cQuery implements a decentralised method of querying file-system contents using “selectors” within a library for Python and a command-line interface.
$ cquery search .Asset /path/to/asset1 /path/to/asset2 /path/to/asset3
How it works¶
Given a directory such as:
cQuery answers the questions:
- What is my Asset?
- What is my Project?
- What rigs do I have?
- Am I Orange?
- How many shaders do I contain?
Here is how it might look when tagging and querying a content hierarchy for a feature animation project.
$ cd MyAsset $ cquery tag .Asset $ cquery search .Asset /path/to/MyAsset
The traditional method of identifying content on a file-system is via keys and maps. E.g. a database stores a key along with an associated path in a map. The path can then be looked up through the map via key.
$ query MyKey $ /path/to/corresponding_content
This means that all keys are stored in one spot, the map, and all content stored elsewhere, the file-system. cQuery joins maps with content in an effort to eliminate this separation and in so doing eliminates many of the added responsibilities such as synchronisation and increased barrier-to-entry.
The idea of selectors are adopted from CSS3 and its use in jQuery (from which the name cQuery was derived). jQuery allows users to operate on the Document Object Model, or DOM, using CSS3 selectors to locate the appropriate Nodes. Similarly, cQuery operates on the Content Object Model, or COM, using CSS3 selectors to locate the appropriate folders.
Up or Down¶
Starting from a root directory, a query can either be made up or down. To find descending matches of a given directory, you would use DOWN. To instead query for ascending matches, you would use UP. To query one-self only, you would use NONE.
Here is how that might look when used in Python:
>>> # Find the associated project of the asset Peter >>> first_match("/projects/spiderman/assets/Peter", ... selector='.Project', direction=UP) >>> >>> # Find all textures >>> for match in matches("/projects/spiderman/assets/Peter", ... selector='.Texture', direction=DOWN): ... print match >>> >>> # Is this asset a Hero? >>> True if first_match("/projects/spiderman/assets/Peter", ... selector='.Hero', direction=NONE) else False
cQuery works upon directories tagged with metadata to indicate its class, ID or name. The tagged directories may then be queried, either from outside a hierarchy looking in or from within a hierarchy looking out.
For tagging, cQuery uses the Open Metadata specification , the process is simple - for each subdirectory within a directory, recursively look for a file by name stored within the Open Metadata container. If a match is found, return the absolute path to said directory. The name of this file is the “selector” argument of your query.
cQuery operates on the hard-drive and is a seek-only algorithm and as such doesn’t perform any reads. Despite this however, disk-access is (seemingly) the prime bottle-neck. A cQuery prototype has been implemented in both Python and Go for performance comparisons, here are some results:
# Scanning a hierarchy of 3601 items # 1 queries, 7 matches in 1.494072 seconds # 1 queries, 7 matches in 1.480471 seconds # 1 queries, 7 matches in 1.477589 seconds # Average time/query: 1.484044 seconds # Scanning a hierarchy of 47715 items # 1 queries, 14 matches in 19.888399 seconds # 1 queries, 14 matches in 20.078811 seconds # 1 queries, 14 matches in 19.879660 seconds # Average time/query: 19.948957 seconds
# Scanning a hierarchy of 3601 items # 1 queries, 7 matches in 1.425702 seconds # 1 queries, 7 matches in 1.420373 seconds # 1 queries, 7 matches in 1.419541 seconds # Average time/query: 1.421872 seconds # Scanning a hierarchy of 47715 items # 1 queries, 14 matches in 18.015012 seconds # 1 queries, 14 matches in 17.951607 seconds # 1 queries, 14 matches in 17.994924 seconds # Average time/query: 17.987181 seconds
For some more encouraging results in file-system search and indexing, here are some resources:
If cQuery is not the place for a full implementation of the DOM, another project will take its place shortly.
|||For more information on Open Metadata, see here https://github.com/abstractfactory/openmetadata|