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Pottery: Redis for Humans 🌎🌍🌏

Redis is awesome, but Redis commands are not always intuitive. Pottery is a Pythonic way to access Redis. If you know how to use Python dicts, then you already know how to use Pottery. Pottery is useful for accessing Redis more easily, and also for implementing microservice resilience patterns; and it has been battle tested in production at scale.

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Table of Contents

Installation

$ pip3 install pottery

Usage

First, set up your Redis client:

>>> from redis import Redis
>>> redis = Redis.from_url('redis://localhost:6379/1')
>>>

Dicts 📖

RedisDict is a Redis-backed container compatible with Python’s dict.

Here is a small example using a RedisDict:

>>> from pottery import RedisDict
>>> tel = RedisDict({'jack': 4098, 'sape': 4139}, redis=redis, key='tel')
>>> tel['guido'] = 4127
>>> tel
RedisDict{'jack': 4098, 'sape': 4139, 'guido': 4127}
>>> tel['jack']
4098
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
RedisDict{'jack': 4098, 'guido': 4127, 'irv': 4127}
>>> list(tel)
['jack', 'guido', 'irv']
>>> sorted(tel)
['guido', 'irv', 'jack']
>>> 'guido' in tel
True
>>> 'jack' not in tel
False
>>>

Notice the first two keyword arguments to RedisDict(): The first is your Redis client. The second is the Redis key name for your dict. Other than that, you can use your RedisDict the same way that you use any other Python dict.

Limitations:

  1. Keys and values must be JSON serializable.

Sets 🛍️

RedisSet is a Redis-backed container compatible with Python’s set.

Here is a brief demonstration:

>>> from pottery import RedisSet
>>> basket = RedisSet({'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}, redis=redis, key='basket')
>>> sorted(basket)
['apple', 'banana', 'orange', 'pear']
>>> 'orange' in basket
True
>>> 'crabgrass' in basket
False

>>> a = RedisSet('abracadabra', redis=redis, key='magic')
>>> b = set('alacazam')
>>> sorted(a)
['a', 'b', 'c', 'd', 'r']
>>> sorted(a - b)
['b', 'd', 'r']
>>> sorted(a | b)
['a', 'b', 'c', 'd', 'l', 'm', 'r', 'z']
>>> sorted(a & b)
['a', 'c']
>>> sorted(a ^ b)
['b', 'd', 'l', 'm', 'r', 'z']
>>>

Notice the two keyword arguments to RedisSet(): The first is your Redis client. The second is the Redis key name for your set. Other than that, you can use your RedisSet the same way that you use any other Python set.

Do more efficient membership testing for multiple elements using .contains_many():

>>> nirvana = RedisSet({'kurt', 'krist', 'dave'}, redis=redis, key='nirvana')
>>> tuple(nirvana.contains_many('kurt', 'krist', 'chat', 'dave'))
(True, True, False, True)
>>>

Limitations:

  1. Elements must be JSON serializable.

Lists

RedisList is a Redis-backed container compatible with Python’s list.

>>> from pottery import RedisList
>>> squares = RedisList([1, 4, 9, 16, 25], redis=redis, key='squares')
>>> squares
RedisList[1, 4, 9, 16, 25]
>>> squares[0]
1
>>> squares[-1]
25
>>> squares[-3:]
[9, 16, 25]
>>> squares[:]
[1, 4, 9, 16, 25]
>>> squares + [36, 49, 64, 81, 100]
RedisList[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
>>>

Notice the two keyword arguments to RedisList(): The first is your Redis client. The second is the Redis key name for your list. Other than that, you can use your RedisList the same way that you use any other Python list.

Limitations:

  1. Elements must be JSON serializable.
  2. Under the hood, Python implements list using an array. Redis implements list using a doubly linked list. As such, inserting elements at the head or tail of a RedisList is fast, O(1). However, accessing RedisList elements by index is slow, O(n). So in terms of performance and ideal use cases, RedisList is more similar to Python’s deque than Python’s list. Instead of RedisList, consider using RedisDeque.

Counters 🧮

RedisCounter is a Redis-backed container compatible with Python’s collections.Counter.

