: Improve performance of saving multi-index Pandas DataFrames by 9x

• Bugfix: authenticate should propagate non-OperationFailure exceptions (e.g. ConnectionFailure) as this might be indicative of socket failures

• Bugfix: return ‘deleted’ state in VersionStore.list_versions() so that callers can pick up on the head version being the delete-sentinel.

1.10 (2015-10-28)

• Bugfix: VersionStore.read(date_range=…) could do the wrong thing with TimeZones (which aren’t yet supported for date_range slicing.).

1.9 (2015-10-06)

• Bugfix: fix authentication race condition when sharing an Arctic instance between multiple threads.

1.8 (2015-09-29)

• Bugfix: compatibility with both 3.0 and pre-3.0 MongoDB for querying current authentications

1.7 (2015-09-18)

• Feature: Add support for reading a subset of a pandas DataFrame in VersionStore.read by passing in an arctic.date.DateRange

• Bugfix: Reauth against admin if not auth’d against a library a specific library’s DB. Sometimes we appear to miss admin DB auths. This is to workaround that until we work out what the issue is.

1.6 (2015-09-16)

• Feature: Add support for multi-index Bitemporal DataFrame storage. This allows persisting data and changes within the DataFrame making it easier to see how old data has been revised over time.

• Bugfix: Ensure we call the error logging hook when exceptions occur

1.5 (2015-09-02)

• Always use the primary cluster node for ‘has_symbol()’, it’s safer

1.4 (2015-08-19)

• Bugfixes for timezone handling, now ensures use of non-naive datetimes

• Bugfix for tickstore read missing images

1.3 (2015-08-011)

• Improvements to command-line control scripts for users and libraries

• Bugfix for pickling top-level Arctic object

1.2 (2015-06-29)

• Allow snapshotting a range of versions in the VersionStore, and snapshot all versions by default.

1.1 (2015-06-16)

• Bugfix for backwards-compatible unpickling of bson-encoded data

• Added switch for enabling parallel lz4 compression

1.0 (2015-06-14)

• Initial public release