AHL Research Versioned TimeSeries and Tick store
Project description
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
import quandl
# 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("WIKI/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 three 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
TickStore: Column oriented tick database. Supports dynamic fields, chunks aren’t versioned. Designed for large continuously ticking data.
Chunkstore: A storage type that allows data to be stored in customizable chunk sizes. Chunks aren’t versioned, and can be appended to and updated in place.
Arctic storage implementations are pluggable. VersionStore is the default.
Requirements
Arctic currently works with:
Python 2.7, 3.4, 3.5
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:
Tope Olukemi
Drake Siard
… and many others …
Contributions welcome!
License
Arctic is licensed under the GNU LGPL v2.1. A copy of which is included in LICENSE
Changelog
1.28 (2016-08-16)
Bugfix: #195 Top level tickstore write with list of dicts now works with timezone aware datetimes
1.27 (2016-08-05)
Bugfix: #187 Compatibility with latest version of pytest-dbfixtures
Feature: #182 Improve ChunkStore read/write performance
Feature: #162 Rename API for ChunkStore
Feature: #186 chunk_range on update
Bugfix: #189 range delete does not update symbol metadata
1.26 (2016-07-20)
Bugfix: Faster TickStore querying for multiple symbols simultaneously
Bugfix: TickStore.read now respects allow_secondary=True
Bugfix: #147 Add get_info method to ChunkStore
Bugfix: Periodically re-cache the library.quota to pick up any changes
Bugfix: #166 Add index on SHA for ChunkStore
Bugfix: #169 Dtype mismatch in chunkstore updates
Feature: #171 allow deleting of values within a date range in ChunkStore
Bugfix: #172 Fix date range bug when querying dates in the middle of chunks
Bugfix: #176 Fix overwrite failures in Chunkstore
Bugfix: #178 - Change how start/end dates are populated in the DB, also fix append so it works as expected.
Bugfix: #43 - Remove dependency on hardcoded Linux timezone files
1.25 (2016-05-23)
Bugfix: Ensure that Tickstore.write doesn’t allow out of order messages
Bugfix: VersionStore.write now allows writing ‘None’ as a value
1.24 (2016-05-10)
Bugfix: Backwards compatibility reading/writing documents with previous versions of Arctic
1.22 (2016-05-09)
Bugfix: #109 Ensure stable sort during Arctic read
Feature: New benchmark suite using ASV
Bugfix: #129 Fixed an issue where some chunks could get skipped during a multiple-symbol TickStore read
Bugfix: #135 Fix issue with different datatype returned from pymongo in python3
Feature: #130 New Chunkstore storage type
1.21 (2016-03-08)
Bugfix: #106 Fix Pandas Panel storage for panels with different dimensions
1.20 (2016-02-03)
Feature: #98 Add initial_image as optional parameter on tickstore write()
Bugfix: #100 Write error on end field when writing with pandas dataframes
1.19 (2016-01-29)
Feature: Add python 3.3/3.4 support
Bugfix: #95 Fix raising NoDataFoundException across multiple low level libraries
1.18 (2016-01-05)
Bugfix: #81 Fix broken read of multi-index DataFrame written by old version of Arctic
Bugfix: #49 Fix strifying tickstore
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
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