>>> from pottery import RedisCounter
>>> c = RedisCounter(redis=redis, key='my-counter')
>>> c = RedisCounter('gallahad', redis=redis, key='my-counter')
>>> c.clear()
>>> c = RedisCounter({'red': 4, 'blue': 2}, redis=redis, key='my-counter')
>>> c.clear()
>>> c = RedisCounter(redis=redis, key='my-counter', cats=4, dogs=8)
>>> c.clear()

>>> c = RedisCounter(['eggs', 'ham'], redis=redis, key='my-counter')
>>> c['bacon']
0
>>> c['sausage'] = 0
>>> del c['sausage']
>>> c.clear()

>>> c = RedisCounter(redis=redis, key='my-counter', a=4, b=2, c=0, d=-2)
>>> sorted(c.elements())
['a', 'a', 'a', 'a', 'b', 'b']
>>> c.clear()

>>> RedisCounter('abracadabra', redis=redis, key='my-counter').most_common(3)
[('a', 5), ('b', 2), ('r', 2)]
>>> c.clear()

>>> c = RedisCounter(redis=redis, key='my-counter', a=4, b=2, c=0, d=-2)
>>> from collections import Counter
>>> d = Counter(a=1, b=2, c=3, d=4)
>>> c.subtract(d)
>>> c
RedisCounter{'a': 3, 'b': 0, 'c': -3, 'd': -6}
>>>

Notice the first two keyword arguments to RedisCounter(): The first is your Redis client. The second is the Redis key name for your counter. Other than that, you can use your RedisCounter the same way that you use any other Python Counter.

Limitations:

  1. Keys must be JSON serializable.

Deques 🖇️

RedisDeque is a Redis-backed container compatible with Python’s collections.deque.

Example:

>>> from pottery import RedisDeque
>>> d = RedisDeque('ghi', redis=redis, key='letters')
>>> for elem in d:
...     print(elem.upper())
G
H
I

>>> d.append('j')
>>> d.appendleft('f')
>>> d
RedisDeque(['f', 'g', 'h', 'i', 'j'])

>>> d.pop()
'j'
>>> d.popleft()
'f'
>>> list(d)
['g', 'h', 'i']
>>> d[0]
'g'
>>> d[-1]
'i'

>>> list(reversed(d))
['i', 'h', 'g']
>>> 'h' in d
True
>>> d.extend('jkl')
>>> d
RedisDeque(['g', 'h', 'i', 'j', 'k', 'l'])
>>> d.rotate(1)
>>> d
RedisDeque(['l', 'g', 'h', 'i', 'j', 'k'])
>>> d.rotate(-1)
>>> d
RedisDeque(['g', 'h', 'i', 'j', 'k', 'l'])

>>> RedisDeque(reversed(d), redis=redis)
RedisDeque(['l', 'k', 'j', 'i', 'h', 'g'])
>>> d.clear()

>>> d.extendleft('abc')
>>> d
RedisDeque(['c', 'b', 'a'])
>>>

Notice the two keyword arguments to RedisDeque(): The first is your Redis client. The second is the Redis key name for your deque. Other than that, you can use your RedisDeque the same way that you use any other Python deque.

Limitations:

  1. Elements must be JSON serializable.

Queues 🚶‍♂️🚶‍♀️🚶‍♂️

RedisSimpleQueue is a Redis-backed multi-producer, multi-consumer FIFO queue compatible with Python’s queue.SimpleQueue. In general, use a Python queue.Queue if you’re using it in one or more threads, use multiprocessing.Queue if you’re using it between processes, and use RedisSimpleQueue if you’re sharing it across machines or if you need for your queue to persist across application crashes or restarts.

Instantiate a RedisSimpleQueue:

>>> from pottery import RedisSimpleQueue
>>> cars = RedisSimpleQueue(redis=redis, key='cars')
>>>

Notice the two keyword arguments to RedisSimpleQueue(): The first is your Redis client. The second is the Redis key name for your queue. Other than that, you can use your RedisSimpleQueue the same way that you use any other Python queue.SimpleQueue.

