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in memory database like object with O(1) queries

Project description

A simple in memory database like object. Replace list of dictionaries with a fast, O(1) lookup, data structure. Also supports multiple items via a list.

It is very simple and designed for small to medium databases which can exist purely in memory. It intended for situations where query speed is prioritized over memory usage and instantiation time.

I built it because I was querying a list of dictionaries by multiple keys inside of a loop. That cause an O(N^2) complexity and made my moderately-sized 30,000 item list intractable!

There are other solutions out there that are more traditionally database focused such as TinyDB, buzhug, etc, but this is purely in-memory and very fast. It is likely at the cost of extra memory overhead.

It passes all tests (with 100% test coverage) on Python 2.7 and Python 3.6

I am not a database expert. This simple met my needs.

Install

Simply

pip install list_dict_DB

Usage:

For more full example usage, including the flexible query methods, see the tests.

Consider the following: (please do not argue accuracy. It is an example)

items = [
    {'first':'John', 'last':'Lennon','born':1940,'role':'guitar'},
    {'first':'Paul', 'last':'McCartney','born':1942,'role':'bass'},
    {'first':'George','last':'Harrison','born':1943,'role':'guitar'},
    {'first':'Ringo','last':'Starr','born':1940,'role':'drums'},
    {'first':'George','last':'Martin','born':1926,'role':'producer'}
]

If we want to find all members of The Beatles who’s name is “George Harrison”, we could do the following:

[item for item in items if item['first']=='George' and item['last']=='Harrison']

Which is an O(N) operation. If we are only doing it once, it is fine, but if we are doing it multiple times (especially in loops) it can cause a major bottleneck.

Instead do:

from list_dict_DB import list_dict_DB
DB = list_dict_DB(items) # Will index them all

DB.query(first='George',last='Harrison')

The creation is O(N) but the query is O(1) and can be done many times.

Queries

There are a few different methods to perform queries. It is designed to be flexible and allow for easy construction

Basic Queries

Basic queries only test equality with an and boolean relationship.

For example, to query band the example DB for band members with the first name ‘George’, you can do either of the following:

DB.query(first='George')
DB.query({'first':'George'})
DB[{'first':'George'}]      # item indecies can be queries or a number
DB(first='George')          # Directly calling the object is a query()

To get George Harrison, you can do the following:

DB.query(first='George',last='Harrison')

Or again, you can use a dictionary or mix and match. For example:

DB.query({'first':'George'},last='Harrison')

Again, you are restricted to equality and AND relationships.

Advanced Queries

Advanced queries are a bit more complex. The require a Qobj. Note, a Qobj expires if the DB index changes (update(), remove(), add(), add_attribute(), and reindex())

An advanced query is constructed as follows. NOTE: Python gets easily messed up with assignment. Use parentheses to separate statements!

For example, to query all elements with the first name George and the last name not Martin, you can do:

Q = DB.Qobj() # Instantiate it with the DB. DB.Q() will also work
DB.query( (Q.first=='George') & (Q.last != 'Martin') )

Or

DB.query( (DB.Q().first=='George') & (DB.Q().last != 'Martin') )

Notice:

  • Use of parentheses. The queries must be separated
  • We are checking equality so == and != are used
    • You can also negate with ~ but again, be careful and deliberate about parentheses
  • We instantiate the Q object with the DB. If the DB index is changed, the Q object will not be allowed to run as a precaution.
  • We used & for and and | for or
  • <, <=, >, >=, and filters are supported but these are O(N) opperations.

You can also do more advanced boolean logic such as:

DB.query( ~( (Q.role=='guitar') | (Q.role=='drums')))

Filters

A filter allows for more advanced queries of the data but, as noted below, are O(N) (as with <, <=, >, >=).

For example, to perform a simple equality, the following return the same entry. But do note that the equality version is much faster.

Edge Case: If an attribute’s name is ‘filter’, the filter method may be accessed through _filter.

# Traditional lookup:
DB.query(Q.first == 'George') # equality is O(1)

# Filter lookup
filt = lambda item: True if item['first'] == 'George' else False
DB.query(Q.filter(filt))

The are flexible for more advanced queries

WARNING about speed

Some of the major speed gains in this are due to the use of dictionaries and sets which are O(1) complexity.

Queries with <, <=, >, >=, and filters are O(N) opperations and should be avoided if possible.

The time complexity of a query will depend on the number of items that match any part of the query.

Loading and Saving (Dumping)

There is intentionally no built in way to dump these as they are intended to be in-memory. Of course, the a good way to save or load is as follows:

Dump:

import json
with open('DB.json','w') as F:
    json.dump(DB.items(),F)

Load:

from list_dict_DB import list_dict_DB
import json
with open('DB.json') as F:
    DB = list_dict_DB(json.load(F))

Lists:

All attributes must be hashable. The only exception are lists in which case the list is expanded for each item. For example, an entry may be:

{'first':'George','last':'Harrison','born':1943,'role':['guitar','sitar']}

and

DB.query(role='sitar')

will return him.

Benchmarks & Complexity Testing

I compared the creating and querying a large database with the following methods. Note that some cache results so I recreated and re-queried from scratch. In practice, even caching the results does not help much if the queries change.

  • list_dict_DB
  • simple looping with a copied list (not deepcopy though)
  • Pandas dataframe (0.16.2)
  • TinyDB (3.2.2) with in-memory storage
  • dataset (0.6.0) with slite3 in-memory storage
    • dataset is a wrapper to SQLAlchemy that (in my words) provides a noSQL interface to SQL.

I tested on my MacBook Pro (Retina, 15-inch, Mid 2014) laptop with 2.8 GHz i7 and 16 gb of ram using Python 2.7.9.

The following figure is the time to build and query the resulting data object. Note that for TinyDB, the object was deleted between tests since it caches queries

benchmarks

From the slope of the plots, you can estimate the complexity. I just calculated from the final point. The order is O(N^{slope})

Tool Query slope Create slope
list_dict_DB 0.12 1.01
loop_copy 1.12 1.27
pandas 0.92 0.99
TinyDB_mem 1.04 1.00
dataset_mem 0.03 1.02

dataset gives this tool a run for its money but it also has a lot more dependancies and was the slowest in creation time (though, if you use it with a file, once it is created, you do not have to recreate it again). Pandas also performs well and only starts to have the O(N) dependency creep in at larger sizes. Of course, this is a scaling analysis. When you look at actual query times, list_dict_DB is orders of magnitude faster!

Which tool is the best will be problem dependent, but these results make a strong argument for list_dict_DB

Known Issues

None at the moment.

There is 100% (!!!) test coverage. Of course that doesn’t mean there aren’t bugs. If you find any, please report them.

Limitations

  • The entire DB exists in memory
  • Serializing (dumping) is not included though is easy to do with JSON or the like. See above
  • The index used in the dictionary is itself a dictionary with keys as any value. Since these are all done as pointers to original list, the memory footprint should be small.
  • This has not been tested for thread-safety!

Project details


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