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A lightweight adapter API for python-ldap with read-through caching to Redis

Project description


A lightweight adapter API for python-ldap that provides simplified access to LDAP objects and attributes in native Python types, as well as transparent read-through caching support using either a simple in-memory cache or Redis.


Ldapter is available from PyPi and can be installed using pip or whatever package management solution you prefer.

pip install ldapter

If you want to use datetime types to represent ISO-8601 formatted date strings in your LDAP entries, you'll also need to install python-dateutil

pip install python-dateutil

Running the Demo

The source repository includes a demo. Of course, you'll need an LDAP server and (optionally) a Redis cache in order to see ldapter at work. To make this easy, the repository includes everything you need to run the demo using Docker and Docker Compose on your local workstation.

Start up the demo LDAP server and Redis cache in a terminal.

cd src/demo/docker
docker compose up --build

Depending on the version of Docker you have installed, you might find that you need to use docker-compose instead of docker compose in the second line above.

Run the demo in another terminal.

cd src/demo/python
PYTHONPATH=../main/python python

Using the ldapter API

The Directory object class is the top-level API.

from ldapter import Directory
from ldapter import inet_org_person_schema

# Create a directory adapter with no cache.
directory = Directory(schema=inet_org_person_schema())

The Directory.fetch_all method returns a list of tuples, each of which contains a DistinguishedName and an Entry. The list will be empty if the LDAP search returned no entries.

from ldapter import Directory, Scope
from ldapter import inet_org_person_schema

directory = Directory(schema=inet_org_person_schema())
base = "dc=example,dc=org"
search_filter = "uid=fletcher"
for dn, entry in directory.fetch_all(base, Scope.SUBTREE, search_filter):
    for k, v in entry.items():
        print(f"{k}: {v}")

A DistinguishedName is an object that implements a subset of the dict protocol, allowing relative names (RDN) to be easily extracted.

An Entry is an object that uses the directory schema to transform attribute values into useful Python data types. It implements a subset of the dict protocol, so that attribute values can be easily accessed by name. A single-valued attribute has a common Python data type (e.g. str, int, bool, datetime) or DistinguishedName. A multi-valued attribute is a Python list whose values are one of these types.

Caching Strategy

When an LDAP search is performed (via the Directory.fetch_all or Directory.fetch_one methods) a resulting set of matching LDAP entries is returned (which may be empty). Each entry has an LDAP distinguished name that serves as the "primary key" for retrieving that specific entry. Each retrieved object is placed into the cache using its distinguished name as the key. Additionally, the search base, scope, and filter expression specified for the search are used as a key for a cache entry that contains the set of distinguished names found by the search.

Conceptually you can think of the cache as a simple hash table where each entry has a key string and corresponding JSON value (an array or object). If we perform a search using dc=example,dc=org as the base, SUBTREE as the scope, and a filter expression such as (&(sn=Smith)(givenName=Mary*), the cache might have entries such as the following:

Key Value
s:dc=example,dc=org:SUBTREE:(&(sn=Smith)(givenName=Mary*) ["uid=liz,dc=example,dc=org", "uid=mas87,dc=example,dc=org", "uid=msmith,dc=example,dc=org", ...]
e:uid=liz,dc=example,dc=org { "uid": "liz", sn="Smith", "givenName": "Mary Elizabeth", ... }
e:uid=mas87,dc=example,dc=org { "uid": "mas87", sn="Smith", "givenName": "Mary Anne", ... }
e:uid=msmith,dc=example,dc=org { "uid": "msmith", sn="Smith", "givenName": "Mary", ... }

The keys are prefixed with a namespace identifier based on the type of the value. In this example, we use s: for a search result and e: for an LDAP entry. This makes the keys easy to distinguish when viewing the cache and prevents any potential for collisions of keys representing different types.

On a subsequent search request with the same base, scope, and filter expression, the cached list of distinguished names is retrieved, and those names are used to retrieve the corresponding LDAP entries from the cache. In this way, different searches can share the same LDAP entries in the cache -- any given LDAP entry is stored at most once.

No attempt is made to analyze the filter expression to predict what might be returned by the LDAP server. Cached searches are simply the list of distinguished names that matched the given search criteria when the search was performed. This strategy works well for accelerating searches that are performed repeatedly with the same search criteria, but it does nothing for a search whose criteria are even slightly different from a previously executed search. As discussed below in Optimizing Single Entry Fetches, additional optimizations are included to accelerate the common case of fetching LDAP entries using the distinguished name as a primary key.

Using a Cache

The module includes two cache providers.