Check the queue state, put some items in the queue, and get those items back out:

>>> cars.empty()
True
>>> cars.qsize()
0
>>> cars.put('Jeep')
>>> cars.put('Honda')
>>> cars.put('Audi')
>>> cars.empty()
False
>>> cars.qsize()
3
>>> cars.get()
'Jeep'
>>> cars.get()
'Honda'
>>> cars.get()
'Audi'
>>> cars.empty()
True
>>> cars.qsize()
0
>>>

Limitations:

  1. Items must be JSON serializable.

Redlock 🔒

Redlock is a safe and reliable lock to coordinate access to a resource shared across threads, processes, and even machines, without a single point of failure. Rationale and algorithm description.

Redlock implements Python’s excellent threading.Lock API as closely as is feasible. In other words, you can use Redlock the same way that you use threading.Lock. The main reason to use Redlock over threading.Lock is that Redlock can coordinate access to a resource shared across different machines; threading.Lock can’t.

Instantiate a Redlock:

>>> from pottery import Redlock
>>> printer_lock = Redlock(key='printer', masters={redis}, auto_release_time=.2)
>>>

The key argument represents the resource, and the masters argument specifies your Redis masters across which to distribute the lock. In production, you should have 5 Redis masters. This is to eliminate a single point of failure — you can lose up to 2 out of the 5 Redis masters and your Redlock will remain available and performant. Now you can protect access to your resource:

>>> if printer_lock.acquire():
...     # Critical section - print stuff here.
...     print('printer_lock is locked')
...     printer_lock.release()
printer_lock is locked
>>> bool(printer_lock.locked())
False
>>>

Or you can protect access to your resource inside a context manager:

>>> with printer_lock:
...     # Critical section - print stuff here.
...     print('printer_lock is locked')
printer_lock is locked
>>> bool(printer_lock.locked())
False
>>>

It’s safest to instantiate a new Redlock object every time you need to protect your resource and to not share Redlock instances across different parts of code. In other words, think of the key as identifying the resource; don’t think of any particular Redlock as identifying the resource. Instantiating a new Redlock every time you need a lock sidesteps bugs by decoupling how you use Redlock from the forking/threading model of your application/service.

Redlocks are automatically released (by default, after 10 seconds). You should take care to ensure that your critical section completes well within that timeout. The reasons that Redlocks are automatically released are to preserve “liveness” and to avoid deadlocks (in the event that a process dies inside a critical section before it releases its lock).

>>> import time
>>> if printer_lock.acquire():
...     # Critical section - print stuff here.
...     time.sleep(printer_lock.auto_release_time)
>>> bool(printer_lock.locked())
False
>>>

If 10 seconds isn’t enough to complete executing your critical section, then you can specify your own auto release time (in seconds):

>>> printer_lock = Redlock(key='printer', masters={redis}, auto_release_time=.2)
>>> if printer_lock.acquire():
...     # Critical section - print stuff here.
...     time.sleep(printer_lock.auto_release_time / 2)
>>> bool(printer_lock.locked())
True
>>> time.sleep(printer_lock.auto_release_time / 2)
>>> bool(printer_lock.locked())
False
>>>

By default, .acquire() blocks indefinitely until the lock is acquired. You can make .acquire() return immediately with the blocking argument. .acquire() returns True if the lock was acquired; False if not.

>>> printer_lock_1 = Redlock(key='printer', masters={redis}, auto_release_time=.2)
>>> printer_lock_2 = Redlock(key='printer', masters={redis}, auto_release_time=.2)
>>> printer_lock_1.acquire(blocking=False)
True
>>> printer_lock_2.acquire(blocking=False)  # Returns immediately.
False
>>> printer_lock_1.release()
>>>

You can make .acquire() block but not indefinitely by specifying the timeout argument (in seconds):

>>> printer_lock_1.acquire()
True
>>> printer_lock_2.acquire(timeout=printer_lock_1.auto_release_time / 2)  # Waits 100 milliseconds.
False
>>> import contextlib
>>> from pottery import ReleaseUnlockedLock
>>> with contextlib.suppress(ReleaseUnlockedLock):
...     printer_lock_1.release()
>>>

You can similarly configure the Redlock context manager’s blocking/timeout behavior during Redlock initialization. If the context manager fails to acquire the lock, it raises the QuorumNotAchieved exception.

