Octopoes is KAT’s knowledge-graph. It stores the knowledge KAT has gathered about its domain. As Octopoes uses XTDB for bi-temporal data-storage, Octopoes keeps the current state of the knowledge-graph, as well as a complete, queryable history of the knowledge-graph.


Install dependencies

python3 -m pip install -r requirements.txt

Run Octopoes API

python3 -m uvicorn octopoes.api.api:app [--port 8000]

Run the event processor

python3 -m celery -A octopoes.tasks.tasks worker -B -s /tmp/celerybeat-schedule --loglevel=WARNING

Note: The -B flag instructs celery start the Celery Beat scheduler in the same process Note: The -s flag is used to specify the beat schedule location and should be writeable by the user the process runs in


# Return XTDB connection info
curl http://localhost:8000/_dev/health
# Return some XTDB objects (or empty list [])
curl http://localhost:8000/_dev/objects

# To request data for a different KAT-client:
curl http://localhost:8000/clientx/healthcheck


The domain of discourse, on which Octopoes operates, is described by the OOI datamodel. The OOI (Object of Interest) model is described by entities and relations between them. The OOI model is currently defined in Octopoes itself (module octopoes.model.ooi). However, it is planned to be defined in the Openkat schema registries, decoupling the applicable domain from the logic.


Each OOI must have an origin to exist in the knowledge-graph. Origins can be supplied to Octopoes in 3 ways:

  • origin through declaration

  • origin through observation

  • origin through inference

Each origin consists of:

  • the identifier of the origin-method

  • a source OOI

  • a set of result OOIs

  • additional metadata. E.g. the task-ID that made the observation

Origin through declaration

An OOI is declared to exist by a user of KAT.

In this case, OOI B is both source and result

flowchart RL D[Declaration D] subgraph result[ ] B[OOI B] end B-.source.-D D-.result.- result

Origin through observation

An observation is reported by a normalizer

  • An observation has a key that identifies the normalizer

  • An observation always has a source OOI

  • An observation always has a (possibly empty) set of result OOIs

flowchart LR A[OOI A] O["Observation O (OOI A)"] subgraph outp[ ] B[OOI B] C[OOI C] end A-.source.-O O-.result.- outp

Origin through inference

An object is inferred from other objects in the knowledge-graph. This is achieved by rules, declared in bits. A bit is a rule that is applied to a pattern in the knowledge-graph.

flowchart TD subgraph pattern[ ] direction TB A[OOI A]---B[OOI B] A---C[OOI C] end subgraph result[ ] direction RL D[OOI D]---E[OOI E] end BIT["Bit B (OOI A)"] A-.source.-BIT pattern-.pattern.-BIT BIT-.result.- result

Graph mutations

Mutations can only be made by supplying an origin to Octopoes. This can be an origin through declaration, or origin through observation. When, after an origin-update, an OOI is no longer referenced by any origin. The OOI will be deleted from the knowledge-graph.

Example: observation O has result B and C

flowchart LR A[OOI A] A-.source.-O O["Observation O (OOI A)"] O-.result.- result subgraph result[ ] direction LR B[OOI B]---C[OOI C] end

After a mutation, observation O has result B. C is no longer referenced, and is deleted from the knowledge-graph.

flowchart LR A[OOI A] O["Observation O (OOI A)"] subgraph result[ ] direction LR B[OOI B] end C[OOI C]:::someclass A-.source.-O O-.result.- result B[OOI B]x--xC[OOI C] classDef someclass fill:#f96, color:#000, stroke:#000;

If C had been referenced by another origin, it would not have been deleted.