  1. A provider that acts as a Redis client.
  2. A provider that uses a simple dict-based in-memory cache.

The simple dict-based cache is suitable mostly for testing. For optimal performance and persistence beyond the runtime of a Python script, Redis is recommended.

To use a cache, specify the cache provider when creating a Directory.

from ldapter import Directory
from ldapter import RedisDirectoryCache
from ldapter import inet_org_person_schema

# Create a directory adapter that uses a Redis cache
cache = RedisDirectoryCache(host="localhost", port=6379, db=0)
directory = Directory(schema=inet_org_person_schema(), cache=cache)

By default, the Redis cache provider uses the same time-to-live (TTL) for all LDAP search result lists and corresponding LDAP entries. In most cases, you will want to differentiate the TTL for an entry, based on the object type, since the probability that an entry will be updated in the source LDAP server tends to vary based on object type. For example, attributes about a person change relatively infrequently, while the composition of groups of people changes more often and it may be important to observe such changes when groups are used for authorization.

When constructing the cache object, you can specify a dict with TTLs for different object types defined in the schema.

from datetime import timedelta
from ldapter import Directory
from ldapter import RedisDirectoryCache
from ldapter import inet_org_person_schema

# Create a directory adapter that uses a Redis cache
cache = RedisDirectoryCache(
    host="localhost", port=6379, db=0,
        "inetOrgPerson": timedelta(hours=4)

directory = Directory(schema=inet_org_person_schema(), cache=cache)

The search_ttl is used when caching the search criteria (base, scope, filter) and corresponding list of distinguished names that were retrieved by a search.

The entry_ttl dict is used when caching the LDAP entries returned by a search. By default, the Redis cache provider uses the objectClass attribute to distinguish LDAP entries by type. It checks the entry_ttl dict for a TTL specific to the given type. If not found it uses the search_ttl as the fallback.

Configuring Redis

When using the Redis cache provider, the cache eviction policy is an important consideration. When the cache encounters memory pressure, Redis can evict some cache entries to make room for new additions to the cache. For LDAP, the Redis Least Frequently Used (LFU) eviction policy tends to be good choice. The LFU policy uses an estimator of cache entry usage frequency, and tends to prefer evicting cache entries that are unlikely to be needed based on frequency, rather than last access time.

All cache entries managed by ldapter have a TTL that controls the maximum lifetime of the cache entry -- whether a search result list or and LDAP object entry. To configure Redis to evict cache entries based on the LFU policy, configure a maximum memory size and specify the volatile LFU policy in the Redis configuration.

maxmemory 2gb
maxmemory-policy volatile-lfu 

Optimizing Single Entry Fetches

When fetching a specific LDAP entry using the entry's distinguished name, the caching implementation can provide an additional optimization, allowing a single object search to be satisfied from cache entries created by prior searches.

A common situation where this optimization is useful is when a UI provides a means to search for a person using the person's name as search criteria.

people = directory.fetch_all(
    "dc=example,dc=org", Scope.SUBTREE, "(&(sn=Smith)(givenName=Mary*))")

Since the surname and given name are both quite common, this is likely to return several matching entries. In the UI, the matches are presented with other distinguishing attributes (middle name, department, etc) allowing the user to select specific person. Often, after the specific person has been selected, there will be a need to fetch the specific person entry again soon after the original search is performed. The subsequent search could be performed using a filter expression based on some unique attribute of LDAP person entry.

person = directory.fetch_one(
    "dc=example,dc=org", Scope.SUBTREE, "uid=liz")

This approach will indeed work. However, it will not necessarily take advantage of the fact the person entry to be fetched was just placed into the cache by the prior search. In fact, it will perform an LDAP search unless the same search criteria (base, scope, filter expression) was performed recently to retrieve this person's LDAP entry.

However, if the subsequent fetch for this person object instead used the distinguished name of the person entry, the previously cached entry would be returned without the need to perform an LDAP search.

import random
from ldapter import Directory, Scope
from ldapter import RedisDirectoryCache
from ldapter import inet_org_person_schema

# Create a directory adapter that uses a Redis cache with default config
cache = RedisDirectoryCache()
directory = Directory(schema=inet_org_person_schema(), cache=cache)

# Find everyone named Mary Smith
people = directory.fetch_all(
    "dc=example,dc=org", Scope.SUBTREE, "(&(sn=Smith)(givenName=Mary*))")

# pick one at random
selected = random.randint(0, len(people))
selected_person = people[selected]

# get the DN from the selected tuple
dn = selected_person[0]

# this fetch will be always be satisfied from cache
person = directory.fetch_one(dn, Scope.BASE)

When the scope is BASE, the search is essentially a primary key search and has no filter expression. If the cache contains the entry with the requested DN, there's no reason to perform the search.

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