>>> import contextlib
>>> from pottery import QuorumNotAchieved
>>> printer_lock_1 = Redlock(key='printer', masters={redis}, context_manager_blocking=True, context_manager_timeout=0.2)
>>> printer_lock_2 = Redlock(key='printer', masters={redis}, context_manager_blocking=True, context_manager_timeout=0.2)
>>> with printer_lock_1:
...     with contextlib.suppress(QuorumNotAchieved):
...         with printer_lock_2:  # Waits 200 milliseconds; raises QuorumNotAchieved.
...             pass
...     print(f"printer_lock_1 is {'locked' if printer_lock_1.locked() else 'unlocked'}")
...     print(f"printer_lock_2 is {'locked' if printer_lock_2.locked() else 'unlocked'}")
printer_lock_1 is locked
printer_lock_2 is unlocked
>>>

synchronize() 👯‍♀️

synchronize() is a decorator that allows only one thread to execute a function at a time. Under the hood, synchronize() uses a Redlock, so refer to the Redlock documentation for more details.

Here’s how to use synchronize():

>>> from pottery import synchronize
>>> @synchronize(key='synchronized-func', masters={redis}, auto_release_time=1.5, blocking=True, timeout=-1)
... def func():
...   # Only one thread can execute this function at a time.
...   return True
...
>>> func()
True
>>>

AIORedlock 🔒

AIORedlock is the asyncio implementation of Redlock, compatible with Python’s asyncio.Lock.

Instantiate an AIORedlock and protect a resource:

>>> import asyncio
>>> from redis.asyncio import Redis as AIORedis
>>> from pottery import AIORedlock
>>> async def main():
...     aioredis = AIORedis.from_url('redis://localhost:6379/1')
...     shower = AIORedlock(key='shower', masters={aioredis})
...     if await shower.acquire():
...         # Critical section - no other coroutine can enter while we hold the lock.
...         print(f"shower is {'occupied' if await shower.locked() else 'available'}")
...         await shower.release()
...     print(f"shower is {'occupied' if await shower.locked() else 'available'}")
...
>>> asyncio.run(main(), debug=True)
shower is occupied
shower is available
>>>

Or you can protect access to your resource inside a context manager:

>>> asyncio.set_event_loop(asyncio.new_event_loop())
>>> async def main():
...     aioredis = AIORedis.from_url('redis://localhost:6379/1')
...     shower = AIORedlock(key='shower', masters={aioredis})
...     async with shower:
...         # Critical section - no other coroutine can enter while we hold the lock.
...         print(f"shower is {'occupied' if await shower.locked() else 'available'}")
...     print(f"shower is {'occupied' if await shower.locked() else 'available'}")
...
>>> asyncio.run(main(), debug=True)
shower is occupied
shower is available
>>>

NextID 🔢

NextID safely and reliably produces increasing IDs across threads, processes, and even machines, without a single point of failure. Rationale and algorithm description.

Instantiate an ID generator:

>>> from pottery import NextID
>>> tweet_ids = NextID(key='tweet-ids', masters={redis})
>>>

The key argument represents the sequence (so that you can have different sequences for user IDs, comment IDs, etc.), and the masters argument specifies your Redis masters across which to distribute ID generation (in production, you should have 5 Redis masters). Now, whenever you need a user ID, call next() on the ID generator:

>>> next(tweet_ids)
1
>>> next(tweet_ids)
2
>>> next(tweet_ids)
3
>>>

Two caveats:

  1. If many clients are generating IDs concurrently, then there may be “holes” in the sequence of IDs (e.g.: 1, 2, 6, 10, 11, 21, …).
  2. This algorithm scales to about 5,000 IDs per second (with 5 Redis masters). If you need IDs faster than that, then you may want to consider other techniques.

redis_cache()

redis_cache() is a simple lightweight unbounded function return value cache, sometimes called “memoize”. redis_cache() implements Python’s excellent functools.cache() API as closely as is feasible. In other words, you can use redis_cache() the same way that you use functools.cache().