OOI C is not deleted, since it’s still referenced by Observation P

flowchart LR A[OOI A] O["Observation O (OOI A)"] subgraph result[ ] direction LR B[OOI B] end C2[OOI C] A-.source.-O O-.result.- result B[OOI B]x--xC2 E[OOI E] P["Observation P (OOI B)"] subgraph result2[ ] direction LR D[OOI D]---C[OOI C] end E-.source.-P P-.result.- result2

Code Architecture

In high level, the code architecture is as follows:

  • Origin gets reported to the API

  • API calls the service layer

  • Service layer calls the data layer

  • Data layer sends out a mutation event

  • Listener catches the mutation event

  • Listener calls service layer to process mutation

flowchart LR Listener API OctopoesService API --> OctopoesService Listener --> OctopoesService OctopoesService --> Repository Repository --> XTDB[(XTDB)] Repository --> EventManager EventManager --> Listener

Sequence: save_origin

sequenceDiagram actor Client participant API participant OctopoesService participant OriginRepository participant OOIRepository participant XTDB participant EventManager Client ->>+ API: save_origin(origin, oois, valid_time, organisation) API ->>+ OctopoesService: save_origin(origin, oois, valid_time) OctopoesService ->>+ OriginRepository: save(origin, valid_time) OriginRepository ->> XTDB: get(origin, valid_time) OriginRepository ->> OriginRepository: compare(origin) OriginRepository ->> XTDB: save(origin, valid_time) OriginRepository ->> EventManager: publish( CREATE_ORIGIN ) OriginRepository ->- OctopoesService: #nbsp OctopoesService ->>+ OOIRepository: save(ooi, valid_time) OOIRepository ->> XTDB: get(ooi, valid_time) OOIRepository ->> OOIRepository: compare(ooi) OOIRepository ->> XTDB: save(ooi, valid_time) OOIRepository ->> EventManager: publish( UPDATE_OOI ) OOIRepository ->- OctopoesService: #nbsp OctopoesService ->- API: #nbsp API ->- Client: #nbsp

Sequence: process update ooi

sequenceDiagram actor EventManager participant Listener participant OctopoesService participant OriginRepository participant XTDB EventManager ->>+ Listener: handle_event(event<UPDATE_OOI>) Listener ->> OctopoesService: handle_update_ooi(event, valid_time) OctopoesService ->> OriginRepository: get_origin(event.origin, type=inference) OriginRepository ->> OctopoesService: bits loop bits OctopoesService ->> OctopoesService: run_bit end Listener ->>- EventManager: #nbsp

Crux / XTDB

Crux is the central database of OOIs within KAT. Crux is a graph-database that can store objects (schemalessly), while providing object history and audit-trail functionality out-of-the-box. The term bitemporal means it tracks every object on 2 time axis: valid-time and transaction-time.

  • Valid-time means the state of an object at a certain time X (mutable).

  • Transaction-time means the state of an object with all transactions-processed until time Y (immutable)

This is especially useful for forensics-type queries like: What was the state of an object at time X (valid-time), with the information we had at time Y (transaction-time).

Good to know: Crux tracks the history of each object by its primary key.

Read more about Crux bitemporality


OOI objects are instances of relatively simple classes, which inherit from OOIBase.

Because all OOIs are stored in Crux and Crux tracks object history by primary key, KAT defines a way to reliably determine the primary key of an object by its attributes. This is called the natural key of an object.

The main advantage of this method, is that when enough attributes of an OOI are discovered, the primary key of this object is known. This allows reasoning about the exact same objects in several subsystems, without having to query a database.

Consider this (oversimplified) Person class

from octopoes.models import OOI

class Person(OOI):
  name: str
  last_name: str
  age: int

  _natural_key_attributes: ['name', 'last_name']

# 2 completely separate systems can instantiate the the following Person OOI:
john = Person(name='John', last_name='Doe', age=42)

# And without having to search this person in a central database, the primary key is known:
john.natural_key # 'John/Doe'
john.primary_key # 'Person/John/Doe'

Note that the primary key consists of the natural key prefixed by the OOI-type, to avoid PK collisions


OOIs can be related to each other. At time of writing the OOI data structure looks like this:

Directional arrows indicate a foreign key pointing to referred object KAT Data Structure

