A embedded database built around the concept of event trees, emphasizing data deduplication and data integrity checks. By structuring data into event trees, OuroborosDB ensures efficient and intuitive data management. Key features include:
- Data Deduplication: Eliminates redundant data through efficient chunking and hashing mechanisms.
- Data Integrity Checks: Uses SHA-512 hashes to verify the integrity of stored data.
- Event-Based Architecture: Organizes data hierarchically for easy retrieval and management.
- Scalable Concurrent Processing: Optimized for concurrent processing to handle large-scale data.
- Log Management and Indexing: Provides efficient logging and indexing for performance monitoring.
- Non-Deletable Events: Once stored, events cannot be deleted or altered, ensuring the immutability and auditability of the data.
- (To be implemented) Temporary Events: Allows the creation of temporary events that can be marked as temporary and safely cleaned up later for short-term data storage needs.
- OuroborosDB
OuroborosDB requires Go 1.21.5+
go get -u github.com/i5heu/OuroborosDBOuroborosDB can be initialized with a configuration struct that includes paths for storage and other settings.
import "OuroborosDB"
func initializeDB() *OuroborosDB.OuroborosDB {
db, err := OuroborosDB.NewOuroborosDB(OuroborosDB.Config{
Paths: []string{"./data/storage"},
MinimumFreeGB: 1,
GarbageCollectionInterval: 10, // Minutes
})
if err != nil {
log.Fatalf("Failed to initialize OuroborosDB: %v", err)
}
return db
}Files can be stored within events using the StoreFile method.
import (
"OuroborosDB/internal/storage"
"OuroborosDB"
)
func storeFile(db *OuroborosDB.OuroborosDB, parentEvent storage.Event) storage.Event {
fileContent := []byte("This is a sample file content")
metadata := []byte("sample.txt")
event, err := db.DB.StoreFile(storage.StoreFileOptions{
EventToAppendTo: parentEvent,
Metadata: metadata,
File: fileContent,
})
if err != nil {
log.Fatalf("Failed to store file: %v", err)
}
return event
}Files can be retrieved by providing the event from which they were stored.
func retrieveFile(db *OuroborosDB.OuroborosDB, event storage.Event) []byte {
content, err := db.DB.GetFile(event)
if err != nil {
log.Fatalf("Failed to retrieve file: %v", err)
}
return content
}Create a root event to represent the top level of an event tree.
func createRootEvent(db *OuroborosDB.OuroborosDB, title string) storage.Event {
rootEvent, err := db.DB.CreateRootEvent(title)
if err != nil {
log.Fatalf("Failed to create root event: %v", err)
}
return rootEvent
}func getRootEventsByTitle(db *OuroborosDB.OuroborosDB, title string) []storage.Event {
events, err := db.DB.GetRootEventsWithTitle(title)
if err != nil {
log.Fatalf("Failed to fetch root events by title: %v", err)
}
return events
}func createChildEvent(db *OuroborosDB.OuroborosDB, parentEvent storage.Event) storage.Event {
childEvent, err := db.DB.CreateNewEvent(storage.EventOptions{
HashOfParentEvent: parentEvent.EventHash,
})
if err != nil {
log.Fatalf("Failed to create child event: %v", err)
}
return childEvent
}func getChildEvents(db *OuroborosDB.OuroborosDB, parentEvent storage.Event) []storage.Event {
children, err := db.Index.GetDirectChildrenOfEvent(parentEvent.EventHash)
if err != nil {
log.Fatalf("Failed to fetch child events: %v", err)
}
return children
} go test ./... go test -run='^$' -bench=.Works with committed changes and version/commits that are reachable by git checkout.
You also need to have installed benchstat to compare the benchmarks, install it with go get golang.org/x/perf/cmd/benchstat@latest
# add versions to bench.sh
bash bench.sh
# Now look in benchmarks/combined_benchmarks_comparison to see the resultsgoos: linux
goarch: amd64
pkg: github.com/i5heu/ouroboros-db
cpu: AMD Ryzen 9 5900X 12-Core Processor
│ benchmarks/v0.0.5.txt │ benchmarks/v0.0.8.txt │ benchmarks/main.txt │
│ sec/op │ sec/op vs base │ sec/op vs base │
_setupDBWithData/RebuildIndex-24 414.3m ± 12% 423.4m ± 7% ~ (p=0.310 n=6)
_Index_RebuildingIndex/RebuildIndex-24 14.85m ± 22% 13.29m ± 30% ~ (p=0.699 n=6) 17.83m ± 12% +20.06% (p=0.015 n=6)
_Index_GetDirectChildrenOfEvent/GetChildrenOfEvent-24 2.408µ ± 11% 2.472µ ± 9% ~ (p=0.937 n=6) 2.288µ ± 11% ~ (p=0.180 n=6)
_Index_GetChildrenHashesOfEvent/GetChildrenHashesOfEvent-24 38.81n ± 6% 40.58n ± 9% ~ (p=0.071 n=6) 38.48n ± 6% ~ (p=0.394 n=6)
_DB_StoreFile/StoreFile-24 109.0µ ± 8% 103.5µ ± 14% ~ (p=0.310 n=6) 106.0µ ± 13% ~ (p=0.132 n=6)
_DB_GetFile/GetFile-24 2.338µ ± 4% 2.374µ ± 6% ~ (p=0.394 n=6) 2.323µ ± 5% ~ (p=0.619 n=6)
_DB_GetEvent/GetEvent-24 3.186µ ± 12% 3.274µ ± 6% ~ (p=0.485 n=6) 3.228µ ± 8% ~ (p=0.699 n=6)