Limitations:

  1. Arguments to the function must be hashable.
  2. Return values from the function must be JSON serializable.
  3. Just like functools.cache(), redis_cache() does not allow for a maximum size, and does not evict old values, and grows unbounded. Only use redis_cache() in one of these cases:
    1. Your function’s argument space has a known small cardinality.
    2. You specify a timeout when calling redis_cache() to decorate your function, to dump your entire return value cache timeout seconds after the last cache access (hit or miss).
    3. You periodically call .cache_clear() to dump your entire return value cache.
    4. You’re ok with your return value cache growing unbounded, and you understand the implications of this for your underlying Redis instance.

In general, you should only use redis_cache() when you want to reuse previously computed values. Accordingly, it doesn’t make sense to cache functions with side-effects or impure functions such as time() or random().

Decorate a function:

>>> import time
>>> from pottery import redis_cache
>>> @redis_cache(redis=redis, key='expensive-function-cache')
... def expensive_function(n):
...     time.sleep(.1)  # Simulate an expensive computation or database lookup.
...     return n
...
>>>

Notice the two keyword arguments to redis_cache(): The first is your Redis client. The second is the Redis key name for your function’s return value cache.

Call your function and observe the cache hit/miss rates:

>>> expensive_function(5)
5
>>> expensive_function.cache_info()
CacheInfo(hits=0, misses=1, maxsize=None, currsize=1)
>>> expensive_function(5)
5
>>> expensive_function.cache_info()
CacheInfo(hits=1, misses=1, maxsize=None, currsize=1)
>>> expensive_function(6)
6
>>> expensive_function.cache_info()
CacheInfo(hits=1, misses=2, maxsize=None, currsize=2)
>>>

Notice that the first call to expensive_function() takes 1 second and results in a cache miss; but the second call returns almost immediately and results in a cache hit. This is because after the first call, redis_cache() cached the return value for the call when n == 5.

You can access your original undecorated underlying expensive_function() as expensive_function.__wrapped__. This is useful for introspection, for bypassing the cache, or for rewrapping the original function with a different cache.

You can force a cache reset for a particular combination of args/kwargs with expensive_function.__bypass__. A call to expensive_function.__bypass__(*args, **kwargs) bypasses the cache lookup, calls the original underlying function, then caches the results for future calls to expensive_function(*args, **kwargs). Note that a call to expensive_function.__bypass__(*args, **kwargs) results in neither a cache hit nor a cache miss.

Finally, clear/invalidate your function’s entire return value cache with expensive_function.cache_clear():

>>> expensive_function.cache_info()
CacheInfo(hits=1, misses=2, maxsize=None, currsize=2)
>>> expensive_function.cache_clear()
>>> expensive_function.cache_info()
CacheInfo(hits=0, misses=0, maxsize=None, currsize=0)
>>>

CachedOrderedDict

The best way that I can explain CachedOrderedDict is through an example use-case. Imagine that your search engine returns document IDs, which then you have to hydrate into full documents via the database to return to the client. The data structure used to represent such search results must have the following properties:

  1. It must preserve the order of the document IDs returned by the search engine.
  2. It must map document IDs to hydrated documents.
  3. It must cache previously hydrated documents.

Properties 1 and 2 are satisfied by Python’s collections.OrderedDict. However, CachedOrderedDict extends Python’s OrderedDict to also satisfy property 3.

The most common usage pattern for CachedOrderedDict is as follows:

  1. Instantiate CachedOrderedDict with the IDs that you must look up or compute passed in as the dict_keys argument to the initializer.
  2. Compute and store the cache misses for future lookups.
  3. Return some representation of your CachedOrderedDict to the client.

Instantiate a CachedOrderedDict:

>>> from pottery import CachedOrderedDict
>>> search_results_1 = CachedOrderedDict(
...     redis_client=redis,
...     redis_key='search-results',
...     dict_keys=(1, 2, 3, 4, 5),
... )
>>>

The redis_client argument to the initializer is your Redis client, and the redis_key argument is the Redis key for the Redis Hash backing your cache. The dict_keys argument represents an ordered iterable of keys to be looked up and automatically populated in your CachedOrderedDict (on cache hits), or that you’ll have to compute and populate for future lookups (on cache misses). Regardless of whether keys are cache hits or misses, CachedOrderedDict preserves the order of dict_keys (like a list), maps those keys to values (like a dict), and maintains an underlying cache for future key lookups.