In a one-to-many relationship (A 1-* B), the relationship is stored in B (B points to A). For example, an IP-address belongs to a Network. So the Network primary key is stored as a foreign key in the IP-address object.

from octopoes.models import OOI, Reference
class Network(OOI):
    name: str
    _natural_key_attrs = ['name']

class IpAddressV6(OOI):
    Network: Reference[Network]
    address: str
    _natural_key_attrs = ['Network', 'address']

A few example records

KAT Data Example

OOI Reference

Even though foreign keys are actually simple strings, for ease of use these strings are represented in Octopoes by a special Reference object.

OOIRefs can be obtained in several ways.

from octopoes.models import Reference
from octopoes.models.ooi.network import Network, IPAddressV6

# Through the .ref() method of an OOI instance
internet = Network(name='internet')
internet_ref = internet.reference

# Or from string
internet_ref = Reference.from_str('Network|internet')

# Create a related object with a ref
ip = IPAddressV6(network=internet_ref, address='2001:db8::1')

Since an OOIRef is a compound key, individual parts of the foreign key can be retrieved by the parsed property.

from octopoes.models import Reference

ref = Reference.from_str('IpPort|internet|2001:db8::1|tcp|5050')

ref.tokenized.protocol # 'tcp'
ref.tokenized.port # '5050'
ref.tokenized.address.address # '2001:db8::1'

KAT Ref Example

Octopoes API


The OctopoesAPIConnector class provides a python interface for connecting with Octopoes API.

Abstract classes / subclassing

Relationships from an OOI class to another OOI class are inferred through its property types. It is possible to define a relationship to an abstract class.

For querying purposes and grouping purposes these abstract classes can also be used.

from octopoes.models import OOI, Reference
from octopoes.connector.octopoes import OctopoesAPIConnector

# Define abstract class and subclasses
class IPAddress(OOI):

class IPAddressV4(IPAddress):

class IPAddressV6(IPAddress):

# Relationships to abstract class
class IPPort(OOI):
    address: Reference[IPAddress] # Any subclass of IPAddress (IPAddressV4, IPAddressV6)
    protocol: str
    port: int

class TagExample(OOI):
    ooi: Reference[OOI] # Any subclass of OOI..
    tag: str

# Query abstract class
OctopoesAPIConnector('http://octopoes', '_dev').list({IPAddress})


Octopoes API uses the OOI model to construct Crux queries. For complex graph-querying, Crux’s pull-syntax is used to build a query tree. Crux can join objects to properties which hold (lists of) foreign keys.

Imagine a query “Give me IpAddressV4 with primary key X and all related objects 2 levels deep”.

What happens under the hood:

  • A relation map is created with all OOI classes and their relations

  • A query plan is created by traversing the relation map 2 levels deep. The queryplan is a tree of QueryNode objects

  • The query plan is transformed into a Crux Datalog query, utilizing its pull syntax to join related objects

Rules: A few rules come into play when planning the query.

  • Relations are not traversed back through the previous relation. E.g.: IpAddressV4 -> IpPort -> IpAddressV4

  • Leaf nodes are OOI classes that have too many relations to effectively traverse if they are not the starting node. Currently these are Network, Finding and Job

Query Plan Visualization: The OOI class tree is traversed 2 levels deep. Bear in mind that both Finding and Job can be related to any OOI, so the following paths are valid:

- IpAddressV4 -> Finding
- IpAddressV4 -> Job
- IpAddressV4 -> DnsARecord -> Finding
- IpAddressV4 -> DnsARecord -> Job
- IpAddressV4 -> IpPort -> Finding
- IpAddressV4 -> IpPort -> Job
- IpAddressV4 -> IpPort -> IpService
- IpAddressV4 -> DnsARecord -> Hostname

Hence the 1 and 2 levels markers on Finding and Job in the image below. KAT Query Plan


The unit tests octopoes/tests are run using

python -m unittest discover octopoes/tests