_DB_GetMetadata/GetMetadata-24 2.547µ ± 14% 2.532µ ± 13% ~ (p=0.818 n=6) 2.531µ ± 7% ~ (p=0.699 n=6)
_DB_GetAllRootEvents/GetAllRootEvents-24 11.17m ± 13% 10.88m ± 17% ~ (p=0.699 n=6) 11.26m ± 11% ~ (p=0.937 n=6)
_DB_GetRootIndex/GetRootIndex-24 1.479m ± 10% 1.488m ± 3% ~ (p=0.589 n=6) 1.428m ± 13% ~ (p=0.699 n=6)
_DB_GetRootEventsWithTitle/GetRootEventsWithTitle-24 6.123µ ± 8% 6.469µ ± 14% ~ (p=0.310 n=6) 6.533µ ± 9% ~ (p=0.093 n=6)
_DB_CreateRootEvent/CreateRootEvent-24 83.08µ ± 14% 80.44µ ± 13% ~ (p=0.937 n=6) 89.10µ ± 18% ~ (p=0.394 n=6)
_DB_CreateNewEvent/CreateNewEvent-24 26.78µ ± 22% 28.04µ ± 13% ~ (p=1.000 n=6) 29.43µ ± 10% ~ (p=0.394 n=6)
_setupDBWithData/setupDBWithData-24 442.0m ± 7%
_DB_fastMeta/CreateNewEvent_with_FastMeta-24 28.64µ ± 13%
_DB_fastMeta/GetEvent_with_FastMeta-24 3.281µ ± 17%
_Index_GetParentHashOfEvent/GetParentHashOfEvent-24 44.01n ± 12%
_Index_RebuildIFastMeta/RebuildFastMeta-24 596.8µ ± 8%
_Index_GetEventHashesByFastMetaParameter/GetEventHashesByFastMetaParameter-24 40.20n ± 13%
_Index_GetEventsByFastMeta/GetEventsByFastMeta-24 2.014m ± 10%
geomean 63.40µ 63.47µ +0.11% 33.13µ +2.51% ¹
¹ benchmark set differs from baseline; geomeans may not be comparable
- v0.0.14 - Major refactor of the Event type and introduction of FastMeta which should speed up search
- v0.0.3 - Switch from
gobtoprotobuffor serialization - v0.0.2 - Create tests and benchmarks
🚧 = currently in development
- Data Deduplication
- Basic Data Store and Retrieval
- Child to Parent Index
- Data Basics
- 🚧 Data Compression with LZMA
- Erasure coding
- Encryption
- Data Integrity Checks
- Distributed System
- 🚧 Bootstrap System
- Authentication
- Message Distribution
- Broadcast
- Unicast
- Data Replication
- DHT for Sharding? - maybe full HT is enough
- Data Collection
- Find and Collect Data in Network
- Allow other nodes that are faster to collect data and send it to the slower with zstd
- Full Text Search - blevesearch/bleve
- Semantic Search with API requests for Embeddings
- Is the deletion of not Temporary Events a good idea?
- Maybe if only some superUser can delete them with a key or something.
- It would be nice to have pipelines that can run custom JS or webassembly to do arbitrary things.
- With http routing we could build a webserver that can run inside a "pipeline" in the database. sksksk
- They should be usable as scraper or time or event based notificators.
- Like if this event gets a child recursively, upload this tree to a server.
- this would need a virtual folder structure that is represented in an event.
- with this we could also build a webdav server that can be used to access parts of the database.
- Garbage Collection would delete Chunks that in the process of being used in a new event
- Deletion of Temporary Events is not yet discovered
- We have EventChilds that are used as either
- A Item in the "category" of the Event
- New Information that replaces it's Parent
- Changes to the Parent (think patches)
We need to reflect this in the Event Structure to lower chunk lookups
If we implement a potential DeltaEvent, we need to provide tooling for it.- is it like git where we have a diff of the file?
- is it a new file that replaces the old one?
- we already have the chunk system in place. But this seams to not be suitable for text files - so we would need a text based delta event?
| ID | Environment | Requirements |
|---|---|---|
| #TARGET_Store-100TB | simulated for each component (real tests cost a lot of money) | 🔜 Store 100TB of row data (400M Chunks) 🔜 having retrieval times of random single chunks and events of under 10ms |
| #TARGET_Retrieval-1GB-16s | 10 nodes globally distributed with test chunks spread globally | 🔜 Retrieve 1GB of data in less than 16 seconds (This is a full 0.5GB/s retrieval speed of 40'000 250KB chunks) |
| #TARGET_SplitBrain-Resilience | 3 partitions, 3 nodes each, 100GB new raw data per partition and 1M new events | 🔜 Recover from a network partition in under 300 seconds |
The name "OuroborosDB" is derived from the ancient symbol "Ouroboros," a representation of cyclical events, continuity, and endless return. Historically, it's been a potent symbol across various cultures, signifying the eternal cycle of life, death, and rebirth. In the context of this database, the Ouroboros symbolizes the perpetual preservation and renewal of data. While the traditional Ouroboros depicts a serpent consuming its tail, our version deviates, hinting at both reverence for historical cycles and the importance of continuous adaptation in the face of change.
OuroborosDB (c) 2024 Mia Heidenstedt and contributors
SPDX-License-Identifier: AGPL-3.0