In the beginning, the cache is empty, so let’s populate it:

>>> sorted(search_results_1.misses())
[1, 2, 3, 4, 5]
>>> search_results_1[1] = 'one'
>>> search_results_1[2] = 'two'
>>> search_results_1[3] = 'three'
>>> search_results_1[4] = 'four'
>>> search_results_1[5] = 'five'
>>> sorted(search_results_1.misses())
[]
>>>

Note that CachedOrderedDict preserves the order of dict_keys:

>>> for key, value in search_results_1.items():
...     print(f'{key}: {value}')
1: one
2: two
3: three
4: four
5: five
>>>

Now, let’s look at a combination of cache hits and misses:

>>> search_results_2 = CachedOrderedDict(
...     redis_client=redis,
...     redis_key='search-results',
...     dict_keys=(2, 4, 6, 8, 10),
... )
>>> sorted(search_results_2.misses())
[6, 8, 10]
>>> search_results_2[2]
'two'
>>> search_results_2[6] = 'six'
>>> search_results_2[8] = 'eight'
>>> search_results_2[10] = 'ten'
>>> sorted(search_results_2.misses())
[]
>>> for key, value in search_results_2.items():
...     print(f'{key}: {value}')
2: two
4: four
6: six
8: eight
10: ten
>>>

Limitations:

  1. Keys and values must be JSON serializable.

Bloom filters 🌸

Bloom filters are a powerful data structure that help you to answer the questions, “Have I seen this element before?” and “How many distinct elements have I seen?”; but not the question, “What are all of the elements that I’ve seen before?” So think of Bloom filters as Python sets that you can add elements to, use to test element membership, and get the length of; but that you can’t iterate through or get elements back out of.

Bloom filters are probabilistic, which means that they can sometimes generate false positives (as in, they may report that you’ve seen a particular element before even though you haven’t). But they will never generate false negatives (so every time that they report that you haven’t seen a particular element before, you really must never have seen it). You can tune your acceptable false positive probability, though at the expense of the storage size and the element insertion/lookup time of your Bloom filter.

Create a BloomFilter:

>>> from pottery import BloomFilter
>>> dilberts = BloomFilter(
...     num_elements=100,
...     false_positives=0.01,
...     redis=redis,
...     key='dilberts',
... )
>>>

Here, num_elements represents the number of elements that you expect to insert into your BloomFilter, and false_positives represents your acceptable false positive probability. Using these two parameters, BloomFilter automatically computes its own storage size and number of times to run its hash functions on element insertion/lookup such that it can guarantee a false positive rate at or below what you can tolerate, given that you’re going to insert your specified number of elements.

Insert an element into the BloomFilter:

>>> dilberts.add('rajiv')
>>>

Test for membership in the BloomFilter:

>>> 'rajiv' in dilberts
True
>>> 'raj' in dilberts
False
>>> 'dan' in dilberts
False
>>>

See how many elements we’ve inserted into the BloomFilter:

>>> len(dilberts)
1
>>>

Note that BloomFilter.__len__() is an approximation, not an exact value, though it’s quite accurate.

Insert multiple elements into the BloomFilter:

>>> dilberts.update({'raj', 'dan'})
>>>

Do more efficient membership testing for multiple elements using .contains_many():

>>> tuple(dilberts.contains_many('rajiv', 'raj', 'dan', 'luis'))
(True, True, True, False)
>>>

Remove all of the elements from the BloomFilter:

>>> dilberts.clear()
>>> len(dilberts)
0
>>>

Limitations:

  1. Elements must be JSON serializable.
  2. len(bf) is probabilistic in that it’s an accurate approximation. You can tune how accurate you want it to be with the num_elements and false_positives arguments to .__init__(), at the expense of storage space and insertion/lookup time.
  3. Membership testing against a Bloom filter is probabilistic in that it may return false positives, but never returns false negatives. This means that if element in bf evaluates to True, then you may have inserted the element into the Bloom filter. But if element in bf evaluates to False, then you must not have inserted it. Again, you can tune accuracy with the num_elements and false_positives arguments to .__init__(), at the expense of storage space and insertion/lookup time.

HyperLogLogs 🪵

HyperLogLogs are an interesting data structure designed to answer the question, “How many distinct elements have I seen?”; but not the questions, “Have I seen this element before?” or “What are all of the elements that I’ve seen before?” So think of HyperLogLogs as Python sets that you can add elements to and get the length of; but that you can’t use to test element membership, iterate through, or get elements out of.

HyperLogLogs are probabilistic, which means that they’re accurate within a margin of error up to 2%. However, they can reasonably accurately estimate the cardinality (size) of vast datasets (like the number of unique Google searches issued in a day) with a tiny amount of storage (1.5 KB).

Create a HyperLogLog:

>>> from pottery import HyperLogLog
>>> google_searches = HyperLogLog(redis=redis, key='google-searches')
>>>

Insert an element into the HyperLogLog:

>>> google_searches.add('sonic the hedgehog video game')
>>>

See how many elements we’ve inserted into the HyperLogLog:

>>> len(google_searches)
1
>>>

Insert multiple elements into the HyperLogLog:

>>> google_searches.update({
...     'google in 1998',
...     'minesweeper',
...     'joey tribbiani',
...     'wizard of oz',
...     'rgb to hex',
...     'pac-man',
...     'breathing exercise',
...     'do a barrel roll',
...     'snake',
... })
>>> len(google_searches)
10
>>>

Through a clever hack, we can do membership testing against a HyperLogLog, even though it was never designed for this purpose. The way that the hack works is that it creates a temporary copy of the HyperLogLog, then inserts the element that you’re running the membership test for into the temporary copy. If the insertion changes the temporary HyperLogLog’s cardinality, then the element must not have been inserted into the original HyperLogLog.

>>> 'joey tribbiani' in google_searches
True
>>> 'jennifer aniston' in google_searches
False
>>>

Do more efficient membership testing for multiple elements using .contains_many():

>>> tuple(google_searches.contains_many('joey tribbiani', 'jennifer aniston'))
(True, False)
>>>

Remove all of the elements from the HyperLogLog:

>>> google_searches.clear()
>>> len(google_searches)
0
>>>

Limitations:

  1. Elements must be JSON serializable.
  2. len(hll) is probabilistic in that it’s an accurate approximation.
  3. Membership testing against a HyperLogLog is probabilistic in that it may return false positives, but never returns false negatives. This means that if element in hll evaluates to True, then you may have inserted the element into the HyperLogLog. But if element in hll evaluates to False, then you must not have inserted it.

ContextTimer ⏱️

ContextTimer helps you easily and accurately measure elapsed time. Note that ContextTimer measures wall (real-world) time, not CPU time; and that elapsed() returns time in milliseconds.

You can use ContextTimer stand-alone…

>>> import time
>>> from pottery import ContextTimer
>>> timer = ContextTimer()
>>> timer.start()
>>> time.sleep(0.1)
>>> 100 <= timer.elapsed() < 200
True
>>> timer.stop()
>>> time.sleep(0.1)
>>> 100 <= timer.elapsed() < 200
True
>>>

…or as a context manager:

>>> tests = []
>>> with ContextTimer() as timer:
...     time.sleep(0.1)
...     tests.append(100 <= timer.elapsed() < 200)
>>> time.sleep(0.1)
>>> tests.append(100 <= timer.elapsed() < 200)
>>> tests
[True, True]
>>>

Contributing

Obtain source code

  1. Clone the git repo:
    1. $ git clone git@github.com:brainix/pottery.git
    2. $ cd pottery/
  2. Install project-level dependencies:
    1. $ make install

Run tests

  1. In one Terminal session:
    1. $ cd pottery/
    2. $ redis-server
  2. In a second Terminal session:
    1. $ cd pottery/
    2. $ make test
    3. $ make test-readme

make test runs all of the unit tests as well as the coverage test. However, sometimes, when debugging, it can be useful to run an individual test module, class, or method:

  1. In one Terminal session:
    1. $ cd pottery/
    2. $ redis-server
  2. In a second Terminal session:
    1. Run a test module with $ make test tests=tests.test_dict
    2. Run a test class with: $ make test tests=tests.test_dict.DictTests
    3. Run a test method with: $ make test tests=tests.test_dict.DictTests.test_keyexistserror

make test-readme doctests the Python code examples in this README to ensure that they’re correct.